Merge remote-tracking branch 'upstream/master'
This commit is contained in:
Bruno Seoane
commit
59ec427dff
13 changed files with 172 additions and 52 deletions
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@ -142,7 +142,7 @@ def prepare_enviroment():
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stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
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taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
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k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
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k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "60e5042ca0da89c14d1dd59d73883280f8fce991")
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codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
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blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
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@ -16,6 +16,7 @@
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"A merger of the two checkpoints will be generated in your": "체크포인트들이 병합된 결과물이 당신의",
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"A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "난수 생성기의 결과물을 지정하는 값 - 동일한 설정값과 동일한 시드를 적용 시, 완전히 똑같은 결과물을 얻게 됩니다.",
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"Action": "작업",
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"Add a button to convert the prompts used in NovelAI for use in the WebUI. In addition, add a button that allows you to recall a previously used prompt.": "NovelAI에서 사용되는 프롬프트를 WebUI에서 사용할 수 있게 변환하는 버튼을 추가합니다. 덤으로 이전에 사용한 프롬프트를 불러오는 버튼도 추가됩니다.",
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"Add a random artist to the prompt.": "프롬프트에 랜덤한 작가 추가",
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"Add a second progress bar to the console that shows progress for an entire job.": "콘솔에 전체 작업의 진행도를 보여주는 2번째 프로그레스 바 추가하기",
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"Add difference": "차이점 추가",
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@ -24,6 +25,7 @@
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"Add model hash to generation information": "생성 정보에 모델 해시 추가",
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"Add model name to generation information": "생성 정보에 모델 이름 추가",
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"Add number to filename when saving": "이미지를 저장할 때 파일명에 숫자 추가하기",
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"Adds a tab that lets you preview how CLIP model would tokenize your text.": "CLIP 모델이 텍스트를 어떻게 토큰화할지 미리 보여주는 탭을 추가합니다.",
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"Adds a tab to the webui that allows the user to automatically extract keyframes from video, and manually extract 512x512 crops of those frames for use in model training.": "WebUI에 비디오로부터 자동으로 키프레임을 추출하고, 그 키프레임으로부터 모델 훈련에 사용될 512x512 이미지를 잘라낼 수 있는 탭을 추가합니다.",
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"Aesthetic Gradients": "스타일 그라디언트",
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"Aesthetic Image Scorer": "스타일 이미지 스코어러",
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@ -33,6 +35,7 @@
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"Aesthetic text for imgs": "스타일 텍스트",
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"Aesthetic weight": "스타일 가중치",
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"Allowed categories for random artists selection when using the Roll button": "랜덤 버튼을 눌러 무작위 작가를 선택할 때 허용된 카테고리",
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"Allows you to include various shortcodes in your prompts. You can pull text from files, set up your own variables, process text through conditional functions, and so much more - it's like wildcards on steroids.": "프롬프트에 다양한 숏코드를 추가할 수 있게 해줍니다. 파일로부터 텍스트 추출, 변수 설정, 조건 함수로 텍스트 처리 등등 - 스테로이드를 맞은 와일드카드라 할 수 있죠.",
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"Always print all generation info to standard output": "기본 아웃풋에 모든 생성 정보 항상 출력하기",
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"Always save all generated image grids": "생성된 이미지 그리드 항상 저장하기",
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"Always save all generated images": "생성된 이미지 항상 저장하기",
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@ -54,6 +57,7 @@
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"Batch Process": "이미지 여러장 처리",
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"Batch size": "배치 크기",
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"behind": "최신 아님",
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"Booru tag autocompletion": "Booru 태그 자동완성",
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"BSRGAN 4x": "BSRGAN 4x",
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"built with gradio": "gradio로 제작되었습니다",
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"Calculates aesthetic score for generated images using CLIP+MLP Aesthetic Score Predictor based on Chad Scorer": "Chad 스코어러를 기반으로 한 CLIP+MLP 스타일 점수 예측기를 이용해 생성된 이미지의 스타일 점수를 계산합니다.",
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@ -114,6 +118,7 @@
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"Directory for saving images using the Save button": "저장 버튼을 이용해 저장하는 이미지들의 저장 경로",
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"Directory name pattern": "디렉토리명 패턴",
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"directory.": "저장 경로에 저장됩니다.",
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"Displays autocompletion hints for tags from image booru boards such as Danbooru. Uses local tag CSV files and includes a config for customization.": "Danbooru 같은 이미지 booru 보드의 태그에 대한 자동완성 힌트를 보여줍니다. 로컬 환경에 저장된 CSV 파일을 사용하고 조정 가능한 설정 파일이 포함되어 있습니다.",
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"Do not add watermark to images": "이미지에 워터마크 추가하지 않기",
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"Do not do anything special": "아무것도 하지 않기",
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"Do not save grids consisting of one picture": "이미지가 1개뿐인 그리드는 저장하지 않기",
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@ -317,6 +322,7 @@
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"None": "없음",
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"Nothing": "없음",
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"Nothing found in the image.": "Nothing found in the image.",
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"novelai-2-local-prompt": "NovelAI 프롬프트 변환기",
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"Number of columns on the page": "각 페이지마다 표시할 가로줄 수",
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"Number of grids in each row": "각 세로줄마다 표시될 그리드 수",
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"number of images to delete consecutively next": "연속적으로 삭제할 이미지 수",
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@ -431,6 +437,7 @@
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"Save images with embedding in PNG chunks": "PNG 청크로 이미지에 임베딩을 포함시켜 저장",
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"Save style": "스타일 저장",
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"Save text information about generation parameters as chunks to png files": "이미지 생성 설정값을 PNG 청크에 텍스트로 저장",
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"Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file.": "옵티마이저 상태를 별개의 *.optim 파일로 저장하기. 하이퍼네트워크 파일과 일치하는 optim 파일로부터 훈련을 재개할 수 있습니다.",
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"Saving images/grids": "이미지/그리드 저장",
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"Saving to a directory": "디렉토리에 저장",
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"Scale by": "스케일링 배수 지정",
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@ -515,6 +522,7 @@
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"Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGAN 업스케일러들의 타일 사이즈. 0 = 타일링 없음.",
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"Tiling": "타일링",
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"Time taken:": "소요 시간 : ",
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"tokenizer": "토크나이저",
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"Torch active/reserved:": "활성화/예약된 Torch 양 : ",
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"Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).": "활성화된 Torch : 생성 도중 캐시된 데이터를 포함해 사용된 VRAM의 최대량\n예약된 Torch : 활성화되고 캐시된 모든 데이터를 포함해 Torch에게 할당된 VRAM의 최대량\n시스템 VRAM : 모든 어플리케이션에 할당된 VRAM 최대량 / 총 GPU VRAM (최고 이용도%)",
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"Train": "훈련",
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@ -10,6 +10,7 @@ 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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@ -34,9 +35,21 @@ def setUpscalers(req: dict):
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def encode_pil_to_base64(image):
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buffer = io.BytesIO()
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image.save(buffer, format="png")
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return base64.b64encode(buffer.getvalue())
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with io.BytesIO() as output_bytes:
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# Copy any text-only metadata
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use_metadata = False
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metadata = PngImagePlugin.PngInfo()
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for key, value in image.info.items():
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if isinstance(key, str) and isinstance(value, str):
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metadata.add_text(key, value)
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use_metadata = True
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image.save(
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output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
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)
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bytes_data = output_bytes.getvalue()
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return base64.b64encode(bytes_data)
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class Api:
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@ -218,6 +231,10 @@ 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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@ -1,11 +1,11 @@
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import inspect
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from pydantic import BaseModel, Field, create_model
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from typing import Any, Optional, Union
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from typing import Any, Optional
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from typing_extensions import Literal
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from inflection import underscore
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
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from modules.shared import sd_upscalers, opts, parser
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from typing import List
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from typing import Dict, List
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API_NOT_ALLOWED = [
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"self",
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@ -186,21 +186,21 @@ for key in _options:
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if(_options[key].dest != 'help'):
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flag = _options[key]
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_type = str
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if(_options[key].default != None): _type = type(_options[key].default)
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if _options[key].default is not None: _type = type(_options[key].default)
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flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
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FlagsModel = create_model("Flags", **flags)
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class SamplerItem(BaseModel):
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name: str = Field(title="Name")
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aliases: list[str] = Field(title="Aliases")
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options: dict[str, str] = Field(title="Options")
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aliases: List[str] = Field(title="Aliases")
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options: Dict[str, str] = Field(title="Options")
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class UpscalerItem(BaseModel):
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name: str = Field(title="Name")
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model_name: str | None = Field(title="Model Name")
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model_path: str | None = Field(title="Path")
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model_url: str | None = Field(title="URL")
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model_name: Optional[str] = Field(title="Model Name")
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model_path: Optional[str] = Field(title="Path")
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model_url: Optional[str] = Field(title="URL")
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class SDModelItem(BaseModel):
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title: str = Field(title="Title")
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@ -211,21 +211,21 @@ class SDModelItem(BaseModel):
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class HypernetworkItem(BaseModel):
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name: str = Field(title="Name")
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path: str | None = Field(title="Path")
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path: Optional[str] = Field(title="Path")
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class FaceRestorerItem(BaseModel):
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name: str = Field(title="Name")
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cmd_dir: str | None = Field(title="Path")
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cmd_dir: Optional[str] = Field(title="Path")
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class RealesrganItem(BaseModel):
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name: str = Field(title="Name")
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path: str | None = Field(title="Path")
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scale: int | None = Field(title="Scale")
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path: Optional[str] = Field(title="Path")
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scale: Optional[int] = Field(title="Scale")
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class PromptStyleItem(BaseModel):
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name: str = Field(title="Name")
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prompt: str | None = Field(title="Prompt")
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negative_prompt: str | None = Field(title="Negative Prompt")
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prompt: Optional[str] = Field(title="Prompt")
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negative_prompt: Optional[str] = Field(title="Negative Prompt")
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class ArtistItem(BaseModel):
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name: str = Field(title="Name")
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@ -34,8 +34,11 @@ class Extension:
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if repo is None or repo.bare:
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self.remote = None
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else:
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self.remote = next(repo.remote().urls, None)
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self.status = 'unknown'
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try:
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self.remote = next(repo.remote().urls, None)
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self.status = 'unknown'
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except Exception:
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self.remote = None
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def list_files(self, subdir, extension):
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from modules import scripts
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@ -22,6 +22,8 @@ from collections import defaultdict, deque
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from statistics import stdev, mean
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optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}
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class HypernetworkModule(torch.nn.Module):
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multiplier = 1.0
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activation_dict = {
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@ -142,6 +144,8 @@ class Hypernetwork:
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self.use_dropout = use_dropout
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self.activate_output = activate_output
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self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True
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self.optimizer_name = None
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self.optimizer_state_dict = None
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for size in enable_sizes or []:
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self.layers[size] = (
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@ -163,6 +167,7 @@ class Hypernetwork:
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def save(self, filename):
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state_dict = {}
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optimizer_saved_dict = {}
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for k, v in self.layers.items():
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state_dict[k] = (v[0].state_dict(), v[1].state_dict())
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@ -179,7 +184,14 @@ class Hypernetwork:
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state_dict['activate_output'] = self.activate_output
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state_dict['last_layer_dropout'] = self.last_layer_dropout
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if self.optimizer_name is not None:
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optimizer_saved_dict['optimizer_name'] = self.optimizer_name
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torch.save(state_dict, filename)
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if shared.opts.save_optimizer_state and self.optimizer_state_dict:
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optimizer_saved_dict['hash'] = sd_models.model_hash(filename)
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optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict
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torch.save(optimizer_saved_dict, filename + '.optim')
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def load(self, filename):
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self.filename = filename
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@ -202,6 +214,18 @@ class Hypernetwork:
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print(f"Activate last layer is set to {self.activate_output}")
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self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
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optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}
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self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')
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print(f"Optimizer name is {self.optimizer_name}")
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if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):
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self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
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else:
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self.optimizer_state_dict = None
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if self.optimizer_state_dict:
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print("Loaded existing optimizer from checkpoint")
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else:
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print("No saved optimizer exists in checkpoint")
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for size, sd in state_dict.items():
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if type(size) == int:
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self.layers[size] = (
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@ -219,11 +243,11 @@ class Hypernetwork:
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def list_hypernetworks(path):
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res = {}
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for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
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for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
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name = os.path.splitext(os.path.basename(filename))[0]
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# Prevent a hypothetical "None.pt" from being listed.
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if name != "None":
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res[name] = filename
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res[name + f"({sd_models.model_hash(filename)})"] = filename
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return res
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@ -358,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
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shared.state.textinfo = "Initializing hypernetwork training..."
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shared.state.job_count = steps
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hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]
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filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
|
||||
|
||||
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
|
||||
|
|
@ -404,8 +429,22 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
|
|||
weights = hypernetwork.weights()
|
||||
for weight in weights:
|
||||
weight.requires_grad = True
|
||||
# if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
|
||||
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
|
||||
|
||||
# Here we use optimizer from saved HN, or we can specify as UI option.
|
||||
if hypernetwork.optimizer_name in optimizer_dict:
|
||||
optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)
|
||||
optimizer_name = hypernetwork.optimizer_name
|
||||
else:
|
||||
print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")
|
||||
optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)
|
||||
optimizer_name = 'AdamW'
|
||||
|
||||
if hypernetwork.optimizer_state_dict: # This line must be changed if Optimizer type can be different from saved optimizer.
|
||||
try:
|
||||
optimizer.load_state_dict(hypernetwork.optimizer_state_dict)
|
||||
except RuntimeError as e:
|
||||
print("Cannot resume from saved optimizer!")
|
||||
print(e)
|
||||
|
||||
steps_without_grad = 0
|
||||
|
||||
|
|
@ -467,7 +506,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
|
|||
# Before saving, change name to match current checkpoint.
|
||||
hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'
|
||||
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')
|
||||
hypernetwork.optimizer_name = optimizer_name
|
||||
if shared.opts.save_optimizer_state:
|
||||
hypernetwork.optimizer_state_dict = optimizer.state_dict()
|
||||
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)
|
||||
hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
|
||||
|
||||
textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
|
||||
"loss": f"{previous_mean_loss:.7f}",
|
||||
|
|
@ -530,8 +573,12 @@ Last saved image: {html.escape(last_saved_image)}<br/>
|
|||
report_statistics(loss_dict)
|
||||
|
||||
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
|
||||
hypernetwork.optimizer_name = optimizer_name
|
||||
if shared.opts.save_optimizer_state:
|
||||
hypernetwork.optimizer_state_dict = optimizer.state_dict()
|
||||
save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)
|
||||
|
||||
del optimizer
|
||||
hypernetwork.optimizer_state_dict = None # dereference it after saving, to save memory.
|
||||
return hypernetwork, filename
|
||||
|
||||
def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared
|
|||
from modules.hypernetworks import hypernetwork
|
||||
|
||||
not_available = ["hardswish", "multiheadattention"]
|
||||
keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
|
||||
keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)
|
||||
|
||||
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
|
||||
# Remove illegal characters from name.
|
||||
|
|
|
|||
|
|
@ -24,11 +24,15 @@ samplers_k_diffusion = [
|
|||
('Heun', 'sample_heun', ['k_heun'], {}),
|
||||
('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
|
||||
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
|
||||
('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
|
||||
('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
|
||||
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
|
||||
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
|
||||
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
|
||||
('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
|
||||
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
|
||||
('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
|
||||
('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
|
||||
]
|
||||
|
||||
samplers_data_k_diffusion = [
|
||||
|
|
|
|||
|
|
@ -86,6 +86,10 @@ parser.add_argument("--nowebui", action='store_true', help="use api=True to laun
|
|||
parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
|
||||
parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
|
||||
parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
|
||||
parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origins", default=None)
|
||||
parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
|
||||
parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
|
||||
parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
|
||||
|
||||
cmd_opts = parser.parse_args()
|
||||
restricted_opts = {
|
||||
|
|
@ -317,6 +321,7 @@ options_templates.update(options_section(('system', "System"), {
|
|||
|
||||
options_templates.update(options_section(('training', "Training"), {
|
||||
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
|
||||
"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
|
||||
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
|
||||
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
|
||||
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
|
||||
|
|
@ -406,7 +411,8 @@ class Options:
|
|||
if key in self.data or key in self.data_labels:
|
||||
assert not cmd_opts.freeze_settings, "changing settings is disabled"
|
||||
|
||||
comp_args = opts.data_labels[key].component_args
|
||||
info = opts.data_labels.get(key, None)
|
||||
comp_args = info.component_args if info else None
|
||||
if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
|
||||
raise RuntimeError(f"not possible to set {key} because it is restricted")
|
||||
|
||||
|
|
|
|||
|
|
@ -1446,17 +1446,19 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
continue
|
||||
|
||||
oldval = opts.data.get(key, None)
|
||||
|
||||
setattr(opts, key, value)
|
||||
|
||||
try:
|
||||
setattr(opts, key, value)
|
||||
except RuntimeError:
|
||||
continue
|
||||
if oldval != value:
|
||||
if opts.data_labels[key].onchange is not None:
|
||||
opts.data_labels[key].onchange()
|
||||
|
||||
changed += 1
|
||||
|
||||
opts.save(shared.config_filename)
|
||||
|
||||
try:
|
||||
opts.save(shared.config_filename)
|
||||
except RuntimeError:
|
||||
return opts.dumpjson(), f'{changed} settings changed without save.'
|
||||
return opts.dumpjson(), f'{changed} settings changed.'
|
||||
|
||||
def run_settings_single(value, key):
|
||||
|
|
|
|||
|
|
@ -188,7 +188,7 @@ def refresh_available_extensions_from_data():
|
|||
|
||||
code += f"""
|
||||
<tr>
|
||||
<td><a href="{html.escape(url)}">{html.escape(name)}</a></td>
|
||||
<td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a></td>
|
||||
<td>{html.escape(description)}</td>
|
||||
<td>{install_code}</td>
|
||||
</tr>
|
||||
|
|
|
|||
|
|
@ -57,10 +57,18 @@ class Upscaler:
|
|||
self.scale = scale
|
||||
dest_w = img.width * scale
|
||||
dest_h = img.height * scale
|
||||
|
||||
for i in range(3):
|
||||
if img.width > dest_w and img.height > dest_h:
|
||||
break
|
||||
shape = (img.width, img.height)
|
||||
|
||||
img = self.do_upscale(img, selected_model)
|
||||
|
||||
if shape == (img.width, img.height):
|
||||
break
|
||||
|
||||
if img.width >= dest_w and img.height >= dest_h:
|
||||
break
|
||||
|
||||
if img.width != dest_w or img.height != dest_h:
|
||||
img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)
|
||||
|
||||
|
|
|
|||
29
webui.py
29
webui.py
|
|
@ -5,6 +5,7 @@ import importlib
|
|||
import signal
|
||||
import threading
|
||||
from fastapi import FastAPI
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.middleware.gzip import GZipMiddleware
|
||||
|
||||
from modules.paths import script_path
|
||||
|
|
@ -34,7 +35,7 @@ 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
|
||||
|
||||
def wrap_queued_call(func):
|
||||
def f(*args, **kwargs):
|
||||
|
|
@ -85,6 +86,20 @@ def initialize():
|
|||
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
|
||||
shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
|
||||
|
||||
if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:
|
||||
|
||||
try:
|
||||
if not os.path.exists(cmd_opts.tls_keyfile):
|
||||
print("Invalid path to TLS keyfile given")
|
||||
if not os.path.exists(cmd_opts.tls_certfile):
|
||||
print(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
|
||||
except TypeError:
|
||||
cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None
|
||||
print("TLS setup invalid, running webui without TLS")
|
||||
else:
|
||||
print("Running with TLS")
|
||||
|
||||
|
||||
# make the program just exit at ctrl+c without waiting for anything
|
||||
def sigint_handler(sig, frame):
|
||||
print(f'Interrupted with signal {sig} in {frame}')
|
||||
|
|
@ -93,6 +108,11 @@ def initialize():
|
|||
signal.signal(signal.SIGINT, sigint_handler)
|
||||
|
||||
|
||||
def setup_cors(app):
|
||||
if cmd_opts.cors_allow_origins:
|
||||
app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_methods=['*'])
|
||||
|
||||
|
||||
def create_api(app):
|
||||
from modules.api.api import Api
|
||||
api = Api(app, queue_lock)
|
||||
|
|
@ -114,6 +134,7 @@ def api_only():
|
|||
initialize()
|
||||
|
||||
app = FastAPI()
|
||||
setup_cors(app)
|
||||
app.add_middleware(GZipMiddleware, minimum_size=1000)
|
||||
api = create_api(app)
|
||||
|
||||
|
|
@ -131,8 +152,10 @@ def webui():
|
|||
|
||||
app, local_url, share_url = demo.launch(
|
||||
share=cmd_opts.share,
|
||||
server_name="0.0.0.0" if cmd_opts.listen else None,
|
||||
server_name=server_name,
|
||||
server_port=cmd_opts.port,
|
||||
ssl_keyfile=cmd_opts.tls_keyfile,
|
||||
ssl_certfile=cmd_opts.tls_certfile,
|
||||
debug=cmd_opts.gradio_debug,
|
||||
auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
|
||||
inbrowser=cmd_opts.autolaunch,
|
||||
|
|
@ -147,6 +170,8 @@ def webui():
|
|||
# runnnig its code. We disable this here. Suggested by RyotaK.
|
||||
app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
|
||||
|
||||
setup_cors(app)
|
||||
|
||||
app.add_middleware(GZipMiddleware, minimum_size=1000)
|
||||
|
||||
if launch_api:
|
||||
|
|
|
|||
Loading…
Reference in a new issue