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