mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-09-19 19:05:28 +00:00
735 lines
25 KiB
Python
735 lines
25 KiB
Python
"""
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Download and convert weights for testing
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To see what weights will be downloaded and converted, run:
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DRY_RUN=1 python scripts/prepare_test_weights.py
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"""
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import hashlib
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import os
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import subprocess
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import sys
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from urllib.parse import urlparse
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import requests
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from tqdm import tqdm
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# Set the base directory to the parent directory of the script
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project_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
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test_weights_dir = os.path.join(project_dir, "tests", "weights")
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previous_line = "\033[F"
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download_count = 0
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bytes_count = 0
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def die(message: str) -> None:
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print(message, file=sys.stderr)
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sys.exit(1)
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def rel(path: str) -> str:
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return os.path.relpath(path, project_dir)
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def calc_hash(filepath: str) -> str:
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with open(filepath, "rb") as f:
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data = f.read()
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found = hashlib.blake2b(data, digest_size=int(32 / 8)).hexdigest()
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return found
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def check_hash(path: str, expected: str) -> str:
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found = calc_hash(path)
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if found != expected:
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die(f"❌ Invalid hash for {path} ({found} != {expected})")
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return found
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def download_file(
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url: str,
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dest_folder: str,
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dry_run: bool | None = None,
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skip_existing: bool = True,
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expected_hash: str | None = None,
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filename: str | None = None,
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):
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"""
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Downloads a file
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Features:
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- shows a progress bar
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- skips existing files
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- uses a temporary file to prevent partial downloads
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- can do a dry run to check the url is valid
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- displays the downloaded file hash
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"""
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global download_count, bytes_count
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filename = os.path.basename(urlparse(url).path) if filename is None else filename
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dest_filename = os.path.join(dest_folder, filename)
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temp_filename = dest_filename + ".part"
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dry_run = bool(os.environ.get("DRY_RUN") == "1") if dry_run is None else dry_run
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is_downloaded = os.path.exists(dest_filename)
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if is_downloaded and skip_existing:
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skip_icon = "✖️ "
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else:
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skip_icon = "🔽"
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if dry_run:
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response = requests.head(url, allow_redirects=True)
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readable_size = ""
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if response.status_code == 200:
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content_length = response.headers.get("content-length")
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if content_length:
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size_in_bytes = int(content_length)
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readable_size = human_readable_size(size_in_bytes)
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download_count += 1
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bytes_count += size_in_bytes
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print(f"✅{skip_icon} {response.status_code} READY {readable_size:<8} {url}")
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else:
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print(f"❌{skip_icon} {response.status_code} ERROR {readable_size:<8} {url}")
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return
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if skip_existing and os.path.exists(dest_filename):
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print(f"{skip_icon}️ Skipping previously downloaded {url}")
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return
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os.makedirs(dest_folder, exist_ok=True)
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print(f"🔽 Downloading {url} => '{rel(dest_filename)}'", end="\n")
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response = requests.get(url, stream=True)
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if response.status_code != 200:
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print(response.content[:1000])
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die(f"Failed to download {url}. Status code: {response.status_code}")
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total = int(response.headers.get("content-length", 0))
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bar = tqdm(
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desc=filename,
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total=total,
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unit="iB",
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unit_scale=True,
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unit_divisor=1024,
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leave=False,
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)
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with open(temp_filename, "wb") as f, bar:
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for data in response.iter_content(chunk_size=1024 * 1000):
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size = f.write(data)
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bar.update(size)
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os.rename(temp_filename, dest_filename)
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calculated_hash = calc_hash(dest_filename)
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print(f"{previous_line}✅ Downloaded {calculated_hash} {url} => '{rel(dest_filename)}' ")
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if expected_hash is not None:
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check_hash(dest_filename, expected_hash)
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def download_files(urls: list[str], dest_folder: str):
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for url in urls:
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download_file(url, dest_folder)
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def human_readable_size(size: int | float, decimal_places: int = 2) -> str:
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for unit in ["B", "KB", "MB", "GB", "TB", "PB"]:
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if size < 1024.0:
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break
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size /= 1024.0
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return f"{size:.{decimal_places}f}{unit}" # type: ignore
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def download_sd_text_encoder(hf_repo_id: str = "runwayml/stable-diffusion-v1-5", subdir: str = "text_encoder"):
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encoder_filename = "model.safetensors" if "inpainting" not in hf_repo_id else "model.fp16.safetensors"
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base_url = f"https://huggingface.co/{hf_repo_id}"
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download_files(
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urls=[
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f"{base_url}/raw/main/{subdir}/config.json",
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f"{base_url}/resolve/main/{subdir}/{encoder_filename}",
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],
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dest_folder=os.path.join(test_weights_dir, hf_repo_id, subdir),
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)
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def download_sd_tokenizer(hf_repo_id: str = "runwayml/stable-diffusion-v1-5", subdir: str = "tokenizer"):
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download_files(
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urls=[
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f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/merges.txt",
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f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/special_tokens_map.json",
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f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/tokenizer_config.json",
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f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/vocab.json",
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],
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dest_folder=os.path.join(test_weights_dir, hf_repo_id, subdir),
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)
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def download_sd_base(hf_repo_id: str = "runwayml/stable-diffusion-v1-5"):
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is_inpainting = "inpainting" in hf_repo_id
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ext = "safetensors" if not is_inpainting else "bin"
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base_folder = os.path.join(test_weights_dir, hf_repo_id)
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download_file(f"https://huggingface.co/{hf_repo_id}/raw/main/model_index.json", base_folder)
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download_file(
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f"https://huggingface.co/{hf_repo_id}/raw/main/scheduler/scheduler_config.json",
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os.path.join(base_folder, "scheduler"),
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)
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for subdir in ["unet", "vae"]:
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subdir_folder = os.path.join(base_folder, subdir)
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download_file(f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/config.json", subdir_folder)
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download_file(
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f"https://huggingface.co/{hf_repo_id}/resolve/main/{subdir}/diffusion_pytorch_model.{ext}", subdir_folder
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)
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# we only need the unet for the inpainting model
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if not is_inpainting:
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download_sd_text_encoder(hf_repo_id, "text_encoder")
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download_sd_tokenizer(hf_repo_id, "tokenizer")
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def download_sd15(hf_repo_id: str = "runwayml/stable-diffusion-v1-5"):
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download_sd_base(hf_repo_id)
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base_folder = os.path.join(test_weights_dir, hf_repo_id)
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subdir = "feature_extractor"
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download_file(
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f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/preprocessor_config.json",
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os.path.join(base_folder, subdir),
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)
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if "inpainting" not in hf_repo_id:
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subdir = "safety_checker"
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subdir_folder = os.path.join(base_folder, subdir)
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download_file(f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/config.json", subdir_folder)
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download_file(f"https://huggingface.co/{hf_repo_id}/resolve/main/{subdir}/model.safetensors", subdir_folder)
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def download_sdxl(hf_repo_id: str = "stabilityai/stable-diffusion-xl-base-1.0"):
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download_sd_base(hf_repo_id)
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download_sd_text_encoder(hf_repo_id, "text_encoder_2")
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download_sd_tokenizer(hf_repo_id, "tokenizer_2")
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def download_vae_fp16_fix():
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download_files(
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urls=[
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"https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/raw/main/config.json",
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"https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/resolve/main/diffusion_pytorch_model.safetensors",
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],
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dest_folder=os.path.join(test_weights_dir, "madebyollin", "sdxl-vae-fp16-fix"),
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)
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def download_vae_ft_mse():
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download_files(
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urls=[
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"https://huggingface.co/stabilityai/sd-vae-ft-mse/raw/main/config.json",
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"https://huggingface.co/stabilityai/sd-vae-ft-mse/resolve/main/diffusion_pytorch_model.safetensors",
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],
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dest_folder=os.path.join(test_weights_dir, "stabilityai", "sd-vae-ft-mse"),
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)
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def download_loras():
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dest_folder = os.path.join(test_weights_dir, "loras", "pokemon-lora")
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download_file("https://huggingface.co/pcuenq/pokemon-lora/resolve/main/pytorch_lora_weights.bin", dest_folder)
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dest_folder = os.path.join(test_weights_dir, "loras", "dpo-lora")
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download_file(
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"https://huggingface.co/radames/sdxl-DPO-LoRA/resolve/main/pytorch_lora_weights.safetensors", dest_folder
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)
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dest_folder = os.path.join(test_weights_dir, "loras", "sliders")
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download_file("https://sliders.baulab.info/weights/xl_sliders/age.pt", dest_folder)
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download_file("https://sliders.baulab.info/weights/xl_sliders/cartoon_style.pt", dest_folder)
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download_file("https://sliders.baulab.info/weights/xl_sliders/eyesize.pt", dest_folder)
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def download_preprocessors():
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dest_folder = os.path.join(test_weights_dir, "carolineec", "informativedrawings")
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download_file("https://huggingface.co/spaces/carolineec/informativedrawings/resolve/main/model2.pth", dest_folder)
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def download_controlnet():
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base_folder = os.path.join(test_weights_dir, "lllyasviel")
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controlnets = [
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"control_v11p_sd15_canny",
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"control_v11f1p_sd15_depth",
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"control_v11p_sd15_normalbae",
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"control_v11p_sd15_lineart",
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]
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for net in controlnets:
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net_folder = os.path.join(base_folder, net)
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urls = [
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f"https://huggingface.co/lllyasviel/{net}/raw/main/config.json",
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f"https://huggingface.co/lllyasviel/{net}/resolve/main/diffusion_pytorch_model.safetensors",
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]
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download_files(urls, net_folder)
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mfidabel_folder = os.path.join(test_weights_dir, "mfidabel", "controlnet-segment-anything")
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urls = [
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"https://huggingface.co/mfidabel/controlnet-segment-anything/raw/main/config.json",
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"https://huggingface.co/mfidabel/controlnet-segment-anything/resolve/main/diffusion_pytorch_model.bin",
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]
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download_files(urls, mfidabel_folder)
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def download_control_lora_fooocus():
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base_folder = os.path.join(test_weights_dir, "lllyasviel", "misc")
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control_loras = [
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"control-lora-canny-rank128.safetensors",
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"fooocus_xl_cpds_128.safetensors",
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]
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for control_lora in control_loras:
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download_file(
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url=f"https://huggingface.co/lllyasviel/misc/resolve/main/{control_lora}",
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dest_folder=base_folder,
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)
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def download_unclip():
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base_folder = os.path.join(test_weights_dir, "stabilityai", "stable-diffusion-2-1-unclip")
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download_file(
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"https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/raw/main/model_index.json", base_folder
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)
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image_encoder_folder = os.path.join(base_folder, "image_encoder")
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urls = [
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"https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/raw/main/image_encoder/config.json",
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"https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/resolve/main/image_encoder/model.safetensors",
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]
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download_files(urls, image_encoder_folder)
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def download_ip_adapter():
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base_folder = os.path.join(test_weights_dir, "h94", "IP-Adapter")
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models_folder = os.path.join(base_folder, "models")
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urls = [
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"https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.bin",
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"https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus_sd15.bin",
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]
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download_files(urls, models_folder)
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sdxl_models_folder = os.path.join(base_folder, "sdxl_models")
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urls = [
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"https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.bin",
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"https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin",
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]
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download_files(urls, sdxl_models_folder)
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def download_t2i_adapter():
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base_folder = os.path.join(test_weights_dir, "TencentARC", "t2iadapter_depth_sd15v2")
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urls = [
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"https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/raw/main/config.json",
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"https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/resolve/main/diffusion_pytorch_model.bin",
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]
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download_files(urls, base_folder)
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canny_sdxl_folder = os.path.join(test_weights_dir, "TencentARC", "t2i-adapter-canny-sdxl-1.0")
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urls = [
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"https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0/raw/main/config.json",
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"https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0/resolve/main/diffusion_pytorch_model.safetensors",
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]
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download_files(urls, canny_sdxl_folder)
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def download_sam():
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weights_folder = os.path.join(test_weights_dir)
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download_file(
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"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth", weights_folder, expected_hash="06785e66"
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)
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def download_dinov2():
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# For conversion
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weights_folder = os.path.join(test_weights_dir)
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urls = [
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"https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_pretrain.pth",
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"https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_pretrain.pth",
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"https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_pretrain.pth",
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"https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_reg4_pretrain.pth",
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"https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_reg4_pretrain.pth",
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"https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_reg4_pretrain.pth",
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]
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download_files(urls, weights_folder)
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# For testing (note: versions with registers are not available yet on HuggingFace)
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for repo in ["dinov2-small", "dinov2-base", "dinov2-large"]:
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base_folder = os.path.join(test_weights_dir, "facebook", repo)
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urls = [
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f"https://huggingface.co/facebook/{repo}/raw/main/config.json",
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f"https://huggingface.co/facebook/{repo}/raw/main/preprocessor_config.json",
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f"https://huggingface.co/facebook/{repo}/resolve/main/pytorch_model.bin",
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]
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download_files(urls, base_folder)
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def download_lcm_base():
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base_folder = os.path.join(test_weights_dir, "latent-consistency/lcm-sdxl")
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download_file(f"https://huggingface.co/latent-consistency/lcm-sdxl/raw/main/config.json", base_folder)
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download_file(
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f"https://huggingface.co/latent-consistency/lcm-sdxl/resolve/main/diffusion_pytorch_model.safetensors",
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base_folder,
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)
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def download_lcm_lora():
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download_file(
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"https://huggingface.co/latent-consistency/lcm-lora-sdxl/resolve/main/pytorch_lora_weights.safetensors",
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dest_folder=test_weights_dir,
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filename="sdxl-lcm-lora.safetensors",
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expected_hash="6312a30a",
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)
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def printg(msg: str):
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"""print in green color"""
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print("\033[92m" + msg + "\033[0m")
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def run_conversion_script(
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script_filename: str,
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from_weights: str,
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to_weights: str,
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half: bool = False,
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expected_hash: str | None = None,
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additional_args: list[str] | None = None,
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skip_existing: bool = True,
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):
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if skip_existing and expected_hash and os.path.exists(to_weights):
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found_hash = check_hash(to_weights, expected_hash)
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if expected_hash == found_hash:
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printg(f"✖️ Skipping converted from {from_weights} to {to_weights} (hash {found_hash} confirmed) ")
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return
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msg = f"Converting {from_weights} to {to_weights}"
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printg(msg)
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args = ["python", f"scripts/conversion/{script_filename}", "--from", from_weights, "--to", to_weights]
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if half:
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args.append("--half")
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if additional_args:
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args.extend(additional_args)
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subprocess.run(args, check=True)
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if expected_hash is not None:
|
||
found_hash = check_hash(to_weights, expected_hash)
|
||
printg(f"✅ Converted from {from_weights} to {to_weights} (hash {found_hash} confirmed) ")
|
||
else:
|
||
printg(f"✅⚠️ Converted from {from_weights} to {to_weights} (no hash check performed)")
|
||
|
||
|
||
def convert_sd15():
|
||
run_conversion_script(
|
||
script_filename="convert_transformers_clip_text_model.py",
|
||
from_weights="tests/weights/runwayml/stable-diffusion-v1-5",
|
||
to_weights="tests/weights/CLIPTextEncoderL.safetensors",
|
||
half=True,
|
||
expected_hash="6c9cbc59",
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_autoencoder_kl.py",
|
||
"tests/weights/runwayml/stable-diffusion-v1-5",
|
||
"tests/weights/lda.safetensors",
|
||
expected_hash="329e369c",
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_unet.py",
|
||
"tests/weights/runwayml/stable-diffusion-v1-5",
|
||
"tests/weights/unet.safetensors",
|
||
half=True,
|
||
expected_hash="f81ac65a",
|
||
)
|
||
os.makedirs("tests/weights/inpainting", exist_ok=True)
|
||
run_conversion_script(
|
||
"convert_diffusers_unet.py",
|
||
"tests/weights/runwayml/stable-diffusion-inpainting",
|
||
"tests/weights/inpainting/unet.safetensors",
|
||
half=True,
|
||
expected_hash="c07a8c61",
|
||
)
|
||
|
||
|
||
def convert_sdxl():
|
||
run_conversion_script(
|
||
"convert_transformers_clip_text_model.py",
|
||
"tests/weights/stabilityai/stable-diffusion-xl-base-1.0",
|
||
"tests/weights/DoubleCLIPTextEncoder.safetensors",
|
||
half=True,
|
||
expected_hash="7f99c30b",
|
||
additional_args=["--subfolder2", "text_encoder_2"],
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_autoencoder_kl.py",
|
||
"tests/weights/stabilityai/stable-diffusion-xl-base-1.0",
|
||
"tests/weights/sdxl-lda.safetensors",
|
||
half=True,
|
||
expected_hash="7464e9dc",
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_unet.py",
|
||
"tests/weights/stabilityai/stable-diffusion-xl-base-1.0",
|
||
"tests/weights/sdxl-unet.safetensors",
|
||
half=True,
|
||
expected_hash="2e5c4911",
|
||
)
|
||
|
||
|
||
def convert_vae_ft_mse():
|
||
run_conversion_script(
|
||
"convert_diffusers_autoencoder_kl.py",
|
||
"tests/weights/stabilityai/sd-vae-ft-mse",
|
||
"tests/weights/lda_ft_mse.safetensors",
|
||
half=True,
|
||
expected_hash="4d0bae7e",
|
||
)
|
||
|
||
|
||
def convert_vae_fp16_fix():
|
||
run_conversion_script(
|
||
"convert_diffusers_autoencoder_kl.py",
|
||
"tests/weights/madebyollin/sdxl-vae-fp16-fix",
|
||
"tests/weights/sdxl-lda-fp16-fix.safetensors",
|
||
additional_args=["--subfolder", "''"],
|
||
half=True,
|
||
expected_hash="98c7e998",
|
||
)
|
||
|
||
|
||
def convert_preprocessors():
|
||
subprocess.run(
|
||
[
|
||
"curl",
|
||
"-L",
|
||
"https://raw.githubusercontent.com/carolineec/informative-drawings/main/model.py",
|
||
"-o",
|
||
"src/model.py",
|
||
],
|
||
check=True,
|
||
)
|
||
run_conversion_script(
|
||
"convert_informative_drawings.py",
|
||
"tests/weights/carolineec/informativedrawings/model2.pth",
|
||
"tests/weights/informative-drawings.safetensors",
|
||
expected_hash="93dca207",
|
||
)
|
||
os.remove("src/model.py")
|
||
|
||
|
||
def convert_controlnet():
|
||
os.makedirs("tests/weights/controlnet", exist_ok=True)
|
||
run_conversion_script(
|
||
"convert_diffusers_controlnet.py",
|
||
"tests/weights/lllyasviel/control_v11p_sd15_canny",
|
||
"tests/weights/controlnet/lllyasviel_control_v11p_sd15_canny.safetensors",
|
||
expected_hash="9a1a48cf",
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_controlnet.py",
|
||
"tests/weights/lllyasviel/control_v11f1p_sd15_depth",
|
||
"tests/weights/controlnet/lllyasviel_control_v11f1p_sd15_depth.safetensors",
|
||
expected_hash="bbe7e5a6",
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_controlnet.py",
|
||
"tests/weights/lllyasviel/control_v11p_sd15_normalbae",
|
||
"tests/weights/controlnet/lllyasviel_control_v11p_sd15_normalbae.safetensors",
|
||
expected_hash="9fa88ed5",
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_controlnet.py",
|
||
"tests/weights/lllyasviel/control_v11p_sd15_lineart",
|
||
"tests/weights/controlnet/lllyasviel_control_v11p_sd15_lineart.safetensors",
|
||
expected_hash="c29e8c03",
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_controlnet.py",
|
||
"tests/weights/mfidabel/controlnet-segment-anything",
|
||
"tests/weights/controlnet/mfidabel_controlnet-segment-anything.safetensors",
|
||
expected_hash="d536eebb",
|
||
)
|
||
|
||
|
||
def convert_unclip():
|
||
run_conversion_script(
|
||
"convert_transformers_clip_image_model.py",
|
||
"tests/weights/stabilityai/stable-diffusion-2-1-unclip",
|
||
"tests/weights/CLIPImageEncoderH.safetensors",
|
||
half=True,
|
||
expected_hash="4ddb44d2",
|
||
)
|
||
|
||
|
||
def convert_ip_adapter():
|
||
run_conversion_script(
|
||
"convert_diffusers_ip_adapter.py",
|
||
"tests/weights/h94/IP-Adapter/models/ip-adapter_sd15.bin",
|
||
"tests/weights/ip-adapter_sd15.safetensors",
|
||
expected_hash="3fb0472e",
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_ip_adapter.py",
|
||
"tests/weights/h94/IP-Adapter/sdxl_models/ip-adapter_sdxl_vit-h.bin",
|
||
"tests/weights/ip-adapter_sdxl_vit-h.safetensors",
|
||
half=True,
|
||
expected_hash="860518fe",
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_ip_adapter.py",
|
||
"tests/weights/h94/IP-Adapter/models/ip-adapter-plus_sd15.bin",
|
||
"tests/weights/ip-adapter-plus_sd15.safetensors",
|
||
half=True,
|
||
expected_hash="aba8503b",
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_ip_adapter.py",
|
||
"tests/weights/h94/IP-Adapter/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin",
|
||
"tests/weights/ip-adapter-plus_sdxl_vit-h.safetensors",
|
||
half=True,
|
||
expected_hash="545d5ce7",
|
||
)
|
||
|
||
|
||
def convert_t2i_adapter():
|
||
os.makedirs("tests/weights/T2I-Adapter", exist_ok=True)
|
||
run_conversion_script(
|
||
"convert_diffusers_t2i_adapter.py",
|
||
"tests/weights/TencentARC/t2iadapter_depth_sd15v2",
|
||
"tests/weights/T2I-Adapter/t2iadapter_depth_sd15v2.safetensors",
|
||
half=True,
|
||
expected_hash="bb2b3115",
|
||
)
|
||
run_conversion_script(
|
||
"convert_diffusers_t2i_adapter.py",
|
||
"tests/weights/TencentARC/t2i-adapter-canny-sdxl-1.0",
|
||
"tests/weights/T2I-Adapter/t2i-adapter-canny-sdxl-1.0.safetensors",
|
||
half=True,
|
||
expected_hash="f07249a6",
|
||
)
|
||
|
||
|
||
def convert_sam():
|
||
run_conversion_script(
|
||
"convert_segment_anything.py",
|
||
"tests/weights/sam_vit_h_4b8939.pth",
|
||
"tests/weights/segment-anything-h.safetensors",
|
||
expected_hash="b62ad5ed",
|
||
)
|
||
|
||
|
||
def convert_dinov2():
|
||
run_conversion_script(
|
||
"convert_dinov2.py",
|
||
"tests/weights/dinov2_vits14_pretrain.pth",
|
||
"tests/weights/dinov2_vits14_pretrain.safetensors",
|
||
expected_hash="b7f9b294",
|
||
)
|
||
run_conversion_script(
|
||
"convert_dinov2.py",
|
||
"tests/weights/dinov2_vitb14_pretrain.pth",
|
||
"tests/weights/dinov2_vitb14_pretrain.safetensors",
|
||
expected_hash="d72c767b",
|
||
)
|
||
run_conversion_script(
|
||
"convert_dinov2.py",
|
||
"tests/weights/dinov2_vitl14_pretrain.pth",
|
||
"tests/weights/dinov2_vitl14_pretrain.safetensors",
|
||
expected_hash="71eb98d1",
|
||
)
|
||
run_conversion_script(
|
||
"convert_dinov2.py",
|
||
"tests/weights/dinov2_vits14_reg4_pretrain.pth",
|
||
"tests/weights/dinov2_vits14_reg4_pretrain.safetensors",
|
||
expected_hash="89118b46",
|
||
)
|
||
run_conversion_script(
|
||
"convert_dinov2.py",
|
||
"tests/weights/dinov2_vitb14_reg4_pretrain.pth",
|
||
"tests/weights/dinov2_vitb14_reg4_pretrain.safetensors",
|
||
expected_hash="b0296f77",
|
||
)
|
||
run_conversion_script(
|
||
"convert_dinov2.py",
|
||
"tests/weights/dinov2_vitl14_reg4_pretrain.pth",
|
||
"tests/weights/dinov2_vitl14_reg4_pretrain.safetensors",
|
||
expected_hash="b3d877dc",
|
||
)
|
||
|
||
|
||
def convert_control_lora_fooocus():
|
||
run_conversion_script(
|
||
"convert_fooocus_control_lora.py",
|
||
"tests/weights/lllyasviel/misc/control-lora-canny-rank128.safetensors",
|
||
"tests/weights/control_lora/refiners_control-lora-canny-rank128.safetensors",
|
||
expected_hash="4d505134",
|
||
)
|
||
run_conversion_script(
|
||
"convert_fooocus_control_lora.py",
|
||
"tests/weights/lllyasviel/misc/fooocus_xl_cpds_128.safetensors",
|
||
"tests/weights/control_lora/refiners_fooocus_xl_cpds_128.safetensors",
|
||
expected_hash="d81aa461",
|
||
)
|
||
|
||
|
||
def convert_lcm_base():
|
||
run_conversion_script(
|
||
"convert_diffusers_unet.py",
|
||
"tests/weights/latent-consistency/lcm-sdxl",
|
||
"tests/weights/sdxl-lcm-unet.safetensors",
|
||
half=True,
|
||
expected_hash="e161b20c",
|
||
)
|
||
|
||
|
||
def download_all():
|
||
print(f"\nAll weights will be downloaded to {test_weights_dir}\n")
|
||
download_sd15("runwayml/stable-diffusion-v1-5")
|
||
download_sd15("runwayml/stable-diffusion-inpainting")
|
||
download_sdxl("stabilityai/stable-diffusion-xl-base-1.0")
|
||
download_vae_ft_mse()
|
||
download_vae_fp16_fix()
|
||
download_loras()
|
||
download_preprocessors()
|
||
download_controlnet()
|
||
download_unclip()
|
||
download_ip_adapter()
|
||
download_t2i_adapter()
|
||
download_sam()
|
||
download_dinov2()
|
||
download_control_lora_fooocus()
|
||
download_lcm_base()
|
||
download_lcm_lora()
|
||
|
||
|
||
def convert_all():
|
||
convert_sd15()
|
||
convert_sdxl()
|
||
convert_vae_ft_mse()
|
||
convert_vae_fp16_fix()
|
||
# Note: no convert loras: this is done at runtime by `SDLoraManager`
|
||
convert_preprocessors()
|
||
convert_controlnet()
|
||
convert_unclip()
|
||
convert_ip_adapter()
|
||
convert_t2i_adapter()
|
||
convert_sam()
|
||
convert_dinov2()
|
||
convert_control_lora_fooocus()
|
||
convert_lcm_base()
|
||
|
||
|
||
def main():
|
||
try:
|
||
download_all()
|
||
print(f"{download_count} files ({human_readable_size(bytes_count)})\n")
|
||
if not bool(os.environ.get("DRY_RUN") == "1"):
|
||
printg("Converting weights to refiners format\n")
|
||
convert_all()
|
||
except KeyboardInterrupt:
|
||
print("Stopped")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|