Sygil-Dev/sygil-webui
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Alejandro Gil 2022-12-05 06:34:05 -08:00 committed by GitHub
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2
.gitignore vendored
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@ -54,10 +54,12 @@ condaenv.*.requirements.txt
# Repo-specific
# =========================================================================== #
/configs/webui/userconfig_streamlit.yaml
/configs/webui/userconfig_flet.yaml
/custom-conda-path.txt
!/src/components/*
!/src/pages/*
/src/*
/inputs
/outputs
/model_cache
/log/**/*.png

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.gitmodules vendored Normal file
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@ -0,0 +1,24 @@
[submodule "backend"]
path = backend
url = ../../Sygil-Dev/dalle-flow.git
[submodule "backend/clip-as-service"]
path = backend/clip-as-service
url = ../../jina-ai/clip-as-service.git
[submodule "backend/clipseg"]
path = backend/clipseg
url = ../../timojl/clipseg.git
[submodule "backend/dalle_flow"]
path = backend/dalle_flow
url = ../../Sygil-Dev/dalle-flow.git
[submodule "backend/glid-3-xl"]
path = backend/glid-3-xl
url = ../../jina-ai/glid-3-xl.git
[submodule "backend/latent-diffusion"]
path = backend/latent-diffusion
url = ../../CompVis/latent-diffusion.git
[submodule "backend/stable-diffusion"]
path = backend/stable-diffusion
url = ../../AmericanPresidentJimmyCarter/stable-diffusion.git
[submodule "backend/SwinIR"]
path = backend/SwinIR
url = ../../jina-ai/SwinIR.git

4
Dockerfile
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@ -9,6 +9,7 @@ SHELL ["/bin/bash", "-c"]
ENV PYTHONPATH=/sd
EXPOSE 8501
COPY ./entrypoint.sh /sd/
COPY ./data/DejaVuSans.ttf /usr/share/fonts/truetype/
COPY ./data/ /sd/data/
copy ./images/ /sd/images/
@ -16,8 +17,9 @@ copy ./scripts/ /sd/scripts/
copy ./ldm/ /sd/ldm/
copy ./frontend/ /sd/frontend/
copy ./configs/ /sd/configs/
copy ./configs/webui/webui_streamlit.yaml /sd/configs/webui/userconfig_streamlit.yaml
copy ./.streamlit/ /sd/.streamlit/
COPY ./entrypoint.sh /sd/
copy ./optimizedSD/ /sd/optimizedSD/
ENTRYPOINT /sd/entrypoint.sh
RUN mkdir -p ~/.streamlit/

3
Dockerfile_base
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@ -6,11 +6,12 @@ SHELL ["/bin/bash", "-c"]
WORKDIR /install
RUN apt-get update && \
apt-get install -y wget curl git build-essential zip unzip nano openssh-server libgl1 && \
apt-get install -y wget curl git build-essential zip unzip nano openssh-server libgl1 libsndfile1-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
COPY ./requirements.txt /install/
COPY ./setup.py /install/
RUN /opt/conda/bin/python -m pip install -r /install/requirements.txt

4
Dockerfile_runpod
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@ -9,17 +9,19 @@ SHELL ["/bin/bash", "-c"]
ENV PYTHONPATH=/sd
EXPOSE 8501
COPY ./runpod_entrypoint.sh /sd/entrypoint.sh
COPY ./data/DejaVuSans.ttf /usr/share/fonts/truetype/
COPY ./configs/ /sd/configs/
copy ./configs/webui/webui_streamlit.yaml /sd/configs/webui/userconfig_streamlit.yaml
COPY ./data/ /sd/data/
COPY ./frontend/ /sd/frontend/
COPY ./gfpgan/ /sd/gfpgan/
COPY ./images/ /sd/images/
COPY ./ldm/ /sd/ldm/
COPY ./models/ /sd/models/
copy ./optimizedSD/ /sd/optimizedSD/
COPY ./scripts/ /sd/scripts/
COPY ./.streamlit/ /sd/.streamlit/
COPY ./runpod_entrypoint.sh /sd/entrypoint.sh
ENTRYPOINT /sd/entrypoint.sh
RUN mkdir -p ~/.streamlit/

16
README.md
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@ -6,8 +6,8 @@
## Installation instructions for:
- **[Windows](https://sygil-dev.github.io/sygil-webui/docs/1.windows-installation.html)**
- **[Linux](https://sygil-dev.github.io/sygil-webui/docs/2.linux-installation.html)**
- **[Windows](https://sygil-dev.github.io/sygil-webui/docs/Installation/windows-installation)**
- **[Linux](https://sygil-dev.github.io/sygil-webui/docs/Installation/linux-installation)**
### Want to ask a question or request a feature?
@ -118,7 +118,7 @@ Please see the [Streamlit Documentation](docs/4.streamlit-interface.md) to learn
**Note: the Gradio interface is no longer being actively developed by Sygil.Dev and is only receiving bug fixes.**
Please see the [Gradio Documentation](docs/5.gradio-interface.md) to learn more.
Please see the [Gradio Documentation](https://sygil-dev.github.io/sygil-webui/docs/Gradio/gradio-interface/) to learn more.
## Image Upscalers
@ -146,13 +146,13 @@ Put them into the `sygil-webui/models/realesrgan` directory.
### LSDR
Download **LDSR** [project.yaml](https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1) and [model last.cpkt](https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1). Rename last.ckpt to model.ckpt and place both under `sygil-webui/models/ldsr/`
Download **LDSR** [project.yaml](https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1) and [model last.cpkt](https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1). Rename `last.ckpt` to `model.ckpt` and place both under `sygil-webui/models/ldsr/`
### GoBig, and GoLatent *(Currently on the Gradio version Only)*
More powerful upscalers that uses a seperate Latent Diffusion model to more cleanly upscale images.
More powerful upscalers that uses a separate Latent Diffusion model to more cleanly upscale images.
Please see the [Image Enhancers Documentation](docs/6.image_enhancers.md) to learn more.
Please see the [Post-Processing Documentation](https://sygil-dev.github.io/sygil-webui/docs/post-processing) to learn more.
-----
@ -162,12 +162,12 @@ Please see the [Image Enhancers Documentation](docs/6.image_enhancers.md) to lea
*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*
[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)<br/>
[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://ommer-lab.com/research/latent-diffusion-models/)
[Robin Rombach](https://github.com/rromb)\*,
[Andreas Blattmann](https://github.com/ablattmann)\*,
[Dominik Lorenz](https://github.com/qp-qp)\,
[Patrick Esser](https://github.com/pesser),
[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)
**CVPR '22 Oral**

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backend/SwinIR Submodule

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Subproject commit 41d8c990adfbeeba929f20ae11d3a8494a83d12d

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backend/clip-as-service Submodule

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Subproject commit 9bb7d1f47d19e15e844108dec5f84cabcce7975d

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backend/clipseg Submodule

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Subproject commit 656e0c662bd1c9a5ae511011642da5b7d8503312

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backend/dalle_flow Submodule

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Subproject commit 491c52af85f6d75d30094974c97a5a0ed53ba6db

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backend/glid-3-xl Submodule

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Subproject commit b21a3acdd478a4fa41c529b55199c8ac3b1b807a

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backend/latent-diffusion Submodule

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Subproject commit a506df5756472e2ebaf9078affdde2c4f1502cd4

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backend/stable-diffusion Submodule

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Subproject commit 2de63ea62862106de27706cd280e692f34c12d9f

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configs/stable-diffusion/v2-inference-v.yaml Normal file
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model:
base_learning_rate: 1.0e-4
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
parameterization: "v"
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False # we set this to false because this is an inference only config
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
use_checkpoint: True
use_fp16: True
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_head_channels: 64 # need to fix for flash-attn
use_spatial_transformer: True
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
#attn_type: "vanilla-xformers"
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
params:
freeze: True
layer: "penultimate"

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model:
base_learning_rate: 1.0e-4
target: ldm.models.diffusion.ddpm.LatentDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false
conditioning_key: crossattn
monitor: val/loss_simple_ema
scale_factor: 0.18215
use_ema: False # we set this to false because this is an inference only config
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
use_checkpoint: True
use_fp16: True
image_size: 32 # unused
in_channels: 4
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_head_channels: 64 # need to fix for flash-attn
use_spatial_transformer: True
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
#attn_type: "vanilla-xformers"
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: []
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
params:
freeze: True
layer: "penultimate"

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configs/stable-diffusion/v2-inpainting-inference.yaml Normal file
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@ -0,0 +1,158 @@
model:
base_learning_rate: 5.0e-05
target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false
conditioning_key: hybrid
scale_factor: 0.18215
monitor: val/loss_simple_ema
finetune_keys: null
use_ema: False
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
use_checkpoint: True
image_size: 32 # unused
in_channels: 9
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_head_channels: 64 # need to fix for flash-attn
use_spatial_transformer: True
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
#attn_type: "vanilla-xformers"
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: [ ]
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
params:
freeze: True
layer: "penultimate"
data:
target: ldm.data.laion.WebDataModuleFromConfig
params:
tar_base: null # for concat as in LAION-A
p_unsafe_threshold: 0.1
filter_word_list: "data/filters.yaml"
max_pwatermark: 0.45
batch_size: 8
num_workers: 6
multinode: True
min_size: 512
train:
shards:
- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -"
- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -"
- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -"
- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -"
- "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar"
shuffle: 10000
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.RandomCrop
params:
size: 512
postprocess:
target: ldm.data.laion.AddMask
params:
mode: "512train-large"
p_drop: 0.25
# NOTE use enough shards to avoid empty validation loops in workers
validation:
shards:
- "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - "
shuffle: 0
image_key: jpg
image_transforms:
- target: torchvision.transforms.Resize
params:
size: 512
interpolation: 3
- target: torchvision.transforms.CenterCrop
params:
size: 512
postprocess:
target: ldm.data.laion.AddMask
params:
mode: "512train-large"
p_drop: 0.25
lightning:
find_unused_parameters: True
modelcheckpoint:
params:
every_n_train_steps: 5000
callbacks:
metrics_over_trainsteps_checkpoint:
params:
every_n_train_steps: 10000
image_logger:
target: main.ImageLogger
params:
enable_autocast: False
disabled: False
batch_frequency: 1000
max_images: 4
increase_log_steps: False
log_first_step: False
log_images_kwargs:
use_ema_scope: False
inpaint: False
plot_progressive_rows: False
plot_diffusion_rows: False
N: 4
unconditional_guidance_scale: 5.0
unconditional_guidance_label: [""]
ddim_steps: 50 # todo check these out for depth2img,
ddim_eta: 0.0 # todo check these out for depth2img,
trainer:
benchmark: True
val_check_interval: 5000000
num_sanity_val_steps: 0
accumulate_grad_batches: 1

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configs/stable-diffusion/v2-midas-inference.yaml Normal file
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model:
base_learning_rate: 5.0e-07
target: ldm.models.diffusion.ddpm.LatentDepth2ImageDiffusion
params:
linear_start: 0.00085
linear_end: 0.0120
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 64
channels: 4
cond_stage_trainable: false
conditioning_key: hybrid
scale_factor: 0.18215
monitor: val/loss_simple_ema
finetune_keys: null
use_ema: False
depth_stage_config:
target: ldm.modules.midas.api.MiDaSInference
params:
model_type: "dpt_hybrid"
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
use_checkpoint: True
image_size: 32 # unused
in_channels: 5
out_channels: 4
model_channels: 320
attention_resolutions: [ 4, 2, 1 ]
num_res_blocks: 2
channel_mult: [ 1, 2, 4, 4 ]
num_head_channels: 64 # need to fix for flash-attn
use_spatial_transformer: True
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
legacy: False
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
monitor: val/rec_loss
ddconfig:
#attn_type: "vanilla-xformers"
double_z: true
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult:
- 1
- 2
- 4
- 4
num_res_blocks: 2
attn_resolutions: [ ]
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
params:
freeze: True
layer: "penultimate"

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configs/stable-diffusion/x4-upscaling.yaml Normal file
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model:
base_learning_rate: 1.0e-04
target: ldm.models.diffusion.ddpm.LatentUpscaleDiffusion
params:
parameterization: "v"
low_scale_key: "lr"
linear_start: 0.0001
linear_end: 0.02
num_timesteps_cond: 1
log_every_t: 200
timesteps: 1000
first_stage_key: "jpg"
cond_stage_key: "txt"
image_size: 128
channels: 4
cond_stage_trainable: false
conditioning_key: "hybrid-adm"
monitor: val/loss_simple_ema
scale_factor: 0.08333
use_ema: False
low_scale_config:
target: ldm.modules.diffusionmodules.upscaling.ImageConcatWithNoiseAugmentation
params:
noise_schedule_config: # image space
linear_start: 0.0001
linear_end: 0.02
max_noise_level: 350
unet_config:
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
params:
use_checkpoint: True
num_classes: 1000 # timesteps for noise conditioning (here constant, just need one)
image_size: 128
in_channels: 7
out_channels: 4
model_channels: 256
attention_resolutions: [ 2,4,8]
num_res_blocks: 2
channel_mult: [ 1, 2, 2, 4]
disable_self_attentions: [True, True, True, False]
disable_middle_self_attn: False
num_heads: 8
use_spatial_transformer: True
transformer_depth: 1
context_dim: 1024
legacy: False
use_linear_in_transformer: True
first_stage_config:
target: ldm.models.autoencoder.AutoencoderKL
params:
embed_dim: 4
ddconfig:
# attn_type: "vanilla-xformers" this model needs efficient attention to be feasible on HR data, also the decoder seems to break in half precision (UNet is fine though)
double_z: True
z_channels: 4
resolution: 256
in_channels: 3
out_ch: 3
ch: 128
ch_mult: [ 1,2,4 ] # num_down = len(ch_mult)-1
num_res_blocks: 2
attn_resolutions: [ ]
dropout: 0.0
lossconfig:
target: torch.nn.Identity
cond_stage_config:
target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
params:
freeze: True
layer: "penultimate"

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configs/webui/webui_flet.yaml Normal file
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# This file is part of sygil-webui (https://github.com/Sygil-Dev/sygil-webui/).
# Copyright 2022 Sygil-Dev team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# UI defaults configuration file. It is automatically loaded if located at configs/webui/webui_flet.yaml.
# Any changes made here will be available automatically on the web app without having to stop it.
# You may add overrides in a file named "userconfig_flet.yaml" in this folder, which can contain any subset
# of the properties below.
# any section labeled '_page' will get it's own tab in settings
# any section without that suffix will still be read by parser and stored in session
#
# display types
# -- every display type must have 'value: '
# -- to do: add 'tooltip : ' to every display type
# --(make optional, not everything needs one.)
# bool
# -value
# dropdown
# -value
# -option_list
# slider
# -value
# -min
# -max
# -step
# textinput
# -value
#
# list of value types
# !!bool boolean 'true' 'false'
# !!float float '0.01'
# !!int integer '23'
# !!str string 'foo' 'bar'
# !!null None
webui_page:
default_theme:
display: dropdown
value: 'dark'
option_list:
- !!str 'dark'
- !!str 'light'
default_text_size:
display: slider
value: !!int '20'
min: !!int '10'
max: !!int '32'
step: !!float '2.0'
max_message_history:
display: slider
value: !!int '20'
min: !!int '1'
max: !!int '100'
step: !!int '1'
general_page:
huggingface_token:
display: textinput
value: !!str ''
stable_horde_api:
display: textinput
value: !!str '0000000000'
global_negative_prompt:
display: textinput
value: !!str " "
default_model:
display: textinput
value: !!str "Stable Diffusion v1.5"
base_model:
display: textinput
value: !!str "Stable Diffusion v1.5"
default_model_config:
display: textinput
value: !!str "configs/stable-diffusion/v1-inference.yaml"
default_model_path:
display: textinput
value: !!str "models/ldm/stable-diffusion-v1/Stable Diffusion v1.5.ckpt"
use_sd_concepts_library:
display: bool
value: !!bool 'true'
sd_concepts_library_folder:
display: textinput
value: !!str "models/custom/sd-concepts-library"
GFPGAN_dir:
display: textinput
value: !!str "./models/gfpgan"
GFPGAN_model:
display: textinput
value: !!str "GFPGANv1.4"
LDSR_dir:
display: textinput
value: !!str "./models/ldsr"
LDSR_model:
display: textinput
value: !!str "model"
RealESRGAN_dir:
display: textinput
value: !!str "./models/realesrgan"
RealESRGAN_model:
display: textinput
value: !!str "RealESRGAN_x4plus"
upscaling_method:
display: textinput
value: !!str "RealESRGAN"
output_page:
outdir:
display: textinput
value: !!str 'outputs'
outdir_txt2img:
display: textinput
value: !!str "outputs/txt2img"
outdir_img2img:
display: textinput
value: !!str "outputs/img2img"
outdir_img2txt:
display: textinput
value: !!str "outputs/img2txt"
save_metadata:
display: bool
value: !!bool true
save_format:
display: dropdown
value: !!str "png"
option_list:
- !!str 'png'
- !!str 'jpeg'
skip_grid:
display: bool
value: !!bool 'false'
skip_save:
display: bool
value: !!bool 'false'
#grid_quality: 95
#n_rows: -1
#update_preview: True
#update_preview_frequency: 10
performance_page:
gpu:
display: dropdown
value: !!str ''
option_list:
- !!str '0:'
gfpgan_cpu:
display: bool
value: !!bool 'false'
esrgan_cpu:
display: bool
value: !!bool 'false'
extra_models_cpu:
display: bool
value: !!bool 'false'
extra_models_gpu:
display: bool
value: !!bool 'false'
gfpgan_gpu:
display: textinput
value: !!int 0
esrgan_gpu:
display: textinput
value: !!int 0
keep_all_models_loaded:
display: bool
value: !!bool 'false'
#no_verify_input: False
#no_half: False
#use_float16: False
#precision: "autocast"
#optimized: False
#optimized_turbo: False
#optimized_config: "optimizedSD/v1-inference.yaml"
#enable_attention_slicing: False
#enable_minimal_memory_usage: False
server_page:
hide_server_setting:
display: bool
value: !!bool 'false'
hide_browser_setting:
display: bool
value: !!bool 'false'
textual_inversion:
pretrained_model_name_or_path: "models/diffusers/stable-diffusion-v1-5"
tokenizer_name: "models/clip-vit-large-patch14"

3
configs/webui/webui_streamlit.yaml
View File

@ -59,6 +59,7 @@ general:
no_half: False
use_float16: False
precision: "autocast"
use_cudnn: False
optimized: False
optimized_turbo: False
optimized_config: "optimizedSD/v1-inference.yaml"
@ -70,6 +71,7 @@ general:
admin:
hide_server_setting: False
hide_browser_setting: False
global_negative_prompt: ""
debug:
enable_hydralit: False
@ -219,6 +221,7 @@ txt2vid:
beta_scheduler_type: "scaled_linear"
max_duration_in_seconds: 30
fps: 30
LDSR_config:
sampling_steps: 50

9
data/img2txt/mediums.txt
View File

@ -1,4 +1,12 @@
a 2 koma
a 2koma
a 3D render
a 4 koma
a 4koma
a 6 koma
a 6koma
a 8 koma
a 8koma
a black and white photo
a bronze sculpture
a cartoon
@ -25,6 +33,7 @@ a gouache
a hologram
a hyperrealistic painting
a jigsaw puzzle
a koma
a low poly render
a macro photograph
a manga drawing

10
docs/1.Installation/1.one-click-installer.md
View File

@ -8,15 +8,21 @@ Home Page: https://github.com/Sygil-Dev/sygil-webui
### Installation on Windows:
- Clone or download the code from the [Repository](https://github.com/Sygil-Dev/sygil-webui).
- Double-click the `installer/install.bat` file and wait for it to handle everything for you.
- Open the `installer` folder and copy the `install.bat` to the root folder next to the `webui.bat`
- Double-click the `install.bat` file and wait for it to handle everything for you.
### Installation on Linux:
- Clone or download the code from the [Repository](https://github.com/Sygil-Dev/sygil-webui).
- Open a terminal on the folder where the code is located and run `./installer/install.sh` ,make sure it has the right permissions and can be executed.
- Open the `installer` folder and copy the `install.sh` to the root folder next to the `webui.sh`
- Open a terminal on the folder where the code is located and run `./install.sh` ,make sure it has the right permissions and can be executed.
- Wait for the installer to handle everything for you.

11
environment.yaml
View File

@ -15,22 +15,21 @@ name: ldm
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
channels:
- conda-forge
- pytorch
- defaults
- nvidia
# Psst. If you change a dependency, make sure it's mirrored in the docker requirement
# files as well.
dependencies:
- nodejs=18.11.0
- conda-forge::nodejs=18.11.0
- yarn=1.22.19
- cudatoolkit=11.3
- cudatoolkit=11.7
- git
- numpy=1.22.3
- numpy=1.23.3
- pip=20.3
- python=3.8.5
- pytorch=1.11.0
- pytorch=1.13.0
- scikit-image=0.19.2
- torchvision=0.12.0
- torchvision=0.14.0
- pip:
- -r requirements.txt

0
ldm/__init__.py Normal file
View File

101
ldm/data/__init__.py
View File

@ -1,101 +0,0 @@
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from data.coco_karpathy_dataset import coco_karpathy_train, coco_karpathy_caption_eval, coco_karpathy_retrieval_eval
from data.nocaps_dataset import nocaps_eval
from data.flickr30k_dataset import flickr30k_train, flickr30k_retrieval_eval
from data.vqa_dataset import vqa_dataset
from data.nlvr_dataset import nlvr_dataset
from data.pretrain_dataset import pretrain_dataset
from transform.randaugment import RandomAugment
def create_dataset(dataset, config, min_scale=0.5):
normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
transform_train = transforms.Compose([
transforms.RandomResizedCrop(config['image_size'],scale=(min_scale, 1.0),interpolation=InterpolationMode.BICUBIC),
transforms.RandomHorizontalFlip(),
RandomAugment(2,5,isPIL=True,augs=['Identity','AutoContrast','Brightness','Sharpness','Equalize',
'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']),
transforms.ToTensor(),
normalize,
])
transform_test = transforms.Compose([
transforms.Resize((config['image_size'],config['image_size']),interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
normalize,
])
if dataset=='pretrain':
dataset = pretrain_dataset(config['train_file'], config['laion_path'], transform_train)
return dataset
elif dataset=='caption_coco':
train_dataset = coco_karpathy_train(transform_train, config['image_root'], config['ann_root'], prompt=config['prompt'])
val_dataset = coco_karpathy_caption_eval(transform_test, config['image_root'], config['ann_root'], 'val')
test_dataset = coco_karpathy_caption_eval(transform_test, config['image_root'], config['ann_root'], 'test')
return train_dataset, val_dataset, test_dataset
elif dataset=='nocaps':
val_dataset = nocaps_eval(transform_test, config['image_root'], config['ann_root'], 'val')
test_dataset = nocaps_eval(transform_test, config['image_root'], config['ann_root'], 'test')
return val_dataset, test_dataset
elif dataset=='retrieval_coco':
train_dataset = coco_karpathy_train(transform_train, config['image_root'], config['ann_root'])
val_dataset = coco_karpathy_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'val')
test_dataset = coco_karpathy_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'test')
return train_dataset, val_dataset, test_dataset
elif dataset=='retrieval_flickr':
train_dataset = flickr30k_train(transform_train, config['image_root'], config['ann_root'])
val_dataset = flickr30k_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'val')
test_dataset = flickr30k_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'test')
return train_dataset, val_dataset, test_dataset
elif dataset=='vqa':
train_dataset = vqa_dataset(transform_train, config['ann_root'], config['vqa_root'], config['vg_root'],
train_files = config['train_files'], split='train')
test_dataset = vqa_dataset(transform_test, config['ann_root'], config['vqa_root'], config['vg_root'], split='test')
return train_dataset, test_dataset
elif dataset=='nlvr':
train_dataset = nlvr_dataset(transform_train, config['image_root'], config['ann_root'],'train')
val_dataset = nlvr_dataset(transform_test, config['image_root'], config['ann_root'],'val')
test_dataset = nlvr_dataset(transform_test, config['image_root'], config['ann_root'],'test')
return train_dataset, val_dataset, test_dataset
def create_sampler(datasets, shuffles, num_tasks, global_rank):
samplers = []
for dataset,shuffle in zip(datasets,shuffles):
sampler = torch.utils.data.DistributedSampler(dataset, num_replicas=num_tasks, rank=global_rank, shuffle=shuffle)
samplers.append(sampler)
return samplers
def create_loader(datasets, samplers, batch_size, num_workers, is_trains, collate_fns):
loaders = []
for dataset,sampler,bs,n_worker,is_train,collate_fn in zip(datasets,samplers,batch_size,num_workers,is_trains,collate_fns):
if is_train:
shuffle = (sampler is None)
drop_last = True
else:
shuffle = False
drop_last = False
loader = DataLoader(
dataset,
batch_size=bs,
num_workers=n_worker,
pin_memory=True,
sampler=sampler,
shuffle=shuffle,
collate_fn=collate_fn,
drop_last=drop_last,
)
loaders.append(loader)
return loaders

16
ldm/data/base.py
View File

@ -1,11 +1,17 @@
from abc import abstractmethod
from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
from torch.utils.data import (
Dataset,
ConcatDataset,
ChainDataset,
IterableDataset,
)
class Txt2ImgIterableBaseDataset(IterableDataset):
'''
"""
Define an interface to make the IterableDatasets for text2img data chainable
'''
"""
def __init__(self, num_records=0, valid_ids=None, size=256):
super().__init__()
self.num_records = num_records
@ -13,7 +19,9 @@ class Txt2ImgIterableBaseDataset(IterableDataset):
self.sample_ids = valid_ids
self.size = size
print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')
print(
f'{self.__class__.__name__} dataset contains {self.__len__()} examples.'
)
def __len__(self):
return self.num_records

310
ldm/data/imagenet.py
View File

@ -11,13 +11,21 @@ from tqdm import tqdm
from torch.utils.data import Dataset, Subset
import taming.data.utils as tdu
from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve
from taming.data.imagenet import (
str_to_indices,
give_synsets_from_indices,
download,
retrieve,
)
from taming.data.imagenet import ImagePaths
from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
from ldm.modules.image_degradation import (
degradation_fn_bsr,
degradation_fn_bsr_light,
)
def synset2idx(path_to_yaml="data/index_synset.yaml"):
def synset2idx(path_to_yaml='data/index_synset.yaml'):
with open(path_to_yaml) as f:
di2s = yaml.load(f)
return dict((v, k) for k, v in di2s.items())
@ -28,7 +36,9 @@ class ImageNetBase(Dataset):
self.config = config or OmegaConf.create()
if not type(self.config) == dict:
self.config = OmegaConf.to_container(self.config)
self.keep_orig_class_label = self.config.get("keep_orig_class_label", False)
self.keep_orig_class_label = self.config.get(
'keep_orig_class_label', False
)
self.process_images = True # if False we skip loading & processing images and self.data contains filepaths
self._prepare()
self._prepare_synset_to_human()
@ -46,17 +56,23 @@ class ImageNetBase(Dataset):
raise NotImplementedError()
def _filter_relpaths(self, relpaths):
ignore = set([
"n06596364_9591.JPEG",
])
relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore]
if "sub_indices" in self.config:
indices = str_to_indices(self.config["sub_indices"])
synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn) # returns a list of strings
ignore = set(
[
'n06596364_9591.JPEG',
]
)
relpaths = [
rpath for rpath in relpaths if not rpath.split('/')[-1] in ignore
]
if 'sub_indices' in self.config:
indices = str_to_indices(self.config['sub_indices'])
synsets = give_synsets_from_indices(
indices, path_to_yaml=self.idx2syn
) # returns a list of strings
self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)
files = []
for rpath in relpaths:
syn = rpath.split("/")[0]
syn = rpath.split('/')[0]
if syn in synsets:
files.append(rpath)
return files
@ -65,64 +81,75 @@ class ImageNetBase(Dataset):
def _prepare_synset_to_human(self):
SIZE = 2655750
URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
self.human_dict = os.path.join(self.root, "synset_human.txt")
if (not os.path.exists(self.human_dict) or
not os.path.getsize(self.human_dict)==SIZE):
URL = 'https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1'
self.human_dict = os.path.join(self.root, 'synset_human.txt')
if (
not os.path.exists(self.human_dict)
or not os.path.getsize(self.human_dict) == SIZE
):
download(URL, self.human_dict)
def _prepare_idx_to_synset(self):
URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
self.idx2syn = os.path.join(self.root, "index_synset.yaml")
if (not os.path.exists(self.idx2syn)):
URL = 'https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1'
self.idx2syn = os.path.join(self.root, 'index_synset.yaml')
if not os.path.exists(self.idx2syn):
download(URL, self.idx2syn)
def _prepare_human_to_integer_label(self):
URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt")
if (not os.path.exists(self.human2integer)):
URL = 'https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1'
self.human2integer = os.path.join(
self.root, 'imagenet1000_clsidx_to_labels.txt'
)
if not os.path.exists(self.human2integer):
download(URL, self.human2integer)
with open(self.human2integer, "r") as f:
with open(self.human2integer, 'r') as f:
lines = f.read().splitlines()
assert len(lines) == 1000
self.human2integer_dict = dict()
for line in lines:
value, key = line.split(":")
value, key = line.split(':')
self.human2integer_dict[key] = int(value)
def _load(self):
with open(self.txt_filelist, "r") as f:
with open(self.txt_filelist, 'r') as f:
self.relpaths = f.read().splitlines()
l1 = len(self.relpaths)
self.relpaths = self._filter_relpaths(self.relpaths)
print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths)))
print(
'Removed {} files from filelist during filtering.'.format(
l1 - len(self.relpaths)
)
)
self.synsets = [p.split("/")[0] for p in self.relpaths]
self.synsets = [p.split('/')[0] for p in self.relpaths]
self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]
unique_synsets = np.unique(self.synsets)
class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
class_dict = dict(
(synset, i) for i, synset in enumerate(unique_synsets)
)
if not self.keep_orig_class_label:
self.class_labels = [class_dict[s] for s in self.synsets]
else:
self.class_labels = [self.synset2idx[s] for s in self.synsets]
with open(self.human_dict, "r") as f:
with open(self.human_dict, 'r') as f:
human_dict = f.read().splitlines()
human_dict = dict(line.split(maxsplit=1) for line in human_dict)
self.human_labels = [human_dict[s] for s in self.synsets]
labels = {
"relpath": np.array(self.relpaths),
"synsets": np.array(self.synsets),
"class_label": np.array(self.class_labels),
"human_label": np.array(self.human_labels),
'relpath': np.array(self.relpaths),
'synsets': np.array(self.synsets),
'class_label': np.array(self.class_labels),
'human_label': np.array(self.human_labels),
}
if self.process_images:
self.size = retrieve(self.config, "size", default=256)
self.data = ImagePaths(self.abspaths,
self.size = retrieve(self.config, 'size', default=256)
self.data = ImagePaths(
self.abspaths,
labels=labels,
size=self.size,
random_crop=self.random_crop,
@ -132,11 +159,11 @@ class ImageNetBase(Dataset):
class ImageNetTrain(ImageNetBase):
NAME = "ILSVRC2012_train"
URL = "http://www.image-net.org/challenges/LSVRC/2012/"
AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
NAME = 'ILSVRC2012_train'
URL = 'http://www.image-net.org/challenges/LSVRC/2012/'
AT_HASH = 'a306397ccf9c2ead27155983c254227c0fd938e2'
FILES = [
"ILSVRC2012_img_train.tar",
'ILSVRC2012_img_train.tar',
]
SIZES = [
147897477120,
@ -151,57 +178,64 @@ class ImageNetTrain(ImageNetBase):
if self.data_root:
self.root = os.path.join(self.data_root, self.NAME)
else:
cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
cachedir = os.environ.get(
'XDG_CACHE_HOME', os.path.expanduser('~/.cache')
)
self.root = os.path.join(cachedir, 'autoencoders/data', self.NAME)
self.datadir = os.path.join(self.root, "data")
self.txt_filelist = os.path.join(self.root, "filelist.txt")
self.datadir = os.path.join(self.root, 'data')
self.txt_filelist = os.path.join(self.root, 'filelist.txt')
self.expected_length = 1281167
self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop",
default=True)
self.random_crop = retrieve(
self.config, 'ImageNetTrain/random_crop', default=True
)
if not tdu.is_prepared(self.root):
# prep
print("Preparing dataset {} in {}".format(self.NAME, self.root))
print('Preparing dataset {} in {}'.format(self.NAME, self.root))
datadir = self.datadir
if not os.path.exists(datadir):
path = os.path.join(self.root, self.FILES[0])
if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
if (
not os.path.exists(path)
or not os.path.getsize(path) == self.SIZES[0]
):
import academictorrents as at
atpath = at.get(self.AT_HASH, datastore=self.root)
assert atpath == path
print("Extracting {} to {}".format(path, datadir))
print('Extracting {} to {}'.format(path, datadir))
os.makedirs(datadir, exist_ok=True)
with tarfile.open(path, "r:") as tar:
with tarfile.open(path, 'r:') as tar:
tar.extractall(path=datadir)
print("Extracting sub-tars.")
subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
print('Extracting sub-tars.')
subpaths = sorted(glob.glob(os.path.join(datadir, '*.tar')))
for subpath in tqdm(subpaths):
subdir = subpath[:-len(".tar")]
subdir = subpath[: -len('.tar')]
os.makedirs(subdir, exist_ok=True)
with tarfile.open(subpath, "r:") as tar:
with tarfile.open(subpath, 'r:') as tar:
tar.extractall(path=subdir)
filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
filelist = glob.glob(os.path.join(datadir, '**', '*.JPEG'))
filelist = [os.path.relpath(p, start=datadir) for p in filelist]
filelist = sorted(filelist)
filelist = "\n".join(filelist)+"\n"
with open(self.txt_filelist, "w") as f:
filelist = '\n'.join(filelist) + '\n'
with open(self.txt_filelist, 'w') as f:
f.write(filelist)
tdu.mark_prepared(self.root)
class ImageNetValidation(ImageNetBase):
NAME = "ILSVRC2012_validation"
URL = "http://www.image-net.org/challenges/LSVRC/2012/"
AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
NAME = 'ILSVRC2012_validation'
URL = 'http://www.image-net.org/challenges/LSVRC/2012/'
AT_HASH = '5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5'
VS_URL = 'https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1'
FILES = [
"ILSVRC2012_img_val.tar",
"validation_synset.txt",
'ILSVRC2012_img_val.tar',
'validation_synset.txt',
]
SIZES = [
6744924160,
@ -217,39 +251,49 @@ class ImageNetValidation(ImageNetBase):
if self.data_root:
self.root = os.path.join(self.data_root, self.NAME)
else:
cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
self.datadir = os.path.join(self.root, "data")
self.txt_filelist = os.path.join(self.root, "filelist.txt")
cachedir = os.environ.get(
'XDG_CACHE_HOME', os.path.expanduser('~/.cache')
)
self.root = os.path.join(cachedir, 'autoencoders/data', self.NAME)
self.datadir = os.path.join(self.root, 'data')
self.txt_filelist = os.path.join(self.root, 'filelist.txt')
self.expected_length = 50000
self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop",
default=False)
self.random_crop = retrieve(
self.config, 'ImageNetValidation/random_crop', default=False
)
if not tdu.is_prepared(self.root):
# prep
print("Preparing dataset {} in {}".format(self.NAME, self.root))
print('Preparing dataset {} in {}'.format(self.NAME, self.root))
datadir = self.datadir
if not os.path.exists(datadir):
path = os.path.join(self.root, self.FILES[0])
if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
if (
not os.path.exists(path)
or not os.path.getsize(path) == self.SIZES[0]
):
import academictorrents as at
atpath = at.get(self.AT_HASH, datastore=self.root)
assert atpath == path
print("Extracting {} to {}".format(path, datadir))
print('Extracting {} to {}'.format(path, datadir))
os.makedirs(datadir, exist_ok=True)
with tarfile.open(path, "r:") as tar:
with tarfile.open(path, 'r:') as tar:
tar.extractall(path=datadir)
vspath = os.path.join(self.root, self.FILES[1])
if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]:
if (
not os.path.exists(vspath)
or not os.path.getsize(vspath) == self.SIZES[1]
):
download(self.VS_URL, vspath)
with open(vspath, "r") as f:
with open(vspath, 'r') as f:
synset_dict = f.read().splitlines()
synset_dict = dict(line.split() for line in synset_dict)
print("Reorganizing into synset folders")
print('Reorganizing into synset folders')
synsets = np.unique(list(synset_dict.values()))
for s in synsets:
os.makedirs(os.path.join(datadir, s), exist_ok=True)
@ -258,21 +302,26 @@ class ImageNetValidation(ImageNetBase):
dst = os.path.join(datadir, v)
shutil.move(src, dst)
filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
filelist = glob.glob(os.path.join(datadir, '**', '*.JPEG'))
filelist = [os.path.relpath(p, start=datadir) for p in filelist]
filelist = sorted(filelist)
filelist = "\n".join(filelist)+"\n"
with open(self.txt_filelist, "w") as f:
filelist = '\n'.join(filelist) + '\n'
with open(self.txt_filelist, 'w') as f:
f.write(filelist)
tdu.mark_prepared(self.root)
class ImageNetSR(Dataset):
def __init__(self, size=None,
degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1.,
random_crop=True):
def __init__(
self,
size=None,
degradation=None,
downscale_f=4,
min_crop_f=0.5,
max_crop_f=1.0,
random_crop=True,
):
"""
Imagenet Superresolution Dataloader
Performs following ops in order:
@ -296,67 +345,86 @@ class ImageNetSR(Dataset):
self.LR_size = int(size / downscale_f)
self.min_crop_f = min_crop_f
self.max_crop_f = max_crop_f
assert(max_crop_f <= 1.)
assert max_crop_f <= 1.0
self.center_crop = not random_crop
self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA)
self.image_rescaler = albumentations.SmallestMaxSize(
max_size=size, interpolation=cv2.INTER_AREA
)
self.pil_interpolation = False # gets reset later if incase interp_op is from pillow
self.pil_interpolation = (
False # gets reset later if incase interp_op is from pillow
)
if degradation == "bsrgan":
self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)
if degradation == 'bsrgan':
self.degradation_process = partial(
degradation_fn_bsr, sf=downscale_f
)
elif degradation == "bsrgan_light":
self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)
elif degradation == 'bsrgan_light':
self.degradation_process = partial(
degradation_fn_bsr_light, sf=downscale_f
)
else:
interpolation_fn = {
"cv_nearest": cv2.INTER_NEAREST,
"cv_bilinear": cv2.INTER_LINEAR,
"cv_bicubic": cv2.INTER_CUBIC,
"cv_area": cv2.INTER_AREA,
"cv_lanczos": cv2.INTER_LANCZOS4,
"pil_nearest": PIL.Image.NEAREST,
"pil_bilinear": PIL.Image.BILINEAR,
"pil_bicubic": PIL.Image.BICUBIC,
"pil_box": PIL.Image.BOX,
"pil_hamming": PIL.Image.HAMMING,
"pil_lanczos": PIL.Image.LANCZOS,
'cv_nearest': cv2.INTER_NEAREST,
'cv_bilinear': cv2.INTER_LINEAR,
'cv_bicubic': cv2.INTER_CUBIC,
'cv_area': cv2.INTER_AREA,
'cv_lanczos': cv2.INTER_LANCZOS4,
'pil_nearest': PIL.Image.NEAREST,
'pil_bilinear': PIL.Image.BILINEAR,
'pil_bicubic': PIL.Image.BICUBIC,
'pil_box': PIL.Image.BOX,
'pil_hamming': PIL.Image.HAMMING,
'pil_lanczos': PIL.Image.LANCZOS,
}[degradation]
self.pil_interpolation = degradation.startswith("pil_")
self.pil_interpolation = degradation.startswith('pil_')
if self.pil_interpolation:
self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn)
self.degradation_process = partial(
TF.resize,
size=self.LR_size,
interpolation=interpolation_fn,
)
else:
self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size,
interpolation=interpolation_fn)
self.degradation_process = albumentations.SmallestMaxSize(
max_size=self.LR_size, interpolation=interpolation_fn
)
def __len__(self):
return len(self.base)
def __getitem__(self, i):
example = self.base[i]
image = Image.open(example["file_path_"])
image = Image.open(example['file_path_'])
if not image.mode == "RGB":
image = image.convert("RGB")
if not image.mode == 'RGB':
image = image.convert('RGB')
image = np.array(image).astype(np.uint8)
min_side_len = min(image.shape[:2])
crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)
crop_side_len = min_side_len * np.random.uniform(
self.min_crop_f, self.max_crop_f, size=None
)
crop_side_len = int(crop_side_len)
if self.center_crop:
self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)
self.cropper = albumentations.CenterCrop(
height=crop_side_len, width=crop_side_len
)
else:
self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)
self.cropper = albumentations.RandomCrop(
height=crop_side_len, width=crop_side_len
)
image = self.cropper(image=image)["image"]
image = self.image_rescaler(image=image)["image"]
image = self.cropper(image=image)['image']
image = self.image_rescaler(image=image)['image']
if self.pil_interpolation:
image_pil = PIL.Image.fromarray(image)
@ -364,10 +432,10 @@ class ImageNetSR(Dataset):
LR_image = np.array(LR_image).astype(np.uint8)
else:
LR_image = self.degradation_process(image=image)["image"]
LR_image = self.degradation_process(image=image)['image']
example["image"] = (image/127.5 - 1.0).astype(np.float32)
example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32)
example['image'] = (image / 127.5 - 1.0).astype(np.float32)
example['LR_image'] = (LR_image / 127.5 - 1.0).astype(np.float32)
return example
@ -377,9 +445,11 @@ class ImageNetSRTrain(ImageNetSR):
super().__init__(**kwargs)
def get_base(self):
with open("data/imagenet_train_hr_indices.p", "rb") as f:
with open('data/imagenet_train_hr_indices.p', 'rb') as f:
indices = pickle.load(f)
dset = ImageNetTrain(process_images=False,)
dset = ImageNetTrain(
process_images=False,
)
return Subset(dset, indices)
@ -388,7 +458,9 @@ class ImageNetSRValidation(ImageNetSR):
super().__init__(**kwargs)
def get_base(self):
with open("data/imagenet_val_hr_indices.p", "rb") as f:
with open('data/imagenet_val_hr_indices.p', 'rb') as f:
indices = pickle.load(f)
dset = ImageNetValidation(process_images=False,)
dset = ImageNetValidation(
process_images=False,
)
return Subset(dset, indices)

94
ldm/data/lsun.py
View File

@ -7,29 +7,32 @@ from torchvision import transforms
class LSUNBase(Dataset):
def __init__(self,
def __init__(
self,
txt_file,
data_root,
size=None,
interpolation="bicubic",
flip_p=0.5
interpolation='bicubic',
flip_p=0.5,
):
self.data_paths = txt_file
self.data_root = data_root
with open(self.data_paths, "r") as f:
with open(self.data_paths, 'r') as f:
self.image_paths = f.read().splitlines()
self._length = len(self.image_paths)
self.labels = {
"relative_file_path_": [l for l in self.image_paths],
"file_path_": [os.path.join(self.data_root, l)
for l in self.image_paths],
'relative_file_path_': [l for l in self.image_paths],
'file_path_': [
os.path.join(self.data_root, l) for l in self.image_paths
],
}
self.size = size
self.interpolation = {"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
self.interpolation = {
'linear': PIL.Image.LINEAR,
'bilinear': PIL.Image.BILINEAR,
'bicubic': PIL.Image.BICUBIC,
'lanczos': PIL.Image.LANCZOS,
}[interpolation]
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
@ -38,55 +41,86 @@ class LSUNBase(Dataset):
def __getitem__(self, i):
example = dict((k, self.labels[k][i]) for k in self.labels)
image = Image.open(example["file_path_"])
if not image.mode == "RGB":
image = image.convert("RGB")
image = Image.open(example['file_path_'])
if not image.mode == 'RGB':
image = image.convert('RGB')
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
crop = min(img.shape[0], img.shape[1])
h, w, = img.shape[0], img.shape[1]
img = img[(h - crop) // 2:(h + crop) // 2,
(w - crop) // 2:(w + crop) // 2]
h, w, = (
img.shape[0],
img.shape[1],
)
img = img[
(h - crop) // 2 : (h + crop) // 2,
(w - crop) // 2 : (w + crop) // 2,
]
image = Image.fromarray(img)
if self.size is not None:
image = image.resize((self.size, self.size), resample=self.interpolation)
image = image.resize(
(self.size, self.size), resample=self.interpolation
)
image = self.flip(image)
image = np.array(image).astype(np.uint8)
example["image"] = (image / 127.5 - 1.0).astype(np.float32)
example['image'] = (image / 127.5 - 1.0).astype(np.float32)
return example
class LSUNChurchesTrain(LSUNBase):
def __init__(self, **kwargs):
super().__init__(txt_file="data/lsun/church_outdoor_train.txt", data_root="data/lsun/churches", **kwargs)
super().__init__(
txt_file='data/lsun/church_outdoor_train.txt',
data_root='data/lsun/churches',
**kwargs
)
class LSUNChurchesValidation(LSUNBase):
def __init__(self, flip_p=0., **kwargs):
super().__init__(txt_file="data/lsun/church_outdoor_val.txt", data_root="data/lsun/churches",
flip_p=flip_p, **kwargs)
def __init__(self, flip_p=0.0, **kwargs):
super().__init__(
txt_file='data/lsun/church_outdoor_val.txt',
data_root='data/lsun/churches',
flip_p=flip_p,
**kwargs
)
class LSUNBedroomsTrain(LSUNBase):
def __init__(self, **kwargs):
super().__init__(txt_file="data/lsun/bedrooms_train.txt", data_root="data/lsun/bedrooms", **kwargs)
super().__init__(
txt_file='data/lsun/bedrooms_train.txt',
data_root='data/lsun/bedrooms',
**kwargs
)
class LSUNBedroomsValidation(LSUNBase):
def __init__(self, flip_p=0.0, **kwargs):
super().__init__(txt_file="data/lsun/bedrooms_val.txt", data_root="data/lsun/bedrooms",
flip_p=flip_p, **kwargs)
super().__init__(
txt_file='data/lsun/bedrooms_val.txt',
data_root='data/lsun/bedrooms',
flip_p=flip_p,
**kwargs
)
class LSUNCatsTrain(LSUNBase):
def __init__(self, **kwargs):
super().__init__(txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs)
super().__init__(
txt_file='data/lsun/cat_train.txt',
data_root='data/lsun/cats',
**kwargs
)
class LSUNCatsValidation(LSUNBase):
def __init__(self, flip_p=0., **kwargs):
super().__init__(txt_file="data/lsun/cat_val.txt", data_root="data/lsun/cats",
flip_p=flip_p, **kwargs)
def __init__(self, flip_p=0.0, **kwargs):
super().__init__(
txt_file='data/lsun/cat_val.txt',
data_root='data/lsun/cats',
flip_p=flip_p,
**kwargs
)

202
ldm/data/personalized.py Normal file
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@ -0,0 +1,202 @@
import os
import numpy as np
import PIL
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
import random
imagenet_templates_smallest = [
'a photo of a {}',
]
imagenet_templates_small = [
'a photo of a {}',
'a rendering of a {}',
'a cropped photo of the {}',
'the photo of a {}',
'a photo of a clean {}',
'a photo of a dirty {}',
'a dark photo of the {}',
'a photo of my {}',
'a photo of the cool {}',
'a close-up photo of a {}',
'a bright photo of the {}',
'a cropped photo of a {}',
'a photo of the {}',
'a good photo of the {}',
'a photo of one {}',
'a close-up photo of the {}',
'a rendition of the {}',
'a photo of the clean {}',
'a rendition of a {}',
'a photo of a nice {}',
'a good photo of a {}',
'a photo of the nice {}',
'a photo of the small {}',
'a photo of the weird {}',
'a photo of the large {}',
'a photo of a cool {}',
'a photo of a small {}',
]
imagenet_dual_templates_small = [
'a photo of a {} with {}',
'a rendering of a {} with {}',
'a cropped photo of the {} with {}',
'the photo of a {} with {}',
'a photo of a clean {} with {}',
'a photo of a dirty {} with {}',
'a dark photo of the {} with {}',
'a photo of my {} with {}',
'a photo of the cool {} with {}',
'a close-up photo of a {} with {}',
'a bright photo of the {} with {}',
'a cropped photo of a {} with {}',
'a photo of the {} with {}',
'a good photo of the {} with {}',
'a photo of one {} with {}',
'a close-up photo of the {} with {}',
'a rendition of the {} with {}',
'a photo of the clean {} with {}',
'a rendition of a {} with {}',
'a photo of a nice {} with {}',
'a good photo of a {} with {}',
'a photo of the nice {} with {}',
'a photo of the small {} with {}',
'a photo of the weird {} with {}',
'a photo of the large {} with {}',
'a photo of a cool {} with {}',
'a photo of a small {} with {}',
]
per_img_token_list = [
'א',
'ב',
'ג',
'ד',
'ה',
'ו',
'ז',
'ח',
'ט',
'י',
'כ',
'ל',
'מ',
'נ',
'ס',
'ע',
'פ',
'צ',
'ק',
'ר',
'ש',
'ת',
]
class PersonalizedBase(Dataset):
def __init__(
self,
data_root,
size=None,
repeats=100,
interpolation='bicubic',
flip_p=0.5,
set='train',
placeholder_token='*',
per_image_tokens=False,
center_crop=False,
mixing_prob=0.25,
coarse_class_text=None,
):
self.data_root = data_root
self.image_paths = [
os.path.join(self.data_root, file_path)
for file_path in os.listdir(self.data_root)
]
# self._length = len(self.image_paths)
self.num_images = len(self.image_paths)
self._length = self.num_images
self.placeholder_token = placeholder_token
self.per_image_tokens = per_image_tokens
self.center_crop = center_crop
self.mixing_prob = mixing_prob
self.coarse_class_text = coarse_class_text
if per_image_tokens:
assert self.num_images < len(
per_img_token_list
), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
if set == 'train':
self._length = self.num_images * repeats
self.size = size
self.interpolation = {
'linear': PIL.Image.LINEAR,
'bilinear': PIL.Image.BILINEAR,
'bicubic': PIL.Image.BICUBIC,
'lanczos': PIL.Image.LANCZOS,
}[interpolation]
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
def __len__(self):
return self._length
def __getitem__(self, i):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
if not image.mode == 'RGB':
image = image.convert('RGB')
placeholder_string = self.placeholder_token
if self.coarse_class_text:
placeholder_string = (
f'{self.coarse_class_text} {placeholder_string}'
)
if self.per_image_tokens and np.random.uniform() < self.mixing_prob:
text = random.choice(imagenet_dual_templates_small).format(
placeholder_string, per_img_token_list[i % self.num_images]
)
else:
text = random.choice(imagenet_templates_small).format(
placeholder_string
)
example['caption'] = text
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
h, w, = (
img.shape[0],
img.shape[1],
)
img = img[
(h - crop) // 2 : (h + crop) // 2,
(w - crop) // 2 : (w + crop) // 2,
]
image = Image.fromarray(img)
if self.size is not None:
image = image.resize(
(self.size, self.size), resample=self.interpolation
)
image = self.flip(image)
image = np.array(image).astype(np.uint8)
example['image'] = (image / 127.5 - 1.0).astype(np.float32)
return example

169
ldm/data/personalized_file.py Normal file
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@ -0,0 +1,169 @@
import os
import numpy as np
import PIL
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
import random
imagenet_templates_small = [
'a painting in the style of {}',
'a rendering in the style of {}',
'a cropped painting in the style of {}',
'the painting in the style of {}',
'a clean painting in the style of {}',
'a dirty painting in the style of {}',
'a dark painting in the style of {}',
'a picture in the style of {}',
'a cool painting in the style of {}',
'a close-up painting in the style of {}',
'a bright painting in the style of {}',
'a cropped painting in the style of {}',
'a good painting in the style of {}',
'a close-up painting in the style of {}',
'a rendition in the style of {}',
'a nice painting in the style of {}',
'a small painting in the style of {}',
'a weird painting in the style of {}',
'a large painting in the style of {}',
]
imagenet_dual_templates_small = [
'a painting in the style of {} with {}',
'a rendering in the style of {} with {}',
'a cropped painting in the style of {} with {}',
'the painting in the style of {} with {}',
'a clean painting in the style of {} with {}',
'a dirty painting in the style of {} with {}',
'a dark painting in the style of {} with {}',
'a cool painting in the style of {} with {}',
'a close-up painting in the style of {} with {}',
'a bright painting in the style of {} with {}',
'a cropped painting in the style of {} with {}',
'a good painting in the style of {} with {}',
'a painting of one {} in the style of {}',
'a nice painting in the style of {} with {}',
'a small painting in the style of {} with {}',
'a weird painting in the style of {} with {}',
'a large painting in the style of {} with {}',
]
per_img_token_list = [
'א',
'ב',
'ג',
'ד',
'ה',
'ו',
'ז',
'ח',
'ט',
'י',
'כ',
'ל',
'מ',
'נ',
'ס',
'ע',
'פ',
'צ',
'ק',
'ר',
'ש',
'ת',
]
class PersonalizedBase(Dataset):
def __init__(
self,
data_root,
size=None,
repeats=100,
interpolation='bicubic',
flip_p=0.5,
set='train',
placeholder_token='*',
per_image_tokens=False,
center_crop=False,
):
self.data_root = data_root
self.image_paths = [
os.path.join(self.data_root, file_path)
for file_path in os.listdir(self.data_root)
]
# self._length = len(self.image_paths)
self.num_images = len(self.image_paths)
self._length = self.num_images
self.placeholder_token = placeholder_token
self.per_image_tokens = per_image_tokens
self.center_crop = center_crop
if per_image_tokens:
assert self.num_images < len(
per_img_token_list
), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
if set == 'train':
self._length = self.num_images * repeats
self.size = size
self.interpolation = {
'linear': PIL.Image.LINEAR,
'bilinear': PIL.Image.BILINEAR,
'bicubic': PIL.Image.BICUBIC,
'lanczos': PIL.Image.LANCZOS,
}[interpolation]
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
def __len__(self):
return self._length
def __getitem__(self, i):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
if not image.mode == 'RGB':
image = image.convert('RGB')
if self.per_image_tokens and np.random.uniform() < 0.25:
text = random.choice(imagenet_dual_templates_small).format(
self.placeholder_token, per_img_token_list[i % self.num_images]
)
else:
text = random.choice(imagenet_templates_small).format(
self.placeholder_token
)
example['caption'] = text
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
h, w, = (
img.shape[0],
img.shape[1],
)
img = img[
(h - crop) // 2 : (h + crop) // 2,
(w - crop) // 2 : (w + crop) // 2,
]
image = Image.fromarray(img)
if self.size is not None:
image = image.resize(
(self.size, self.size), resample=self.interpolation
)
image = self.flip(image)
image = np.array(image).astype(np.uint8)
example['image'] = (image / 127.5 - 1.0).astype(np.float32)
return example

24
ldm/data/util.py Normal file
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@ -0,0 +1,24 @@
import torch
from ldm.modules.midas.api import load_midas_transform
class AddMiDaS(object):
def __init__(self, model_type):
super().__init__()
self.transform = load_midas_transform(model_type)
def pt2np(self, x):
x = ((x + 1.0) * .5).detach().cpu().numpy()
return x
def np2pt(self, x):
x = torch.from_numpy(x) * 2 - 1.
return x
def __call__(self, sample):
# sample['jpg'] is tensor hwc in [-1, 1] at this point
x = self.pt2np(sample['jpg'])
x = self.transform({"image": x})["image"]
sample['midas_in'] = x
return sample

1
ldm/devices/__init__.py Normal file
View File

@ -0,0 +1 @@
from ldm.devices.devices import choose_autocast_device, choose_torch_device

24
ldm/devices/devices.py Normal file
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@ -0,0 +1,24 @@
import torch
from torch import autocast
from contextlib import contextmanager, nullcontext
def choose_torch_device() -> str:
'''Convenience routine for guessing which GPU device to run model on'''
if torch.cuda.is_available():
return 'cuda'
if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
return 'mps'
return 'cpu'
def choose_autocast_device(device):
'''Returns an autocast compatible device from a torch device'''
device_type = device.type # this returns 'mps' on M1
# autocast only for cuda, but GTX 16xx have issues with it
if device_type == 'cuda':
device_name = torch.cuda.get_device_name()
if 'GeForce GTX 1660' in device_name or 'GeForce GTX 1650' in device_name:
return device_type,nullcontext
else:
return device_type,autocast
else:
return 'cpu',nullcontext

87
ldm/lr_scheduler.py
View File

@ -5,27 +5,44 @@ class LambdaWarmUpCosineScheduler:
"""
note: use with a base_lr of 1.0
"""
def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
def __init__(
self,
warm_up_steps,
lr_min,
lr_max,
lr_start,
max_decay_steps,
verbosity_interval=0,
):
self.lr_warm_up_steps = warm_up_steps
self.lr_start = lr_start
self.lr_min = lr_min
self.lr_max = lr_max
self.lr_max_decay_steps = max_decay_steps
self.last_lr = 0.
self.last_lr = 0.0
self.verbosity_interval = verbosity_interval
def schedule(self, n, **kwargs):
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
if n % self.verbosity_interval == 0:
print(
f'current step: {n}, recent lr-multiplier: {self.last_lr}'
)
if n < self.lr_warm_up_steps:
lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
lr = (
self.lr_max - self.lr_start
) / self.lr_warm_up_steps * n + self.lr_start
self.last_lr = lr
return lr
else:
t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
t = (n - self.lr_warm_up_steps) / (
self.lr_max_decay_steps - self.lr_warm_up_steps
)
t = min(t, 1.0)
lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
1 + np.cos(t * np.pi))
1 + np.cos(t * np.pi)
)
self.last_lr = lr
return lr
@ -38,15 +55,30 @@ class LambdaWarmUpCosineScheduler2:
supports repeated iterations, configurable via lists
note: use with a base_lr of 1.0.
"""
def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
def __init__(
self,
warm_up_steps,
f_min,
f_max,
f_start,
cycle_lengths,
verbosity_interval=0,
):
assert (
len(warm_up_steps)
== len(f_min)
== len(f_max)
== len(f_start)
== len(cycle_lengths)
)
self.lr_warm_up_steps = warm_up_steps
self.f_start = f_start
self.f_min = f_min
self.f_max = f_max
self.cycle_lengths = cycle_lengths
self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
self.last_f = 0.
self.last_f = 0.0
self.verbosity_interval = verbosity_interval
def find_in_interval(self, n):
@ -60,17 +92,25 @@ class LambdaWarmUpCosineScheduler2:
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
f"current cycle {cycle}")
if n % self.verbosity_interval == 0:
print(
f'current step: {n}, recent lr-multiplier: {self.last_f}, '
f'current cycle {cycle}'
)
if n < self.lr_warm_up_steps[cycle]:
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
f = (
self.f_max[cycle] - self.f_start[cycle]
) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
self.last_f = f
return f
else:
t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
t = (n - self.lr_warm_up_steps[cycle]) / (
self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]
)
t = min(t, 1.0)
f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
1 + np.cos(t * np.pi))
f = self.f_min[cycle] + 0.5 * (
self.f_max[cycle] - self.f_min[cycle]
) * (1 + np.cos(t * np.pi))
self.last_f = f
return f
@ -79,20 +119,25 @@ class LambdaWarmUpCosineScheduler2:
class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
def schedule(self, n, **kwargs):
cycle = self.find_in_interval(n)
n = n - self.cum_cycles[cycle]
if self.verbosity_interval > 0:
if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
f"current cycle {cycle}")
if n % self.verbosity_interval == 0:
print(
f'current step: {n}, recent lr-multiplier: {self.last_f}, '
f'current cycle {cycle}'
)
if n < self.lr_warm_up_steps[cycle]:
f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
f = (
self.f_max[cycle] - self.f_start[cycle]
) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
self.last_f = f
return f
else:
f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (
self.cycle_lengths[cycle] - n
) / (self.cycle_lengths[cycle])
self.last_f = f
return f

359
ldm/models/autoencoder.py
View File

@ -6,20 +6,23 @@ from contextlib import contextmanager
from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from ldm.modules.distributions.distributions import (
DiagonalGaussianDistribution,
)
from ldm.util import instantiate_from_config
class VQModel(pl.LightningModule):
def __init__(self,
def __init__(
self,
ddconfig,
lossconfig,
n_embed,
embed_dim,
ckpt_path=None,
ignore_keys=[],
image_key="image",
image_key='image',
colorize_nlabels=None,
monitor=None,
batch_resize_range=None,
@ -27,7 +30,7 @@ class VQModel(pl.LightningModule):
lr_g_factor=1.0,
remap=None,
sane_index_shape=False, # tell vector quantizer to return indices as bhw
use_ema=False
use_ema=False,
):
super().__init__()
self.embed_dim = embed_dim
@ -36,24 +39,34 @@ class VQModel(pl.LightningModule):
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.loss = instantiate_from_config(lossconfig)
self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
self.quantize = VectorQuantizer(
n_embed,
embed_dim,
beta=0.25,
remap=remap,
sane_index_shape=sane_index_shape)
self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
sane_index_shape=sane_index_shape,
)
self.quant_conv = torch.nn.Conv2d(ddconfig['z_channels'], embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(
embed_dim, ddconfig['z_channels'], 1
)
if colorize_nlabels is not None:
assert type(colorize_nlabels) == int
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
self.register_buffer(
'colorize', torch.randn(3, colorize_nlabels, 1, 1)
)
if monitor is not None:
self.monitor = monitor
self.batch_resize_range = batch_resize_range
if self.batch_resize_range is not None:
print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
print(
f'{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.'
)
self.use_ema = use_ema
if self.use_ema:
self.model_ema = LitEma(self)
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
print(f'Keeping EMAs of {len(list(self.model_ema.buffers()))}.')
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
@ -66,28 +79,30 @@ class VQModel(pl.LightningModule):
self.model_ema.store(self.parameters())
self.model_ema.copy_to(self)
if context is not None:
print(f"{context}: Switched to EMA weights")
print(f'{context}: Switched to EMA weights')
try:
yield None
finally:
if self.use_ema:
self.model_ema.restore(self.parameters())
if context is not None:
print(f"{context}: Restored training weights")
print(f'{context}: Restored training weights')
def init_from_ckpt(self, path, ignore_keys=list()):
sd = torch.load(path, map_location="cpu")["state_dict"]
sd = torch.load(path, map_location='cpu')['state_dict']
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
print('Deleting key {} from state_dict.'.format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
print(
f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
)
if len(missing) > 0:
print(f"Missing Keys: {missing}")
print(f"Unexpected Keys: {unexpected}")
print(f'Missing Keys: {missing}')
print(f'Unexpected Keys: {unexpected}')
def on_train_batch_end(self, *args, **kwargs):
if self.use_ema:
@ -125,7 +140,11 @@ class VQModel(pl.LightningModule):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
x = (
x.permute(0, 3, 1, 2)
.to(memory_format=torch.contiguous_format)
.float()
)
if self.batch_resize_range is not None:
lower_size = self.batch_resize_range[0]
upper_size = self.batch_resize_range[1]
@ -133,9 +152,11 @@ class VQModel(pl.LightningModule):
# do the first few batches with max size to avoid later oom
new_resize = upper_size
else:
new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
new_resize = np.random.choice(
np.arange(lower_size, upper_size + 16, 16)
)
if new_resize != x.shape[2]:
x = F.interpolate(x, size=new_resize, mode="bicubic")
x = F.interpolate(x, size=new_resize, mode='bicubic')
x = x.detach()
return x
@ -147,49 +168,99 @@ class VQModel(pl.LightningModule):
if optimizer_idx == 0:
# autoencode
aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train",
predicted_indices=ind)
aeloss, log_dict_ae = self.loss(
qloss,
x,
xrec,
optimizer_idx,
self.global_step,
last_layer=self.get_last_layer(),
split='train',
predicted_indices=ind,
)
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
self.log_dict(
log_dict_ae,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=True,
)
return aeloss
if optimizer_idx == 1:
# discriminator
discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
discloss, log_dict_disc = self.loss(
qloss,
x,
xrec,
optimizer_idx,
self.global_step,
last_layer=self.get_last_layer(),
split='train',
)
self.log_dict(
log_dict_disc,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=True,
)
return discloss
def validation_step(self, batch, batch_idx):
log_dict = self._validation_step(batch, batch_idx)
with self.ema_scope():
log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
log_dict_ema = self._validation_step(
batch, batch_idx, suffix='_ema'
)
return log_dict
def _validation_step(self, batch, batch_idx, suffix=""):
def _validation_step(self, batch, batch_idx, suffix=''):
x = self.get_input(batch, self.image_key)
xrec, qloss, ind = self(x, return_pred_indices=True)
aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
aeloss, log_dict_ae = self.loss(
qloss,
x,
xrec,
0,
self.global_step,
last_layer=self.get_last_layer(),
split="val"+suffix,
predicted_indices=ind
split='val' + suffix,
predicted_indices=ind,
)
discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
discloss, log_dict_disc = self.loss(
qloss,
x,
xrec,
1,
self.global_step,
last_layer=self.get_last_layer(),
split="val"+suffix,
predicted_indices=ind
split='val' + suffix,
predicted_indices=ind,
)
rec_loss = log_dict_ae[f'val{suffix}/rec_loss']
self.log(
f'val{suffix}/rec_loss',
rec_loss,
prog_bar=True,
logger=True,
on_step=False,
on_epoch=True,
sync_dist=True,
)
self.log(
f'val{suffix}/aeloss',
aeloss,
prog_bar=True,
logger=True,
on_step=False,
on_epoch=True,
sync_dist=True,
)
rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
self.log(f"val{suffix}/rec_loss", rec_loss,
prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
self.log(f"val{suffix}/aeloss", aeloss,
prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
if version.parse(pl.__version__) >= version.parse('1.4.0'):
del log_dict_ae[f"val{suffix}/rec_loss"]
del log_dict_ae[f'val{suffix}/rec_loss']
self.log_dict(log_dict_ae)
self.log_dict(log_dict_disc)
return self.log_dict
@ -197,31 +268,39 @@ class VQModel(pl.LightningModule):
def configure_optimizers(self):
lr_d = self.learning_rate
lr_g = self.lr_g_factor * self.learning_rate
print("lr_d", lr_d)
print("lr_g", lr_g)
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
list(self.decoder.parameters())+
list(self.quantize.parameters())+
list(self.quant_conv.parameters())+
list(self.post_quant_conv.parameters()),
lr=lr_g, betas=(0.5, 0.9))
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
lr=lr_d, betas=(0.5, 0.9))
print('lr_d', lr_d)
print('lr_g', lr_g)
opt_ae = torch.optim.Adam(
list(self.encoder.parameters())
+ list(self.decoder.parameters())
+ list(self.quantize.parameters())
+ list(self.quant_conv.parameters())
+ list(self.post_quant_conv.parameters()),
lr=lr_g,
betas=(0.5, 0.9),
)
opt_disc = torch.optim.Adam(
self.loss.discriminator.parameters(), lr=lr_d, betas=(0.5, 0.9)
)
if self.scheduler_config is not None:
scheduler = instantiate_from_config(self.scheduler_config)
print("Setting up LambdaLR scheduler...")
print('Setting up LambdaLR scheduler...')
scheduler = [
{
'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
'scheduler': LambdaLR(
opt_ae, lr_lambda=scheduler.schedule
),
'interval': 'step',
'frequency': 1
'frequency': 1,
},
{
'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
'scheduler': LambdaLR(
opt_disc, lr_lambda=scheduler.schedule
),
'interval': 'step',
'frequency': 1
'frequency': 1,
},
]
return [opt_ae, opt_disc], scheduler
@ -235,7 +314,7 @@ class VQModel(pl.LightningModule):
x = self.get_input(batch, self.image_key)
x = x.to(self.device)
if only_inputs:
log["inputs"] = x
log['inputs'] = x
return log
xrec, _ = self(x)
if x.shape[1] > 3:
@ -243,21 +322,24 @@ class VQModel(pl.LightningModule):
assert xrec.shape[1] > 3
x = self.to_rgb(x)
xrec = self.to_rgb(xrec)
log["inputs"] = x
log["reconstructions"] = xrec
log['inputs'] = x
log['reconstructions'] = xrec
if plot_ema:
with self.ema_scope():
xrec_ema, _ = self(x)
if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
log["reconstructions_ema"] = xrec_ema
if x.shape[1] > 3:
xrec_ema = self.to_rgb(xrec_ema)
log['reconstructions_ema'] = xrec_ema
return log
def to_rgb(self, x):
assert self.image_key == "segmentation"
if not hasattr(self, "colorize"):
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
assert self.image_key == 'segmentation'
if not hasattr(self, 'colorize'):
self.register_buffer(
'colorize', torch.randn(3, x.shape[1], 1, 1).to(x)
)
x = F.conv2d(x, weight=self.colorize)
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
return x
@ -283,13 +365,14 @@ class VQModelInterface(VQModel):
class AutoencoderKL(pl.LightningModule):
def __init__(self,
def __init__(
self,
ddconfig,
lossconfig,
embed_dim,
ckpt_path=None,
ignore_keys=[],
image_key="image",
image_key='image',
colorize_nlabels=None,
monitor=None,
):
@ -298,28 +381,34 @@ class AutoencoderKL(pl.LightningModule):
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.loss = instantiate_from_config(lossconfig)
assert ddconfig["double_z"]
self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
assert ddconfig['double_z']
self.quant_conv = torch.nn.Conv2d(
2 * ddconfig['z_channels'], 2 * embed_dim, 1
)
self.post_quant_conv = torch.nn.Conv2d(
embed_dim, ddconfig['z_channels'], 1
)
self.embed_dim = embed_dim
if colorize_nlabels is not None:
assert type(colorize_nlabels) == int
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
self.register_buffer(
'colorize', torch.randn(3, colorize_nlabels, 1, 1)
)
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def init_from_ckpt(self, path, ignore_keys=list()):
sd = torch.load(path, map_location="cpu")["state_dict"]
sd = torch.load(path, map_location='cpu')['state_dict']
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
print('Deleting key {} from state_dict.'.format(k))
del sd[k]
self.load_state_dict(sd, strict=False)
print(f"Restored from {path}")
print(f'Restored from {path}')
def encode(self, x):
h = self.encoder(x)
@ -345,7 +434,11 @@ class AutoencoderKL(pl.LightningModule):
x = batch[k]
if len(x.shape) == 3:
x = x[..., None]
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
x = (
x.permute(0, 3, 1, 2)
.to(memory_format=torch.contiguous_format)
.float()
)
return x
def training_step(self, batch, batch_idx, optimizer_idx):
@ -354,44 +447,102 @@ class AutoencoderKL(pl.LightningModule):
if optimizer_idx == 0:
# train encoder+decoder+logvar
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
aeloss, log_dict_ae = self.loss(
inputs,
reconstructions,
posterior,
optimizer_idx,
self.global_step,
last_layer=self.get_last_layer(),
split='train',
)
self.log(
'aeloss',
aeloss,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=True,
)
self.log_dict(
log_dict_ae,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=False,
)
return aeloss
if optimizer_idx == 1:
# train the discriminator
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
discloss, log_dict_disc = self.loss(
inputs,
reconstructions,
posterior,
optimizer_idx,
self.global_step,
last_layer=self.get_last_layer(),
split='train',
)
self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
self.log(
'discloss',
discloss,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=True,
)
self.log_dict(
log_dict_disc,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=False,
)
return discloss
def validation_step(self, batch, batch_idx):
inputs = self.get_input(batch, self.image_key)
reconstructions, posterior = self(inputs)
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
last_layer=self.get_last_layer(), split="val")
aeloss, log_dict_ae = self.loss(
inputs,
reconstructions,
posterior,
0,
self.global_step,
last_layer=self.get_last_layer(),
split='val',
)
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
last_layer=self.get_last_layer(), split="val")
discloss, log_dict_disc = self.loss(
inputs,
reconstructions,
posterior,
1,
self.global_step,
last_layer=self.get_last_layer(),
split='val',
)
self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
self.log('val/rec_loss', log_dict_ae['val/rec_loss'])
self.log_dict(log_dict_ae)
self.log_dict(log_dict_disc)
return self.log_dict
def configure_optimizers(self):
lr = self.learning_rate
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
list(self.decoder.parameters())+
list(self.quant_conv.parameters())+
list(self.post_quant_conv.parameters()),
lr=lr, betas=(0.5, 0.9))
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
lr=lr, betas=(0.5, 0.9))
opt_ae = torch.optim.Adam(
list(self.encoder.parameters())
+ list(self.decoder.parameters())
+ list(self.quant_conv.parameters())
+ list(self.post_quant_conv.parameters()),
lr=lr,
betas=(0.5, 0.9),
)
opt_disc = torch.optim.Adam(
self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)
)
return [opt_ae, opt_disc], []
def get_last_layer(self):
@ -409,17 +560,19 @@ class AutoencoderKL(pl.LightningModule):
assert xrec.shape[1] > 3
x = self.to_rgb(x)
xrec = self.to_rgb(xrec)
log["samples"] = self.decode(torch.randn_like(posterior.sample()))
log["reconstructions"] = xrec
log["inputs"] = x
log['samples'] = self.decode(torch.randn_like(posterior.sample()))
log['reconstructions'] = xrec
log['inputs'] = x
return log
def to_rgb(self, x):
assert self.image_key == "segmentation"
if not hasattr(self, "colorize"):
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
assert self.image_key == 'segmentation'
if not hasattr(self, 'colorize'):
self.register_buffer(
'colorize', torch.randn(3, x.shape[1], 1, 1).to(x)
)
x = F.conv2d(x, weight=self.colorize)
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
x = 2.0 * (x - x.min()) / (x.max() - x.min()) - 1.0
return x

206
ldm/models/diffusion/classifier.py
View File

@ -10,13 +10,13 @@ from einops import rearrange
from glob import glob
from natsort import natsorted
from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
from ldm.modules.diffusionmodules.openaimodel import (
EncoderUNetModel,
UNetModel,
)
from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
__models__ = {
'class_label': EncoderUNetModel,
'segmentation': UNetModel
}
__models__ = {'class_label': EncoderUNetModel, 'segmentation': UNetModel}
def disabled_train(self, mode=True):
@ -26,8 +26,8 @@ def disabled_train(self, mode=True):
class NoisyLatentImageClassifier(pl.LightningModule):
def __init__(self,
def __init__(
self,
diffusion_path,
num_classes,
ckpt_path=None,
@ -35,28 +35,40 @@ class NoisyLatentImageClassifier(pl.LightningModule):
label_key=None,
diffusion_ckpt_path=None,
scheduler_config=None,
weight_decay=1.e-2,
weight_decay=1.0e-2,
log_steps=10,
monitor='val/loss',
*args,
**kwargs):
**kwargs,
):
super().__init__(*args, **kwargs)
self.num_classes = num_classes
# get latest config of diffusion model
diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1]
diffusion_config = natsorted(
glob(os.path.join(diffusion_path, 'configs', '*-project.yaml'))
)[-1]
self.diffusion_config = OmegaConf.load(diffusion_config).model
self.diffusion_config.params.ckpt_path = diffusion_ckpt_path
self.load_diffusion()
self.monitor = monitor
self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
self.log_time_interval = self.diffusion_model.num_timesteps // log_steps
self.numd = (
self.diffusion_model.first_stage_model.encoder.num_resolutions - 1
)
self.log_time_interval = (
self.diffusion_model.num_timesteps // log_steps
)
self.log_steps = log_steps
self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \
self.label_key = (
label_key
if not hasattr(self.diffusion_model, 'cond_stage_key')
else self.diffusion_model.cond_stage_key
)
assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params'
assert (
self.label_key is not None
), 'label_key neither in diffusion model nor in model.params'
if self.label_key not in __models__:
raise NotImplementedError()
@ -68,22 +80,27 @@ class NoisyLatentImageClassifier(pl.LightningModule):
self.weight_decay = weight_decay
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
sd = torch.load(path, map_location='cpu')
if 'state_dict' in list(sd.keys()):
sd = sd['state_dict']
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
print('Deleting key {} from state_dict.'.format(k))
del sd[k]
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
sd, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
missing, unexpected = (
self.load_state_dict(sd, strict=False)
if not only_model
else self.model.load_state_dict(sd, strict=False)
)
print(
f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
)
if len(missing) > 0:
print(f"Missing Keys: {missing}")
print(f'Missing Keys: {missing}')
if len(unexpected) > 0:
print(f"Unexpected Keys: {unexpected}")
print(f'Unexpected Keys: {unexpected}')
def load_diffusion(self):
model = instantiate_from_config(self.diffusion_config)
@ -93,17 +110,25 @@ class NoisyLatentImageClassifier(pl.LightningModule):
param.requires_grad = False
def load_classifier(self, ckpt_path, pool):
model_config = deepcopy(self.diffusion_config.params.unet_config.params)
model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels
model_config = deepcopy(
self.diffusion_config.params.unet_config.params
)
model_config.in_channels = (
self.diffusion_config.params.unet_config.params.out_channels
)
model_config.out_channels = self.num_classes
if self.label_key == 'class_label':
model_config.pool = pool
self.model = __models__[self.label_key](**model_config)
if ckpt_path is not None:
print('#####################################################################')
print(
'#####################################################################'
)
print(f'load from ckpt "{ckpt_path}"')
print('#####################################################################')
print(
'#####################################################################'
)
self.init_from_ckpt(ckpt_path)
@torch.no_grad()
@ -111,11 +136,19 @@ class NoisyLatentImageClassifier(pl.LightningModule):
noise = default(noise, lambda: torch.randn_like(x))
continuous_sqrt_alpha_cumprod = None
if self.diffusion_model.use_continuous_noise:
continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1)
continuous_sqrt_alpha_cumprod = (
self.diffusion_model.sample_continuous_noise_level(
x.shape[0], t + 1
)
)
# todo: make sure t+1 is correct here
return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise,
continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod)
return self.diffusion_model.q_sample(
x_start=x,
t=t,
noise=noise,
continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod,
)
def forward(self, x_noisy, t, *args, **kwargs):
return self.model(x_noisy, t)
@ -141,17 +174,21 @@ class NoisyLatentImageClassifier(pl.LightningModule):
targets = rearrange(targets, 'b h w c -> b c h w')
for down in range(self.numd):
h, w = targets.shape[-2:]
targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest')
targets = F.interpolate(
targets, size=(h // 2, w // 2), mode='nearest'
)
# targets = rearrange(targets,'b c h w -> b h w c')
return targets
def compute_top_k(self, logits, labels, k, reduction="mean"):
def compute_top_k(self, logits, labels, k, reduction='mean'):
_, top_ks = torch.topk(logits, k, dim=1)
if reduction == "mean":
return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
elif reduction == "none":
if reduction == 'mean':
return (
(top_ks == labels[:, None]).float().sum(dim=-1).mean().item()
)
elif reduction == 'none':
return (top_ks == labels[:, None]).float().sum(dim=-1)
def on_train_epoch_start(self):
@ -162,29 +199,59 @@ class NoisyLatentImageClassifier(pl.LightningModule):
def write_logs(self, loss, logits, targets):
log_prefix = 'train' if self.training else 'val'
log = {}
log[f"{log_prefix}/loss"] = loss.mean()
log[f"{log_prefix}/acc@1"] = self.compute_top_k(
logits, targets, k=1, reduction="mean"
log[f'{log_prefix}/loss'] = loss.mean()
log[f'{log_prefix}/acc@1'] = self.compute_top_k(
logits, targets, k=1, reduction='mean'
)
log[f"{log_prefix}/acc@5"] = self.compute_top_k(
logits, targets, k=5, reduction="mean"
log[f'{log_prefix}/acc@5'] = self.compute_top_k(
logits, targets, k=5, reduction='mean'
)
self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True)
self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False)
self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True)
self.log_dict(
log,
prog_bar=False,
logger=True,
on_step=self.training,
on_epoch=True,
)
self.log(
'loss', log[f'{log_prefix}/loss'], prog_bar=True, logger=False
)
self.log(
'global_step',
self.global_step,
logger=False,
on_epoch=False,
prog_bar=True,
)
lr = self.optimizers().param_groups[0]['lr']
self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True)
self.log(
'lr_abs',
lr,
on_step=True,
logger=True,
on_epoch=False,
prog_bar=True,
)
def shared_step(self, batch, t=None):
x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key)
x, *_ = self.diffusion_model.get_input(
batch, k=self.diffusion_model.first_stage_key
)
targets = self.get_conditioning(batch)
if targets.dim() == 4:
targets = targets.argmax(dim=1)
if t is None:
t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long()
t = torch.randint(
0,
self.diffusion_model.num_timesteps,
(x.shape[0],),
device=self.device,
).long()
else:
t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long()
t = torch.full(
size=(x.shape[0],), fill_value=t, device=self.device
).long()
x_noisy = self.get_x_noisy(x, t)
logits = self(x_noisy, t)
@ -200,8 +267,14 @@ class NoisyLatentImageClassifier(pl.LightningModule):
return loss
def reset_noise_accs(self):
self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in
range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)}
self.noisy_acc = {
t: {'acc@1': [], 'acc@5': []}
for t in range(
0,
self.diffusion_model.num_timesteps,
self.diffusion_model.log_every_t,
)
}
def on_validation_start(self):
self.reset_noise_accs()
@ -212,24 +285,35 @@ class NoisyLatentImageClassifier(pl.LightningModule):
for t in self.noisy_acc:
_, logits, _, targets = self.shared_step(batch, t)
self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean'))
self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean'))
self.noisy_acc[t]['acc@1'].append(
self.compute_top_k(logits, targets, k=1, reduction='mean')
)
self.noisy_acc[t]['acc@5'].append(
self.compute_top_k(logits, targets, k=5, reduction='mean')
)
return loss
def configure_optimizers(self):
optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
optimizer = AdamW(
self.model.parameters(),
lr=self.learning_rate,
weight_decay=self.weight_decay,
)
if self.use_scheduler:
scheduler = instantiate_from_config(self.scheduler_config)
print("Setting up LambdaLR scheduler...")
print('Setting up LambdaLR scheduler...')
scheduler = [
{
'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule),
'scheduler': LambdaLR(
optimizer, lr_lambda=scheduler.schedule
),
'interval': 'step',
'frequency': 1
}]
'frequency': 1,
}
]
return [optimizer], scheduler
return optimizer
@ -243,7 +327,7 @@ class NoisyLatentImageClassifier(pl.LightningModule):
y = self.get_conditioning(batch)
if self.label_key == 'class_label':
y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
y = log_txt_as_img((x.shape[2], x.shape[3]), batch['human_label'])
log['labels'] = y
if ismap(y):
@ -256,10 +340,14 @@ class NoisyLatentImageClassifier(pl.LightningModule):
log[f'inputs@t{current_time}'] = x_noisy
pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes)
pred = F.one_hot(
logits.argmax(dim=1), num_classes=self.num_classes
)
pred = rearrange(pred, 'b h w c -> b c h w')
log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred)
log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(
pred
)
for key in log:
log[key] = log[key][:N]

345
ldm/models/diffusion/ddim.py
View File

@ -4,57 +4,111 @@ import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.devices import choose_torch_device
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
extract_into_tensor
from ldm.modules.diffusionmodules.util import (
make_ddim_sampling_parameters,
make_ddim_timesteps,
noise_like,
extract_into_tensor,
)
class DDIMSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
def __init__(self, model, schedule='linear', device=None, **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
self.device = device or choose_torch_device()
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
if attr.device != torch.device(self.device):
attr = attr.to(dtype=torch.float32, device=self.device)
setattr(self, name, attr)
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
def make_schedule(
self,
ddim_num_steps,
ddim_discretize='uniform',
ddim_eta=0.0,
verbose=True,
):
self.ddim_timesteps = make_ddim_timesteps(
ddim_discr_method=ddim_discretize,
num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.ddpm_num_timesteps,
verbose=verbose,
)
alphas_cumprod = self.model.alphas_cumprod
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
assert (
alphas_cumprod.shape[0] == self.ddpm_num_timesteps
), 'alphas have to be defined for each timestep'
to_torch = (
lambda x: x.clone()
.detach()
.to(torch.float32)
.to(self.model.device)
)
self.register_buffer('betas', to_torch(self.model.betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
self.register_buffer(
'alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)
)
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
self.register_buffer(
'sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))
)
self.register_buffer(
'sqrt_one_minus_alphas_cumprod',
to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
)
self.register_buffer(
'log_one_minus_alphas_cumprod',
to_torch(np.log(1.0 - alphas_cumprod.cpu())),
)
self.register_buffer(
'sqrt_recip_alphas_cumprod',
to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())),
)
self.register_buffer(
'sqrt_recipm1_alphas_cumprod',
to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
)
# ddim sampling parameters
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
(
ddim_sigmas,
ddim_alphas,
ddim_alphas_prev,
) = make_ddim_sampling_parameters(
alphacums=alphas_cumprod.cpu(),
ddim_timesteps=self.ddim_timesteps,
eta=ddim_eta,verbose=verbose)
eta=ddim_eta,
verbose=verbose,
)
self.register_buffer('ddim_sigmas', ddim_sigmas)
self.register_buffer('ddim_alphas', ddim_alphas)
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
self.register_buffer(
'ddim_sqrt_one_minus_alphas', np.sqrt(1.0 - ddim_alphas)
)
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
(1 - self.alphas_cumprod_prev)
/ (1 - self.alphas_cumprod)
* (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
)
self.register_buffer(
'ddim_sigmas_for_original_num_steps',
sigmas_for_original_sampling_steps,
)
@torch.no_grad()
def sample(self,
def sample(
self,
S,
batch_size,
shape,
@ -63,29 +117,33 @@ class DDIMSampler(object):
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
eta=0.0,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
**kwargs,
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
print(
f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'
)
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
print(
f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'
)
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
# sampling
@ -93,11 +151,14 @@ class DDIMSampler(object):
size = (batch_size, C, H, W)
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
samples, intermediates = self.ddim_sampling(conditioning, size,
samples, intermediates = self.ddim_sampling(
conditioning,
size,
callback=callback,
img_callback=img_callback,
quantize_denoised=quantize_x0,
mask=mask, x0=x0,
mask=mask,
x0=x0,
ddim_use_original_steps=False,
noise_dropout=noise_dropout,
temperature=temperature,
@ -110,13 +171,28 @@ class DDIMSampler(object):
)
return samples, intermediates
# This routine gets called from img2img
@torch.no_grad()
def ddim_sampling(self, cond, shape,
x_T=None, ddim_use_original_steps=False,
callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, log_every_t=100,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None,):
def ddim_sampling(
self,
cond,
shape,
x_T=None,
ddim_use_original_steps=False,
callback=None,
timesteps=None,
quantize_denoised=False,
mask=None,
x0=None,
img_callback=None,
log_every_t=100,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
):
device = self.model.betas.device
b = shape[0]
if x_T is None:
@ -125,17 +201,38 @@ class DDIMSampler(object):
img = x_T
if timesteps is None:
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
timesteps = (
self.ddpm_num_timesteps
if ddim_use_original_steps
else self.ddim_timesteps
)
elif timesteps is not None and not ddim_use_original_steps:
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
subset_end = (
int(
min(timesteps / self.ddim_timesteps.shape[0], 1)
* self.ddim_timesteps.shape[0]
)
- 1
)
timesteps = self.ddim_timesteps[:subset_end]
intermediates = {'x_inter': [img], 'pred_x0': [img]}
time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
print(f"Running DDIM Sampling with {total_steps} timesteps")
time_range = (
reversed(range(0, timesteps))
if ddim_use_original_steps
else np.flip(timesteps)
)
total_steps = (
timesteps if ddim_use_original_steps else timesteps.shape[0]
)
print(f'\nRunning DDIM Sampling with {total_steps} timesteps')
iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
iterator = tqdm(
time_range,
desc='DDIM Sampler',
total=total_steps,
dynamic_ncols=True,
)
for i, step in enumerate(iterator):
index = total_steps - i - 1
@ -143,18 +240,30 @@ class DDIMSampler(object):
if mask is not None:
assert x0 is not None
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
img = img_orig * mask + (1. - mask) * img
img_orig = self.model.q_sample(
x0, ts
) # TODO: deterministic forward pass?
img = img_orig * mask + (1.0 - mask) * img
outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
quantize_denoised=quantize_denoised, temperature=temperature,
noise_dropout=noise_dropout, score_corrector=score_corrector,
outs = self.p_sample_ddim(
img,
cond,
ts,
index=index,
use_original_steps=ddim_use_original_steps,
quantize_denoised=quantize_denoised,
temperature=temperature,
noise_dropout=noise_dropout,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning)
unconditional_conditioning=unconditional_conditioning,
)
img, pred_x0 = outs
if callback: callback(i)
if img_callback: img_callback(pred_x0, i)
if callback:
callback(i)
if img_callback:
img_callback(pred_x0, i)
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['x_inter'].append(img)
@ -162,43 +271,84 @@ class DDIMSampler(object):
return img, intermediates
# This routine gets called from ddim_sampling() and decode()
@torch.no_grad()
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None):
def p_sample_ddim(
self,
x,
c,
t,
index,
repeat_noise=False,
use_original_steps=False,
quantize_denoised=False,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
):
b, *_, device = *x.shape, x.device
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
if (
unconditional_conditioning is None
or unconditional_guidance_scale == 1.0
):
e_t = self.model.apply_model(x, t, c)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([unconditional_conditioning, c])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
e_t = e_t_uncond + unconditional_guidance_scale * (
e_t - e_t_uncond
)
if score_corrector is not None:
assert self.model.parameterization == "eps"
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
assert self.model.parameterization == 'eps'
e_t = score_corrector.modify_score(
self.model, e_t, x, t, c, **corrector_kwargs
)
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
alphas = (
self.model.alphas_cumprod
if use_original_steps
else self.ddim_alphas
)
alphas_prev = (
self.model.alphas_cumprod_prev
if use_original_steps
else self.ddim_alphas_prev
)
sqrt_one_minus_alphas = (
self.model.sqrt_one_minus_alphas_cumprod
if use_original_steps
else self.ddim_sqrt_one_minus_alphas
)
sigmas = (
self.model.ddim_sigmas_for_original_num_steps
if use_original_steps
else self.ddim_sigmas
)
# select parameters corresponding to the currently considered timestep
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
sqrt_one_minus_at = torch.full(
(b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
)
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
# direction pointing to x_t
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
noise = (
sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
)
if noise_dropout > 0.0:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev, pred_x0
@ -216,33 +366,68 @@ class DDIMSampler(object):
if noise is None:
noise = torch.randn_like(x0)
return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
return (
extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0
+ extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape)
* noise
)
@torch.no_grad()
def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
use_original_steps=False, z_mask = None, x0=None):
def decode(
self,
x_latent,
cond,
t_start,
img_callback=None,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
use_original_steps=False,
init_latent = None,
mask = None,
):
timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
timesteps = (
np.arange(self.ddpm_num_timesteps)
if use_original_steps
else self.ddim_timesteps
)
timesteps = timesteps[:t_start]
time_range = np.flip(timesteps)
total_steps = timesteps.shape[0]
print(f"Running DDIM Sampling with {total_steps} timesteps")
print(f'Running DDIM Sampling with {total_steps} timesteps')
iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
x_dec = x_latent
x0 = init_latent
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
ts = torch.full(
(x_latent.shape[0],),
step,
device=x_latent.device,
dtype=torch.long,
)
if z_mask is not None and i < total_steps - 2:
if mask is not None:
assert x0 is not None
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
mask_inv = 1. - z_mask
x_dec = (img_orig * mask_inv) + (z_mask * x_dec)
xdec_orig = self.model.q_sample(
x0, ts
) # TODO: deterministic forward pass?
x_dec = xdec_orig * mask + (1.0 - mask) * x_dec
x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
x_dec, _ = self.p_sample_ddim(
x_dec,
cond,
ts,
index=index,
use_original_steps=use_original_steps,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning)
unconditional_conditioning=unconditional_conditioning,
)
if img_callback:
img_callback(x_dec, i)
return x_dec

1609
ldm/models/diffusion/ddpm.py
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1
ldm/models/diffusion/dpm_solver/__init__.py Normal file
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@ -0,0 +1 @@
from .sampler import DPMSolverSampler

1154
ldm/models/diffusion/dpm_solver/dpm_solver.py Normal file
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87
ldm/models/diffusion/dpm_solver/sampler.py Normal file
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@ -0,0 +1,87 @@
"""SAMPLING ONLY."""
import torch
from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
MODEL_TYPES = {
"eps": "noise",
"v": "v"
}
class DPMSolverSampler(object):
def __init__(self, model, **kwargs):
super().__init__()
self.model = model
to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
setattr(self, name, attr)
@torch.no_grad()
def sample(self,
S,
batch_size,
shape,
conditioning=None,
callback=None,
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
device = self.model.betas.device
if x_T is None:
img = torch.randn(size, device=device)
else:
img = x_T
ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
model_fn = model_wrapper(
lambda x, t, c: self.model.apply_model(x, t, c),
ns,
model_type=MODEL_TYPES[self.model.parameterization],
guidance_type="classifier-free",
condition=conditioning,
unconditional_condition=unconditional_conditioning,
guidance_scale=unconditional_guidance_scale,
)
dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
return x.to(device), None

309
ldm/models/diffusion/plms.py
View File

@ -4,58 +4,112 @@ import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.devices import choose_torch_device
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
from ldm.modules.diffusionmodules.util import (
make_ddim_sampling_parameters,
make_ddim_timesteps,
noise_like,
)
class PLMSSampler(object):
def __init__(self, model, schedule="linear", **kwargs):
def __init__(self, model, schedule='linear', device=None, **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
self.device = device if device else choose_torch_device()
def register_buffer(self, name, attr):
if type(attr) == torch.Tensor:
if attr.device != torch.device("cuda"):
attr = attr.to(torch.device("cuda"))
if attr.device != torch.device(self.device):
attr = attr.to(torch.float32).to(torch.device(self.device))
setattr(self, name, attr)
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
def make_schedule(
self,
ddim_num_steps,
ddim_discretize='uniform',
ddim_eta=0.0,
verbose=True,
):
if ddim_eta != 0:
raise ValueError('ddim_eta must be 0 for PLMS')
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
self.ddim_timesteps = make_ddim_timesteps(
ddim_discr_method=ddim_discretize,
num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.ddpm_num_timesteps,
verbose=verbose,
)
alphas_cumprod = self.model.alphas_cumprod
assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
assert (
alphas_cumprod.shape[0] == self.ddpm_num_timesteps
), 'alphas have to be defined for each timestep'
to_torch = (
lambda x: x.clone()
.detach()
.to(torch.float32)
.to(self.model.device)
)
self.register_buffer('betas', to_torch(self.model.betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
self.register_buffer(
'alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)
)
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
self.register_buffer(
'sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))
)
self.register_buffer(
'sqrt_one_minus_alphas_cumprod',
to_torch(np.sqrt(1.0 - alphas_cumprod.cpu())),
)
self.register_buffer(
'log_one_minus_alphas_cumprod',
to_torch(np.log(1.0 - alphas_cumprod.cpu())),
)
self.register_buffer(
'sqrt_recip_alphas_cumprod',
to_torch(np.sqrt(1.0 / alphas_cumprod.cpu())),
)
self.register_buffer(
'sqrt_recipm1_alphas_cumprod',
to_torch(np.sqrt(1.0 / alphas_cumprod.cpu() - 1)),
)
# ddim sampling parameters
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
(
ddim_sigmas,
ddim_alphas,
ddim_alphas_prev,
) = make_ddim_sampling_parameters(
alphacums=alphas_cumprod.cpu(),
ddim_timesteps=self.ddim_timesteps,
eta=ddim_eta,verbose=verbose)
eta=ddim_eta,
verbose=verbose,
)
self.register_buffer('ddim_sigmas', ddim_sigmas)
self.register_buffer('ddim_alphas', ddim_alphas)
self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
self.register_buffer(
'ddim_sqrt_one_minus_alphas', np.sqrt(1.0 - ddim_alphas)
)
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
(1 - self.alphas_cumprod_prev)
/ (1 - self.alphas_cumprod)
* (1 - self.alphas_cumprod / self.alphas_cumprod_prev)
)
self.register_buffer(
'ddim_sigmas_for_original_num_steps',
sigmas_for_original_sampling_steps,
)
@torch.no_grad()
def sample(self,
def sample(
self,
S,
batch_size,
shape,
@ -64,41 +118,48 @@ class PLMSSampler(object):
normals_sequence=None,
img_callback=None,
quantize_x0=False,
eta=0.,
eta=0.0,
mask=None,
x0=None,
temperature=1.,
noise_dropout=0.,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
verbose=True,
x_T=None,
log_every_t=100,
unconditional_guidance_scale=1.,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
**kwargs
**kwargs,
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
print(
f'Warning: Got {cbs} conditionings but batch-size is {batch_size}'
)
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
print(
f'Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}'
)
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
print(f'Data shape for PLMS sampling is {size}')
# print(f'Data shape for PLMS sampling is {size}')
samples, intermediates = self.plms_sampling(conditioning, size,
samples, intermediates = self.plms_sampling(
conditioning,
size,
callback=callback,
img_callback=img_callback,
quantize_denoised=quantize_x0,
mask=mask, x0=x0,
mask=mask,
x0=x0,
ddim_use_original_steps=False,
noise_dropout=noise_dropout,
temperature=temperature,
@ -112,12 +173,26 @@ class PLMSSampler(object):
return samples, intermediates
@torch.no_grad()
def plms_sampling(self, cond, shape,
x_T=None, ddim_use_original_steps=False,
callback=None, timesteps=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, log_every_t=100,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None,):
def plms_sampling(
self,
cond,
shape,
x_T=None,
ddim_use_original_steps=False,
callback=None,
timesteps=None,
quantize_denoised=False,
mask=None,
x0=None,
img_callback=None,
log_every_t=100,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
):
device = self.model.betas.device
b = shape[0]
if x_T is None:
@ -126,42 +201,81 @@ class PLMSSampler(object):
img = x_T
if timesteps is None:
timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
timesteps = (
self.ddpm_num_timesteps
if ddim_use_original_steps
else self.ddim_timesteps
)
elif timesteps is not None and not ddim_use_original_steps:
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
subset_end = (
int(
min(timesteps / self.ddim_timesteps.shape[0], 1)
* self.ddim_timesteps.shape[0]
)
- 1
)
timesteps = self.ddim_timesteps[:subset_end]
intermediates = {'x_inter': [img], 'pred_x0': [img]}
time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
print(f"Running PLMS Sampling with {total_steps} timesteps")
time_range = (
list(reversed(range(0, timesteps)))
if ddim_use_original_steps
else np.flip(timesteps)
)
total_steps = (
timesteps if ddim_use_original_steps else timesteps.shape[0]
)
# print(f"Running PLMS Sampling with {total_steps} timesteps")
iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
iterator = tqdm(
time_range,
desc='PLMS Sampler',
total=total_steps,
dynamic_ncols=True,
)
old_eps = []
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((b,), step, device=device, dtype=torch.long)
ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
ts_next = torch.full(
(b,),
time_range[min(i + 1, len(time_range) - 1)],
device=device,
dtype=torch.long,
)
if mask is not None:
assert x0 is not None
img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
img = img_orig * mask + (1. - mask) * img
img_orig = self.model.q_sample(
x0, ts
) # TODO: deterministic forward pass?
img = img_orig * mask + (1.0 - mask) * img
outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
quantize_denoised=quantize_denoised, temperature=temperature,
noise_dropout=noise_dropout, score_corrector=score_corrector,
outs = self.p_sample_plms(
img,
cond,
ts,
index=index,
use_original_steps=ddim_use_original_steps,
quantize_denoised=quantize_denoised,
temperature=temperature,
noise_dropout=noise_dropout,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
old_eps=old_eps, t_next=ts_next)
old_eps=old_eps,
t_next=ts_next,
)
img, pred_x0, e_t = outs
old_eps.append(e_t)
if len(old_eps) >= 4:
old_eps.pop(0)
if callback: callback(i)
if img_callback: img_callback(pred_x0, i)
if callback:
callback(i)
if img_callback:
img_callback(pred_x0, i)
if index % log_every_t == 0 or index == total_steps - 1:
intermediates['x_inter'].append(img)
@ -170,47 +284,95 @@ class PLMSSampler(object):
return img, intermediates
@torch.no_grad()
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
def p_sample_plms(
self,
x,
c,
t,
index,
repeat_noise=False,
use_original_steps=False,
quantize_denoised=False,
temperature=1.0,
noise_dropout=0.0,
score_corrector=None,
corrector_kwargs=None,
unconditional_guidance_scale=1.0,
unconditional_conditioning=None,
old_eps=None,
t_next=None,
):
b, *_, device = *x.shape, x.device
def get_model_output(x, t):
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
if (
unconditional_conditioning is None
or unconditional_guidance_scale == 1.0
):
e_t = self.model.apply_model(x, t, c)
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([unconditional_conditioning, c])
e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
e_t_uncond, e_t = self.model.apply_model(
x_in, t_in, c_in
).chunk(2)
e_t = e_t_uncond + unconditional_guidance_scale * (
e_t - e_t_uncond
)
if score_corrector is not None:
assert self.model.parameterization == "eps"
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
assert self.model.parameterization == 'eps'
e_t = score_corrector.modify_score(
self.model, e_t, x, t, c, **corrector_kwargs
)
return e_t
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
alphas = (
self.model.alphas_cumprod
if use_original_steps
else self.ddim_alphas
)
alphas_prev = (
self.model.alphas_cumprod_prev
if use_original_steps
else self.ddim_alphas_prev
)
sqrt_one_minus_alphas = (
self.model.sqrt_one_minus_alphas_cumprod
if use_original_steps
else self.ddim_sqrt_one_minus_alphas
)
sigmas = (
self.model.ddim_sigmas_for_original_num_steps
if use_original_steps
else self.ddim_sigmas
)
def get_x_prev_and_pred_x0(e_t, index):
# select parameters corresponding to the currently considered timestep
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
a_prev = torch.full(
(b, 1, 1, 1), alphas_prev[index], device=device
)
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
sqrt_one_minus_at = torch.full(
(b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
)
# current prediction for x_0
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
if quantize_denoised:
pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
# direction pointing to x_t
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
if noise_dropout > 0.:
dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
noise = (
sigma_t
* noise_like(x.shape, device, repeat_noise)
* temperature
)
if noise_dropout > 0.0:
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev, pred_x0
@ -229,7 +391,12 @@ class PLMSSampler(object):
e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
elif len(old_eps) >= 3:
# 4nd order Pseudo Linear Multistep (Adams-Bashforth)
e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
e_t_prime = (
55 * e_t
- 59 * old_eps[-1]
+ 37 * old_eps[-2]
- 9 * old_eps[-3]
) / 24
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)

22
ldm/models/diffusion/sampling_util.py Normal file
View File

@ -0,0 +1,22 @@
import torch
import numpy as np
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions.
From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
return x[(...,) + (None,) * dims_to_append]
def norm_thresholding(x0, value):
s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
return x0 * (value / s)
def spatial_norm_thresholding(x0, value):
# b c h w
s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
return x0 * (value / s)

0
ldm/modules/__init__.py Normal file
View File

241
ldm/modules/attention.py
View File

@ -1,3 +1,5 @@
import os
from typing import Any, Optional
from inspect import isfunction
import math
import torch
@ -9,7 +11,6 @@ from ldm.modules.diffusionmodules.util import checkpoint
import psutil
def exists(val):
return val is not None
@ -169,98 +170,84 @@ class CrossAttention(nn.Module):
nn.Dropout(dropout)
)
if torch.cuda.is_available():
self.einsum_op = self.einsum_op_cuda
else:
self.mem_total = psutil.virtual_memory().total / (1024**3)
self.einsum_op = self.einsum_op_mps_v1 if self.mem_total >= 32 else self.einsum_op_mps_v2
self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
def einsum_op_compvis(self, q, k, v, r1):
s1 = einsum('b i d, b j d -> b i j', q, k) * self.scale # faster
s2 = s1.softmax(dim=-1, dtype=q.dtype)
del s1
r1 = einsum('b i j, b j d -> b i d', s2, v)
del s2
return r1
def einsum_op_compvis(self, q, k, v):
s = einsum('b i d, b j d -> b i j', q, k)
s = s.softmax(dim=-1, dtype=s.dtype)
return einsum('b i j, b j d -> b i d', s, v)
def einsum_op_mps_v1(self, q, k, v, r1):
if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
r1 = self.einsum_op_compvis(q, k, v, r1)
else:
slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
def einsum_op_slice_0(self, q, k, v, slice_size):
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
for i in range(0, q.shape[0], slice_size):
end = i + slice_size
r[i:end] = self.einsum_op_compvis(q[i:end], k[i:end], v[i:end])
return r
def einsum_op_slice_1(self, q, k, v, slice_size):
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
s2 = s1.softmax(dim=-1, dtype=r1.dtype)
del s1
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
del s2
return r1
r[:, i:end] = self.einsum_op_compvis(q[:, i:end], k, v)
return r
def einsum_op_mps_v2(self, q, k, v, r1):
if self.mem_total >= 8 and q.shape[1] <= 4096:
r1 = self.einsum_op_compvis(q, k, v, r1)
def einsum_op_mps_v1(self, q, k, v):
if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
return self.einsum_op_compvis(q, k, v)
else:
slice_size = 1
for i in range(0, q.shape[0], slice_size):
end = min(q.shape[0], i + slice_size)
s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
s1 *= self.scale
s2 = s1.softmax(dim=-1, dtype=r1.dtype)
del s1
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
del s2
return r1
slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
return self.einsum_op_slice_1(q, k, v, slice_size)
def einsum_op_cuda(self, q, k, v, r1):
def einsum_op_mps_v2(self, q, k, v):
if self.mem_total_gb > 8 and q.shape[1] <= 4096:
return self.einsum_op_compvis(q, k, v)
else:
return self.einsum_op_slice_0(q, k, v, 1)
def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
if size_mb <= max_tensor_mb:
return self.einsum_op_compvis(q, k, v)
div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
if div <= q.shape[0]:
return self.einsum_op_slice_0(q, k, v, q.shape[0] // div)
return self.einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
def einsum_op_cuda(self, q, k, v):
stats = torch.cuda.memory_stats(q.device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
mem_free_torch = mem_reserved - mem_active
mem_free_total = mem_free_cuda + mem_free_torch
# Divide factor of safety as there's copying and fragmentation
return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
gb = 1024 ** 3
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * 4
mem_required = tensor_size * 2.5
steps = 1
def einsum_op(self, q, k, v):
if q.device.type == 'cuda':
return self.einsum_op_cuda(q, k, v)
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
if q.device.type == 'mps':
if self.mem_total_gb >= 32:
return self.einsum_op_mps_v1(q, k, v)
return self.einsum_op_mps_v2(q, k, v)
if steps > 64:
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = min(q.shape[1], i + slice_size)
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)# * self.scale
s2 = s1.softmax(dim=-1, dtype=r1.dtype)
del s1
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
del s2
return r1
# Smaller slices are faster due to L2/L3/SLC caches.
# Tested on i7 with 8MB L3 cache.
return self.einsum_op_tensor_mem(q, k, v, 32)
def forward(self, x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
del x
k = self.to_k(context) * self.scale
v = self.to_v(context)
del context
del context, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
r1 = self.einsum_op(q, k, v, r1)
del q, k, v
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
return self.to_out(r2)
r = self.einsum_op(q, k, v)
return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
class BasicTransformerBlock(nn.Module):
@ -280,12 +267,118 @@ class BasicTransformerBlock(nn.Module):
def _forward(self, x, context=None):
x = x.contiguous() if x.device.type == 'mps' else x
x += self.attn1(self.norm1(x))
x += self.attn2(self.norm2(x), context=context)
x += self.ff(self.norm3(x))
x += self.attn1(self.norm1(x.clone()))
x += self.attn2(self.norm2(x.clone()), context=context)
x += self.ff(self.norm3(x.clone()))
return x
class BasicTransformerBlockMECA(nn.Module):
'''
Memory efficient cross-attention transformer block.
'''
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
super().__init__()
AttentionBuilder = MemoryEfficientCrossAttention
self.attn1 = AttentionBuilder(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
self.attn2 = AttentionBuilder(query_dim=dim, context_dim=context_dim,
heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(dim)
self.checkpoint = checkpoint
def _set_attention_slice(self, slice_size):
self.attn1._slice_size = slice_size
self.attn2._slice_size = slice_size
def forward(self, hidden_states, context=None):
hidden_states = hidden_states.contiguous() if hidden_states.device.type == "mps" else hidden_states
hidden_states = self.attn1(self.norm1(hidden_states)) + hidden_states
hidden_states = self.attn2(self.norm2(hidden_states), context=context) + hidden_states
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
return hidden_states
class MemoryEfficientCrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = dim_head**-0.5
self.heads = heads
self.dim_head = dim_head
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None
def _maybe_init(self, x):
"""
Initialize the attention operator, if required We expect the head dimension to be exposed here, meaning that x
: B, Head, Length
"""
from xformers.ops import AttentionOpDispatch
if self.attention_op is not None:
return
# _, K, M = x.shape
_, M, K = x.shape
try:
self.attention_op = AttentionOpDispatch(
dtype=x.dtype,
device=x.device,
k=K,
attn_bias_type=type(None),
has_dropout=False,
kv_len=M,
q_len=M,
).op
except NotImplementedError as err:
raise NotImplementedError(f"Please install xformers with the flash attention / cutlass components.\n{err}")
def forward(self, x, context=None, mask=None):
from xformers.ops import memory_efficient_attention
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
b, _, _ = q.shape
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, t.shape[1], self.heads, self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b * self.heads, t.shape[1], self.dim_head)
.contiguous(),
(q, k, v),
)
# init the attention op, if required, using the proper dimensions
self._maybe_init(q)
# actually compute the attention, what we cannot get enough of
out = memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
# TODO: Use this directly in the attention operation, as a bias
if exists(mask):
raise NotImplementedError
out = (
out.unsqueeze(0)
.reshape(b, self.heads, out.shape[1], self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b, out.shape[1], self.heads * self.dim_head)
)
return self.to_out(out)
class SpatialTransformer(nn.Module):
"""
Transformer block for image-like data.
@ -307,6 +400,12 @@ class SpatialTransformer(nn.Module):
stride=1,
padding=0)
if os.environ.get('MEMORY_EFFICIENT_CROSS_ATTENTION', False):
self.transformer_blocks = nn.ModuleList(
[BasicTransformerBlockMECA(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
for d in range(depth)]
)
else:
self.transformer_blocks = nn.ModuleList(
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
for d in range(depth)]

100
ldm/modules/diffusionmodules/model.py
View File

@ -3,12 +3,14 @@ import gc
import math
import torch
import torch.nn as nn
from torch.nn.functional import silu
import numpy as np
from einops import rearrange
from ldm.util import instantiate_from_config
from ldm.modules.attention import LinearAttention
import psutil
def get_timestep_embedding(timesteps, embedding_dim):
"""
@ -31,11 +33,6 @@ def get_timestep_embedding(timesteps, embedding_dim):
return emb
def nonlinearity(x):
# swish
return x*torch.sigmoid(x)
def Normalize(in_channels, num_groups=32):
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
@ -120,30 +117,17 @@ class ResnetBlock(nn.Module):
padding=0)
def forward(self, x, temb):
h1 = x
h2 = self.norm1(h1)
del h1
h3 = nonlinearity(h2)
del h2
h4 = self.conv1(h3)
del h3
h = self.norm1(x)
h = silu(h)
h = self.conv1(h)
if temb is not None:
h4 = h4 + self.temb_proj(nonlinearity(temb))[:,:,None,None]
h = h + self.temb_proj(silu(temb))[:,:,None,None]
h5 = self.norm2(h4)
del h4
h6 = nonlinearity(h5)
del h5
h7 = self.dropout(h6)
del h6
h8 = self.conv2(h7)
del h7
h = self.norm2(h)
h = silu(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
@ -151,8 +135,7 @@ class ResnetBlock(nn.Module):
else:
x = self.nin_shortcut(x)
return x + h8
return x + h
class LinAttnBlock(LinearAttention):
"""to match AttnBlock usage"""
@ -209,6 +192,7 @@ class AttnBlock(nn.Module):
h_ = torch.zeros_like(k, device=q.device)
if q.device.type == 'cuda':
stats = torch.cuda.memory_stats(q.device)
mem_active = stats['active_bytes.all.current']
mem_reserved = stats['reserved_bytes.all.current']
@ -224,6 +208,13 @@ class AttnBlock(nn.Module):
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
else:
if psutil.virtual_memory().available / (1024**3) < 12:
slice_size = 1
else:
slice_size = min(q.shape[1], math.floor(2**30 / (q.shape[0] * q.shape[1])))
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
@ -373,7 +364,7 @@ class Model(nn.Module):
assert t is not None
temb = get_timestep_embedding(t, self.ch)
temb = self.temb.dense[0](temb)
temb = nonlinearity(temb)
temb = silu(temb)
temb = self.temb.dense[1](temb)
else:
temb = None
@ -407,7 +398,7 @@ class Model(nn.Module):
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = silu(h)
h = self.conv_out(h)
return h
@ -504,7 +495,7 @@ class Encoder(nn.Module):
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = silu(h)
h = self.conv_out(h)
return h
@ -590,20 +581,16 @@ class Decoder(nn.Module):
temb = None
# z to block_in
h1 = self.conv_in(z)
h = self.conv_in(z)
# middle
h2 = self.mid.block_1(h1, temb)
del h1
h3 = self.mid.attn_1(h2)
del h2
h = self.mid.block_2(h3, temb)
del h3
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# prepare for up sampling
gc.collect()
if h.device.type == 'cuda':
torch.cuda.empty_cache()
# upsampling
@ -611,33 +598,19 @@ class Decoder(nn.Module):
for i_block in range(self.num_res_blocks+1):
h = self.up[i_level].block[i_block](h, temb)
if len(self.up[i_level].attn) > 0:
t = h
h = self.up[i_level].attn[i_block](t)
del t
h = self.up[i_level].attn[i_block](h)
if i_level != 0:
t = h
h = self.up[i_level].upsample(t)
del t
h = self.up[i_level].upsample(h)
# end
if self.give_pre_end:
return h
h1 = self.norm_out(h)
del h
h2 = nonlinearity(h1)
del h1
h = self.conv_out(h2)
del h2
h = self.norm_out(h)
h = silu(h)
h = self.conv_out(h)
if self.tanh_out:
t = h
h = torch.tanh(t)
del t
h = torch.tanh(h)
return h
@ -672,7 +645,7 @@ class SimpleDecoder(nn.Module):
x = layer(x)
h = self.norm_out(x)
h = nonlinearity(h)
h = silu(h)
x = self.conv_out(h)
return x
@ -720,7 +693,7 @@ class UpsampleDecoder(nn.Module):
if i_level != self.num_resolutions - 1:
h = self.upsample_blocks[k](h)
h = self.norm_out(h)
h = nonlinearity(h)
h = silu(h)
h = self.conv_out(h)
return h
@ -896,7 +869,7 @@ class FirstStagePostProcessor(nn.Module):
z_fs = self.encode_with_pretrained(x)
z = self.proj_norm(z_fs)
z = self.proj(z)
z = nonlinearity(z)
z = silu(z)
for submodel, downmodel in zip(self.model,self.downsampler):
z = submodel(z,temb=None)
@ -905,4 +878,3 @@ class FirstStagePostProcessor(nn.Module):
if self.do_reshape:
z = rearrange(z,'b c h w -> b (h w) c')
return z

173
ldm/modules/diffusionmodules/openaimodel.py
View File

@ -24,6 +24,7 @@ from ldm.modules.attention import SpatialTransformer
def convert_module_to_f16(x):
pass
def convert_module_to_f32(x):
pass
@ -42,7 +43,9 @@ class AttentionPool2d(nn.Module):
output_dim: int = None,
):
super().__init__()
self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
self.positional_embedding = nn.Parameter(
th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5
)
self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
self.num_heads = embed_dim // num_heads_channels
@ -97,35 +100,43 @@ class Upsample(nn.Module):
upsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
def __init__(
self, channels, use_conv, dims=2, out_channels=None, padding=1
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
if use_conv:
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
self.conv = conv_nd(
dims, self.channels, self.out_channels, 3, padding=padding
)
def forward(self, x):
assert x.shape[1] == self.channels
if self.dims == 3:
x = F.interpolate(
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest'
)
else:
x = F.interpolate(x, scale_factor=2, mode="nearest")
x = F.interpolate(x, scale_factor=2, mode='nearest')
if self.use_conv:
x = self.conv(x)
return x
class TransposedUpsample(nn.Module):
'Learned 2x upsampling without padding'
"""Learned 2x upsampling without padding"""
def __init__(self, channels, out_channels=None, ks=5):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
self.up = nn.ConvTranspose2d(
self.channels, self.out_channels, kernel_size=ks, stride=2
)
def forward(self, x):
return self.up(x)
@ -140,7 +151,9 @@ class Downsample(nn.Module):
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
def __init__(
self, channels, use_conv, dims=2, out_channels=None, padding=1
):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
@ -149,7 +162,12 @@ class Downsample(nn.Module):
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
dims,
self.channels,
self.out_channels,
3,
stride=stride,
padding=padding,
)
else:
assert self.channels == self.out_channels
@ -219,7 +237,9 @@ class ResBlock(TimestepBlock):
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
2 * self.out_channels
if use_scale_shift_norm
else self.out_channels,
),
)
self.out_layers = nn.Sequential(
@ -227,7 +247,9 @@ class ResBlock(TimestepBlock):
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
conv_nd(
dims, self.out_channels, self.out_channels, 3, padding=1
)
),
)
@ -238,7 +260,9 @@ class ResBlock(TimestepBlock):
dims, channels, self.out_channels, 3, padding=1
)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
self.skip_connection = conv_nd(
dims, channels, self.out_channels, 1
)
def forward(self, x, emb):
"""
@ -251,7 +275,6 @@ class ResBlock(TimestepBlock):
self._forward, (x, emb), self.parameters(), self.use_checkpoint
)
def _forward(self, x, emb):
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
@ -297,7 +320,7 @@ class AttentionBlock(nn.Module):
else:
assert (
channels % num_head_channels == 0
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
), f'q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}'
self.num_heads = channels // num_head_channels
self.use_checkpoint = use_checkpoint
self.norm = normalization(channels)
@ -312,7 +335,9 @@ class AttentionBlock(nn.Module):
self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
def forward(self, x):
return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
return checkpoint(
self._forward, (x,), self.parameters(), True
) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
# return pt_checkpoint(self._forward, x) # pytorch
def _forward(self, x):
@ -362,13 +387,15 @@ class QKVAttentionLegacy(nn.Module):
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(
ch, dim=1
)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
"bct,bcs->bts", q * scale, k * scale
'bct,bcs->bts', q * scale, k * scale
) # More stable with f16 than dividing afterwards
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
a = th.einsum("bts,bcs->bct", weight, v)
a = th.einsum('bts,bcs->bct', weight, v)
return a.reshape(bs, -1, length)
@staticmethod
@ -397,12 +424,14 @@ class QKVAttention(nn.Module):
q, k, v = qkv.chunk(3, dim=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
"bct,bcs->bts",
'bct,bcs->bts',
(q * scale).view(bs * self.n_heads, ch, length),
(k * scale).view(bs * self.n_heads, ch, length),
) # More stable with f16 than dividing afterwards
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
a = th.einsum(
'bts,bcs->bct', weight, v.reshape(bs * self.n_heads, ch, length)
)
return a.reshape(bs, -1, length)
@staticmethod
@ -469,11 +498,16 @@ class UNetModel(nn.Module):
):
super().__init__()
if use_spatial_transformer:
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
assert (
context_dim is not None
), 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
assert (
use_spatial_transformer
), 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
from omegaconf.listconfig import ListConfig
if type(context_dim) == ListConfig:
context_dim = list(context_dim)
@ -481,10 +515,14 @@ class UNetModel(nn.Module):
num_heads_upsample = num_heads
if num_heads == -1:
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
assert (
num_head_channels != -1
), 'Either num_heads or num_head_channels has to be set'
if num_head_channels == -1:
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
assert (
num_heads != -1
), 'Either num_heads or num_head_channels has to be set'
self.image_size = image_size
self.in_channels = in_channels
@ -546,7 +584,11 @@ class UNetModel(nn.Module):
dim_head = num_head_channels
if legacy:
# num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
dim_head = (
ch // num_heads
if use_spatial_transformer
else num_head_channels
)
layers.append(
AttentionBlock(
ch,
@ -554,8 +596,14 @@ class UNetModel(nn.Module):
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
)
if not use_spatial_transformer
else SpatialTransformer(
ch,
num_heads,
dim_head,
depth=transformer_depth,
context_dim=context_dim,
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
@ -593,7 +641,11 @@ class UNetModel(nn.Module):
dim_head = num_head_channels
if legacy:
# num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
dim_head = (
ch // num_heads
if use_spatial_transformer
else num_head_channels
)
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
@ -609,8 +661,14 @@ class UNetModel(nn.Module):
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
)
if not use_spatial_transformer
else SpatialTransformer(
ch,
num_heads,
dim_head,
depth=transformer_depth,
context_dim=context_dim,
),
ResBlock(
ch,
@ -647,7 +705,11 @@ class UNetModel(nn.Module):
dim_head = num_head_channels
if legacy:
# num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
dim_head = (
ch // num_heads
if use_spatial_transformer
else num_head_channels
)
layers.append(
AttentionBlock(
ch,
@ -655,8 +717,14 @@ class UNetModel(nn.Module):
num_heads=num_heads_upsample,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
)
if not use_spatial_transformer
else SpatialTransformer(
ch,
num_heads,
dim_head,
depth=transformer_depth,
context_dim=context_dim,
)
)
if level and i == num_res_blocks:
@ -673,7 +741,9 @@ class UNetModel(nn.Module):
up=True,
)
if resblock_updown
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
else Upsample(
ch, conv_resample, dims=dims, out_channels=out_ch
)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
@ -682,7 +752,9 @@ class UNetModel(nn.Module):
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
zero_module(
conv_nd(dims, model_channels, out_channels, 3, padding=1)
),
)
if self.predict_codebook_ids:
self.id_predictor = nn.Sequential(
@ -718,9 +790,11 @@ class UNetModel(nn.Module):
"""
assert (y is not None) == (
self.num_classes is not None
), "must specify y if and only if the model is class-conditional"
), 'must specify y if and only if the model is class-conditional'
hs = []
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
t_emb = timestep_embedding(
timesteps, self.model_channels, repeat_only=False
)
emb = self.time_embed(t_emb)
if self.num_classes is not None:
@ -733,6 +807,8 @@ class UNetModel(nn.Module):
hs.append(h)
h = self.middle_block(h, emb, context)
for module in self.output_blocks:
if h.shape[-2:] != hs[-1].shape[-2:]:
h = F.interpolate(h, hs[-1].shape[-2:], mode="nearest")
h = th.cat([h, hs.pop()], dim=1)
h = module(h, emb, context)
h = h.type(x.dtype)
@ -768,9 +844,9 @@ class EncoderUNetModel(nn.Module):
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
pool="adaptive",
pool='adaptive',
*args,
**kwargs
**kwargs,
):
super().__init__()
@ -888,7 +964,7 @@ class EncoderUNetModel(nn.Module):
)
self._feature_size += ch
self.pool = pool
if pool == "adaptive":
if pool == 'adaptive':
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
@ -896,7 +972,7 @@ class EncoderUNetModel(nn.Module):
zero_module(conv_nd(dims, ch, out_channels, 1)),
nn.Flatten(),
)
elif pool == "attention":
elif pool == 'attention':
assert num_head_channels != -1
self.out = nn.Sequential(
normalization(ch),
@ -905,13 +981,13 @@ class EncoderUNetModel(nn.Module):
(image_size // ds), ch, num_head_channels, out_channels
),
)
elif pool == "spatial":
elif pool == 'spatial':
self.out = nn.Sequential(
nn.Linear(self._feature_size, 2048),
nn.ReLU(),
nn.Linear(2048, self.out_channels),
)
elif pool == "spatial_v2":
elif pool == 'spatial_v2':
self.out = nn.Sequential(
nn.Linear(self._feature_size, 2048),
normalization(2048),
@ -919,7 +995,7 @@ class EncoderUNetModel(nn.Module):
nn.Linear(2048, self.out_channels),
)
else:
raise NotImplementedError(f"Unexpected {pool} pooling")
raise NotImplementedError(f'Unexpected {pool} pooling')
def convert_to_fp16(self):
"""
@ -942,20 +1018,21 @@ class EncoderUNetModel(nn.Module):
:param timesteps: a 1-D batch of timesteps.
:return: an [N x K] Tensor of outputs.
"""
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
emb = self.time_embed(
timestep_embedding(timesteps, self.model_channels)
)
results = []
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb)
if self.pool.startswith("spatial"):
if self.pool.startswith('spatial'):
results.append(h.type(x.dtype).mean(dim=(2, 3)))
h = self.middle_block(h, emb)
if self.pool.startswith("spatial"):
if self.pool.startswith('spatial'):
results.append(h.type(x.dtype).mean(dim=(2, 3)))
h = th.cat(results, axis=-1)
return self.out(h)
else:
h = h.type(x.dtype)
return self.out(h)

81
ldm/modules/diffusionmodules/upscaling.py Normal file
View File

@ -0,0 +1,81 @@
import torch
import torch.nn as nn
import numpy as np
from functools import partial
from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
from ldm.util import default
class AbstractLowScaleModel(nn.Module):
# for concatenating a downsampled image to the latent representation
def __init__(self, noise_schedule_config=None):
super(AbstractLowScaleModel, self).__init__()
if noise_schedule_config is not None:
self.register_schedule(**noise_schedule_config)
def register_schedule(self, beta_schedule="linear", timesteps=1000,
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
def forward(self, x):
return x, None
def decode(self, x):
return x
class SimpleImageConcat(AbstractLowScaleModel):
# no noise level conditioning
def __init__(self):
super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
self.max_noise_level = 0
def forward(self, x):
# fix to constant noise level
return x, torch.zeros(x.shape[0], device=x.device).long()
class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
super().__init__(noise_schedule_config=noise_schedule_config)
self.max_noise_level = max_noise_level
def forward(self, x, noise_level=None):
if noise_level is None:
noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
else:
assert isinstance(noise_level, torch.Tensor)
z = self.q_sample(x, noise_level)
return z, noise_level

114
ldm/modules/diffusionmodules/util.py
View File

@ -18,15 +18,24 @@ from einops import repeat
from ldm.util import instantiate_from_config
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if schedule == "linear":
def make_beta_schedule(
schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3
):
if schedule == 'linear':
betas = (
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
torch.linspace(
linear_start**0.5,
linear_end**0.5,
n_timestep,
dtype=torch.float64,
)
** 2
)
elif schedule == "cosine":
elif schedule == 'cosine':
timesteps = (
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep
+ cosine_s
)
alphas = timesteps / (1 + cosine_s) * np.pi / 2
alphas = torch.cos(alphas).pow(2)
@ -34,44 +43,73 @@ def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2,
betas = 1 - alphas[1:] / alphas[:-1]
betas = np.clip(betas, a_min=0, a_max=0.999)
elif schedule == "sqrt_linear":
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
elif schedule == "sqrt":
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
elif schedule == 'sqrt_linear':
betas = torch.linspace(
linear_start, linear_end, n_timestep, dtype=torch.float64
)
elif schedule == 'sqrt':
betas = (
torch.linspace(
linear_start, linear_end, n_timestep, dtype=torch.float64
)
** 0.5
)
else:
raise ValueError(f"schedule '{schedule}' unknown.")
return betas.numpy()
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
def make_ddim_timesteps(
ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True
):
if ddim_discr_method == 'uniform':
c = num_ddpm_timesteps // num_ddim_timesteps
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
elif ddim_discr_method == 'quad':
ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
ddim_timesteps = (
(
np.linspace(
0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps
)
)
** 2
).astype(int)
else:
raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
raise NotImplementedError(
f'There is no ddim discretization method called "{ddim_discr_method}"'
)
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
# add one to get the final alpha values right (the ones from first scale to data during sampling)
# steps_out = ddim_timesteps + 1 # removed due to some issues when reaching 1000
steps_out = np.where(ddim_timesteps != 999, ddim_timesteps+1, ddim_timesteps)
# steps_out = ddim_timesteps + 1
steps_out = ddim_timesteps
if verbose:
print(f'Selected timesteps for ddim sampler: {steps_out}')
return steps_out
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
def make_ddim_sampling_parameters(
alphacums, ddim_timesteps, eta, verbose=True
):
# select alphas for computing the variance schedule
alphas = alphacums[ddim_timesteps]
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
alphas_prev = np.asarray(
[alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()
)
# according the the formula provided in https://arxiv.org/abs/2010.02502
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
sigmas = eta * np.sqrt(
(1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)
)
if verbose:
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
print(f'For the chosen value of eta, which is {eta}, '
f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
print(
f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}'
)
print(
f'For the chosen value of eta, which is {eta}, '
f'this results in the following sigma_t schedule for ddim sampler {sigmas}'
)
return sigmas, alphas, alphas_prev
@ -110,7 +148,9 @@ def checkpoint(func, inputs, params, flag):
explicitly take as arguments.
:param flag: if False, disable gradient checkpointing.
"""
if flag:
if (
False
): # disabled checkpointing to allow requires_grad = False for main model
args = tuple(inputs) + tuple(params)
return CheckpointFunction.apply(func, len(inputs), *args)
else:
@ -130,7 +170,9 @@ class CheckpointFunction(torch.autograd.Function):
@staticmethod
def backward(ctx, *output_grads):
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
ctx.input_tensors = [
x.detach().requires_grad_(True) for x in ctx.input_tensors
]
with torch.enable_grad():
# Fixes a bug where the first op in run_function modifies the
# Tensor storage in place, which is not allowed for detach()'d
@ -161,12 +203,16 @@ def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
if not repeat_only:
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-math.log(max_period)
* torch.arange(start=0, end=half, dtype=torch.float32)
/ half
).to(device=timesteps.device)
args = timesteps[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
embedding = torch.cat(
[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
)
else:
embedding = repeat(timesteps, 'b -> b d', d=dim)
return embedding
@ -206,16 +252,11 @@ def normalization(channels):
return GroupNorm32(32, channels)
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
def forward(self, x):
return x * torch.sigmoid(x)
class GroupNorm32(nn.GroupNorm):
def forward(self, x):
return super().forward(x.float()).type(x.dtype)
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
@ -226,7 +267,7 @@ def conv_nd(dims, *args, **kwargs):
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
raise ValueError(f'unsupported dimensions: {dims}')
def linear(*args, **kwargs):
@ -246,15 +287,16 @@ def avg_pool_nd(dims, *args, **kwargs):
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}")
raise ValueError(f'unsupported dimensions: {dims}')
class HybridConditioner(nn.Module):
def __init__(self, c_concat_config, c_crossattn_config):
super().__init__()
self.concat_conditioner = instantiate_from_config(c_concat_config)
self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
self.crossattn_conditioner = instantiate_from_config(
c_crossattn_config
)
def forward(self, c_concat, c_crossattn):
c_concat = self.concat_conditioner(c_concat)
@ -263,6 +305,8 @@ class HybridConditioner(nn.Module):
def noise_like(shape, device, repeat=False):
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(
shape[0], *((1,) * (len(shape) - 1))
)
noise = lambda: torch.randn(shape, device=device)
return repeat_noise() if repeat else noise()

36
ldm/modules/distributions/distributions.py
View File

@ -30,33 +30,45 @@ class DiagonalGaussianDistribution(object):
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
self.var = self.std = torch.zeros_like(self.mean).to(
device=self.parameters.device
)
def sample(self):
x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
x = self.mean + self.std * torch.randn(self.mean.shape).to(
device=self.parameters.device
)
return x
def kl(self, other=None):
if self.deterministic:
return torch.Tensor([0.])
return torch.Tensor([0.0])
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean, 2)
+ self.var - 1.0 - self.logvar,
dim=[1, 2, 3])
return 0.5 * torch.sum(
torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
dim=[1, 2, 3],
)
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
dim=[1, 2, 3])
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar,
dim=[1, 2, 3],
)
def nll(self, sample, dims=[1, 2, 3]):
if self.deterministic:
return torch.Tensor([0.])
return torch.Tensor([0.0])
logtwopi = np.log(2.0 * np.pi)
return 0.5 * torch.sum(
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
dim=dims)
logtwopi
+ self.logvar
+ torch.pow(sample - self.mean, 2) / self.var,
dim=dims,
)
def mode(self):
return self.mean
@ -74,7 +86,7 @@ def normal_kl(mean1, logvar1, mean2, logvar2):
if isinstance(obj, torch.Tensor):
tensor = obj
break
assert tensor is not None, "at least one argument must be a Tensor"
assert tensor is not None, 'at least one argument must be a Tensor'
# Force variances to be Tensors. Broadcasting helps convert scalars to
# Tensors, but it does not work for torch.exp().

24
ldm/modules/ema.py
View File

@ -10,8 +10,12 @@ class LitEma(nn.Module):
self.m_name2s_name = {}
self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates
else torch.tensor(-1,dtype=torch.int))
self.register_buffer(
'num_updates',
torch.tensor(0, dtype=torch.int)
if use_num_upates
else torch.tensor(-1, dtype=torch.int),
)
for name, p in model.named_parameters():
if p.requires_grad:
@ -27,7 +31,9 @@ class LitEma(nn.Module):
if self.num_updates >= 0:
self.num_updates += 1
decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates))
decay = min(
self.decay, (1 + self.num_updates) / (10 + self.num_updates)
)
one_minus_decay = 1.0 - decay
@ -38,8 +44,12 @@ class LitEma(nn.Module):
for key in m_param:
if m_param[key].requires_grad:
sname = self.m_name2s_name[key]
shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
shadow_params[sname] = shadow_params[sname].type_as(
m_param[key]
)
shadow_params[sname].sub_(
one_minus_decay * (shadow_params[sname] - m_param[key])
)
else:
assert not key in self.m_name2s_name
@ -48,7 +58,9 @@ class LitEma(nn.Module):
shadow_params = dict(self.named_buffers())
for key in m_param:
if m_param[key].requires_grad:
m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
m_param[key].data.copy_(
shadow_params[self.m_name2s_name[key]].data
)
else:
assert not key in self.m_name2s_name

273
ldm/modules/embedding_manager.py Normal file
View File

@ -0,0 +1,273 @@
from cmath import log
import torch
from torch import nn
import sys
from ldm.data.personalized import per_img_token_list
from transformers import CLIPTokenizer
from functools import partial
DEFAULT_PLACEHOLDER_TOKEN = ['*']
PROGRESSIVE_SCALE = 2000
def get_clip_token_for_string(tokenizer, string):
batch_encoding = tokenizer(
string,
truncation=True,
max_length=77,
return_length=True,
return_overflowing_tokens=False,
padding='max_length',
return_tensors='pt',
)
tokens = batch_encoding['input_ids']
""" assert (
torch.count_nonzero(tokens - 49407) == 2
), f"String '{string}' maps to more than a single token. Please use another string" """
return tokens[0, 1]
def get_bert_token_for_string(tokenizer, string):
token = tokenizer(string)
# assert torch.count_nonzero(token) == 3, f"String '{string}' maps to more than a single token. Please use another string"
token = token[0, 1]
return token
def get_embedding_for_clip_token(embedder, token):
return embedder(token.unsqueeze(0))[0, 0]
class EmbeddingManager(nn.Module):
def __init__(
self,
embedder,
placeholder_strings=None,
initializer_words=None,
per_image_tokens=False,
num_vectors_per_token=1,
progressive_words=False,
**kwargs,
):
super().__init__()
self.embedder = embedder
device = embedder.device
self.string_to_token_dict = {}
self.string_to_param_dict = nn.ParameterDict()
self.initial_embeddings = (
nn.ParameterDict()
) # These should not be optimized
self.progressive_words = progressive_words
self.progressive_counter = 0
self.max_vectors_per_token = num_vectors_per_token
if hasattr(
embedder, 'tokenizer'
): # using Stable Diffusion's CLIP encoder
self.is_clip = True
get_token_for_string = partial(
get_clip_token_for_string, embedder.tokenizer
)
get_embedding_for_tkn = partial(
get_embedding_for_clip_token,
embedder.transformer.text_model.embeddings,
)
# per bug report #572
#token_dim = 1280
token_dim = 768
else: # using LDM's BERT encoder
self.is_clip = False
get_token_for_string = partial(
get_bert_token_for_string, embedder.tknz_fn
)
get_embedding_for_tkn = embedder.transformer.token_emb
token_dim = 1280
if per_image_tokens:
placeholder_strings.extend(per_img_token_list)
for idx, placeholder_string in enumerate(placeholder_strings):
token = get_token_for_string(placeholder_string)
if initializer_words and idx < len(initializer_words):
init_word_token = get_token_for_string(initializer_words[idx])
with torch.no_grad():
init_word_embedding = get_embedding_for_tkn(
init_word_token.to(device)
)
token_params = torch.nn.Parameter(
init_word_embedding.unsqueeze(0).repeat(
num_vectors_per_token, 1
),
requires_grad=True,
)
self.initial_embeddings[
placeholder_string
] = torch.nn.Parameter(
init_word_embedding.unsqueeze(0).repeat(
num_vectors_per_token, 1
),
requires_grad=False,
)
else:
token_params = torch.nn.Parameter(
torch.rand(
size=(num_vectors_per_token, token_dim),
requires_grad=True,
)
)
self.string_to_token_dict[placeholder_string] = token
self.string_to_param_dict[placeholder_string] = token_params
def forward(
self,
tokenized_text,
embedded_text,
):
b, n, device = *tokenized_text.shape, tokenized_text.device
for (
placeholder_string,
placeholder_token,
) in self.string_to_token_dict.items():
placeholder_embedding = self.string_to_param_dict[
placeholder_string
].to(device)
if (
self.max_vectors_per_token == 1
): # If there's only one vector per token, we can do a simple replacement
placeholder_idx = torch.where(
tokenized_text == placeholder_token.to(device)
)
embedded_text[placeholder_idx] = placeholder_embedding
else: # otherwise, need to insert and keep track of changing indices
if self.progressive_words:
self.progressive_counter += 1
max_step_tokens = (
1 + self.progressive_counter // PROGRESSIVE_SCALE
)
else:
max_step_tokens = self.max_vectors_per_token
num_vectors_for_token = min(
placeholder_embedding.shape[0], max_step_tokens
)
placeholder_rows, placeholder_cols = torch.where(
tokenized_text == placeholder_token.to(device)
)
if placeholder_rows.nelement() == 0:
continue
sorted_cols, sort_idx = torch.sort(
placeholder_cols, descending=True
)
sorted_rows = placeholder_rows[sort_idx]
for idx in range(len(sorted_rows)):
row = sorted_rows[idx]
col = sorted_cols[idx]
new_token_row = torch.cat(
[
tokenized_text[row][:col],
placeholder_token.repeat(num_vectors_for_token).to(
device
),
tokenized_text[row][col + 1 :],
],
axis=0,
)[:n]
new_embed_row = torch.cat(
[
embedded_text[row][:col],
placeholder_embedding[:num_vectors_for_token],
embedded_text[row][col + 1 :],
],
axis=0,
)[:n]
embedded_text[row] = new_embed_row
tokenized_text[row] = new_token_row
return embedded_text
def save(self, ckpt_path):
torch.save(
{
'string_to_token': self.string_to_token_dict,
'string_to_param': self.string_to_param_dict,
},
ckpt_path,
)
def load(self, ckpt_path, full=True):
ckpt = torch.load(ckpt_path, map_location='cpu')
# Handle .pt textual inversion files
if 'string_to_token' in ckpt and 'string_to_param' in ckpt:
self.string_to_token_dict = ckpt["string_to_token"]
self.string_to_param_dict = ckpt["string_to_param"]
# Handle .bin textual inversion files from Huggingface Concepts
# https://huggingface.co/sd-concepts-library
else:
for token_str in list(ckpt.keys()):
token = get_clip_token_for_string(self.embedder.tokenizer, token_str)
self.string_to_token_dict[token_str] = token
ckpt[token_str] = torch.nn.Parameter(ckpt[token_str])
self.string_to_param_dict.update(ckpt)
if not full:
for key, value in self.string_to_param_dict.items():
self.string_to_param_dict[key] = torch.nn.Parameter(value.half())
def get_embedding_norms_squared(self):
all_params = torch.cat(
list(self.string_to_param_dict.values()), axis=0
) # num_placeholders x embedding_dim
param_norm_squared = (all_params * all_params).sum(
axis=-1
) # num_placeholders
return param_norm_squared
def embedding_parameters(self):
return self.string_to_param_dict.parameters()
def embedding_to_coarse_loss(self):
loss = 0.0
num_embeddings = len(self.initial_embeddings)
for key in self.initial_embeddings:
optimized = self.string_to_param_dict[key]
coarse = self.initial_embeddings[key].clone().to(optimized.device)
loss = (
loss
+ (optimized - coarse)
@ (optimized - coarse).T
/ num_embeddings
)
return loss

416
ldm/modules/encoders/modules.py
View File

@ -5,8 +5,40 @@ import clip
from einops import rearrange, repeat
from transformers import CLIPTokenizer, CLIPTextModel
import kornia
import os
from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
from ldm.devices import choose_torch_device
from ldm.modules.x_transformer import (
Encoder,
TransformerWrapper,
) # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
def _expand_mask(mask, dtype, tgt_len=None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = (
mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
)
def _build_causal_attention_mask(bsz, seq_len, dtype):
# lazily create causal attention mask, with full attention between the vision tokens
# pytorch uses additive attention mask; fill with -inf
mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
mask.fill_(torch.tensor(torch.finfo(dtype).min))
mask.triu_(1) # zero out the lower diagonal
mask = mask.unsqueeze(1) # expand mask
return mask
class AbstractEncoder(nn.Module):
@ -17,7 +49,6 @@ class AbstractEncoder(nn.Module):
raise NotImplementedError
class ClassEmbedder(nn.Module):
def __init__(self, embed_dim, n_classes=1000, key='class'):
super().__init__()
@ -35,11 +66,22 @@ class ClassEmbedder(nn.Module):
class TransformerEmbedder(AbstractEncoder):
"""Some transformer encoder layers"""
def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
def __init__(
self,
n_embed,
n_layer,
vocab_size,
max_seq_len=77,
device=choose_torch_device(),
):
super().__init__()
self.device = device
self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
attn_layers=Encoder(dim=n_embed, depth=n_layer))
self.transformer = TransformerWrapper(
num_tokens=vocab_size,
max_seq_len=max_seq_len,
attn_layers=Encoder(dim=n_embed, depth=n_layer),
)
def forward(self, tokens):
tokens = tokens.to(self.device) # meh
@ -52,18 +94,43 @@ class TransformerEmbedder(AbstractEncoder):
class BERTTokenizer(AbstractEncoder):
"""Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
def __init__(self, device="cuda", vq_interface=True, max_length=77):
def __init__(
self, device=choose_torch_device(), vq_interface=True, max_length=77
):
super().__init__()
from transformers import BertTokenizerFast # TODO: add to reuquirements
self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
from transformers import (
BertTokenizerFast,
) # TODO: add to reuquirements
# Modified to allow to run on non-internet connected compute nodes.
# Model needs to be loaded into cache from an internet-connected machine
# by running:
# from transformers import BertTokenizerFast
# BertTokenizerFast.from_pretrained("bert-base-uncased")
try:
self.tokenizer = BertTokenizerFast.from_pretrained(
'bert-base-uncased', local_files_only=False
)
except OSError:
raise SystemExit(
"* Couldn't load Bert tokenizer files. Try running scripts/preload_models.py from an internet-conected machine."
)
self.device = device
self.vq_interface = vq_interface
self.max_length = max_length
def forward(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.device)
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=True,
return_overflowing_tokens=False,
padding='max_length',
return_tensors='pt',
)
tokens = batch_encoding['input_ids'].to(self.device)
return tokens
@torch.no_grad()
@ -79,54 +146,84 @@ class BERTTokenizer(AbstractEncoder):
class BERTEmbedder(AbstractEncoder):
"""Uses the BERT tokenizr model and add some transformer encoder layers"""
def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
device="cuda",use_tokenizer=True, embedding_dropout=0.0):
def __init__(
self,
n_embed,
n_layer,
vocab_size=30522,
max_seq_len=77,
device=choose_torch_device(),
use_tokenizer=True,
embedding_dropout=0.0,
):
super().__init__()
self.use_tknz_fn = use_tokenizer
if self.use_tknz_fn:
self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
self.tknz_fn = BERTTokenizer(
vq_interface=False, max_length=max_seq_len
)
self.device = device
self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
self.transformer = TransformerWrapper(
num_tokens=vocab_size,
max_seq_len=max_seq_len,
attn_layers=Encoder(dim=n_embed, depth=n_layer),
emb_dropout=embedding_dropout)
emb_dropout=embedding_dropout,
)
def forward(self, text):
def forward(self, text, embedding_manager=None):
if self.use_tknz_fn:
tokens = self.tknz_fn(text) # .to(self.device)
else:
tokens = text
z = self.transformer(tokens, return_embeddings=True)
z = self.transformer(
tokens, return_embeddings=True, embedding_manager=embedding_manager
)
return z
def encode(self, text):
def encode(self, text, **kwargs):
# output of length 77
return self(text)
return self(text, **kwargs)
class SpatialRescaler(nn.Module):
def __init__(self,
def __init__(
self,
n_stages=1,
method='bilinear',
multiplier=0.5,
in_channels=3,
out_channels=None,
bias=False):
bias=False,
):
super().__init__()
self.n_stages = n_stages
assert self.n_stages >= 0
assert method in ['nearest','linear','bilinear','trilinear','bicubic','area']
assert method in [
'nearest',
'linear',
'bilinear',
'trilinear',
'bicubic',
'area',
]
self.multiplier = multiplier
self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
self.interpolator = partial(
torch.nn.functional.interpolate, mode=method
)
self.remap_output = out_channels is not None
if self.remap_output:
print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.')
self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias)
print(
f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.'
)
self.channel_mapper = nn.Conv2d(
in_channels, out_channels, 1, bias=bias
)
def forward(self, x):
for stage in range(self.n_stages):
x = self.interpolator(x, scale_factor=self.multiplier)
if self.remap_output:
x = self.channel_mapper(x)
return x
@ -134,45 +231,244 @@ class SpatialRescaler(nn.Module):
def encode(self, x):
return self(x)
class FrozenCLIPEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77):
def __init__(
self,
version='openai/clip-vit-large-patch14',
device=choose_torch_device(),
max_length=77,
):
super().__init__()
if os.path.exists("models/clip-vit-large-patch14"):
self.tokenizer = CLIPTokenizer.from_pretrained("models/clip-vit-large-patch14")
self.transformer = CLIPTextModel.from_pretrained("models/clip-vit-large-patch14")
else:
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.transformer = CLIPTextModel.from_pretrained(version)
self.tokenizer = CLIPTokenizer.from_pretrained(
version, local_files_only=False
)
self.transformer = CLIPTextModel.from_pretrained(
version, local_files_only=False
)
self.device = device
self.max_length = max_length
self.freeze()
def embedding_forward(
self,
input_ids=None,
position_ids=None,
inputs_embeds=None,
embedding_manager=None,
) -> torch.Tensor:
seq_length = (
input_ids.shape[-1]
if input_ids is not None
else inputs_embeds.shape[-2]
)
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if inputs_embeds is None:
inputs_embeds = self.token_embedding(input_ids)
if embedding_manager is not None:
inputs_embeds = embedding_manager(input_ids, inputs_embeds)
position_embeddings = self.position_embedding(position_ids)
embeddings = inputs_embeds + position_embeddings
return embeddings
self.transformer.text_model.embeddings.forward = (
embedding_forward.__get__(self.transformer.text_model.embeddings)
)
def encoder_forward(
self,
inputs_embeds,
attention_mask=None,
causal_attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict
if return_dict is not None
else self.config.use_return_dict
)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
return hidden_states
self.transformer.text_model.encoder.forward = encoder_forward.__get__(
self.transformer.text_model.encoder
)
def text_encoder_forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
embedding_manager=None,
):
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict
if return_dict is not None
else self.config.use_return_dict
)
if input_ids is None:
raise ValueError('You have to specify either input_ids')
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
hidden_states = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
embedding_manager=embedding_manager,
)
bsz, seq_len = input_shape
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
causal_attention_mask = _build_causal_attention_mask(
bsz, seq_len, hidden_states.dtype
).to(hidden_states.device)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(
attention_mask, hidden_states.dtype
)
last_hidden_state = self.encoder(
inputs_embeds=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = self.final_layer_norm(last_hidden_state)
return last_hidden_state
self.transformer.text_model.forward = text_encoder_forward.__get__(
self.transformer.text_model
)
def transformer_forward(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
embedding_manager=None,
):
return self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
embedding_manager=embedding_manager,
)
self.transformer.forward = transformer_forward.__get__(
self.transformer
)
def freeze(self):
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"].to(self.device)
outputs = self.transformer(input_ids=tokens)
def forward(self, text, **kwargs):
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=True,
return_overflowing_tokens=False,
padding='max_length',
return_tensors='pt',
)
tokens = batch_encoding['input_ids'].to(self.device)
z = self.transformer(input_ids=tokens, **kwargs)
z = outputs.last_hidden_state
return z
def encode(self, text):
return self(text)
def encode(self, text, **kwargs):
return self(text, **kwargs)
class FrozenCLIPTextEmbedder(nn.Module):
"""
Uses the CLIP transformer encoder for text.
"""
def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True):
def __init__(
self,
version='ViT-L/14',
device=choose_torch_device(),
max_length=77,
n_repeat=1,
normalize=True,
):
super().__init__()
self.model, _ = clip.load(version, jit=False, device="cpu")
self.model, _ = clip.load(version, jit=False, device=device)
self.device = device
self.max_length = max_length
self.n_repeat = n_repeat
@ -202,11 +498,12 @@ class FrozenClipImageEmbedder(nn.Module):
"""
Uses the CLIP image encoder.
"""
def __init__(
self,
model,
jit=False,
device='cuda' if torch.cuda.is_available() else 'cpu',
device=choose_torch_device(),
antialias=False,
):
super().__init__()
@ -214,15 +511,27 @@ class FrozenClipImageEmbedder(nn.Module):
self.antialias = antialias
self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False)
self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False)
self.register_buffer(
'mean',
torch.Tensor([0.48145466, 0.4578275, 0.40821073]),
persistent=False,
)
self.register_buffer(
'std',
torch.Tensor([0.26862954, 0.26130258, 0.27577711]),
persistent=False,
)
def preprocess(self, x):
# normalize to [0,1]
x = kornia.geometry.resize(x, (224, 224),
interpolation='bicubic',align_corners=True,
antialias=self.antialias)
x = (x + 1.) / 2.
x = kornia.geometry.resize(
x,
(224, 224),
interpolation='bicubic',
align_corners=True,
antialias=self.antialias,
)
x = (x + 1.0) / 2.0
# renormalize according to clip
x = kornia.enhance.normalize(x, self.mean, self.std)
return x
@ -232,7 +541,8 @@ class FrozenClipImageEmbedder(nn.Module):
return self.model.encode_image(self.preprocess(x))
if __name__ == "__main__":
if __name__ == '__main__':
from ldm.util import count_params
model = FrozenCLIPEmbedder()
count_params(model, verbose=True)

170
ldm/modules/midas/api.py Normal file
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@ -0,0 +1,170 @@
# based on https://github.com/isl-org/MiDaS
import cv2
import torch
import torch.nn as nn
from torchvision.transforms import Compose
from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
from ldm.modules.midas.midas.midas_net import MidasNet
from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
ISL_PATHS = {
"dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
"dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
"midas_v21": "",
"midas_v21_small": "",
}
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def load_midas_transform(model_type):
# https://github.com/isl-org/MiDaS/blob/master/run.py
# load transform only
if model_type == "dpt_large": # DPT-Large
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid": # DPT-Hybrid
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "midas_v21":
net_w, net_h = 384, 384
resize_mode = "upper_bound"
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
elif model_type == "midas_v21_small":
net_w, net_h = 256, 256
resize_mode = "upper_bound"
normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
else:
assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
transform = Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method=resize_mode,
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
return transform
def load_model(model_type):
# https://github.com/isl-org/MiDaS/blob/master/run.py
# load network
model_path = ISL_PATHS[model_type]
if model_type == "dpt_large": # DPT-Large
model = DPTDepthModel(
path=model_path,
backbone="vitl16_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "dpt_hybrid": # DPT-Hybrid
model = DPTDepthModel(
path=model_path,
backbone="vitb_rn50_384",
non_negative=True,
)
net_w, net_h = 384, 384
resize_mode = "minimal"
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
elif model_type == "midas_v21":
model = MidasNet(model_path, non_negative=True)
net_w, net_h = 384, 384
resize_mode = "upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
elif model_type == "midas_v21_small":
model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
non_negative=True, blocks={'expand': True})
net_w, net_h = 256, 256
resize_mode = "upper_bound"
normalization = NormalizeImage(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
else:
print(f"model_type '{model_type}' not implemented, use: --model_type large")
assert False
transform = Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method=resize_mode,
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
return model.eval(), transform
class MiDaSInference(nn.Module):
MODEL_TYPES_TORCH_HUB = [
"DPT_Large",
"DPT_Hybrid",
"MiDaS_small"
]
MODEL_TYPES_ISL = [
"dpt_large",
"dpt_hybrid",
"midas_v21",
"midas_v21_small",
]
def __init__(self, model_type):
super().__init__()
assert (model_type in self.MODEL_TYPES_ISL)
model, _ = load_model(model_type)
self.model = model
self.model.train = disabled_train
def forward(self, x):
# x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
# NOTE: we expect that the correct transform has been called during dataloading.
with torch.no_grad():
prediction = self.model(x)
prediction = torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=x.shape[2:],
mode="bicubic",
align_corners=False,
)
assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
return prediction

0
ldm/modules/midas/midas/__init__.py Normal file
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16
ldm/modules/midas/midas/base_model.py Normal file
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@ -0,0 +1,16 @@
import torch
class BaseModel(torch.nn.Module):
def load(self, path):
"""Load model from file.
Args:
path (str): file path
"""
parameters = torch.load(path, map_location=torch.device('cpu'))
if "optimizer" in parameters:
parameters = parameters["model"]
self.load_state_dict(parameters)

342
ldm/modules/midas/midas/blocks.py Normal file
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@ -0,0 +1,342 @@
import torch
import torch.nn as nn
from .vit import (
_make_pretrained_vitb_rn50_384,
_make_pretrained_vitl16_384,
_make_pretrained_vitb16_384,
forward_vit,
)
def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
if backbone == "vitl16_384":
pretrained = _make_pretrained_vitl16_384(
use_pretrained, hooks=hooks, use_readout=use_readout
)
scratch = _make_scratch(
[256, 512, 1024, 1024], features, groups=groups, expand=expand
) # ViT-L/16 - 85.0% Top1 (backbone)
elif backbone == "vitb_rn50_384":
pretrained = _make_pretrained_vitb_rn50_384(
use_pretrained,
hooks=hooks,
use_vit_only=use_vit_only,
use_readout=use_readout,
)
scratch = _make_scratch(
[256, 512, 768, 768], features, groups=groups, expand=expand
) # ViT-H/16 - 85.0% Top1 (backbone)
elif backbone == "vitb16_384":
pretrained = _make_pretrained_vitb16_384(
use_pretrained, hooks=hooks, use_readout=use_readout
)
scratch = _make_scratch(
[96, 192, 384, 768], features, groups=groups, expand=expand
) # ViT-B/16 - 84.6% Top1 (backbone)
elif backbone == "resnext101_wsl":
pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3
elif backbone == "efficientnet_lite3":
pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3
else:
print(f"Backbone '{backbone}' not implemented")
assert False
return pretrained, scratch
def _make_scratch(in_shape, out_shape, groups=1, expand=False):
scratch = nn.Module()
out_shape1 = out_shape
out_shape2 = out_shape
out_shape3 = out_shape
out_shape4 = out_shape
if expand==True:
out_shape1 = out_shape
out_shape2 = out_shape*2
out_shape3 = out_shape*4
out_shape4 = out_shape*8
scratch.layer1_rn = nn.Conv2d(
in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
scratch.layer2_rn = nn.Conv2d(
in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
scratch.layer3_rn = nn.Conv2d(
in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
scratch.layer4_rn = nn.Conv2d(
in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
)
return scratch
def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
efficientnet = torch.hub.load(
"rwightman/gen-efficientnet-pytorch",
"tf_efficientnet_lite3",
pretrained=use_pretrained,
exportable=exportable
)
return _make_efficientnet_backbone(efficientnet)
def _make_efficientnet_backbone(effnet):
pretrained = nn.Module()
pretrained.layer1 = nn.Sequential(
effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
)
pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
return pretrained
def _make_resnet_backbone(resnet):
pretrained = nn.Module()
pretrained.layer1 = nn.Sequential(
resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
)
pretrained.layer2 = resnet.layer2
pretrained.layer3 = resnet.layer3
pretrained.layer4 = resnet.layer4
return pretrained
def _make_pretrained_resnext101_wsl(use_pretrained):
resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
return _make_resnet_backbone(resnet)
class Interpolate(nn.Module):
"""Interpolation module.
"""
def __init__(self, scale_factor, mode, align_corners=False):
"""Init.
Args:
scale_factor (float): scaling
mode (str): interpolation mode
"""
super(Interpolate, self).__init__()
self.interp = nn.functional.interpolate
self.scale_factor = scale_factor
self.mode = mode
self.align_corners = align_corners
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: interpolated data
"""
x = self.interp(
x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
)
return x
class ResidualConvUnit(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.conv1 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True
)
self.conv2 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: output
"""
out = self.relu(x)
out = self.conv1(out)
out = self.relu(out)
out = self.conv2(out)
return out + x
class FeatureFusionBlock(nn.Module):
"""Feature fusion block.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super(FeatureFusionBlock, self).__init__()
self.resConfUnit1 = ResidualConvUnit(features)
self.resConfUnit2 = ResidualConvUnit(features)
def forward(self, *xs):
"""Forward pass.
Returns:
tensor: output
"""
output = xs[0]
if len(xs) == 2:
output += self.resConfUnit1(xs[1])
output = self.resConfUnit2(output)
output = nn.functional.interpolate(
output, scale_factor=2, mode="bilinear", align_corners=True
)
return output
class ResidualConvUnit_custom(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features, activation, bn):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.bn = bn
self.groups=1
self.conv1 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
)
self.conv2 = nn.Conv2d(
features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
)
if self.bn==True:
self.bn1 = nn.BatchNorm2d(features)
self.bn2 = nn.BatchNorm2d(features)
self.activation = activation
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: output
"""
out = self.activation(x)
out = self.conv1(out)
if self.bn==True:
out = self.bn1(out)
out = self.activation(out)
out = self.conv2(out)
if self.bn==True:
out = self.bn2(out)
if self.groups > 1:
out = self.conv_merge(out)
return self.skip_add.add(out, x)
# return out + x
class FeatureFusionBlock_custom(nn.Module):
"""Feature fusion block.
"""
def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
"""Init.
Args:
features (int): number of features
"""
super(FeatureFusionBlock_custom, self).__init__()
self.deconv = deconv
self.align_corners = align_corners
self.groups=1
self.expand = expand
out_features = features
if self.expand==True:
out_features = features//2
self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, *xs):
"""Forward pass.
Returns:
tensor: output
"""
output = xs[0]
if len(xs) == 2:
res = self.resConfUnit1(xs[1])
output = self.skip_add.add(output, res)
# output += res
output = self.resConfUnit2(output)
output = nn.functional.interpolate(
output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
)
output = self.out_conv(output)
return output

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ldm/modules/midas/midas/dpt_depth.py Normal file
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import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_model import BaseModel
from .blocks import (
FeatureFusionBlock,
FeatureFusionBlock_custom,
Interpolate,
_make_encoder,
forward_vit,
)
def _make_fusion_block(features, use_bn):
return FeatureFusionBlock_custom(
features,
nn.ReLU(False),
deconv=False,
bn=use_bn,
expand=False,
align_corners=True,
)
class DPT(BaseModel):
def __init__(
self,
head,
features=256,
backbone="vitb_rn50_384",
readout="project",
channels_last=False,
use_bn=False,
):
super(DPT, self).__init__()
self.channels_last = channels_last
hooks = {
"vitb_rn50_384": [0, 1, 8, 11],
"vitb16_384": [2, 5, 8, 11],
"vitl16_384": [5, 11, 17, 23],
}
# Instantiate backbone and reassemble blocks
self.pretrained, self.scratch = _make_encoder(
backbone,
features,
False, # Set to true of you want to train from scratch, uses ImageNet weights
groups=1,
expand=False,
exportable=False,
hooks=hooks[backbone],
use_readout=readout,
)
self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
self.scratch.output_conv = head
def forward(self, x):
if self.channels_last == True:
x.contiguous(memory_format=torch.channels_last)
layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn)
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv(path_1)
return out
class DPTDepthModel(DPT):
def __init__(self, path=None, non_negative=True, **kwargs):
features = kwargs["features"] if "features" in kwargs else 256
head = nn.Sequential(
nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(True),
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
nn.ReLU(True) if non_negative else nn.Identity(),
nn.Identity(),
)
super().__init__(head, **kwargs)
if path is not None:
self.load(path)
def forward(self, x):
return super().forward(x).squeeze(dim=1)

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"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
class MidasNet(BaseModel):
"""Network for monocular depth estimation.
"""
def __init__(self, path=None, features=256, non_negative=True):
"""Init.
Args:
path (str, optional): Path to saved model. Defaults to None.
features (int, optional): Number of features. Defaults to 256.
backbone (str, optional): Backbone network for encoder. Defaults to resnet50
"""
print("Loading weights: ", path)
super(MidasNet, self).__init__()
use_pretrained = False if path is None else True
self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
self.scratch.refinenet4 = FeatureFusionBlock(features)
self.scratch.refinenet3 = FeatureFusionBlock(features)
self.scratch.refinenet2 = FeatureFusionBlock(features)
self.scratch.refinenet1 = FeatureFusionBlock(features)
self.scratch.output_conv = nn.Sequential(
nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
Interpolate(scale_factor=2, mode="bilinear"),
nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(True),
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
nn.ReLU(True) if non_negative else nn.Identity(),
)
if path:
self.load(path)
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input data (image)
Returns:
tensor: depth
"""
layer_1 = self.pretrained.layer1(x)
layer_2 = self.pretrained.layer2(layer_1)
layer_3 = self.pretrained.layer3(layer_2)
layer_4 = self.pretrained.layer4(layer_3)
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn)
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv(path_1)
return torch.squeeze(out, dim=1)

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"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from .base_model import BaseModel
from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
class MidasNet_small(BaseModel):
"""Network for monocular depth estimation.
"""
def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
blocks={'expand': True}):
"""Init.
Args:
path (str, optional): Path to saved model. Defaults to None.
features (int, optional): Number of features. Defaults to 256.
backbone (str, optional): Backbone network for encoder. Defaults to resnet50
"""
print("Loading weights: ", path)
super(MidasNet_small, self).__init__()
use_pretrained = False if path else True
self.channels_last = channels_last
self.blocks = blocks
self.backbone = backbone
self.groups = 1
features1=features
features2=features
features3=features
features4=features
self.expand = False
if "expand" in self.blocks and self.blocks['expand'] == True:
self.expand = True
features1=features
features2=features*2
features3=features*4
features4=features*8
self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
self.scratch.activation = nn.ReLU(False)
self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
self.scratch.output_conv = nn.Sequential(
nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
Interpolate(scale_factor=2, mode="bilinear"),
nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
self.scratch.activation,
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
nn.ReLU(True) if non_negative else nn.Identity(),
nn.Identity(),
)
if path:
self.load(path)
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input data (image)
Returns:
tensor: depth
"""
if self.channels_last==True:
print("self.channels_last = ", self.channels_last)
x.contiguous(memory_format=torch.channels_last)
layer_1 = self.pretrained.layer1(x)
layer_2 = self.pretrained.layer2(layer_1)
layer_3 = self.pretrained.layer3(layer_2)
layer_4 = self.pretrained.layer4(layer_3)
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn)
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv(path_1)
return torch.squeeze(out, dim=1)
def fuse_model(m):
prev_previous_type = nn.Identity()
prev_previous_name = ''
previous_type = nn.Identity()
previous_name = ''
for name, module in m.named_modules():
if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
# print("FUSED ", prev_previous_name, previous_name, name)
torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
# print("FUSED ", prev_previous_name, previous_name)
torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
# elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
# print("FUSED ", previous_name, name)
# torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
prev_previous_type = previous_type
prev_previous_name = previous_name
previous_type = type(module)
previous_name = name

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import numpy as np
import cv2
import math
def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
"""Rezise the sample to ensure the given size. Keeps aspect ratio.
Args:
sample (dict): sample
size (tuple): image size
Returns:
tuple: new size
"""
shape = list(sample["disparity"].shape)
if shape[0] >= size[0] and shape[1] >= size[1]:
return sample
scale = [0, 0]
scale[0] = size[0] / shape[0]
scale[1] = size[1] / shape[1]
scale = max(scale)
shape[0] = math.ceil(scale * shape[0])
shape[1] = math.ceil(scale * shape[1])
# resize
sample["image"] = cv2.resize(
sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
)
sample["disparity"] = cv2.resize(
sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
)
sample["mask"] = cv2.resize(
sample["mask"].astype(np.float32),
tuple(shape[::-1]),
interpolation=cv2.INTER_NEAREST,
)
sample["mask"] = sample["mask"].astype(bool)
return tuple(shape)
class Resize(object):
"""Resize sample to given size (width, height).
"""
def __init__(
self,
width,
height,
resize_target=True,
keep_aspect_ratio=False,
ensure_multiple_of=1,
resize_method="lower_bound",
image_interpolation_method=cv2.INTER_AREA,
):
"""Init.
Args:
width (int): desired output width
height (int): desired output height
resize_target (bool, optional):
True: Resize the full sample (image, mask, target).
False: Resize image only.
Defaults to True.
keep_aspect_ratio (bool, optional):
True: Keep the aspect ratio of the input sample.
Output sample might not have the given width and height, and
resize behaviour depends on the parameter 'resize_method'.
Defaults to False.
ensure_multiple_of (int, optional):
Output width and height is constrained to be multiple of this parameter.
Defaults to 1.
resize_method (str, optional):
"lower_bound": Output will be at least as large as the given size.
"upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
"minimal": Scale as least as possible. (Output size might be smaller than given size.)
Defaults to "lower_bound".
"""
self.__width = width
self.__height = height
self.__resize_target = resize_target
self.__keep_aspect_ratio = keep_aspect_ratio
self.__multiple_of = ensure_multiple_of
self.__resize_method = resize_method
self.__image_interpolation_method = image_interpolation_method
def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
if max_val is not None and y > max_val:
y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
if y < min_val:
y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
return y
def get_size(self, width, height):
# determine new height and width
scale_height = self.__height / height
scale_width = self.__width / width
if self.__keep_aspect_ratio:
if self.__resize_method == "lower_bound":
# scale such that output size is lower bound
if scale_width > scale_height:
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
elif self.__resize_method == "upper_bound":
# scale such that output size is upper bound
if scale_width < scale_height:
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
elif self.__resize_method == "minimal":
# scale as least as possbile
if abs(1 - scale_width) < abs(1 - scale_height):
# fit width
scale_height = scale_width
else:
# fit height
scale_width = scale_height
else:
raise ValueError(
f"resize_method {self.__resize_method} not implemented"
)
if self.__resize_method == "lower_bound":
new_height = self.constrain_to_multiple_of(
scale_height * height, min_val=self.__height
)
new_width = self.constrain_to_multiple_of(
scale_width * width, min_val=self.__width
)
elif self.__resize_method == "upper_bound":
new_height = self.constrain_to_multiple_of(
scale_height * height, max_val=self.__height
)
new_width = self.constrain_to_multiple_of(
scale_width * width, max_val=self.__width
)
elif self.__resize_method == "minimal":
new_height = self.constrain_to_multiple_of(scale_height * height)
new_width = self.constrain_to_multiple_of(scale_width * width)
else:
raise ValueError(f"resize_method {self.__resize_method} not implemented")
return (new_width, new_height)
def __call__(self, sample):
width, height = self.get_size(
sample["image"].shape[1], sample["image"].shape[0]
)
# resize sample
sample["image"] = cv2.resize(
sample["image"],
(width, height),
interpolation=self.__image_interpolation_method,
)
if self.__resize_target:
if "disparity" in sample:
sample["disparity"] = cv2.resize(
sample["disparity"],
(width, height),
interpolation=cv2.INTER_NEAREST,
)
if "depth" in sample:
sample["depth"] = cv2.resize(
sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
)
sample["mask"] = cv2.resize(
sample["mask"].astype(np.float32),
(width, height),
interpolation=cv2.INTER_NEAREST,
)
sample["mask"] = sample["mask"].astype(bool)
return sample
class NormalizeImage(object):
"""Normlize image by given mean and std.
"""
def __init__(self, mean, std):
self.__mean = mean
self.__std = std
def __call__(self, sample):
sample["image"] = (sample["image"] - self.__mean) / self.__std
return sample
class PrepareForNet(object):
"""Prepare sample for usage as network input.
"""
def __init__(self):
pass
def __call__(self, sample):
image = np.transpose(sample["image"], (2, 0, 1))
sample["image"] = np.ascontiguousarray(image).astype(np.float32)
if "mask" in sample:
sample["mask"] = sample["mask"].astype(np.float32)
sample["mask"] = np.ascontiguousarray(sample["mask"])
if "disparity" in sample:
disparity = sample["disparity"].astype(np.float32)
sample["disparity"] = np.ascontiguousarray(disparity)
if "depth" in sample:
depth = sample["depth"].astype(np.float32)
sample["depth"] = np.ascontiguousarray(depth)
return sample

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import torch
import torch.nn as nn
import timm
import types
import math
import torch.nn.functional as F
class Slice(nn.Module):
def __init__(self, start_index=1):
super(Slice, self).__init__()
self.start_index = start_index
def forward(self, x):
return x[:, self.start_index :]
class AddReadout(nn.Module):
def __init__(self, start_index=1):
super(AddReadout, self).__init__()
self.start_index = start_index
def forward(self, x):
if self.start_index == 2:
readout = (x[:, 0] + x[:, 1]) / 2
else:
readout = x[:, 0]
return x[:, self.start_index :] + readout.unsqueeze(1)
class ProjectReadout(nn.Module):
def __init__(self, in_features, start_index=1):
super(ProjectReadout, self).__init__()
self.start_index = start_index
self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
def forward(self, x):
readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
features = torch.cat((x[:, self.start_index :], readout), -1)
return self.project(features)
class Transpose(nn.Module):
def __init__(self, dim0, dim1):
super(Transpose, self).__init__()
self.dim0 = dim0
self.dim1 = dim1
def forward(self, x):
x = x.transpose(self.dim0, self.dim1)
return x
def forward_vit(pretrained, x):
b, c, h, w = x.shape
glob = pretrained.model.forward_flex(x)
layer_1 = pretrained.activations["1"]
layer_2 = pretrained.activations["2"]
layer_3 = pretrained.activations["3"]
layer_4 = pretrained.activations["4"]
layer_1 = pretrained.act_postprocess1[0:2](layer_1)
layer_2 = pretrained.act_postprocess2[0:2](layer_2)
layer_3 = pretrained.act_postprocess3[0:2](layer_3)
layer_4 = pretrained.act_postprocess4[0:2](layer_4)
unflatten = nn.Sequential(
nn.Unflatten(
2,
torch.Size(
[
h // pretrained.model.patch_size[1],
w // pretrained.model.patch_size[0],
]
),
)
)
if layer_1.ndim == 3:
layer_1 = unflatten(layer_1)
if layer_2.ndim == 3:
layer_2 = unflatten(layer_2)
if layer_3.ndim == 3:
layer_3 = unflatten(layer_3)
if layer_4.ndim == 3:
layer_4 = unflatten(layer_4)
layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
return layer_1, layer_2, layer_3, layer_4
def _resize_pos_embed(self, posemb, gs_h, gs_w):
posemb_tok, posemb_grid = (
posemb[:, : self.start_index],
posemb[0, self.start_index :],
)
gs_old = int(math.sqrt(len(posemb_grid)))
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
return posemb
def forward_flex(self, x):
b, c, h, w = x.shape
pos_embed = self._resize_pos_embed(
self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
)
B = x.shape[0]
if hasattr(self.patch_embed, "backbone"):
x = self.patch_embed.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
if getattr(self, "dist_token", None) is not None:
cls_tokens = self.cls_token.expand(
B, -1, -1
) # stole cls_tokens impl from Phil Wang, thanks
dist_token = self.dist_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, dist_token, x), dim=1)
else:
cls_tokens = self.cls_token.expand(
B, -1, -1
) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_tokens, x), dim=1)
x = x + pos_embed
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
return x
activations = {}
def get_activation(name):
def hook(model, input, output):
activations[name] = output
return hook
def get_readout_oper(vit_features, features, use_readout, start_index=1):
if use_readout == "ignore":
readout_oper = [Slice(start_index)] * len(features)
elif use_readout == "add":
readout_oper = [AddReadout(start_index)] * len(features)
elif use_readout == "project":
readout_oper = [
ProjectReadout(vit_features, start_index) for out_feat in features
]
else:
assert (
False
), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
return readout_oper
def _make_vit_b16_backbone(
model,
features=[96, 192, 384, 768],
size=[384, 384],
hooks=[2, 5, 8, 11],
vit_features=768,
use_readout="ignore",
start_index=1,
):
pretrained = nn.Module()
pretrained.model = model
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
pretrained.activations = activations
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
# 32, 48, 136, 384
pretrained.act_postprocess1 = nn.Sequential(
readout_oper[0],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[0],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[0],
out_channels=features[0],
kernel_size=4,
stride=4,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
pretrained.act_postprocess2 = nn.Sequential(
readout_oper[1],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[1],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[1],
out_channels=features[1],
kernel_size=2,
stride=2,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
pretrained.act_postprocess3 = nn.Sequential(
readout_oper[2],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[2],
kernel_size=1,
stride=1,
padding=0,
),
)
pretrained.act_postprocess4 = nn.Sequential(
readout_oper[3],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[3],
kernel_size=1,
stride=1,
padding=0,
),
nn.Conv2d(
in_channels=features[3],
out_channels=features[3],
kernel_size=3,
stride=2,
padding=1,
),
)
pretrained.model.start_index = start_index
pretrained.model.patch_size = [16, 16]
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
pretrained.model._resize_pos_embed = types.MethodType(
_resize_pos_embed, pretrained.model
)
return pretrained
def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
hooks = [5, 11, 17, 23] if hooks == None else hooks
return _make_vit_b16_backbone(
model,
features=[256, 512, 1024, 1024],
hooks=hooks,
vit_features=1024,
use_readout=use_readout,
)
def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
hooks = [2, 5, 8, 11] if hooks == None else hooks
return _make_vit_b16_backbone(
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
)
def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
hooks = [2, 5, 8, 11] if hooks == None else hooks
return _make_vit_b16_backbone(
model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
)
def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model(
"vit_deit_base_distilled_patch16_384", pretrained=pretrained
)
hooks = [2, 5, 8, 11] if hooks == None else hooks
return _make_vit_b16_backbone(
model,
features=[96, 192, 384, 768],
hooks=hooks,
use_readout=use_readout,
start_index=2,
)
def _make_vit_b_rn50_backbone(
model,
features=[256, 512, 768, 768],
size=[384, 384],
hooks=[0, 1, 8, 11],
vit_features=768,
use_vit_only=False,
use_readout="ignore",
start_index=1,
):
pretrained = nn.Module()
pretrained.model = model
if use_vit_only == True:
pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
else:
pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
get_activation("1")
)
pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
get_activation("2")
)
pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
pretrained.activations = activations
readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
if use_vit_only == True:
pretrained.act_postprocess1 = nn.Sequential(
readout_oper[0],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[0],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[0],
out_channels=features[0],
kernel_size=4,
stride=4,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
pretrained.act_postprocess2 = nn.Sequential(
readout_oper[1],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[1],
kernel_size=1,
stride=1,
padding=0,
),
nn.ConvTranspose2d(
in_channels=features[1],
out_channels=features[1],
kernel_size=2,
stride=2,
padding=0,
bias=True,
dilation=1,
groups=1,
),
)
else:
pretrained.act_postprocess1 = nn.Sequential(
nn.Identity(), nn.Identity(), nn.Identity()
)
pretrained.act_postprocess2 = nn.Sequential(
nn.Identity(), nn.Identity(), nn.Identity()
)
pretrained.act_postprocess3 = nn.Sequential(
readout_oper[2],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[2],
kernel_size=1,
stride=1,
padding=0,
),
)
pretrained.act_postprocess4 = nn.Sequential(
readout_oper[3],
Transpose(1, 2),
nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
nn.Conv2d(
in_channels=vit_features,
out_channels=features[3],
kernel_size=1,
stride=1,
padding=0,
),
nn.Conv2d(
in_channels=features[3],
out_channels=features[3],
kernel_size=3,
stride=2,
padding=1,
),
)
pretrained.model.start_index = start_index
pretrained.model.patch_size = [16, 16]
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
# We inject this function into the VisionTransformer instances so that
# we can use it with interpolated position embeddings without modifying the library source.
pretrained.model._resize_pos_embed = types.MethodType(
_resize_pos_embed, pretrained.model
)
return pretrained
def _make_pretrained_vitb_rn50_384(
pretrained, use_readout="ignore", hooks=None, use_vit_only=False
):
model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
hooks = [0, 1, 8, 11] if hooks == None else hooks
return _make_vit_b_rn50_backbone(
model,
features=[256, 512, 768, 768],
size=[384, 384],
hooks=hooks,
use_vit_only=use_vit_only,
use_readout=use_readout,
)

189
ldm/modules/midas/utils.py Normal file
View File

@ -0,0 +1,189 @@
"""Utils for monoDepth."""
import sys
import re
import numpy as np
import cv2
import torch
def read_pfm(path):
"""Read pfm file.
Args:
path (str): path to file
Returns:
tuple: (data, scale)
"""
with open(path, "rb") as file:
color = None
width = None
height = None
scale = None
endian = None
header = file.readline().rstrip()
if header.decode("ascii") == "PF":
color = True
elif header.decode("ascii") == "Pf":
color = False
else:
raise Exception("Not a PFM file: " + path)
dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
if dim_match:
width, height = list(map(int, dim_match.groups()))
else:
raise Exception("Malformed PFM header.")
scale = float(file.readline().decode("ascii").rstrip())
if scale < 0:
# little-endian
endian = "<"
scale = -scale
else:
# big-endian
endian = ">"
data = np.fromfile(file, endian + "f")
shape = (height, width, 3) if color else (height, width)
data = np.reshape(data, shape)
data = np.flipud(data)
return data, scale
def write_pfm(path, image, scale=1):
"""Write pfm file.
Args:
path (str): pathto file
image (array): data
scale (int, optional): Scale. Defaults to 1.
"""
with open(path, "wb") as file:
color = None
if image.dtype.name != "float32":
raise Exception("Image dtype must be float32.")
image = np.flipud(image)
if len(image.shape) == 3 and image.shape[2] == 3: # color image
color = True
elif (
len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
): # greyscale
color = False
else:
raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
file.write("PF\n" if color else "Pf\n".encode())
file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
endian = image.dtype.byteorder
if endian == "<" or endian == "=" and sys.byteorder == "little":
scale = -scale
file.write("%f\n".encode() % scale)
image.tofile(file)
def read_image(path):
"""Read image and output RGB image (0-1).
Args:
path (str): path to file
Returns:
array: RGB image (0-1)
"""
img = cv2.imread(path)
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
return img
def resize_image(img):
"""Resize image and make it fit for network.
Args:
img (array): image
Returns:
tensor: data ready for network
"""
height_orig = img.shape[0]
width_orig = img.shape[1]
if width_orig > height_orig:
scale = width_orig / 384
else:
scale = height_orig / 384
height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
img_resized = (
torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
)
img_resized = img_resized.unsqueeze(0)
return img_resized
def resize_depth(depth, width, height):
"""Resize depth map and bring to CPU (numpy).
Args:
depth (tensor): depth
width (int): image width
height (int): image height
Returns:
array: processed depth
"""
depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
depth_resized = cv2.resize(
depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
)
return depth_resized
def write_depth(path, depth, bits=1):
"""Write depth map to pfm and png file.
Args:
path (str): filepath without extension
depth (array): depth
"""
write_pfm(path + ".pfm", depth.astype(np.float32))
depth_min = depth.min()
depth_max = depth.max()
max_val = (2**(8*bits))-1
if depth_max - depth_min > np.finfo("float").eps:
out = max_val * (depth - depth_min) / (depth_max - depth_min)
else:
out = np.zeros(depth.shape, dtype=depth.type)
if bits == 1:
cv2.imwrite(path + ".png", out.astype("uint8"))
elif bits == 2:
cv2.imwrite(path + ".png", out.astype("uint16"))
return

261
ldm/modules/x_transformer.py
View File

@ -11,15 +11,13 @@ from einops import rearrange, repeat, reduce
DEFAULT_DIM_HEAD = 64
Intermediates = namedtuple('Intermediates', [
'pre_softmax_attn',
'post_softmax_attn'
])
Intermediates = namedtuple(
'Intermediates', ['pre_softmax_attn', 'post_softmax_attn']
)
LayerIntermediates = namedtuple('Intermediates', [
'hiddens',
'attn_intermediates'
])
LayerIntermediates = namedtuple(
'Intermediates', ['hiddens', 'attn_intermediates']
)
class AbsolutePositionalEmbedding(nn.Module):
@ -39,11 +37,16 @@ class AbsolutePositionalEmbedding(nn.Module):
class FixedPositionalEmbedding(nn.Module):
def __init__(self, dim):
super().__init__()
inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer('inv_freq', inv_freq)
def forward(self, x, seq_dim=1, offset=0):
t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
t = (
torch.arange(x.shape[seq_dim], device=x.device).type_as(
self.inv_freq
)
+ offset
)
sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
return emb[None, :, :]
@ -51,6 +54,7 @@ class FixedPositionalEmbedding(nn.Module):
# helpers
def exists(val):
return val is not None
@ -64,18 +68,21 @@ def default(val, d):
def always(val):
def inner(*args, **kwargs):
return val
return inner
def not_equals(val):
def inner(x):
return x != val
return inner
def equals(val):
def inner(x):
return x == val
return inner
@ -85,6 +92,7 @@ def max_neg_value(tensor):
# keyword argument helpers
def pick_and_pop(keys, d):
values = list(map(lambda key: d.pop(key), keys))
return dict(zip(keys, values))
@ -108,8 +116,15 @@ def group_by_key_prefix(prefix, d):
def groupby_prefix_and_trim(prefix, d):
kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
kwargs_with_prefix, kwargs = group_dict_by_key(
partial(string_begins_with, prefix), d
)
kwargs_without_prefix = dict(
map(
lambda x: (x[0][len(prefix) :], x[1]),
tuple(kwargs_with_prefix.items()),
)
)
return kwargs_without_prefix, kwargs
@ -173,7 +188,7 @@ class GRUGating(nn.Module):
def forward(self, x, residual):
gated_output = self.gru(
rearrange(x, 'b n d -> (b n) d'),
rearrange(residual, 'b n d -> (b n) d')
rearrange(residual, 'b n d -> (b n) d'),
)
return gated_output.reshape_as(x)
@ -181,6 +196,7 @@ class GRUGating(nn.Module):
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
@ -192,19 +208,18 @@ class GEGLU(nn.Module):
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
nn.Linear(dim, inner_dim),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
project_in = (
nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
if not glu
else GEGLU(dim, inner_dim)
)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
nn.Linear(inner_dim, dim_out)
project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
)
def forward(self, x):
@ -224,12 +239,14 @@ class Attention(nn.Module):
sparse_topk=None,
use_entmax15=False,
num_mem_kv=0,
dropout=0.,
on_attn=False
dropout=0.0,
on_attn=False,
):
super().__init__()
if use_entmax15:
raise NotImplementedError("Check out entmax activation instead of softmax activation!")
raise NotImplementedError(
'Check out entmax activation instead of softmax activation!'
)
self.scale = dim_head**-0.5
self.heads = heads
self.causal = causal
@ -263,7 +280,11 @@ class Attention(nn.Module):
# attention on attention
self.attn_on_attn = on_attn
self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
self.to_out = (
nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU())
if on_attn
else nn.Linear(inner_dim, dim)
)
def forward(
self,
@ -274,9 +295,14 @@ class Attention(nn.Module):
rel_pos=None,
sinusoidal_emb=None,
prev_attn=None,
mem=None
mem=None,
):
b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
b, n, _, h, talking_heads, device = (
*x.shape,
self.heads,
self.talking_heads,
x.device,
)
kv_input = default(context, x)
q_input = x
@ -297,23 +323,35 @@ class Attention(nn.Module):
k = self.to_k(k_input)
v = self.to_v(v_input)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
q, k, v = map(
lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)
)
input_mask = None
if any(map(exists, (mask, context_mask))):
q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
q_mask = default(
mask, lambda: torch.ones((b, n), device=device).bool()
)
k_mask = q_mask if not exists(context) else context_mask
k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
k_mask = default(
k_mask,
lambda: torch.ones((b, k.shape[-2]), device=device).bool(),
)
q_mask = rearrange(q_mask, 'b i -> b () i ()')
k_mask = rearrange(k_mask, 'b j -> b () () j')
input_mask = q_mask * k_mask
if self.num_mem_kv > 0:
mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
mem_k, mem_v = map(
lambda t: repeat(t, 'h n d -> b h n d', b=b),
(self.mem_k, self.mem_v),
)
k = torch.cat((mem_k, k), dim=-2)
v = torch.cat((mem_v, v), dim=-2)
if exists(input_mask):
input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
input_mask = F.pad(
input_mask, (self.num_mem_kv, 0), value=True
)
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
mask_value = max_neg_value(dots)
@ -324,7 +362,9 @@ class Attention(nn.Module):
pre_softmax_attn = dots
if talking_heads:
dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
dots = einsum(
'b h i j, h k -> b k i j', dots, self.pre_softmax_proj
).contiguous()
if exists(rel_pos):
dots = rel_pos(dots)
@ -336,7 +376,9 @@ class Attention(nn.Module):
if self.causal:
i, j = dots.shape[-2:]
r = torch.arange(i, device=device)
mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
mask = rearrange(r, 'i -> () () i ()') < rearrange(
r, 'j -> () () () j'
)
mask = F.pad(mask, (j - i, 0), value=False)
dots.masked_fill_(mask, mask_value)
del mask
@ -354,14 +396,16 @@ class Attention(nn.Module):
attn = self.dropout(attn)
if talking_heads:
attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
attn = einsum(
'b h i j, h k -> b k i j', attn, self.post_softmax_proj
).contiguous()
out = einsum('b h i j, b h j d -> b h i d', attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
intermediates = Intermediates(
pre_softmax_attn=pre_softmax_attn,
post_softmax_attn=post_softmax_attn
post_softmax_attn=post_softmax_attn,
)
return self.to_out(out), intermediates
@ -390,7 +434,7 @@ class AttentionLayers(nn.Module):
macaron=False,
pre_norm=True,
gate_residual=False,
**kwargs
**kwargs,
):
super().__init__()
ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
@ -403,10 +447,14 @@ class AttentionLayers(nn.Module):
self.layers = nn.ModuleList([])
self.has_pos_emb = position_infused_attn
self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
self.pia_pos_emb = (
FixedPositionalEmbedding(dim) if position_infused_attn else None
)
self.rotary_pos_emb = always(None)
assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
assert (
rel_pos_num_buckets <= rel_pos_max_distance
), 'number of relative position buckets must be less than the relative position max distance'
self.rel_pos = None
self.pre_norm = pre_norm
@ -438,15 +486,27 @@ class AttentionLayers(nn.Module):
assert 1 < par_ratio <= par_depth, 'par ratio out of range'
default_block = tuple(filter(not_equals('f'), default_block))
par_attn = par_depth // par_ratio
depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper
depth_cut = (
par_depth * 2 // 3
) # 2 / 3 attention layer cutoff suggested by PAR paper
par_width = (depth_cut + depth_cut // par_attn) // par_attn
assert len(default_block) <= par_width, 'default block is too large for par_ratio'
par_block = default_block + ('f',) * (par_width - len(default_block))
assert (
len(default_block) <= par_width
), 'default block is too large for par_ratio'
par_block = default_block + ('f',) * (
par_width - len(default_block)
)
par_head = par_block * par_attn
layer_types = par_head + ('f',) * (par_depth - len(par_head))
elif exists(sandwich_coef):
assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
assert (
sandwich_coef > 0 and sandwich_coef <= depth
), 'sandwich coefficient should be less than the depth'
layer_types = (
('a',) * sandwich_coef
+ default_block * (depth - sandwich_coef)
+ ('f',) * sandwich_coef
)
else:
layer_types = default_block * depth
@ -455,7 +515,9 @@ class AttentionLayers(nn.Module):
for layer_type in self.layer_types:
if layer_type == 'a':
layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
layer = Attention(
dim, heads=heads, causal=causal, **attn_kwargs
)
elif layer_type == 'c':
layer = Attention(dim, heads=heads, **attn_kwargs)
elif layer_type == 'f':
@ -472,11 +534,7 @@ class AttentionLayers(nn.Module):
else:
residual_fn = Residual()
self.layers.append(nn.ModuleList([
norm_fn(),
layer,
residual_fn
]))
self.layers.append(nn.ModuleList([norm_fn(), layer, residual_fn]))
def forward(
self,
@ -485,7 +543,8 @@ class AttentionLayers(nn.Module):
mask=None,
context_mask=None,
mems=None,
return_hiddens=False
return_hiddens=False,
**kwargs,
):
hiddens = []
intermediates = []
@ -494,7 +553,9 @@ class AttentionLayers(nn.Module):
mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
for ind, (layer_type, (norm, block, residual_fn)) in enumerate(
zip(self.layer_types, self.layers)
):
is_last = ind == (len(self.layers) - 1)
if layer_type == 'a':
@ -507,10 +568,22 @@ class AttentionLayers(nn.Module):
x = norm(x)
if layer_type == 'a':
out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
prev_attn=prev_attn, mem=layer_mem)
out, inter = block(
x,
mask=mask,
sinusoidal_emb=self.pia_pos_emb,
rel_pos=self.rel_pos,
prev_attn=prev_attn,
mem=layer_mem,
)
elif layer_type == 'c':
out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
out, inter = block(
x,
context=context,
mask=mask,
context_mask=context_mask,
prev_attn=prev_cross_attn,
)
elif layer_type == 'f':
out = block(x)
@ -529,8 +602,7 @@ class AttentionLayers(nn.Module):
if return_hiddens:
intermediates = LayerIntermediates(
hiddens=hiddens,
attn_intermediates=intermediates
hiddens=hiddens, attn_intermediates=intermediates
)
return x, intermediates
@ -544,7 +616,6 @@ class Encoder(AttentionLayers):
super().__init__(causal=False, **kwargs)
class TransformerWrapper(nn.Module):
def __init__(
self,
@ -553,14 +624,16 @@ class TransformerWrapper(nn.Module):
max_seq_len,
attn_layers,
emb_dim=None,
max_mem_len=0.,
emb_dropout=0.,
max_mem_len=0.0,
emb_dropout=0.0,
num_memory_tokens=None,
tie_embedding=False,
use_pos_emb=True
use_pos_emb=True,
):
super().__init__()
assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
assert isinstance(
attn_layers, AttentionLayers
), 'attention layers must be one of Encoder or Decoder'
dim = attn_layers.dim
emb_dim = default(emb_dim, dim)
@ -570,23 +643,34 @@ class TransformerWrapper(nn.Module):
self.num_tokens = num_tokens
self.token_emb = nn.Embedding(num_tokens, emb_dim)
self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
use_pos_emb and not attn_layers.has_pos_emb) else always(0)
self.pos_emb = (
AbsolutePositionalEmbedding(emb_dim, max_seq_len)
if (use_pos_emb and not attn_layers.has_pos_emb)
else always(0)
)
self.emb_dropout = nn.Dropout(emb_dropout)
self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
self.project_emb = (
nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
)
self.attn_layers = attn_layers
self.norm = nn.LayerNorm(dim)
self.init_()
self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
self.to_logits = (
nn.Linear(dim, num_tokens)
if not tie_embedding
else lambda t: t @ self.token_emb.weight.t()
)
# memory tokens (like [cls]) from Memory Transformers paper
num_memory_tokens = default(num_memory_tokens, 0)
self.num_memory_tokens = num_memory_tokens
if num_memory_tokens > 0:
self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
self.memory_tokens = nn.Parameter(
torch.randn(num_memory_tokens, dim)
)
# let funnel encoder know number of memory tokens, if specified
if hasattr(attn_layers, 'num_memory_tokens'):
@ -603,11 +687,19 @@ class TransformerWrapper(nn.Module):
return_mems=False,
return_attn=False,
mems=None,
**kwargs
embedding_manager=None,
**kwargs,
):
b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
x = self.token_emb(x)
x += self.pos_emb(x)
embedded_x = self.token_emb(x)
if embedding_manager:
x = embedding_manager(x, embedded_x)
else:
x = embedded_x
x = x + self.pos_emb(x)
x = self.emb_dropout(x)
x = self.project_emb(x)
@ -620,7 +712,9 @@ class TransformerWrapper(nn.Module):
if exists(mask):
mask = F.pad(mask, (num_mem, 0), value=True)
x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
x, intermediates = self.attn_layers(
x, mask=mask, mems=mems, return_hiddens=True, **kwargs
)
x = self.norm(x)
mem, x = x[:, :num_mem], x[:, num_mem:]
@ -629,13 +723,30 @@ class TransformerWrapper(nn.Module):
if return_mems:
hiddens = intermediates.hiddens
new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
new_mems = (
list(
map(
lambda pair: torch.cat(pair, dim=-2),
zip(mems, hiddens),
)
)
if exists(mems)
else hiddens
)
new_mems = list(
map(
lambda t: t[..., -self.max_mem_len :, :].detach(), new_mems
)
)
return out, new_mems
if return_attn:
attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
attn_maps = list(
map(
lambda t: t.post_softmax_attn,
intermediates.attn_intermediates,
)
)
return out, attn_maps
return out

57
ldm/util.py
View File

@ -20,16 +20,18 @@ def log_txt_as_img(wh, xc, size=10):
b = len(xc)
txts = list()
for bi in range(b):
txt = Image.new("RGB", wh, color="white")
txt = Image.new('RGB', wh, color='white')
draw = ImageDraw.Draw(txt)
font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
font = ImageFont.load_default()
nc = int(40 * (wh[0] / 256))
lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
lines = '\n'.join(
xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)
)
try:
draw.text((0, 0), lines, fill="black", font=font)
draw.text((0, 0), lines, fill='black', font=font)
except UnicodeEncodeError:
print("Cant encode string for logging. Skipping.")
print('Cant encode string for logging. Skipping.')
txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
txts.append(txt)
@ -71,22 +73,26 @@ def mean_flat(tensor):
def count_params(model, verbose=False):
total_params = sum(p.numel() for p in model.parameters())
if verbose:
print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
print(
f'{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.'
)
return total_params
def instantiate_from_config(config):
if not "target" in config:
def instantiate_from_config(config, **kwargs):
if not 'target' in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
elif config == '__is_unconditional__':
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
raise KeyError('Expected key `target` to instantiate.')
return get_obj_from_str(config['target'])(
**config.get('params', dict()), **kwargs
)
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
module, cls = string.rsplit('.', 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
@ -102,31 +108,36 @@ def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
else:
res = func(data)
Q.put([idx, res])
Q.put("Done")
Q.put('Done')
def parallel_data_prefetch(
func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False
func: callable,
data,
n_proc,
target_data_type='ndarray',
cpu_intensive=True,
use_worker_id=False,
):
# if target_data_type not in ["ndarray", "list"]:
# raise ValueError(
# "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
# )
if isinstance(data, np.ndarray) and target_data_type == "list":
raise ValueError("list expected but function got ndarray.")
if isinstance(data, np.ndarray) and target_data_type == 'list':
raise ValueError('list expected but function got ndarray.')
elif isinstance(data, abc.Iterable):
if isinstance(data, dict):
print(
f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
)
data = list(data.values())
if target_data_type == "ndarray":
if target_data_type == 'ndarray':
data = np.asarray(data)
else:
data = list(data)
else:
raise TypeError(
f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
f'The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}.'
)
if cpu_intensive:
@ -136,7 +147,7 @@ def parallel_data_prefetch(
Q = Queue(1000)
proc = Thread
# spawn processes
if target_data_type == "ndarray":
if target_data_type == 'ndarray':
arguments = [
[func, Q, part, i, use_worker_id]
for i, part in enumerate(np.array_split(data, n_proc))
@ -159,7 +170,7 @@ def parallel_data_prefetch(
processes += [p]
# start processes
print(f"Start prefetching...")
print(f'Start prefetching...')
import time
start = time.time()
@ -172,13 +183,13 @@ def parallel_data_prefetch(
while k < n_proc:
# get result
res = Q.get()
if res == "Done":
if res == 'Done':
k += 1
else:
gather_res[res[0]] = res[1]
except Exception as e:
print("Exception: ", e)
print('Exception: ', e)
for p in processes:
p.terminate()
@ -186,7 +197,7 @@ def parallel_data_prefetch(
finally:
for p in processes:
p.join()
print(f"Prefetching complete. [{time.time() - start} sec.]")
print(f'Prefetching complete. [{time.time() - start} sec.]')
if target_data_type == 'ndarray':
if not isinstance(gather_res[0], np.ndarray):

454
optimizedSD/ddpm.py
View File

@ -6,7 +6,8 @@ https://github.com/CompVis/taming-transformers
-- merci
"""
import time
import time, math
from tqdm.auto import trange, tqdm
import torch
from einops import rearrange
from tqdm import tqdm
@ -21,7 +22,7 @@ from ldm.util import exists, default, instantiate_from_config
from ldm.modules.diffusionmodules.util import make_beta_schedule
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from .samplers import CompVisDenoiser, get_ancestral_step, to_d, append_dims,linear_multistep_coeff
def disabled_train(self):
"""Overwrite model.train with this function to make sure train/eval mode
@ -92,7 +93,6 @@ class DDPM(pl.LightningModule):
cosine_s=cosine_s)
alphas = 1. - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
timesteps, = betas.shape
self.num_timesteps = int(timesteps)
@ -104,7 +104,6 @@ class DDPM(pl.LightningModule):
self.register_buffer('betas', to_torch(betas))
self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
class FirstStage(DDPM):
@ -403,7 +402,7 @@ class UNet(DDPM):
h,emb,hs = self.model1(x_noisy[0:step], t[:step], cond[:step])
bs = cond.shape[0]
assert bs%2 == 0
# assert bs%2 == 0
lenhs = len(hs)
for i in range(step,bs,step):
@ -446,15 +445,14 @@ class UNet(DDPM):
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
num_ddpm_timesteps=self.num_timesteps,verbose=verbose)
alphas_cumprod = self.alphas_cumprod
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
assert self.alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
to_torch = lambda x: x.to(self.cdevice)
self.register_buffer1('betas', to_torch(self.betas))
self.register_buffer1('alphas_cumprod', to_torch(alphas_cumprod))
self.register_buffer1('alphas_cumprod_prev', to_torch(self.alphas_cumprod_prev))
self.register_buffer1('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
self.register_buffer1('alphas_cumprod', to_torch(self.alphas_cumprod))
# ddim sampling parameters
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=self.alphas_cumprod.cpu(),
ddim_timesteps=self.ddim_timesteps,
@ -463,25 +461,21 @@ class UNet(DDPM):
self.register_buffer1('ddim_alphas', ddim_alphas)
self.register_buffer1('ddim_alphas_prev', ddim_alphas_prev)
self.register_buffer1('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
self.ddim_sqrt_one_minus_alphas = np.sqrt(1. - ddim_alphas)
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
self.register_buffer1('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
@torch.no_grad()
def sample(self,
S,
batch_size,
shape,
seed,
conditioning=None,
conditioning,
x0=None,
shape = None,
seed=1234,
callback=None,
img_callback=None,
quantize_x0=False,
eta=0.,
mask=None,
x0=None,
sampler = "plms",
temperature=1.,
noise_dropout=0.,
score_corrector=None,
@ -492,27 +486,31 @@ class UNet(DDPM):
unconditional_guidance_scale=1.,
unconditional_conditioning=None,
):
if conditioning is not None:
if isinstance(conditioning, dict):
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
if cbs != batch_size:
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
else:
if conditioning.shape[0] != batch_size:
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
# sampling
C, H, W = shape
size = (batch_size, C, H, W)
print(f'Data shape for PLMS sampling is {size}')
if(self.turbo):
self.model1.to(self.cdevice)
self.model2.to(self.cdevice)
samples = self.plms_sampling(conditioning, size, seed,
if x0 is None:
batch_size, b1, b2, b3 = shape
img_shape = (1, b1, b2, b3)
tens = []
print("seeds used = ", [seed+s for s in range(batch_size)])
for _ in range(batch_size):
torch.manual_seed(seed)
tens.append(torch.randn(img_shape, device=self.cdevice))
seed+=1
noise = torch.cat(tens)
del tens
x_latent = noise if x0 is None else x0
# sampling
if sampler == "plms":
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
print(f'Data shape for PLMS sampling is {shape}')
samples = self.plms_sampling(conditioning, batch_size, x_latent,
callback=callback,
img_callback=img_callback,
quantize_denoised=quantize_x0,
@ -522,12 +520,41 @@ class UNet(DDPM):
temperature=temperature,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs,
x_T=x_T,
log_every_t=log_every_t,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
)
elif sampler == "ddim":
samples = self.ddim_sampling(x_latent, conditioning, S, unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning,
mask = mask,init_latent=x_T,use_original_steps=False)
elif sampler == "euler":
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
samples = self.euler_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
unconditional_guidance_scale=unconditional_guidance_scale)
elif sampler == "euler_a":
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
samples = self.euler_ancestral_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
unconditional_guidance_scale=unconditional_guidance_scale)
elif sampler == "dpm2":
samples = self.dpm_2_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
unconditional_guidance_scale=unconditional_guidance_scale)
elif sampler == "heun":
samples = self.heun_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
unconditional_guidance_scale=unconditional_guidance_scale)
elif sampler == "dpm2_a":
samples = self.dpm_2_ancestral_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
unconditional_guidance_scale=unconditional_guidance_scale)
elif sampler == "lms":
samples = self.lms_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
unconditional_guidance_scale=unconditional_guidance_scale)
if(self.turbo):
self.model1.to("cpu")
self.model2.to("cpu")
@ -535,36 +562,17 @@ class UNet(DDPM):
return samples
@torch.no_grad()
def plms_sampling(self, cond, shape, seed,
x_T=None, ddim_use_original_steps=False,
callback=None, timesteps=None, quantize_denoised=False,
def plms_sampling(self, cond,b, img,
ddim_use_original_steps=False,
callback=None, quantize_denoised=False,
mask=None, x0=None, img_callback=None, log_every_t=100,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
unconditional_guidance_scale=1., unconditional_conditioning=None,):
device = self.betas.device
b = shape[0]
if x_T is None:
_, b1, b2, b3 = shape
img_shape = (1, b1, b2, b3)
tens = []
print("seeds used = ", [seed+s for s in range(b)])
for _ in range(b):
torch.manual_seed(seed)
tens.append(torch.randn(img_shape, device=device))
seed+=1
img = torch.cat(tens)
del tens
else:
img = x_T
if timesteps is None:
timesteps = self.num_timesteps if ddim_use_original_steps else self.ddim_timesteps
elif timesteps is not None and not ddim_use_original_steps:
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
timesteps = self.ddim_timesteps[:subset_end]
time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
timesteps = self.ddim_timesteps
time_range = np.flip(timesteps)
total_steps = timesteps.shape[0]
print(f"Running PLMS Sampling with {total_steps} timesteps")
iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
@ -618,10 +626,10 @@ class UNet(DDPM):
return e_t
alphas = self.alphas_cumprod if use_original_steps else self.ddim_alphas
alphas_prev = self.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
sqrt_one_minus_alphas = self.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
alphas = self.ddim_alphas
alphas_prev = self.ddim_alphas_prev
sqrt_one_minus_alphas = self.ddim_sqrt_one_minus_alphas
sigmas = self.ddim_sigmas
def get_x_prev_and_pred_x0(e_t, index):
# select parameters corresponding to the currently considered timestep
@ -664,17 +672,11 @@ class UNet(DDPM):
@torch.no_grad()
def stochastic_encode(self, x0, t, seed, ddim_eta,ddim_steps,use_original_steps=False, noise=None, mask=None):
def stochastic_encode(self, x0, t, seed, ddim_eta,ddim_steps,use_original_steps=False, noise=None):
# fast, but does not allow for exact reconstruction
# t serves as an index to gather the correct alphas
self.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=ddim_eta, verbose=False)
if use_original_steps:
sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
else:
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
if noise is None:
b0, b1, b2, b3 = x0.shape
@ -687,50 +689,53 @@ class UNet(DDPM):
seed+=1
noise = torch.cat(tens)
del tens
if mask is not None:
noise = noise*mask
return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
extract_into_tensor(sqrt_one_minus_alphas_cumprod.to(self.cdevice), t, x0.shape) * noise)
extract_into_tensor(self.ddim_sqrt_one_minus_alphas, t, x0.shape) * noise)
@torch.no_grad()
def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
mask = None,use_original_steps=False):
def add_noise(self, x0, t):
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
noise = torch.randn(x0.shape, device=x0.device)
# print(extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape),
# extract_into_tensor(self.ddim_sqrt_one_minus_alphas, t, x0.shape))
return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
extract_into_tensor(self.ddim_sqrt_one_minus_alphas, t, x0.shape) * noise)
if(self.turbo):
self.model1.to(self.cdevice)
self.model2.to(self.cdevice)
@torch.no_grad()
def ddim_sampling(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
mask = None,init_latent=None,use_original_steps=False):
timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
timesteps = self.ddim_timesteps
timesteps = timesteps[:t_start]
time_range = np.flip(timesteps)
total_steps = timesteps.shape[0]
print(f"Running DDIM Sampling with {total_steps} timesteps")
iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
x_dec = x_latent
# x0 = x_latent
x0 = init_latent
for i, step in enumerate(iterator):
index = total_steps - i - 1
ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
# if mask is not None:
# x_dec = x0 * mask + (1. - mask) * x_dec
if mask is not None:
# x0_noisy = self.add_noise(mask, torch.tensor([index] * x0.shape[0]).to(self.cdevice))
x0_noisy = x0
x_dec = x0_noisy* mask + (1. - mask) * x_dec
x_dec = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=unconditional_conditioning)
# if mask is not None:
# return x0 * mask + (1. - mask) * x_dec
if(self.turbo):
self.model1.to("cpu")
self.model2.to("cpu")
if mask is not None:
return x0 * mask + (1. - mask) * x_dec
return x_dec
@torch.no_grad()
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
@ -743,7 +748,6 @@ class UNet(DDPM):
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
c_in = torch.cat([unconditional_conditioning, c])
# print("xin shape = ", x_in.shape)
e_t_uncond, e_t = self.apply_model(x_in, t_in, c_in).chunk(2)
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
@ -751,10 +755,10 @@ class UNet(DDPM):
assert self.model.parameterization == "eps"
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
alphas = self.ddim_alphas
alphas_prev = self.ddim_alphas_prev
sqrt_one_minus_alphas = self.ddim_sqrt_one_minus_alphas
sigmas = self.ddim_sigmas
# select parameters corresponding to the currently considered timestep
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
@ -772,3 +776,255 @@ class UNet(DDPM):
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
return x_prev
@torch.no_grad()
def euler_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None,callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
cvd = CompVisDenoiser(ac)
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
s_in = x.new_ones([x.shape[0]]).half()
for i in trange(len(sigmas) - 1, disable=disable):
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
eps = torch.randn_like(x) * s_noise
sigma_hat = (sigmas[i] * (gamma + 1)).half()
if gamma > 0:
x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
s_i = sigma_hat * s_in
x_in = torch.cat([x] * 2)
t_in = torch.cat([s_i] * 2)
cond_in = torch.cat([unconditional_conditioning, cond])
c_out, c_in = [append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)]
eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
d = to_d(x, sigma_hat, denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
dt = sigmas[i + 1] - sigma_hat
# Euler method
x = x + d * dt
return x
@torch.no_grad()
def euler_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None):
"""Ancestral sampling with Euler method steps."""
extra_args = {} if extra_args is None else extra_args
cvd = CompVisDenoiser(ac)
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
s_in = x.new_ones([x.shape[0]]).half()
for i in trange(len(sigmas) - 1, disable=disable):
s_i = sigmas[i] * s_in
x_in = torch.cat([x] * 2)
t_in = torch.cat([s_i] * 2)
cond_in = torch.cat([unconditional_conditioning, cond])
c_out, c_in = [append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)]
eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
d = to_d(x, sigmas[i], denoised)
# Euler method
dt = sigma_down - sigmas[i]
x = x + d * dt
x = x + torch.randn_like(x) * sigma_up
return x
@torch.no_grad()
def heun_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
"""Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
cvd = CompVisDenoiser(alphas_cumprod=ac)
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
s_in = x.new_ones([x.shape[0]]).half()
for i in trange(len(sigmas) - 1, disable=disable):
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
eps = torch.randn_like(x) * s_noise
sigma_hat = (sigmas[i] * (gamma + 1)).half()
if gamma > 0:
x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
s_i = sigma_hat * s_in
x_in = torch.cat([x] * 2)
t_in = torch.cat([s_i] * 2)
cond_in = torch.cat([unconditional_conditioning, cond])
c_out, c_in = [append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)]
eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
d = to_d(x, sigma_hat, denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
dt = sigmas[i + 1] - sigma_hat
if sigmas[i + 1] == 0:
# Euler method
x = x + d * dt
else:
# Heun's method
x_2 = x + d * dt
s_i = sigmas[i + 1] * s_in
x_in = torch.cat([x_2] * 2)
t_in = torch.cat([s_i] * 2)
cond_in = torch.cat([unconditional_conditioning, cond])
c_out, c_in = [append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)]
eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
denoised_2 = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
d_prime = (d + d_2) / 2
x = x + d_prime * dt
return x
@torch.no_grad()
def dpm_2_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
"""A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
cvd = CompVisDenoiser(ac)
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
s_in = x.new_ones([x.shape[0]]).half()
for i in trange(len(sigmas) - 1, disable=disable):
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
eps = torch.randn_like(x) * s_noise
sigma_hat = sigmas[i] * (gamma + 1)
if gamma > 0:
x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
s_i = sigma_hat * s_in
x_in = torch.cat([x] * 2)
t_in = torch.cat([s_i] * 2)
cond_in = torch.cat([unconditional_conditioning, cond])
c_out, c_in = [append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)]
eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
d = to_d(x, sigma_hat, denoised)
# Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
sigma_mid = ((sigma_hat ** (1 / 3) + sigmas[i + 1] ** (1 / 3)) / 2) ** 3
dt_1 = sigma_mid - sigma_hat
dt_2 = sigmas[i + 1] - sigma_hat
x_2 = x + d * dt_1
s_i = sigma_mid * s_in
x_in = torch.cat([x_2] * 2)
t_in = torch.cat([s_i] * 2)
cond_in = torch.cat([unconditional_conditioning, cond])
c_out, c_in = [append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)]
eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
denoised_2 = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
d_2 = to_d(x_2, sigma_mid, denoised_2)
x = x + d_2 * dt_2
return x
@torch.no_grad()
def dpm_2_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None):
"""Ancestral sampling with DPM-Solver inspired second-order steps."""
extra_args = {} if extra_args is None else extra_args
cvd = CompVisDenoiser(ac)
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
s_in = x.new_ones([x.shape[0]]).half()
for i in trange(len(sigmas) - 1, disable=disable):
s_i = sigmas[i] * s_in
x_in = torch.cat([x] * 2)
t_in = torch.cat([s_i] * 2)
cond_in = torch.cat([unconditional_conditioning, cond])
c_out, c_in = [append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)]
eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
d = to_d(x, sigmas[i], denoised)
# Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
sigma_mid = ((sigmas[i] ** (1 / 3) + sigma_down ** (1 / 3)) / 2) ** 3
dt_1 = sigma_mid - sigmas[i]
dt_2 = sigma_down - sigmas[i]
x_2 = x + d * dt_1
s_i = sigma_mid * s_in
x_in = torch.cat([x_2] * 2)
t_in = torch.cat([s_i] * 2)
cond_in = torch.cat([unconditional_conditioning, cond])
c_out, c_in = [append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)]
eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
denoised_2 = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
d_2 = to_d(x_2, sigma_mid, denoised_2)
x = x + d_2 * dt_2
x = x + torch.randn_like(x) * sigma_up
return x
@torch.no_grad()
def lms_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, order=4):
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
cvd = CompVisDenoiser(ac)
sigmas = cvd.get_sigmas(S)
x = x*sigmas[0]
ds = []
for i in trange(len(sigmas) - 1, disable=disable):
s_i = sigmas[i] * s_in
x_in = torch.cat([x] * 2)
t_in = torch.cat([s_i] * 2)
cond_in = torch.cat([unconditional_conditioning, cond])
c_out, c_in = [append_dims(tmp, x_in.ndim) for tmp in cvd.get_scalings(t_in)]
eps = self.apply_model(x_in * c_in, cvd.sigma_to_t(t_in), cond_in)
e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
d = to_d(x, sigmas[i], denoised)
ds.append(d)
if len(ds) > order:
ds.pop(0)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
cur_order = min(i + 1, order)
coeffs = [linear_multistep_coeff(cur_order, sigmas.cpu(), i, j) for j in range(cur_order)]
x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
return x

13
optimizedSD/diffusers_txt2img.py Normal file
View File

@ -0,0 +1,13 @@
import torch
from diffusers import LDMTextToImagePipeline
pipe = LDMTextToImagePipeline.from_pretrained("CompVis/stable-diffusion-v1-3-diffusers", use_auth_token=True)
prompt = "19th Century wooden engraving of Elon musk"
seed = torch.manual_seed(1024)
images = pipe([prompt], batch_size=1, num_inference_steps=50, guidance_scale=7, generator=seed,torch_device="cpu" )["sample"]
# save images
for idx, image in enumerate(images):
image.save(f"image-{idx}.png")

2
optimizedSD/openaimodelSplit.py
View File

@ -13,7 +13,7 @@ from ldm.modules.diffusionmodules.util import (
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer
from .splitAttention import SpatialTransformer
class AttentionPool2d(nn.Module):

362
optimizedSD/optimized_img2img.py Normal file
View File

@ -0,0 +1,362 @@
import argparse, os, re
import torch
import numpy as np
from random import randint
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange
from torchvision.utils import make_grid
import time
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import contextmanager, nullcontext
from einops import rearrange, repeat
from ldm.util import instantiate_from_config
from optimUtils import split_weighted_subprompts, logger
from transformers import logging
import pandas as pd
logging.set_verbosity_error()
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ())
def load_model_from_config(ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
return sd
def load_img(path, h0, w0):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
if h0 is not None and w0 is not None:
h, w = h0, w0
w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 32
print(f"New image size ({w}, {h})")
image = image.resize((w, h), resample=Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0
config = "optimizedSD/v1-inference.yaml"
ckpt = "models/ldm/stable-diffusion-v1/model.ckpt"
parser = argparse.ArgumentParser()
parser.add_argument(
"--prompt", type=str, nargs="?", default="a painting of a virus monster playing guitar", help="the prompt to render"
)
parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/img2img-samples")
parser.add_argument("--init-img", type=str, nargs="?", help="path to the input image")
parser.add_argument(
"--skip_grid",
action="store_true",
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
"--skip_save",
action="store_true",
help="do not save individual samples. For speed measurements.",
)
parser.add_argument(
"--ddim_steps",
type=int,
default=50,
help="number of ddim sampling steps",
)
parser.add_argument(
"--ddim_eta",
type=float,
default=0.0,
help="ddim eta (eta=0.0 corresponds to deterministic sampling",
)
parser.add_argument(
"--n_iter",
type=int,
default=1,
help="sample this often",
)
parser.add_argument(
"--H",
type=int,
default=None,
help="image height, in pixel space",
)
parser.add_argument(
"--W",
type=int,
default=None,
help="image width, in pixel space",
)
parser.add_argument(
"--strength",
type=float,
default=0.75,
help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image",
)
parser.add_argument(
"--n_samples",
type=int,
default=5,
help="how many samples to produce for each given prompt. A.k.a. batch size",
)
parser.add_argument(
"--n_rows",
type=int,
default=0,
help="rows in the grid (default: n_samples)",
)
parser.add_argument(
"--scale",
type=float,
default=7.5,
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
"--from-file",
type=str,
help="if specified, load prompts from this file",
)
parser.add_argument(
"--seed",
type=int,
default=None,
help="the seed (for reproducible sampling)",
)
parser.add_argument(
"--device",
type=str,
default="cuda",
help="CPU or GPU (cuda/cuda:0/cuda:1/...)",
)
parser.add_argument(
"--unet_bs",
type=int,
default=1,
help="Slightly reduces inference time at the expense of high VRAM (value > 1 not recommended )",
)
parser.add_argument(
"--turbo",
action="store_true",
help="Reduces inference time on the expense of 1GB VRAM",
)
parser.add_argument(
"--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast"
)
parser.add_argument(
"--format",
type=str,
help="output image format",
choices=["jpg", "png"],
default="png",
)
parser.add_argument(
"--sampler",
type=str,
help="sampler",
choices=["ddim"],
default="ddim",
)
opt = parser.parse_args()
tic = time.time()
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
grid_count = len(os.listdir(outpath)) - 1
if opt.seed == None:
opt.seed = randint(0, 1000000)
seed_everything(opt.seed)
# Logging
logger(vars(opt), log_csv = "logs/img2img_logs.csv")
sd = load_model_from_config(f"{ckpt}")
li, lo = [], []
for key, value in sd.items():
sp = key.split(".")
if (sp[0]) == "model":
if "input_blocks" in sp:
li.append(key)
elif "middle_block" in sp:
li.append(key)
elif "time_embed" in sp:
li.append(key)
else:
lo.append(key)
for key in li:
sd["model1." + key[6:]] = sd.pop(key)
for key in lo:
sd["model2." + key[6:]] = sd.pop(key)
config = OmegaConf.load(f"{config}")
assert os.path.isfile(opt.init_img)
init_image = load_img(opt.init_img, opt.H, opt.W).to(opt.device)
model = instantiate_from_config(config.modelUNet)
_, _ = model.load_state_dict(sd, strict=False)
model.eval()
model.cdevice = opt.device
model.unet_bs = opt.unet_bs
model.turbo = opt.turbo
modelCS = instantiate_from_config(config.modelCondStage)
_, _ = modelCS.load_state_dict(sd, strict=False)
modelCS.eval()
modelCS.cond_stage_model.device = opt.device
modelFS = instantiate_from_config(config.modelFirstStage)
_, _ = modelFS.load_state_dict(sd, strict=False)
modelFS.eval()
del sd
if opt.device != "cpu" and opt.precision == "autocast":
model.half()
modelCS.half()
modelFS.half()
init_image = init_image.half()
batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
assert opt.prompt is not None
prompt = opt.prompt
data = [batch_size * [prompt]]
else:
print(f"reading prompts from {opt.from_file}")
with open(opt.from_file, "r") as f:
data = f.read().splitlines()
data = batch_size * list(data)
data = list(chunk(sorted(data), batch_size))
modelFS.to(opt.device)
init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
init_latent = modelFS.get_first_stage_encoding(modelFS.encode_first_stage(init_image)) # move to latent space
if opt.device != "cpu":
mem = torch.cuda.memory_allocated(device=opt.device) / 1e6
modelFS.to("cpu")
while torch.cuda.memory_allocated(device=opt.device) / 1e6 >= mem:
time.sleep(1)
assert 0.0 <= opt.strength <= 1.0, "can only work with strength in [0.0, 1.0]"
t_enc = int(opt.strength * opt.ddim_steps)
print(f"target t_enc is {t_enc} steps")
if opt.precision == "autocast" and opt.device != "cpu":
precision_scope = autocast
else:
precision_scope = nullcontext
seeds = ""
with torch.no_grad():
all_samples = list()
for n in trange(opt.n_iter, desc="Sampling"):
for prompts in tqdm(data, desc="data"):
sample_path = os.path.join(outpath, "_".join(re.split(":| ", prompts[0])))[:150]
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
with precision_scope("cuda"):
modelCS.to(opt.device)
uc = None
if opt.scale != 1.0:
uc = modelCS.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
subprompts, weights = split_weighted_subprompts(prompts[0])
if len(subprompts) > 1:
c = torch.zeros_like(uc)
totalWeight = sum(weights)
# normalize each "sub prompt" and add it
for i in range(len(subprompts)):
weight = weights[i]
# if not skip_normalize:
weight = weight / totalWeight
c = torch.add(c, modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
else:
c = modelCS.get_learned_conditioning(prompts)
if opt.device != "cpu":
mem = torch.cuda.memory_allocated(device=opt.device) / 1e6
modelCS.to("cpu")
while torch.cuda.memory_allocated(device=opt.device) / 1e6 >= mem:
time.sleep(1)
# encode (scaled latent)
z_enc = model.stochastic_encode(
init_latent,
torch.tensor([t_enc] * batch_size).to(opt.device),
opt.seed,
opt.ddim_eta,
opt.ddim_steps,
)
# decode it
samples_ddim = model.sample(
t_enc,
c,
z_enc,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
sampler = opt.sampler
)
modelFS.to(opt.device)
print("saving images")
for i in range(batch_size):
x_samples_ddim = modelFS.decode_first_stage(samples_ddim[i].unsqueeze(0))
x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c")
Image.fromarray(x_sample.astype(np.uint8)).save(
os.path.join(sample_path, "seed_" + str(opt.seed) + "_" + f"{base_count:05}.{opt.format}")
)
seeds += str(opt.seed) + ","
opt.seed += 1
base_count += 1
if opt.device != "cpu":
mem = torch.cuda.memory_allocated(device=opt.device) / 1e6
modelFS.to("cpu")
while torch.cuda.memory_allocated(device=opt.device) / 1e6 >= mem:
time.sleep(1)
del samples_ddim
print("memory_final = ", torch.cuda.memory_allocated(device=opt.device) / 1e6)
toc = time.time()
time_taken = (toc - tic) / 60.0
print(
(
"Samples finished in {0:.2f} minutes and exported to "
+ sample_path
+ "\n Seeds used = "
+ seeds[:-1]
).format(time_taken)
)

208
optimizedSD/optimized_txt2img.py
View File

@ -1,7 +1,7 @@
import argparse, os, sys, glob, random
import argparse, os, re
import torch
import numpy as np
import copy
from random import randint
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
@ -13,6 +13,10 @@ from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import contextmanager, nullcontext
from ldm.util import instantiate_from_config
from optimUtils import split_weighted_subprompts, logger
from transformers import logging
# from samplers import CompVisDenoiser
logging.set_verbosity_error()
def chunk(it, size):
@ -30,33 +34,22 @@ def load_model_from_config(ckpt, verbose=False):
config = "optimizedSD/v1-inference.yaml"
ckpt = "models/ldm/stable-diffusion-v1/model.ckpt"
device = "cuda"
DEFAULT_CKPT = "models/ldm/stable-diffusion-v1/model.ckpt"
parser = argparse.ArgumentParser()
parser.add_argument(
"--prompt",
type=str,
nargs="?",
default="a painting of a virus monster playing guitar",
help="the prompt to render"
)
parser.add_argument(
"--outdir",
type=str,
nargs="?",
help="dir to write results to",
default="outputs/txt2img-samples"
"--prompt", type=str, nargs="?", default="a painting of a virus monster playing guitar", help="the prompt to render"
)
parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default="outputs/txt2img-samples")
parser.add_argument(
"--skip_grid",
action='store_true',
action="store_true",
help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
)
parser.add_argument(
"--skip_save",
action='store_true',
action="store_true",
help="do not save individual samples. For speed measurements.",
)
parser.add_argument(
@ -68,7 +61,7 @@ parser.add_argument(
parser.add_argument(
"--fixed_code",
action='store_true',
action="store_true",
help="if enabled, uses the same starting code across samples ",
)
parser.add_argument(
@ -125,6 +118,12 @@ parser.add_argument(
default=7.5,
help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
)
parser.add_argument(
"--device",
type=str,
default="cuda",
help="specify GPU (cuda/cuda:0/cuda:1/...)",
)
parser.add_argument(
"--from-file",
type=str,
@ -133,13 +132,19 @@ parser.add_argument(
parser.add_argument(
"--seed",
type=int,
default=42,
default=None,
help="the seed (for reproducible sampling)",
)
parser.add_argument(
"--small_batch",
action='store_true',
help="Reduce inference time when generate a smaller batch of images",
"--unet_bs",
type=int,
default=1,
help="Slightly reduces inference time at the expense of high VRAM (value > 1 not recommended )",
)
parser.add_argument(
"--turbo",
action="store_true",
help="Reduces inference time on the expense of 1GB VRAM",
)
parser.add_argument(
"--precision",
@ -148,150 +153,195 @@ parser.add_argument(
choices=["full", "autocast"],
default="autocast"
)
parser.add_argument(
"--format",
type=str,
help="output image format",
choices=["jpg", "png"],
default="png",
)
parser.add_argument(
"--sampler",
type=str,
help="sampler",
choices=["ddim", "plms","heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"],
default="plms",
)
parser.add_argument(
"--ckpt",
type=str,
help="path to checkpoint of model",
default=DEFAULT_CKPT,
)
opt = parser.parse_args()
tic = time.time()
os.makedirs(opt.outdir, exist_ok=True)
outpath = opt.outdir
sample_path = os.path.join(outpath, "samples", "_".join(opt.prompt.split())[:255])
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
grid_count = len(os.listdir(outpath)) - 1
if opt.seed == None:
opt.seed = randint(0, 1000000)
seed_everything(opt.seed)
sd = load_model_from_config(f"{ckpt}")
li = []
lo = []
# Logging
logger(vars(opt), log_csv = "logs/txt2img_logs.csv")
sd = load_model_from_config(f"{opt.ckpt}")
li, lo = [], []
for key, value in sd.items():
sp = key.split('.')
if(sp[0]) == 'model':
if('input_blocks' in sp):
sp = key.split(".")
if (sp[0]) == "model":
if "input_blocks" in sp:
li.append(key)
elif('middle_block' in sp):
elif "middle_block" in sp:
li.append(key)
elif('time_embed' in sp):
elif "time_embed" in sp:
li.append(key)
else:
lo.append(key)
for key in li:
sd['model1.' + key[6:]] = sd.pop(key)
sd["model1." + key[6:]] = sd.pop(key)
for key in lo:
sd['model2.' + key[6:]] = sd.pop(key)
sd["model2." + key[6:]] = sd.pop(key)
config = OmegaConf.load(f"{config}")
config.modelUNet.params.ddim_steps = opt.ddim_steps
if opt.small_batch:
config.modelUNet.params.small_batch = True
else:
config.modelUNet.params.small_batch = False
model = instantiate_from_config(config.modelUNet)
_, _ = model.load_state_dict(sd, strict=False)
model.eval()
model.unet_bs = opt.unet_bs
model.cdevice = opt.device
model.turbo = opt.turbo
modelCS = instantiate_from_config(config.modelCondStage)
_, _ = modelCS.load_state_dict(sd, strict=False)
modelCS.eval()
modelCS.cond_stage_model.device = opt.device
modelFS = instantiate_from_config(config.modelFirstStage)
_, _ = modelFS.load_state_dict(sd, strict=False)
modelFS.eval()
del sd
if opt.precision == "autocast":
if opt.device != "cpu" and opt.precision == "autocast":
model.half()
modelCS.half()
start_code = None
if opt.fixed_code:
start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=opt.device)
batch_size = opt.n_samples
n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
if not opt.from_file:
assert opt.prompt is not None
prompt = opt.prompt
assert prompt is not None
print(f"Using prompt: {prompt}")
data = [batch_size * [prompt]]
else:
print(f"reading prompts from {opt.from_file}")
with open(opt.from_file, "r") as f:
data = f.read().splitlines()
data = list(chunk(data, batch_size))
text = f.read()
print(f"Using prompt: {text.strip()}")
data = text.splitlines()
data = batch_size * list(data)
data = list(chunk(sorted(data), batch_size))
precision_scope = autocast if opt.precision=="autocast" else nullcontext
if opt.precision == "autocast" and opt.device != "cpu":
precision_scope = autocast
else:
precision_scope = nullcontext
seeds = ""
with torch.no_grad():
all_samples = list()
for n in trange(opt.n_iter, desc="Sampling"):
for prompts in tqdm(data, desc="data"):
sample_path = os.path.join(outpath, "_".join(re.split(":| ", prompts[0])))[:150]
os.makedirs(sample_path, exist_ok=True)
base_count = len(os.listdir(sample_path))
with precision_scope("cuda"):
modelCS.to(device)
modelCS.to(opt.device)
uc = None
if opt.scale != 1.0:
uc = modelCS.get_learned_conditioning(batch_size * [""])
if isinstance(prompts, tuple):
prompts = list(prompts)
subprompts, weights = split_weighted_subprompts(prompts[0])
if len(subprompts) > 1:
c = torch.zeros_like(uc)
totalWeight = sum(weights)
# normalize each "sub prompt" and add it
for i in range(len(subprompts)):
weight = weights[i]
# if not skip_normalize:
weight = weight / totalWeight
c = torch.add(c, modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
else:
c = modelCS.get_learned_conditioning(prompts)
shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
shape = [opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f]
if opt.device != "cpu":
mem = torch.cuda.memory_allocated() / 1e6
modelCS.to("cpu")
while(torch.cuda.memory_allocated()/1e6 >= mem):
while torch.cuda.memory_allocated() / 1e6 >= mem:
time.sleep(1)
samples_ddim = model.sample(S=opt.ddim_steps,
samples_ddim = model.sample(
S=opt.ddim_steps,
conditioning=c,
batch_size=opt.n_samples,
seed=opt.seed,
shape=shape,
verbose=False,
unconditional_guidance_scale=opt.scale,
unconditional_conditioning=uc,
eta=opt.ddim_eta,
x_T=start_code)
x_T=start_code,
sampler = opt.sampler,
)
modelFS.to(device)
modelFS.to(opt.device)
print(samples_ddim.shape)
print("saving images")
for i in range(batch_size):
x_samples_ddim = modelFS.decode_first_stage(samples_ddim[i].unsqueeze(0))
x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
# for x_sample in x_samples_ddim:
x_sample = 255. * rearrange(x_sample[0].cpu().numpy(), 'c h w -> h w c')
x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c")
Image.fromarray(x_sample.astype(np.uint8)).save(
os.path.join(sample_path, f"{base_count:05}.png"))
os.path.join(sample_path, "seed_" + str(opt.seed) + "_" + f"{base_count:05}.{opt.format}")
)
seeds += str(opt.seed) + ","
opt.seed += 1
base_count += 1
if opt.device != "cpu":
mem = torch.cuda.memory_allocated() / 1e6
modelFS.to("cpu")
while(torch.cuda.memory_allocated()/1e6 >= mem):
while torch.cuda.memory_allocated() / 1e6 >= mem:
time.sleep(1)
# if not opt.skip_grid:
# all_samples.append(x_samples_ddim)
del samples_ddim
print("memory_final = ", torch.cuda.memory_allocated() / 1e6)
# if not skip_grid:
# # additionally, save as grid
# grid = torch.stack(all_samples, 0)
# grid = rearrange(grid, 'n b c h w -> (n b) c h w')
# grid = make_grid(grid, nrow=n_rows)
# # to image
# grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
# Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
# grid_count += 1
toc = time.time()
time_taken = (toc - tic) / 60.0
print(("Your samples are ready in {0:.2f} minutes and waiting for you here \n" + sample_path).format(time_taken))
print(
(
"Samples finished in {0:.2f} minutes and exported to "
+ sample_path
+ "\n Seeds used = "
+ seeds[:-1]
).format(time_taken)
)

252
optimizedSD/samplers.py Normal file
View File

@ -0,0 +1,252 @@
from scipy import integrate
import torch
from tqdm.auto import trange, tqdm
import torch.nn as nn
def append_zero(x):
return torch.cat([x, x.new_zeros([1])])
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
if dims_to_append < 0:
raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
return x[(...,) + (None,) * dims_to_append]
def get_ancestral_step(sigma_from, sigma_to):
"""Calculates the noise level (sigma_down) to step down to and the amount
of noise to add (sigma_up) when doing an ancestral sampling step."""
sigma_up = (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5
sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
return sigma_down, sigma_up
class DiscreteSchedule(nn.Module):
"""A mapping between continuous noise levels (sigmas) and a list of discrete noise
levels."""
def __init__(self, sigmas, quantize):
super().__init__()
self.register_buffer('sigmas', sigmas)
self.quantize = quantize
def get_sigmas(self, n=None):
if n is None:
return append_zero(self.sigmas.flip(0))
t_max = len(self.sigmas) - 1
t = torch.linspace(t_max, 0, n, device=self.sigmas.device)
return append_zero(self.t_to_sigma(t))
def sigma_to_t(self, sigma, quantize=None):
quantize = self.quantize if quantize is None else quantize
dists = torch.abs(sigma - self.sigmas[:, None])
if quantize:
return torch.argmin(dists, dim=0).view(sigma.shape)
low_idx, high_idx = torch.sort(torch.topk(dists, dim=0, k=2, largest=False).indices, dim=0)[0]
low, high = self.sigmas[low_idx], self.sigmas[high_idx]
w = (low - sigma) / (low - high)
w = w.clamp(0, 1)
t = (1 - w) * low_idx + w * high_idx
return t.view(sigma.shape)
def t_to_sigma(self, t):
t = t.float()
low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac()
# print(low_idx, high_idx, w )
return (1 - w) * self.sigmas[low_idx] + w * self.sigmas[high_idx]
class DiscreteEpsDDPMDenoiser(DiscreteSchedule):
"""A wrapper for discrete schedule DDPM models that output eps (the predicted
noise)."""
def __init__(self, alphas_cumprod, quantize):
super().__init__(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, quantize)
self.sigma_data = 1.
def get_scalings(self, sigma):
c_out = -sigma
c_in = 1 / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
return c_out, c_in
def get_eps(self, *args, **kwargs):
return self.inner_model(*args, **kwargs)
def forward(self, input, sigma, **kwargs):
c_out, c_in = [append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
return input + eps * c_out
class CompVisDenoiser(DiscreteEpsDDPMDenoiser):
"""A wrapper for CompVis diffusion models."""
def __init__(self, alphas_cumprod, quantize=False, device='cpu'):
super().__init__(alphas_cumprod, quantize=quantize)
def get_eps(self, *args, **kwargs):
return self.inner_model.apply_model(*args, **kwargs)
def to_d(x, sigma, denoised):
"""Converts a denoiser output to a Karras ODE derivative."""
return (x - denoised) / append_dims(sigma, x.ndim)
def get_ancestral_step(sigma_from, sigma_to):
"""Calculates the noise level (sigma_down) to step down to and the amount
of noise to add (sigma_up) when doing an ancestral sampling step."""
sigma_up = (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5
sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
return sigma_down, sigma_up
@torch.no_grad()
def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
eps = torch.randn_like(x) * s_noise
sigma_hat = sigmas[i] * (gamma + 1)
if gamma > 0:
x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
denoised = model(x, sigma_hat * s_in, **extra_args)
d = to_d(x, sigma_hat, denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
dt = sigmas[i + 1] - sigma_hat
# Euler method
x = x + d * dt
return x
@torch.no_grad()
def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None):
"""Ancestral sampling with Euler method steps."""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
d = to_d(x, sigmas[i], denoised)
# Euler method
dt = sigma_down - sigmas[i]
x = x + d * dt
x = x + torch.randn_like(x) * sigma_up
return x
@torch.no_grad()
def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
"""Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
eps = torch.randn_like(x) * s_noise
sigma_hat = sigmas[i] * (gamma + 1)
if gamma > 0:
x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
denoised = model(x, sigma_hat * s_in, **extra_args)
d = to_d(x, sigma_hat, denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
dt = sigmas[i + 1] - sigma_hat
if sigmas[i + 1] == 0:
# Euler method
x = x + d * dt
else:
# Heun's method
x_2 = x + d * dt
denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
d_prime = (d + d_2) / 2
x = x + d_prime * dt
return x
@torch.no_grad()
def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
"""A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
eps = torch.randn_like(x) * s_noise
sigma_hat = sigmas[i] * (gamma + 1)
if gamma > 0:
x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
denoised = model(x, sigma_hat * s_in, **extra_args)
d = to_d(x, sigma_hat, denoised)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
# Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
sigma_mid = ((sigma_hat ** (1 / 3) + sigmas[i + 1] ** (1 / 3)) / 2) ** 3
dt_1 = sigma_mid - sigma_hat
dt_2 = sigmas[i + 1] - sigma_hat
x_2 = x + d * dt_1
denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
d_2 = to_d(x_2, sigma_mid, denoised_2)
x = x + d_2 * dt_2
return x
@torch.no_grad()
def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None):
"""Ancestral sampling with DPM-Solver inspired second-order steps."""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
d = to_d(x, sigmas[i], denoised)
# Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
sigma_mid = ((sigmas[i] ** (1 / 3) + sigma_down ** (1 / 3)) / 2) ** 3
dt_1 = sigma_mid - sigmas[i]
dt_2 = sigma_down - sigmas[i]
x_2 = x + d * dt_1
denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
d_2 = to_d(x_2, sigma_mid, denoised_2)
x = x + d_2 * dt_2
x = x + torch.randn_like(x) * sigma_up
return x
def linear_multistep_coeff(order, t, i, j):
if order - 1 > i:
raise ValueError(f'Order {order} too high for step {i}')
def fn(tau):
prod = 1.
for k in range(order):
if j == k:
continue
prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
return prod
return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
@torch.no_grad()
def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4):
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
ds = []
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
d = to_d(x, sigmas[i], denoised)
ds.append(d)
if len(ds) > order:
ds.pop(0)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
cur_order = min(i + 1, order)
coeffs = [linear_multistep_coeff(cur_order, sigmas.cpu(), i, j) for j in range(cur_order)]
x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
return x

280
optimizedSD/splitAttention.py Normal file
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@ -0,0 +1,280 @@
from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in arr}.keys()
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def max_neg_value(t):
return -torch.finfo(t.dtype).max
def init_(tensor):
dim = tensor.shape[-1]
std = 1 / math.sqrt(dim)
tensor.uniform_(-std, std)
return tensor
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
nn.Linear(dim, inner_dim),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
nn.Linear(inner_dim, dim_out)
)
def forward(self, x):
return self.net(x)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def Normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
class LinearAttention(nn.Module):
def __init__(self, dim, heads=4, dim_head=32):
super().__init__()
self.heads = heads
hidden_dim = dim_head * heads
self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
self.to_out = nn.Conv2d(hidden_dim, dim, 1)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.to_qkv(x)
q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
k = k.softmax(dim=-1)
context = torch.einsum('bhdn,bhen->bhde', k, v)
out = torch.einsum('bhde,bhdn->bhen', context, q)
out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
return self.to_out(out)
class SpatialSelfAttention(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = rearrange(q, 'b c h w -> b (h w) c')
k = rearrange(k, 'b c h w -> b c (h w)')
w_ = torch.einsum('bij,bjk->bik', q, k)
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = rearrange(v, 'b c h w -> b c (h w)')
w_ = rearrange(w_, 'b i j -> b j i')
h_ = torch.einsum('bij,bjk->bik', v, w_)
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
h_ = self.proj_out(h_)
return x+h_
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., att_step=1):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = dim_head ** -0.5
self.heads = heads
self.att_step = att_step
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, query_dim),
nn.Dropout(dropout)
)
def forward(self, x, context=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
del context, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
limit = k.shape[0]
att_step = self.att_step
q_chunks = list(torch.tensor_split(q, limit//att_step, dim=0))
k_chunks = list(torch.tensor_split(k, limit//att_step, dim=0))
v_chunks = list(torch.tensor_split(v, limit//att_step, dim=0))
q_chunks.reverse()
k_chunks.reverse()
v_chunks.reverse()
sim = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
del k, q, v
for i in range (0, limit, att_step):
q_buffer = q_chunks.pop()
k_buffer = k_chunks.pop()
v_buffer = v_chunks.pop()
sim_buffer = einsum('b i d, b j d -> b i j', q_buffer, k_buffer) * self.scale
del k_buffer, q_buffer
# attention, what we cannot get enough of, by chunks
sim_buffer = sim_buffer.softmax(dim=-1)
sim_buffer = einsum('b i j, b j d -> b i d', sim_buffer, v_buffer)
del v_buffer
sim[i:i+att_step,:,:] = sim_buffer
del sim_buffer
sim = rearrange(sim, '(b h) n d -> b n (h d)', h=h)
return self.to_out(sim)
class BasicTransformerBlock(nn.Module):
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
super().__init__()
self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(dim)
self.checkpoint = checkpoint
def forward(self, x, context=None):
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
def _forward(self, x, context=None):
x = self.attn1(self.norm1(x)) + x
x = self.attn2(self.norm2(x), context=context) + x
x = self.ff(self.norm3(x)) + x
return x
class SpatialTransformer(nn.Module):
"""
Transformer block for image-like data.
First, project the input (aka embedding)
and reshape to b, t, d.
Then apply standard transformer action.
Finally, reshape to image
"""
def __init__(self, in_channels, n_heads, d_head,
depth=1, dropout=0., context_dim=None):
super().__init__()
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = Normalize(in_channels)
self.proj_in = nn.Conv2d(in_channels,
inner_dim,
kernel_size=1,
stride=1,
padding=0)
self.transformer_blocks = nn.ModuleList(
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
for d in range(depth)]
)
self.proj_out = zero_module(nn.Conv2d(inner_dim,
in_channels,
kernel_size=1,
stride=1,
padding=0))
def forward(self, x, context=None):
# note: if no context is given, cross-attention defaults to self-attention
b, c, h, w = x.shape
x_in = x
x = self.norm(x)
x = self.proj_in(x)
x = rearrange(x, 'b c h w -> b (h w) c')
for block in self.transformer_blocks:
x = block(x, context=context)
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
x = self.proj_out(x)
return x + x_in

6
requirements.txt
View File

@ -29,7 +29,7 @@ streamlit==1.14.0
streamlit-on-Hover-tabs==1.0.1
streamlit-option-menu==0.3.2
streamlit_nested_layout==0.1.1
streamlit-server-state==0.14.2
streamlit-server-state==0.15.0
streamlit-tensorboard==0.0.2
streamlit-elements==0.1.* # used for the draggable dashboard and new UI design (WIP)
streamlit-ace==0.1.1 # used to replace the text area on the prompt and also for the code editor tool.
@ -43,11 +43,12 @@ jsonmerge==1.8.
matplotlib==3.6.
resize-right==0.0.2
torchdiffeq==0.2.3
barfi==0.7.0
# Environment Dependencies for WebUI (flet)
# txt2vid
diffusers==0.6.0
diffusers==0.7.2
librosa==0.9.2
# img2img inpainting
@ -66,6 +67,7 @@ retry==0.9.2 # used by sd_utils
python-slugify==6.1.2 # used by sd_utils
piexif==1.1.3 # used by sd_utils
pywebview==3.6.3 # used by streamlit_webview.py
shutup==0.2.0 # remove all the annoying warnings
accelerate==0.12.0
albumentations==0.4.3

5
scripts/Settings.py
View File

@ -125,9 +125,14 @@ def layout():
st.session_state["defaults"].general.no_half = st.checkbox("No Half", value=st.session_state['defaults'].general.no_half,
help="DO NOT switch the model to 16-bit floats. Default: False")
st.session_state["defaults"].general.use_cudnn = st.checkbox("Use cudnn", value=st.session_state['defaults'].general.use_cudnn,
help="Switch the pytorch backend to use cudnn, this should help with fixing Nvidia 16xx cards getting"
"a black or green image. Default: False")
st.session_state["defaults"].general.use_float16 = st.checkbox("Use float16", value=st.session_state['defaults'].general.use_float16,
help="Switch the model to 16-bit floats. Default: False")
precision_list = ['full', 'autocast']
st.session_state["defaults"].general.precision = st.selectbox("Precision", precision_list, index=precision_list.index(st.session_state['defaults'].general.precision),
help="Evaluates at this precision. Default: autocast")

754
scripts/convert_original_stable_diffusion_to_diffusers.py Normal file
View File

@ -0,0 +1,754 @@
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Conversion script for the LDM checkpoints. """
import argparse
import os
import torch
try:
from omegaconf import OmegaConf
except ImportError:
raise ImportError(
"OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`."
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
#DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LDMTextToImagePipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet2DConditionModel,
)
from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer
def shave_segments(path, n_shave_prefix_segments=1):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
"""
if n_shave_prefix_segments >= 0:
return ".".join(path.split(".")[n_shave_prefix_segments:])
else:
return ".".join(path.split(".")[:n_shave_prefix_segments])
def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item.replace("in_layers.0", "norm1")
new_item = new_item.replace("in_layers.2", "conv1")
new_item = new_item.replace("out_layers.0", "norm2")
new_item = new_item.replace("out_layers.3", "conv2")
new_item = new_item.replace("emb_layers.1", "time_emb_proj")
new_item = new_item.replace("skip_connection", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside resnets to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("nin_shortcut", "conv_shortcut")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
# new_item = new_item.replace('norm.weight', 'group_norm.weight')
# new_item = new_item.replace('norm.bias', 'group_norm.bias')
# new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
# new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
# new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
"""
Updates paths inside attentions to the new naming scheme (local renaming)
"""
mapping = []
for old_item in old_list:
new_item = old_item
new_item = new_item.replace("norm.weight", "group_norm.weight")
new_item = new_item.replace("norm.bias", "group_norm.bias")
new_item = new_item.replace("q.weight", "query.weight")
new_item = new_item.replace("q.bias", "query.bias")
new_item = new_item.replace("k.weight", "key.weight")
new_item = new_item.replace("k.bias", "key.bias")
new_item = new_item.replace("v.weight", "value.weight")
new_item = new_item.replace("v.bias", "value.bias")
new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
mapping.append({"old": old_item, "new": new_item})
return mapping
def assign_to_checkpoint(
paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
):
"""
This does the final conversion step: take locally converted weights and apply a global renaming
to them. It splits attention layers, and takes into account additional replacements
that may arise.
Assigns the weights to the new checkpoint.
"""
assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
old_tensor = old_checkpoint[path]
channels = old_tensor.shape[0] // 3
target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
query, key, value = old_tensor.split(channels // num_heads, dim=1)
checkpoint[path_map["query"]] = query.reshape(target_shape)
checkpoint[path_map["key"]] = key.reshape(target_shape)
checkpoint[path_map["value"]] = value.reshape(target_shape)
for path in paths:
new_path = path["new"]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
if additional_replacements is not None:
for replacement in additional_replacements:
new_path = new_path.replace(replacement["old"], replacement["new"])
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
else:
checkpoint[new_path] = old_checkpoint[path["old"]]
def conv_attn_to_linear(checkpoint):
keys = list(checkpoint.keys())
attn_keys = ["query.weight", "key.weight", "value.weight"]
for key in keys:
if ".".join(key.split(".")[-2:]) in attn_keys:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0, 0]
elif "proj_attn.weight" in key:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0]
def create_unet_diffusers_config(original_config):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
unet_params = original_config.model.params.unet_config.params
block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
down_block_types = []
resolution = 1
for i in range(len(block_out_channels)):
block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
down_block_types.append(block_type)
if i != len(block_out_channels) - 1:
resolution *= 2
up_block_types = []
for i in range(len(block_out_channels)):
block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
up_block_types.append(block_type)
resolution //= 2
config = dict(
sample_size=unet_params.image_size,
in_channels=unet_params.in_channels,
out_channels=unet_params.out_channels,
down_block_types=tuple(down_block_types),
up_block_types=tuple(up_block_types),
block_out_channels=tuple(block_out_channels),
layers_per_block=unet_params.num_res_blocks,
cross_attention_dim=unet_params.context_dim,
attention_head_dim=unet_params.num_heads,
)
return config
def create_vae_diffusers_config(original_config):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
vae_params = original_config.model.params.first_stage_config.params.ddconfig
_ = original_config.model.params.first_stage_config.params.embed_dim
block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
config = dict(
sample_size=vae_params.resolution,
in_channels=vae_params.in_channels,
out_channels=vae_params.out_ch,
down_block_types=tuple(down_block_types),
up_block_types=tuple(up_block_types),
block_out_channels=tuple(block_out_channels),
latent_channels=vae_params.z_channels,
layers_per_block=vae_params.num_res_blocks,
)
return config
def create_diffusers_schedular(original_config):
schedular = DDIMScheduler(
num_train_timesteps=original_config.model.params.timesteps,
beta_start=original_config.model.params.linear_start,
beta_end=original_config.model.params.linear_end,
beta_schedule="scaled_linear",
)
return schedular
def create_ldm_bert_config(original_config):
bert_params = original_config.model.parms.cond_stage_config.params
config = LDMBertConfig(
d_model=bert_params.n_embed,
encoder_layers=bert_params.n_layer,
encoder_ffn_dim=bert_params.n_embed * 4,
)
return config
def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):
"""
Takes a state dict and a config, and returns a converted checkpoint.
"""
# extract state_dict for UNet
unet_state_dict = {}
keys = list(checkpoint.keys())
unet_key = "model.diffusion_model."
# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
if sum(k.startswith("model_ema") for k in keys) > 100:
print(f"Checkpoint {path} has both EMA and non-EMA weights.")
if extract_ema:
print(
"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
)
for key in keys:
if key.startswith("model.diffusion_model"):
flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
else:
print(
"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
" weights (usually better for inference), please make sure to add the `--extract_ema` flag."
)
for key in keys:
if key.startswith(unet_key):
unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
new_checkpoint = {}
new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
# Retrieves the keys for the input blocks only
num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
input_blocks = {
layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
for layer_id in range(num_input_blocks)
}
# Retrieves the keys for the middle blocks only
num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
middle_blocks = {
layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
for layer_id in range(num_middle_blocks)
}
# Retrieves the keys for the output blocks only
num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
output_blocks = {
layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
for layer_id in range(num_output_blocks)
}
for i in range(1, num_input_blocks):
block_id = (i - 1) // (config["layers_per_block"] + 1)
layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
resnets = [
key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
]
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.weight"
)
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
f"input_blocks.{i}.0.op.bias"
)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
resnet_0 = middle_blocks[0]
attentions = middle_blocks[1]
resnet_1 = middle_blocks[2]
resnet_0_paths = renew_resnet_paths(resnet_0)
assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
resnet_1_paths = renew_resnet_paths(resnet_1)
assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
attentions_paths = renew_attention_paths(attentions)
meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(
attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
for i in range(num_output_blocks):
block_id = i // (config["layers_per_block"] + 1)
layer_in_block_id = i % (config["layers_per_block"] + 1)
output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
output_block_list = {}
for layer in output_block_layers:
layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
if layer_id in output_block_list:
output_block_list[layer_id].append(layer_name)
else:
output_block_list[layer_id] = [layer_name]
if len(output_block_list) > 1:
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
resnet_0_paths = renew_resnet_paths(resnets)
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
if ["conv.weight", "conv.bias"] in output_block_list.values():
index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
f"output_blocks.{i}.{index}.conv.weight"
]
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
f"output_blocks.{i}.{index}.conv.bias"
]
# Clear attentions as they have been attributed above.
if len(attentions) == 2:
attentions = []
if len(attentions):
paths = renew_attention_paths(attentions)
meta_path = {
"old": f"output_blocks.{i}.1",
"new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
else:
resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
for path in resnet_0_paths:
old_path = ".".join(["output_blocks", str(i), path["old"]])
new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
new_checkpoint[new_path] = unet_state_dict[old_path]
return new_checkpoint
def convert_ldm_vae_checkpoint(checkpoint, config):
# extract state dict for VAE
vae_state_dict = {}
vae_key = "first_stage_model."
keys = list(checkpoint.keys())
for key in keys:
if key.startswith(vae_key):
vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
new_checkpoint = {}
new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
down_blocks = {
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
}
# Retrieves the keys for the decoder up blocks only
num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
up_blocks = {
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
}
for i in range(num_down_blocks):
resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.weight"
)
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.bias"
)
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
conv_attn_to_linear(new_checkpoint)
for i in range(num_up_blocks):
block_id = num_up_blocks - 1 - i
resnets = [
key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
]
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.weight"
]
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.bias"
]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
num_mid_res_blocks = 2
for i in range(1, num_mid_res_blocks + 1):
resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
paths = renew_vae_resnet_paths(resnets)
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
paths = renew_vae_attention_paths(mid_attentions)
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
conv_attn_to_linear(new_checkpoint)
return new_checkpoint
def convert_ldm_bert_checkpoint(checkpoint, config):
def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight
hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias
def _copy_linear(hf_linear, pt_linear):
hf_linear.weight = pt_linear.weight
hf_linear.bias = pt_linear.bias
def _copy_layer(hf_layer, pt_layer):
# copy layer norms
_copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
_copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])
# copy attn
_copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])
# copy MLP
pt_mlp = pt_layer[1][1]
_copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
_copy_linear(hf_layer.fc2, pt_mlp.net[2])
def _copy_layers(hf_layers, pt_layers):
for i, hf_layer in enumerate(hf_layers):
if i != 0:
i += i
pt_layer = pt_layers[i : i + 2]
_copy_layer(hf_layer, pt_layer)
hf_model = LDMBertModel(config).eval()
# copy embeds
hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
# copy layer norm
_copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
# copy hidden layers
_copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)
_copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)
return hf_model
def convert_ldm_clip_checkpoint(checkpoint):
text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
keys = list(checkpoint.keys())
text_model_dict = {}
for key in keys:
if key.startswith("cond_stage_model.transformer"):
text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
try:
text_model.load_state_dict(text_model_dict)
except RuntimeError:
pass
return text_model
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
"--original_config_file",
default=None,
type=str,
help="The YAML config file corresponding to the original architecture.",
)
parser.add_argument(
"--scheduler_type",
default="pndm",
type=str,
help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancest', 'dpm']",
)
parser.add_argument(
"--extract_ema",
action="store_true",
help=(
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
),
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
args = parser.parse_args()
if args.original_config_file is None:
os.system(
"wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
)
args.original_config_file = "./v1-inference.yaml"
original_config = OmegaConf.load(args.original_config_file)
checkpoint = torch.load(args.checkpoint_path)
checkpoint = checkpoint["state_dict"]
num_train_timesteps = original_config.model.params.timesteps
beta_start = original_config.model.params.linear_start
beta_end = original_config.model.params.linear_end
if args.scheduler_type == "pndm":
scheduler = PNDMScheduler(
beta_end=beta_end,
beta_schedule="scaled_linear",
beta_start=beta_start,
num_train_timesteps=num_train_timesteps,
skip_prk_steps=True,
)
elif args.scheduler_type == "lms":
scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
elif args.scheduler_type == "euler":
scheduler = EulerDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear")
elif args.scheduler_type == "euler-ancestral":
scheduler = EulerAncestralDiscreteScheduler(
beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
)
elif args.scheduler_type == "dpm":
scheduler = DPMSolverMultistepScheduler(
beta_start=beta_start, beta_end=beta_end, beta_schedule="scaled_linear"
)
elif args.scheduler_type == "ddim":
scheduler = DDIMScheduler(
beta_start=beta_start,
beta_end=beta_end,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
else:
raise ValueError(f"Scheduler of type {args.scheduler_type} doesn't exist!")
# Convert the UNet2DConditionModel model.
unet_config = create_unet_diffusers_config(original_config)
converted_unet_checkpoint = convert_ldm_unet_checkpoint(
checkpoint, unet_config, path=args.checkpoint_path, extract_ema=args.extract_ema
)
unet = UNet2DConditionModel(**unet_config)
unet.load_state_dict(converted_unet_checkpoint)
# Convert the VAE model.
vae_config = create_vae_diffusers_config(original_config)
converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
vae = AutoencoderKL(**vae_config)
vae.load_state_dict(converted_vae_checkpoint)
# Convert the text model.
text_model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
if text_model_type == "FrozenCLIPEmbedder":
text_model = convert_ldm_clip_checkpoint(checkpoint)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
pipe = StableDiffusionPipeline(
vae=vae,
text_encoder=text_model,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
else:
text_config = create_ldm_bert_config(original_config)
text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
pipe.save_pretrained(args.dump_path)

7
scripts/img2img.py
View File

@ -14,7 +14,7 @@
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import st, server_state, \
from sd_utils import st, server_state, no_rerun, \
generation_callback, process_images, KDiffusionSampler, \
custom_models_available, RealESRGAN_available, GFPGAN_available, \
LDSR_available, load_models, hc, seed_to_int, logger, \
@ -379,6 +379,10 @@ def layout():
prompt = st.text_area("Input Text","", placeholder=placeholder, height=54)
sygil_suggestions.suggestion_area(placeholder)
if "defaults" in st.session_state:
if st.session_state['defaults'].admin.global_negative_prompt:
prompt += f"### {st.session_state['defaults'].admin.global_negative_prompt}"
# Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
img2img_generate_col.write("")
img2img_generate_col.write("")
@ -690,6 +694,7 @@ def layout():
#print("Loading models")
# load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
with col3_img2img_layout:
with no_rerun:
with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]):
load_models(use_LDSR=st.session_state["use_LDSR"], LDSR_model=st.session_state["LDSR_model"],
use_GFPGAN=st.session_state["use_GFPGAN"], GFPGAN_model=st.session_state["GFPGAN_model"] ,

58
scripts/prune-ckpt.py Normal file
View File

@ -0,0 +1,58 @@
import os
import torch
import argparse
import glob
parser = argparse.ArgumentParser(description='Pruning')
parser.add_argument('--ckpt', type=str, default=None, help='path to model ckpt')
args = parser.parse_args()
ckpt = args.ckpt
def prune_it(p, keep_only_ema=False):
print(f"prunin' in path: {p}")
size_initial = os.path.getsize(p)
nsd = dict()
sd = torch.load(p, map_location="cpu")
print(sd.keys())
for k in sd.keys():
if k != "optimizer_states":
nsd[k] = sd[k]
else:
print(f"removing optimizer states for path {p}")
if "global_step" in sd:
print(f"This is global step {sd['global_step']}.")
if keep_only_ema:
sd = nsd["state_dict"].copy()
# infer ema keys
ema_keys = {k: "model_ema." + k[6:].replace(".", ".") for k in sd.keys() if k.startswith("model.")}
new_sd = dict()
for k in sd:
if k in ema_keys:
new_sd[k] = sd[ema_keys[k]].half()
elif not k.startswith("model_ema.") or k in ["model_ema.num_updates", "model_ema.decay"]:
new_sd[k] = sd[k].half()
assert len(new_sd) == len(sd) - len(ema_keys)
nsd["state_dict"] = new_sd
else:
sd = nsd['state_dict'].copy()
new_sd = dict()
for k in sd:
new_sd[k] = sd[k].half()
nsd['state_dict'] = new_sd
fn = f"{os.path.splitext(p)[0]}-pruned.ckpt" if not keep_only_ema else f"{os.path.splitext(p)[0]}-ema-pruned.ckpt"
print(f"saving pruned checkpoint at: {fn}")
torch.save(nsd, fn)
newsize = os.path.getsize(fn)
MSG = f"New ckpt size: {newsize*1e-9:.2f} GB. " + \
f"Saved {(size_initial - newsize)*1e-9:.2f} GB by removing optimizer states"
if keep_only_ema:
MSG += " and non-EMA weights"
print(MSG)
if __name__ == "__main__":
prune_it(ckpt)

2
scripts/sd_concept_library.py
View File

@ -107,7 +107,7 @@ def getConceptsFromPath(page, conceptPerPage, searchText=""):
# Maintain the aspect ratio (max 200x200)
resizedImage = originalImage.copy()
resizedImage.thumbnail((200, 200), Image.ANTIALIAS)
resizedImage.thumbnail((200, 200), Image.Resampling.LANCZOS)
# concept["images"].append(resizedImage)

29
scripts/sd_utils/__init__.py
View File

@ -22,7 +22,7 @@ from streamlit.runtime.scriptrunner import StopException
#from streamlit.runtime.scriptrunner import script_run_context
#streamlit components section
from streamlit_server_state import server_state, server_state_lock
from streamlit_server_state import server_state, server_state_lock, no_rerun
import hydralit_components as hc
from hydralit import HydraHeadApp
import streamlit_nested_layout
@ -72,6 +72,7 @@ from io import BytesIO
from packaging import version
from pathlib import Path
from huggingface_hub import hf_hub_download
import shutup
#import librosa
from logger import logger, set_logger_verbosity, quiesce_logger
@ -91,6 +92,15 @@ except ImportError as e:
# end of imports
#---------------------------------------------------------------------------------------------------------------
# remove all the annoying python warnings.
shutup.please()
# the following lines should help fixing an issue with nvidia 16xx cards.
if "defaults" in st.session_state:
if st.session_state["defaults"].general.use_cudnn:
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.enabled = True
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
@ -261,10 +271,13 @@ def set_page_title(title):
def make_grid(n_items=5, n_cols=5):
# Compute number of rows
n_rows = 1 + n_items // int(n_cols)
# Create rows
rows = [st.container() for _ in range(n_rows)]
# Create columns in each row
cols_per_row = [r.columns(n_cols) for r in rows]
cols = [column for row in cols_per_row for column in row]
@ -272,29 +285,29 @@ def make_grid(n_items=5, n_cols=5):
def merge(file1, file2, out, weight):
alpha = (weight)/100
if not(file1.endswith(".ckpt")):
file1 += ".ckpt"
if not(file2.endswith(".ckpt")):
file2 += ".ckpt"
if not(out.endswith(".ckpt")):
out += ".ckpt"
try:
#Load Models
model_0 = torch.load(file1)
model_1 = torch.load(file2)
theta_0 = model_0['state_dict']
theta_1 = model_1['state_dict']
alpha = (weight)/100
for key in theta_0.keys():
if 'model' in key and key in theta_1:
theta_0[key] = (alpha) * theta_0[key] + (1-alpha) * theta_1[key]
logger.info("RUNNING...\n(STAGE 2)")
for key in theta_1.keys():
if 'model' in key and key not in theta_0:
theta_0[key] = theta_1[key]
torch.save(model_0, out)
except:
logger.error("Error in merging")
def human_readable_size(size, decimal_places=3):
@ -483,7 +496,7 @@ def load_model_from_config(config, ckpt, verbose=False):
if "global_step" in pl_sd:
logger.info(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
model = instantiate_from_config(config.model, personalization_config='')
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
logger.info("missing keys:")
@ -1606,6 +1619,10 @@ def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='Re
#
@retry(tries=5)
def generation_callback(img, i=0):
# try to do garbage collection before decoding the image
torch_gc()
if "update_preview_frequency" not in st.session_state:
raise StopException

9
scripts/txt2img.py
View File

@ -14,7 +14,7 @@
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
from sd_utils import st, MemUsageMonitor, server_state, \
from sd_utils import st, MemUsageMonitor, server_state, no_rerun, \
get_next_sequence_number, check_prompt_length, torch_gc, \
save_sample, generation_callback, process_images, \
KDiffusionSampler, \
@ -427,6 +427,12 @@ def layout():
prompt = st.text_area("Input Text","", placeholder=placeholder, height=54)
sygil_suggestions.suggestion_area(placeholder)
if "defaults" in st.session_state:
if st.session_state['defaults'].admin.global_negative_prompt:
prompt += f"### {st.session_state['defaults'].admin.global_negative_prompt}"
#print(prompt)
# creating the page layout using columns
col1, col2, col3 = st.columns([2,5,2], gap="large")
@ -652,6 +658,7 @@ def layout():
if generate_button:
with col2:
with no_rerun:
if not use_stable_horde:
with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]):
load_models(use_LDSR=st.session_state["use_LDSR"], LDSR_model=st.session_state["LDSR_model"],

62
scripts/txt2vid.py
View File

@ -21,7 +21,7 @@ https://github.com/nateraw/stable-diffusion-videos
repo and the original gist script from
https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355
"""
from sd_utils import st, MemUsageMonitor, server_state, torch_gc, \
from sd_utils import st, MemUsageMonitor, server_state, no_rerun, torch_gc, \
custom_models_available, RealESRGAN_available, GFPGAN_available, \
LDSR_available, hc, seed_to_int, logger, slerp, optimize_update_preview_frequency, \
load_learned_embed_in_clip, load_GFPGAN, RealESRGANModel
@ -54,7 +54,7 @@ from diffusers import StableDiffusionPipeline, DiffusionPipeline
#from stable_diffusion_videos import StableDiffusionWalkPipeline
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, \
PNDMScheduler
PNDMScheduler, DDPMScheduler
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNet2DConditionModel
@ -946,6 +946,7 @@ def diffuse(
num_inference_steps,
cfg_scale,
eta,
fps=30
):
torch_device = cond_latents.get_device()
@ -1055,8 +1056,7 @@ def diffuse(
speed = "it/s"
duration = 1 / duration
#
total_frames = (st.session_state.sampling_steps + st.session_state.num_inference_steps) * st.session_state.max_duration_in_seconds
total_frames = st.session_state.max_duration_in_seconds * fps
total_steps = st.session_state.sampling_steps + st.session_state.num_inference_steps
if i > st.session_state.sampling_steps:
@ -1124,6 +1124,8 @@ def load_diffusers_model(weights_path,torch_device):
if weights_path == "runwayml/stable-diffusion-v1-5":
model_path = os.path.join("models", "diffusers", "stable-diffusion-v1-5")
else:
model_path = weights_path
if not os.path.exists(model_path + "/model_index.json"):
server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
@ -1195,13 +1197,13 @@ def load_diffusers_model(weights_path,torch_device):
st.session_state["progress_bar_text"].error(e)
#
def save_video_to_disk(frames, seeds, sanitized_prompt, fps=6,save_video=True, outdir='outputs'):
def save_video_to_disk(frames, seeds, sanitized_prompt, fps=30,save_video=True, outdir='outputs'):
if save_video:
# write video to memory
#output = io.BytesIO()
#writer = imageio.get_writer(os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid"), im, extension=".mp4", fps=30)
#try:
video_path = os.path.join(os.getcwd(), outdir, "txt2vid",f"{seeds}_{sanitized_prompt}{datetime.now().strftime('%Y%m-%d%H-%M%S-') + str(uuid4())[:8]}.mp4")
video_path = os.path.join(os.getcwd(), outdir, "txt2vid",f"{seeds}_{sanitized_prompt}{datetime.datetime.now().strftime('%Y%m-%d%H-%M%S-') + str(uuid4())[:8]}.mp4")
writer = imageio.get_writer(video_path, fps=fps)
for frame in frames:
writer.append_data(frame)
@ -1358,7 +1360,29 @@ def txt2vid(
beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
)
SCHEDULERS = dict(default=default_scheduler, ddim=ddim_scheduler, klms=klms_scheduler)
#flaxddims_scheduler = FlaxDDIMScheduler(
#beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
#)
#flaxddpms_scheduler = FlaxDDPMScheduler(
#beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
#)
#flaxpndms_scheduler = FlaxPNDMScheduler(
#beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
#)
ddpms_scheduler = DDPMScheduler(
beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
)
SCHEDULERS = dict(default=default_scheduler, ddim=ddim_scheduler,
klms=klms_scheduler,
ddpms=ddpms_scheduler,
#flaxddims=flaxddims_scheduler,
#flaxddpms=flaxddpms_scheduler,
#flaxpndms=flaxpndms_scheduler,
)
with st.session_state["progress_bar_text"].container():
with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]):
@ -1482,9 +1506,9 @@ def txt2vid(
#)
# old code
total_frames = (st.session_state.sampling_steps + st.session_state.num_inference_steps) * st.session_state.max_duration_in_seconds
total_frames = st.session_state.max_duration_in_seconds * fps
while second_count < max_duration_in_seconds:
while frame_index+1 <= total_frames:
st.session_state["frame_duration"] = 0
st.session_state["frame_speed"] = 0
st.session_state["current_frame"] = frame_index
@ -1506,7 +1530,7 @@ def txt2vid(
#init = slerp(gpu, float(t), init1, init2)
with autocast("cuda"):
image = diffuse(server_state["pipe"], cond_embeddings, init, num_inference_steps, cfg_scale, eta)
image = diffuse(server_state["pipe"], cond_embeddings, init, num_inference_steps, cfg_scale, eta, fps=fps)
if st.session_state["save_individual_images"] and not st.session_state["use_GFPGAN"] and not st.session_state["use_RealESRGAN"]:
#im = Image.fromarray(image)
@ -1560,6 +1584,8 @@ def txt2vid(
st.session_state["frame_duration"] = duration
st.session_state["frame_speed"] = speed
if frame_index+1 > total_frames:
break
init1 = init2
@ -1602,6 +1628,10 @@ def layout():
prompt = st.text_area("Input Text","", placeholder=placeholder, height=54)
sygil_suggestions.suggestion_area(placeholder)
if "defaults" in st.session_state:
if st.session_state['defaults'].admin.global_negative_prompt:
prompt += f"### {st.session_state['defaults'].admin.global_negative_prompt}"
# Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
generate_col1.write("")
generate_col1.write("")
@ -1633,6 +1663,9 @@ def layout():
st.session_state["max_duration_in_seconds"] = st.number_input("Max Duration In Seconds:", value=st.session_state['defaults'].txt2vid.max_duration_in_seconds,
help="Specify the max duration in seconds you want your video to be.")
st.session_state["fps"] = st.number_input("Frames per Second (FPS):", value=st.session_state['defaults'].txt2vid.fps,
help="Specify the frame rate of the video.")
with st.expander("Preview Settings"):
#st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2vid.update_preview,
#help="If enabled the image preview will be updated during the generation instead of at the end. \
@ -1713,7 +1746,9 @@ def layout():
#sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
#sampler_name = st.selectbox("Sampling method", sampler_name_list,
#index=sampler_name_list.index(st.session_state['defaults'].txt2vid.default_sampler), help="Sampling method to use. Default: k_euler")
scheduler_name_list = ["klms", "ddim"]
scheduler_name_list = ["klms", "ddim", "ddpms",
#"flaxddims", "flaxddpms", "flaxpndms"
]
scheduler_name = st.selectbox("Scheduler:", scheduler_name_list,
index=scheduler_name_list.index(st.session_state['defaults'].txt2vid.scheduler_name), help="Scheduler to use. Default: klms")
@ -1874,7 +1909,7 @@ def layout():
#print("Loading models")
# load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
#load_models(False, st.session_state["use_GFPGAN"], True, st.session_state["RealESRGAN_model"])
with no_rerun:
if st.session_state["use_GFPGAN"]:
if "GFPGAN" in server_state:
logger.info("GFPGAN already loaded")
@ -1907,9 +1942,10 @@ def layout():
height=height, weights_path=custom_model, scheduler=scheduler_name,
disable_tqdm=False, beta_start=st.session_state['defaults'].txt2vid.beta_start.value,
beta_end=st.session_state['defaults'].txt2vid.beta_end.value,
beta_schedule=beta_scheduler_type, starting_image=None)
beta_schedule=beta_scheduler_type, starting_image=None, fps=st.session_state.fps)
if video and save_video_on_stop:
if os.path.exists(video): # temporary solution to bypass exception
# show video preview on the UI after we hit the stop button
# currently not working as session_state is cleared on StopException
preview_video.video(open(video, 'rb').read())

1239
scripts/webui_flet.py Normal file
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65
scripts/webui_flet_utils.py Normal file
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@ -0,0 +1,65 @@
# webui_utils.py
# imports
import os, yaml
from PIL import Image
from pprint import pprint
# logging
log_file = 'webui_flet.log'
def log_message(message):
with open(log_file,'a+') as log:
log.write(message)
# Settings
path_to_default_config = 'configs/webui/webui_flet.yaml'
path_to_user_config = 'configs/webui/userconfig_flet.yaml'
def get_default_settings_from_config():
with open(path_to_default_config) as f:
default_settings = yaml.safe_load(f)
return default_settings
def get_user_settings_from_config():
settings = get_default_settings_from_config()
if os.path.exists(path_to_user_config):
with open(path_to_user_config) as f:
user_settings = yaml.safe_load(f)
settings.update(user_settings)
return settings
def save_user_settings_to_config(settings):
with open(path_to_user_config, 'w+') as f:
yaml.dump(settings, f, default_flow_style=False)
# Image handling
def load_images(images): # just for testing, needs love to function
images_loaded = {}
images_not_loaded = []
for i in images:
try:
img = Image.open(images[i]['path'])
if img:
images_loaded.update({images[i].name:img})
except:
images_not_loaded.append(i)
return images_loaded, images_not_loaded
def create_blank_image():
img = Image.new('RGBA',(512,512),(0,0,0,0))
return img
# Textual Inversion
textual_inversion_grid_row_list = [
'model', 'medium', 'artist', 'trending', 'movement', 'flavors', 'techniques', 'tags',
]
def run_textual_inversion(args):
pass