diff options
Diffstat (limited to 'modules')
30 files changed, 1079 insertions, 228 deletions
diff --git a/modules/api/api.py b/modules/api/api.py index 536e3f16..688469ad 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -1,14 +1,19 @@ +import base64 +import io import time import uvicorn +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, HTTPException +from fastapi import APIRouter, Depends, FastAPI, HTTPException import modules.shared as shared -from modules import devices 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 +from modules.realesrgan_model import get_realesrgan_models +from typing import List def upscaler_to_index(name: str): try: @@ -29,8 +34,26 @@ def setUpscalers(req: dict): return reqDict +def encode_pil_to_base64(image): + with io.BytesIO() as output_bytes: + + # Copy any text-only metadata + use_metadata = False + metadata = PngImagePlugin.PngInfo() + for key, value in image.info.items(): + if isinstance(key, str) and isinstance(value, str): + metadata.add_text(key, value) + use_metadata = True + + image.save( + output_bytes, "PNG", pnginfo=(metadata if use_metadata else None) + ) + bytes_data = output_bytes.getvalue() + return base64.b64encode(bytes_data) + + class Api: - def __init__(self, app, queue_lock): + def __init__(self, app: FastAPI, queue_lock: Lock): self.router = APIRouter() self.app = app self.queue_lock = queue_lock @@ -41,6 +64,19 @@ class Api: self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse) self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse) self.app.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"]) + self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"]) + self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel) + self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"]) + self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel) + self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem]) + self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem]) + self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem]) + self.app.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem]) + self.app.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem]) + self.app.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem]) + self.app.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem]) + self.app.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str]) + 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) @@ -171,6 +207,8 @@ class Api: progress = min(progress, 1) + shared.state.set_current_image() + current_image = None if shared.state.current_image and not req.skip_current_image: current_image = encode_pil_to_base64(shared.state.current_image) @@ -190,6 +228,75 @@ class Api: return InterrogateResponse(caption=processed) + def interruptapi(self): + shared.state.interrupt() + + return {} + + def get_config(self): + options = {} + for key in shared.opts.data.keys(): + metadata = shared.opts.data_labels.get(key) + if(metadata is not None): + options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)}) + else: + options.update({key: shared.opts.data.get(key, None)}) + + 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]) + + shared.opts.save(shared.config_filename) + return + + def get_cmd_flags(self): + return vars(shared.cmd_opts) + + def get_samplers(self): + return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers] + + def get_upscalers(self): + upscalers = [] + + for upscaler in shared.sd_upscalers: + u = upscaler.scaler + upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url}) + + return upscalers + + def get_sd_models(self): + return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": x.config} for x in checkpoints_list.values()] + + def get_hypernetworks(self): + return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks] + + def get_face_restorers(self): + return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers] + + def get_realesrgan_models(self): + return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)] + + def get_promp_styles(self): + styleList = [] + for k in shared.prompt_styles.styles: + style = shared.prompt_styles.styles[k] + styleList.append({"name":style[0], "prompt": style[1], "negative_prompr": style[2]}) + + return styleList + + def get_artists_categories(self): + return shared.artist_db.cats + + def get_artists(self): + return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists] + def launch(self, server_name, port): self.app.include_router(self.router) uvicorn.run(self.app, host=server_name, port=port) diff --git a/modules/api/models.py b/modules/api/models.py index 82ab29b8..34dbfa16 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -1,11 +1,11 @@ import inspect -from click import prompt from pydantic import BaseModel, Field, create_model from typing import Any, Optional from typing_extensions import Literal from inflection import underscore from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img -from modules.shared import sd_upscalers +from modules.shared import sd_upscalers, opts, parser +from typing import Dict, List API_NOT_ALLOWED = [ "self", @@ -110,12 +110,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( ).generate_model() class TextToImageResponse(BaseModel): - images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") parameters: dict info: str class ImageToImageResponse(BaseModel): - images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") parameters: dict info: str @@ -132,6 +132,7 @@ class ExtrasBaseRequest(BaseModel): upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.") + upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?") class ExtraBaseResponse(BaseModel): html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.") @@ -147,10 +148,10 @@ class FileData(BaseModel): name: str = Field(title="File name") class ExtrasBatchImagesRequest(ExtrasBaseRequest): - imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings") + imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings") class ExtrasBatchImagesResponse(ExtraBaseResponse): - images: list[str] = Field(title="Images", description="The generated images in base64 format.") + images: List[str] = Field(title="Images", description="The generated images in base64 format.") class PNGInfoRequest(BaseModel): image: str = Field(title="Image", description="The base64 encoded PNG image") @@ -172,3 +173,69 @@ class InterrogateRequest(BaseModel): 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)) + + if (metadata is not None): + fields.update({key: (Optional[optType], Field( + default=metadata.default ,description=metadata.label))}) + else: + fields.update({key: (Optional[optType], Field())}) + +OptionsModel = create_model("Options", **fields) + +flags = {} +_options = vars(parser)['_option_string_actions'] +for key in _options: + if(_options[key].dest != 'help'): + flag = _options[key] + _type = str + if _options[key].default is not None: _type = type(_options[key].default) + flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))}) + +FlagsModel = create_model("Flags", **flags) + +class SamplerItem(BaseModel): + name: str = Field(title="Name") + aliases: List[str] = Field(title="Aliases") + options: Dict[str, str] = Field(title="Options") + +class UpscalerItem(BaseModel): + name: str = Field(title="Name") + model_name: Optional[str] = Field(title="Model Name") + model_path: Optional[str] = Field(title="Path") + model_url: Optional[str] = Field(title="URL") + +class SDModelItem(BaseModel): + title: str = Field(title="Title") + model_name: str = Field(title="Model Name") + hash: str = Field(title="Hash") + filename: str = Field(title="Filename") + config: str = Field(title="Config file") + +class HypernetworkItem(BaseModel): + name: str = Field(title="Name") + path: Optional[str] = Field(title="Path") + +class FaceRestorerItem(BaseModel): + name: str = Field(title="Name") + cmd_dir: Optional[str] = Field(title="Path") + +class RealesrganItem(BaseModel): + name: str = Field(title="Name") + path: Optional[str] = Field(title="Path") + scale: Optional[int] = Field(title="Scale") + +class PromptStyleItem(BaseModel): + name: str = Field(title="Name") + prompt: Optional[str] = Field(title="Prompt") + negative_prompt: Optional[str] = Field(title="Negative Prompt") + +class ArtistItem(BaseModel): + name: str = Field(title="Name") + score: float = Field(title="Score") + category: str = Field(title="Category") + diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index a13cf6ac..c61669b4 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -50,6 +50,7 @@ def mod2normal(state_dict): def resrgan2normal(state_dict, nb=23):
# this code is copied from https://github.com/victorca25/iNNfer
if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
+ re8x = 0
crt_net = {}
items = []
for k, v in state_dict.items():
@@ -75,10 +76,18 @@ def resrgan2normal(state_dict, nb=23): crt_net['model.3.bias'] = state_dict['conv_up1.bias']
crt_net['model.6.weight'] = state_dict['conv_up2.weight']
crt_net['model.6.bias'] = state_dict['conv_up2.bias']
- crt_net['model.8.weight'] = state_dict['conv_hr.weight']
- crt_net['model.8.bias'] = state_dict['conv_hr.bias']
- crt_net['model.10.weight'] = state_dict['conv_last.weight']
- crt_net['model.10.bias'] = state_dict['conv_last.bias']
+
+ if 'conv_up3.weight' in state_dict:
+ # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
+ re8x = 3
+ crt_net['model.9.weight'] = state_dict['conv_up3.weight']
+ crt_net['model.9.bias'] = state_dict['conv_up3.bias']
+
+ crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
+ crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
+ crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
+ crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
+
state_dict = crt_net
return state_dict
diff --git a/modules/extensions.py b/modules/extensions.py index 8d6ae848..8e0977fd 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -34,8 +34,11 @@ class Extension: if repo is None or repo.bare:
self.remote = None
else:
- self.remote = next(repo.remote().urls, None)
- self.status = 'unknown'
+ try:
+ self.remote = next(repo.remote().urls, None)
+ self.status = 'unknown'
+ except Exception:
+ self.remote = None
def list_files(self, subdir, extension):
from modules import scripts
@@ -46,7 +49,7 @@ class Extension: res = []
for filename in sorted(os.listdir(dirpath)):
- res.append(scripts.ScriptFile(dirpath, filename, os.path.join(dirpath, filename)))
+ res.append(scripts.ScriptFile(self.path, filename, os.path.join(dirpath, filename)))
res = [x for x in res if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
diff --git a/modules/extras.py b/modules/extras.py index 4d51088b..71b93a06 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -136,10 +136,11 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
blended_result: Image.Image = None
+ image_hash: str = hash(np.array(image.getdata()).tobytes())
for upscaler in params:
upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
upscaling_resize_w, upscaling_resize_h, upscaling_crop)
- cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
+ cache_key = LruCache.Key(image_hash=image_hash,
info_hash=hash(info),
args_hash=hash(upscale_args))
cached_entry = cached_images.get(cache_key)
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index a11e01d6..7f182712 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -22,6 +22,8 @@ from collections import defaultdict, deque from statistics import stdev, mean
+optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
+
class HypernetworkModule(torch.nn.Module):
multiplier = 1.0
activation_dict = {
@@ -35,7 +37,8 @@ class HypernetworkModule(torch.nn.Module): }
activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
- def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
+ def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
+ add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=True):
super().__init__()
assert layer_structure is not None, "layer_structure must not be None"
@@ -48,8 +51,8 @@ class HypernetworkModule(torch.nn.Module): # Add a fully-connected layer
linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
- # Add an activation func
- if activation_func == "linear" or activation_func is None:
+ # Add an activation func except last layer
+ if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output):
pass
elif activation_func in self.activation_dict:
linears.append(self.activation_dict[activation_func]())
@@ -60,8 +63,8 @@ class HypernetworkModule(torch.nn.Module): if add_layer_norm:
linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
- # Add dropout expect last layer
- if use_dropout and i < len(layer_structure) - 3:
+ # Add dropout except last layer
+ if use_dropout and (i < len(layer_structure) - 3 or last_layer_dropout and i < len(layer_structure) - 2):
linears.append(torch.nn.Dropout(p=0.3))
self.linear = torch.nn.Sequential(*linears)
@@ -75,7 +78,7 @@ class HypernetworkModule(torch.nn.Module): w, b = layer.weight.data, layer.bias.data
if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:
normal_(w, mean=0.0, std=0.01)
- normal_(b, mean=0.0, std=0.005)
+ normal_(b, mean=0.0, std=0)
elif weight_init == 'XavierUniform':
xavier_uniform_(w)
zeros_(b)
@@ -127,7 +130,7 @@ class Hypernetwork: filename = None
name = None
- def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
+ def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
self.filename = None
self.name = name
self.layers = {}
@@ -139,11 +142,17 @@ class Hypernetwork: self.weight_init = weight_init
self.add_layer_norm = add_layer_norm
self.use_dropout = use_dropout
+ self.activate_output = activate_output
+ self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True
+ self.optimizer_name = None
+ self.optimizer_state_dict = None
for size in enable_sizes or []:
self.layers[size] = (
- HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
- HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+ HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+ self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
+ HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+ self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
)
def weights(self):
@@ -158,6 +167,7 @@ class Hypernetwork: def save(self, filename):
state_dict = {}
+ optimizer_saved_dict = {}
for k, v in self.layers.items():
state_dict[k] = (v[0].state_dict(), v[1].state_dict())
@@ -171,8 +181,17 @@ class Hypernetwork: state_dict['use_dropout'] = self.use_dropout
state_dict['sd_checkpoint'] = self.sd_checkpoint
state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
+ state_dict['activate_output'] = self.activate_output
+ state_dict['last_layer_dropout'] = self.last_layer_dropout
+
+ if self.optimizer_name is not None:
+ optimizer_saved_dict['optimizer_name'] = self.optimizer_name
torch.save(state_dict, filename)
+ if shared.opts.save_optimizer_state and self.optimizer_state_dict:
+ optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
+ optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
+ torch.save(optimizer_saved_dict, filename + '.optim')
def load(self, filename):
self.filename = filename
@@ -191,12 +210,29 @@ class Hypernetwork: print(f"Layer norm is set to {self.add_layer_norm}")
self.use_dropout = state_dict.get('use_dropout', False)
print(f"Dropout usage is set to {self.use_dropout}" )
+ self.activate_output = state_dict.get('activate_output', True)
+ print(f"Activate last layer is set to {self.activate_output}")
+ self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
+
+ optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
+ self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
+ print(f"Optimizer name is {self.optimizer_name}")
+ if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
+ self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+ else:
+ self.optimizer_state_dict = None
+ if self.optimizer_state_dict:
+ print("Loaded existing optimizer from checkpoint")
+ else:
+ print("No saved optimizer exists in checkpoint")
for size, sd in state_dict.items():
if type(size) == int:
self.layers[size] = (
- HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
- HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+ HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
+ self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
+ HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
+ self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
)
self.name = state_dict.get('name', self.name)
@@ -207,11 +243,11 @@ class Hypernetwork: def list_hypernetworks(path):
res = {}
- for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
+ for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
name = os.path.splitext(os.path.basename(filename))[0]
# Prevent a hypothetical "None.pt" from being listed.
if name != "None":
- res[name] = filename
+ res[name + f"({sd_models.model_hash(filename)})"] = filename
return res
@@ -346,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log shared.state.textinfo = "Initializing hypernetwork training..."
shared.state.job_count = steps
+ hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
@@ -392,8 +429,22 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log weights = hypernetwork.weights()
for weight in weights:
weight.requires_grad = True
- # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
- optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
+
+ # Here we use optimizer from saved HN, or we can specify as UI option.
+ if hypernetwork.optimizer_name in optimizer_dict:
+ optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
+ optimizer_name = hypernetwork.optimizer_name
+ else:
+ print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
+ optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
+ optimizer_name = 'AdamW'
+
+ if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer.
+ try:
+ optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
+ except RuntimeError as e:
+ print("Cannot resume from saved optimizer!")
+ print(e)
steps_without_grad = 0
@@ -455,7 +506,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log # Before saving, change name to match current checkpoint.
hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
+ hypernetwork.optimizer_name = optimizer_name
+ if shared.opts.save_optimizer_state:
+ hypernetwork.optimizer_state_dict = optimizer.state_dict()
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
+ hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
"loss": f"{previous_mean_loss:.7f}",
@@ -518,8 +573,12 @@ Last saved image: {html.escape(last_saved_image)}<br/> report_statistics(loss_dict)
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
+ hypernetwork.optimizer_name = optimizer_name
+ if shared.opts.save_optimizer_state:
+ hypernetwork.optimizer_state_dict = optimizer.state_dict()
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
-
+ del optimizer
+ hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
return hypernetwork, filename
def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index aad09ffc..c2d4b51c 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared from modules.hypernetworks import hypernetwork
not_available = ["hardswish", "multiheadattention"]
-keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
+keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
# Remove illegal characters from name.
diff --git a/modules/images.py b/modules/images.py index a0728553..ae705cbd 100644 --- a/modules/images.py +++ b/modules/images.py @@ -510,8 +510,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i if extension.lower() == '.png':
pnginfo_data = PngImagePlugin.PngInfo()
- for k, v in params.pnginfo.items():
- pnginfo_data.add_text(k, str(v))
+ if opts.enable_pnginfo:
+ for k, v in params.pnginfo.items():
+ pnginfo_data.add_text(k, str(v))
image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
diff --git a/modules/img2img.py b/modules/img2img.py index efda26e1..be9f3653 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -55,6 +55,7 @@ def process_batch(p, input_dir, output_dir, args): filename = f"{left}-{n}{right}"
if not save_normally:
+ os.makedirs(output_dir, exist_ok=True)
processed_image.save(os.path.join(output_dir, filename))
@@ -80,7 +81,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro mask = None
# Use the EXIF orientation of photos taken by smartphones.
- image = ImageOps.exif_transpose(image)
+ if image is not None:
+ image = ImageOps.exif_transpose(image)
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
@@ -136,6 +138,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro if processed is None:
processed = process_images(p)
+ p.close()
+
shared.total_tqdm.clear()
generation_info_js = processed.js()
diff --git a/modules/interrogate.py b/modules/interrogate.py index 65b05d34..9769aa34 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -56,9 +56,9 @@ class InterrogateModels: import clip
if self.running_on_cpu:
- model, preprocess = clip.load(clip_model_name, device="cpu")
+ model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.cmd_opts.clip_models_path)
else:
- model, preprocess = clip.load(clip_model_name)
+ model, preprocess = clip.load(clip_model_name, download_root=shared.cmd_opts.clip_models_path)
model.eval()
model = model.to(devices.device_interrogate)
diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py index 14db5076..90e0a2f0 100644 --- a/modules/ldsr_model_arch.py +++ b/modules/ldsr_model_arch.py @@ -101,8 +101,8 @@ class LDSR: down_sample_rate = target_scale / 4 wd = width_og * down_sample_rate hd = height_og * down_sample_rate - width_downsampled_pre = int(wd) - height_downsampled_pre = int(hd) + width_downsampled_pre = int(np.ceil(wd)) + height_downsampled_pre = int(np.ceil(hd)) if down_sample_rate != 1: print( @@ -110,7 +110,12 @@ class LDSR: im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS) else: print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)") - logs = self.run(model["model"], im_og, diffusion_steps, eta) + + # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts + pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size + im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge')) + + logs = self.run(model["model"], im_padded, diffusion_steps, eta) sample = logs["sample"] sample = sample.detach().cpu() @@ -120,6 +125,9 @@ class LDSR: sample = np.transpose(sample, (0, 2, 3, 1)) a = Image.fromarray(sample[0]) + # remove padding + a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4)) + del model gc.collect() torch.cuda.empty_cache() diff --git a/modules/localization.py b/modules/localization.py index b1810cda..f6a6f2fb 100644 --- a/modules/localization.py +++ b/modules/localization.py @@ -3,6 +3,7 @@ import os import sys
import traceback
+
localizations = {}
@@ -16,6 +17,11 @@ def list_localizations(dirname): localizations[fn] = os.path.join(dirname, file)
+ from modules import scripts
+ for file in scripts.list_scripts("localizations", ".json"):
+ fn, ext = os.path.splitext(file.filename)
+ localizations[fn] = file.path
+
def localization_js(current_localization_name):
fn = localizations.get(current_localization_name, None)
diff --git a/modules/lowvram.py b/modules/lowvram.py index f327c3df..a4652cb1 100644 --- a/modules/lowvram.py +++ b/modules/lowvram.py @@ -38,13 +38,18 @@ def setup_for_low_vram(sd_model, use_medvram): # see below for register_forward_pre_hook;
# first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
# useless here, and we just replace those methods
- def first_stage_model_encode_wrap(self, encoder, x):
- send_me_to_gpu(self, None)
- return encoder(x)
- def first_stage_model_decode_wrap(self, decoder, z):
- send_me_to_gpu(self, None)
- return decoder(z)
+ first_stage_model = sd_model.first_stage_model
+ first_stage_model_encode = sd_model.first_stage_model.encode
+ first_stage_model_decode = sd_model.first_stage_model.decode
+
+ def first_stage_model_encode_wrap(x):
+ send_me_to_gpu(first_stage_model, None)
+ return first_stage_model_encode(x)
+
+ def first_stage_model_decode_wrap(z):
+ send_me_to_gpu(first_stage_model, None)
+ return first_stage_model_decode(z)
# remove three big modules, cond, first_stage, and unet from the model and then
# send the model to GPU. Then put modules back. the modules will be in CPU.
@@ -56,8 +61,8 @@ def setup_for_low_vram(sd_model, use_medvram): # register hooks for those the first two models
sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
- sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x)
- sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z)
+ sd_model.first_stage_model.encode = first_stage_model_encode_wrap
+ sd_model.first_stage_model.decode = first_stage_model_decode_wrap
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
if use_medvram:
diff --git a/modules/masking.py b/modules/masking.py index fd8d9241..a5c4d2da 100644 --- a/modules/masking.py +++ b/modules/masking.py @@ -49,7 +49,7 @@ def expand_crop_region(crop_region, processing_width, processing_height, image_w ratio_processing = processing_width / processing_height
if ratio_crop_region > ratio_processing:
- desired_height = (x2 - x1) * ratio_processing
+ desired_height = (x2 - x1) / ratio_processing
desired_height_diff = int(desired_height - (y2-y1))
y1 -= desired_height_diff//2
y2 += desired_height_diff - desired_height_diff//2
diff --git a/modules/modelloader.py b/modules/modelloader.py index b0f2f33d..e4a6f8ac 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -85,6 +85,9 @@ def cleanup_models(): src_path = os.path.join(root_path, "ESRGAN") dest_path = os.path.join(models_path, "ESRGAN") move_files(src_path, dest_path) + src_path = os.path.join(models_path, "BSRGAN") + dest_path = os.path.join(models_path, "ESRGAN") + move_files(src_path, dest_path, ".pth") src_path = os.path.join(root_path, "gfpgan") dest_path = os.path.join(models_path, "GFPGAN") move_files(src_path, dest_path) diff --git a/modules/processing.py b/modules/processing.py index b1df4918..03c9143d 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -134,11 +134,7 @@ class StableDiffusionProcessing(): # Dummy zero conditioning if we're not using inpainting model.
# Still takes up a bit of memory, but no encoder call.
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
- return torch.zeros(
- x.shape[0], 5, 1, 1,
- dtype=x.dtype,
- device=x.device
- )
+ return x.new_zeros(x.shape[0], 5, 1, 1)
height = height or self.height
width = width or self.width
@@ -156,11 +152,7 @@ class StableDiffusionProcessing(): def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
# Dummy zero conditioning if we're not using inpainting model.
- return torch.zeros(
- latent_image.shape[0], 5, 1, 1,
- dtype=latent_image.dtype,
- device=latent_image.device
- )
+ return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
# Handle the different mask inputs
if image_mask is not None:
@@ -174,11 +166,11 @@ class StableDiffusionProcessing(): # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
conditioning_mask = torch.round(conditioning_mask)
else:
- conditioning_mask = torch.ones(1, 1, *source_image.shape[-2:])
+ conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
# Create another latent image, this time with a masked version of the original input.
# Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
- conditioning_mask = conditioning_mask.to(source_image.device)
+ conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)
conditioning_image = torch.lerp(
source_image,
source_image * (1.0 - conditioning_mask),
@@ -199,9 +191,13 @@ class StableDiffusionProcessing(): def init(self, all_prompts, all_seeds, all_subseeds):
pass
- def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+ def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
raise NotImplementedError()
+ def close(self):
+ self.sd_model = None
+ self.sampler = None
+
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):
@@ -422,13 +418,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed: try:
for k, v in p.override_settings.items():
- opts.data[k] = v # we don't call onchange for simplicity which makes changing model, hypernet impossible
+ setattr(opts, k, v) # we don't call onchange for simplicity which makes changing model, hypernet impossible
res = process_images_inner(p)
finally:
for k, v in stored_opts.items():
- opts.data[k] = v
+ setattr(opts, k, v)
return res
@@ -505,6 +501,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if len(prompts) == 0:
break
+ if p.scripts is not None:
+ 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)
c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
@@ -517,7 +516,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: shared.state.job = f"Batch {n+1} out of {p.n_iter}"
with devices.autocast():
- samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
+ samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
samples_ddim = samples_ddim.to(devices.dtype_vae)
x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
@@ -645,7 +644,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
- def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+ 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)
if not self.enable_hr:
@@ -658,9 +657,28 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
+ """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images"""
+ def save_intermediate(image, index):
+ if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
+ return
+
+ if not isinstance(image, Image.Image):
+ image = sd_samplers.sample_to_image(image, index)
+
+ images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
+
if opts.use_scale_latent_for_hires_fix:
+ for i in range(samples.shape[0]):
+ save_intermediate(samples, i)
+
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+ # Avoid making the inpainting conditioning unless necessary as
+ # this does need some extra compute to decode / encode the image again.
+ if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
+ image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
+ else:
+ image_conditioning = self.txt2img_image_conditioning(samples)
else:
decoded_samples = decode_first_stage(self.sd_model, samples)
lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
@@ -670,6 +688,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
image = Image.fromarray(x_sample)
+
+ save_intermediate(image, i)
+
image = images.resize_image(0, image, self.width, self.height)
image = np.array(image).astype(np.float32) / 255.0
image = np.moveaxis(image, 2, 0)
@@ -681,14 +702,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
+ image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
+
shared.state.nextjob()
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, 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)
- image_conditioning = self.txt2img_image_conditioning(x)
-
# GC now before running the next img2img to prevent running out of memory
x = None
devices.torch_gc()
@@ -827,8 +848,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
-
- def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
+ 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)
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
@@ -839,4 +859,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): del x
devices.torch_gc()
- return samples
\ No newline at end of file + return samples
diff --git a/modules/safe.py b/modules/safe.py index 399165a1..a9209e38 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -23,16 +23,23 @@ def encode(*args): class RestrictedUnpickler(pickle.Unpickler):
+ extra_handler = None
+
def persistent_load(self, saved_id):
assert saved_id[0] == 'storage'
return TypedStorage()
def find_class(self, module, name):
+ if self.extra_handler is not None:
+ res = self.extra_handler(module, name)
+ if res is not None:
+ return res
+
if module == 'collections' and name == 'OrderedDict':
return getattr(collections, name)
if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
return getattr(torch._utils, name)
- if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
+ if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage']:
return getattr(torch, name)
if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
return getattr(torch.nn.modules.container, name)
@@ -52,7 +59,7 @@ class RestrictedUnpickler(pickle.Unpickler): return set
# Forbid everything else.
- raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
+ raise Exception(f"global '{module}/{name}' is forbidden")
allowed_zip_names = ["archive/data.pkl", "archive/version"]
@@ -69,7 +76,7 @@ def check_zip_filenames(filename, names): raise Exception(f"bad file inside {filename}: {name}")
-def check_pt(filename):
+def check_pt(filename, extra_handler):
try:
# new pytorch format is a zip file
@@ -78,6 +85,7 @@ def check_pt(filename): with z.open('archive/data.pkl') as file:
unpickler = RestrictedUnpickler(file)
+ unpickler.extra_handler = extra_handler
unpickler.load()
except zipfile.BadZipfile:
@@ -85,16 +93,42 @@ def check_pt(filename): # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
with open(filename, "rb") as file:
unpickler = RestrictedUnpickler(file)
+ unpickler.extra_handler = extra_handler
for i in range(5):
unpickler.load()
def load(filename, *args, **kwargs):
+ return load_with_extra(filename, *args, **kwargs)
+
+
+def load_with_extra(filename, extra_handler=None, *args, **kwargs):
+ """
+ this functon is intended to be used by extensions that want to load models with
+ some extra classes in them that the usual unpickler would find suspicious.
+
+ Use the extra_handler argument to specify a function that takes module and field name as text,
+ and returns that field's value:
+
+ ```python
+ def extra(module, name):
+ if module == 'collections' and name == 'OrderedDict':
+ return collections.OrderedDict
+
+ return None
+
+ safe.load_with_extra('model.pt', extra_handler=extra)
+ ```
+
+ The alternative to this is just to use safe.unsafe_torch_load('model.pt'), which as the name implies is
+ definitely unsafe.
+ """
+
from modules import shared
try:
if not shared.cmd_opts.disable_safe_unpickle:
- check_pt(filename)
+ check_pt(filename, extra_handler)
except pickle.UnpicklingError:
print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 6ea58d61..74dfb880 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -2,7 +2,10 @@ import sys import traceback
from collections import namedtuple
import inspect
+from typing import Optional
+from fastapi import FastAPI
+from gradio import Blocks
def report_exception(c, job):
print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
@@ -24,24 +27,50 @@ class ImageSaveParams: """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
+class CFGDenoiserParams:
+ def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
+ self.x = x
+ """Latent image representation in the process of being denoised"""
+
+ self.image_cond = image_cond
+ """Conditioning image"""
+
+ self.sigma = sigma
+ """Current sigma noise step value"""
+
+ self.sampling_step = sampling_step
+ """Current Sampling step number"""
+
+ self.total_sampling_steps = total_sampling_steps
+ """Total number of sampling steps planned"""
+
+
ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-callbacks_model_loaded = []
-callbacks_ui_tabs = []
-callbacks_ui_settings = []
-callbacks_before_image_saved = []
-callbacks_image_saved = []
+callback_map = dict(
+ callbacks_app_started=[],
+ callbacks_model_loaded=[],
+ callbacks_ui_tabs=[],
+ callbacks_ui_settings=[],
+ callbacks_before_image_saved=[],
+ callbacks_image_saved=[],
+ callbacks_cfg_denoiser=[]
+)
def clear_callbacks():
- callbacks_model_loaded.clear()
- callbacks_ui_tabs.clear()
- callbacks_ui_settings.clear()
- callbacks_before_image_saved.clear()
- callbacks_image_saved.clear()
+ for callback_list in callback_map.values():
+ callback_list.clear()
+
+def app_started_callback(demo: Optional[Blocks], app: FastAPI):
+ for c in callback_map['callbacks_app_started']:
+ try:
+ c.callback(demo, app)
+ except Exception:
+ report_exception(c, 'app_started_callback')
def model_loaded_callback(sd_model):
- for c in callbacks_model_loaded:
+ for c in callback_map['callbacks_model_loaded']:
try:
c.callback(sd_model)
except Exception:
@@ -51,7 +80,7 @@ def model_loaded_callback(sd_model): def ui_tabs_callback():
res = []
- for c in callbacks_ui_tabs:
+ for c in callback_map['callbacks_ui_tabs']:
try:
res += c.callback() or []
except Exception:
@@ -61,7 +90,7 @@ def ui_tabs_callback(): def ui_settings_callback():
- for c in callbacks_ui_settings:
+ for c in callback_map['callbacks_ui_settings']:
try:
c.callback()
except Exception:
@@ -69,7 +98,7 @@ def ui_settings_callback(): def before_image_saved_callback(params: ImageSaveParams):
- for c in callbacks_image_saved:
+ for c in callback_map['callbacks_before_image_saved']:
try:
c.callback(params)
except Exception:
@@ -77,24 +106,54 @@ def before_image_saved_callback(params: ImageSaveParams): def image_saved_callback(params: ImageSaveParams):
- for c in callbacks_image_saved:
+ for c in callback_map['callbacks_image_saved']:
try:
c.callback(params)
except Exception:
report_exception(c, 'image_saved_callback')
+def cfg_denoiser_callback(params: CFGDenoiserParams):
+ for c in callback_map['callbacks_cfg_denoiser']:
+ try:
+ c.callback(params)
+ except Exception:
+ report_exception(c, 'cfg_denoiser_callback')
+
+
def add_callback(callbacks, fun):
stack = [x for x in inspect.stack() if x.filename != __file__]
filename = stack[0].filename if len(stack) > 0 else 'unknown file'
callbacks.append(ScriptCallback(filename, fun))
+
+def remove_current_script_callbacks():
+ stack = [x for x in inspect.stack() if x.filename != __file__]
+ filename = stack[0].filename if len(stack) > 0 else 'unknown file'
+ if filename == 'unknown file':
+ return
+ for callback_list in callback_map.values():
+ for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
+ callback_list.remove(callback_to_remove)
+
+
+def remove_callbacks_for_function(callback_func):
+ for callback_list in callback_map.values():
+ for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
+ callback_list.remove(callback_to_remove)
+
+
+def on_app_started(callback):
+ """register a function to be called when the webui started, the gradio `Block` component and
+ fastapi `FastAPI` object are passed as the arguments"""
+ add_callback(callback_map['callbacks_app_started'], callback)
+
def on_model_loaded(callback):
"""register a function to be called when the stable diffusion model is created; the model is
passed as an argument"""
- add_callback(callbacks_model_loaded, callback)
+ add_callback(callback_map['callbacks_model_loaded'], callback)
def on_ui_tabs(callback):
@@ -107,13 +166,13 @@ def on_ui_tabs(callback): title is tab text displayed to user in the UI
elem_id is HTML id for the tab
"""
- add_callback(callbacks_ui_tabs, callback)
+ add_callback(callback_map['callbacks_ui_tabs'], callback)
def on_ui_settings(callback):
"""register a function to be called before UI settings are populated; add your settings
by using shared.opts.add_option(shared.OptionInfo(...)) """
- add_callback(callbacks_ui_settings, callback)
+ add_callback(callback_map['callbacks_ui_settings'], callback)
def on_before_image_saved(callback):
@@ -121,7 +180,7 @@ def on_before_image_saved(callback): The callback is called with one argument:
- params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
"""
- add_callback(callbacks_before_image_saved, callback)
+ add_callback(callback_map['callbacks_before_image_saved'], callback)
def on_image_saved(callback):
@@ -129,4 +188,12 @@ def on_image_saved(callback): The callback is called with one argument:
- params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
"""
- add_callback(callbacks_image_saved, callback)
+ add_callback(callback_map['callbacks_image_saved'], callback)
+
+
+def on_cfg_denoiser(callback):
+ """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
+ The callback is called with one argument:
+ - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
+ """
+ add_callback(callback_map['callbacks_cfg_denoiser'], callback)
diff --git a/modules/scripts.py b/modules/scripts.py index 533db45c..637b2329 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -3,7 +3,6 @@ import sys import traceback
from collections import namedtuple
-import modules.ui as ui
import gradio as gr
from modules.processing import StableDiffusionProcessing
@@ -18,6 +17,9 @@ class Script: args_to = None
alwayson = False
+ """A gr.Group component that has all script's UI inside it"""
+ group = None
+
infotext_fields = None
"""if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
@@ -70,6 +72,19 @@ class Script: pass
+ def process_batch(self, p, *args, **kwargs):
+ """
+ Same as process(), but called for every batch.
+
+ **kwargs will have those items:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+ - seeds - list of seeds for current batch
+ - subseeds - list of subseeds for current batch
+ """
+
+ pass
+
def postprocess(self, p, processed, *args):
"""
This function is called after processing ends for AlwaysVisible scripts.
@@ -218,8 +233,6 @@ class ScriptRunner: for control in controls:
control.custom_script_source = os.path.basename(script.filename)
- if not script.alwayson:
- control.visible = False
if script.infotext_fields is not None:
self.infotext_fields += script.infotext_fields
@@ -229,40 +242,41 @@ class ScriptRunner: script.args_to = len(inputs)
for script in self.alwayson_scripts:
- with gr.Group():
+ with gr.Group() as group:
create_script_ui(script, inputs, inputs_alwayson)
+ script.group = group
+
dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
dropdown.save_to_config = True
inputs[0] = dropdown
for script in self.selectable_scripts:
- create_script_ui(script, inputs, inputs_alwayson)
+ with gr.Group(visible=False) as group:
+ create_script_ui(script, inputs, inputs_alwayson)
+
+ script.group = group
def select_script(script_index):
- if 0 < script_index <= len(self.selectable_scripts):
- script = self.selectable_scripts[script_index-1]
- args_from = script.args_from
- args_to = script.args_to
- else:
- args_from = 0
- args_to = 0
+ selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
- return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]
+ return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
def init_field(title):
+ """called when an initial value is set from ui-config.json to show script's UI components"""
+
if title == 'None':
return
+
script_index = self.titles.index(title)
- script = self.selectable_scripts[script_index]
- for i in range(script.args_from, script.args_to):
- inputs[i].visible = True
+ self.selectable_scripts[script_index].group.visible = True
dropdown.init_field = init_field
+
dropdown.change(
fn=select_script,
inputs=[dropdown],
- outputs=inputs
+ outputs=[script.group for script in self.selectable_scripts]
)
return inputs
@@ -294,6 +308,15 @@ class ScriptRunner: print(f"Error running process: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
+ def process_batch(self, p, **kwargs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.process_batch(p, *script_args, **kwargs)
+ except Exception:
+ print(f"Error running process_batch: {script.filename}", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+
def postprocess(self, p, processed):
for script in self.alwayson_scripts:
try:
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 0f10828e..bc49d235 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -94,6 +94,10 @@ class StableDiffusionModelHijack: if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
+ self.layers = None
+ self.circular_enabled = False
+ self.clip = None
+
def apply_circular(self, enable):
if self.circular_enabled == enable:
return
diff --git a/modules/sd_models.py b/modules/sd_models.py index f86dc3ed..34c57bfa 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -1,6 +1,7 @@ import collections
import os.path
import sys
+import gc
from collections import namedtuple
import torch
import re
@@ -8,7 +9,7 @@ from omegaconf import OmegaConf from ldm.util import instantiate_from_config
-from modules import shared, modelloader, devices, script_callbacks
+from modules import shared, modelloader, devices, script_callbacks, sd_vae
from modules.paths import models_path
from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
@@ -158,13 +159,16 @@ def get_state_dict_from_checkpoint(pl_sd): return pl_sd
-vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
-
-
-def load_model_weights(model, checkpoint_info):
+def load_model_weights(model, checkpoint_info, vae_file="auto"):
checkpoint_file = checkpoint_info.filename
sd_model_hash = checkpoint_info.hash
+ if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
+ sd_vae.restore_base_vae(model)
+ checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
+
+ vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+
if checkpoint_info not in checkpoints_loaded:
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
@@ -181,37 +185,36 @@ def load_model_weights(model, checkpoint_info): model.to(memory_format=torch.channels_last)
if not shared.cmd_opts.no_half:
+ vae = model.first_stage_model
+
+ # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
+ if shared.cmd_opts.no_half_vae:
+ model.first_stage_model = None
+
model.half()
+ model.first_stage_model = vae
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
- vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
-
- if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
- vae_file = shared.cmd_opts.vae_path
-
- if os.path.exists(vae_file):
- print(f"Loading VAE weights from: {vae_file}")
- vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
- vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
- model.first_stage_model.load_state_dict(vae_dict)
-
model.first_stage_model.to(devices.dtype_vae)
- if shared.opts.sd_checkpoint_cache > 0:
- checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
- while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
- checkpoints_loaded.popitem(last=False) # LRU
else:
- print(f"Loading weights [{sd_model_hash}] from cache")
- checkpoints_loaded.move_to_end(checkpoint_info)
+ 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")
model.load_state_dict(checkpoints_loaded[checkpoint_info])
+ if shared.opts.sd_checkpoint_cache > 0:
+ while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+ checkpoints_loaded.popitem(last=False) # LRU
+
model.sd_model_hash = sd_model_hash
model.sd_model_checkpoint = checkpoint_file
model.sd_checkpoint_info = checkpoint_info
+ sd_vae.load_vae(model, vae_file)
+
def load_model(checkpoint_info=None):
from modules import lowvram, sd_hijack
@@ -220,6 +223,12 @@ def load_model(checkpoint_info=None): if checkpoint_info.config != shared.cmd_opts.config:
print(f"Loading config from: {checkpoint_info.config}")
+ if shared.sd_model:
+ sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+ shared.sd_model = None
+ gc.collect()
+ devices.torch_gc()
+
sd_config = OmegaConf.load(checkpoint_info.config)
if should_hijack_inpainting(checkpoint_info):
@@ -233,6 +242,7 @@ def load_model(checkpoint_info=None): checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
do_inpainting_hijack()
+
sd_model = instantiate_from_config(sd_config.model)
load_model_weights(sd_model, checkpoint_info)
@@ -252,14 +262,18 @@ def load_model(checkpoint_info=None): return sd_model
-def reload_model_weights(sd_model, info=None):
+def reload_model_weights(sd_model=None, info=None):
from modules import lowvram, devices, sd_hijack
checkpoint_info = info or select_checkpoint()
+
+ if not sd_model:
+ sd_model = shared.sd_model
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return
if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+ del sd_model
checkpoints_loaded.clear()
load_model(checkpoint_info)
return shared.sd_model
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 3670b57d..783992d2 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -1,5 +1,6 @@ from collections import namedtuple
import numpy as np
+from math import floor
import torch
import tqdm
from PIL import Image
@@ -11,6 +12,7 @@ from modules import prompt_parser, devices, processing, images from modules.shared import opts, cmd_opts, state
import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
@@ -22,11 +24,15 @@ samplers_k_diffusion = [ ('Heun', 'sample_heun', ['k_heun'], {}),
('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
+ ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+ ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
+ ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+ ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
]
samplers_data_k_diffusion = [
@@ -91,8 +97,8 @@ def single_sample_to_image(sample): return Image.fromarray(x_sample)
-def sample_to_image(samples):
- return single_sample_to_image(samples[0])
+def sample_to_image(samples, index=0):
+ return single_sample_to_image(samples[index])
def samples_to_image_grid(samples):
@@ -205,17 +211,22 @@ class VanillaStableDiffusionSampler: self.mask = p.mask if hasattr(p, 'mask') else None
self.nmask = p.nmask if hasattr(p, 'nmask') else None
+
+ def adjust_steps_if_invalid(self, p, num_steps):
+ if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
+ valid_step = 999 / (1000 // num_steps)
+ if valid_step == floor(valid_step):
+ return int(valid_step) + 1
+
+ return num_steps
+
+
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
steps, t_enc = setup_img2img_steps(p, steps)
-
+ steps = self.adjust_steps_if_invalid(p, steps)
self.initialize(p)
- # existing code fails with certain step counts, like 9
- try:
- self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
- except Exception:
- self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-
+ self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
self.init_latent = x
@@ -239,18 +250,14 @@ class VanillaStableDiffusionSampler: self.last_latent = x
self.step = 0
- steps = steps or p.steps
+ steps = self.adjust_steps_if_invalid(p, steps or p.steps)
# Wrap the conditioning models with additional image conditioning for inpainting model
if image_conditioning is not None:
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
- # existing code fails with certain step counts, like 9
- try:
- samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
- except Exception:
- samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
+ samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
return samples_ddim
@@ -278,6 +285,12 @@ class CFGDenoiser(torch.nn.Module): image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+ denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
+ cfg_denoiser_callback(denoiser_params)
+ x_in = denoiser_params.x
+ image_cond_in = denoiser_params.image_cond
+ sigma_in = denoiser_params.sigma
+
if tensor.shape[1] == uncond.shape[1]:
cond_in = torch.cat([tensor, uncond])
diff --git a/modules/sd_vae.py b/modules/sd_vae.py new file mode 100644 index 00000000..71e7a6e6 --- /dev/null +++ b/modules/sd_vae.py @@ -0,0 +1,207 @@ +import torch +import os +from collections import namedtuple +from modules import shared, devices, script_callbacks +from modules.paths import models_path +import glob + + +model_dir = "Stable-diffusion" +model_path = os.path.abspath(os.path.join(models_path, model_dir)) +vae_dir = "VAE" +vae_path = os.path.abspath(os.path.join(models_path, vae_dir)) + + +vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"} + + +default_vae_dict = {"auto": "auto", "None": "None"} +default_vae_list = ["auto", "None"] + + +default_vae_values = [default_vae_dict[x] for x in default_vae_list] +vae_dict = dict(default_vae_dict) +vae_list = list(default_vae_list) +first_load = True + + +base_vae = None +loaded_vae_file = None +checkpoint_info = None + + +def get_base_vae(model): + if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model: + return base_vae + return None + + +def store_base_vae(model): + global base_vae, checkpoint_info + if checkpoint_info != model.sd_checkpoint_info: + base_vae = model.first_stage_model.state_dict().copy() + checkpoint_info = model.sd_checkpoint_info + + +def delete_base_vae(): + global base_vae, checkpoint_info + base_vae = None + checkpoint_info = None + + +def restore_base_vae(model): + global base_vae, checkpoint_info + if base_vae is not None and checkpoint_info == model.sd_checkpoint_info: + load_vae_dict(model, base_vae) + delete_base_vae() + + +def get_filename(filepath): + return os.path.splitext(os.path.basename(filepath))[0] + + +def refresh_vae_list(vae_path=vae_path, model_path=model_path): + global vae_dict, vae_list + res = {} + candidates = [ + *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True), + *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True), + *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True), + *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True) + ] + if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path): + candidates.append(shared.cmd_opts.vae_path) + for filepath in candidates: + name = get_filename(filepath) + res[name] = filepath + vae_list.clear() + vae_list.extend(default_vae_list) + vae_list.extend(list(res.keys())) + vae_dict.clear() + vae_dict.update(res) + vae_dict.update(default_vae_dict) + 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 + 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") + # 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") + # 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") + # 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") + # No more fallbacks for auto + if vae_file == "auto": + vae_file = None + # Last check, just because + if vae_file and not os.path.exists(vae_file): + vae_file = None + + return vae_file + + +def load_vae(model, vae_file=None): + global first_load, vae_dict, vae_list, loaded_vae_file + # save_settings = False + + if 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} + load_vae_dict(model, vae_dict_1) + + # If vae used is not in dict, update it + # It will be removed on refresh though + vae_opt = get_filename(vae_file) + if vae_opt not in vae_dict: + vae_dict[vae_opt] = vae_file + vae_list.append(vae_opt) + + loaded_vae_file = vae_file + + """ + # Save current VAE to VAE settings, maybe? will it work? + if save_settings: + if vae_file is None: + vae_opt = "None" + + # shared.opts.sd_vae = vae_opt + """ + + first_load = False + + +# don't call this from outside +def load_vae_dict(model, vae_dict_1=None): + if vae_dict_1: + store_base_vae(model) + model.first_stage_model.load_state_dict(vae_dict_1) + else: + restore_base_vae() + model.first_stage_model.to(devices.dtype_vae) + + +def reload_vae_weights(sd_model=None, vae_file="auto"): + from modules import lowvram, devices, sd_hijack + + if not sd_model: + sd_model = shared.sd_model + + checkpoint_info = sd_model.sd_checkpoint_info + checkpoint_file = checkpoint_info.filename + vae_file = resolve_vae(checkpoint_file, vae_file=vae_file) + + if loaded_vae_file == vae_file: + return + + if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: + lowvram.send_everything_to_cpu() + else: + sd_model.to(devices.cpu) + + sd_hijack.model_hijack.undo_hijack(sd_model) + + load_vae(sd_model, vae_file) + + sd_hijack.model_hijack.hijack(sd_model) + script_callbacks.model_loaded_callback(sd_model) + + if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram: + sd_model.to(devices.device) + + print(f"VAE Weights loaded.") + return sd_model diff --git a/modules/shared.py b/modules/shared.py index a27c654e..e8bacd3c 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -4,6 +4,7 @@ import json import os
import sys
from collections import OrderedDict
+import time
import gradio as gr
import tqdm
@@ -14,7 +15,7 @@ import modules.memmon import modules.sd_models
import modules.styles
import modules.devices as devices
-from modules import sd_samplers, sd_models, localization
+from modules import sd_samplers, sd_models, localization, sd_vae
from modules.hypernetworks import hypernetwork
from modules.paths import models_path, script_path, sd_path
@@ -43,6 +44,7 @@ parser.add_argument("--precision", type=str, help="evaluate at this precision", parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
+parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
@@ -51,6 +53,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
+parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
@@ -83,6 +86,10 @@ parser.add_argument("--nowebui", action='store_true', help="use api=True to laun parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origins", default=None)
+parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
+parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
+parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
cmd_opts = parser.parse_args()
restricted_opts = {
@@ -97,8 +104,7 @@ restricted_opts = { "outdir_save",
}
-if cmd_opts.share or cmd_opts.listen:
- cmd_opts.disable_extension_access = True
+cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen) and not cmd_opts.enable_insecure_extension_access
devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
@@ -135,6 +141,7 @@ class State: current_image = None
current_image_sampling_step = 0
textinfo = None
+ time_start = None
need_restart = False
def skip(self):
@@ -144,6 +151,9 @@ class State: self.interrupted = True
def nextjob(self):
+ if opts.show_progress_every_n_steps == -1:
+ self.do_set_current_image()
+
self.job_no += 1
self.sampling_step = 0
self.current_image_sampling_step = 0
@@ -172,6 +182,7 @@ class State: self.skipped = False
self.interrupted = False
self.textinfo = None
+ self.time_start = time.time()
devices.torch_gc()
@@ -181,6 +192,24 @@ class State: devices.torch_gc()
+ """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
+ def set_current_image(self):
+ if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
+ self.do_set_current_image()
+
+ def do_set_current_image(self):
+ if not parallel_processing_allowed:
+ return
+ if self.current_latent is None:
+ return
+
+ if opts.show_progress_grid:
+ self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
+ else:
+ self.current_image = sd_samplers.sample_to_image(self.current_latent)
+
+ self.current_image_sampling_step = self.sampling_step
+
state = State()
artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
@@ -192,8 +221,6 @@ interrogator = modules.interrogate.InterrogateModels("interrogate") face_restorers = []
-localization.list_localizations(cmd_opts.localizations_dir)
-
def realesrgan_models_names():
import modules.realesrgan_model
@@ -238,6 +265,8 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
+ "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
+ "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
"export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
@@ -289,21 +318,23 @@ options_templates.update(options_section(('system', "System"), { }))
options_templates.update(options_section(('training', "Training"), {
- "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
+ "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
+ "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
"training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
+ "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while 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_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}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
- "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
"enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
@@ -331,7 +362,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), options_templates.update(options_section(('ui', "User interface"), {
"show_progressbar": OptionInfo(True, "Show progressbar"),
- "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+ "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
"return_grid": OptionInfo(True, "Show grid in results for web"),
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
@@ -376,6 +407,16 @@ class Options: def __setattr__(self, key, value):
if self.data is not None:
if key in self.data or key in self.data_labels:
+ assert not cmd_opts.freeze_settings, "changing settings is disabled"
+
+ info = opts.data_labels.get(key, None)
+ comp_args = info.component_args if info else None
+ if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
+ raise RuntimeError(f"not possible to set {key} because it is restricted")
+
+ if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+ raise RuntimeError(f"not possible to set {key} because it is restricted")
+
self.data[key] = value
return
@@ -392,6 +433,8 @@ class Options: return super(Options, self).__getattribute__(item)
def save(self, filename):
+ assert not cmd_opts.freeze_settings, "saving settings is disabled"
+
with open(filename, "w", encoding="utf8") as file:
json.dump(self.data, file, indent=4)
@@ -418,11 +461,12 @@ class Options: if bad_settings > 0:
print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
- def onchange(self, key, func):
+ def onchange(self, key, func, call=True):
item = self.data_labels.get(key)
item.onchange = func
- func()
+ if call:
+ func()
def dumpjson(self):
d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index e0babb46..0aeb0459 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -235,6 +235,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
+ unload = shared.opts.unload_models_when_training
if save_embedding_every > 0:
embedding_dir = os.path.join(log_directory, "embeddings")
@@ -272,6 +273,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
with torch.autocast("cuda"):
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
+ if unload:
+ shared.sd_model.first_stage_model.to(devices.cpu)
embedding.vec.requires_grad = True
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
@@ -328,6 +331,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc if images_dir is not None and steps_done % create_image_every == 0:
forced_filename = f'{embedding_name}-{steps_done}'
last_saved_image = os.path.join(images_dir, forced_filename)
+
+ shared.sd_model.first_stage_model.to(devices.device)
+
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
do_not_save_grid=True,
@@ -355,6 +361,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc processed = processing.process_images(p)
image = processed.images[0]
+ if unload:
+ shared.sd_model.first_stage_model.to(devices.cpu)
+
shared.state.current_image = image
if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
@@ -400,6 +409,7 @@ Last saved image: {html.escape(last_saved_image)}<br/> filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+ shared.sd_model.first_stage_model.to(devices.device)
return embedding, filename
diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py index e712284d..d679e6f4 100644 --- a/modules/textual_inversion/ui.py +++ b/modules/textual_inversion/ui.py @@ -25,8 +25,10 @@ def train_embedding(*args): assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
+ apply_optimizations = shared.opts.training_xattention_optimizations
try:
- sd_hijack.undo_optimizations()
+ if not apply_optimizations:
+ sd_hijack.undo_optimizations()
embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
@@ -38,5 +40,6 @@ Embedding saved to {html.escape(filename)} except Exception:
raise
finally:
- sd_hijack.apply_optimizations()
+ if not apply_optimizations:
+ sd_hijack.apply_optimizations()
diff --git a/modules/txt2img.py b/modules/txt2img.py index c9d5a090..8e4e8677 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -47,6 +47,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: if processed is None:
processed = process_images(p)
+ p.close()
+
shared.total_tqdm.clear()
generation_info_js = processed.js()
diff --git a/modules/ui.py b/modules/ui.py index 2c15abb7..c946ad59 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -174,9 +174,9 @@ def save_pil_to_file(pil_image, dir=None): gr.processing_utils.save_pil_to_file = save_pil_to_file
-def wrap_gradio_call(func, extra_outputs=None):
+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
- run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
+ run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
if run_memmon:
shared.mem_mon.monitor()
t = time.perf_counter()
@@ -203,11 +203,18 @@ def wrap_gradio_call(func, extra_outputs=None): res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
+ shared.state.skipped = False
+ shared.state.interrupted = False
+ shared.state.job_count = 0
+
+ if not add_stats:
+ return tuple(res)
+
elapsed = time.perf_counter() - t
elapsed_m = int(elapsed // 60)
elapsed_s = elapsed % 60
elapsed_text = f"{elapsed_s:.2f}s"
- if (elapsed_m > 0):
+ if elapsed_m > 0:
elapsed_text = f"{elapsed_m}m "+elapsed_text
if run_memmon:
@@ -225,10 +232,6 @@ def wrap_gradio_call(func, extra_outputs=None): # last item is always HTML
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
- shared.state.skipped = False
- shared.state.interrupted = False
- shared.state.job_count = 0
-
return tuple(res)
return f
@@ -276,16 +279,8 @@ def check_progress_call(id_part): image = gr_show(False)
preview_visibility = gr_show(False)
- if opts.show_progress_every_n_steps > 0:
- if shared.parallel_processing_allowed:
-
- if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:
- if opts.show_progress_grid:
- shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent)
- else:
- shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
- shared.state.current_image_sampling_step = shared.state.sampling_step
-
+ if opts.show_progress_every_n_steps != 0:
+ shared.state.set_current_image()
image = shared.state.current_image
if image is None:
@@ -671,6 +666,8 @@ def create_ui(wrap_gradio_gpu_call): import modules.img2img
import modules.txt2img
+ reload_javascript()
+
parameters_copypaste.reset()
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
@@ -1058,9 +1055,11 @@ def create_ui(wrap_gradio_gpu_call): extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
show_extras_results = gr.Checkbox(label='Show result images', value=True)
+ submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
with gr.Tabs(elem_id="extras_resize_mode"):
with gr.TabItem('Scale by'):
- upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
+ upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
with gr.TabItem('Scale to'):
with gr.Group():
with gr.Row():
@@ -1085,8 +1084,6 @@ def create_ui(wrap_gradio_gpu_call): with gr.Group():
upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
- submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
submit.click(
@@ -1188,8 +1185,8 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_name = gr.Textbox(label="Name")
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
- new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
- new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
+ new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+ new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
@@ -1442,50 +1439,41 @@ def create_ui(wrap_gradio_gpu_call): opts.reorder()
def run_settings(*args):
- changed = 0
-
- assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
+ changed = []
for key, value, comp in zip(opts.data_labels.keys(), args, components):
- if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
- return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
+ assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
for key, value, comp in zip(opts.data_labels.keys(), args, components):
if comp == dummy_component:
continue
- comp_args = opts.data_labels[key].component_args
- if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
- continue
-
- if cmd_opts.hide_ui_dir_config and key in restricted_opts:
- continue
-
oldval = opts.data.get(key, None)
- opts.data[key] = value
-
+ try:
+ setattr(opts, key, value)
+ except RuntimeError:
+ continue
if oldval != value:
if opts.data_labels[key].onchange is not None:
opts.data_labels[key].onchange()
- changed += 1
-
- opts.save(shared.config_filename)
-
- return f'{changed} settings changed.', opts.dumpjson()
+ changed.append(key)
+ try:
+ opts.save(shared.config_filename)
+ except RuntimeError:
+ return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'
+ return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'
def run_settings_single(value, key):
- assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
-
if not opts.same_type(value, opts.data_labels[key].default):
return gr.update(visible=True), opts.dumpjson()
oldval = opts.data.get(key, None)
- if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+ try:
+ setattr(opts, key, value)
+ except Exception:
return gr.update(value=oldval), opts.dumpjson()
- opts.data[key] = value
-
if oldval != value:
if opts.data_labels[key].onchange is not None:
opts.data_labels[key].onchange()
@@ -1570,8 +1558,7 @@ def create_ui(wrap_gradio_gpu_call): reload_script_bodies.click(
fn=reload_scripts,
inputs=[],
- outputs=[],
- _js='function(){}'
+ outputs=[]
)
def request_restart():
@@ -1579,11 +1566,10 @@ def create_ui(wrap_gradio_gpu_call): shared.state.need_restart = True
restart_gradio.click(
-
fn=request_restart,
+ _js='restart_reload',
inputs=[],
outputs=[],
- _js='function(){restart_reload()}'
)
if column is not None:
@@ -1639,9 +1625,9 @@ def create_ui(wrap_gradio_gpu_call): text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
settings_submit.click(
- fn=run_settings,
+ fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
inputs=components,
- outputs=[result, text_settings],
+ outputs=[text_settings, result],
)
for i, k, item in quicksettings_list:
@@ -1782,4 +1768,3 @@ def load_javascript(raw_response): reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)
-reload_javascript()
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index e74b7d68..02ab9643 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -13,6 +13,9 @@ import html from modules import extensions, shared, paths
+available_extensions = {"extensions": []}
+
+
def check_access():
assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
@@ -83,7 +86,7 @@ def extension_table(): code += f"""
<tr>
<td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
- <td><a href="{html.escape(ext.remote or '')}">{html.escape(ext.remote or '')}</a></td>
+ <td><a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape(ext.remote or '')}</a></td>
<td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
</tr>
"""
@@ -96,6 +99,14 @@ def extension_table(): return code
+def normalize_git_url(url):
+ if url is None:
+ return ""
+
+ url = url.replace(".git", "")
+ return url
+
+
def install_extension_from_url(dirname, url):
check_access()
@@ -103,14 +114,15 @@ def install_extension_from_url(dirname, url): if dirname is None or dirname == "":
*parts, last_part = url.split('/')
- last_part = last_part.replace(".git", "")
+ last_part = normalize_git_url(last_part)
dirname = last_part
target_dir = os.path.join(extensions.extensions_dir, dirname)
assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
- assert len([x for x in extensions.extensions if x.remote == url]) == 0, 'Extension with this URL is already installed'
+ normalized_url = normalize_git_url(url)
+ assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
tmpdir = os.path.join(paths.script_path, "tmp", dirname)
@@ -128,18 +140,104 @@ def install_extension_from_url(dirname, url): shutil.rmtree(tmpdir, True)
+def install_extension_from_index(url, hide_tags):
+ ext_table, message = install_extension_from_url(None, url)
+
+ code, _ = refresh_available_extensions_from_data(hide_tags)
+
+ return code, ext_table, message
+
+
+def refresh_available_extensions(url, hide_tags):
+ global available_extensions
+
+ import urllib.request
+ with urllib.request.urlopen(url) as response:
+ text = response.read()
+
+ available_extensions = json.loads(text)
+
+ code, tags = refresh_available_extensions_from_data(hide_tags)
+
+ return url, code, gr.CheckboxGroup.update(choices=tags), ''
+
+
+def refresh_available_extensions_for_tags(hide_tags):
+ code, _ = refresh_available_extensions_from_data(hide_tags)
+
+ return code, ''
+
+
+def refresh_available_extensions_from_data(hide_tags):
+ extlist = available_extensions["extensions"]
+ installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
+
+ tags = available_extensions.get("tags", {})
+ tags_to_hide = set(hide_tags)
+ hidden = 0
+
+ code = f"""<!-- {time.time()} -->
+ <table id="available_extensions">
+ <thead>
+ <tr>
+ <th>Extension</th>
+ <th>Description</th>
+ <th>Action</th>
+ </tr>
+ </thead>
+ <tbody>
+ """
+
+ for ext in extlist:
+ name = ext.get("name", "noname")
+ url = ext.get("url", None)
+ description = ext.get("description", "")
+ extension_tags = ext.get("tags", [])
+
+ if url is None:
+ continue
+
+ if len([x for x in extension_tags if x in tags_to_hide]) > 0:
+ hidden += 1
+ continue
+
+ existing = installed_extension_urls.get(normalize_git_url(url), None)
+
+ install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
+
+ tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
+
+ code += f"""
+ <tr>
+ <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a><br />{tags_text}</td>
+ <td>{html.escape(description)}</td>
+ <td>{install_code}</td>
+ </tr>
+ """
+
+ code += """
+ </tbody>
+ </table>
+ """
+
+ if hidden > 0:
+ code += f"<p>Extension hidden: {hidden}</p>"
+
+ return code, list(tags)
+
+
def create_ui():
import modules.ui
with gr.Blocks(analytics_enabled=False) as ui:
with gr.Tabs(elem_id="tabs_extensions") as tabs:
with gr.TabItem("Installed"):
- extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False)
- extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False)
with gr.Row():
apply = gr.Button(value="Apply and restart UI", variant="primary")
check = gr.Button(value="Check for updates")
+ extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
+ extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
extensions_table = gr.HTML(lambda: extension_table())
@@ -157,16 +255,47 @@ def create_ui(): outputs=[extensions_table],
)
+ with gr.TabItem("Available"):
+ with gr.Row():
+ refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
+ available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/wiki/AUTOMATIC1111/stable-diffusion-webui/Extensions-index.md", label="Extension index URL").style(container=False)
+ extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
+ install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
+
+ with gr.Row():
+ hide_tags = gr.CheckboxGroup(value=["ads", "localization"], label="Hide extensions with tags", choices=["script", "ads", "localization"])
+
+ install_result = gr.HTML()
+ available_extensions_table = gr.HTML()
+
+ refresh_available_extensions_button.click(
+ fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
+ inputs=[available_extensions_index, hide_tags],
+ outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result],
+ )
+
+ install_extension_button.click(
+ fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
+ inputs=[extension_to_install, hide_tags],
+ outputs=[available_extensions_table, extensions_table, install_result],
+ )
+
+ hide_tags.change(
+ fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
+ inputs=[hide_tags],
+ outputs=[available_extensions_table, install_result]
+ )
+
with gr.TabItem("Install from URL"):
install_url = gr.Text(label="URL for extension's git repository")
install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
- intall_button = gr.Button(value="Install", variant="primary")
- intall_result = gr.HTML(elem_id="extension_install_result")
+ install_button = gr.Button(value="Install", variant="primary")
+ install_result = gr.HTML(elem_id="extension_install_result")
- intall_button.click(
+ install_button.click(
fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
inputs=[install_dirname, install_url],
- outputs=[extensions_table, intall_result],
+ outputs=[extensions_table, install_result],
)
return ui
diff --git a/modules/upscaler.py b/modules/upscaler.py index 6ab2fb40..c4e6e6bd 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -10,6 +10,7 @@ import modules.shared from modules import modelloader, shared LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) +NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST) from modules.paths import models_path @@ -56,10 +57,18 @@ class Upscaler: self.scale = scale dest_w = img.width * scale dest_h = img.height * scale + for i in range(3): + shape = (img.width, img.height) + + img = self.do_upscale(img, selected_model) + + if shape == (img.width, img.height): + break + if img.width >= dest_w and img.height >= dest_h: break - img = self.do_upscale(img, selected_model) + if img.width != dest_w or img.height != dest_h: img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS) @@ -120,3 +129,17 @@ class UpscalerLanczos(Upscaler): self.name = "Lanczos" self.scalers = [UpscalerData("Lanczos", None, self)] + +class UpscalerNearest(Upscaler): + scalers = [] + + def do_upscale(self, img, selected_model=None): + return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=NEAREST) + + def load_model(self, _): + pass + + def __init__(self, dirname=None): + super().__init__(False) + self.name = "Nearest" + self.scalers = [UpscalerData("Nearest", None, self)]
\ No newline at end of file |