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https://github.com/sd-webui/stable-diffusion-webui.git
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444 lines
27 KiB
Python
444 lines
27 KiB
Python
# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
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# Copyright 2022 sd-webui team.
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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# base webui import and utils.
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from sd_utils import *
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# streamlit imports
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from streamlit import StopException
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#from streamlit.elements import image as STImage
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import streamlit.components.v1 as components
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from streamlit.runtime.media_file_manager import media_file_manager
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from streamlit.elements.image import image_to_url
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#other imports
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import uuid
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from typing import Union
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.models.diffusion.plms import PLMSSampler
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# Temp imports
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# end of imports
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#---------------------------------------------------------------------------------------------------------------
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try:
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# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
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from transformers import logging
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logging.set_verbosity_error()
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except:
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pass
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#
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# Dev mode (server)
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# _component_func = components.declare_component(
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# "sd-gallery",
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# url="http://localhost:3001",
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# )
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# Init Vuejs component
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_component_func = components.declare_component(
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"sd-gallery", "./frontend/dists/sd-gallery/dist")
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def sdGallery(images=[], key=None):
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component_value = _component_func(images=imgsToGallery(images), key=key, default="")
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return component_value
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def imgsToGallery(images):
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urls = []
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for i in images:
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# random string for id
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random_id = str(uuid.uuid4())
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url = image_to_url(
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image=i,
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image_id= random_id,
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width=i.width,
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clamp=False,
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channels="RGB",
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output_format="PNG"
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)
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# image_io = BytesIO()
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# i.save(image_io, 'PNG')
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# width, height = i.size
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# image_id = "%s" % (str(images.index(i)))
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# (data, mimetype) = STImage._normalize_to_bytes(image_io.getvalue(), width, 'auto')
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# this_file = media_file_manager.add(data, mimetype, image_id)
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# img_str = this_file.url
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urls.append(url)
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return urls
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class plugin_info():
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plugname = "txt2img"
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description = "Text to Image"
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isTab = True
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displayPriority = 1
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#
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def txt2img(prompt: str, ddim_steps: int, sampler_name: str, realesrgan_model_name: str,
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n_iter: int, batch_size: int, cfg_scale: float, seed: Union[int, str, None],
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height: int, width: int, separate_prompts:bool = False, normalize_prompt_weights:bool = True,
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save_individual_images: bool = True, save_grid: bool = True, group_by_prompt: bool = True,
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save_as_jpg: bool = True, use_GFPGAN: bool = True, GFPGAN_model: str = 'GFPGANv1.3', use_RealESRGAN: bool = True,
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RealESRGAN_model: str = "RealESRGAN_x4plus_anime_6B", use_LDSR: bool = True,
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LDSR_model: str = "model",
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fp = None, variant_amount: float = None,
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variant_seed: int = None, ddim_eta:float = 0.0, write_info_files:bool = True):
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outpath = st.session_state['defaults'].general.outdir_txt2img or st.session_state['defaults'].general.outdir or "outputs/txt2img-samples"
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seed = seed_to_int(seed)
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if sampler_name == 'PLMS':
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sampler = PLMSSampler(server_state["model"])
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elif sampler_name == 'DDIM':
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sampler = DDIMSampler(server_state["model"])
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elif sampler_name == 'k_dpm_2_a':
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sampler = KDiffusionSampler(server_state["model"],'dpm_2_ancestral')
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elif sampler_name == 'k_dpm_2':
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sampler = KDiffusionSampler(server_state["model"],'dpm_2')
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elif sampler_name == 'k_euler_a':
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sampler = KDiffusionSampler(server_state["model"],'euler_ancestral')
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elif sampler_name == 'k_euler':
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sampler = KDiffusionSampler(server_state["model"],'euler')
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elif sampler_name == 'k_heun':
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sampler = KDiffusionSampler(server_state["model"],'heun')
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elif sampler_name == 'k_lms':
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sampler = KDiffusionSampler(server_state["model"],'lms')
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else:
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raise Exception("Unknown sampler: " + sampler_name)
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def init():
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pass
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def sample(init_data, x, conditioning, unconditional_conditioning, sampler_name):
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samples_ddim, _ = sampler.sample(S=ddim_steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=unconditional_conditioning, eta=ddim_eta, x_T=x, img_callback=generation_callback,
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log_every_t=int(st.session_state.update_preview_frequency))
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return samples_ddim
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#try:
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output_images, seed, info, stats = process_images(
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outpath=outpath,
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func_init=init,
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func_sample=sample,
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prompt=prompt,
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seed=seed,
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sampler_name=sampler_name,
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save_grid=save_grid,
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batch_size=batch_size,
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n_iter=n_iter,
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steps=ddim_steps,
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cfg_scale=cfg_scale,
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width=width,
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height=height,
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prompt_matrix=separate_prompts,
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use_GFPGAN=st.session_state["use_GFPGAN"],
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GFPGAN_model=st.session_state["GFPGAN_model"],
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use_RealESRGAN=st.session_state["use_RealESRGAN"],
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realesrgan_model_name=realesrgan_model_name,
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use_LDSR=st.session_state["use_LDSR"],
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LDSR_model_name=LDSR_model,
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ddim_eta=ddim_eta,
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normalize_prompt_weights=normalize_prompt_weights,
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save_individual_images=save_individual_images,
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sort_samples=group_by_prompt,
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write_info_files=write_info_files,
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jpg_sample=save_as_jpg,
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variant_amount=variant_amount,
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variant_seed=variant_seed,
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)
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del sampler
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return output_images, seed, info, stats
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#except RuntimeError as e:
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#err = e
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#err_msg = f'CRASHED:<br><textarea rows="5" style="color:white;background: black;width: -webkit-fill-available;font-family: monospace;font-size: small;font-weight: bold;">{str(e)}</textarea><br><br>Please wait while the program restarts.'
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#stats = err_msg
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#return [], seed, 'err', stats
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#
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def layout():
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with st.form("txt2img-inputs"):
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st.session_state["generation_mode"] = "txt2img"
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input_col1, generate_col1 = st.columns([10,1])
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with input_col1:
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#prompt = st.text_area("Input Text","")
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prompt = st.text_input("Input Text","", placeholder="A corgi wearing a top hat as an oil painting.")
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# creating the page layout using columns
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col1, col2, col3 = st.columns([1,2,1], gap="large")
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with col1:
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width = st.slider("Width:", min_value=st.session_state['defaults'].txt2img.width.min_value, max_value=st.session_state['defaults'].txt2img.width.max_value,
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value=st.session_state['defaults'].txt2img.width.value, step=st.session_state['defaults'].txt2img.width.step)
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height = st.slider("Height:", min_value=st.session_state['defaults'].txt2img.height.min_value, max_value=st.session_state['defaults'].txt2img.height.max_value,
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value=st.session_state['defaults'].txt2img.height.value, step=st.session_state['defaults'].txt2img.height.step)
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cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].txt2img.cfg_scale.min_value,
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max_value=st.session_state['defaults'].txt2img.cfg_scale.max_value,
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value=st.session_state['defaults'].txt2img.cfg_scale.value, step=st.session_state['defaults'].txt2img.cfg_scale.step,
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help="How strongly the image should follow the prompt.")
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seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
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with st.expander("Batch Options"):
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batch_count = st.slider("Batch count.", min_value=st.session_state['defaults'].txt2img.batch_count.min_value, max_value=st.session_state['defaults'].txt2img.batch_count.max_value,
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value=st.session_state['defaults'].txt2img.batch_count.value, step=st.session_state['defaults'].txt2img.batch_count.step,
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help="How many iterations or batches of images to generate in total.")
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batch_size = st.slider("Batch size", min_value=st.session_state['defaults'].txt2img.batch_size.min_value, max_value=st.session_state['defaults'].txt2img.batch_size.max_value,
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value=st.session_state.defaults.txt2img.batch_size.value, step=st.session_state.defaults.txt2img.batch_size.step,
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help="How many images are at once in a batch.\
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It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
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Default: 1")
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with st.expander("Preview Settings"):
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st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2img.update_preview,
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help="If enabled the image preview will be updated during the generation instead of at the end. \
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You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
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By default this is enabled and the frequency is set to 1 step.")
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st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].txt2img.update_preview_frequency,
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help="Frequency in steps at which the the preview image is updated. By default the frequency \
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is set to 1 step.")
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with col2:
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preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])
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with preview_tab:
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#st.write("Image")
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#Image for testing
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#image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
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#new_image = image.resize((175, 240))
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#preview_image = st.image(image)
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# create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
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st.session_state["preview_image"] = st.empty()
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st.session_state["progress_bar_text"] = st.empty()
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st.session_state["progress_bar_text"].info("Nothing but crickets here, try generating something first.")
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st.session_state["progress_bar"] = st.empty()
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message = st.empty()
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with gallery_tab:
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st.session_state["gallery"] = st.empty()
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st.session_state["gallery"].info("Nothing but crickets here, try generating something first.")
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with col3:
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# If we have custom models available on the "models/custom"
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#folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
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custom_models_available()
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if server_state["CustomModel_available"]:
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st.session_state["custom_model"] = st.selectbox("Custom Model:", server_state["custom_models"],
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index=server_state["custom_models"].index(st.session_state['defaults'].general.default_model),
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help="Select the model you want to use. This option is only available if you have custom models \
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on your 'models/custom' folder. The model name that will be shown here is the same as the name\
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the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
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will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.4")
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st.session_state.sampling_steps = st.slider("Sampling Steps", value=st.session_state.defaults.txt2img.sampling_steps.value,
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min_value=st.session_state.defaults.txt2img.sampling_steps.min_value,
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max_value=st.session_state['defaults'].txt2img.sampling_steps.max_value,
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step=st.session_state['defaults'].txt2img.sampling_steps.step)
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sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
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sampler_name = st.selectbox("Sampling method", sampler_name_list,
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index=sampler_name_list.index(st.session_state['defaults'].txt2img.default_sampler), help="Sampling method to use. Default: k_euler")
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with st.expander("Advanced"):
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separate_prompts = st.checkbox("Create Prompt Matrix.", value=st.session_state['defaults'].txt2img.separate_prompts,
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help="Separate multiple prompts using the `|` character, and get all combinations of them.")
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normalize_prompt_weights = st.checkbox("Normalize Prompt Weights.", value=st.session_state['defaults'].txt2img.normalize_prompt_weights,
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help="Ensure the sum of all weights add up to 1.0")
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save_individual_images = st.checkbox("Save individual images.", value=st.session_state['defaults'].txt2img.save_individual_images,
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help="Save each image generated before any filter or enhancement is applied.")
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save_grid = st.checkbox("Save grid",value=st.session_state['defaults'].txt2img.save_grid, help="Save a grid with all the images generated into a single image.")
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group_by_prompt = st.checkbox("Group results by prompt", value=st.session_state['defaults'].txt2img.group_by_prompt,
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help="Saves all the images with the same prompt into the same folder. When using a prompt matrix each prompt combination will have its own folder.")
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write_info_files = st.checkbox("Write Info file", value=st.session_state['defaults'].txt2img.write_info_files,
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help="Save a file next to the image with informartion about the generation.")
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save_as_jpg = st.checkbox("Save samples as jpg", value=st.session_state['defaults'].txt2img.save_as_jpg, help="Saves the images as jpg instead of png.")
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# check if GFPGAN, RealESRGAN and LDSR are available.
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if "GFPGAN_available" not in st.session_state:
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GFPGAN_available()
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if "RealESRGAN_available" not in st.session_state:
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RealESRGAN_available()
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if "LDSR_available" not in st.session_state:
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LDSR_available()
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if st.session_state["GFPGAN_available"] or st.session_state["RealESRGAN_available"] or st.session_state["LDSR_available"]:
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with st.expander("Post-Processing"):
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face_restoration_tab, upscaling_tab = st.tabs(["Face Restoration", "Upscaling"])
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with face_restoration_tab:
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# GFPGAN used for face restoration
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if st.session_state["GFPGAN_available"]:
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#with st.expander("Face Restoration"):
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#if st.session_state["GFPGAN_available"]:
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#with st.expander("GFPGAN"):
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st.session_state["use_GFPGAN"] = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].txt2img.use_GFPGAN,
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help="Uses the GFPGAN model to improve faces after the generation.\
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This greatly improve the quality and consistency of faces but uses\
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extra VRAM. Disable if you need the extra VRAM.")
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st.session_state["GFPGAN_model"] = st.selectbox("GFPGAN model", st.session_state["GFPGAN_models"],
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index=st.session_state["GFPGAN_models"].index(st.session_state['defaults'].general.GFPGAN_model))
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#st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')
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else:
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st.session_state["use_GFPGAN"] = False
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with upscaling_tab:
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#with st.expander("Upscaling"):
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# RealESRGAN and LDSR used for upscaling.
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if st.session_state["RealESRGAN_available"] or st.session_state["LDSR_available"]:
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upscaling_method_list = []
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if st.session_state["RealESRGAN_available"]:
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upscaling_method_list.append("RealESRGAN")
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if st.session_state["LDSR_available"]:
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upscaling_method_list.append("LDSR")
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st.session_state["upscaling_method"] = st.selectbox("Upscaling Method", upscaling_method_list,
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index=upscaling_method_list.index(st.session_state['defaults'].general.upscaling_method))
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if st.session_state["RealESRGAN_available"]:
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# with st.expander("RealESRGAN"):
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st.session_state["use_RealESRGAN"] = st.checkbox("Use RealESRGAN", value=st.session_state['defaults'].txt2img.use_RealESRGAN,
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help="Uses the RealESRGAN model to upscale the images after the generation.\
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This greatly improve the quality and lets you have high resolution images but \
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uses extra VRAM. Disable if you need the extra VRAM.")
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st.session_state["RealESRGAN_model"] = st.selectbox("RealESRGAN model", st.session_state["RealESRGAN_models"],
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index=st.session_state["RealESRGAN_models"].index(st.session_state['defaults'].general.RealESRGAN_model))
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else:
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st.session_state["use_RealESRGAN"] = False
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st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"
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#
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if st.session_state["LDSR_available"]:
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#with st.expander("LDSR"):
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st.session_state["use_LDSR"] = st.checkbox("Use LDSR", value=st.session_state['defaults'].txt2img.use_LDSR,
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help="Uses the LDSR model to upscale the images after the generation.\
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This greatly improve the quality and lets you have high resolution images but \
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uses extra VRAM. Disable if you need the extra VRAM.")
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st.session_state["LDSR_model"] = st.selectbox("LDSR model", st.session_state["LDSR_models"],
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index=st.session_state["LDSR_models"].index(st.session_state['defaults'].general.LDSR_model))
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else:
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st.session_state["use_LDSR"] = False
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st.session_state["LDSR_model"] = "model"
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with st.expander("Variant"):
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variant_amount = st.slider("Variant Amount:", value=st.session_state['defaults'].txt2img.variant_amount.value,
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min_value=st.session_state['defaults'].txt2img.variant_amount.min_value, max_value=st.session_state['defaults'].txt2img.variant_amount.max_value,
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step=st.session_state['defaults'].txt2img.variant_amount.step)
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variant_seed = st.text_input("Variant Seed:", value=st.session_state['defaults'].txt2img.seed,
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help="The seed to use when generating a variant, if left blank a random seed will be generated.")
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#galleryCont = st.empty()
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# 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.
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generate_col1.write("")
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generate_col1.write("")
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generate_button = generate_col1.form_submit_button("Generate")
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#
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if generate_button:
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with col2:
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with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]):
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load_models(st.session_state["use_LDSR"], st.session_state["LDSR_model"], st.session_state["use_GFPGAN"], st.session_state["GFPGAN_model"] , st.session_state["use_RealESRGAN"],
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st.session_state["RealESRGAN_model"], server_state["CustomModel_available"], st.session_state["custom_model"])
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#try:
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#
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output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, st.session_state["RealESRGAN_model"], batch_count, batch_size,
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cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
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save_grid, group_by_prompt, save_as_jpg, st.session_state["use_GFPGAN"], st.session_state['GFPGAN_model'],
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use_LDSR=st.session_state["use_LDSR"], LDSR_model=st.session_state["LDSR_model"],
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variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
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message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
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#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
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#if 'latestImages' in st.session_state:
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#for i in output_images:
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##push the new image to the list of latest images and remove the oldest one
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##remove the last index from the list\
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#st.session_state['latestImages'].pop()
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##add the new image to the start of the list
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#st.session_state['latestImages'].insert(0, i)
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#PlaceHolder.empty()
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#with PlaceHolder.container():
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#col1, col2, col3 = st.columns(3)
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#col1_cont = st.container()
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#col2_cont = st.container()
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#col3_cont = st.container()
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#images = st.session_state['latestImages']
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#with col1_cont:
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#with col1:
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#[st.image(images[index]) for index in [0, 3, 6] if index < len(images)]
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#with col2_cont:
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#with col2:
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#[st.image(images[index]) for index in [1, 4, 7] if index < len(images)]
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#with col3_cont:
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#with col3:
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#[st.image(images[index]) for index in [2, 5, 8] if index < len(images)]
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#historyGallery = st.empty()
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## check if output_images length is the same as seeds length
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#with gallery_tab:
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#st.markdown(createHTMLGallery(output_images,seeds), unsafe_allow_html=True)
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#st.session_state['historyTab'] = [history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont]
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with gallery_tab:
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print(seeds)
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sdGallery(output_images)
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#except (StopException, KeyError):
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#print(f"Received Streamlit StopException")
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# this will render all the images at the end of the generation but its better if its moved to a second tab inside col2 and shown as a gallery.
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# use the current col2 first tab to show the preview_img and update it as its generated.
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#preview_image.image(output_images)
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