Sygil-Dev/sygil-webui
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Textual inversion support

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Enabled only if textual-inversion exists.  i.e.
> config (configs/stable-diffusion/v1-inference.yaml) is updated for textual inversion
> ldm/modules/embedding_manager.py exists (copied from textual-inversion repo)
> ldm/data/personalized.py exists (copied from textual-inversion repo)
> ldm/data/personalized_style.py exists (copied from textual-inversion repo)
> ldm/models/diffusion/ddpm.py is replaced with textual-inversion version
> ldm/util.py is replaced with textual-inversion version

Without textual-inversion installed script will function as normal

Please note:
Once textual-inversion changes have been applied do not remove personalization_config from config .yaml without also replacing the files changed with the original versions
This commit is contained in:
hlky 2022-08-24 22:28:32 +01:00
parent 22e225ad1e
commit 8d6c046a08
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GPG Key ID: 55A99F1E80D907D5
1 changed files with 15 additions and 4 deletions
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webui.py
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@ -236,6 +236,9 @@ model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckp
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model = (model if opt.no_half else model.half()).to(device)
def load_embeddings(fp):
if fp is not None and hasattr(model, "embedding_manager"):
model.embedding_manager.load(fp.name)
def image_grid(imgs, batch_size, round_down=False, force_n_rows=None):
if force_n_rows is not None:
@ -386,7 +389,7 @@ def check_prompt_length(prompt, comments):
comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, skip_grid, skip_save, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, do_not_save_grid=False, normalize_prompt_weights=True):
def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name, skip_grid, skip_save, batch_size, n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, fp, do_not_save_grid=False, normalize_prompt_weights=True):
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
assert prompt is not None
torch_gc()
@ -396,6 +399,9 @@ def process_images(outpath, func_init, func_sample, prompt, seed, sampler_name,
mem_mon = MemUsageMonitor('MemMon')
mem_mon.start()
if hasattr(model, "embedding_manager"):
load_embeddings(fp)
os.makedirs(outpath, exist_ok=True)
sample_path = os.path.join(outpath, "samples")
@ -537,7 +543,7 @@ Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_0
return output_images, seed, info, stats
def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, skip_grid: bool, skip_save: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, normalize_prompt_weights: bool):
def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix: bool, skip_grid: bool, skip_save: bool, ddim_eta: float, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, normalize_prompt_weights: bool, fp):
outpath = opt.outdir or "outputs/txt2img-samples"
err = False
seed = seed_to_int(seed)
@ -576,6 +582,7 @@ def txt2img(prompt: str, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, p
height=height,
prompt_matrix=prompt_matrix,
use_GFPGAN=use_GFPGAN,
fp=fp,
normalize_prompt_weights=normalize_prompt_weights
)
@ -651,6 +658,7 @@ txt2img_interface = gr.Interface(
gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512),
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
gr.Checkbox(label="Normalize Prompt Weights (ensure sum of weights add up to 1.0)", value=True),
gr.File(label = "Embeddings file for textual inversion", visible=hasattr(model, "embedding_manager")),
],
outputs=[
gr.Gallery(label="Images"),
@ -665,7 +673,7 @@ txt2img_interface = gr.Interface(
)
def img2img(prompt: str, init_img, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix, loopback: bool, skip_grid: bool, skip_save: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, normalize_prompt_weights: bool):
def img2img(prompt: str, init_img, ddim_steps: int, sampler_name: str, use_GFPGAN: bool, prompt_matrix, loopback: bool, skip_grid: bool, skip_save: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, normalize_prompt_weights: bool, fp):
outpath = opt.outdir or "outputs/img2img-samples"
err = False
seed = seed_to_int(seed)
@ -740,6 +748,7 @@ def img2img(prompt: str, init_img, ddim_steps: int, sampler_name: str, use_GFPGA
height=height,
prompt_matrix=prompt_matrix,
use_GFPGAN=use_GFPGAN,
fp=fp,
do_not_save_grid=True
)
@ -779,7 +788,8 @@ def img2img(prompt: str, init_img, ddim_steps: int, sampler_name: str, use_GFPGA
height=height,
prompt_matrix=prompt_matrix,
use_GFPGAN=use_GFPGAN,
normalize_prompt_weights=normalize_prompt_weights
fp=fp,
normalize_prompt_weights=normalize_prompt_weights
)
del sampler
@ -820,6 +830,7 @@ img2img_interface = gr.Interface(
gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512),
gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize"),
gr.Checkbox(label="Normalize Prompt Weights (ensure sum of weights add up to 1.0)", value=True),
gr.File(label = "Embeddings file for textual inversion", visible=hasattr(model, "embedding_manager")),
],
outputs=[
gr.Gallery(),