IOPaint/lama_cleaner/download.py

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import json
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import os
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from typing import List
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from loguru import logger
from pathlib import Path
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from lama_cleaner.const import DIFFUSERS_MODEL_FP16_REVERSION, DEFAULT_MODEL_DIR
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from lama_cleaner.schema import (
ModelInfo,
ModelType,
DIFFUSERS_SD_INPAINT_CLASS_NAME,
DIFFUSERS_SDXL_INPAINT_CLASS_NAME,
DIFFUSERS_SD_CLASS_NAME,
DIFFUSERS_SDXL_CLASS_NAME,
)
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def cli_download_model(model: str, model_dir: str):
if os.path.isfile(model_dir):
raise ValueError(f"invalid --model-dir: {model_dir} is a file")
if not os.path.exists(model_dir):
logger.info(f"Create model cache directory: {model_dir}")
Path(model_dir).mkdir(exist_ok=True, parents=True)
os.environ["XDG_CACHE_HOME"] = model_dir
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from lama_cleaner.model import models
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if model in models:
logger.info(f"Downloading {model}...")
models[model].download()
logger.info(f"Done.")
else:
logger.info(f"Downloading model from Huggingface: {model}")
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from diffusers import DiffusionPipeline
downloaded_path = DiffusionPipeline.download(
pretrained_model_name=model,
revision="fp16" if model in DIFFUSERS_MODEL_FP16_REVERSION else "main",
resume_download=True,
)
logger.info(f"Done. Downloaded to {downloaded_path}")
def folder_name_to_show_name(name: str) -> str:
return name.replace("models--", "").replace("--", "/")
def scan_diffusers_models(
cache_dir, class_name: List[str], model_type: ModelType
) -> List[ModelInfo]:
cache_dir = Path(cache_dir)
res = []
for it in cache_dir.glob("**/*/model_index.json"):
with open(it, "r", encoding="utf-8") as f:
data = json.load(f)
if data["_class_name"] in class_name:
name = folder_name_to_show_name(it.parent.parent.parent.name)
if name not in res:
res.append(
ModelInfo(
name=name,
path=name,
model_type=model_type,
)
)
return res
def scan_single_file_diffusion_models(cache_dir) -> List[ModelInfo]:
cache_dir = Path(cache_dir)
res = []
for it in cache_dir.glob(f"*.*"):
if it.suffix not in [".safetensors", ".ckpt"]:
continue
if "inpaint" in str(it).lower():
if "sdxl" in str(it).lower():
model_type = ModelType.DIFFUSERS_SDXL_INPAINT
else:
model_type = ModelType.DIFFUSERS_SD_INPAINT
else:
if "sdxl" in str(it).lower():
model_type = ModelType.DIFFUSERS_SDXL
else:
model_type = ModelType.DIFFUSERS_SD
res.append(
ModelInfo(
name=it.name,
path=str(it.absolute()),
model_type=model_type,
is_single_file_diffusers=True,
)
)
return res
def scan_inpaint_models() -> List[ModelInfo]:
res = []
from lama_cleaner.model import models
for name, m in models.items():
if m.is_erase_model:
res.append(
ModelInfo(
name=name,
path=name,
model_type=ModelType.INPAINT,
)
)
return res
def scan_models() -> List[ModelInfo]:
from diffusers.utils import DIFFUSERS_CACHE
available_models = []
available_models.extend(scan_inpaint_models())
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available_models.extend(scan_single_file_diffusion_models(DEFAULT_MODEL_DIR))
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cache_dir = Path(DIFFUSERS_CACHE)
diffusers_model_names = []
for it in cache_dir.glob("**/*/model_index.json"):
with open(it, "r", encoding="utf-8") as f:
data = json.load(f)
_class_name = data["_class_name"]
name = folder_name_to_show_name(it.parent.parent.parent.name)
if name in diffusers_model_names:
continue
if _class_name == DIFFUSERS_SD_CLASS_NAME:
model_type = ModelType.DIFFUSERS_SD
elif _class_name == DIFFUSERS_SD_INPAINT_CLASS_NAME:
model_type = ModelType.DIFFUSERS_SD_INPAINT
elif _class_name == DIFFUSERS_SDXL_CLASS_NAME:
model_type = ModelType.DIFFUSERS_SDXL
elif _class_name == DIFFUSERS_SDXL_INPAINT_CLASS_NAME:
model_type = ModelType.DIFFUSERS_SDXL_INPAINT
elif _class_name in [
"StableDiffusionInstructPix2PixPipeline",
"PaintByExamplePipeline",
"KandinskyV22InpaintPipeline",
]:
model_type = ModelType.DIFFUSERS_OTHER
else:
continue
diffusers_model_names.append(name)
available_models.append(
ModelInfo(
name=name,
path=name,
model_type=model_type,
)
)
return available_models