184 lines
7.5 KiB
Python
184 lines
7.5 KiB
Python
#credit to huchenlei for this module
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#from https://github.com/huchenlei/ComfyUI-IC-Light-Native
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import torch
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import numpy as np
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from typing import Tuple, TypedDict, Callable
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import comfy.model_management
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from comfy.sd import load_unet
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from comfy.ldm.models.autoencoder import AutoencoderKL
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from comfy.model_base import BaseModel
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from comfy.model_patcher import ModelPatcher
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from PIL import Image
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from nodes import VAEEncode
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from ...libs.image import np2tensor, pil2tensor
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class UnetParams(TypedDict):
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input: torch.Tensor
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timestep: torch.Tensor
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c: dict
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cond_or_uncond: torch.Tensor
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class VAEEncodeArgMax(VAEEncode):
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def encode(self, vae, pixels):
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assert isinstance(
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vae.first_stage_model, AutoencoderKL
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), "ArgMax only supported for AutoencoderKL"
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original_sample_mode = vae.first_stage_model.regularization.sample
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vae.first_stage_model.regularization.sample = False
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ret = super().encode(vae, pixels)
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vae.first_stage_model.regularization.sample = original_sample_mode
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return ret
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class ICLight:
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@staticmethod
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def apply_c_concat(params: UnetParams, concat_conds) -> UnetParams:
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"""Apply c_concat on unet call."""
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sample = params["input"]
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params["c"]["c_concat"] = torch.cat(
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(
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[concat_conds.to(sample.device)]
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* (sample.shape[0] // concat_conds.shape[0])
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),
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dim=0,
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)
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return params
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@staticmethod
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def create_custom_conv(
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original_conv: torch.nn.Module,
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dtype: torch.dtype,
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device=torch.device,
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) -> torch.nn.Module:
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with torch.no_grad():
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new_conv_in = torch.nn.Conv2d(
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8,
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original_conv.out_channels,
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original_conv.kernel_size,
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original_conv.stride,
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original_conv.padding,
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)
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new_conv_in.weight.zero_()
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new_conv_in.weight[:, :4, :, :].copy_(original_conv.weight)
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new_conv_in.bias = original_conv.bias
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return new_conv_in.to(dtype=dtype, device=device)
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def generate_lighting_image(self, original_image, direction):
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_, image_height, image_width, _ = original_image.shape
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if direction == 'Left Light':
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gradient = np.linspace(255, 0, image_width)
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image = np.tile(gradient, (image_height, 1))
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input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
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return np2tensor(input_bg)
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elif direction == 'Right Light':
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gradient = np.linspace(0, 255, image_width)
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image = np.tile(gradient, (image_height, 1))
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input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
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return np2tensor(input_bg)
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elif direction == 'Top Light':
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gradient = np.linspace(255, 0, image_height)[:, None]
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image = np.tile(gradient, (1, image_width))
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input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
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return np2tensor(input_bg)
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elif direction == 'Bottom Light':
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gradient = np.linspace(0, 255, image_height)[:, None]
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image = np.tile(gradient, (1, image_width))
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input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
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return np2tensor(input_bg)
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elif direction == 'Circle Light':
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x = np.linspace(-1, 1, image_width)
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y = np.linspace(-1, 1, image_height)
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x, y = np.meshgrid(x, y)
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r = np.sqrt(x ** 2 + y ** 2)
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r = r / r.max()
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color1 = np.array([0, 0, 0])[np.newaxis, np.newaxis, :]
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color2 = np.array([255, 255, 255])[np.newaxis, np.newaxis, :]
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gradient = (color1 * r[..., np.newaxis] + color2 * (1 - r)[..., np.newaxis]).astype(np.uint8)
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image = pil2tensor(Image.fromarray(gradient))
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return image
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else:
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image = pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
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return image
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def generate_source_image(self, original_image, source):
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batch_size, image_height, image_width, _ = original_image.shape
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if source == 'Use Flipped Background Image':
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if batch_size < 2:
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raise ValueError('Must be at least 2 image to use flipped background image.')
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original_image = [img.unsqueeze(0) for img in original_image]
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image = torch.flip(original_image[1], [2])
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return image
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elif source == 'Ambient':
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input_bg = np.zeros(shape=(image_height, image_width, 3), dtype=np.uint8) + 64
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return np2tensor(input_bg)
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elif source == 'Left Light':
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gradient = np.linspace(224, 32, image_width)
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image = np.tile(gradient, (image_height, 1))
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input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
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return np2tensor(input_bg)
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elif source == 'Right Light':
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gradient = np.linspace(32, 224, image_width)
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image = np.tile(gradient, (image_height, 1))
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input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
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return np2tensor(input_bg)
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elif source == 'Top Light':
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gradient = np.linspace(224, 32, image_height)[:, None]
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image = np.tile(gradient, (1, image_width))
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input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
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return np2tensor(input_bg)
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elif source == 'Bottom Light':
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gradient = np.linspace(32, 224, image_height)[:, None]
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image = np.tile(gradient, (1, image_width))
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input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
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return np2tensor(input_bg)
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else:
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image = pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
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return image
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def apply(self, ic_model_path, model, c_concat: dict, ic_model=None) -> Tuple[ModelPatcher]:
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device = comfy.model_management.get_torch_device()
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dtype = comfy.model_management.unet_dtype()
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work_model = model.clone()
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# Apply scale factor.
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base_model: BaseModel = work_model.model
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scale_factor = base_model.model_config.latent_format.scale_factor
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# [B, 4, H, W]
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concat_conds: torch.Tensor = c_concat["samples"] * scale_factor
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# [1, 4 * B, H, W]
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concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1)
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def unet_dummy_apply(unet_apply: Callable, params: UnetParams):
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"""A dummy unet apply wrapper serving as the endpoint of wrapper
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chain."""
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return unet_apply(x=params["input"], t=params["timestep"], **params["c"])
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existing_wrapper = work_model.model_options.get(
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"model_function_wrapper", unet_dummy_apply
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)
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def wrapper_func(unet_apply: Callable, params: UnetParams):
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return existing_wrapper(unet_apply, params=self.apply_c_concat(params, concat_conds))
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work_model.set_model_unet_function_wrapper(wrapper_func)
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if not ic_model:
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ic_model = load_unet(ic_model_path)
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ic_model_state_dict = ic_model.model.diffusion_model.state_dict()
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work_model.add_patches(
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patches={
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("diffusion_model." + key): (
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'diff',
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[
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value.to(dtype=dtype, device=device),
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{"pad_weight": key == 'input_blocks.0.0.weight'}
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]
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)
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for key, value in ic_model_state_dict.items()
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}
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)
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return (work_model, ic_model) |