implement foreground conditioned ic light

This commit is contained in:
limiteinductive 2024-08-12 09:33:15 +00:00 committed by Benjamin Trom
parent 928da1ee1c
commit 51dcd7772c
7 changed files with 351 additions and 0 deletions

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@ -19,6 +19,7 @@ class Args(argparse.Namespace):
half: bool half: bool
verbose: bool verbose: bool
skip_init_check: bool skip_init_check: bool
override_weights: str | None
def setup_converter(args: Args) -> ModelConverter: def setup_converter(args: Args) -> ModelConverter:

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@ -0,0 +1,89 @@
import argparse
from pathlib import Path
from convert_diffusers_unet import Args as UNetArgs, setup_converter as setup_unet_converter
from huggingface_hub import hf_hub_download # type: ignore
from refiners.fluxion.utils import load_from_safetensors, save_to_safetensors
class Args(argparse.Namespace):
source_path: str
output_path: str | None
subfolder: str
half: bool
verbose: bool
reference_unet_path: str
def main() -> None:
parser = argparse.ArgumentParser(description="Converts IC-Light patch weights to work with Refiners")
parser.add_argument(
"--from",
type=str,
dest="source_path",
default="lllyasviel/ic-light",
help=(
"Can be a path to a .bin file, a .safetensors file or a model name from the Hugging Face Hub. Default:"
" lllyasviel/ic-light"
),
)
parser.add_argument("--filename", type=str, default="iclight_sd15_fc.safetensors", help="Filename inside the hub.")
parser.add_argument(
"--to",
type=str,
dest="output_path",
default=None,
help=(
"Output path (.safetensors) for converted model. If not provided, the output path will be the same as the"
" source path."
),
)
parser.add_argument(
"--verbose",
action="store_true",
default=False,
help="Prints additional information during conversion. Default: False",
)
parser.add_argument(
"--reference-unet-path",
type=str,
dest="reference_unet_path",
default="runwayml/stable-diffusion-v1-5",
help="Path to the reference UNet weights.",
)
args = parser.parse_args(namespace=Args())
if args.output_path is None:
args.output_path = f"{Path(args.filename).stem}-refiners.safetensors"
patch_file = (
Path(args.source_path)
if args.source_path.endswith(".safetensors")
else Path(
hf_hub_download(
repo_id=args.source_path,
filename=args.filename,
)
)
)
patch_weights = load_from_safetensors(patch_file)
unet_args = UNetArgs(
source_path=args.reference_unet_path,
subfolder="unet",
half=False,
verbose=False,
skip_init_check=True,
override_weights=None,
)
converter = setup_unet_converter(args=unet_args)
result = converter._convert_state_dict( # pyright: ignore[reportPrivateUsage]
source_state_dict=patch_weights,
target_state_dict=converter.target_model.state_dict(),
state_dict_mapping=converter.get_mapping(),
)
save_to_safetensors(path=args.output_path, tensors=result)
if __name__ == "__main__":
main()

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@ -438,6 +438,14 @@ def download_sdxl_lightning_lora():
) )
def download_ic_light():
download_file(
"https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fc.safetensors",
dest_folder=test_weights_dir,
expected_hash="bce70123",
)
def printg(msg: str): def printg(msg: str):
"""print in green color""" """print in green color"""
print("\033[92m" + msg + "\033[0m") print("\033[92m" + msg + "\033[0m")
@ -790,6 +798,16 @@ def convert_sdxl_lightning_base():
) )
def convert_ic_light():
run_conversion_script(
"convert_ic_light.py",
"tests/weights/iclight_sd15_fc.safetensors",
"tests/weights/iclight_sd15_fc-refiners.safetensors",
half=False,
expected_hash="be315c1f",
)
def download_all(): def download_all():
print(f"\nAll weights will be downloaded to {test_weights_dir}\n") print(f"\nAll weights will be downloaded to {test_weights_dir}\n")
download_sd15("runwayml/stable-diffusion-v1-5") download_sd15("runwayml/stable-diffusion-v1-5")
@ -811,6 +829,7 @@ def download_all():
download_lcm_lora() download_lcm_lora()
download_sdxl_lightning_base() download_sdxl_lightning_base()
download_sdxl_lightning_lora() download_sdxl_lightning_lora()
download_ic_light()
def convert_all(): def convert_all():
@ -830,6 +849,7 @@ def convert_all():
convert_control_lora_fooocus() convert_control_lora_fooocus()
convert_lcm_base() convert_lcm_base()
convert_sdxl_lightning_base() convert_sdxl_lightning_base()
convert_ic_light()
def main(): def main():

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@ -0,0 +1,182 @@
import torch
from PIL import Image
from torch.nn.init import zeros_ as zero_init
from refiners.fluxion import layers as fl
from refiners.fluxion.utils import image_to_tensor, no_grad
from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
from refiners.foundationals.latent_diffusion.solvers.solver import Solver
from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder, StableDiffusion_1
from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import DownBlocks, SD1UNet
class ICLight(StableDiffusion_1):
"""
IC-Light is a Stable Diffusion model that can be used to relight a reference image.
At initialization, the UNet will be patched to accept four additional input channels. Only the text-conditioned relighting model is supported for now.
```example
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from refiners.fluxion.utils import load_from_safetensors, manual_seed, no_grad
from refiners.foundationals.clip import CLIPTextEncoderL
from refiners.foundationals.latent_diffusion.stable_diffusion_1 import SD1Autoencoder, SD1UNet
from refiners.foundationals.latent_diffusion.stable_diffusion_1.ic_light import ICLight
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.float32
no_grad().__enter__()
manual_seed(42)
sd = ICLight(
patch_weights=load_from_safetensors(
path=hf_hub_download(
repo_id="refiners/ic_light.sd1_5.fc",
filename="model.safetensors",
),
device=device,
),
unet=SD1UNet(in_channels=4, device=device, dtype=dtype).load_from_safetensors(
tensors_path=hf_hub_download(
repo_id="refiners/realistic_vision.v5_1.sd1_5.unet",
filename="model.safetensors",
)
),
clip_text_encoder=CLIPTextEncoderL(device=device, dtype=dtype).load_from_safetensors(
tensors_path=hf_hub_download(
repo_id="refiners/realistic_vision.v5_1.sd1_5.text_encoder",
filename="model.safetensors",
)
),
lda=SD1Autoencoder(device=device, dtype=dtype).load_from_safetensors(
tensors_path=hf_hub_download(
repo_id="refiners/realistic_vision.v5_1.sd1_5.autoencoder",
filename="model.safetensors",
)
),
device=device,
dtype=dtype,
)
prompt = "soft lighting, high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
image = Image.open("reference-image.png").resize((512, 512))
sd.set_ic_light_condition(image)
x = torch.randn(
size=(1, 4, 64, 64),
device=device,
dtype=dtype,
)
for step in sd.steps:
x = sd(
x=x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=1.5,
)
predicted_image = sd.lda.latents_to_image(x)
predicted_image.save("ic-light-output.png")
"""
def __init__(
self,
patch_weights: dict[str, torch.Tensor],
unet: SD1UNet,
lda: SD1Autoencoder | None = None,
clip_text_encoder: CLIPTextEncoderL | None = None,
solver: Solver | None = None,
device: torch.device | str = "cpu",
dtype: torch.dtype = torch.float32,
) -> None:
super().__init__(
unet=unet,
lda=lda,
clip_text_encoder=clip_text_encoder,
solver=solver,
device=device,
dtype=dtype,
)
self._extend_conv_in()
self._apply_patch(weights=patch_weights)
@no_grad()
def _extend_conv_in(self) -> None:
"""
Extend to 8 the input channels of the first convolutional layer of the UNet.
"""
down_blocks = self.unet.ensure_find(DownBlocks)
first_block = down_blocks.layer(0, fl.Chain)
conv_in = first_block.ensure_find(fl.Conv2d)
new_conv_in = fl.Conv2d(
in_channels=conv_in.in_channels + 4,
out_channels=conv_in.out_channels,
kernel_size=(conv_in.kernel_size[0], conv_in.kernel_size[1]),
padding=(int(conv_in.padding[0]), int(conv_in.padding[1])),
device=conv_in.device,
dtype=conv_in.dtype,
)
zero_init(new_conv_in.weight)
new_conv_in.bias = conv_in.bias
new_conv_in.weight[:, :4, :, :] = conv_in.weight
first_block.replace(old_module=conv_in, new_module=new_conv_in)
def _apply_patch(self, weights: dict[str, torch.Tensor]) -> None:
"""
Apply the patch weights to the UNet, modifying inplace the state dict.
"""
current_state_dict = self.unet.state_dict()
new_state_dict = {
key: tensor + weights[key].to(tensor.device, tensor.dtype) for key, tensor in current_state_dict.items()
}
self.unet.load_state_dict(new_state_dict)
@staticmethod
def compute_gray_composite(image: Image.Image, mask: Image.Image) -> Image.Image:
"""
Compute a grayscale composite of an image and a mask.
"""
assert mask.mode == "L", "Mask must be a grayscale image"
assert image.size == mask.size, "Image and mask must have the same size"
background = Image.new("RGB", image.size, (127, 127, 127))
return Image.composite(image, background, mask)
def set_ic_light_condition(
self, image: Image.Image, mask: Image.Image | None = None, use_rescaled_image: bool = False
) -> None:
"""
Set the IC light condition.
If a mask is provided, it will be used to compute a grayscale composite of the image and the mask ; otherwise,
the image will be used as is, but note that IC-Light requires a 127-valued gray background to work.
`use_rescaled_image` is used to rescale the image to [-1, 1] range. This is the expected range when using the
Stable Diffusion autoencoder. But in the original code this part is skipped, giving different results.
see https://github.com/lllyasviel/IC-Light/blob/788687452a2bad59633a401281c8aee91bdd3750/gradio_demo.py#L262-L265
"""
if mask is not None:
image = self.compute_gray_composite(image=image, mask=mask)
image_tensor = image_to_tensor(image, device=self.device, dtype=self.dtype)
if use_rescaled_image:
image_tensor = 2 * image_tensor - 1
latents = self.lda.encode(image_tensor)
self._ic_light_condition = latents
def __call__(
self, x: torch.Tensor, step: int, *, clip_text_embedding: torch.Tensor, condition_scale: float = 2.0
) -> torch.Tensor:
assert self._ic_light_condition is not None, "Reference image not set, use `set_ic_light_condition` first"
x = torch.cat((x, self._ic_light_condition), dim=1)
return super().__call__(
x,
step,
clip_text_embedding=clip_text_embedding,
condition_scale=condition_scale,
)

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@ -12,6 +12,7 @@ from tests.utils import ensure_similar_images
from refiners.fluxion.layers.attentions import ScaledDotProductAttention from refiners.fluxion.layers.attentions import ScaledDotProductAttention
from refiners.fluxion.utils import image_to_tensor, load_from_safetensors, load_tensors, manual_seed, no_grad from refiners.fluxion.utils import image_to_tensor, load_from_safetensors, load_tensors, manual_seed, no_grad
from refiners.foundationals.clip.concepts import ConceptExtender from refiners.foundationals.clip.concepts import ConceptExtender
from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
from refiners.foundationals.latent_diffusion import ( from refiners.foundationals.latent_diffusion import (
ControlLoraAdapter, ControlLoraAdapter,
SD1ControlnetAdapter, SD1ControlnetAdapter,
@ -30,6 +31,8 @@ from refiners.foundationals.latent_diffusion.reference_only_control import Refer
from refiners.foundationals.latent_diffusion.restart import Restart from refiners.foundationals.latent_diffusion.restart import Restart
from refiners.foundationals.latent_diffusion.solvers import DDIM, Euler, NoiseSchedule, SolverParams from refiners.foundationals.latent_diffusion.solvers import DDIM, Euler, NoiseSchedule, SolverParams
from refiners.foundationals.latent_diffusion.solvers.dpm import DPMSolver from refiners.foundationals.latent_diffusion.solvers.dpm import DPMSolver
from refiners.foundationals.latent_diffusion.stable_diffusion_1.ic_light import ICLight
from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder
from refiners.foundationals.latent_diffusion.stable_diffusion_1.multi_diffusion import ( from refiners.foundationals.latent_diffusion.stable_diffusion_1.multi_diffusion import (
SD1DiffusionTarget, SD1DiffusionTarget,
SD1MultiDiffusion, SD1MultiDiffusion,
@ -2564,3 +2567,58 @@ def test_multi_upscaler(
) -> None: ) -> None:
predicted_image = multi_upscaler.upscale(clarity_example) predicted_image = multi_upscaler.upscale(clarity_example)
ensure_similar_images(predicted_image, expected_multi_upscaler, min_psnr=35, min_ssim=0.99) ensure_similar_images(predicted_image, expected_multi_upscaler, min_psnr=35, min_ssim=0.99)
@pytest.fixture(scope="module")
def expected_ic_light(ref_path: Path) -> Image.Image:
return _img_open(ref_path / "expected_ic_light.png").convert("RGB")
@pytest.fixture(scope="module")
def ic_light_sd15_fc_weights(test_weights_path: Path) -> Path:
return test_weights_path / "iclight_sd15_fc-refiners.safetensors"
@pytest.fixture(scope="module")
def ic_light_sd15_fc(
ic_light_sd15_fc_weights: Path,
unet_weights_std: Path,
lda_weights: Path,
text_encoder_weights: Path,
test_device: torch.device,
) -> ICLight:
return ICLight(
patch_weights=load_from_safetensors(ic_light_sd15_fc_weights),
unet=SD1UNet(in_channels=4).load_from_safetensors(unet_weights_std),
lda=SD1Autoencoder().load_from_safetensors(lda_weights),
clip_text_encoder=CLIPTextEncoderL().load_from_safetensors(text_encoder_weights),
device=test_device,
)
@no_grad()
def test_ic_light(
kitchen_dog: Image.Image,
kitchen_dog_mask: Image.Image,
ic_light_sd15_fc: ICLight,
expected_ic_light: Image.Image,
test_device: torch.device,
) -> None:
sd = ic_light_sd15_fc
manual_seed(2)
clip_text_embedding = sd.compute_clip_text_embedding(
text="a photo of dog, purple neon lighting",
negative_text="lowres, bad anatomy, bad hands, cropped, worst quality",
)
ic_light_condition = sd.compute_gray_composite(image=kitchen_dog, mask=kitchen_dog_mask.convert("L"))
sd.set_ic_light_condition(ic_light_condition)
x = torch.randn(1, 4, 64, 64, device=test_device)
for step in sd.steps:
x = sd(
x,
step=step,
clip_text_embedding=clip_text_embedding,
condition_scale=2.0,
)
predicted_image = sd.lda.latents_to_image(x)
ensure_similar_images(predicted_image, expected_ic_light, min_psnr=35, min_ssim=0.99)

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@ -60,6 +60,7 @@ Special cases:
- `expected_controlnet_canny_scale_decay.png` - `expected_controlnet_canny_scale_decay.png`
- `expected_multi_diffusion_dpm.png` - `expected_multi_diffusion_dpm.png`
- `expected_multi_upscaler.png` - `expected_multi_upscaler.png`
- `expected_ic_light.png`
## Other images ## Other images

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