149 lines
4.4 KiB
Python
149 lines
4.4 KiB
Python
from __future__ import annotations
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import os
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import diffusers
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from modal import Image, Mount, Secret, Stub
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BASE_CACHE_PATH = "/vol/cache"
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BASE_CACHE_PATH_LORA = "/vol/cache/lora"
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BASE_CACHE_PATH_TEXTUAL_INVERSION = "/vol/cache/textual_inversion"
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BASE_CACHE_PATH_CONTROLNET = "/vol/cache/controlnet"
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def download_file(url, file_name, file_path):
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"""
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Download files.
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"""
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from urllib.request import Request, urlopen
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req = Request(url, headers={"User-Agent": "Mozilla/5.0"})
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downloaded = urlopen(req).read()
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dir_names = os.path.join(file_path, file_name)
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os.makedirs(os.path.dirname(dir_names), exist_ok=True)
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with open(dir_names, mode="wb") as f:
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f.write(downloaded)
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def download_controlnet(name: str, repo_id: str, token: str):
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"""
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Download a controlnet.
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"""
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cache_path = os.path.join(BASE_CACHE_PATH_CONTROLNET, name)
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controlnet = diffusers.ControlNetModel.from_pretrained(
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repo_id,
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use_auth_token=token,
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cache_dir=cache_path,
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)
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controlnet.save_pretrained(cache_path, safe_serialization=True)
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def download_vae(name: str, model_url: str, token: str):
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"""
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Download a vae.
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"""
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cache_path = os.path.join(BASE_CACHE_PATH, name)
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vae = diffusers.AutoencoderKL.from_single_file(
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pretrained_model_link_or_path=model_url,
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use_auth_token=token,
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cache_dir=cache_path,
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)
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vae.save_pretrained(cache_path, safe_serialization=True)
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def download_model(name: str, model_url: str, token: str):
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"""
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Download a model.
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"""
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cache_path = os.path.join(BASE_CACHE_PATH, name)
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pipe = diffusers.StableDiffusionPipeline.from_single_file(
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pretrained_model_link_or_path=model_url,
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use_auth_token=token,
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cache_dir=cache_path,
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)
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pipe.save_pretrained(cache_path, safe_serialization=True)
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def download_model_sdxl(name: str, model_url: str, token: str):
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"""
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Download a sdxl model.
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"""
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cache_path = os.path.join(BASE_CACHE_PATH, name)
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pipe = diffusers.StableDiffusionXLPipeline.from_single_file(
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pretrained_model_link_or_path=model_url,
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use_auth_token=token,
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cache_dir=cache_path,
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)
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pipe.save_pretrained(cache_path, safe_serialization=True)
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refiner_cache_path = cache_path + "-refiner"
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refiner = diffusers.StableDiffusionXLImg2ImgPipeline.from_single_file(
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"https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/blob/main/sd_xl_refiner_1.0.safetensors",
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cache_dir=refiner_cache_path,
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)
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refiner.save_pretrained(refiner_cache_path, safe_serialization=True)
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def build_image():
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"""
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Build the Docker image.
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"""
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import yaml
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token = os.environ["HUGGING_FACE_TOKEN"]
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config = {}
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with open("/config.yml", "r") as file:
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config = yaml.safe_load(file)
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model = config.get("model")
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use_xl = config.get("use_xl")
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if model is not None:
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if use_xl is not None and use_xl:
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download_model_sdxl(name=model["name"], model_url=model["url"], token=token)
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else:
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download_model(name=model["name"], model_url=model["url"], token=token)
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vae = config.get("vae")
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if vae is not None:
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download_vae(name=model["name"], model_url=vae["url"], token=token)
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controlnets = config.get("controlnets")
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if controlnets is not None:
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for controlnet in controlnets:
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download_controlnet(name=controlnet["name"], repo_id=controlnet["repo_id"], token=token)
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loras = config.get("loras")
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if loras is not None:
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for lora in loras:
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download_file(
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url=lora["url"],
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file_name=lora["name"],
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file_path=BASE_CACHE_PATH_LORA,
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)
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textual_inversions = config.get("textual_inversions")
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if textual_inversions is not None:
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for textual_inversion in textual_inversions:
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download_file(
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url=textual_inversion["url"],
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file_name=textual_inversion["name"],
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file_path=BASE_CACHE_PATH_TEXTUAL_INVERSION,
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)
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stub = Stub("stable-diffusion-cli")
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base_stub = Image.from_dockerfile(
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path="Dockerfile",
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context_mount=Mount.from_local_file("requirements.txt"),
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)
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stub.image = base_stub.extend(
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dockerfile_commands=[
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"FROM base",
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"COPY config.yml /",
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],
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context_mount=Mount.from_local_file("config.yml"),
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).run_function(
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build_image,
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secrets=[Secret.from_dotenv(__file__)],
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)
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