mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-03-01 19:19:52 +00:00
Gradio 4 + WebUI 1.10
This commit is contained in:
layerdiffusion
@@ -2,13 +2,11 @@ import base64
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import io
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import os
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import time
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import itertools
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import datetime
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import uvicorn
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import ipaddress
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import requests
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import gradio as gr
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import numpy as np
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from threading import Lock
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from io import BytesIO
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from fastapi import APIRouter, Depends, FastAPI, Request, Response
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@@ -19,13 +17,13 @@ from fastapi.encoders import jsonable_encoder
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from secrets import compare_digest
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import modules.shared as shared
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from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models
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from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models, sd_schedulers
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from modules.api import models
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from modules.shared import opts
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
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from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
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from PIL import PngImagePlugin, Image
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from PIL import PngImagePlugin
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from modules.sd_models_config import find_checkpoint_config_near_filename
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from modules.realesrgan_model import get_realesrgan_models
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from modules import devices
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@@ -45,7 +43,7 @@ def script_name_to_index(name, scripts):
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def validate_sampler_name(name):
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config = sd_samplers.all_samplers_map.get(name, None)
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if config is None:
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raise HTTPException(status_code=404, detail="Sampler not found")
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raise HTTPException(status_code=400, detail="Sampler not found")
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return name
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@@ -87,7 +85,7 @@ def decode_base64_to_image(encoding):
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headers = {'user-agent': opts.api_useragent} if opts.api_useragent else {}
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response = requests.get(encoding, timeout=30, headers=headers)
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try:
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image = Image.open(BytesIO(response.content))
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image = images.read(BytesIO(response.content))
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return image
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except Exception as e:
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raise HTTPException(status_code=500, detail="Invalid image url") from e
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@@ -95,7 +93,7 @@ def decode_base64_to_image(encoding):
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if encoding.startswith("data:image/"):
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encoding = encoding.split(";")[1].split(",")[1]
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try:
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image = Image.open(BytesIO(base64.b64decode(encoding)))
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image = images.read(BytesIO(base64.b64decode(encoding)))
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return image
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except Exception as e:
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raise HTTPException(status_code=500, detail="Invalid encoded image") from e
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@@ -105,8 +103,6 @@ def encode_pil_to_base64(image):
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with io.BytesIO() as output_bytes:
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if isinstance(image, str):
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return image
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if isinstance(image, np.ndarray):
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image = Image.fromarray(image)
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if opts.samples_format.lower() == 'png':
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use_metadata = False
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metadata = PngImagePlugin.PngInfo()
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@@ -117,7 +113,7 @@ def encode_pil_to_base64(image):
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image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
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elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
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if image.mode == "RGBA":
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if image.mode in ("RGBA", "P"):
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image = image.convert("RGB")
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parameters = image.info.get('parameters', None)
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exif_bytes = piexif.dump({
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@@ -211,7 +207,7 @@ class Api:
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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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api_middleware(self.app)
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#api_middleware(self.app) # XXX this will have to be fixed
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self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=models.TextToImageResponse)
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self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=models.ImageToImageResponse)
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self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=models.ExtrasSingleImageResponse)
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@@ -225,6 +221,7 @@ class Api:
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self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
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self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
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self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem])
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self.add_api_route("/sdapi/v1/schedulers", self.get_schedulers, methods=["GET"], response_model=list[models.SchedulerItem])
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self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem])
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self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem])
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self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem])
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@@ -364,7 +361,7 @@ class Api:
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return script_args
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def apply_infotext(self, request, tabname, *, script_runner=None, mentioned_script_args=None):
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"""Processes `infotext` field from the `request`, and sets other fields of the `request` accoring to what's in infotext.
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"""Processes `infotext` field from the `request`, and sets other fields of the `request` according to what's in infotext.
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If request already has a field set, and that field is encountered in infotext too, the value from infotext is ignored.
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@@ -375,7 +372,7 @@ class Api:
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return {}
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possible_fields = infotext_utils.paste_fields[tabname]["fields"]
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set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True) # pydantic v1/v2 have differenrt names for this
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set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True) # pydantic v1/v2 have different names for this
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params = infotext_utils.parse_generation_parameters(request.infotext)
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def get_field_value(field, params):
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@@ -413,8 +410,8 @@ class Api:
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if request.override_settings is None:
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request.override_settings = {}
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overriden_settings = infotext_utils.get_override_settings(params)
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for _, setting_name, value in overriden_settings:
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overridden_settings = infotext_utils.get_override_settings(params)
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for _, setting_name, value in overridden_settings:
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if setting_name not in request.override_settings:
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request.override_settings[setting_name] = value
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@@ -441,15 +438,19 @@ class Api:
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self.apply_infotext(txt2imgreq, "txt2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
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selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)
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sampler, scheduler = sd_samplers.get_sampler_and_scheduler(txt2imgreq.sampler_name or txt2imgreq.sampler_index, txt2imgreq.scheduler)
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populate = txt2imgreq.copy(update={ # Override __init__ params
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"sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
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"sampler_name": validate_sampler_name(sampler),
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"do_not_save_samples": not txt2imgreq.save_images,
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"do_not_save_grid": not txt2imgreq.save_images,
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})
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if populate.sampler_name:
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populate.sampler_index = None # prevent a warning later on
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if not populate.scheduler and scheduler != "Automatic":
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populate.scheduler = scheduler
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args = vars(populate)
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args.pop('script_name', None)
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args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
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@@ -484,11 +485,7 @@ class Api:
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shared.state.end()
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shared.total_tqdm.clear()
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b64images = [
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encode_pil_to_base64(image)
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for image in itertools.chain(processed.images, processed.extra_images)
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if send_images
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]
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b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
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return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
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@@ -509,9 +506,10 @@ class Api:
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self.apply_infotext(img2imgreq, "img2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
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selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
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sampler, scheduler = sd_samplers.get_sampler_and_scheduler(img2imgreq.sampler_name or img2imgreq.sampler_index, img2imgreq.scheduler)
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populate = img2imgreq.copy(update={ # Override __init__ params
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"sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
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"sampler_name": validate_sampler_name(sampler),
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"do_not_save_samples": not img2imgreq.save_images,
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"do_not_save_grid": not img2imgreq.save_images,
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"mask": mask,
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@@ -519,6 +517,9 @@ class Api:
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if populate.sampler_name:
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populate.sampler_index = None # prevent a warning later on
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if not populate.scheduler and scheduler != "Automatic":
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populate.scheduler = scheduler
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args = vars(populate)
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args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
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args.pop('script_name', None)
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@@ -555,11 +556,7 @@ class Api:
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shared.state.end()
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shared.total_tqdm.clear()
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b64images = [
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encode_pil_to_base64(image)
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for image in itertools.chain(processed.images, processed.extra_images)
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if send_images
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]
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b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
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if not img2imgreq.include_init_images:
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img2imgreq.init_images = None
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@@ -695,6 +692,17 @@ class Api:
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def get_samplers(self):
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return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
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def get_schedulers(self):
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return [
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{
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"name": scheduler.name,
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"label": scheduler.label,
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"aliases": scheduler.aliases,
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"default_rho": scheduler.default_rho,
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"need_inner_model": scheduler.need_inner_model,
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}
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for scheduler in sd_schedulers.schedulers]
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def get_upscalers(self):
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return [
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{
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@@ -1,6 +1,6 @@
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import inspect
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from pydantic import BaseModel, Field, create_model
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from pydantic import BaseModel, Field, create_model, ConfigDict
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from typing import Any, Optional, Literal
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from inflection import underscore
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
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@@ -92,9 +92,7 @@ class PydanticModelGenerator:
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fields = {
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d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) 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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DynamicModel = create_model(self._model_name, __config__=ConfigDict(populate_by_name=True, frozen=False), **fields)
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return DynamicModel
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StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
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@@ -102,13 +100,13 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
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StableDiffusionProcessingTxt2Img,
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[
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{"key": "sampler_index", "type": str, "default": "Euler"},
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{"key": "script_name", "type": str, "default": None},
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{"key": "script_name", "type": str | None, "default": None},
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{"key": "script_args", "type": list, "default": []},
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{"key": "send_images", "type": bool, "default": True},
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{"key": "save_images", "type": bool, "default": False},
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{"key": "alwayson_scripts", "type": dict, "default": {}},
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{"key": "force_task_id", "type": str, "default": None},
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{"key": "infotext", "type": str, "default": None},
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{"key": "force_task_id", "type": str | None, "default": None},
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{"key": "infotext", "type": str | None, "default": None},
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]
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).generate_model()
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@@ -117,27 +115,27 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
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StableDiffusionProcessingImg2Img,
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[
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{"key": "sampler_index", "type": str, "default": "Euler"},
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{"key": "init_images", "type": list, "default": None},
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{"key": "init_images", "type": list | None, "default": None},
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{"key": "denoising_strength", "type": float, "default": 0.75},
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{"key": "mask", "type": str, "default": None},
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{"key": "mask", "type": str | None, "default": None},
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{"key": "include_init_images", "type": bool, "default": False, "exclude" : True},
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{"key": "script_name", "type": str, "default": None},
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{"key": "script_name", "type": str | None, "default": None},
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{"key": "script_args", "type": list, "default": []},
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{"key": "send_images", "type": bool, "default": True},
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{"key": "save_images", "type": bool, "default": False},
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{"key": "alwayson_scripts", "type": dict, "default": {}},
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{"key": "force_task_id", "type": str, "default": None},
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{"key": "infotext", "type": str, "default": None},
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{"key": "force_task_id", "type": str | None, "default": None},
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{"key": "infotext", "type": str | None, "default": None},
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]
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).generate_model()
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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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images: list[str] | None = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: dict
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info: str
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class ImageToImageResponse(BaseModel):
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images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
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images: list[str] | None = Field(default=None, title="Image", description="The generated image in base64 format.")
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parameters: dict
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info: str
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@@ -147,7 +145,7 @@ class ExtrasBaseRequest(BaseModel):
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gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
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codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
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codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
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upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=8, description="By how much to upscale the image, only used when resize_mode=0.")
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upscaling_resize: float = Field(default=2, title="Upscaling Factor", gt=0, description="By how much to upscale the image, only used when resize_mode=0.")
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upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
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upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
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upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
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@@ -163,7 +161,7 @@ class ExtrasSingleImageRequest(ExtrasBaseRequest):
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image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
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class ExtrasSingleImageResponse(ExtraBaseResponse):
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image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
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image: str | None = Field(default=None, title="Image", description="The generated image in base64 format.")
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class FileData(BaseModel):
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data: str = Field(title="File data", description="Base64 representation of the file")
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@@ -190,15 +188,15 @@ class ProgressResponse(BaseModel):
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progress: float = Field(title="Progress", description="The progress with a range of 0 to 1")
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eta_relative: float = Field(title="ETA in secs")
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state: dict = Field(title="State", description="The current state snapshot")
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current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
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textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.")
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current_image: str | None = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
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textinfo: str | None = Field(default=None, title="Info text", description="Info text used by WebUI.")
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class InterrogateRequest(BaseModel):
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image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
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model: str = Field(default="clip", title="Model", description="The interrogate model used.")
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class InterrogateResponse(BaseModel):
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caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
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caption: str | None = Field(default=None, title="Caption", description="The generated caption for the image.")
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class TrainResponse(BaseModel):
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info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.")
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@@ -223,7 +221,7 @@ _options = vars(parser)['_option_string_actions']
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for key in _options:
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if(_options[key].dest != 'help'):
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flag = _options[key]
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_type = str
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_type = str | None
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if _options[key].default is not None:
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_type = type(_options[key].default)
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flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))})
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@@ -233,9 +231,19 @@ FlagsModel = create_model("Flags", **flags)
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class SamplerItem(BaseModel):
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name: str = Field(title="Name")
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aliases: list[str] = Field(title="Aliases")
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options: dict[str, str] = Field(title="Options")
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options: dict[str, Any] = Field(title="Options")
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class SchedulerItem(BaseModel):
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name: str = Field(title="Name")
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label: str = Field(title="Label")
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aliases: Optional[list[str]] = Field(title="Aliases")
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default_rho: Optional[float] = Field(title="Default Rho")
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need_inner_model: Optional[bool] = Field(title="Needs Inner Model")
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||||
|
||||
class UpscalerItem(BaseModel):
|
||||
class Config:
|
||||
protected_namespaces = ()
|
||||
|
||||
name: str = Field(title="Name")
|
||||
model_name: Optional[str] = Field(title="Model Name")
|
||||
model_path: Optional[str] = Field(title="Path")
|
||||
@@ -246,6 +254,9 @@ class LatentUpscalerModeItem(BaseModel):
|
||||
name: str = Field(title="Name")
|
||||
|
||||
class SDModelItem(BaseModel):
|
||||
class Config:
|
||||
protected_namespaces = ()
|
||||
|
||||
title: str = Field(title="Title")
|
||||
model_name: str = Field(title="Model Name")
|
||||
hash: Optional[str] = Field(title="Short hash")
|
||||
@@ -254,6 +265,9 @@ class SDModelItem(BaseModel):
|
||||
config: Optional[str] = Field(title="Config file")
|
||||
|
||||
class SDVaeItem(BaseModel):
|
||||
class Config:
|
||||
protected_namespaces = ()
|
||||
|
||||
model_name: str = Field(title="Model Name")
|
||||
filename: str = Field(title="Filename")
|
||||
|
||||
@@ -293,12 +307,12 @@ class MemoryResponse(BaseModel):
|
||||
|
||||
|
||||
class ScriptsList(BaseModel):
|
||||
txt2img: list = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)")
|
||||
img2img: list = Field(default=None, title="Img2img", description="Titles of scripts (img2img)")
|
||||
txt2img: list | None = Field(default=None, title="Txt2img", description="Titles of scripts (txt2img)")
|
||||
img2img: list | None = Field(default=None, title="Img2img", description="Titles of scripts (img2img)")
|
||||
|
||||
|
||||
class ScriptArg(BaseModel):
|
||||
label: str = Field(default=None, title="Label", description="Name of the argument in UI")
|
||||
label: str | None = Field(default=None, title="Label", description="Name of the argument in UI")
|
||||
value: Optional[Any] = Field(default=None, title="Value", description="Default value of the argument")
|
||||
minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
|
||||
maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
|
||||
@@ -307,9 +321,9 @@ class ScriptArg(BaseModel):
|
||||
|
||||
|
||||
class ScriptInfo(BaseModel):
|
||||
name: str = Field(default=None, title="Name", description="Script name")
|
||||
is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
|
||||
is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
|
||||
name: str | None = Field(default=None, title="Name", description="Script name")
|
||||
is_alwayson: bool | None = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
|
||||
is_img2img: bool | None = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
|
||||
args: list[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
|
||||
|
||||
class ExtensionItem(BaseModel):
|
||||
|
||||
Reference in New Issue
Block a user