openvinotoolkit/stable-diffusion-webui
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Merge branch 'master' into api-authorization

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Maiko Tan 2022-11-19 20:13:07 +08:00
commit 336c341a7c
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28 changed files with 193 additions and 150 deletions
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42
modules/api/api.py
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@ -9,9 +9,9 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
from secrets import compare_digest
import modules.shared as shared
from modules import sd_samplers
from modules.api.models import *
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.sd_samplers import all_samplers
from modules.extras import run_extras, run_pnginfo
from PIL import PngImagePlugin
from modules.sd_models import checkpoints_list
@ -28,8 +28,12 @@ def upscaler_to_index(name: str):
raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
def validate_sampler_name(name):
config = sd_samplers.all_samplers_map.get(name, None)
if config is None:
raise HTTPException(status_code=404, detail="Sampler not found")
return name
def setUpscalers(req: dict):
reqDict = vars(req)
@ -77,6 +81,7 @@ class Api:
self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
@ -103,14 +108,9 @@ class Api:
raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
sampler_index = sampler_to_index(txt2imgreq.sampler_index)
if sampler_index is None:
raise HTTPException(status_code=404, detail="Sampler not found")
populate = txt2imgreq.copy(update={ # Override __init__ params
"sd_model": shared.sd_model,
"sampler_index": sampler_index[0],
"sampler_name": validate_sampler_name(txt2imgreq.sampler_index),
"do_not_save_samples": True,
"do_not_save_grid": True
}
@ -130,12 +130,6 @@ class Api:
return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
sampler_index = sampler_to_index(img2imgreq.sampler_index)
if sampler_index is None:
raise HTTPException(status_code=404, detail="Sampler not found")
init_images = img2imgreq.init_images
if init_images is None:
raise HTTPException(status_code=404, detail="Init image not found")
@ -144,10 +138,9 @@ class Api:
if mask:
mask = decode_base64_to_image(mask)
populate = img2imgreq.copy(update={ # Override __init__ params
"sd_model": shared.sd_model,
"sampler_index": sampler_index[0],
"sampler_name": validate_sampler_name(img2imgreq.sampler_index),
"do_not_save_samples": True,
"do_not_save_grid": True,
"mask": mask
@ -266,6 +259,9 @@ class Api:
return {}
def skip(self):
shared.state.skip()
def get_config(self):
options = {}
for key in shared.opts.data.keys():
@ -277,14 +273,10 @@ class Api:
return options
def set_config(self, req: OptionsModel):
# currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will
# overwrite all options with default values.
raise RuntimeError('Setting options via API is not supported')
reqDict = vars(req)
for o in reqDict:
setattr(shared.opts, o, reqDict[o])
def set_config(self, req: Dict[str, Any]):
for o in req:
setattr(shared.opts, o, req[o])
shared.opts.save(shared.config_filename)
return
@ -293,7 +285,7 @@ class Api:
return vars(shared.cmd_opts)
def get_samplers(self):
return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
def get_upscalers(self):
upscalers = []

6
modules/api/models.py
View File

@ -176,9 +176,9 @@ class InterrogateResponse(BaseModel):
caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
fields = {}
for key, value in opts.data.items():
metadata = opts.data_labels.get(key)
optType = opts.typemap.get(type(value), type(value))
for key, metadata in opts.data_labels.items():
value = opts.data.get(key)
optType = opts.typemap.get(type(metadata.default), type(value))
if (metadata is not None):
fields.update({key: (Optional[optType], Field(

7
modules/extensions.py
View File

@ -65,9 +65,12 @@ class Extension:
self.can_update = False
self.status = "latest"
def pull(self):
def fetch_and_reset_hard(self):
repo = git.Repo(self.path)
repo.remotes.origin.pull()
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
repo.git.fetch('--all')
repo.git.reset('--hard', 'origin')
def list_extensions():

1
modules/generation_parameters_copypaste.py
View File

@ -73,6 +73,7 @@ def integrate_settings_paste_fields(component_dict):
'sd_hypernetwork': 'Hypernet',
'sd_hypernetwork_strength': 'Hypernet strength',
'CLIP_stop_at_last_layers': 'Clip skip',
'inpainting_mask_weight': 'Conditional mask weight',
'sd_model_checkpoint': 'Model hash',
}
settings_paste_fields = [

4
modules/hypernetworks/hypernetwork.py
View File

@ -12,7 +12,7 @@ import torch
import tqdm
from einops import rearrange, repeat
from ldm.util import default
from modules import devices, processing, sd_models, shared
from modules import devices, processing, sd_models, shared, sd_samplers
from modules.textual_inversion import textual_inversion
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from torch import einsum
@ -535,7 +535,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
p.prompt = preview_prompt
p.negative_prompt = preview_negative_prompt
p.steps = preview_steps
p.sampler_index = preview_sampler_index
p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
p.cfg_scale = preview_cfg_scale
p.seed = preview_seed
p.width = preview_width

2
modules/images.py
View File

@ -303,7 +303,7 @@ class FilenameGenerator:
'width': lambda self: self.image.width,
'height': lambda self: self.image.height,
'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]

4
modules/img2img.py
View File

@ -6,7 +6,7 @@ import traceback
import numpy as np
from PIL import Image, ImageOps, ImageChops
from modules import devices
from modules import devices, sd_samplers
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
import modules.shared as shared
@ -99,7 +99,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
seed_resize_from_h=seed_resize_from_h,
seed_resize_from_w=seed_resize_from_w,
seed_enable_extras=seed_enable_extras,
sampler_index=sampler_index,
sampler_index=sd_samplers.samplers_for_img2img[sampler_index].name,
batch_size=batch_size,
n_iter=n_iter,
steps=steps,

111
modules/processing.py
View File

@ -2,6 +2,7 @@ import json
import math
import os
import sys
import warnings
import torch
import numpy as np
@ -66,19 +67,15 @@ def apply_overlay(image, paste_loc, index, overlays):
return image
def get_correct_sampler(p):
if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
return sd_samplers.samplers
elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
return sd_samplers.samplers_for_img2img
elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
return sd_samplers.samplers
class StableDiffusionProcessing():
"""
The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
"""
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_index: int = 0, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None):
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, sampler_index: int = None):
if sampler_index is not None:
warnings.warn("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name")
self.sd_model = sd_model
self.outpath_samples: str = outpath_samples
self.outpath_grids: str = outpath_grids
@ -91,7 +88,7 @@ class StableDiffusionProcessing():
self.subseed_strength: float = subseed_strength
self.seed_resize_from_h: int = seed_resize_from_h
self.seed_resize_from_w: int = seed_resize_from_w
self.sampler_index: int = sampler_index
self.sampler_name: str = sampler_name
self.batch_size: int = batch_size
self.n_iter: int = n_iter
self.steps: int = steps
@ -116,6 +113,7 @@ class StableDiffusionProcessing():
self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option
self.s_noise = s_noise or opts.s_noise
self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
self.is_using_inpainting_conditioning = False
if not seed_enable_extras:
self.subseed = -1
@ -126,6 +124,7 @@ class StableDiffusionProcessing():
self.scripts = None
self.script_args = None
self.all_prompts = None
self.all_negative_prompts = None
self.all_seeds = None
self.all_subseeds = None
@ -136,6 +135,8 @@ class StableDiffusionProcessing():
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
return x.new_zeros(x.shape[0], 5, 1, 1)
self.is_using_inpainting_conditioning = True
height = height or self.height
width = width or self.width
@ -154,6 +155,8 @@ class StableDiffusionProcessing():
# Dummy zero conditioning if we're not using inpainting model.
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
self.is_using_inpainting_conditioning = True
# Handle the different mask inputs
if image_mask is not None:
if torch.is_tensor(image_mask):
@ -200,7 +203,7 @@ class StableDiffusionProcessing():
class Processed:
def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
self.images = images_list
self.prompt = p.prompt
self.negative_prompt = p.negative_prompt
@ -210,8 +213,7 @@ class Processed:
self.info = info
self.width = p.width
self.height = p.height
self.sampler_index = p.sampler_index
self.sampler = sd_samplers.samplers[p.sampler_index].name
self.sampler_name = p.sampler_name
self.cfg_scale = p.cfg_scale
self.steps = p.steps
self.batch_size = p.batch_size
@ -238,17 +240,20 @@ class Processed:
self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
self.all_prompts = all_prompts or [self.prompt]
self.all_seeds = all_seeds or [self.seed]
self.all_subseeds = all_subseeds or [self.subseed]
self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
self.all_seeds = all_seeds or p.all_seeds or [self.seed]
self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
self.infotexts = infotexts or [info]
def js(self):
obj = {
"prompt": self.prompt,
"prompt": self.all_prompts[0],
"all_prompts": self.all_prompts,
"negative_prompt": self.negative_prompt,
"negative_prompt": self.all_negative_prompts[0],
"all_negative_prompts": self.all_negative_prompts,
"seed": self.seed,
"all_seeds": self.all_seeds,
"subseed": self.subseed,
@ -256,8 +261,7 @@ class Processed:
"subseed_strength": self.subseed_strength,
"width": self.width,
"height": self.height,
"sampler_index": self.sampler_index,
"sampler": self.sampler,
"sampler_name": self.sampler_name,
"cfg_scale": self.cfg_scale,
"steps": self.steps,
"batch_size": self.batch_size,
@ -273,6 +277,7 @@ class Processed:
"styles": self.styles,
"job_timestamp": self.job_timestamp,
"clip_skip": self.clip_skip,
"is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
}
return json.dumps(obj)
@ -384,7 +389,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
generation_params = {
"Steps": p.steps,
"Sampler": get_correct_sampler(p)[p.sampler_index].name,
"Sampler": p.sampler_name,
"CFG scale": p.cfg_scale,
"Seed": all_seeds[index],
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
@ -399,6 +404,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"Denoising strength": getattr(p, 'denoising_strength', None),
"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
"Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
"Clip skip": None if clip_skip <= 1 else clip_skip,
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
@ -408,7 +414,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[0] if p.all_negative_prompts[0] else ""
return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
@ -437,10 +443,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else:
assert p.prompt is not None
with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
processed = Processed(p, [], p.seed, "")
file.write(processed.infotext(p, 0))
devices.torch_gc()
seed = get_fixed_seed(p.seed)
@ -451,12 +453,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
comments = {}
shared.prompt_styles.apply_styles(p)
if type(p.prompt) == list:
p.all_prompts = p.prompt
p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
else:
p.all_prompts = p.batch_size * p.n_iter * [p.prompt]
p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
if type(p.negative_prompt) == list:
p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
else:
p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
if type(seed) == list:
p.all_seeds = seed
@ -471,6 +476,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
def infotext(iteration=0, position_in_batch=0):
return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
processed = Processed(p, [], p.seed, "")
file.write(processed.infotext(p, 0))
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
@ -495,6 +504,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
break
prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
@ -505,7 +515,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
with devices.autocast():
uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
uc = prompt_parser.get_learned_conditioning(shared.sd_model, negative_prompts, p.steps)
c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
if len(model_hijack.comments) > 0:
@ -591,7 +601,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
devices.torch_gc()
res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
if p.scripts is not None:
p.scripts.postprocess(p, res)
@ -645,7 +655,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
if not self.enable_hr:
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@ -706,7 +716,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
shared.state.nextjob()
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
@ -730,7 +740,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.denoising_strength: float = denoising_strength
self.init_latent = None
self.image_mask = mask
#self.image_unblurred_mask = None
self.latent_mask = None
self.mask_for_overlay = None
self.mask_blur = mask_blur
@ -743,39 +752,39 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.image_conditioning = None
def init(self, all_prompts, all_seeds, all_subseeds):
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
crop_region = None
if self.image_mask is not None:
self.image_mask = self.image_mask.convert('L')
image_mask = self.image_mask
if image_mask is not None:
image_mask = image_mask.convert('L')
if self.inpainting_mask_invert:
self.image_mask = ImageOps.invert(self.image_mask)
#self.image_unblurred_mask = self.image_mask
image_mask = ImageOps.invert(image_mask)
if self.mask_blur > 0:
self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
if self.inpaint_full_res:
self.mask_for_overlay = self.image_mask
mask = self.image_mask.convert('L')
self.mask_for_overlay = image_mask
mask = image_mask.convert('L')
crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
x1, y1, x2, y2 = crop_region
mask = mask.crop(crop_region)
self.image_mask = images.resize_image(2, mask, self.width, self.height)
image_mask = images.resize_image(2, mask, self.width, self.height)
self.paste_to = (x1, y1, x2-x1, y2-y1)
else:
self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
np_mask = np.array(self.image_mask)
image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
np_mask = np.array(image_mask)
np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
self.mask_for_overlay = Image.fromarray(np_mask)
self.overlay_images = []
latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
latent_mask = self.latent_mask if self.latent_mask is not None else image_mask
add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
if add_color_corrections:
@ -787,7 +796,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
if crop_region is None:
image = images.resize_image(self.resize_mode, image, self.width, self.height)
if self.image_mask is not None:
if image_mask is not None:
image_masked = Image.new('RGBa', (image.width, image.height))
image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
@ -797,7 +806,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
image = image.crop(crop_region)
image = images.resize_image(2, image, self.width, self.height)
if self.image_mask is not None:
if image_mask is not None:
if self.inpainting_fill != 1:
image = masking.fill(image, latent_mask)
@ -829,7 +838,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
if self.image_mask is not None:
if image_mask is not None:
init_mask = latent_mask
latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
@ -846,7 +855,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
elif self.inpainting_fill == 3:
self.init_latent = self.init_latent * self.mask
self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

2
modules/sd_hijack.py
View File

@ -96,8 +96,8 @@ class StableDiffusionModelHijack:
if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
self.apply_circular(False)
self.layers = None
self.circular_enabled = False
self.clip = None
def apply_circular(self, enable):

10
modules/sd_models.py
View File

@ -165,16 +165,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
cache_enabled = shared.opts.sd_checkpoint_cache > 0
if cache_enabled:
sd_vae.restore_base_vae(model)
vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
if cache_enabled and checkpoint_info in checkpoints_loaded:
# use checkpoint cache
vae_name = sd_vae.get_filename(vae_file) if vae_file else None
vae_message = f" with {vae_name} VAE" if vae_name else ""
print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
print(f"Loading weights [{sd_model_hash}] from cache")
model.load_state_dict(checkpoints_loaded[checkpoint_info])
else:
# load from file
@ -220,6 +213,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
model.sd_model_checkpoint = checkpoint_file
model.sd_checkpoint_info = checkpoint_info
vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
sd_vae.load_vae(model, vae_file)

13
modules/sd_samplers.py
View File

@ -46,16 +46,23 @@ all_samplers = [
SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
]
all_samplers_map = {x.name: x for x in all_samplers}
samplers = []
samplers_for_img2img = []
def create_sampler_with_index(list_of_configs, index, model):
config = list_of_configs[index]
def create_sampler(name, model):
if name is not None:
config = all_samplers_map.get(name, None)
else:
config = all_samplers[0]
assert config is not None, f'bad sampler name: {name}'
sampler = config.constructor(model)
sampler.config = config
return sampler

48
modules/sd_vae.py
View File

@ -83,47 +83,54 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
return vae_list
def resolve_vae(checkpoint_file, vae_file="auto"):
global first_load, vae_dict, vae_list
# if vae_file argument is provided, it takes priority, but not saved
if vae_file and vae_file not in default_vae_list:
if not os.path.isfile(vae_file):
vae_file = "auto"
print("VAE provided as function argument doesn't exist")
# for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
if first_load and shared.cmd_opts.vae_path is not None:
if os.path.isfile(shared.cmd_opts.vae_path):
vae_file = shared.cmd_opts.vae_path
shared.opts.data['sd_vae'] = get_filename(vae_file)
else:
print("VAE provided as command line argument doesn't exist")
# else, we load from settings
def get_vae_from_settings(vae_file="auto"):
# else, we load from settings, if not set to be default
if vae_file == "auto" and shared.opts.sd_vae is not None:
# if saved VAE settings isn't recognized, fallback to auto
vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
# if VAE selected but not found, fallback to auto
if vae_file not in default_vae_values and not os.path.isfile(vae_file):
vae_file = "auto"
print("Selected VAE doesn't exist")
print(f"Selected VAE doesn't exist: {vae_file}")
return vae_file
def resolve_vae(checkpoint_file=None, vae_file="auto"):
global first_load, vae_dict, vae_list
# if vae_file argument is provided, it takes priority, but not saved
if vae_file and vae_file not in default_vae_list:
if not os.path.isfile(vae_file):
print(f"VAE provided as function argument doesn't exist: {vae_file}")
vae_file = "auto"
# for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
if first_load and shared.cmd_opts.vae_path is not None:
if os.path.isfile(shared.cmd_opts.vae_path):
vae_file = shared.cmd_opts.vae_path
shared.opts.data['sd_vae'] = get_filename(vae_file)
else:
print(f"VAE provided as command line argument doesn't exist: {vae_file}")
# fallback to selector in settings, if vae selector not set to act as default fallback
if not shared.opts.sd_vae_as_default:
vae_file = get_vae_from_settings(vae_file)
# vae-path cmd arg takes priority for auto
if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
if os.path.isfile(shared.cmd_opts.vae_path):
vae_file = shared.cmd_opts.vae_path
print("Using VAE provided as command line argument")
print(f"Using VAE provided as command line argument: {vae_file}")
# if still not found, try look for ".vae.pt" beside model
model_path = os.path.splitext(checkpoint_file)[0]
if vae_file == "auto":
vae_file_try = model_path + ".vae.pt"
if os.path.isfile(vae_file_try):
vae_file = vae_file_try
print("Using VAE found beside selected model")
print(f"Using VAE found similar to selected model: {vae_file}")
# if still not found, try look for ".vae.ckpt" beside model
if vae_file == "auto":
vae_file_try = model_path + ".vae.ckpt"
if os.path.isfile(vae_file_try):
vae_file = vae_file_try
print("Using VAE found beside selected model")
print(f"Using VAE found similar to selected model: {vae_file}")
# No more fallbacks for auto
if vae_file == "auto":
vae_file = None
@ -139,6 +146,7 @@ def load_vae(model, vae_file=None):
# save_settings = False
if vae_file:
assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
print(f"Loading VAE weights from: {vae_file}")
vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}

3
modules/shared.py
View File

@ -335,7 +335,8 @@ options_templates.update(options_section(('training', "Training"), {
options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
"sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
"sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

11
modules/styles.py
View File

@ -65,17 +65,6 @@ class StyleDatabase:
def apply_negative_styles_to_prompt(self, prompt, styles):
return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
def apply_styles(self, p: StableDiffusionProcessing) -> None:
if isinstance(p.prompt, list):
p.prompt = [self.apply_styles_to_prompt(prompt, p.styles) for prompt in p.prompt]
else:
p.prompt = self.apply_styles_to_prompt(p.prompt, p.styles)
if isinstance(p.negative_prompt, list):
p.negative_prompt = [self.apply_negative_styles_to_prompt(prompt, p.styles) for prompt in p.negative_prompt]
else:
p.negative_prompt = self.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)
def save_styles(self, path: str) -> None:
# Write to temporary file first, so we don't nuke the file if something goes wrong
fd, temp_path = tempfile.mkstemp(".csv")

4
modules/textual_inversion/textual_inversion.py
View File

@ -10,7 +10,7 @@ import csv
from PIL import Image, PngImagePlugin
from modules import shared, devices, sd_hijack, processing, sd_models, images
from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers
import modules.textual_inversion.dataset
from modules.textual_inversion.learn_schedule import LearnRateScheduler
@ -345,7 +345,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
p.prompt = preview_prompt
p.negative_prompt = preview_negative_prompt
p.steps = preview_steps
p.sampler_index = preview_sampler_index
p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
p.cfg_scale = preview_cfg_scale
p.seed = preview_seed
p.width = preview_width

2
modules/textual_inversion/ui.py
View File

@ -18,7 +18,7 @@ def create_embedding(name, initialization_text, nvpt, overwrite_old):
def preprocess(*args):
modules.textual_inversion.preprocess.preprocess(*args)
return "Preprocessing finished.", ""
return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
def train_embedding(*args):

3
modules/txt2img.py
View File

@ -1,4 +1,5 @@
import modules.scripts
from modules import sd_samplers
from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, cmd_opts
@ -21,7 +22,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
seed_resize_from_h=seed_resize_from_h,
seed_resize_from_w=seed_resize_from_w,
seed_enable_extras=seed_enable_extras,
sampler_index=sampler_index,
sampler_name=sd_samplers.samplers[sampler_index].name,
batch_size=batch_size,
n_iter=n_iter,
steps=steps,

15
modules/ui.py
View File

@ -69,8 +69,11 @@ sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
css_hide_progressbar = """
.wrap .m-12 svg { display:none!important; }
.wrap .m-12::before { content:"Loading..." }
.wrap .z-20 svg { display:none!important; }
.wrap .z-20::before { content:"Loading..." }
.progress-bar { display:none!important; }
.meta-text { display:none!important; }
.meta-text-center { display:none!important; }
"""
# Using constants for these since the variation selector isn't visible.
@ -142,7 +145,7 @@ def save_files(js_data, images, do_make_zip, index):
filenames.append(os.path.basename(txt_fullfn))
fullfns.append(txt_fullfn)
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
# Make Zip
if do_make_zip:
@ -1249,7 +1252,9 @@ def create_ui(wrap_gradio_gpu_call):
gr.HTML(value="")
with gr.Column():
run_preprocess = gr.Button(value="Preprocess", variant='primary')
with gr.Row():
interrupt_preprocessing = gr.Button("Interrupt")
run_preprocess = gr.Button(value="Preprocess", variant='primary')
process_split.change(
fn=lambda show: gr_show(show),
@ -1422,6 +1427,12 @@ def create_ui(wrap_gradio_gpu_call):
outputs=[],
)
interrupt_preprocessing.click(
fn=lambda: shared.state.interrupt(),
inputs=[],
outputs=[],
)
def create_setting_component(key, is_quicksettings=False):
def fun():
return opts.data[key] if key in opts.data else opts.data_labels[key].default

4
modules/ui_extensions.py
View File

@ -36,9 +36,9 @@ def apply_and_restart(disable_list, update_list):
continue
try:
ext.pull()
ext.fetch_and_reset_hard()
except Exception:
print(f"Error pulling updates for {ext.name}:", file=sys.stderr)
print(f"Error getting updates for {ext.name}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
shared.opts.disabled_extensions = disabled

1
requirements.txt
View File

@ -1,3 +1,4 @@
accelerate
basicsr
diffusers
fairscale==0.4.4

1
requirements_versions.txt
View File

@ -1,5 +1,6 @@
transformers==4.19.2
diffusers==0.3.0
accelerate==0.12.0
basicsr==1.4.2
gfpgan==1.3.8
gradio==3.9

4
scripts/img2imgalt.py
View File

@ -157,7 +157,7 @@ class Script(scripts.Script):
def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
# Override
if override_sampler:
p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
p.sampler_name = "Euler"
if override_prompt:
p.prompt = original_prompt
p.negative_prompt = original_negative_prompt
@ -191,7 +191,7 @@ class Script(scripts.Script):
combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
sigmas = sampler.model_wrap.get_sigmas(p.steps)

12
scripts/xy_grid.py
View File

@ -10,9 +10,9 @@ import numpy as np
import modules.scripts as scripts
import gradio as gr
from modules import images
from modules import images, sd_samplers
from modules.hypernetworks import hypernetwork
from modules.processing import process_images, Processed, get_correct_sampler, StableDiffusionProcessingTxt2Img
from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.sd_samplers
@ -60,9 +60,9 @@ def apply_order(p, x, xs):
p.prompt = prompt_tmp + p.prompt
def build_samplers_dict(p):
def build_samplers_dict():
samplers_dict = {}
for i, sampler in enumerate(get_correct_sampler(p)):
for i, sampler in enumerate(sd_samplers.all_samplers):
samplers_dict[sampler.name.lower()] = i
for alias in sampler.aliases:
samplers_dict[alias.lower()] = i
@ -70,7 +70,7 @@ def build_samplers_dict(p):
def apply_sampler(p, x, xs):
sampler_index = build_samplers_dict(p).get(x.lower(), None)
sampler_index = build_samplers_dict().get(x.lower(), None)
if sampler_index is None:
raise RuntimeError(f"Unknown sampler: {x}")
@ -78,7 +78,7 @@ def apply_sampler(p, x, xs):
def confirm_samplers(p, xs):
samplers_dict = build_samplers_dict(p)
samplers_dict = build_samplers_dict()
for x in xs:
if x.lower() not in samplers_dict.keys():
raise RuntimeError(f"Unknown sampler: {x}")

1
webui-user.bat
View File

@ -4,5 +4,6 @@ set PYTHON=
set GIT=
set VENV_DIR=
set COMMANDLINE_ARGS=
set ACCELERATE=
call webui.bat

3
webui-user.sh
View File

@ -40,4 +40,7 @@ export COMMANDLINE_ARGS=""
#export CODEFORMER_COMMIT_HASH=""
#export BLIP_COMMIT_HASH=""
# Uncomment to enable accelerated launch
#export ACCELERATE="True"
###########################################

12
webui.bat
View File

@ -28,15 +28,27 @@ goto :show_stdout_stderr
:activate_venv
set PYTHON="%~dp0%VENV_DIR%\Scripts\Python.exe"
echo venv %PYTHON%
if [%ACCELERATE%] == ["True"] goto :accelerate
goto :launch
:skip_venv
:accelerate
echo "Checking for accelerate"
set ACCELERATE="%~dp0%VENV_DIR%\Scripts\accelerate.exe"
if EXIST %ACCELERATE% goto :accelerate_launch
:launch
%PYTHON% launch.py %*
pause
exit /b
:accelerate_launch
echo "Accelerating"
%ACCELERATE% launch --num_cpu_threads_per_process=6 launch.py
pause
exit /b
:show_stdout_stderr
echo.

1
webui.py
View File

@ -82,6 +82,7 @@ def initialize():
modules.sd_models.load_model()
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)

16
webui.sh
View File

@ -134,7 +134,15 @@ else
exit 1
fi
printf "\n%s\n" "${delimiter}"
printf "Launching launch.py..."
printf "\n%s\n" "${delimiter}"
"${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]
then
printf "\n%s\n" "${delimiter}"
printf "Accelerating launch.py..."
printf "\n%s\n" "${delimiter}"
accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@"
else
printf "\n%s\n" "${delimiter}"
printf "Launching launch.py..."
printf "\n%s\n" "${delimiter}"
"${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
fi