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import json
import os
from functools import lru_cache
from typing import List
from iopaint.schema import ModelType, ModelInfo
from loguru import logger
from pathlib import Path
from iopaint.const import (
DEFAULT_MODEL_DIR,
DIFFUSERS_SD_CLASS_NAME,
DIFFUSERS_SD_INPAINT_CLASS_NAME,
DIFFUSERS_SDXL_CLASS_NAME,
DIFFUSERS_SDXL_INPAINT_CLASS_NAME,
ANYTEXT_NAME,
)
from iopaint.model.original_sd_configs import get_config_files
def cli_download_model(model: str):
from iopaint.model import models
from iopaint.model.utils import handle_from_pretrained_exceptions
if model in models and models[model].is_erase_model:
logger.info(f"Downloading {model}...")
models[model].download()
logger.info(f"Done.")
elif model == ANYTEXT_NAME:
logger.info(f"Downloading {model}...")
models[model].download()
logger.info(f"Done.")
else:
logger.info(f"Downloading model from Huggingface: {model}")
from diffusers import DiffusionPipeline
downloaded_path = handle_from_pretrained_exceptions(
DiffusionPipeline.download,
pretrained_model_name=model,
variant="fp16",
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("--", "/")
@lru_cache(maxsize=512)
def get_sd_model_type(model_abs_path: str) -> ModelType:
if "inpaint" in Path(model_abs_path).name.lower():
model_type = ModelType.DIFFUSERS_SD_INPAINT
else:
# load once to check num_in_channels
from diffusers import StableDiffusionInpaintPipeline
try:
StableDiffusionInpaintPipeline.from_single_file(
model_abs_path,
load_safety_checker=False,
num_in_channels=9,
config_files=get_config_files(),
)
model_type = ModelType.DIFFUSERS_SD_INPAINT
except ValueError as e:
if "Trying to set a tensor of shape torch.Size([320, 4, 3, 3])" in str(e):
model_type = ModelType.DIFFUSERS_SD
else:
raise e
return model_type
@lru_cache()
def get_sdxl_model_type(model_abs_path: str) -> ModelType:
if "inpaint" in model_abs_path:
model_type = ModelType.DIFFUSERS_SDXL_INPAINT
else:
# load once to check num_in_channels
from diffusers import StableDiffusionXLInpaintPipeline
try:
model = StableDiffusionXLInpaintPipeline.from_single_file(
model_abs_path,
load_safety_checker=False,
num_in_channels=9,
config_files=get_config_files(),
)
if model.unet.config.in_channels == 9:
# https://github.com/huggingface/diffusers/issues/6610
model_type = ModelType.DIFFUSERS_SDXL_INPAINT
else:
model_type = ModelType.DIFFUSERS_SDXL
except ValueError as e:
if "Trying to set a tensor of shape torch.Size([320, 4, 3, 3])" in str(e):
model_type = ModelType.DIFFUSERS_SDXL
else:
raise e
return model_type
def scan_single_file_diffusion_models(cache_dir) -> List[ModelInfo]:
cache_dir = Path(cache_dir)
stable_diffusion_dir = cache_dir / "stable_diffusion"
cache_file = stable_diffusion_dir / "iopaint_cache.json"
model_type_cache = {}
if cache_file.exists():
try:
with open(cache_file, "r", encoding="utf-8") as f:
model_type_cache = json.load(f)
assert isinstance(model_type_cache, dict)
except:
pass
res = []
for it in stable_diffusion_dir.glob(f"*.*"):
if it.suffix not in [".safetensors", ".ckpt"]:
continue
model_abs_path = str(it.absolute())
model_type = model_type_cache.get(it.name)
if model_type is None:
model_type = get_sd_model_type(model_abs_path)
model_type_cache[it.name] = model_type
res.append(
ModelInfo(
name=it.name,
path=model_abs_path,
model_type=model_type,
is_single_file_diffusers=True,
)
)
if stable_diffusion_dir.exists():
with open(cache_file, "w", encoding="utf-8") as fw:
json.dump(model_type_cache, fw, indent=2, ensure_ascii=False)
stable_diffusion_xl_dir = cache_dir / "stable_diffusion_xl"
sdxl_cache_file = stable_diffusion_xl_dir / "iopaint_cache.json"
sdxl_model_type_cache = {}
if sdxl_cache_file.exists():
try:
with open(sdxl_cache_file, "r", encoding="utf-8") as f:
sdxl_model_type_cache = json.load(f)
assert isinstance(sdxl_model_type_cache, dict)
except:
pass
for it in stable_diffusion_xl_dir.glob(f"*.*"):
if it.suffix not in [".safetensors", ".ckpt"]:
continue
model_abs_path = str(it.absolute())
model_type = sdxl_model_type_cache.get(it.name)
if model_type is None:
model_type = get_sdxl_model_type(model_abs_path)
sdxl_model_type_cache[it.name] = model_type
if stable_diffusion_xl_dir.exists():
with open(sdxl_cache_file, "w", encoding="utf-8") as fw:
json.dump(sdxl_model_type_cache, fw, indent=2, ensure_ascii=False)
res.append(
ModelInfo(
name=it.name,
path=model_abs_path,
model_type=model_type,
is_single_file_diffusers=True,
)
)
return res
def scan_inpaint_models(model_dir: Path) -> List[ModelInfo]:
res = []
from iopaint.model import models
# logger.info(f"Scanning inpaint models in {model_dir}")
for name, m in models.items():
if m.is_erase_model and m.is_downloaded():
res.append(
ModelInfo(
name=name,
path=name,
model_type=ModelType.INPAINT,
)
)
return res
def scan_diffusers_models() -> List[ModelInfo]:
from huggingface_hub.constants import HF_HUB_CACHE
available_models = []
cache_dir = Path(HF_HUB_CACHE)
# logger.info(f"Scanning diffusers models in {cache_dir}")
diffusers_model_names = []
for it in cache_dir.glob("**/*/model_index.json"):
with open(it, "r", encoding="utf-8") as f:
try:
data = json.load(f)
except:
continue
_class_name = data["_class_name"]
name = folder_name_to_show_name(it.parent.parent.parent.name)
if name in diffusers_model_names:
continue
if "PowerPaint" in name:
model_type = ModelType.DIFFUSERS_OTHER
elif _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",
"AnyText",
]:
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
def _scan_converted_diffusers_models(cache_dir) -> List[ModelInfo]:
cache_dir = Path(cache_dir)
available_models = []
diffusers_model_names = []
for it in cache_dir.glob("**/*/model_index.json"):
with open(it, "r", encoding="utf-8") as f:
try:
data = json.load(f)
except:
logger.error(
f"Failed to load {it}, please try revert from original model or fix model_index.json by hand."
)
continue
_class_name = data["_class_name"]
name = folder_name_to_show_name(it.parent.name)
if name in diffusers_model_names:
continue
elif _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
else:
continue
diffusers_model_names.append(name)
available_models.append(
ModelInfo(
name=name,
path=str(it.parent.absolute()),
model_type=model_type,
)
)
return available_models
def scan_converted_diffusers_models(cache_dir) -> List[ModelInfo]:
cache_dir = Path(cache_dir)
available_models = []
stable_diffusion_dir = cache_dir / "stable_diffusion"
stable_diffusion_xl_dir = cache_dir / "stable_diffusion_xl"
available_models.extend(_scan_converted_diffusers_models(stable_diffusion_dir))
available_models.extend(_scan_converted_diffusers_models(stable_diffusion_xl_dir))
return available_models
def scan_models() -> List[ModelInfo]:
model_dir = os.getenv("XDG_CACHE_HOME", DEFAULT_MODEL_DIR)
available_models = []
available_models.extend(scan_inpaint_models(model_dir))
available_models.extend(scan_single_file_diffusion_models(model_dir))
available_models.extend(scan_diffusers_models())
available_models.extend(scan_converted_diffusers_models(model_dir))
return available_models
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