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https://github.com/finegrain-ai/refiners.git
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update ic_light adapter, bugfix + improve docstrings
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Laurent
Laureηt
parent
2cb0f06119
commit
d7bbf9ad5d
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@ -1,4 +1,5 @@
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import SD1ControlnetAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.ic_light import ICLight
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.image_prompt import SD1IPAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import (
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SD1Autoencoder,
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@ -16,4 +17,5 @@ __all__ = [
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"SD1ControlnetAdapter",
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"SD1IPAdapter",
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"SD1T2IAdapter",
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"ICLight",
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]
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@ -3,7 +3,7 @@ from PIL import Image
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from torch.nn.init import zeros_ as zero_init
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from refiners.fluxion import layers as fl
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from refiners.fluxion.utils import image_to_tensor, no_grad
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from refiners.fluxion.utils import no_grad
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from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
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from refiners.foundationals.latent_diffusion.solvers.solver import Solver
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder, StableDiffusion_1
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@ -11,79 +11,82 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import Down
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class ICLight(StableDiffusion_1):
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"""
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IC-Light is a Stable Diffusion model that can be used to relight a reference image.
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"""IC-Light is a Stable Diffusion model that can be used to relight a reference image.
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At initialization, the UNet will be patched to accept four additional input channels. Only the text-conditioned relighting model is supported for now.
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At initialization, the UNet will be patched to accept four additional input channels.
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Only the text-conditioned relighting model is supported for now.
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```example
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import torch
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from huggingface_hub import hf_hub_download
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from PIL import Image
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from refiners.fluxion.utils import load_from_safetensors, manual_seed, no_grad
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from refiners.foundationals.clip import CLIPTextEncoderL
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from refiners.foundationals.latent_diffusion.stable_diffusion_1 import SD1Autoencoder, SD1UNet
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.ic_light import ICLight
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Example:
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```py
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import torch
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from huggingface_hub import hf_hub_download
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from PIL import Image
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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dtype = torch.float32
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no_grad().__enter__()
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manual_seed(42)
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from refiners.fluxion.utils import load_from_safetensors, manual_seed, no_grad
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from refiners.foundationals.clip import CLIPTextEncoderL
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from refiners.foundationals.latent_diffusion.stable_diffusion_1 import SD1Autoencoder, SD1UNet
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.ic_light import ICLight
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sd = ICLight(
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patch_weights=load_from_safetensors(
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path=hf_hub_download(
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repo_id="refiners/ic_light.sd1_5.fc",
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filename="model.safetensors",
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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dtype = torch.float32
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no_grad().__enter__()
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manual_seed(42)
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sd = ICLight(
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patch_weights=load_from_safetensors(
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path=hf_hub_download(
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repo_id="refiners/ic_light.sd1_5.fc",
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filename="model.safetensors",
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),
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device=device,
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),
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unet=SD1UNet(in_channels=4, device=device, dtype=dtype).load_from_safetensors(
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tensors_path=hf_hub_download(
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repo_id="refiners/realistic_vision.v5_1.sd1_5.unet",
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filename="model.safetensors",
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)
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),
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clip_text_encoder=CLIPTextEncoderL(device=device, dtype=dtype).load_from_safetensors(
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tensors_path=hf_hub_download(
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repo_id="refiners/realistic_vision.v5_1.sd1_5.text_encoder",
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filename="model.safetensors",
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)
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),
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lda=SD1Autoencoder(device=device, dtype=dtype).load_from_safetensors(
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tensors_path=hf_hub_download(
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repo_id="refiners/realistic_vision.v5_1.sd1_5.autoencoder",
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filename="model.safetensors",
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)
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),
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device=device,
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),
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unet=SD1UNet(in_channels=4, device=device, dtype=dtype).load_from_safetensors(
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tensors_path=hf_hub_download(
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repo_id="refiners/realistic_vision.v5_1.sd1_5.unet",
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filename="model.safetensors",
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)
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),
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clip_text_encoder=CLIPTextEncoderL(device=device, dtype=dtype).load_from_safetensors(
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tensors_path=hf_hub_download(
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repo_id="refiners/realistic_vision.v5_1.sd1_5.text_encoder",
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filename="model.safetensors",
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)
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),
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lda=SD1Autoencoder(device=device, dtype=dtype).load_from_safetensors(
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tensors_path=hf_hub_download(
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repo_id="refiners/realistic_vision.v5_1.sd1_5.autoencoder",
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filename="model.safetensors",
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)
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),
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device=device,
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dtype=dtype,
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)
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prompt = "soft lighting, high-quality professional image"
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negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
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clip_text_embedding = sd.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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image = Image.open("reference-image.png").resize((512, 512))
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sd.set_ic_light_condition(image)
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x = torch.randn(
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size=(1, 4, 64, 64),
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device=device,
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dtype=dtype,
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)
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for step in sd.steps:
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x = sd(
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x=x,
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step=step,
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clip_text_embedding=clip_text_embedding,
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condition_scale=1.5,
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dtype=dtype,
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)
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predicted_image = sd.lda.latents_to_image(x)
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predicted_image.save("ic-light-output.png")
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prompt = "soft lighting, high-quality professional image"
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negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
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clip_text_embedding = sd.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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image = Image.open("reference-image.png").resize((512, 512))
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sd.set_ic_light_condition(image)
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x = torch.randn(
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size=(1, 4, 64, 64),
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device=device,
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dtype=dtype,
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)
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for step in sd.steps:
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x = sd(
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x=x,
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step=step,
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clip_text_embedding=clip_text_embedding,
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condition_scale=1.5,
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)
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predicted_image = sd.lda.latents_to_image(x)
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predicted_image.save("ic-light-output.png")
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```
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"""
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def __init__(
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@ -109,9 +112,7 @@ class ICLight(StableDiffusion_1):
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@no_grad()
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def _extend_conv_in(self) -> None:
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"""
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Extend to 8 the input channels of the first convolutional layer of the UNet.
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"""
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"""Extend to 8 the input channels of the first convolutional layer of the UNet."""
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down_blocks = self.unet.ensure_find(DownBlocks)
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first_block = down_blocks.layer(0, fl.Chain)
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conv_in = first_block.ensure_find(fl.Conv2d)
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@ -129,9 +130,7 @@ class ICLight(StableDiffusion_1):
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first_block.replace(old_module=conv_in, new_module=new_conv_in)
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def _apply_patch(self, weights: dict[str, torch.Tensor]) -> None:
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"""
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Apply the patch weights to the UNet, modifying inplace the state dict.
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"""
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"""Apply the weights patch to the UNet, modifying inplace the state dict."""
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current_state_dict = self.unet.state_dict()
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new_state_dict = {
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key: tensor + weights[key].to(tensor.device, tensor.dtype) for key, tensor in current_state_dict.items()
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@ -139,9 +138,17 @@ class ICLight(StableDiffusion_1):
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self.unet.load_state_dict(new_state_dict)
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@staticmethod
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def compute_gray_composite(image: Image.Image, mask: Image.Image) -> Image.Image:
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"""
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Compute a grayscale composite of an image and a mask.
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def compute_gray_composite(
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image: Image.Image,
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mask: Image.Image,
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) -> Image.Image:
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"""Compute a grayscale composite of an image and a mask.
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IC-Light will recreate the image
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Args:
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image: The image to composite.
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mask: The mask to use for the composite.
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"""
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assert mask.mode == "L", "Mask must be a grayscale image"
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assert image.size == mask.size, "Image and mask must have the same size"
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@ -149,28 +156,31 @@ class ICLight(StableDiffusion_1):
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return Image.composite(image, background, mask)
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def set_ic_light_condition(
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self, image: Image.Image, mask: Image.Image | None = None, use_rescaled_image: bool = False
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self,
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image: Image.Image,
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mask: Image.Image | None = None,
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) -> None:
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"""
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Set the IC light condition.
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"""Set the IC light condition.
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Args:
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image: The reference image.
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mask: The mask to use for the reference image.
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If a mask is provided, it will be used to compute a grayscale composite of the image and the mask ; otherwise,
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the image will be used as is, but note that IC-Light requires a 127-valued gray background to work.
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`use_rescaled_image` is used to rescale the image to [-1, 1] range. This is the expected range when using the
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Stable Diffusion autoencoder. But in the original code this part is skipped, giving different results.
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see https://github.com/lllyasviel/IC-Light/blob/788687452a2bad59633a401281c8aee91bdd3750/gradio_demo.py#L262-L265
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"""
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if mask is not None:
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image = self.compute_gray_composite(image=image, mask=mask)
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image_tensor = image_to_tensor(image, device=self.device, dtype=self.dtype)
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if use_rescaled_image:
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image_tensor = 2 * image_tensor - 1
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latents = self.lda.encode(image_tensor)
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latents = self.lda.image_to_latents(image)
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self._ic_light_condition = latents
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def __call__(
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self, x: torch.Tensor, step: int, *, clip_text_embedding: torch.Tensor, condition_scale: float = 2.0
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self,
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x: torch.Tensor,
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step: int,
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*,
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clip_text_embedding: torch.Tensor,
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condition_scale: float = 2.0,
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) -> torch.Tensor:
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assert self._ic_light_condition is not None, "Reference image not set, use `set_ic_light_condition` first"
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x = torch.cat((x, self._ic_light_condition), dim=1)
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