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