Mirror/stable-diffusion-webui
1
0
Fork 0
mirror of https://github.com/AUTOMATIC1111/stable-diffusion-webui.git synced 2024-06-07 21:20:49 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'a1111' into vae-fix-none

Browse Source
This commit is contained in:
Muhammad Rizqi Nur 2022-11-19 16:38:21 +07:00
commit 8662b5e57f
15 changed files with 66 additions and 72 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

26
modules/api/api.py
View File

@ -6,9 +6,9 @@ from threading import Lock
from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
from fastapi import APIRouter, Depends, FastAPI, HTTPException
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
@ -25,8 +25,12 @@ 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)
@ -82,14 +86,9 @@ class Api:
self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
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
}
@ -109,12 +108,6 @@ 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")
@ -123,10 +116,9 @@ 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
@ -272,7 +264,7 @@ 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 = []

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():

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,

30
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
@ -210,8 +207,7 @@ 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
@ -256,8 +252,7 @@ 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,
@ -384,7 +379,7 @@ 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 +394,7 @@ 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),
"Inpainting strength": (None if getattr(p, 'denoising_strength', None) is None else getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)),
"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,
@ -645,7 +641,7 @@ 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 +702,7 @@ 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)
@ -743,7 +739,7 @@ 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:

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
@ -222,6 +215,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
sd_vae.delete_base_vae()
sd_vae.clear_loaded_vae()
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

13
modules/sd_vae.py
View File

@ -95,7 +95,7 @@ def get_vae_from_settings(vae_file="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
@ -105,15 +105,15 @@ def resolve_vae(checkpoint_file=None, vae_file="auto"):
# 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"
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")
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)
@ -121,20 +121,20 @@ def resolve_vae(checkpoint_file=None, vae_file="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
@ -150,6 +150,7 @@ 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}")
store_base_vae(model)
vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)

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

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,

2
modules/ui.py
View File

@ -142,7 +142,7 @@ 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:

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

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}")