Merge branch 'master' into test_resolve_conflicts
This commit is contained in:
MalumaDev
GitHub
commit
2362d5f00e
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@ -0,0 +1,68 @@
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from modules.api.processing import StableDiffusionProcessingAPI
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from modules.processing import StableDiffusionProcessingTxt2Img, process_images
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from modules.sd_samplers import all_samplers
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from modules.extras import run_pnginfo
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import modules.shared as shared
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import uvicorn
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from fastapi import Body, APIRouter, HTTPException
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from fastapi.responses import JSONResponse
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from pydantic import BaseModel, Field, Json
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import json
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import io
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import base64
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sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
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class TextToImageResponse(BaseModel):
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images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: Json
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info: Json
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class Api:
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def __init__(self, app, queue_lock):
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self.router = APIRouter()
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self.app = app
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self.queue_lock = queue_lock
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self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
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def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ):
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sampler_index = sampler_to_index(txt2imgreq.sampler_index)
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if sampler_index is None:
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raise HTTPException(status_code=404, detail="Sampler not found")
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populate = txt2imgreq.copy(update={ # Override __init__ params
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"sd_model": shared.sd_model,
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"sampler_index": sampler_index[0],
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"do_not_save_samples": True,
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"do_not_save_grid": True
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}
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)
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p = StableDiffusionProcessingTxt2Img(**vars(populate))
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# Override object param
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with self.queue_lock:
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processed = process_images(p)
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b64images = []
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for i in processed.images:
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buffer = io.BytesIO()
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i.save(buffer, format="png")
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b64images.append(base64.b64encode(buffer.getvalue()))
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return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
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def img2imgapi(self):
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raise NotImplementedError
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def extrasapi(self):
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raise NotImplementedError
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def pnginfoapi(self):
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raise NotImplementedError
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def launch(self, server_name, port):
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self.app.include_router(self.router)
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uvicorn.run(self.app, host=server_name, port=port)
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@ -0,0 +1,99 @@
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from inflection import underscore
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from typing import Any, Dict, Optional
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from pydantic import BaseModel, Field, create_model
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from modules.processing import StableDiffusionProcessingTxt2Img
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import inspect
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API_NOT_ALLOWED = [
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"self",
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"kwargs",
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"sd_model",
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"outpath_samples",
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"outpath_grids",
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"sampler_index",
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"do_not_save_samples",
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"do_not_save_grid",
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"extra_generation_params",
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"overlay_images",
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"do_not_reload_embeddings",
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"seed_enable_extras",
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"prompt_for_display",
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"sampler_noise_scheduler_override",
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"ddim_discretize"
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]
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class ModelDef(BaseModel):
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"""Assistance Class for Pydantic Dynamic Model Generation"""
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field: str
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field_alias: str
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field_type: Any
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field_value: Any
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class PydanticModelGenerator:
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"""
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Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about:
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source_data is a snapshot of the default values produced by the class
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params are the names of the actual keys required by __init__
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"""
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def __init__(
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self,
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model_name: str = None,
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class_instance = None,
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additional_fields = None,
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):
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def field_type_generator(k, v):
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# field_type = str if not overrides.get(k) else overrides[k]["type"]
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# print(k, v.annotation, v.default)
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field_type = v.annotation
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return Optional[field_type]
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def merge_class_params(class_):
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all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_)))
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parameters = {}
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for classes in all_classes:
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parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
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return parameters
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self._model_name = model_name
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self._class_data = merge_class_params(class_instance)
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self._model_def = [
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ModelDef(
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field=underscore(k),
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field_alias=k,
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field_type=field_type_generator(k, v),
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field_value=v.default
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)
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for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
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]
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for fields in additional_fields:
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self._model_def.append(ModelDef(
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field=underscore(fields["key"]),
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field_alias=fields["key"],
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field_type=fields["type"],
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field_value=fields["default"]))
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def generate_model(self):
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"""
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Creates a pydantic BaseModel
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from the json and overrides provided at initialization
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"""
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fields = {
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d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
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}
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DynamicModel = create_model(self._model_name, **fields)
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DynamicModel.__config__.allow_population_by_field_name = True
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DynamicModel.__config__.allow_mutation = True
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return DynamicModel
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StableDiffusionProcessingAPI = PydanticModelGenerator(
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"StableDiffusionProcessingTxt2Img",
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StableDiffusionProcessingTxt2Img,
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[{"key": "sampler_index", "type": str, "default": "Euler"}]
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).generate_model()
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@ -196,7 +196,7 @@ def stack_conds(conds):
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return torch.stack(conds)
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def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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assert hypernetwork_name, 'hypernetwork not selected'
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path = shared.hypernetworks.get(hypernetwork_name, None)
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@ -225,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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with torch.autocast("cuda"):
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
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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=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
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if unload:
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shared.sd_model.cond_stage_model.to(devices.cpu)
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@ -9,6 +9,7 @@ from PIL import Image, ImageFilter, ImageOps
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import random
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import cv2
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from skimage import exposure
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from typing import Any, Dict, List, Optional
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import modules.sd_hijack
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from modules import devices, prompt_parser, masking, sd_samplers, lowvram
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@ -50,10 +51,16 @@ def get_correct_sampler(p):
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return sd_samplers.samplers
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elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
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return sd_samplers.samplers_for_img2img
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elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
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return sd_samplers.samplers
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class StableDiffusionProcessing:
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None, do_not_reload_embeddings=False):
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class StableDiffusionProcessing():
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"""
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The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
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"""
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str="", styles: List[str]=None, seed: int=-1, subseed: int=-1, subseed_strength: float=0, seed_resize_from_h: int=-1, seed_resize_from_w: int=-1, seed_enable_extras: bool=True, sampler_index: int=0, batch_size: int=1, n_iter: int=1, steps:int =50, cfg_scale:float=7.0, width:int=512, height:int=512, restore_faces:bool=False, tiling:bool=False, do_not_save_samples:bool=False, do_not_save_grid:bool=False, extra_generation_params: Dict[Any,Any]=None, overlay_images: Any=None, negative_prompt: str=None, eta: float =None, do_not_reload_embeddings: bool=False, denoising_strength: float = 0, ddim_discretize: str = "uniform", s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0):
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self.sd_model = sd_model
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self.outpath_samples: str = outpath_samples
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self.outpath_grids: str = outpath_grids
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@ -86,10 +93,10 @@ class StableDiffusionProcessing:
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self.denoising_strength: float = 0
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self.sampler_noise_scheduler_override = None
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self.ddim_discretize = opts.ddim_discretize
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self.s_churn = opts.s_churn
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self.s_tmin = opts.s_tmin
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self.s_tmax = float('inf') # not representable as a standard ui option
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self.s_noise = opts.s_noise
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self.s_churn = s_churn or opts.s_churn
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self.s_tmin = s_tmin or opts.s_tmin
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self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option
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self.s_noise = s_noise or opts.s_noise
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if not seed_enable_extras:
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self.subseed = -1
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@ -97,6 +104,7 @@ class StableDiffusionProcessing:
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self.seed_resize_from_h = 0
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self.seed_resize_from_w = 0
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def init(self, all_prompts, all_seeds, all_subseeds):
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pass
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@ -515,7 +523,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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sampler = None
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def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
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def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
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super().__init__(**kwargs)
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self.enable_hr = enable_hr
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self.denoising_strength = denoising_strength
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@ -759,4 +767,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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del x
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devices.torch_gc()
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return samples
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return samples
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@ -181,7 +181,7 @@ def einsum_op_cuda(q, k, v):
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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# Divide factor of safety as there's copying and fragmentation
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return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
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return einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
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def einsum_op(q, k, v):
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if q.device.type == 'cuda':
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|
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@ -122,11 +122,33 @@ def select_checkpoint():
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return checkpoint_info
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chckpoint_dict_replacements = {
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'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
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'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
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'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
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}
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def transform_checkpoint_dict_key(k):
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for text, replacement in chckpoint_dict_replacements.items():
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if k.startswith(text):
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k = replacement + k[len(text):]
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return k
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def get_state_dict_from_checkpoint(pl_sd):
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if "state_dict" in pl_sd:
|
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return pl_sd["state_dict"]
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pl_sd = pl_sd["state_dict"]
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|
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return pl_sd
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sd = {}
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for k, v in pl_sd.items():
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new_key = transform_checkpoint_dict_key(k)
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|
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if new_key is not None:
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sd[new_key] = v
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return sd
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|
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def load_model_weights(model, checkpoint_info):
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|
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@ -141,7 +163,7 @@ def load_model_weights(model, checkpoint_info):
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print(f"Global Step: {pl_sd['global_step']}")
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sd = get_state_dict_from_checkpoint(pl_sd)
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model.load_state_dict(sd, strict=False)
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missing, extra = model.load_state_dict(sd, strict=False)
|
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|
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if shared.cmd_opts.opt_channelslast:
|
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model.to(memory_format=torch.channels_last)
|
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|
|
|
|||
|
|
@ -79,6 +79,8 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help=
|
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parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
|
||||
parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
|
||||
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
|
||||
parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
|
||||
parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
|
||||
|
||||
cmd_opts = parser.parse_args()
|
||||
restricted_opts = [
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ import time
|
|||
import traceback
|
||||
import platform
|
||||
import subprocess as sp
|
||||
from functools import reduce
|
||||
from functools import partial, reduce
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
|
@ -266,6 +266,19 @@ def wrap_gradio_call(func, extra_outputs=None):
|
|||
return f
|
||||
|
||||
|
||||
def calc_time_left(progress, threshold, label, force_display):
|
||||
if progress == 0:
|
||||
return ""
|
||||
else:
|
||||
time_since_start = time.time() - shared.state.time_start
|
||||
eta = (time_since_start/progress)
|
||||
eta_relative = eta-time_since_start
|
||||
if (eta_relative > threshold and progress > 0.02) or force_display:
|
||||
return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
|
||||
else:
|
||||
return ""
|
||||
|
||||
|
||||
def check_progress_call(id_part):
|
||||
if shared.state.job_count == 0:
|
||||
return "", gr_show(False), gr_show(False), gr_show(False)
|
||||
|
|
@ -277,11 +290,15 @@ def check_progress_call(id_part):
|
|||
if shared.state.sampling_steps > 0:
|
||||
progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
|
||||
|
||||
time_left = calc_time_left( progress, 60, " ETA:", shared.state.time_left_force_display )
|
||||
if time_left != "":
|
||||
shared.state.time_left_force_display = True
|
||||
|
||||
progress = min(progress, 1)
|
||||
|
||||
progressbar = ""
|
||||
if opts.show_progressbar:
|
||||
progressbar = f"""<div class='progressDiv'><div class='progress' style="width:{progress * 100}%">{str(int(progress*100))+"%" if progress > 0.01 else ""}</div></div>"""
|
||||
progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:hidden;width:{progress * 100}%">{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}</div></div>"""
|
||||
|
||||
image = gr_show(False)
|
||||
preview_visibility = gr_show(False)
|
||||
|
|
@ -313,6 +330,8 @@ def check_progress_call_initial(id_part):
|
|||
shared.state.current_latent = None
|
||||
shared.state.current_image = None
|
||||
shared.state.textinfo = None
|
||||
shared.state.time_start = time.time()
|
||||
shared.state.time_left_force_display = False
|
||||
|
||||
return check_progress_call(id_part)
|
||||
|
||||
|
|
@ -1417,6 +1436,8 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
batch_size,
|
||||
dataset_directory,
|
||||
log_directory,
|
||||
training_width,
|
||||
training_height,
|
||||
steps,
|
||||
create_image_every,
|
||||
save_embedding_every,
|
||||
|
|
@ -1613,6 +1634,7 @@ Requested path was: {f}
|
|||
|
||||
def reload_scripts():
|
||||
modules.scripts.reload_script_body_only()
|
||||
reload_javascript() # need to refresh the html page
|
||||
|
||||
reload_script_bodies.click(
|
||||
fn=reload_scripts,
|
||||
|
|
@ -1871,26 +1893,30 @@ Requested path was: {f}
|
|||
return demo
|
||||
|
||||
|
||||
with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
|
||||
javascript = f'<script>{jsfile.read()}</script>'
|
||||
def load_javascript(raw_response):
|
||||
with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
|
||||
javascript = f'<script>{jsfile.read()}</script>'
|
||||
|
||||
jsdir = os.path.join(script_path, "javascript")
|
||||
for filename in sorted(os.listdir(jsdir)):
|
||||
with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
|
||||
javascript += f"\n<script>{jsfile.read()}</script>"
|
||||
jsdir = os.path.join(script_path, "javascript")
|
||||
for filename in sorted(os.listdir(jsdir)):
|
||||
with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
|
||||
javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
|
||||
|
||||
if cmd_opts.theme is not None:
|
||||
javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
|
||||
if cmd_opts.theme is not None:
|
||||
javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
|
||||
|
||||
javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
|
||||
javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
|
||||
|
||||
if 'gradio_routes_templates_response' not in globals():
|
||||
def template_response(*args, **kwargs):
|
||||
res = gradio_routes_templates_response(*args, **kwargs)
|
||||
res.body = res.body.replace(b'</head>', f'{javascript}</head>'.encode("utf8"))
|
||||
res = raw_response(*args, **kwargs)
|
||||
res.body = res.body.replace(
|
||||
b'</head>', f'{javascript}</head>'.encode("utf8"))
|
||||
res.init_headers()
|
||||
return res
|
||||
|
||||
gradio_routes_templates_response = gradio.routes.templates.TemplateResponse
|
||||
gradio.routes.templates.TemplateResponse = template_response
|
||||
|
||||
|
||||
reload_javascript = partial(load_javascript,
|
||||
gradio.routes.templates.TemplateResponse)
|
||||
reload_javascript()
|
||||
|
|
|
|||
|
|
@ -23,3 +23,4 @@ resize-right
|
|||
torchdiffeq
|
||||
kornia
|
||||
lark
|
||||
inflection
|
||||
|
|
|
|||
|
|
@ -22,3 +22,4 @@ resize-right==0.0.2
|
|||
torchdiffeq==0.2.3
|
||||
kornia==0.6.7
|
||||
lark==1.1.2
|
||||
inflection==0.5.1
|
||||
|
|
|
|||
58
webui.py
58
webui.py
|
|
@ -4,7 +4,7 @@ import time
|
|||
import importlib
|
||||
import signal
|
||||
import threading
|
||||
|
||||
from fastapi import FastAPI
|
||||
from fastapi.middleware.gzip import GZipMiddleware
|
||||
|
||||
from modules.paths import script_path
|
||||
|
|
@ -31,7 +31,6 @@ from modules.paths import script_path
|
|||
from modules.shared import cmd_opts
|
||||
import modules.hypernetworks.hypernetwork
|
||||
|
||||
|
||||
queue_lock = threading.Lock()
|
||||
|
||||
|
||||
|
|
@ -87,10 +86,6 @@ def initialize():
|
|||
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
|
||||
shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
|
||||
|
||||
|
||||
def webui():
|
||||
initialize()
|
||||
|
||||
# make the program just exit at ctrl+c without waiting for anything
|
||||
def sigint_handler(sig, frame):
|
||||
print(f'Interrupted with signal {sig} in {frame}')
|
||||
|
|
@ -98,10 +93,37 @@ def webui():
|
|||
|
||||
signal.signal(signal.SIGINT, sigint_handler)
|
||||
|
||||
while 1:
|
||||
|
||||
def create_api(app):
|
||||
from modules.api.api import Api
|
||||
api = Api(app, queue_lock)
|
||||
return api
|
||||
|
||||
def wait_on_server(demo=None):
|
||||
while 1:
|
||||
time.sleep(0.5)
|
||||
if demo and getattr(demo, 'do_restart', False):
|
||||
time.sleep(0.5)
|
||||
demo.close()
|
||||
time.sleep(0.5)
|
||||
break
|
||||
|
||||
def api_only():
|
||||
initialize()
|
||||
|
||||
app = FastAPI()
|
||||
app.add_middleware(GZipMiddleware, minimum_size=1000)
|
||||
api = create_api(app)
|
||||
|
||||
api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
|
||||
|
||||
|
||||
def webui(launch_api=False):
|
||||
initialize()
|
||||
|
||||
while 1:
|
||||
demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
|
||||
|
||||
|
||||
app, local_url, share_url = demo.launch(
|
||||
share=cmd_opts.share,
|
||||
server_name="0.0.0.0" if cmd_opts.listen else None,
|
||||
|
|
@ -111,17 +133,14 @@ def webui():
|
|||
inbrowser=cmd_opts.autolaunch,
|
||||
prevent_thread_lock=True
|
||||
)
|
||||
|
||||
|
||||
app.add_middleware(GZipMiddleware, minimum_size=1000)
|
||||
|
||||
while 1:
|
||||
time.sleep(0.5)
|
||||
if getattr(demo, 'do_restart', False):
|
||||
time.sleep(0.5)
|
||||
demo.close()
|
||||
time.sleep(0.5)
|
||||
break
|
||||
if (launch_api):
|
||||
create_api(app)
|
||||
|
||||
wait_on_server(demo)
|
||||
|
||||
sd_samplers.set_samplers()
|
||||
|
||||
print('Reloading Custom Scripts')
|
||||
|
|
@ -133,5 +152,10 @@ def webui():
|
|||
print('Restarting Gradio')
|
||||
|
||||
|
||||
|
||||
task = []
|
||||
if __name__ == "__main__":
|
||||
webui()
|
||||
if cmd_opts.nowebui:
|
||||
api_only()
|
||||
else:
|
||||
webui(cmd_opts.api)
|
||||
Loading…
Reference in New Issue