Merge branch 'AUTOMATIC1111:master' into draft
This commit is contained in:
xucj98
GitHub
commit
263b323de1
34 changed files with 1132 additions and 373 deletions
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@ -70,7 +70,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
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- separate prompts using uppercase `AND`
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- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
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- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
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- DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
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- DeepDanbooru integration, creates danbooru style tags for anime prompts
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- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
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- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
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- Generate forever option
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@ -134,14 +134,13 @@ def prepare_enviroment():
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gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
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clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
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deepdanbooru_package = os.environ.get('DEEPDANBOORU_PACKAGE', "git+https://github.com/KichangKim/DeepDanbooru.git@d91a2963bf87c6a770d74894667e9ffa9f6de7ff")
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xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
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stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/CompVis/stable-diffusion.git")
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taming_transformers_repo = os.environ.get('TAMING_REANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
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taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
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k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
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codeformer_repo = os.environ.get('CODEFORMET_REPO', 'https://github.com/sczhou/CodeFormer.git')
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codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
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blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
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stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
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@ -158,7 +157,6 @@ def prepare_enviroment():
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sys.argv, update_check = extract_arg(sys.argv, '--update-check')
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sys.argv, run_tests = extract_arg(sys.argv, '--tests')
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xformers = '--xformers' in sys.argv
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deepdanbooru = '--deepdanbooru' in sys.argv
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ngrok = '--ngrok' in sys.argv
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try:
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@ -193,9 +191,6 @@ def prepare_enviroment():
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elif platform.system() == "Linux":
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run_pip("install xformers", "xformers")
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if not is_installed("deepdanbooru") and deepdanbooru:
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run_pip(f"install {deepdanbooru_package}#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
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if not is_installed("pyngrok") and ngrok:
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run_pip("install pyngrok", "ngrok")
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@ -5,19 +5,19 @@ import uvicorn
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from threading import Lock
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from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
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from fastapi import APIRouter, Depends, FastAPI, HTTPException
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from fastapi.security import HTTPBasic, HTTPBasicCredentials
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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
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from modules.api.models import *
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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from modules.sd_samplers import all_samplers
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from modules.extras import run_extras, run_pnginfo
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from PIL import PngImagePlugin
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from modules.sd_models import checkpoints_list
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from modules.realesrgan_model import get_realesrgan_models
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from typing import List
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if shared.cmd_opts.deepdanbooru:
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from modules.deepbooru import get_deepbooru_tags
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def upscaler_to_index(name: str):
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try:
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return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
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@ -25,8 +25,12 @@ def upscaler_to_index(name: str):
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raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
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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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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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return name
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def setUpscalers(req: dict):
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reqDict = vars(req)
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@ -57,39 +61,53 @@ def encode_pil_to_base64(image):
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class Api:
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def __init__(self, app: FastAPI, queue_lock: Lock):
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if shared.cmd_opts.api_auth:
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self.credenticals = dict()
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for auth in shared.cmd_opts.api_auth.split(","):
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user, password = auth.split(":")
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self.credenticals[user] = password
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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"], response_model=TextToImageResponse)
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self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
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self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
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self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
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self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
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self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
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self.app.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
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self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
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self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
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self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
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self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
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self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
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self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
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self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
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self.app.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
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self.app.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
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self.app.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
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self.app.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
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self.app.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
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self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
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self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
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self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
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self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
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self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
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self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
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self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
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self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
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self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
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self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
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self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
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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=FlagsModel)
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self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
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self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
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self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
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self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
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self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
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self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
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self.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
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self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
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self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
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def add_api_route(self, path: str, endpoint, **kwargs):
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if shared.cmd_opts.api_auth:
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return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
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return self.app.add_api_route(path, endpoint, **kwargs)
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def auth(self, credenticals: HTTPBasicCredentials = Depends(HTTPBasic())):
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if credenticals.username in self.credenticals:
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if compare_digest(credenticals.password, self.credenticals[credenticals.username]):
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return True
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raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
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def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
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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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"sampler_name": validate_sampler_name(txt2imgreq.sampler_index),
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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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@ -109,12 +127,6 @@ class Api:
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return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
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def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
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sampler_index = sampler_to_index(img2imgreq.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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init_images = img2imgreq.init_images
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if init_images is None:
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raise HTTPException(status_code=404, detail="Init image not found")
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@ -123,10 +135,9 @@ class Api:
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if mask:
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mask = decode_base64_to_image(mask)
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populate = img2imgreq.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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"sampler_name": validate_sampler_name(img2imgreq.sampler_index),
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"do_not_save_samples": True,
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"do_not_save_grid": True,
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"mask": mask
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@ -231,10 +242,7 @@ class Api:
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if interrogatereq.model == "clip":
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processed = shared.interrogator.interrogate(img)
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elif interrogatereq.model == "deepdanbooru":
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if shared.cmd_opts.deepdanbooru:
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processed = get_deepbooru_tags(img)
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else:
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raise HTTPException(status_code=404, detail="Model not found. Add --deepdanbooru when launching for using the model.")
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processed = deepbooru.model.tag(img)
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else:
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raise HTTPException(status_code=404, detail="Model not found")
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@ -245,6 +253,9 @@ class Api:
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return {}
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def skip(self):
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shared.state.skip()
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def get_config(self):
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options = {}
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for key in shared.opts.data.keys():
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@ -256,14 +267,9 @@ class Api:
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return options
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def set_config(self, req: OptionsModel):
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# currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will
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# overwrite all options with default values.
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raise RuntimeError('Setting options via API is not supported')
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reqDict = vars(req)
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for o in reqDict:
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setattr(shared.opts, o, reqDict[o])
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def set_config(self, req: Dict[str, Any]):
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for k, v in req.items():
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shared.opts.set(k, v)
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shared.opts.save(shared.config_filename)
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return
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@ -272,7 +278,7 @@ class Api:
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return vars(shared.cmd_opts)
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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 all_samplers]
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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_upscalers(self):
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upscalers = []
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@ -176,9 +176,9 @@ 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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fields = {}
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for key, value in opts.data.items():
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metadata = opts.data_labels.get(key)
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optType = opts.typemap.get(type(value), type(value))
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for key, metadata in opts.data_labels.items():
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value = opts.data.get(key)
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optType = opts.typemap.get(type(metadata.default), type(value))
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if (metadata is not None):
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fields.update({key: (Optional[optType], Field(
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@ -1,173 +1,97 @@
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import os.path
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from concurrent.futures import ProcessPoolExecutor
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import multiprocessing
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import time
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import os
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import re
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import torch
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from PIL import Image
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import numpy as np
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from modules import modelloader, paths, deepbooru_model, devices, images, shared
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re_special = re.compile(r'([\\()])')
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def get_deepbooru_tags(pil_image):
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"""
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This method is for running only one image at a time for simple use. Used to the img2img interrogate.
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"""
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from modules import shared # prevents circular reference
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try:
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create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts())
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return get_tags_from_process(pil_image)
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finally:
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release_process()
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class DeepDanbooru:
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def __init__(self):
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self.model = None
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def load(self):
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if self.model is not None:
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return
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OPT_INCLUDE_RANKS = "include_ranks"
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def create_deepbooru_opts():
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from modules import shared
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files = modelloader.load_models(
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model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
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model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
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ext_filter=".pt",
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download_name='model-resnet_custom_v3.pt',
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)
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return {
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"use_spaces": shared.opts.deepbooru_use_spaces,
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"use_escape": shared.opts.deepbooru_escape,
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"alpha_sort": shared.opts.deepbooru_sort_alpha,
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OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks,
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}
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self.model = deepbooru_model.DeepDanbooruModel()
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self.model.load_state_dict(torch.load(files[0], map_location="cpu"))
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self.model.eval()
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self.model.to(devices.cpu, devices.dtype)
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def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts):
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model, tags = get_deepbooru_tags_model()
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while True: # while process is running, keep monitoring queue for new image
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pil_image = queue.get()
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if pil_image == "QUIT":
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break
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else:
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deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts)
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def start(self):
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self.load()
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self.model.to(devices.device)
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def stop(self):
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if not shared.opts.interrogate_keep_models_in_memory:
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self.model.to(devices.cpu)
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devices.torch_gc()
|
||||
|
||||
def create_deepbooru_process(threshold, deepbooru_opts):
|
||||
"""
|
||||
Creates deepbooru process. A queue is created to send images into the process. This enables multiple images
|
||||
to be processed in a row without reloading the model or creating a new process. To return the data, a shared
|
||||
dictionary is created to hold the tags created. To wait for tags to be returned, a value of -1 is assigned
|
||||
to the dictionary and the method adding the image to the queue should wait for this value to be updated with
|
||||
the tags.
|
||||
"""
|
||||
from modules import shared # prevents circular reference
|
||||
context = multiprocessing.get_context("spawn")
|
||||
shared.deepbooru_process_manager = context.Manager()
|
||||
shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
|
||||
shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
|
||||
shared.deepbooru_process_return["value"] = -1
|
||||
shared.deepbooru_process = context.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
|
||||
shared.deepbooru_process.start()
|
||||
def tag(self, pil_image):
|
||||
self.start()
|
||||
res = self.tag_multi(pil_image)
|
||||
self.stop()
|
||||
|
||||
return res
|
||||
|
||||
def get_tags_from_process(image):
|
||||
from modules import shared
|
||||
def tag_multi(self, pil_image, force_disable_ranks=False):
|
||||
threshold = shared.opts.interrogate_deepbooru_score_threshold
|
||||
use_spaces = shared.opts.deepbooru_use_spaces
|
||||
use_escape = shared.opts.deepbooru_escape
|
||||
alpha_sort = shared.opts.deepbooru_sort_alpha
|
||||
include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks
|
||||
|
||||
shared.deepbooru_process_return["value"] = -1
|
||||
shared.deepbooru_process_queue.put(image)
|
||||
while shared.deepbooru_process_return["value"] == -1:
|
||||
time.sleep(0.2)
|
||||
caption = shared.deepbooru_process_return["value"]
|
||||
shared.deepbooru_process_return["value"] = -1
|
||||
pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512)
|
||||
a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
|
||||
|
||||
return caption
|
||||
with torch.no_grad(), devices.autocast():
|
||||
x = torch.from_numpy(a).cuda()
|
||||
y = self.model(x)[0].detach().cpu().numpy()
|
||||
|
||||
probability_dict = {}
|
||||
|
||||
def release_process():
|
||||
"""
|
||||
Stops the deepbooru process to return used memory
|
||||
"""
|
||||
from modules import shared # prevents circular reference
|
||||
shared.deepbooru_process_queue.put("QUIT")
|
||||
shared.deepbooru_process.join()
|
||||
shared.deepbooru_process_queue = None
|
||||
shared.deepbooru_process = None
|
||||
shared.deepbooru_process_return = None
|
||||
shared.deepbooru_process_manager = None
|
||||
for tag, probability in zip(self.model.tags, y):
|
||||
if probability < threshold:
|
||||
continue
|
||||
|
||||
def get_deepbooru_tags_model():
|
||||
import deepdanbooru as dd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
this_folder = os.path.dirname(__file__)
|
||||
model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
|
||||
if not os.path.exists(os.path.join(model_path, 'project.json')):
|
||||
# there is no point importing these every time
|
||||
import zipfile
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
load_file_from_url(
|
||||
r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
|
||||
model_path)
|
||||
with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
|
||||
zip_ref.extractall(model_path)
|
||||
os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
|
||||
|
||||
tags = dd.project.load_tags_from_project(model_path)
|
||||
model = dd.project.load_model_from_project(
|
||||
model_path, compile_model=False
|
||||
)
|
||||
return model, tags
|
||||
|
||||
|
||||
def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts):
|
||||
import deepdanbooru as dd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
|
||||
alpha_sort = deepbooru_opts['alpha_sort']
|
||||
use_spaces = deepbooru_opts['use_spaces']
|
||||
use_escape = deepbooru_opts['use_escape']
|
||||
include_ranks = deepbooru_opts['include_ranks']
|
||||
|
||||
width = model.input_shape[2]
|
||||
height = model.input_shape[1]
|
||||
image = np.array(pil_image)
|
||||
image = tf.image.resize(
|
||||
image,
|
||||
size=(height, width),
|
||||
method=tf.image.ResizeMethod.AREA,
|
||||
preserve_aspect_ratio=True,
|
||||
)
|
||||
image = image.numpy() # EagerTensor to np.array
|
||||
image = dd.image.transform_and_pad_image(image, width, height)
|
||||
image = image / 255.0
|
||||
image_shape = image.shape
|
||||
image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
|
||||
|
||||
y = model.predict(image)[0]
|
||||
|
||||
result_dict = {}
|
||||
|
||||
for i, tag in enumerate(tags):
|
||||
result_dict[tag] = y[i]
|
||||
|
||||
unsorted_tags_in_theshold = []
|
||||
result_tags_print = []
|
||||
for tag in tags:
|
||||
if result_dict[tag] >= threshold:
|
||||
if tag.startswith("rating:"):
|
||||
continue
|
||||
unsorted_tags_in_theshold.append((result_dict[tag], tag))
|
||||
result_tags_print.append(f'{result_dict[tag]} {tag}')
|
||||
|
||||
# sort tags
|
||||
result_tags_out = []
|
||||
sort_ndx = 0
|
||||
if alpha_sort:
|
||||
sort_ndx = 1
|
||||
probability_dict[tag] = probability
|
||||
|
||||
# sort by reverse by likelihood and normal for alpha, and format tag text as requested
|
||||
unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
|
||||
for weight, tag in unsorted_tags_in_theshold:
|
||||
tag_outformat = tag
|
||||
if use_spaces:
|
||||
tag_outformat = tag_outformat.replace('_', ' ')
|
||||
if use_escape:
|
||||
tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
|
||||
if include_ranks:
|
||||
tag_outformat = f"({tag_outformat}:{weight:.3f})"
|
||||
if alpha_sort:
|
||||
tags = sorted(probability_dict)
|
||||
else:
|
||||
tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])]
|
||||
|
||||
result_tags_out.append(tag_outformat)
|
||||
res = []
|
||||
|
||||
print('\n'.join(sorted(result_tags_print, reverse=True)))
|
||||
for tag in tags:
|
||||
probability = probability_dict[tag]
|
||||
tag_outformat = tag
|
||||
if use_spaces:
|
||||
tag_outformat = tag_outformat.replace('_', ' ')
|
||||
if use_escape:
|
||||
tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
|
||||
if include_ranks:
|
||||
tag_outformat = f"({tag_outformat}:{probability:.3f})"
|
||||
|
||||
return ', '.join(result_tags_out)
|
||||
res.append(tag_outformat)
|
||||
|
||||
return ", ".join(res)
|
||||
|
||||
|
||||
model = DeepDanbooru()
|
||||
|
|
|
|||
676
modules/deepbooru_model.py
Normal file
676
modules/deepbooru_model.py
Normal file
|
|
@ -0,0 +1,676 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more
|
||||
|
||||
|
||||
class DeepDanbooruModel(nn.Module):
|
||||
def __init__(self):
|
||||
super(DeepDanbooruModel, self).__init__()
|
||||
|
||||
self.tags = []
|
||||
|
||||
self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2))
|
||||
self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))
|
||||
self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
|
||||
self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64)
|
||||
self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
|
||||
self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
|
||||
self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
|
||||
self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
|
||||
self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
|
||||
self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)
|
||||
self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)
|
||||
self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)
|
||||
self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2))
|
||||
self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128)
|
||||
self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2))
|
||||
self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
|
||||
self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
|
||||
self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
|
||||
self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
|
||||
self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
|
||||
self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
|
||||
self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
|
||||
self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
|
||||
self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
|
||||
self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
|
||||
self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
|
||||
self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
|
||||
self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
|
||||
self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
|
||||
self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
|
||||
self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
|
||||
self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
|
||||
self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
|
||||
self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
|
||||
self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)
|
||||
self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)
|
||||
self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)
|
||||
self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2))
|
||||
self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256)
|
||||
self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
|
||||
self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))
|
||||
self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2))
|
||||
self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)
|
||||
self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)
|
||||
self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)
|
||||
self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2))
|
||||
self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512)
|
||||
self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2))
|
||||
self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
|
||||
self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
|
||||
self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
|
||||
self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
|
||||
self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)
|
||||
self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)
|
||||
self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)
|
||||
self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2))
|
||||
self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024)
|
||||
self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2))
|
||||
self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
|
||||
self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
|
||||
self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
|
||||
self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
|
||||
self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)
|
||||
self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)
|
||||
self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)
|
||||
self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False)
|
||||
|
||||
def forward(self, *inputs):
|
||||
t_358, = inputs
|
||||
t_359 = t_358.permute(*[0, 3, 1, 2])
|
||||
t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
|
||||
t_360 = self.n_Conv_0(t_359_padded)
|
||||
t_361 = F.relu(t_360)
|
||||
t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
|
||||
t_362 = self.n_MaxPool_0(t_361)
|
||||
t_363 = self.n_Conv_1(t_362)
|
||||
t_364 = self.n_Conv_2(t_362)
|
||||
t_365 = F.relu(t_364)
|
||||
t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0)
|
||||
t_366 = self.n_Conv_3(t_365_padded)
|
||||
t_367 = F.relu(t_366)
|
||||
t_368 = self.n_Conv_4(t_367)
|
||||
t_369 = torch.add(t_368, t_363)
|
||||
t_370 = F.relu(t_369)
|
||||
t_371 = self.n_Conv_5(t_370)
|
||||
t_372 = F.relu(t_371)
|
||||
t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0)
|
||||
t_373 = self.n_Conv_6(t_372_padded)
|
||||
t_374 = F.relu(t_373)
|
||||
t_375 = self.n_Conv_7(t_374)
|
||||
t_376 = torch.add(t_375, t_370)
|
||||
t_377 = F.relu(t_376)
|
||||
t_378 = self.n_Conv_8(t_377)
|
||||
t_379 = F.relu(t_378)
|
||||
t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0)
|
||||
t_380 = self.n_Conv_9(t_379_padded)
|
||||
t_381 = F.relu(t_380)
|
||||
t_382 = self.n_Conv_10(t_381)
|
||||
t_383 = torch.add(t_382, t_377)
|
||||
t_384 = F.relu(t_383)
|
||||
t_385 = self.n_Conv_11(t_384)
|
||||
t_386 = self.n_Conv_12(t_384)
|
||||
t_387 = F.relu(t_386)
|
||||
t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0)
|
||||
t_388 = self.n_Conv_13(t_387_padded)
|
||||
t_389 = F.relu(t_388)
|
||||
t_390 = self.n_Conv_14(t_389)
|
||||
t_391 = torch.add(t_390, t_385)
|
||||
t_392 = F.relu(t_391)
|
||||
t_393 = self.n_Conv_15(t_392)
|
||||
t_394 = F.relu(t_393)
|
||||
t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0)
|
||||
t_395 = self.n_Conv_16(t_394_padded)
|
||||
t_396 = F.relu(t_395)
|
||||
t_397 = self.n_Conv_17(t_396)
|
||||
t_398 = torch.add(t_397, t_392)
|
||||
t_399 = F.relu(t_398)
|
||||
t_400 = self.n_Conv_18(t_399)
|
||||
t_401 = F.relu(t_400)
|
||||
t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0)
|
||||
t_402 = self.n_Conv_19(t_401_padded)
|
||||
t_403 = F.relu(t_402)
|
||||
t_404 = self.n_Conv_20(t_403)
|
||||
t_405 = torch.add(t_404, t_399)
|
||||
t_406 = F.relu(t_405)
|
||||
t_407 = self.n_Conv_21(t_406)
|
||||
t_408 = F.relu(t_407)
|
||||
t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0)
|
||||
t_409 = self.n_Conv_22(t_408_padded)
|
||||
t_410 = F.relu(t_409)
|
||||
t_411 = self.n_Conv_23(t_410)
|
||||
t_412 = torch.add(t_411, t_406)
|
||||
t_413 = F.relu(t_412)
|
||||
t_414 = self.n_Conv_24(t_413)
|
||||
t_415 = F.relu(t_414)
|
||||
t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0)
|
||||
t_416 = self.n_Conv_25(t_415_padded)
|
||||
t_417 = F.relu(t_416)
|
||||
t_418 = self.n_Conv_26(t_417)
|
||||
t_419 = torch.add(t_418, t_413)
|
||||
t_420 = F.relu(t_419)
|
||||
t_421 = self.n_Conv_27(t_420)
|
||||
t_422 = F.relu(t_421)
|
||||
t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0)
|
||||
t_423 = self.n_Conv_28(t_422_padded)
|
||||
t_424 = F.relu(t_423)
|
||||
t_425 = self.n_Conv_29(t_424)
|
||||
t_426 = torch.add(t_425, t_420)
|
||||
t_427 = F.relu(t_426)
|
||||
t_428 = self.n_Conv_30(t_427)
|
||||
t_429 = F.relu(t_428)
|
||||
t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0)
|
||||
t_430 = self.n_Conv_31(t_429_padded)
|
||||
t_431 = F.relu(t_430)
|
||||
t_432 = self.n_Conv_32(t_431)
|
||||
t_433 = torch.add(t_432, t_427)
|
||||
t_434 = F.relu(t_433)
|
||||
t_435 = self.n_Conv_33(t_434)
|
||||
t_436 = F.relu(t_435)
|
||||
t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0)
|
||||
t_437 = self.n_Conv_34(t_436_padded)
|
||||
t_438 = F.relu(t_437)
|
||||
t_439 = self.n_Conv_35(t_438)
|
||||
t_440 = torch.add(t_439, t_434)
|
||||
t_441 = F.relu(t_440)
|
||||
t_442 = self.n_Conv_36(t_441)
|
||||
t_443 = self.n_Conv_37(t_441)
|
||||
t_444 = F.relu(t_443)
|
||||
t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0)
|
||||
t_445 = self.n_Conv_38(t_444_padded)
|
||||
t_446 = F.relu(t_445)
|
||||
t_447 = self.n_Conv_39(t_446)
|
||||
t_448 = torch.add(t_447, t_442)
|
||||
t_449 = F.relu(t_448)
|
||||
t_450 = self.n_Conv_40(t_449)
|
||||
t_451 = F.relu(t_450)
|
||||
t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0)
|
||||
t_452 = self.n_Conv_41(t_451_padded)
|
||||
t_453 = F.relu(t_452)
|
||||
t_454 = self.n_Conv_42(t_453)
|
||||
t_455 = torch.add(t_454, t_449)
|
||||
t_456 = F.relu(t_455)
|
||||
t_457 = self.n_Conv_43(t_456)
|
||||
t_458 = F.relu(t_457)
|
||||
t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0)
|
||||
t_459 = self.n_Conv_44(t_458_padded)
|
||||
t_460 = F.relu(t_459)
|
||||
t_461 = self.n_Conv_45(t_460)
|
||||
t_462 = torch.add(t_461, t_456)
|
||||
t_463 = F.relu(t_462)
|
||||
t_464 = self.n_Conv_46(t_463)
|
||||
t_465 = F.relu(t_464)
|
||||
t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0)
|
||||
t_466 = self.n_Conv_47(t_465_padded)
|
||||
t_467 = F.relu(t_466)
|
||||
t_468 = self.n_Conv_48(t_467)
|
||||
t_469 = torch.add(t_468, t_463)
|
||||
t_470 = F.relu(t_469)
|
||||
t_471 = self.n_Conv_49(t_470)
|
||||
t_472 = F.relu(t_471)
|
||||
t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0)
|
||||
t_473 = self.n_Conv_50(t_472_padded)
|
||||
t_474 = F.relu(t_473)
|
||||
t_475 = self.n_Conv_51(t_474)
|
||||
t_476 = torch.add(t_475, t_470)
|
||||
t_477 = F.relu(t_476)
|
||||
t_478 = self.n_Conv_52(t_477)
|
||||
t_479 = F.relu(t_478)
|
||||
t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0)
|
||||
t_480 = self.n_Conv_53(t_479_padded)
|
||||
t_481 = F.relu(t_480)
|
||||
t_482 = self.n_Conv_54(t_481)
|
||||
t_483 = torch.add(t_482, t_477)
|
||||
t_484 = F.relu(t_483)
|
||||
t_485 = self.n_Conv_55(t_484)
|
||||
t_486 = F.relu(t_485)
|
||||
t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0)
|
||||
t_487 = self.n_Conv_56(t_486_padded)
|
||||
t_488 = F.relu(t_487)
|
||||
t_489 = self.n_Conv_57(t_488)
|
||||
t_490 = torch.add(t_489, t_484)
|
||||
t_491 = F.relu(t_490)
|
||||
t_492 = self.n_Conv_58(t_491)
|
||||
t_493 = F.relu(t_492)
|
||||
t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0)
|
||||
t_494 = self.n_Conv_59(t_493_padded)
|
||||
t_495 = F.relu(t_494)
|
||||
t_496 = self.n_Conv_60(t_495)
|
||||
t_497 = torch.add(t_496, t_491)
|
||||
t_498 = F.relu(t_497)
|
||||
t_499 = self.n_Conv_61(t_498)
|
||||
t_500 = F.relu(t_499)
|
||||
t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0)
|
||||
t_501 = self.n_Conv_62(t_500_padded)
|
||||
t_502 = F.relu(t_501)
|
||||
t_503 = self.n_Conv_63(t_502)
|
||||
t_504 = torch.add(t_503, t_498)
|
||||
t_505 = F.relu(t_504)
|
||||
t_506 = self.n_Conv_64(t_505)
|
||||
t_507 = F.relu(t_506)
|
||||
t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0)
|
||||
t_508 = self.n_Conv_65(t_507_padded)
|
||||
t_509 = F.relu(t_508)
|
||||
t_510 = self.n_Conv_66(t_509)
|
||||
t_511 = torch.add(t_510, t_505)
|
||||
t_512 = F.relu(t_511)
|
||||
t_513 = self.n_Conv_67(t_512)
|
||||
t_514 = F.relu(t_513)
|
||||
t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0)
|
||||
t_515 = self.n_Conv_68(t_514_padded)
|
||||
t_516 = F.relu(t_515)
|
||||
t_517 = self.n_Conv_69(t_516)
|
||||
t_518 = torch.add(t_517, t_512)
|
||||
t_519 = F.relu(t_518)
|
||||
t_520 = self.n_Conv_70(t_519)
|
||||
t_521 = F.relu(t_520)
|
||||
t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0)
|
||||
t_522 = self.n_Conv_71(t_521_padded)
|
||||
t_523 = F.relu(t_522)
|
||||
t_524 = self.n_Conv_72(t_523)
|
||||
t_525 = torch.add(t_524, t_519)
|
||||
t_526 = F.relu(t_525)
|
||||
t_527 = self.n_Conv_73(t_526)
|
||||
t_528 = F.relu(t_527)
|
||||
t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0)
|
||||
t_529 = self.n_Conv_74(t_528_padded)
|
||||
t_530 = F.relu(t_529)
|
||||
t_531 = self.n_Conv_75(t_530)
|
||||
t_532 = torch.add(t_531, t_526)
|
||||
t_533 = F.relu(t_532)
|
||||
t_534 = self.n_Conv_76(t_533)
|
||||
t_535 = F.relu(t_534)
|
||||
t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0)
|
||||
t_536 = self.n_Conv_77(t_535_padded)
|
||||
t_537 = F.relu(t_536)
|
||||
t_538 = self.n_Conv_78(t_537)
|
||||
t_539 = torch.add(t_538, t_533)
|
||||
t_540 = F.relu(t_539)
|
||||
t_541 = self.n_Conv_79(t_540)
|
||||
t_542 = F.relu(t_541)
|
||||
t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0)
|
||||
t_543 = self.n_Conv_80(t_542_padded)
|
||||
t_544 = F.relu(t_543)
|
||||
t_545 = self.n_Conv_81(t_544)
|
||||
t_546 = torch.add(t_545, t_540)
|
||||
t_547 = F.relu(t_546)
|
||||
t_548 = self.n_Conv_82(t_547)
|
||||
t_549 = F.relu(t_548)
|
||||
t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0)
|
||||
t_550 = self.n_Conv_83(t_549_padded)
|
||||
t_551 = F.relu(t_550)
|
||||
t_552 = self.n_Conv_84(t_551)
|
||||
t_553 = torch.add(t_552, t_547)
|
||||
t_554 = F.relu(t_553)
|
||||
t_555 = self.n_Conv_85(t_554)
|
||||
t_556 = F.relu(t_555)
|
||||
t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0)
|
||||
t_557 = self.n_Conv_86(t_556_padded)
|
||||
t_558 = F.relu(t_557)
|
||||
t_559 = self.n_Conv_87(t_558)
|
||||
t_560 = torch.add(t_559, t_554)
|
||||
t_561 = F.relu(t_560)
|
||||
t_562 = self.n_Conv_88(t_561)
|
||||
t_563 = F.relu(t_562)
|
||||
t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0)
|
||||
t_564 = self.n_Conv_89(t_563_padded)
|
||||
t_565 = F.relu(t_564)
|
||||
t_566 = self.n_Conv_90(t_565)
|
||||
t_567 = torch.add(t_566, t_561)
|
||||
t_568 = F.relu(t_567)
|
||||
t_569 = self.n_Conv_91(t_568)
|
||||
t_570 = F.relu(t_569)
|
||||
t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0)
|
||||
t_571 = self.n_Conv_92(t_570_padded)
|
||||
t_572 = F.relu(t_571)
|
||||
t_573 = self.n_Conv_93(t_572)
|
||||
t_574 = torch.add(t_573, t_568)
|
||||
t_575 = F.relu(t_574)
|
||||
t_576 = self.n_Conv_94(t_575)
|
||||
t_577 = F.relu(t_576)
|
||||
t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0)
|
||||
t_578 = self.n_Conv_95(t_577_padded)
|
||||
t_579 = F.relu(t_578)
|
||||
t_580 = self.n_Conv_96(t_579)
|
||||
t_581 = torch.add(t_580, t_575)
|
||||
t_582 = F.relu(t_581)
|
||||
t_583 = self.n_Conv_97(t_582)
|
||||
t_584 = F.relu(t_583)
|
||||
t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0)
|
||||
t_585 = self.n_Conv_98(t_584_padded)
|
||||
t_586 = F.relu(t_585)
|
||||
t_587 = self.n_Conv_99(t_586)
|
||||
t_588 = self.n_Conv_100(t_582)
|
||||
t_589 = torch.add(t_587, t_588)
|
||||
t_590 = F.relu(t_589)
|
||||
t_591 = self.n_Conv_101(t_590)
|
||||
t_592 = F.relu(t_591)
|
||||
t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0)
|
||||
t_593 = self.n_Conv_102(t_592_padded)
|
||||
t_594 = F.relu(t_593)
|
||||
t_595 = self.n_Conv_103(t_594)
|
||||
t_596 = torch.add(t_595, t_590)
|
||||
t_597 = F.relu(t_596)
|
||||
t_598 = self.n_Conv_104(t_597)
|
||||
t_599 = F.relu(t_598)
|
||||
t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0)
|
||||
t_600 = self.n_Conv_105(t_599_padded)
|
||||
t_601 = F.relu(t_600)
|
||||
t_602 = self.n_Conv_106(t_601)
|
||||
t_603 = torch.add(t_602, t_597)
|
||||
t_604 = F.relu(t_603)
|
||||
t_605 = self.n_Conv_107(t_604)
|
||||
t_606 = F.relu(t_605)
|
||||
t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0)
|
||||
t_607 = self.n_Conv_108(t_606_padded)
|
||||
t_608 = F.relu(t_607)
|
||||
t_609 = self.n_Conv_109(t_608)
|
||||
t_610 = torch.add(t_609, t_604)
|
||||
t_611 = F.relu(t_610)
|
||||
t_612 = self.n_Conv_110(t_611)
|
||||
t_613 = F.relu(t_612)
|
||||
t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0)
|
||||
t_614 = self.n_Conv_111(t_613_padded)
|
||||
t_615 = F.relu(t_614)
|
||||
t_616 = self.n_Conv_112(t_615)
|
||||
t_617 = torch.add(t_616, t_611)
|
||||
t_618 = F.relu(t_617)
|
||||
t_619 = self.n_Conv_113(t_618)
|
||||
t_620 = F.relu(t_619)
|
||||
t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0)
|
||||
t_621 = self.n_Conv_114(t_620_padded)
|
||||
t_622 = F.relu(t_621)
|
||||
t_623 = self.n_Conv_115(t_622)
|
||||
t_624 = torch.add(t_623, t_618)
|
||||
t_625 = F.relu(t_624)
|
||||
t_626 = self.n_Conv_116(t_625)
|
||||
t_627 = F.relu(t_626)
|
||||
t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0)
|
||||
t_628 = self.n_Conv_117(t_627_padded)
|
||||
t_629 = F.relu(t_628)
|
||||
t_630 = self.n_Conv_118(t_629)
|
||||
t_631 = torch.add(t_630, t_625)
|
||||
t_632 = F.relu(t_631)
|
||||
t_633 = self.n_Conv_119(t_632)
|
||||
t_634 = F.relu(t_633)
|
||||
t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0)
|
||||
t_635 = self.n_Conv_120(t_634_padded)
|
||||
t_636 = F.relu(t_635)
|
||||
t_637 = self.n_Conv_121(t_636)
|
||||
t_638 = torch.add(t_637, t_632)
|
||||
t_639 = F.relu(t_638)
|
||||
t_640 = self.n_Conv_122(t_639)
|
||||
t_641 = F.relu(t_640)
|
||||
t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0)
|
||||
t_642 = self.n_Conv_123(t_641_padded)
|
||||
t_643 = F.relu(t_642)
|
||||
t_644 = self.n_Conv_124(t_643)
|
||||
t_645 = torch.add(t_644, t_639)
|
||||
t_646 = F.relu(t_645)
|
||||
t_647 = self.n_Conv_125(t_646)
|
||||
t_648 = F.relu(t_647)
|
||||
t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0)
|
||||
t_649 = self.n_Conv_126(t_648_padded)
|
||||
t_650 = F.relu(t_649)
|
||||
t_651 = self.n_Conv_127(t_650)
|
||||
t_652 = torch.add(t_651, t_646)
|
||||
t_653 = F.relu(t_652)
|
||||
t_654 = self.n_Conv_128(t_653)
|
||||
t_655 = F.relu(t_654)
|
||||
t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0)
|
||||
t_656 = self.n_Conv_129(t_655_padded)
|
||||
t_657 = F.relu(t_656)
|
||||
t_658 = self.n_Conv_130(t_657)
|
||||
t_659 = torch.add(t_658, t_653)
|
||||
t_660 = F.relu(t_659)
|
||||
t_661 = self.n_Conv_131(t_660)
|
||||
t_662 = F.relu(t_661)
|
||||
t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0)
|
||||
t_663 = self.n_Conv_132(t_662_padded)
|
||||
t_664 = F.relu(t_663)
|
||||
t_665 = self.n_Conv_133(t_664)
|
||||
t_666 = torch.add(t_665, t_660)
|
||||
t_667 = F.relu(t_666)
|
||||
t_668 = self.n_Conv_134(t_667)
|
||||
t_669 = F.relu(t_668)
|
||||
t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0)
|
||||
t_670 = self.n_Conv_135(t_669_padded)
|
||||
t_671 = F.relu(t_670)
|
||||
t_672 = self.n_Conv_136(t_671)
|
||||
t_673 = torch.add(t_672, t_667)
|
||||
t_674 = F.relu(t_673)
|
||||
t_675 = self.n_Conv_137(t_674)
|
||||
t_676 = F.relu(t_675)
|
||||
t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0)
|
||||
t_677 = self.n_Conv_138(t_676_padded)
|
||||
t_678 = F.relu(t_677)
|
||||
t_679 = self.n_Conv_139(t_678)
|
||||
t_680 = torch.add(t_679, t_674)
|
||||
t_681 = F.relu(t_680)
|
||||
t_682 = self.n_Conv_140(t_681)
|
||||
t_683 = F.relu(t_682)
|
||||
t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0)
|
||||
t_684 = self.n_Conv_141(t_683_padded)
|
||||
t_685 = F.relu(t_684)
|
||||
t_686 = self.n_Conv_142(t_685)
|
||||
t_687 = torch.add(t_686, t_681)
|
||||
t_688 = F.relu(t_687)
|
||||
t_689 = self.n_Conv_143(t_688)
|
||||
t_690 = F.relu(t_689)
|
||||
t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0)
|
||||
t_691 = self.n_Conv_144(t_690_padded)
|
||||
t_692 = F.relu(t_691)
|
||||
t_693 = self.n_Conv_145(t_692)
|
||||
t_694 = torch.add(t_693, t_688)
|
||||
t_695 = F.relu(t_694)
|
||||
t_696 = self.n_Conv_146(t_695)
|
||||
t_697 = F.relu(t_696)
|
||||
t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0)
|
||||
t_698 = self.n_Conv_147(t_697_padded)
|
||||
t_699 = F.relu(t_698)
|
||||
t_700 = self.n_Conv_148(t_699)
|
||||
t_701 = torch.add(t_700, t_695)
|
||||
t_702 = F.relu(t_701)
|
||||
t_703 = self.n_Conv_149(t_702)
|
||||
t_704 = F.relu(t_703)
|
||||
t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0)
|
||||
t_705 = self.n_Conv_150(t_704_padded)
|
||||
t_706 = F.relu(t_705)
|
||||
t_707 = self.n_Conv_151(t_706)
|
||||
t_708 = torch.add(t_707, t_702)
|
||||
t_709 = F.relu(t_708)
|
||||
t_710 = self.n_Conv_152(t_709)
|
||||
t_711 = F.relu(t_710)
|
||||
t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0)
|
||||
t_712 = self.n_Conv_153(t_711_padded)
|
||||
t_713 = F.relu(t_712)
|
||||
t_714 = self.n_Conv_154(t_713)
|
||||
t_715 = torch.add(t_714, t_709)
|
||||
t_716 = F.relu(t_715)
|
||||
t_717 = self.n_Conv_155(t_716)
|
||||
t_718 = F.relu(t_717)
|
||||
t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0)
|
||||
t_719 = self.n_Conv_156(t_718_padded)
|
||||
t_720 = F.relu(t_719)
|
||||
t_721 = self.n_Conv_157(t_720)
|
||||
t_722 = torch.add(t_721, t_716)
|
||||
t_723 = F.relu(t_722)
|
||||
t_724 = self.n_Conv_158(t_723)
|
||||
t_725 = self.n_Conv_159(t_723)
|
||||
t_726 = F.relu(t_725)
|
||||
t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0)
|
||||
t_727 = self.n_Conv_160(t_726_padded)
|
||||
t_728 = F.relu(t_727)
|
||||
t_729 = self.n_Conv_161(t_728)
|
||||
t_730 = torch.add(t_729, t_724)
|
||||
t_731 = F.relu(t_730)
|
||||
t_732 = self.n_Conv_162(t_731)
|
||||
t_733 = F.relu(t_732)
|
||||
t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0)
|
||||
t_734 = self.n_Conv_163(t_733_padded)
|
||||
t_735 = F.relu(t_734)
|
||||
t_736 = self.n_Conv_164(t_735)
|
||||
t_737 = torch.add(t_736, t_731)
|
||||
t_738 = F.relu(t_737)
|
||||
t_739 = self.n_Conv_165(t_738)
|
||||
t_740 = F.relu(t_739)
|
||||
t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0)
|
||||
t_741 = self.n_Conv_166(t_740_padded)
|
||||
t_742 = F.relu(t_741)
|
||||
t_743 = self.n_Conv_167(t_742)
|
||||
t_744 = torch.add(t_743, t_738)
|
||||
t_745 = F.relu(t_744)
|
||||
t_746 = self.n_Conv_168(t_745)
|
||||
t_747 = self.n_Conv_169(t_745)
|
||||
t_748 = F.relu(t_747)
|
||||
t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0)
|
||||
t_749 = self.n_Conv_170(t_748_padded)
|
||||
t_750 = F.relu(t_749)
|
||||
t_751 = self.n_Conv_171(t_750)
|
||||
t_752 = torch.add(t_751, t_746)
|
||||
t_753 = F.relu(t_752)
|
||||
t_754 = self.n_Conv_172(t_753)
|
||||
t_755 = F.relu(t_754)
|
||||
t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0)
|
||||
t_756 = self.n_Conv_173(t_755_padded)
|
||||
t_757 = F.relu(t_756)
|
||||
t_758 = self.n_Conv_174(t_757)
|
||||
t_759 = torch.add(t_758, t_753)
|
||||
t_760 = F.relu(t_759)
|
||||
t_761 = self.n_Conv_175(t_760)
|
||||
t_762 = F.relu(t_761)
|
||||
t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0)
|
||||
t_763 = self.n_Conv_176(t_762_padded)
|
||||
t_764 = F.relu(t_763)
|
||||
t_765 = self.n_Conv_177(t_764)
|
||||
t_766 = torch.add(t_765, t_760)
|
||||
t_767 = F.relu(t_766)
|
||||
t_768 = self.n_Conv_178(t_767)
|
||||
t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:])
|
||||
t_770 = torch.squeeze(t_769, 3)
|
||||
t_770 = torch.squeeze(t_770, 2)
|
||||
t_771 = torch.sigmoid(t_770)
|
||||
return t_771
|
||||
|
||||
def load_state_dict(self, state_dict, **kwargs):
|
||||
self.tags = state_dict.get('tags', [])
|
||||
|
||||
super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'})
|
||||
|
||||
|
|
@ -65,9 +65,12 @@ class Extension:
|
|||
self.can_update = False
|
||||
self.status = "latest"
|
||||
|
||||
def pull(self):
|
||||
def fetch_and_reset_hard(self):
|
||||
repo = git.Repo(self.path)
|
||||
repo.remotes.origin.pull()
|
||||
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
|
||||
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
|
||||
repo.git.fetch('--all')
|
||||
repo.git.reset('--hard', 'origin')
|
||||
|
||||
|
||||
def list_extensions():
|
||||
|
|
|
|||
|
|
@ -73,6 +73,7 @@ def integrate_settings_paste_fields(component_dict):
|
|||
'sd_hypernetwork': 'Hypernet',
|
||||
'sd_hypernetwork_strength': 'Hypernet strength',
|
||||
'CLIP_stop_at_last_layers': 'Clip skip',
|
||||
'inpainting_mask_weight': 'Conditional mask weight',
|
||||
'sd_model_checkpoint': 'Model hash',
|
||||
}
|
||||
settings_paste_fields = [
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ import torch
|
|||
import tqdm
|
||||
from einops import rearrange, repeat
|
||||
from ldm.util import default
|
||||
from modules import devices, processing, sd_models, shared
|
||||
from modules import devices, processing, sd_models, shared, sd_samplers
|
||||
from modules.textual_inversion import textual_inversion
|
||||
from modules.textual_inversion.learn_schedule import LearnRateScheduler
|
||||
from torch import einsum
|
||||
|
|
@ -535,7 +535,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
|
|||
p.prompt = preview_prompt
|
||||
p.negative_prompt = preview_negative_prompt
|
||||
p.steps = preview_steps
|
||||
p.sampler_index = preview_sampler_index
|
||||
p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
|
||||
p.cfg_scale = preview_cfg_scale
|
||||
p.seed = preview_seed
|
||||
p.width = preview_width
|
||||
|
|
|
|||
|
|
@ -303,7 +303,7 @@ class FilenameGenerator:
|
|||
'width': lambda self: self.image.width,
|
||||
'height': lambda self: self.image.height,
|
||||
'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
|
||||
'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
|
||||
'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
|
||||
'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
|
||||
'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
|
||||
'datetime': lambda self, *args: self.datetime(*args), # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ import traceback
|
|||
import numpy as np
|
||||
from PIL import Image, ImageOps, ImageChops
|
||||
|
||||
from modules import devices
|
||||
from modules import devices, sd_samplers
|
||||
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
|
||||
from modules.shared import opts, state
|
||||
import modules.shared as shared
|
||||
|
|
@ -99,7 +99,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
|
|||
seed_resize_from_h=seed_resize_from_h,
|
||||
seed_resize_from_w=seed_resize_from_w,
|
||||
seed_enable_extras=seed_enable_extras,
|
||||
sampler_index=sampler_index,
|
||||
sampler_index=sd_samplers.samplers_for_img2img[sampler_index].name,
|
||||
batch_size=batch_size,
|
||||
n_iter=n_iter,
|
||||
steps=steps,
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import json
|
|||
import math
|
||||
import os
|
||||
import sys
|
||||
import warnings
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
|
|
@ -66,19 +67,15 @@ def apply_overlay(image, paste_loc, index, overlays):
|
|||
|
||||
return image
|
||||
|
||||
def get_correct_sampler(p):
|
||||
if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
|
||||
return sd_samplers.samplers
|
||||
elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
|
||||
return sd_samplers.samplers_for_img2img
|
||||
elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):
|
||||
return sd_samplers.samplers
|
||||
|
||||
class StableDiffusionProcessing():
|
||||
"""
|
||||
The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
|
||||
"""
|
||||
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_index: int = 0, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None):
|
||||
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, sampler_index: int = None):
|
||||
if sampler_index is not None:
|
||||
warnings.warn("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name")
|
||||
|
||||
self.sd_model = sd_model
|
||||
self.outpath_samples: str = outpath_samples
|
||||
self.outpath_grids: str = outpath_grids
|
||||
|
|
@ -91,7 +88,7 @@ class StableDiffusionProcessing():
|
|||
self.subseed_strength: float = subseed_strength
|
||||
self.seed_resize_from_h: int = seed_resize_from_h
|
||||
self.seed_resize_from_w: int = seed_resize_from_w
|
||||
self.sampler_index: int = sampler_index
|
||||
self.sampler_name: str = sampler_name
|
||||
self.batch_size: int = batch_size
|
||||
self.n_iter: int = n_iter
|
||||
self.steps: int = steps
|
||||
|
|
@ -116,6 +113,7 @@ class StableDiffusionProcessing():
|
|||
self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option
|
||||
self.s_noise = s_noise or opts.s_noise
|
||||
self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
|
||||
self.is_using_inpainting_conditioning = False
|
||||
|
||||
if not seed_enable_extras:
|
||||
self.subseed = -1
|
||||
|
|
@ -126,6 +124,7 @@ class StableDiffusionProcessing():
|
|||
self.scripts = None
|
||||
self.script_args = None
|
||||
self.all_prompts = None
|
||||
self.all_negative_prompts = None
|
||||
self.all_seeds = None
|
||||
self.all_subseeds = None
|
||||
|
||||
|
|
@ -136,6 +135,8 @@ class StableDiffusionProcessing():
|
|||
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
|
||||
return x.new_zeros(x.shape[0], 5, 1, 1)
|
||||
|
||||
self.is_using_inpainting_conditioning = True
|
||||
|
||||
height = height or self.height
|
||||
width = width or self.width
|
||||
|
||||
|
|
@ -154,6 +155,8 @@ class StableDiffusionProcessing():
|
|||
# Dummy zero conditioning if we're not using inpainting model.
|
||||
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
|
||||
|
||||
self.is_using_inpainting_conditioning = True
|
||||
|
||||
# Handle the different mask inputs
|
||||
if image_mask is not None:
|
||||
if torch.is_tensor(image_mask):
|
||||
|
|
@ -200,7 +203,7 @@ class StableDiffusionProcessing():
|
|||
|
||||
|
||||
class Processed:
|
||||
def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
|
||||
def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
|
||||
self.images = images_list
|
||||
self.prompt = p.prompt
|
||||
self.negative_prompt = p.negative_prompt
|
||||
|
|
@ -210,8 +213,7 @@ class Processed:
|
|||
self.info = info
|
||||
self.width = p.width
|
||||
self.height = p.height
|
||||
self.sampler_index = p.sampler_index
|
||||
self.sampler = sd_samplers.samplers[p.sampler_index].name
|
||||
self.sampler_name = p.sampler_name
|
||||
self.cfg_scale = p.cfg_scale
|
||||
self.steps = p.steps
|
||||
self.batch_size = p.batch_size
|
||||
|
|
@ -238,17 +240,20 @@ class Processed:
|
|||
self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
|
||||
self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
|
||||
self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
|
||||
self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
|
||||
|
||||
self.all_prompts = all_prompts or [self.prompt]
|
||||
self.all_seeds = all_seeds or [self.seed]
|
||||
self.all_subseeds = all_subseeds or [self.subseed]
|
||||
self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
|
||||
self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
|
||||
self.all_seeds = all_seeds or p.all_seeds or [self.seed]
|
||||
self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
|
||||
self.infotexts = infotexts or [info]
|
||||
|
||||
def js(self):
|
||||
obj = {
|
||||
"prompt": self.prompt,
|
||||
"prompt": self.all_prompts[0],
|
||||
"all_prompts": self.all_prompts,
|
||||
"negative_prompt": self.negative_prompt,
|
||||
"negative_prompt": self.all_negative_prompts[0],
|
||||
"all_negative_prompts": self.all_negative_prompts,
|
||||
"seed": self.seed,
|
||||
"all_seeds": self.all_seeds,
|
||||
"subseed": self.subseed,
|
||||
|
|
@ -256,8 +261,7 @@ class Processed:
|
|||
"subseed_strength": self.subseed_strength,
|
||||
"width": self.width,
|
||||
"height": self.height,
|
||||
"sampler_index": self.sampler_index,
|
||||
"sampler": self.sampler,
|
||||
"sampler_name": self.sampler_name,
|
||||
"cfg_scale": self.cfg_scale,
|
||||
"steps": self.steps,
|
||||
"batch_size": self.batch_size,
|
||||
|
|
@ -273,6 +277,7 @@ class Processed:
|
|||
"styles": self.styles,
|
||||
"job_timestamp": self.job_timestamp,
|
||||
"clip_skip": self.clip_skip,
|
||||
"is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
|
||||
}
|
||||
|
||||
return json.dumps(obj)
|
||||
|
|
@ -384,7 +389,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
|
|||
|
||||
generation_params = {
|
||||
"Steps": p.steps,
|
||||
"Sampler": get_correct_sampler(p)[p.sampler_index].name,
|
||||
"Sampler": p.sampler_name,
|
||||
"CFG scale": p.cfg_scale,
|
||||
"Seed": all_seeds[index],
|
||||
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
|
||||
|
|
@ -399,6 +404,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
|
|||
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
|
||||
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
|
||||
"Denoising strength": getattr(p, 'denoising_strength', None),
|
||||
"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
|
||||
"Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
|
||||
"Clip skip": None if clip_skip <= 1 else clip_skip,
|
||||
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
|
||||
|
|
@ -408,7 +414,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
|
|||
|
||||
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
|
||||
|
||||
negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
|
||||
negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[0] if p.all_negative_prompts[0] else ""
|
||||
|
||||
return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
|
||||
|
||||
|
|
@ -418,13 +424,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
|
||||
try:
|
||||
for k, v in p.override_settings.items():
|
||||
setattr(opts, 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 impossible
|
||||
if k == 'sd_hypernetwork': shared.reload_hypernetworks() # make onchange call for changing hypernet since it is relatively fast to load on-change, while SD models are not
|
||||
|
||||
res = process_images_inner(p)
|
||||
|
||||
finally:
|
||||
finally: # restore opts to original state
|
||||
for k, v in stored_opts.items():
|
||||
setattr(opts, k, v)
|
||||
if k == 'sd_hypernetwork': shared.reload_hypernetworks()
|
||||
|
||||
return res
|
||||
|
||||
|
|
@ -437,10 +445,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
else:
|
||||
assert p.prompt is not None
|
||||
|
||||
with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
|
||||
processed = Processed(p, [], p.seed, "")
|
||||
file.write(processed.infotext(p, 0))
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
seed = get_fixed_seed(p.seed)
|
||||
|
|
@ -451,12 +455,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
|
||||
comments = {}
|
||||
|
||||
shared.prompt_styles.apply_styles(p)
|
||||
|
||||
if type(p.prompt) == list:
|
||||
p.all_prompts = p.prompt
|
||||
p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
|
||||
else:
|
||||
p.all_prompts = p.batch_size * p.n_iter * [p.prompt]
|
||||
p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
|
||||
|
||||
if type(p.negative_prompt) == list:
|
||||
p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
|
||||
else:
|
||||
p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
|
||||
|
||||
if type(seed) == list:
|
||||
p.all_seeds = seed
|
||||
|
|
@ -471,6 +478,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
def infotext(iteration=0, position_in_batch=0):
|
||||
return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
|
||||
|
||||
with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
|
||||
processed = Processed(p, [], p.seed, "")
|
||||
file.write(processed.infotext(p, 0))
|
||||
|
||||
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
|
||||
model_hijack.embedding_db.load_textual_inversion_embeddings()
|
||||
|
||||
|
|
@ -495,6 +506,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
break
|
||||
|
||||
prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
|
||||
|
||||
|
|
@ -505,7 +517,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
|
||||
|
||||
with devices.autocast():
|
||||
uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
|
||||
uc = prompt_parser.get_learned_conditioning(shared.sd_model, negative_prompts, p.steps)
|
||||
c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
|
||||
|
||||
if len(model_hijack.comments) > 0:
|
||||
|
|
@ -591,7 +603,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
|
||||
devices.torch_gc()
|
||||
|
||||
res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
|
||||
res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
|
||||
|
||||
if p.scripts is not None:
|
||||
p.scripts.postprocess(p, res)
|
||||
|
|
@ -645,7 +657,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
|||
self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
|
||||
|
||||
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
|
||||
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
|
||||
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
|
||||
|
||||
if not self.enable_hr:
|
||||
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
|
||||
|
|
@ -706,7 +718,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
|||
|
||||
shared.state.nextjob()
|
||||
|
||||
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
|
||||
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
|
||||
|
||||
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
|
||||
|
||||
|
|
@ -730,7 +742,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
self.denoising_strength: float = denoising_strength
|
||||
self.init_latent = None
|
||||
self.image_mask = mask
|
||||
#self.image_unblurred_mask = None
|
||||
self.latent_mask = None
|
||||
self.mask_for_overlay = None
|
||||
self.mask_blur = mask_blur
|
||||
|
|
@ -743,39 +754,39 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
self.image_conditioning = None
|
||||
|
||||
def init(self, all_prompts, all_seeds, all_subseeds):
|
||||
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
|
||||
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
|
||||
crop_region = None
|
||||
|
||||
if self.image_mask is not None:
|
||||
self.image_mask = self.image_mask.convert('L')
|
||||
image_mask = self.image_mask
|
||||
|
||||
if image_mask is not None:
|
||||
image_mask = image_mask.convert('L')
|
||||
|
||||
if self.inpainting_mask_invert:
|
||||
self.image_mask = ImageOps.invert(self.image_mask)
|
||||
|
||||
#self.image_unblurred_mask = self.image_mask
|
||||
image_mask = ImageOps.invert(image_mask)
|
||||
|
||||
if self.mask_blur > 0:
|
||||
self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
|
||||
image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
|
||||
|
||||
if self.inpaint_full_res:
|
||||
self.mask_for_overlay = self.image_mask
|
||||
mask = self.image_mask.convert('L')
|
||||
self.mask_for_overlay = image_mask
|
||||
mask = image_mask.convert('L')
|
||||
crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
|
||||
crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
|
||||
x1, y1, x2, y2 = crop_region
|
||||
|
||||
mask = mask.crop(crop_region)
|
||||
self.image_mask = images.resize_image(2, mask, self.width, self.height)
|
||||
image_mask = images.resize_image(2, mask, self.width, self.height)
|
||||
self.paste_to = (x1, y1, x2-x1, y2-y1)
|
||||
else:
|
||||
self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
|
||||
np_mask = np.array(self.image_mask)
|
||||
image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
|
||||
np_mask = np.array(image_mask)
|
||||
np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
|
||||
self.mask_for_overlay = Image.fromarray(np_mask)
|
||||
|
||||
self.overlay_images = []
|
||||
|
||||
latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
|
||||
latent_mask = self.latent_mask if self.latent_mask is not None else image_mask
|
||||
|
||||
add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
|
||||
if add_color_corrections:
|
||||
|
|
@ -787,7 +798,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
if crop_region is None:
|
||||
image = images.resize_image(self.resize_mode, image, self.width, self.height)
|
||||
|
||||
if self.image_mask is not None:
|
||||
if image_mask is not None:
|
||||
image_masked = Image.new('RGBa', (image.width, image.height))
|
||||
image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
|
||||
|
||||
|
|
@ -797,7 +808,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
image = image.crop(crop_region)
|
||||
image = images.resize_image(2, image, self.width, self.height)
|
||||
|
||||
if self.image_mask is not None:
|
||||
if image_mask is not None:
|
||||
if self.inpainting_fill != 1:
|
||||
image = masking.fill(image, latent_mask)
|
||||
|
||||
|
|
@ -829,7 +840,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
|
||||
self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
|
||||
|
||||
if self.image_mask is not None:
|
||||
if image_mask is not None:
|
||||
init_mask = latent_mask
|
||||
latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
|
||||
latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
|
||||
|
|
@ -846,7 +857,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
elif self.inpainting_fill == 3:
|
||||
self.init_latent = self.init_latent * self.mask
|
||||
|
||||
self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
|
||||
self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
|
||||
|
||||
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
|
||||
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
|
||||
|
|
|
|||
|
|
@ -61,6 +61,8 @@ callback_map = dict(
|
|||
callbacks_before_image_saved=[],
|
||||
callbacks_image_saved=[],
|
||||
callbacks_cfg_denoiser=[],
|
||||
callbacks_before_component=[],
|
||||
callbacks_after_component=[],
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -137,6 +139,22 @@ def cfg_denoiser_callback(params: CFGDenoiserParams):
|
|||
report_exception(c, 'cfg_denoiser_callback')
|
||||
|
||||
|
||||
def before_component_callback(component, **kwargs):
|
||||
for c in callback_map['callbacks_before_component']:
|
||||
try:
|
||||
c.callback(component, **kwargs)
|
||||
except Exception:
|
||||
report_exception(c, 'before_component_callback')
|
||||
|
||||
|
||||
def after_component_callback(component, **kwargs):
|
||||
for c in callback_map['callbacks_after_component']:
|
||||
try:
|
||||
c.callback(component, **kwargs)
|
||||
except Exception:
|
||||
report_exception(c, 'after_component_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'
|
||||
|
|
@ -220,3 +238,20 @@ def on_cfg_denoiser(callback):
|
|||
- params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
|
||||
"""
|
||||
add_callback(callback_map['callbacks_cfg_denoiser'], callback)
|
||||
|
||||
|
||||
def on_before_component(callback):
|
||||
"""register a function to be called before a component is created.
|
||||
The callback is called with arguments:
|
||||
- component - gradio component that is about to be created.
|
||||
- **kwargs - args to gradio.components.IOComponent.__init__ function
|
||||
|
||||
Use elem_id/label fields of kwargs to figure out which component it is.
|
||||
This can be useful to inject your own components somewhere in the middle of vanilla UI.
|
||||
"""
|
||||
add_callback(callback_map['callbacks_before_component'], callback)
|
||||
|
||||
|
||||
def on_after_component(callback):
|
||||
"""register a function to be called after a component is created. See on_before_component for more."""
|
||||
add_callback(callback_map['callbacks_after_component'], callback)
|
||||
|
|
|
|||
|
|
@ -17,6 +17,9 @@ class Script:
|
|||
args_to = None
|
||||
alwayson = False
|
||||
|
||||
is_txt2img = False
|
||||
is_img2img = False
|
||||
|
||||
"""A gr.Group component that has all script's UI inside it"""
|
||||
group = None
|
||||
|
||||
|
|
@ -93,6 +96,23 @@ class Script:
|
|||
|
||||
pass
|
||||
|
||||
def before_component(self, component, **kwargs):
|
||||
"""
|
||||
Called before a component is created.
|
||||
Use elem_id/label fields of kwargs to figure out which component it is.
|
||||
This can be useful to inject your own components somewhere in the middle of vanilla UI.
|
||||
You can return created components in the ui() function to add them to the list of arguments for your processing functions
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
def after_component(self, component, **kwargs):
|
||||
"""
|
||||
Called after a component is created. Same as above.
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
def describe(self):
|
||||
"""unused"""
|
||||
return ""
|
||||
|
|
@ -195,12 +215,18 @@ class ScriptRunner:
|
|||
self.titles = []
|
||||
self.infotext_fields = []
|
||||
|
||||
def setup_ui(self, is_img2img):
|
||||
def initialize_scripts(self, is_img2img):
|
||||
self.scripts.clear()
|
||||
self.alwayson_scripts.clear()
|
||||
self.selectable_scripts.clear()
|
||||
|
||||
for script_class, path, basedir in scripts_data:
|
||||
script = script_class()
|
||||
script.filename = path
|
||||
script.is_txt2img = not is_img2img
|
||||
script.is_img2img = is_img2img
|
||||
|
||||
visibility = script.show(is_img2img)
|
||||
visibility = script.show(script.is_img2img)
|
||||
|
||||
if visibility == AlwaysVisible:
|
||||
self.scripts.append(script)
|
||||
|
|
@ -211,6 +237,7 @@ class ScriptRunner:
|
|||
self.scripts.append(script)
|
||||
self.selectable_scripts.append(script)
|
||||
|
||||
def setup_ui(self):
|
||||
self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
|
||||
|
||||
inputs = [None]
|
||||
|
|
@ -220,7 +247,7 @@ class ScriptRunner:
|
|||
script.args_from = len(inputs)
|
||||
script.args_to = len(inputs)
|
||||
|
||||
controls = wrap_call(script.ui, script.filename, "ui", is_img2img)
|
||||
controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
|
||||
|
||||
if controls is None:
|
||||
return
|
||||
|
|
@ -320,6 +347,22 @@ class ScriptRunner:
|
|||
print(f"Error running postprocess: {script.filename}", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
def before_component(self, component, **kwargs):
|
||||
for script in self.scripts:
|
||||
try:
|
||||
script.before_component(component, **kwargs)
|
||||
except Exception:
|
||||
print(f"Error running before_component: {script.filename}", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
def after_component(self, component, **kwargs):
|
||||
for script in self.scripts:
|
||||
try:
|
||||
script.after_component(component, **kwargs)
|
||||
except Exception:
|
||||
print(f"Error running after_component: {script.filename}", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
def reload_sources(self, cache):
|
||||
for si, script in list(enumerate(self.scripts)):
|
||||
args_from = script.args_from
|
||||
|
|
@ -341,6 +384,7 @@ class ScriptRunner:
|
|||
|
||||
scripts_txt2img = ScriptRunner()
|
||||
scripts_img2img = ScriptRunner()
|
||||
scripts_current: ScriptRunner = None
|
||||
|
||||
|
||||
def reload_script_body_only():
|
||||
|
|
@ -357,3 +401,22 @@ def reload_scripts():
|
|||
scripts_txt2img = ScriptRunner()
|
||||
scripts_img2img = ScriptRunner()
|
||||
|
||||
|
||||
def IOComponent_init(self, *args, **kwargs):
|
||||
if scripts_current is not None:
|
||||
scripts_current.before_component(self, **kwargs)
|
||||
|
||||
script_callbacks.before_component_callback(self, **kwargs)
|
||||
|
||||
res = original_IOComponent_init(self, *args, **kwargs)
|
||||
|
||||
script_callbacks.after_component_callback(self, **kwargs)
|
||||
|
||||
if scripts_current is not None:
|
||||
scripts_current.after_component(self, **kwargs)
|
||||
|
||||
return res
|
||||
|
||||
|
||||
original_IOComponent_init = gr.components.IOComponent.__init__
|
||||
gr.components.IOComponent.__init__ = IOComponent_init
|
||||
|
|
|
|||
|
|
@ -96,8 +96,8 @@ class StableDiffusionModelHijack:
|
|||
if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
|
||||
model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
|
||||
|
||||
self.apply_circular(False)
|
||||
self.layers = None
|
||||
self.circular_enabled = False
|
||||
self.clip = None
|
||||
|
||||
def apply_circular(self, enable):
|
||||
|
|
|
|||
|
|
@ -165,16 +165,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
|
|||
|
||||
cache_enabled = shared.opts.sd_checkpoint_cache > 0
|
||||
|
||||
if cache_enabled:
|
||||
sd_vae.restore_base_vae(model)
|
||||
|
||||
vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
|
||||
|
||||
if cache_enabled and checkpoint_info in checkpoints_loaded:
|
||||
# use checkpoint cache
|
||||
vae_name = sd_vae.get_filename(vae_file) if vae_file else None
|
||||
vae_message = f" with {vae_name} VAE" if vae_name else ""
|
||||
print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
|
||||
print(f"Loading weights [{sd_model_hash}] from cache")
|
||||
model.load_state_dict(checkpoints_loaded[checkpoint_info])
|
||||
else:
|
||||
# load from file
|
||||
|
|
@ -220,6 +213,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
|
|||
model.sd_model_checkpoint = checkpoint_file
|
||||
model.sd_checkpoint_info = checkpoint_info
|
||||
|
||||
vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
|
||||
sd_vae.load_vae(model, vae_file)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -46,13 +46,20 @@ all_samplers = [
|
|||
SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
|
||||
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
|
||||
]
|
||||
all_samplers_map = {x.name: x for x in all_samplers}
|
||||
|
||||
samplers = []
|
||||
samplers_for_img2img = []
|
||||
|
||||
|
||||
def create_sampler_with_index(list_of_configs, index, model):
|
||||
config = list_of_configs[index]
|
||||
def create_sampler(name, model):
|
||||
if name is not None:
|
||||
config = all_samplers_map.get(name, None)
|
||||
else:
|
||||
config = all_samplers[0]
|
||||
|
||||
assert config is not None, f'bad sampler name: {name}'
|
||||
|
||||
sampler = config.constructor(model)
|
||||
sampler.config = config
|
||||
|
||||
|
|
|
|||
|
|
@ -83,47 +83,54 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
|
|||
return vae_list
|
||||
|
||||
|
||||
def resolve_vae(checkpoint_file, vae_file="auto"):
|
||||
global first_load, vae_dict, vae_list
|
||||
|
||||
# if vae_file argument is provided, it takes priority, but not saved
|
||||
if vae_file and vae_file not in default_vae_list:
|
||||
if not os.path.isfile(vae_file):
|
||||
vae_file = "auto"
|
||||
print("VAE provided as function argument doesn't exist")
|
||||
# for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
|
||||
if first_load and shared.cmd_opts.vae_path is not None:
|
||||
if os.path.isfile(shared.cmd_opts.vae_path):
|
||||
vae_file = shared.cmd_opts.vae_path
|
||||
shared.opts.data['sd_vae'] = get_filename(vae_file)
|
||||
else:
|
||||
print("VAE provided as command line argument doesn't exist")
|
||||
# else, we load from settings
|
||||
def get_vae_from_settings(vae_file="auto"):
|
||||
# else, we load from settings, if not set to be default
|
||||
if vae_file == "auto" and shared.opts.sd_vae is not None:
|
||||
# if saved VAE settings isn't recognized, fallback to auto
|
||||
vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
|
||||
# if VAE selected but not found, fallback to auto
|
||||
if vae_file not in default_vae_values and not os.path.isfile(vae_file):
|
||||
vae_file = "auto"
|
||||
print("Selected VAE doesn't exist")
|
||||
print(f"Selected VAE doesn't exist: {vae_file}")
|
||||
return vae_file
|
||||
|
||||
|
||||
def resolve_vae(checkpoint_file=None, vae_file="auto"):
|
||||
global first_load, vae_dict, vae_list
|
||||
|
||||
# if vae_file argument is provided, it takes priority, but not saved
|
||||
if vae_file and vae_file not in default_vae_list:
|
||||
if not os.path.isfile(vae_file):
|
||||
print(f"VAE provided as function argument doesn't exist: {vae_file}")
|
||||
vae_file = "auto"
|
||||
# for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
|
||||
if first_load and shared.cmd_opts.vae_path is not None:
|
||||
if os.path.isfile(shared.cmd_opts.vae_path):
|
||||
vae_file = shared.cmd_opts.vae_path
|
||||
shared.opts.data['sd_vae'] = get_filename(vae_file)
|
||||
else:
|
||||
print(f"VAE provided as command line argument doesn't exist: {vae_file}")
|
||||
# fallback to selector in settings, if vae selector not set to act as default fallback
|
||||
if not shared.opts.sd_vae_as_default:
|
||||
vae_file = get_vae_from_settings(vae_file)
|
||||
# vae-path cmd arg takes priority for auto
|
||||
if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
|
||||
if os.path.isfile(shared.cmd_opts.vae_path):
|
||||
vae_file = shared.cmd_opts.vae_path
|
||||
print("Using VAE provided as command line argument")
|
||||
print(f"Using VAE provided as command line argument: {vae_file}")
|
||||
# if still not found, try look for ".vae.pt" beside model
|
||||
model_path = os.path.splitext(checkpoint_file)[0]
|
||||
if vae_file == "auto":
|
||||
vae_file_try = model_path + ".vae.pt"
|
||||
if os.path.isfile(vae_file_try):
|
||||
vae_file = vae_file_try
|
||||
print("Using VAE found beside selected model")
|
||||
print(f"Using VAE found similar to selected model: {vae_file}")
|
||||
# if still not found, try look for ".vae.ckpt" beside model
|
||||
if vae_file == "auto":
|
||||
vae_file_try = model_path + ".vae.ckpt"
|
||||
if os.path.isfile(vae_file_try):
|
||||
vae_file = vae_file_try
|
||||
print("Using VAE found beside selected model")
|
||||
print(f"Using VAE found similar to selected model: {vae_file}")
|
||||
# No more fallbacks for auto
|
||||
if vae_file == "auto":
|
||||
vae_file = None
|
||||
|
|
@ -139,6 +146,7 @@ def load_vae(model, vae_file=None):
|
|||
# save_settings = False
|
||||
|
||||
if vae_file:
|
||||
assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
|
||||
print(f"Loading VAE weights from: {vae_file}")
|
||||
vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
|
||||
vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
|
|||
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")
|
||||
parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
|
||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
|
||||
parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
|
||||
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
|
||||
|
|
@ -81,6 +81,7 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print
|
|||
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("--api-auth", type=str, help='Set authentication for api like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
|
||||
parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
|
||||
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)
|
||||
|
|
@ -106,7 +107,7 @@ restricted_opts = {
|
|||
"outdir_save",
|
||||
}
|
||||
|
||||
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen) and not cmd_opts.enable_insecure_extension_access
|
||||
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) 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'])
|
||||
|
|
@ -334,7 +335,8 @@ options_templates.update(options_section(('training', "Training"), {
|
|||
options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
|
||||
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
||||
"sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
|
||||
"sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
|
||||
"sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
|
||||
"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
|
||||
"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
|
||||
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
|
|
@ -436,6 +438,23 @@ class Options:
|
|||
|
||||
return super(Options, self).__getattribute__(item)
|
||||
|
||||
def set(self, key, value):
|
||||
"""sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
|
||||
|
||||
oldval = self.data.get(key, None)
|
||||
if oldval == value:
|
||||
return False
|
||||
|
||||
try:
|
||||
setattr(self, key, value)
|
||||
except RuntimeError:
|
||||
return False
|
||||
|
||||
if self.data_labels[key].onchange is not None:
|
||||
self.data_labels[key].onchange()
|
||||
|
||||
return True
|
||||
|
||||
def save(self, filename):
|
||||
assert not cmd_opts.freeze_settings, "saving settings is disabled"
|
||||
|
||||
|
|
|
|||
|
|
@ -65,17 +65,6 @@ class StyleDatabase:
|
|||
def apply_negative_styles_to_prompt(self, prompt, styles):
|
||||
return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
|
||||
|
||||
def apply_styles(self, p: StableDiffusionProcessing) -> None:
|
||||
if isinstance(p.prompt, list):
|
||||
p.prompt = [self.apply_styles_to_prompt(prompt, p.styles) for prompt in p.prompt]
|
||||
else:
|
||||
p.prompt = self.apply_styles_to_prompt(p.prompt, p.styles)
|
||||
|
||||
if isinstance(p.negative_prompt, list):
|
||||
p.negative_prompt = [self.apply_negative_styles_to_prompt(prompt, p.styles) for prompt in p.negative_prompt]
|
||||
else:
|
||||
p.negative_prompt = self.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)
|
||||
|
||||
def save_styles(self, path: str) -> None:
|
||||
# Write to temporary file first, so we don't nuke the file if something goes wrong
|
||||
fd, temp_path = tempfile.mkstemp(".csv")
|
||||
|
|
|
|||
|
|
@ -6,12 +6,10 @@ import sys
|
|||
import tqdm
|
||||
import time
|
||||
|
||||
from modules import shared, images
|
||||
from modules import shared, images, deepbooru
|
||||
from modules.paths import models_path
|
||||
from modules.shared import opts, cmd_opts
|
||||
from modules.textual_inversion import autocrop
|
||||
if cmd_opts.deepdanbooru:
|
||||
import modules.deepbooru as deepbooru
|
||||
|
||||
|
||||
def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):
|
||||
|
|
@ -20,9 +18,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
|
|||
shared.interrogator.load()
|
||||
|
||||
if process_caption_deepbooru:
|
||||
db_opts = deepbooru.create_deepbooru_opts()
|
||||
db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
|
||||
deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
|
||||
deepbooru.model.start()
|
||||
|
||||
preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug)
|
||||
|
||||
|
|
@ -32,7 +28,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
|
|||
shared.interrogator.send_blip_to_ram()
|
||||
|
||||
if process_caption_deepbooru:
|
||||
deepbooru.release_process()
|
||||
deepbooru.model.stop()
|
||||
|
||||
|
||||
def listfiles(dirname):
|
||||
|
|
@ -58,7 +54,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
|
|||
if params.process_caption_deepbooru:
|
||||
if len(caption) > 0:
|
||||
caption += ", "
|
||||
caption += deepbooru.get_tags_from_process(image)
|
||||
caption += deepbooru.model.tag_multi(image)
|
||||
|
||||
filename_part = params.src
|
||||
filename_part = os.path.splitext(filename_part)[0]
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ import csv
|
|||
|
||||
from PIL import Image, PngImagePlugin
|
||||
|
||||
from modules import shared, devices, sd_hijack, processing, sd_models, images
|
||||
from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers
|
||||
import modules.textual_inversion.dataset
|
||||
from modules.textual_inversion.learn_schedule import LearnRateScheduler
|
||||
|
||||
|
|
@ -345,7 +345,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
|
|||
p.prompt = preview_prompt
|
||||
p.negative_prompt = preview_negative_prompt
|
||||
p.steps = preview_steps
|
||||
p.sampler_index = preview_sampler_index
|
||||
p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
|
||||
p.cfg_scale = preview_cfg_scale
|
||||
p.seed = preview_seed
|
||||
p.width = preview_width
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ def create_embedding(name, initialization_text, nvpt, overwrite_old):
|
|||
def preprocess(*args):
|
||||
modules.textual_inversion.preprocess.preprocess(*args)
|
||||
|
||||
return "Preprocessing finished.", ""
|
||||
return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
|
||||
|
||||
|
||||
def train_embedding(*args):
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import modules.scripts
|
||||
from modules import sd_samplers
|
||||
from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
|
||||
StableDiffusionProcessingImg2Img, process_images
|
||||
from modules.shared import opts, cmd_opts
|
||||
|
|
@ -21,7 +22,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
|
|||
seed_resize_from_h=seed_resize_from_h,
|
||||
seed_resize_from_w=seed_resize_from_w,
|
||||
seed_enable_extras=seed_enable_extras,
|
||||
sampler_index=sampler_index,
|
||||
sampler_name=sd_samplers.samplers[sampler_index].name,
|
||||
batch_size=batch_size,
|
||||
n_iter=n_iter,
|
||||
steps=steps,
|
||||
|
|
|
|||
|
|
@ -19,14 +19,11 @@ import numpy as np
|
|||
from PIL import Image, PngImagePlugin
|
||||
|
||||
|
||||
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions
|
||||
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru
|
||||
from modules.paths import script_path
|
||||
|
||||
from modules.shared import opts, cmd_opts, restricted_opts
|
||||
|
||||
if cmd_opts.deepdanbooru:
|
||||
from modules.deepbooru import get_deepbooru_tags
|
||||
|
||||
import modules.codeformer_model
|
||||
import modules.generation_parameters_copypaste as parameters_copypaste
|
||||
import modules.gfpgan_model
|
||||
|
|
@ -69,8 +66,11 @@ sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
|
|||
css_hide_progressbar = """
|
||||
.wrap .m-12 svg { display:none!important; }
|
||||
.wrap .m-12::before { content:"Loading..." }
|
||||
.wrap .z-20 svg { display:none!important; }
|
||||
.wrap .z-20::before { content:"Loading..." }
|
||||
.progress-bar { display:none!important; }
|
||||
.meta-text { display:none!important; }
|
||||
.meta-text-center { display:none!important; }
|
||||
"""
|
||||
|
||||
# Using constants for these since the variation selector isn't visible.
|
||||
|
|
@ -142,7 +142,7 @@ def save_files(js_data, images, do_make_zip, index):
|
|||
filenames.append(os.path.basename(txt_fullfn))
|
||||
fullfns.append(txt_fullfn)
|
||||
|
||||
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
|
||||
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
|
||||
|
||||
# Make Zip
|
||||
if do_make_zip:
|
||||
|
|
@ -349,7 +349,7 @@ def interrogate(image):
|
|||
|
||||
|
||||
def interrogate_deepbooru(image):
|
||||
prompt = get_deepbooru_tags(image)
|
||||
prompt = deepbooru.model.tag(image)
|
||||
return gr_show(True) if prompt is None else prompt
|
||||
|
||||
|
||||
|
|
@ -692,6 +692,9 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
|
||||
parameters_copypaste.reset()
|
||||
|
||||
modules.scripts.scripts_current = modules.scripts.scripts_txt2img
|
||||
modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
|
||||
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
|
||||
dummy_component = gr.Label(visible=False)
|
||||
|
|
@ -734,7 +737,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
|
||||
|
||||
with gr.Group():
|
||||
custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
|
||||
custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
|
||||
|
||||
txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
|
||||
parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)
|
||||
|
|
@ -843,6 +846,9 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
|
||||
token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
|
||||
|
||||
modules.scripts.scripts_current = modules.scripts.scripts_img2img
|
||||
modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
|
||||
|
||||
|
|
@ -913,7 +919,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
|
||||
|
||||
with gr.Group():
|
||||
custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
|
||||
custom_inputs = modules.scripts.scripts_img2img.setup_ui()
|
||||
|
||||
img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
|
||||
parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)
|
||||
|
|
@ -1062,6 +1068,8 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
|
||||
parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
|
||||
|
||||
modules.scripts.scripts_current = None
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as extras_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Column(variant='panel'):
|
||||
|
|
@ -1249,7 +1257,9 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
run_preprocess = gr.Button(value="Preprocess", variant='primary')
|
||||
with gr.Row():
|
||||
interrupt_preprocessing = gr.Button("Interrupt")
|
||||
run_preprocess = gr.Button(value="Preprocess", variant='primary')
|
||||
|
||||
process_split.change(
|
||||
fn=lambda show: gr_show(show),
|
||||
|
|
@ -1422,6 +1432,12 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
outputs=[],
|
||||
)
|
||||
|
||||
interrupt_preprocessing.click(
|
||||
fn=lambda: shared.state.interrupt(),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
def create_setting_component(key, is_quicksettings=False):
|
||||
def fun():
|
||||
return opts.data[key] if key in opts.data else opts.data_labels[key].default
|
||||
|
|
@ -1473,16 +1489,9 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
if comp == dummy_component:
|
||||
continue
|
||||
|
||||
oldval = opts.data.get(key, None)
|
||||
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()
|
||||
|
||||
if opts.set(key, value):
|
||||
changed.append(key)
|
||||
|
||||
try:
|
||||
opts.save(shared.config_filename)
|
||||
except RuntimeError:
|
||||
|
|
@ -1493,15 +1502,8 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
if not opts.same_type(value, opts.data_labels[key].default):
|
||||
return gr.update(visible=True), opts.dumpjson()
|
||||
|
||||
oldval = opts.data.get(key, None)
|
||||
try:
|
||||
setattr(opts, key, value)
|
||||
except Exception:
|
||||
return gr.update(value=oldval), opts.dumpjson()
|
||||
|
||||
if oldval != value:
|
||||
if opts.data_labels[key].onchange is not None:
|
||||
opts.data_labels[key].onchange()
|
||||
if not opts.set(key, value):
|
||||
return gr.update(value=getattr(opts, key)), opts.dumpjson()
|
||||
|
||||
opts.save(shared.config_filename)
|
||||
|
||||
|
|
|
|||
|
|
@ -36,9 +36,9 @@ def apply_and_restart(disable_list, update_list):
|
|||
continue
|
||||
|
||||
try:
|
||||
ext.pull()
|
||||
ext.fetch_and_reset_hard()
|
||||
except Exception:
|
||||
print(f"Error pulling updates for {ext.name}:", file=sys.stderr)
|
||||
print(f"Error getting updates for {ext.name}:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
shared.opts.disabled_extensions = disabled
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
accelerate
|
||||
basicsr
|
||||
diffusers
|
||||
fairscale==0.4.4
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
transformers==4.19.2
|
||||
diffusers==0.3.0
|
||||
accelerate==0.12.0
|
||||
basicsr==1.4.2
|
||||
gfpgan==1.3.8
|
||||
gradio==3.9
|
||||
|
|
|
|||
|
|
@ -157,7 +157,7 @@ class Script(scripts.Script):
|
|||
def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
|
||||
# Override
|
||||
if override_sampler:
|
||||
p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
|
||||
p.sampler_name = "Euler"
|
||||
if override_prompt:
|
||||
p.prompt = original_prompt
|
||||
p.negative_prompt = original_negative_prompt
|
||||
|
|
@ -191,7 +191,7 @@ class Script(scripts.Script):
|
|||
|
||||
combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
|
||||
|
||||
sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
|
||||
sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
|
||||
|
||||
sigmas = sampler.model_wrap.get_sigmas(p.steps)
|
||||
|
||||
|
|
|
|||
|
|
@ -10,9 +10,9 @@ import numpy as np
|
|||
import modules.scripts as scripts
|
||||
import gradio as gr
|
||||
|
||||
from modules import images
|
||||
from modules import images, sd_samplers
|
||||
from modules.hypernetworks import hypernetwork
|
||||
from modules.processing import process_images, Processed, get_correct_sampler, StableDiffusionProcessingTxt2Img
|
||||
from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
import modules.shared as shared
|
||||
import modules.sd_samplers
|
||||
|
|
@ -60,9 +60,9 @@ def apply_order(p, x, xs):
|
|||
p.prompt = prompt_tmp + p.prompt
|
||||
|
||||
|
||||
def build_samplers_dict(p):
|
||||
def build_samplers_dict():
|
||||
samplers_dict = {}
|
||||
for i, sampler in enumerate(get_correct_sampler(p)):
|
||||
for i, sampler in enumerate(sd_samplers.all_samplers):
|
||||
samplers_dict[sampler.name.lower()] = i
|
||||
for alias in sampler.aliases:
|
||||
samplers_dict[alias.lower()] = i
|
||||
|
|
@ -70,7 +70,7 @@ def build_samplers_dict(p):
|
|||
|
||||
|
||||
def apply_sampler(p, x, xs):
|
||||
sampler_index = build_samplers_dict(p).get(x.lower(), None)
|
||||
sampler_index = build_samplers_dict().get(x.lower(), None)
|
||||
if sampler_index is None:
|
||||
raise RuntimeError(f"Unknown sampler: {x}")
|
||||
|
||||
|
|
@ -78,7 +78,7 @@ def apply_sampler(p, x, xs):
|
|||
|
||||
|
||||
def confirm_samplers(p, xs):
|
||||
samplers_dict = build_samplers_dict(p)
|
||||
samplers_dict = build_samplers_dict()
|
||||
for x in xs:
|
||||
if x.lower() not in samplers_dict.keys():
|
||||
raise RuntimeError(f"Unknown sampler: {x}")
|
||||
|
|
|
|||
|
|
@ -40,4 +40,7 @@ export COMMANDLINE_ARGS=""
|
|||
#export CODEFORMER_COMMIT_HASH=""
|
||||
#export BLIP_COMMIT_HASH=""
|
||||
|
||||
# Uncomment to enable accelerated launch
|
||||
#export ACCELERATE="True"
|
||||
|
||||
###########################################
|
||||
|
|
|
|||
12
webui.bat
12
webui.bat
|
|
@ -28,15 +28,27 @@ goto :show_stdout_stderr
|
|||
:activate_venv
|
||||
set PYTHON="%~dp0%VENV_DIR%\Scripts\Python.exe"
|
||||
echo venv %PYTHON%
|
||||
if [%ACCELERATE%] == ["True"] goto :accelerate
|
||||
goto :launch
|
||||
|
||||
:skip_venv
|
||||
|
||||
:accelerate
|
||||
echo "Checking for accelerate"
|
||||
set ACCELERATE="%~dp0%VENV_DIR%\Scripts\accelerate.exe"
|
||||
if EXIST %ACCELERATE% goto :accelerate_launch
|
||||
|
||||
:launch
|
||||
%PYTHON% launch.py %*
|
||||
pause
|
||||
exit /b
|
||||
|
||||
:accelerate_launch
|
||||
echo "Accelerating"
|
||||
%ACCELERATE% launch --num_cpu_threads_per_process=6 launch.py
|
||||
pause
|
||||
exit /b
|
||||
|
||||
:show_stdout_stderr
|
||||
|
||||
echo.
|
||||
|
|
|
|||
6
webui.py
6
webui.py
|
|
@ -33,7 +33,10 @@ from modules.shared import cmd_opts
|
|||
import modules.hypernetworks.hypernetwork
|
||||
|
||||
queue_lock = threading.Lock()
|
||||
server_name = "0.0.0.0" if cmd_opts.listen else cmd_opts.server_name
|
||||
if cmd_opts.server_name:
|
||||
server_name = cmd_opts.server_name
|
||||
else:
|
||||
server_name = "0.0.0.0" if cmd_opts.listen else None
|
||||
|
||||
def wrap_queued_call(func):
|
||||
def f(*args, **kwargs):
|
||||
|
|
@ -82,6 +85,7 @@ def initialize():
|
|||
modules.sd_models.load_model()
|
||||
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
|
||||
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
|
||||
shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
|
||||
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
|
||||
shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
|
||||
|
||||
|
|
|
|||
16
webui.sh
16
webui.sh
|
|
@ -134,7 +134,15 @@ else
|
|||
exit 1
|
||||
fi
|
||||
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "Launching launch.py..."
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
"${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
|
||||
if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]
|
||||
then
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "Accelerating launch.py..."
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@"
|
||||
else
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "Launching launch.py..."
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
"${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
|
||||
fi
|
||||
|
|
|
|||
Loading…
Reference in a new issue