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refactor: convert bash script to python

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Ran successfully to completion. But on a repeat run `convert_unclip` didn't pass the hash check for some reason.

- fix inpainting model download urls
- shows a progress bar for downloads
- skips downloading existing files
- uses a temporary file to prevent partial downloads
- can do a dry run to check if url is valid `DRY_RUN=1 python scripts/prepare_test_weights.py`
- displays the downloaded file hash
This commit is contained in:
Bryce 2023-12-13 19:53:06 -08:00 committed by Pierre Chapuis
parent 77fb8032c2
commit 5ca1549c96
11 changed files with 618 additions and 397 deletions
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2
CONTRIBUTING.md
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@ -48,7 +48,7 @@ rye sync --features test,conversion
Then, download and convert all the necessary weights. Be aware that this will use around 50 GB of disk space:
```bash
./scripts/prepare-test-weights.sh
rye run python scripts/prepare_test_weights.py
```
Finally, run the tests:

2
pyproject.toml
View file

@ -41,6 +41,8 @@ conversion = [
"diffusers>=0.24.0",
"transformers>=4.35.2",
"segment-anything-py>=1.0",
"requests>=2.26.0",
"tqdm>=4.62.3",
]
[build-system]

5
scripts/conversion/convert_diffusers_autoencoder_kl.py
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@ -18,8 +18,11 @@ class Args(argparse.Namespace):
def setup_converter(args: Args) -> ModelConverter:
target = LatentDiffusionAutoencoder()
# low_cpu_mem_usage=False stops some annoying console messages us to `pip install accelerate`
source: nn.Module = AutoencoderKL.from_pretrained( # type: ignore
pretrained_model_name_or_path=args.source_path, subfolder=args.subfolder
pretrained_model_name_or_path=args.source_path,
subfolder=args.subfolder,
low_cpu_mem_usage=False,
) # type: ignore
x = torch.randn(1, 3, 512, 512)
converter = ModelConverter(source_model=source, target_model=target, skip_output_check=True, verbose=args.verbose)

6
scripts/conversion/convert_diffusers_controlnet.py
View file

@ -22,7 +22,11 @@ class Args(argparse.Namespace):
@torch.no_grad()
def convert(args: Args) -> dict[str, torch.Tensor]:
controlnet_src: nn.Module = ControlNetModel.from_pretrained(pretrained_model_name_or_path=args.source_path) # type: ignore
# low_cpu_mem_usage=False stops some annoying console messages us to `pip install accelerate`
controlnet_src: nn.Module = ControlNetModel.from_pretrained( # type: ignore
pretrained_model_name_or_path=args.source_path,
low_cpu_mem_usage=False,
)
unet = SD1UNet(in_channels=4)
adapter = SD1ControlnetAdapter(unet, name="mycn").inject()
controlnet = unet.Controlnet

6
scripts/conversion/convert_diffusers_lora.py
View file

@ -40,7 +40,11 @@ class Args(argparse.Namespace):
@torch.no_grad()
def process(args: Args) -> None:
diffusers_state_dict = cast(dict[str, Tensor], torch.load(args.source_path, map_location="cpu")) # type: ignore
diffusers_sd = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path=args.base_model) # type: ignore
# low_cpu_mem_usage=False stops some annoying console messages us to `pip install accelerate`
diffusers_sd = DiffusionPipeline.from_pretrained( # type: ignore
pretrained_model_name_or_path=args.base_model,
low_cpu_mem_usage=False,
)
diffusers_model = cast(fl.Module, diffusers_sd.unet) # type: ignore
refiners_model = SD1UNet(in_channels=4)

6
scripts/conversion/convert_diffusers_t2i_adapter.py
View file

@ -48,7 +48,11 @@ if __name__ == "__main__":
sdxl = "xl" in args.source_path
target = ConditionEncoderXL() if sdxl else ConditionEncoder()
source: nn.Module = T2IAdapter.from_pretrained(pretrained_model_name_or_path=args.source_path) # type: ignore
# low_cpu_mem_usage=False stops some annoying console messages us to `pip install accelerate`
source: nn.Module = T2IAdapter.from_pretrained( # type: ignore
pretrained_model_name_or_path=args.source_path,
low_cpu_mem_usage=False,
)
assert isinstance(source, nn.Module), "Source model is not a nn.Module"
x = torch.randn(1, 3, 1024, 1024) if sdxl else torch.randn(1, 3, 512, 512)

5
scripts/conversion/convert_diffusers_unet.py
View file

@ -17,8 +17,11 @@ class Args(argparse.Namespace):
def setup_converter(args: Args) -> ModelConverter:
# low_cpu_mem_usage=False stops some annoying console messages us to `pip install accelerate`
source: nn.Module = UNet2DConditionModel.from_pretrained( # type: ignore
pretrained_model_name_or_path=args.source_path, subfolder="unet"
pretrained_model_name_or_path=args.source_path,
subfolder="unet",
low_cpu_mem_usage=False,
)
source_in_channels: int = source.config.in_channels # type: ignore
source_clip_embedding_dim: int = source.config.cross_attention_dim # type: ignore

5
scripts/conversion/convert_transformers_clip_image_model.py
View file

@ -21,8 +21,11 @@ class Args(argparse.Namespace):
def setup_converter(args: Args) -> ModelConverter:
# low_cpu_mem_usage=False stops some annoying console messages us to `pip install accelerate`
source: nn.Module = CLIPVisionModelWithProjection.from_pretrained( # type: ignore
pretrained_model_name_or_path=args.source_path, subfolder=args.subfolder
pretrained_model_name_or_path=args.source_path,
subfolder=args.subfolder,
low_cpu_mem_usage=False,
)
assert isinstance(source, nn.Module), "Source model is not a nn.Module"
architecture: str = source.config.architectures[0] # type: ignore

5
scripts/conversion/convert_transformers_clip_text_model.py
View file

@ -22,8 +22,11 @@ class Args(argparse.Namespace):
def setup_converter(args: Args) -> ModelConverter:
# low_cpu_mem_usage=False stops some annoying console messages us to `pip install accelerate`
source: nn.Module = CLIPTextModelWithProjection.from_pretrained( # type: ignore
pretrained_model_name_or_path=args.source_path, subfolder=args.subfolder
pretrained_model_name_or_path=args.source_path,
subfolder=args.subfolder,
low_cpu_mem_usage=False,
)
assert isinstance(source, nn.Module), "Source model is not a nn.Module"
architecture: str = source.config.architectures[0] # type: ignore

389
scripts/prepare-test-weights.sh
View file

@ -1,389 +0,0 @@
#!/bin/bash
# This script downloads source weights from Hugging Face using cURL.
# We want to convert from local directories (not use the network in conversion
# scripts) but we also do not want to clone full repositories to save space.
# This approach is more verbose but it lets us pick and choose.
set -x
cd "$(dirname "$0")/.."
die () { >&2 echo "$@" ; exit 1 ; }
check_hash () { # (path, hash)
_path="$1"; shift
_expected="$1"
_found="$(b2sum -l 32 "$_path" | cut -d' ' -f1)"
[ "$_found" = "$_expected" ] || die "invalid hash for $_path ($_found != $_expected)"
}
download_sd_text_encoder () { # (base="runwayml/stable-diffusion-v1-5" subdir="text_encoder")
_base="$1"; shift
_subdir="$1"
mkdir tests/weights/$_base/$_subdir
pushd tests/weights/$_base/$_subdir
curl -LO https://huggingface.co/$_base/raw/main/$_subdir/config.json
curl -LO https://huggingface.co/$_base/resolve/main/$_subdir/model.safetensors
popd
}
download_sd_tokenizer () { # (base="runwayml/stable-diffusion-v1-5" subdir="tokenizer")
_base="$1"; shift
_subdir="$1"
mkdir tests/weights/$_base/$_subdir
pushd tests/weights/$_base/$_subdir
curl -LO https://huggingface.co/$_base/raw/main/$_subdir/merges.txt
curl -LO https://huggingface.co/$_base/raw/main/$_subdir/special_tokens_map.json
curl -LO https://huggingface.co/$_base/raw/main/$_subdir/tokenizer_config.json
curl -LO https://huggingface.co/$_base/raw/main/$_subdir/vocab.json
popd
}
download_sd_base () { # (base="runwayml/stable-diffusion-v1-5")
_base="$1"
# Inpainting source does not have safetensors.
_ext="safetensors"
grep -q "inpainting" <<< $_base && _ext="bin"
mkdir -p tests/weights/$_base
pushd tests/weights/$_base
curl -LO https://huggingface.co/$_base/raw/main/model_index.json
mkdir scheduler unet vae
pushd scheduler
curl -LO https://huggingface.co/$_base/raw/main/scheduler/scheduler_config.json
popd
pushd unet
curl -LO https://huggingface.co/$_base/raw/main/unet/config.json
curl -LO https://huggingface.co/$_base/resolve/main/unet/diffusion_pytorch_model.$_ext
popd
pushd vae
curl -LO https://huggingface.co/$_base/raw/main/vae/config.json
curl -LO https://huggingface.co/$_base/resolve/main/vae/diffusion_pytorch_model.$_ext
popd
popd
download_sd_text_encoder $_base text_encoder
download_sd_tokenizer $_base tokenizer
}
download_sd15 () { # (base="runwayml/stable-diffusion-v1-5")
_base="$1"
download_sd_base $_base
pushd tests/weights/$_base
mkdir feature_extractor safety_checker
pushd feature_extractor
curl -LO https://huggingface.co/$_base/raw/main/feature_extractor/preprocessor_config.json
popd
pushd safety_checker
curl -LO https://huggingface.co/$_base/raw/main/safety_checker/config.json
curl -LO https://huggingface.co/$_base/resolve/main/safety_checker/model.safetensors
popd
popd
}
download_sdxl () { # (base="stabilityai/stable-diffusion-xl-base-1.0")
_base="$1"
download_sd_base $_base
download_sd_text_encoder $_base text_encoder_2
download_sd_tokenizer $_base tokenizer_2
}
download_vae_ft_mse () {
mkdir -p tests/weights/stabilityai/sd-vae-ft-mse
pushd tests/weights/stabilityai/sd-vae-ft-mse
curl -LO https://huggingface.co/stabilityai/sd-vae-ft-mse/raw/main/config.json
curl -LO https://huggingface.co/stabilityai/sd-vae-ft-mse/resolve/main/diffusion_pytorch_model.safetensors
popd
}
download_lora () {
mkdir -p tests/weights/pcuenq/pokemon-lora
pushd tests/weights/pcuenq/pokemon-lora
curl -LO https://huggingface.co/pcuenq/pokemon-lora/resolve/main/pytorch_lora_weights.bin
popd
}
download_preprocessors () {
mkdir -p tests/weights/carolineec/informativedrawings
pushd tests/weights/carolineec/informativedrawings
curl -LO https://huggingface.co/spaces/carolineec/informativedrawings/resolve/main/model2.pth
popd
}
download_controlnet () {
mkdir -p tests/weights/lllyasviel
pushd tests/weights/lllyasviel
mkdir control_v11p_sd15_canny
pushd control_v11p_sd15_canny
curl -LO https://huggingface.co/lllyasviel/control_v11p_sd15_canny/raw/main/config.json
curl -LO https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/diffusion_pytorch_model.safetensors
popd
mkdir control_v11f1p_sd15_depth
pushd control_v11f1p_sd15_depth
curl -LO https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/raw/main/config.json
curl -LO https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/diffusion_pytorch_model.safetensors
popd
mkdir control_v11p_sd15_normalbae
pushd control_v11p_sd15_normalbae
curl -LO https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/raw/main/config.json
curl -LO https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/diffusion_pytorch_model.safetensors
popd
mkdir control_v11p_sd15_lineart
pushd control_v11p_sd15_lineart
curl -LO https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/raw/main/config.json
curl -LO https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/diffusion_pytorch_model.safetensors
popd
popd
mkdir -p tests/weights/mfidabel/controlnet-segment-anything
pushd tests/weights/mfidabel/controlnet-segment-anything
curl -LO https://huggingface.co/mfidabel/controlnet-segment-anything/raw/main/config.json
curl -LO https://huggingface.co/mfidabel/controlnet-segment-anything/resolve/main/diffusion_pytorch_model.bin
popd
}
download_unclip () {
mkdir -p tests/weights/stabilityai/stable-diffusion-2-1-unclip
pushd tests/weights/stabilityai/stable-diffusion-2-1-unclip
curl -LO https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/raw/main/model_index.json
mkdir image_encoder
pushd image_encoder
curl -LO https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/raw/main/image_encoder/config.json
curl -LO https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/resolve/main/image_encoder/model.safetensors
popd
popd
}
download_ip_adapter () {
mkdir -p tests/weights/h94/IP-Adapter
pushd tests/weights/h94/IP-Adapter
mkdir -p models
pushd models
curl -LO https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.bin
curl -LO https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus_sd15.bin
popd
mkdir -p sdxl_models
pushd sdxl_models
curl -LO https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.bin
curl -LO https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin
popd
popd
}
download_t2i_adapter () {
mkdir -p tests/weights/TencentARC/t2iadapter_depth_sd15v2
pushd tests/weights/TencentARC/t2iadapter_depth_sd15v2
curl -LO https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/raw/main/config.json
curl -LO https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/resolve/main/diffusion_pytorch_model.bin
popd
mkdir -p tests/weights/TencentARC/t2i-adapter-canny-sdxl-1.0
pushd tests/weights/TencentARC/t2i-adapter-canny-sdxl-1.0
curl -LO https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0/raw/main/config.json
curl -LO https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0/resolve/main/diffusion_pytorch_model.safetensors
popd
}
download_sam () {
mkdir -p tests/weights
pushd tests/weights
curl -LO https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
popd
check_hash "tests/weights/sam_vit_h_4b8939.pth" 06785e66
}
convert_sd15 () {
python scripts/conversion/convert_transformers_clip_text_model.py \
--from "tests/weights/runwayml/stable-diffusion-v1-5" \
--to "tests/weights/CLIPTextEncoderL.safetensors" \
--half
check_hash "tests/weights/CLIPTextEncoderL.safetensors" 6c9cbc59
python scripts/conversion/convert_diffusers_autoencoder_kl.py \
--from "tests/weights/runwayml/stable-diffusion-v1-5" \
--to "tests/weights/lda.safetensors"
check_hash "tests/weights/lda.safetensors" 329e369c
python scripts/conversion/convert_diffusers_unet.py \
--from "tests/weights/runwayml/stable-diffusion-v1-5" \
--to "tests/weights/unet.safetensors" \
--half
check_hash "tests/weights/unet.safetensors" f81ac65a
mkdir tests/weights/inpainting
python scripts/conversion/convert_diffusers_unet.py \
--from "tests/weights/runwayml/stable-diffusion-inpainting" \
--to "tests/weights/inpainting/unet.safetensors" \
--half
check_hash "tests/weights/inpainting/unet.safetensors" c07a8c61
}
convert_sdxl () {
python scripts/conversion/convert_transformers_clip_text_model.py \
--from "tests/weights/stabilityai/stable-diffusion-xl-base-1.0" \
--to "tests/weights/DoubleCLIPTextEncoder.safetensors" \
--subfolder2 text_encoder_2 \
--half
check_hash "tests/weights/DoubleCLIPTextEncoder.safetensors" 7f99c30b
python scripts/conversion/convert_diffusers_autoencoder_kl.py \
--from "tests/weights/stabilityai/stable-diffusion-xl-base-1.0" \
--to "tests/weights/sdxl-lda.safetensors" \
--half
check_hash "tests/weights/sdxl-lda.safetensors" 7464e9dc
python scripts/conversion/convert_diffusers_unet.py \
--from "tests/weights/stabilityai/stable-diffusion-xl-base-1.0" \
--to "tests/weights/sdxl-unet.safetensors" \
--half
check_hash "tests/weights/sdxl-unet.safetensors" 2e5c4911
}
convert_vae_ft_mse () {
python scripts/conversion/convert_diffusers_autoencoder_kl.py \
--from "tests/weights/stabilityai/sd-vae-ft-mse" \
--to "tests/weights/lda_ft_mse.safetensors" \
--half
check_hash "tests/weights/lda_ft_mse.safetensors" 4d0bae7e
}
convert_lora () {
mkdir tests/weights/loras
python scripts/conversion/convert_diffusers_lora.py \
--from "tests/weights/pcuenq/pokemon-lora/pytorch_lora_weights.bin" \
--base-model "tests/weights/runwayml/stable-diffusion-v1-5" \
--to "tests/weights/loras/pcuenq_pokemon_lora.safetensors"
check_hash "tests/weights/loras/pcuenq_pokemon_lora.safetensors" a9d7e08e
}
convert_preprocessors () {
curl -L https://raw.githubusercontent.com/carolineec/informative-drawings/main/model.py \
-o src/model.py
python scripts/conversion/convert_informative_drawings.py \
--from "tests/weights/carolineec/informativedrawings/model2.pth" \
--to "tests/weights/informative-drawings.safetensors"
rm -f src/model.py
check_hash "tests/weights/informative-drawings.safetensors" 93dca207
}
convert_controlnet () {
mkdir tests/weights/controlnet
python scripts/conversion/convert_diffusers_controlnet.py \
--from "tests/weights/lllyasviel/control_v11p_sd15_canny" \
--to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_canny.safetensors"
check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_canny.safetensors" 9a1a48cf
python scripts/conversion/convert_diffusers_controlnet.py \
--from "tests/weights/lllyasviel/control_v11f1p_sd15_depth" \
--to "tests/weights/controlnet/lllyasviel_control_v11f1p_sd15_depth.safetensors"
check_hash "tests/weights/controlnet/lllyasviel_control_v11f1p_sd15_depth.safetensors" bbe7e5a6
python scripts/conversion/convert_diffusers_controlnet.py \
--from "tests/weights/lllyasviel/control_v11p_sd15_normalbae" \
--to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_normalbae.safetensors"
check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_normalbae.safetensors" 9fa88ed5
python scripts/conversion/convert_diffusers_controlnet.py \
--from "tests/weights/lllyasviel/control_v11p_sd15_lineart" \
--to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_lineart.safetensors"
check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_lineart.safetensors" c29e8c03
python scripts/conversion/convert_diffusers_controlnet.py \
--from "tests/weights/mfidabel/controlnet-segment-anything" \
--to "tests/weights/controlnet/mfidabel_controlnet-segment-anything.safetensors"
check_hash "tests/weights/controlnet/mfidabel_controlnet-segment-anything.safetensors" d536eebb
}
convert_unclip () {
python scripts/conversion/convert_transformers_clip_image_model.py \
--from "tests/weights/stabilityai/stable-diffusion-2-1-unclip" \
--to "tests/weights/CLIPImageEncoderH.safetensors" \
--half
check_hash "tests/weights/CLIPImageEncoderH.safetensors" 4ddb44d2
}
convert_ip_adapter () {
python scripts/conversion/convert_diffusers_ip_adapter.py \
--from "tests/weights/h94/IP-Adapter/models/ip-adapter_sd15.bin" \
--to "tests/weights/ip-adapter_sd15.safetensors"
check_hash "tests/weights/ip-adapter_sd15.safetensors" 9579b465
python scripts/conversion/convert_diffusers_ip_adapter.py \
--from "tests/weights/h94/IP-Adapter/sdxl_models/ip-adapter_sdxl_vit-h.bin" \
--to "tests/weights/ip-adapter_sdxl_vit-h.safetensors" \
--half
check_hash "tests/weights/ip-adapter_sdxl_vit-h.safetensors" 739504c6
python scripts/conversion/convert_diffusers_ip_adapter.py \
--from "tests/weights/h94/IP-Adapter/models/ip-adapter-plus_sd15.bin" \
--to "tests/weights/ip-adapter-plus_sd15.safetensors" \
--half
check_hash "tests/weights/ip-adapter-plus_sd15.safetensors" 842b20e2
python scripts/conversion/convert_diffusers_ip_adapter.py \
--from "tests/weights/h94/IP-Adapter/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin" \
--to "tests/weights/ip-adapter-plus_sdxl_vit-h.safetensors" \
--half
check_hash "tests/weights/ip-adapter-plus_sdxl_vit-h.safetensors" 0409974b
}
convert_t2i_adapter () {
mkdir tests/weights/T2I-Adapter
python scripts/conversion/convert_diffusers_t2i_adapter.py \
--from "tests/weights/TencentARC/t2iadapter_depth_sd15v2" \
--to "tests/weights/T2I-Adapter/t2iadapter_depth_sd15v2.safetensors" \
--half
check_hash "tests/weights/T2I-Adapter/t2iadapter_depth_sd15v2.safetensors" bb2b3115
python scripts/conversion/convert_diffusers_t2i_adapter.py \
--from "tests/weights/TencentARC/t2i-adapter-canny-sdxl-1.0" \
--to "tests/weights/T2I-Adapter/t2i-adapter-canny-sdxl-1.0.safetensors" \
--half
check_hash "tests/weights/T2I-Adapter/t2i-adapter-canny-sdxl-1.0.safetensors" f07249a6
}
convert_sam () {
python scripts/conversion/convert_segment_anything.py \
--from "tests/weights/sam_vit_h_4b8939.pth" \
--to "tests/weights/segment-anything-h.safetensors"
check_hash "tests/weights/segment-anything-h.safetensors" 6b843800
}
download_all () {
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_lora
download_preprocessors
download_controlnet
download_unclip
download_ip_adapter
download_t2i_adapter
download_sam
}
convert_all () {
convert_sd15
convert_sdxl
convert_vae_ft_mse
convert_lora
convert_preprocessors
convert_controlnet
convert_unclip
convert_ip_adapter
convert_t2i_adapter
convert_sam
}
main () {
git lfs install || die "could not install git lfs"
rm -rf tests/weights
download_all
convert_all
}
main

584
scripts/prepare_test_weights.py Normal file
View file

@ -0,0 +1,584 @@
"""
Download and convert weights for testing
To see what weights will be downloaded and converted, run:
DRY_RUN=1 python scripts/prepare_test_weights.py
"""
import hashlib
import os
import subprocess
import sys
from urllib.parse import urlparse
import requests
from tqdm import tqdm
# Set the base directory to the parent directory of the script
project_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
test_weights_dir = os.path.join(project_dir, "tests", "weights")
previous_line = "\033[F"
download_count = 0
bytes_count = 0
def die(message: str) -> None:
print(message, file=sys.stderr)
sys.exit(1)
def rel(path: str) -> str:
return os.path.relpath(path, project_dir)
def calc_hash(filepath: str) -> str:
with open(filepath, "rb") as f:
data = f.read()
found = hashlib.blake2b(data, digest_size=int(32 / 8)).hexdigest()
return found
def check_hash(path: str, expected: str) -> str:
found = calc_hash(path)
if found != expected:
die(f"❌ Invalid hash for {path} ({found} != {expected})")
return found
def download_file(
url: str,
dest_folder: str,
dry_run: bool | None = None,
skip_existing: bool = True,
expected_hash: str | None = None,
):
"""
Downloads a file
Features:
- shows a progress bar
- skips existing files
- uses a temporary file to prevent partial downloads
- can do a dry run to check the url is valid
- displays the downloaded file hash
"""
global download_count, bytes_count
filename = os.path.basename(urlparse(url).path)
dest_filename = os.path.join(dest_folder, filename)
temp_filename = dest_filename + ".part"
dry_run = bool(os.environ.get("DRY_RUN") == "1") if dry_run is None else dry_run
is_downloaded = os.path.exists(dest_filename)
if is_downloaded and skip_existing:
skip_icon = "✖️ "
else:
skip_icon = "🔽"
if dry_run:
response = requests.head(url, allow_redirects=True)
readable_size = ""
if response.status_code == 200:
content_length = response.headers.get("content-length")
if content_length:
size_in_bytes = int(content_length)
readable_size = human_readable_size(size_in_bytes)
download_count += 1
bytes_count += size_in_bytes
print(f"✅{skip_icon} {response.status_code} READY {readable_size:<8} {url}")
else:
print(f"❌{skip_icon} {response.status_code} ERROR {readable_size:<8} {url}")
return
if skip_existing and os.path.exists(dest_filename):
print(f"{skip_icon}️ Skipping previously downloaded {url}")
return
os.makedirs(dest_folder, exist_ok=True)
print(f"🔽 Downloading {url} => '{rel(dest_filename)}'", end="\n")
response = requests.get(url, stream=True)
if response.status_code != 200:
print(response.content[:1000])
die(f"Failed to download {url}. Status code: {response.status_code}")
total = int(response.headers.get("content-length", 0))
bar = tqdm(
desc=filename,
total=total,
unit="iB",
unit_scale=True,
unit_divisor=1024,
leave=False,
)
with open(temp_filename, "wb") as f, bar:
for data in response.iter_content(chunk_size=1024 * 1000):
size = f.write(data)
bar.update(size)
os.rename(temp_filename, dest_filename)
calculated_hash = calc_hash(dest_filename)
print(f"{previous_line}✅ Downloaded {calculated_hash} {url} => '{rel(dest_filename)}' ")
if expected_hash is not None:
check_hash(dest_filename, expected_hash)
def download_files(urls: list[str], dest_folder: str):
for url in urls:
download_file(url, dest_folder)
def human_readable_size(size: int | float, decimal_places: int = 2) -> str:
for unit in ["B", "KB", "MB", "GB", "TB", "PB"]:
if size < 1024.0:
break
size /= 1024.0
return f"{size:.{decimal_places}f}{unit}" # type: ignore
def download_sd_text_encoder(hf_repo_id: str = "runwayml/stable-diffusion-v1-5", subdir: str = "text_encoder"):
encoder_filename = "model.safetensors" if "inpainting" not in hf_repo_id else "model.fp16.safetensors"
base_url = f"https://huggingface.co/{hf_repo_id}"
download_files(
urls=[
f"{base_url}/raw/main/{subdir}/config.json",
f"{base_url}/resolve/main/{subdir}/{encoder_filename}",
],
dest_folder=os.path.join(test_weights_dir, hf_repo_id, subdir),
)
def download_sd_tokenizer(hf_repo_id: str = "runwayml/stable-diffusion-v1-5", subdir: str = "tokenizer"):
download_files(
urls=[
f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/merges.txt",
f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/special_tokens_map.json",
f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/tokenizer_config.json",
f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/vocab.json",
],
dest_folder=os.path.join(test_weights_dir, hf_repo_id, subdir),
)
def download_sd_base(hf_repo_id: str = "runwayml/stable-diffusion-v1-5"):
is_inpainting = "inpainting" in hf_repo_id
ext = "safetensors" if not is_inpainting else "bin"
base_folder = os.path.join(test_weights_dir, hf_repo_id)
download_file(f"https://huggingface.co/{hf_repo_id}/raw/main/model_index.json", base_folder)
download_file(
f"https://huggingface.co/{hf_repo_id}/raw/main/scheduler/scheduler_config.json",
os.path.join(base_folder, "scheduler"),
)
for subdir in ["unet", "vae"]:
subdir_folder = os.path.join(base_folder, subdir)
download_file(f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/config.json", subdir_folder)
download_file(
f"https://huggingface.co/{hf_repo_id}/resolve/main/{subdir}/diffusion_pytorch_model.{ext}", subdir_folder
)
# we only need the unet for the inpainting model
if not is_inpainting:
download_sd_text_encoder(hf_repo_id, "text_encoder")
download_sd_tokenizer(hf_repo_id, "tokenizer")
def download_sd15(hf_repo_id: str = "runwayml/stable-diffusion-v1-5"):
download_sd_base(hf_repo_id)
base_folder = os.path.join(test_weights_dir, hf_repo_id)
subdir = "feature_extractor"
download_file(
f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/preprocessor_config.json",
os.path.join(base_folder, subdir),
)
if "inpainting" not in hf_repo_id:
subdir = "safety_checker"
subdir_folder = os.path.join(base_folder, subdir)
download_file(f"https://huggingface.co/{hf_repo_id}/raw/main/{subdir}/config.json", subdir_folder)
download_file(f"https://huggingface.co/{hf_repo_id}/resolve/main/{subdir}/model.safetensors", subdir_folder)
def download_sdxl(hf_repo_id: str = "stabilityai/stable-diffusion-xl-base-1.0"):
download_sd_base(hf_repo_id)
download_sd_text_encoder(hf_repo_id, "text_encoder_2")
download_sd_tokenizer(hf_repo_id, "tokenizer_2")
def download_vae_ft_mse():
download_files(
urls=[
"https://huggingface.co/stabilityai/sd-vae-ft-mse/raw/main/config.json",
"https://huggingface.co/stabilityai/sd-vae-ft-mse/resolve/main/diffusion_pytorch_model.safetensors",
],
dest_folder=os.path.join(test_weights_dir, "stabilityai", "sd-vae-ft-mse"),
)
def download_lora():
dest_folder = os.path.join(test_weights_dir, "pcuenq", "pokemon-lora")
download_file("https://huggingface.co/pcuenq/pokemon-lora/resolve/main/pytorch_lora_weights.bin", dest_folder)
def download_preprocessors():
dest_folder = os.path.join(test_weights_dir, "carolineec", "informativedrawings")
download_file("https://huggingface.co/spaces/carolineec/informativedrawings/resolve/main/model2.pth", dest_folder)
def download_controlnet():
base_folder = os.path.join(test_weights_dir, "lllyasviel")
controlnets = [
"control_v11p_sd15_canny",
"control_v11f1p_sd15_depth",
"control_v11p_sd15_normalbae",
"control_v11p_sd15_lineart",
]
for net in controlnets:
net_folder = os.path.join(base_folder, net)
urls = [
f"https://huggingface.co/lllyasviel/{net}/raw/main/config.json",
f"https://huggingface.co/lllyasviel/{net}/resolve/main/diffusion_pytorch_model.safetensors",
]
download_files(urls, net_folder)
mfidabel_folder = os.path.join(test_weights_dir, "mfidabel", "controlnet-segment-anything")
urls = [
"https://huggingface.co/mfidabel/controlnet-segment-anything/raw/main/config.json",
"https://huggingface.co/mfidabel/controlnet-segment-anything/resolve/main/diffusion_pytorch_model.bin",
]
download_files(urls, mfidabel_folder)
def download_unclip():
base_folder = os.path.join(test_weights_dir, "stabilityai", "stable-diffusion-2-1-unclip")
download_file(
"https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/raw/main/model_index.json", base_folder
)
image_encoder_folder = os.path.join(base_folder, "image_encoder")
urls = [
"https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/raw/main/image_encoder/config.json",
"https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/resolve/main/image_encoder/model.safetensors",
]
download_files(urls, image_encoder_folder)
def download_ip_adapter():
base_folder = os.path.join(test_weights_dir, "h94", "IP-Adapter")
models_folder = os.path.join(base_folder, "models")
urls = [
"https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.bin",
"https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter-plus_sd15.bin",
]
download_files(urls, models_folder)
sdxl_models_folder = os.path.join(base_folder, "sdxl_models")
urls = [
"https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl_vit-h.bin",
"https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin",
]
download_files(urls, sdxl_models_folder)
def download_t2i_adapter():
base_folder = os.path.join(test_weights_dir, "TencentARC", "t2iadapter_depth_sd15v2")
urls = [
"https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/raw/main/config.json",
"https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/resolve/main/diffusion_pytorch_model.bin",
]
download_files(urls, base_folder)
canny_sdxl_folder = os.path.join(test_weights_dir, "TencentARC", "t2i-adapter-canny-sdxl-1.0")
urls = [
"https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0/raw/main/config.json",
"https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0/resolve/main/diffusion_pytorch_model.safetensors",
]
download_files(urls, canny_sdxl_folder)
def download_sam():
weights_folder = os.path.join(test_weights_dir)
download_file(
"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth", weights_folder, expected_hash="06785e66"
)
def printg(msg: str):
"""print in green color"""
print("\033[92m" + msg + "\033[0m")
def run_conversion_script(
script_filename: str,
from_weights: str,
to_weights: str,
half: bool = False,
expected_hash: str | None = None,
additional_args: list[str] | None = None,
skip_existing: bool = True,
):
if skip_existing and expected_hash and os.path.exists(to_weights):
found_hash = check_hash(to_weights, expected_hash)
if expected_hash == found_hash:
printg(f"✖️ Skipping converted from {from_weights} to {to_weights} (hash {found_hash} confirmed) ")
return
msg = f"Converting {from_weights} to {to_weights}"
printg(msg)
args = ["python", f"scripts/conversion/{script_filename}", "--from", from_weights, "--to", to_weights]
if half:
args.append("--half")
if additional_args:
args.extend(additional_args)
subprocess.run(args, check=True)
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_lora():
os.makedirs("tests/weights/loras", exist_ok=True)
run_conversion_script(
"convert_diffusers_lora.py",
"tests/weights/pcuenq/pokemon-lora/pytorch_lora_weights.bin",
"tests/weights/loras/pcuenq_pokemon_lora.safetensors",
additional_args=["--base-model", "tests/weights/runwayml/stable-diffusion-v1-5"],
expected_hash="a9d7e08e",
)
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="9579b465",
)
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="739504c6",
)
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="842b20e2",
)
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="0409974b",
)
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="6b843800",
)
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_lora()
download_preprocessors()
download_controlnet()
download_unclip()
download_ip_adapter()
download_t2i_adapter()
download_sam()
def convert_all():
convert_sd15()
convert_sdxl()
convert_vae_ft_mse()
convert_lora()
convert_preprocessors()
convert_controlnet()
convert_unclip()
convert_ip_adapter()
convert_t2i_adapter()
convert_sam()
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()