1173 lines
50 KiB
Python
1173 lines
50 KiB
Python
import cv2
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import numpy as np
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from PIL import Image, ImageFilter, ImageChops, ImageEnhance
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import node_helpers
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import torch
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import comfy.model_management
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import comfy.samplers
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import comfy.sample
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import comfy.utils
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import latent_preview
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from typing import Any, List, Tuple, Optional, Union, Dict
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def pil2tensor(image: Image) -> torch.Tensor:
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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def tensor2pil(t_image: torch.Tensor) -> Image:
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return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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def apply_gaussian_blur(image_np, ksize=5, sigmaX=1.0):
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if ksize % 2 == 0:
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ksize += 1 # ksize must be odd
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blurred_image = cv2.GaussianBlur(image_np, (ksize, ksize), sigmaX=sigmaX)
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return blurred_image
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class TTPlanet_Tile_Preprocessor_Simple:
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def __init__(self, blur_strength=3.0):
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self.blur_strength = blur_strength
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"scale_factor": ("FLOAT", {"default": 2.00, "min": 1.00, "max": 8.00, "step": 0.05}),
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"blur_strength": ("FLOAT", {"default": 1.0, "min": 1.0, "max": 20.0, "step": 0.1}),
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},
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"optional": {}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image_output",)
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FUNCTION = 'process_image'
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CATEGORY = 'TTP/TILE'
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def process_image(self, image, scale_factor, blur_strength):
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ret_images = []
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for i in image:
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# Convert tensor to PIL for processing
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_canvas = tensor2pil(torch.unsqueeze(i, 0)).convert('RGB')
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# Convert PIL image to OpenCV format
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img_np = np.array(_canvas)[:, :, ::-1] # RGB to BGR
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# Resize image first if you want blur to apply after resizing
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height, width = img_np.shape[:2]
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new_width = int(width / scale_factor)
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new_height = int(height / scale_factor)
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resized_down = cv2.resize(img_np, (new_width, new_height), interpolation=cv2.INTER_AREA)
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resized_img = cv2.resize(resized_down, (width, height), interpolation=cv2.INTER_LINEAR)
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# Apply Gaussian blur after resizing
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img_np = apply_gaussian_blur(resized_img, ksize=int(blur_strength), sigmaX=blur_strength / 2)
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# Convert OpenCV back to PIL and then to tensor
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_canvas = Image.fromarray(img_np[:, :, ::-1]) # BGR to RGB
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tensor_img = pil2tensor(_canvas)
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ret_images.append(tensor_img)
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return (torch.cat(ret_images, dim=0),)
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class TTP_Image_Tile_Batch:
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def __init__(self, *args, **kwargs):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"tile_width": ("INT", {"default": 1024, "min": 1}),
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"tile_height": ("INT", {"default": 1024, "min": 1}),
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}
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}
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RETURN_TYPES = ("IMAGE", "LIST", "TUPLE", "TUPLE")
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RETURN_NAMES = ("IMAGES", "POSITIONS", "ORIGINAL_SIZE", "GRID_SIZE")
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FUNCTION = "tile_image"
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CATEGORY = "TTP/Image"
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def tile_image(self, image, tile_width=1024, tile_height=1024):
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image = tensor2pil(image.squeeze(0))
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img_width, img_height = image.size
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if img_width <= tile_width and img_height <= tile_height:
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return (pil2tensor(image), [(0, 0, img_width, img_height)], (img_width, img_height), (1, 1))
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def calculate_step(size, tile_size):
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if size <= tile_size:
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return 1, 0
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else:
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num_tiles = (size + tile_size - 1) // tile_size
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overlap = (num_tiles * tile_size - size) // (num_tiles - 1)
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step = tile_size - overlap
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return num_tiles, step
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num_cols, step_x = calculate_step(img_width, tile_width)
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num_rows, step_y = calculate_step(img_height, tile_height)
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tiles = []
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positions = []
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for y in range(num_rows):
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for x in range(num_cols):
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left = x * step_x
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upper = y * step_y
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right = min(left + tile_width, img_width)
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lower = min(upper + tile_height, img_height)
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if right - left < tile_width:
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left = max(0, img_width - tile_width)
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if lower - upper < tile_height:
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upper = max(0, img_height - tile_height)
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tile = image.crop((left, upper, right, lower))
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tile_tensor = pil2tensor(tile)
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tiles.append(tile_tensor)
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positions.append((left, upper, right, lower))
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tiles = torch.stack(tiles, dim=0).squeeze(1)
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return (tiles, positions, (img_width, img_height), (num_cols, num_rows))
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class TTP_Image_Assy:
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def __init__(self, *args, **kwargs):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"tiles": ("IMAGE",),
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"positions": ("LIST",),
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"original_size": ("TUPLE",),
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"grid_size": ("TUPLE",),
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"padding": ("INT", {"default": 64, "min": 0}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("RECONSTRUCTED_IMAGE",)
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FUNCTION = "assemble_image"
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CATEGORY = "TTP/Image"
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def create_gradient_mask(self, size, direction):
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"""Create a gradient mask for blending."""
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mask = Image.new("L", size)
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for i in range(size[0] if direction == 'horizontal' else size[1]):
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value = int(255 * (1 - (i / size[0] if direction == 'horizontal' else i / size[1])))
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if direction == 'horizontal':
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mask.paste(value, (i, 0, i+1, size[1]))
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else:
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mask.paste(value, (0, i, size[0], i+1))
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return mask
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def blend_tiles(self, tile1, tile2, overlap_size, direction, padding):
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"""Blend two tiles with a smooth transition."""
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blend_size = padding
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if blend_size > overlap_size:
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blend_size = overlap_size
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if blend_size == 0:
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# No blending, just concatenate the images at the correct overlap
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if direction == 'horizontal':
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result = Image.new("RGB", (tile1.width + tile2.width - overlap_size, tile1.height))
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# Paste the left part of tile1 excluding the overlap
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result.paste(tile1.crop((0, 0, tile1.width - overlap_size, tile1.height)), (0, 0))
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# Paste tile2 directly after tile1
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result.paste(tile2, (tile1.width - overlap_size, 0))
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else:
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# For vertical direction
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result = Image.new("RGB", (tile1.width, tile1.height + tile2.height - overlap_size))
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result.paste(tile1.crop((0, 0, tile1.width, tile1.height - overlap_size)), (0, 0))
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result.paste(tile2, (0, tile1.height - overlap_size))
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return result
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# 以下为原有的混合代码，当 blend_size > 0 时执行
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offset_total = overlap_size - blend_size
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offset_left = offset_total // 2
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offset_right = offset_total - offset_left
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size = (blend_size, tile1.height) if direction == 'horizontal' else (tile1.width, blend_size)
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mask = self.create_gradient_mask(size, direction)
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if direction == 'horizontal':
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crop_tile1 = tile1.crop((tile1.width - overlap_size + offset_left, 0, tile1.width - offset_right, tile1.height))
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crop_tile2 = tile2.crop((offset_left, 0, offset_left + blend_size, tile2.height))
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if crop_tile1.size != crop_tile2.size:
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raise ValueError(f"Crop sizes do not match: {crop_tile1.size} vs {crop_tile2.size}")
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blended = Image.composite(crop_tile1, crop_tile2, mask)
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result = Image.new("RGB", (tile1.width + tile2.width - overlap_size, tile1.height))
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result.paste(tile1.crop((0, 0, tile1.width - overlap_size + offset_left, tile1.height)), (0, 0))
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result.paste(blended, (tile1.width - overlap_size + offset_left, 0))
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result.paste(tile2.crop((offset_left + blend_size, 0, tile2.width, tile2.height)), (tile1.width - offset_right, 0))
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else:
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offset_total = overlap_size - blend_size
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offset_top = offset_total // 2
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offset_bottom = offset_total - offset_top
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size = (tile1.width, blend_size)
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mask = self.create_gradient_mask(size, direction)
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crop_tile1 = tile1.crop((0, tile1.height - overlap_size + offset_top, tile1.width, tile1.height - offset_bottom))
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crop_tile2 = tile2.crop((0, offset_top, tile2.width, offset_top + blend_size))
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if crop_tile1.size != crop_tile2.size:
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raise ValueError(f"Crop sizes do not match: {crop_tile1.size} vs {crop_tile2.size}")
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blended = Image.composite(crop_tile1, crop_tile2, mask)
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result = Image.new("RGB", (tile1.width, tile1.height + tile2.height - overlap_size))
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result.paste(tile1.crop((0, 0, tile1.width, tile1.height - overlap_size + offset_top)), (0, 0))
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result.paste(blended, (0, tile1.height - overlap_size + offset_top))
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result.paste(tile2.crop((0, offset_top + blend_size, tile2.width, tile2.height)), (0, tile1.height - offset_bottom))
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return result
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def assemble_image(self, tiles, positions, original_size, grid_size, padding):
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num_cols, num_rows = grid_size
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reconstructed_image = Image.new("RGB", original_size)
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# First, blend each row independently
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row_images = []
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for row in range(num_rows):
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row_image = tensor2pil(tiles[row * num_cols].unsqueeze(0))
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for col in range(1, num_cols):
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index = row * num_cols + col
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tile_image = tensor2pil(tiles[index].unsqueeze(0))
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prev_right = positions[index - 1][2]
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left = positions[index][0]
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overlap_width = prev_right - left
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if overlap_width > 0:
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row_image = self.blend_tiles(row_image, tile_image, overlap_width, 'horizontal', padding)
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else:
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# Adjust the size of row_image to accommodate the new tile
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new_width = row_image.width + tile_image.width
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new_height = max(row_image.height, tile_image.height)
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new_row_image = Image.new("RGB", (new_width, new_height))
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new_row_image.paste(row_image, (0, 0))
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new_row_image.paste(tile_image, (row_image.width, 0))
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row_image = new_row_image
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row_images.append(row_image)
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# Now, blend each row together vertically
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final_image = row_images[0]
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for row in range(1, num_rows):
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prev_lower = positions[(row - 1) * num_cols][3]
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upper = positions[row * num_cols][1]
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overlap_height = prev_lower - upper
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if overlap_height > 0:
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final_image = self.blend_tiles(final_image, row_images[row], overlap_height, 'vertical', padding)
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else:
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# Adjust the size of final_image to accommodate the new row image
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new_width = max(final_image.width, row_images[row].width)
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new_height = final_image.height + row_images[row].height
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new_final_image = Image.new("RGB", (new_width, new_height))
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new_final_image.paste(final_image, (0, 0))
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new_final_image.paste(row_images[row], (0, final_image.height))
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final_image = new_final_image
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return pil2tensor(final_image).unsqueeze(0)
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class TTP_CoordinateSplitter:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"Positions": ("LIST", {"forceInput": True}),
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}
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}
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RETURN_TYPES = ("LIST",)
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RETURN_NAMES = ("Coordinates",)
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FUNCTION = "split_coordinates"
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CATEGORY = "TTP/Conditioning"
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def split_coordinates(self, Positions):
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coordinates = []
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for i, coords in enumerate(Positions):
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if len(coords) != 4:
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raise ValueError(f"Coordinate group {i+1} must contain exactly 4 values, but got {len(coords)}")
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x, y, x2, y2 = coords
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width = x2 - x
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height = y2 - y
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coordinates.append((x, y, width, height)) # Create a tuple for each coordinate group
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return (coordinates,) # Return as a tuple containing a list of tuples
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class TTP_condtobatch:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"conditionings": ("CONDITIONING", {"forceInput": True}),
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}
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}
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INPUT_IS_LIST = True
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "combine_to_batch"
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CATEGORY = "TTP/Conditioning"
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def combine_to_batch(self, conditionings):
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# 直接将所有conditioning组合在一起并返回
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combined_conditioning = sum(conditionings, [])
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return (combined_conditioning,)
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class TTP_condsetarea_merge:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"conditioning_batch": ("CONDITIONING", {"forceInput": True}),
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"coordinates": ("LIST", {"forceInput": True}),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "apply_coordinates_to_batch"
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CATEGORY = "TTP/Conditioning"
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def apply_coordinates_to_batch(self, conditioning_batch, coordinates, strength):
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# 确保coordinates和conditioning_batch的数量一致
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if len(coordinates) != len(conditioning_batch):
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raise ValueError(f"The number of coordinates ({len(coordinates)}) does not match the number of conditionings ({len(conditioning_batch)})")
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updated_conditionings = []
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# 遍历每个conditioning和相应的coordinate
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for conditioning, coord in zip(conditioning_batch, coordinates):
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if len(coord) != 4:
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raise ValueError(f"Each coordinate should have exactly 4 values, but got {len(coord)}")
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x, y, width, height = coord
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# Print x, y, width, height for debugging
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print(f"Processing coordinate - x: {x}, y: {y}, width: {width}, height: {height}")
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# 将每个 conditioning 处理为列表格式
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single_conditioning = [conditioning]
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# 使用标准的 node_helpers.conditioning_set_values 方法进行区域设置
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updated_conditioning = node_helpers.conditioning_set_values(
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single_conditioning,
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{
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"area": (height // 8, width // 8, y // 8, x // 8),
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"strength": strength,
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"set_area_to_bounds": False,
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}
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)
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updated_conditionings.append(updated_conditioning)
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# 将所有更新后的conditioning重新组合为一个batch
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combined_conditioning = sum(updated_conditionings, [])
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return (combined_conditioning,)
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class TTP_condsetarea_merge_test:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"conditioning_batch": ("CONDITIONING", {"forceInput": True}),
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"coordinates": ("LIST", {"forceInput": True}),
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"group_size": ("INT", {"default": 1, "min": 1, "step": 1}),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "apply_coordinates_to_batch"
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CATEGORY = "TTP/Conditioning"
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def apply_coordinates_to_batch(self, conditioning_batch, coordinates, group_size, strength):
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import math
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# 计算 conditioning 中的组数
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num_conditionings = len(conditioning_batch)
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num_groups = math.ceil(num_conditionings / group_size)
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# 如果坐标数量大于组数，需要复制 conditioning
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if len(coordinates) > num_groups:
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# 计算需要的倍数
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multiplier = math.ceil(len(coordinates) * group_size / num_conditionings)
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# 复制 conditioning_batch
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conditioning_batch = conditioning_batch * multiplier
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num_conditionings = len(conditioning_batch)
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num_groups = math.ceil(num_conditionings / group_size)
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# 重新计算需要的坐标数量
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required_coords = num_groups
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# 检查坐标数量是否足够
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if len(coordinates) != required_coords:
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raise ValueError(f"The number of coordinates ({len(coordinates)}) does not match the required number ({required_coords}) based on group size ({group_size}) and conditioning length ({num_conditionings})")
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updated_conditionings = []
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conditioning_index = 0
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# 遍历坐标和分组
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for coord in coordinates:
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if len(coord) != 4:
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raise ValueError(f"Each coordinate should have exactly 4 values, but got {len(coord)}")
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x, y, width, height = coord
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# 打印调试信息
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print(f"Processing coordinate - x: {x}, y: {y}, width: {width}, height: {height}")
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# 获取当前组的 conditioning
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group_conditionings = conditioning_batch[conditioning_index:conditioning_index + group_size]
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for conditioning in group_conditionings:
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# 使用标准的 node_helpers.conditioning_set_values 方法进行区域设置
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updated_conditioning = node_helpers.conditioning_set_values(
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[conditioning],
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{
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"area": (height // 8, width // 8, y // 8, x // 8),
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"strength": strength,
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"set_area_to_bounds": False,
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}
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)
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updated_conditionings.append(updated_conditioning)
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conditioning_index += group_size
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# 将所有更新后的 conditioning 重新组合为一个批次
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combined_conditioning = sum(updated_conditionings, [])
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return (combined_conditioning,)
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class Tile_imageSize:
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@classmethod
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def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"width_factor": ("INT", {"default": 3, "min": 1, "max": 10, "step": 1}),
|
|
"height_factor": ("INT", {"default": 3, "min": 1, "max": 10, "step": 1}),
|
|
"overlap_rate": ("FLOAT", {"default": 0.1, "min": 0.00, "max": 0.95, "step": 0.05}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("INT", "INT")
|
|
RETURN_NAMES = ("tile_width", "tile_height")
|
|
CATEGORY = "TTP/Image"
|
|
FUNCTION = "image_width_height"
|
|
|
|
def image_width_height(self, image, width_factor, height_factor, overlap_rate):
|
|
_, raw_H, raw_W, _ = image.shape
|
|
if overlap_rate == 0:
|
|
# 水平方向
|
|
if width_factor == 1:
|
|
tile_width = raw_W
|
|
else:
|
|
tile_width = int(raw_W / width_factor)
|
|
if tile_width % 8 != 0:
|
|
tile_width = ((tile_width + 7) // 8) * 8
|
|
# 垂直方向
|
|
if height_factor == 1:
|
|
tile_height = raw_H
|
|
else:
|
|
tile_height = int(raw_H / height_factor)
|
|
if tile_height % 8 != 0:
|
|
tile_height = ((tile_height + 7) // 8) * 8
|
|
|
|
else:
|
|
# 水平方向
|
|
if width_factor == 1:
|
|
tile_width = raw_W
|
|
else:
|
|
tile_width = int(raw_W / (1 + (width_factor - 1) * (1 - overlap_rate)))
|
|
if tile_width % 8 != 0:
|
|
tile_width = (tile_width // 8) * 8
|
|
# 垂直方向
|
|
if height_factor == 1:
|
|
tile_height = raw_H
|
|
else:
|
|
tile_height = int(raw_H / (1 + (height_factor - 1) * (1 - overlap_rate)))
|
|
if tile_height % 8 != 0:
|
|
tile_height = (tile_height // 8) * 8
|
|
|
|
return (tile_width, tile_height)
|
|
|
|
class TTP_Expand_And_Mask:
|
|
"""
|
|
这是一个节点类，用于将输入图片在指定方向扩展一定数量的块并创建相应蒙版。
|
|
|
|
功能：
|
|
1. 支持同时在多个方向上扩展图像。
|
|
2. 分别控制每个方向的扩展块数量。
|
|
3. 将输入图像的透明通道（Alpha 通道）信息转换为蒙版，并与新创建的蒙版合并。
|
|
4. 添加一个布尔参数 fill_alpha_decision 来决定是否将输出图片中的透明区域填充为指定颜色，并输出 RGB 图像。
|
|
"""
|
|
def __init__(self, *args, **kwargs):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
directions = ["left", "right", "top", "bottom"]
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",), # 输入一张图片
|
|
"fill_mode": (["duplicate", "white"], {"default": "duplicate", "label": "Fill Mode"}),
|
|
"fill_alpha_decision": ("BOOLEAN", {"default": False, "label": "Fill Alpha with Color"}),
|
|
"fill_color": ("STRING", {"default": "#7F7F7F", "label": "Fill Color"}),
|
|
},
|
|
"optional": {
|
|
**{f"expand_{dir}": ("BOOLEAN", {"default": False, "label": f"Expand {dir.capitalize()}"}) for dir in directions},
|
|
**{f"num_blocks_{dir}": ("INT", {"default": 1, "min": 0, "max": 3, "step": 1, "label": f"Blocks {dir.capitalize()}"}) for dir in directions},
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "MASK")
|
|
RETURN_NAMES = ("EXPANDED_IMAGE", "MASK")
|
|
FUNCTION = "expand_and_mask"
|
|
CATEGORY = "TTP/Image"
|
|
|
|
def hex_to_rgba(self, hex_color):
|
|
# 去除可能存在的 '#' 字符
|
|
hex_color = hex_color.lstrip('#')
|
|
# 如果为6位十六进制字符串，默认为不透明
|
|
if len(hex_color) == 6:
|
|
r = int(hex_color[0:2], 16)
|
|
g = int(hex_color[2:4], 16)
|
|
b = int(hex_color[4:6], 16)
|
|
return (r, g, b, 255)
|
|
# 如果为8位，则最后两位为透明度
|
|
elif len(hex_color) == 8:
|
|
r = int(hex_color[0:2], 16)
|
|
g = int(hex_color[2:4], 16)
|
|
b = int(hex_color[4:6], 16)
|
|
a = int(hex_color[6:8], 16)
|
|
return (r, g, b, a)
|
|
else:
|
|
raise ValueError("Invalid hex color format")
|
|
|
|
def expand_and_mask(self, image, fill_mode="duplicate", fill_alpha_decision=False, fill_color="#7F7F7F", **kwargs):
|
|
pil_image = tensor2pil(image)
|
|
orig_width, orig_height = pil_image.size
|
|
has_alpha = (pil_image.mode == 'RGBA')
|
|
|
|
# 解析方向和块数
|
|
directions = ["left", "right", "top", "bottom"]
|
|
expand_directions = {dir: kwargs.get(f"expand_{dir}", False) for dir in directions}
|
|
num_blocks = {dir: kwargs.get(f"num_blocks_{dir}", 0) if expand_directions[dir] else 0 for dir in directions}
|
|
|
|
# 计算扩展后的尺寸
|
|
total_width = orig_width + orig_width * (num_blocks["left"] + num_blocks["right"])
|
|
total_height = orig_height + orig_height * (num_blocks["top"] + num_blocks["bottom"])
|
|
|
|
# 创建扩展后的图像
|
|
expanded_image_mode = pil_image.mode
|
|
expanded_image = Image.new(expanded_image_mode, (total_width, total_height))
|
|
|
|
# 根据 fill_mode 创建填充图像
|
|
def create_fill_image():
|
|
if pil_image.mode == 'RGBA':
|
|
return Image.new("RGBA", (orig_width, orig_height), color=(255, 255, 255, 255))
|
|
elif pil_image.mode == 'RGB':
|
|
return Image.new("RGB", (orig_width, orig_height), color=(255, 255, 255))
|
|
elif pil_image.mode == 'L':
|
|
return Image.new("L", (orig_width, orig_height), color=255)
|
|
else:
|
|
raise ValueError(f"Unsupported image mode for fill: {pil_image.mode}")
|
|
|
|
if fill_mode == "duplicate":
|
|
fill_image = pil_image.copy()
|
|
elif fill_mode == "white":
|
|
fill_image = create_fill_image()
|
|
else:
|
|
fill_image = pil_image.copy()
|
|
|
|
# 计算原图在扩展图像中的位置
|
|
left_offset = orig_width * num_blocks["left"]
|
|
top_offset = orig_height * num_blocks["top"]
|
|
|
|
# 粘贴原始图像
|
|
expanded_image.paste(pil_image, (left_offset, top_offset))
|
|
|
|
# 粘贴填充区域
|
|
for dir in directions:
|
|
blocks = num_blocks[dir]
|
|
for i in range(blocks):
|
|
if dir == "left":
|
|
x = left_offset - orig_width * (i + 1)
|
|
y = top_offset
|
|
elif dir == "right":
|
|
x = left_offset + orig_width * (i + 1)
|
|
y = top_offset
|
|
elif dir == "top":
|
|
x = left_offset
|
|
y = top_offset - orig_height * (i + 1)
|
|
elif dir == "bottom":
|
|
x = left_offset
|
|
y = top_offset + orig_height * (i + 1)
|
|
else:
|
|
continue
|
|
expanded_image.paste(fill_image, (x, y))
|
|
|
|
# 粘贴角落填充区域（处理同时选择多个方向的情况）
|
|
corner_positions = []
|
|
if expand_directions["left"] and expand_directions["top"]:
|
|
for i in range(num_blocks["left"]):
|
|
for j in range(num_blocks["top"]):
|
|
x = left_offset - orig_width * (i + 1)
|
|
y = top_offset - orig_height * (j + 1)
|
|
corner_positions.append((x, y))
|
|
if expand_directions["left"] and expand_directions["bottom"]:
|
|
for i in range(num_blocks["left"]):
|
|
for j in range(num_blocks["bottom"]):
|
|
x = left_offset - orig_width * (i + 1)
|
|
y = top_offset + orig_height * (j + 1)
|
|
corner_positions.append((x, y))
|
|
if expand_directions["right"] and expand_directions["top"]:
|
|
for i in range(num_blocks["right"]):
|
|
for j in range(num_blocks["top"]):
|
|
x = left_offset + orig_width * (i + 1)
|
|
y = top_offset - orig_height * (j + 1)
|
|
corner_positions.append((x, y))
|
|
if expand_directions["right"] and expand_directions["bottom"]:
|
|
for i in range(num_blocks["right"]):
|
|
for j in range(num_blocks["bottom"]):
|
|
x = left_offset + orig_width * (i + 1)
|
|
y = top_offset + orig_height * (j + 1)
|
|
corner_positions.append((x, y))
|
|
|
|
for pos in corner_positions:
|
|
expanded_image.paste(fill_image, pos)
|
|
|
|
# 创建蒙版
|
|
mask_array = np.zeros((total_height, total_width), dtype=np.float32)
|
|
|
|
# 原始图像区域蒙版处理
|
|
if has_alpha:
|
|
alpha_array = np.array(pil_image.getchannel("A"), dtype=np.float32) / 255.0
|
|
alpha_mask_array = 1.0 - alpha_array
|
|
mask_array[top_offset:top_offset + orig_height, left_offset:left_offset + orig_width] = alpha_mask_array
|
|
|
|
# 填充区域蒙版设置为1.0
|
|
# 左右扩展区域
|
|
for dir in ["left", "right"]:
|
|
blocks = num_blocks[dir]
|
|
for i in range(blocks):
|
|
if dir == "left":
|
|
x_start = left_offset - orig_width * (i + 1)
|
|
x_end = left_offset - orig_width * i
|
|
elif dir == "right":
|
|
x_start = left_offset + orig_width * (i + 1)
|
|
x_end = left_offset + orig_width * (i + 2)
|
|
else:
|
|
continue
|
|
mask_array[top_offset:top_offset + orig_height, x_start:x_end] = 1.0
|
|
|
|
# 上下扩展区域
|
|
for dir in ["top", "bottom"]:
|
|
blocks = num_blocks[dir]
|
|
for i in range(blocks):
|
|
if dir == "top":
|
|
y_start = top_offset - orig_height * (i + 1)
|
|
y_end = top_offset - orig_height * i
|
|
elif dir == "bottom":
|
|
y_start = top_offset + orig_height * (i + 1)
|
|
y_end = top_offset + orig_height * (i + 2)
|
|
else:
|
|
continue
|
|
mask_array[y_start:y_end, left_offset:left_offset + orig_width] = 1.0
|
|
|
|
# 角落区域蒙版设置为1.0
|
|
for pos in corner_positions:
|
|
x, y = pos
|
|
mask_array[y:y + orig_height, x:x + orig_width] = 1.0
|
|
|
|
# 创建蒙版张量 (1, 1, height, width)
|
|
mask_tensor = torch.from_numpy(mask_array).unsqueeze(0).unsqueeze(0)
|
|
|
|
# 根据 fill_alpha_decision 参数决定是否将输出图像中的透明区域填充为指定颜色
|
|
if fill_alpha_decision and has_alpha:
|
|
expanded_image = expanded_image.convert('RGBA') # 确保图像是RGBA模式
|
|
# 使用自定义填充颜色代替纯白色
|
|
fill_rgba = self.hex_to_rgba(fill_color)
|
|
background = Image.new('RGBA', expanded_image.size, fill_rgba)
|
|
expanded_image = Image.alpha_composite(background, expanded_image)
|
|
expanded_image = expanded_image.convert('RGB') # 转换为RGB模式
|
|
expanded_image_mode = 'RGB'
|
|
|
|
expanded_image_tensor = pil2tensor(expanded_image)
|
|
|
|
return (expanded_image_tensor, mask_tensor)
|
|
|
|
class TTP_text_mix:
|
|
def __init__(self, *args, **kwargs):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"text1": ("STRING", {"default": "", "multiline": True, "label": "Text Box 1"}),
|
|
"text2": ("STRING", {"default": "", "multiline": True, "label": "Text Box 2"}),
|
|
"text3": ("STRING", {"default": "", "multiline": True, "label": "Text Box 3"}),
|
|
"template": ("STRING", {"default": "", "multiline": True, "label": "Template Text Box"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING")
|
|
RETURN_NAMES = ("text1", "text2", "text3", "final_text")
|
|
FUNCTION = "mix_texts"
|
|
CATEGORY = "TTP/text"
|
|
|
|
def mix_texts(self, text1, text2, text3, template):
|
|
# 使用replace方法替换模板中的占位符{text1}和{text2}
|
|
final_text = template.replace("{text1}", text1).replace("{text2}", text2).replace("{text3}", text3)
|
|
|
|
return (text1, text2, text3, final_text)
|
|
|
|
def horner_poly(x: torch.Tensor, coefficients: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
使用 Horner's scheme 计算多项式:
|
|
c[0]*x^(n-1) + c[1]*x^(n-2) + ... + c[n-2]*x + c[n-1]
|
|
其中 coefficients = [c[0], c[1], ..., c[n-1]].
|
|
"""
|
|
out = torch.zeros_like(x)
|
|
for c in coefficients:
|
|
out = out * x + c
|
|
return out
|
|
|
|
def modulate(x, shift, scale):
|
|
"""Modulate layer implementation for HunyuanVideo"""
|
|
try:
|
|
# Ensure consistent data types
|
|
shift = shift.to(dtype=x.dtype, device=x.device)
|
|
scale = scale.to(dtype=x.dtype, device=x.device)
|
|
|
|
# Reshape shift and scale to match x dimensions
|
|
B = x.shape[0] # batch size
|
|
|
|
if len(x.shape) == 3: # [B, L, D]
|
|
shift = shift.view(B, 1, -1) # [B, 1, D]
|
|
scale = scale.view(B, 1, -1) # [B, 1, D]
|
|
shift = shift.expand(-1, x.shape[1], -1) # [B, L, D]
|
|
scale = scale.expand(-1, x.shape[1], -1) # [B, L, D]
|
|
elif len(x.shape) == 5: # [B, C, T, H, W]
|
|
shift = shift.view(B, -1, 1, 1, 1) # [B, C, 1, 1, 1]
|
|
scale = scale.view(B, -1, 1, 1, 1) # [B, C, 1, 1, 1]
|
|
shift = shift.expand(-1, -1, x.shape[2], x.shape[3], x.shape[4]) # [B, C, T, H, W]
|
|
scale = scale.expand(-1, -1, x.shape[2], x.shape[3], x.shape[4]) # [B, C, T, H, W]
|
|
else:
|
|
raise ValueError(f"Unsupported input shape: {x.shape}")
|
|
|
|
# Step-by-step calculation to reduce memory usage
|
|
result = x.mul_(1 + scale) # in-place operation
|
|
result.add_(shift) # in-place operation
|
|
|
|
return result
|
|
|
|
except Exception as e:
|
|
raise RuntimeError(f"Modulation failed: {str(e)}")
|
|
|
|
def modulate(x, shift, scale):
|
|
"""Modulate layer implementation for HunyuanVideo"""
|
|
try:
|
|
# Ensure consistent data types
|
|
shift = shift.to(dtype=x.dtype, device=x.device)
|
|
scale = scale.to(dtype=x.dtype, device=x.device)
|
|
|
|
# Reshape shift and scale to match x dimensions
|
|
B = x.shape[0] # batch size
|
|
|
|
if len(x.shape) == 3: # [B, L, D]
|
|
shift = shift.view(B, 1, -1) # [B, 1, D]
|
|
scale = scale.view(B, 1, -1) # [B, 1, D]
|
|
shift = shift.expand(-1, x.shape[1], -1) # [B, L, D]
|
|
scale = scale.expand(-1, x.shape[1], -1) # [B, L, D]
|
|
elif len(x.shape) == 5: # [B, C, T, H, W]
|
|
shift = shift.view(B, -1, 1, 1, 1) # [B, C, 1, 1, 1]
|
|
scale = scale.view(B, -1, 1, 1, 1) # [B, C, 1, 1, 1]
|
|
shift = shift.expand(-1, -1, x.shape[2], x.shape[3], x.shape[4]) # [B, C, T, H, W]
|
|
scale = scale.expand(-1, -1, x.shape[2], x.shape[3], x.shape[4]) # [B, C, T, H, W]
|
|
else:
|
|
raise ValueError(f"Unsupported input shape: {x.shape}")
|
|
|
|
# Step-by-step calculation to reduce memory usage
|
|
result = x.mul_(1 + scale) # in-place operation
|
|
result.add_(shift) # in-place operation
|
|
|
|
return result
|
|
|
|
except Exception as e:
|
|
raise RuntimeError(f"Modulation failed: {str(e)}")
|
|
|
|
class TeaCacheHunyuanVideoSampler:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"noise": ("NOISE",),
|
|
"guider": ("GUIDER",),
|
|
"sampler": ("SAMPLER",),
|
|
"sigmas": ("SIGMAS",),
|
|
"latent_image": ("LATENT",),
|
|
"speedup": ([
|
|
"Original (1x)",
|
|
"Fast (1.6x)",
|
|
"Faster (2.1x)",
|
|
"Ultra Fast (3.2x)",
|
|
"Shapeless Fast (4.4x)"
|
|
], {
|
|
"default": "Fast (1.6x)",
|
|
"tooltip": (
|
|
"Control TeaCache speed/quality trade-off:\n"
|
|
"Original: Base quality\n"
|
|
"Fast: 1.6x speedup\n"
|
|
"Faster: 2.1x speedup\n"
|
|
"Ultra Fast: 3.2x speedup\n"
|
|
"Shapeless Fast: 4.4x speedup"
|
|
)
|
|
}),
|
|
"enable_custom_speed": ("BOOLEAN", {
|
|
"default": False,
|
|
"label": "Enable Custom Speed"
|
|
}),
|
|
"custom_speed": ("FLOAT", {
|
|
"default": 1.0,
|
|
"min": 1.0,
|
|
"max": 4.4,
|
|
"step": 0.1,
|
|
"label": "Custom Speed Multiplier"
|
|
})
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("LATENT", "LATENT")
|
|
RETURN_NAMES = ("output", "denoised_output")
|
|
FUNCTION = "sample"
|
|
CATEGORY = "sampling/custom_sampling"
|
|
|
|
def calculate_threshold(self, speed_multiplier: float) -> float:
|
|
"""根据预设速度点进行线性插值，计算自定义速度对应的阈值"""
|
|
# 预设的速度倍数和对应阈值
|
|
predefined_speeds = [1.0, 1.6, 2.1, 3.2, 4.4]
|
|
predefined_thresholds = [0.0, 0.1, 0.15, 0.25, 0.35]
|
|
|
|
# 使用 numpy 的线性插值函数
|
|
threshold = np.interp(speed_multiplier, predefined_speeds, predefined_thresholds)
|
|
|
|
# 确保阈值不超过最大值
|
|
threshold = min(threshold, 0.35)
|
|
|
|
return threshold
|
|
|
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def teacache_forward(
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self,
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transformer,
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x: torch.Tensor,
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timestep: torch.Tensor, # Should be in range(0, 1000).
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context: Optional[torch.Tensor] = None,
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y: Optional[torch.Tensor] = None, # Text embedding for modulation.
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guidance: Optional[torch.Tensor] = None, # Guidance for modulation, should be cfg_scale x 1000.
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attention_mask: Optional[torch.Tensor] = None,
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control: Any = None,
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transformer_options: Dict = {},
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**kwargs
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) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
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"""TeaCache forward implementation"""
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should_calc = True
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if transformer.enable_teacache:
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try:
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# 获取输入维度
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B, C, T, H, W = x.shape
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# 准备调制向量
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try:
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# HunyuanVideo 使用 timestep_embedding 进行时间步编码
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time_emb = comfy.ldm.flux.layers.timestep_embedding(timestep, 256, time_factor=1.0).to(x.dtype)
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vec = transformer.time_in(time_emb) # [B, hidden_size]
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# 文本调制 - HunyuanVideo 使用 vector_in 处理 y 而不是 context
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if y is not None:
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if not hasattr(transformer, 'params') or not hasattr(transformer.params, 'vec_in_dim'):
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raise AttributeError("Transformer missing required attributes: params.vec_in_dim")
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vec = vec + transformer.vector_in(y[:, :transformer.params.vec_in_dim])
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# 指导调制
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if guidance is not None and getattr(transformer, 'params', None) and transformer.params.guidance_embed:
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guidance_emb = comfy.ldm.flux.layers.timestep_embedding(guidance, 256).to(x.dtype)
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guidance_vec = transformer.guidance_in(guidance_emb)
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vec = vec + guidance_vec
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except Exception as e:
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raise RuntimeError(f"Failed to prepare modulation vector: {str(e)}")
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# 嵌入图像
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try:
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img = transformer.img_in(x)
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except Exception as e:
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raise RuntimeError(f"Failed to embed image: {str(e)}")
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if transformer.enable_teacache:
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try:
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# 使用原地操作减少内存使用
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inp = img.clone()
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vec_ = vec.clone()
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# 获取调制参数
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modulation_output = transformer.double_blocks[0].img_mod(vec_)
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# 处理调制输出
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if isinstance(modulation_output, tuple):
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if len(modulation_output) >= 2:
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mod_shift = modulation_output[0]
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mod_scale = modulation_output[1]
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if hasattr(mod_shift, 'shift') and hasattr(mod_scale, 'scale'):
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img_mod1_shift = mod_shift.shift
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img_mod1_scale = mod_scale.scale
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else:
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img_mod1_shift = mod_shift
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img_mod1_scale = mod_scale
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else:
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raise ValueError(f"Tuple too short, expected at least 2 elements, got {len(modulation_output)}")
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elif hasattr(modulation_output, 'shift') and hasattr(modulation_output, 'scale'):
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img_mod1_shift = modulation_output.shift
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img_mod1_scale = modulation_output.scale
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elif hasattr(modulation_output, 'chunk'):
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chunks = modulation_output.chunk(6, dim=-1)
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img_mod1_shift = chunks[0]
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img_mod1_scale = chunks[1]
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else:
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raise ValueError(f"Unsupported modulation output format: {type(modulation_output)}")
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# 确保获取到的是张量
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if not isinstance(img_mod1_shift, torch.Tensor) or not isinstance(img_mod1_scale, torch.Tensor):
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raise ValueError(f"Failed to get tensor values for shift and scale")
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# 应用归一化和调制
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normed_inp = transformer.double_blocks[0].img_norm1(inp)
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del inp # 释放内存
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modulated_inp = modulate(normed_inp, shift=img_mod1_shift, scale=img_mod1_scale)
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del normed_inp # 释放内存
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# 计算相对 L1 距离并决定是否需要计算
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if transformer.cnt == 0 or transformer.cnt == transformer.num_steps - 1:
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should_calc = True
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transformer.accumulated_rel_l1_distance = 0
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else:
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try:
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coefficients = [7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02]
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rescale_func = np.poly1d(coefficients)
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rel_l1 = ((modulated_inp - transformer.previous_modulated_input).abs().mean() /
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(transformer.previous_modulated_input.abs().mean() + 1e-6)).cpu().item()
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transformer.accumulated_rel_l1_distance += rescale_func(rel_l1)
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if transformer.accumulated_rel_l1_distance < transformer.rel_l1_thresh:
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should_calc = False
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else:
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should_calc = True
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transformer.accumulated_rel_l1_distance = 0
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except Exception as e:
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should_calc = True
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transformer.previous_modulated_input = modulated_inp
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transformer.cnt += 1
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except Exception as e:
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should_calc = True
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|
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except Exception as e:
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should_calc = True
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|
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# 如果需要计算，调用原始的 forward 方法
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if should_calc:
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try:
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out = transformer.original_forward(x, timestep, context, y, guidance,
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attention_mask=attention_mask,
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control=control,
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transformer_options=transformer_options,
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**kwargs)
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transformer.previous_residual = out
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return out
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except Exception as e:
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raise
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else:
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# 如果不需要计算，返回之前的结果
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return transformer.previous_residual
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def sample(self, noise, guider, sampler, sigmas, latent_image, speedup, enable_custom_speed=False, custom_speed=1.0):
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"""Sampling implementation"""
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device = comfy.model_management.get_torch_device()
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# 定义预设速度的阈值映射
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predefined_speeds = [1.0, 1.6, 2.1, 3.2, 4.4]
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predefined_thresholds = [0.0, 0.1, 0.15, 0.25, 0.35]
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# 根据是否启用自定义速度来决定使用哪个阈值
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if enable_custom_speed:
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if not (1.0 <= custom_speed <= 4.4):
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raise ValueError("Custom speed must be between 1.0 and 4.4")
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threshold = self.calculate_threshold(custom_speed)
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else:
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# 定义预设的速度选项
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thresh_map = {
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"Original (1x)": 0.0,
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"Fast (1.6x)": 0.1,
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"Faster (2.1x)": 0.15,
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"Ultra Fast (3.2x)": 0.25,
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"Shapeless Fast (4.4x)": 0.35
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}
|
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if speedup not in thresh_map:
|
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raise ValueError(f"Unsupported speedup option: {speedup}")
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threshold = thresh_map[speedup]
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try:
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# 获取 transformer
|
|
transformer = guider.model_patcher.model.diffusion_model
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|
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# 初始化 TeaCache 状态
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|
transformer.enable_teacache = True
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transformer.cnt = 0
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transformer.num_steps = len(sigmas) - 1
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transformer.rel_l1_thresh = threshold
|
|
transformer.accumulated_rel_l1_distance = 0
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|
transformer.previous_modulated_input = None
|
|
transformer.previous_residual = None
|
|
|
|
latent = latent_image
|
|
latent_image = latent["samples"].clone()
|
|
latent = latent.copy()
|
|
|
|
noise_mask = None
|
|
if "noise_mask" in latent:
|
|
noise_mask = latent["noise_mask"].clone()
|
|
|
|
# 保存原始 forward 方法
|
|
transformer.original_forward = transformer.forward
|
|
|
|
# 使用 lambda 替换 forward 方法，确保正确绑定 self
|
|
transformer.forward = lambda x, t, context=None, y=None, guidance=None, attention_mask=None, control=None, transformer_options={}, **kwargs: self.teacache_forward(
|
|
transformer, x, t, context, y, guidance, attention_mask, control, transformer_options, **kwargs
|
|
)
|
|
|
|
try:
|
|
x0_output = {}
|
|
callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
|
|
|
|
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
|
|
samples = guider.sample(
|
|
noise.generate_noise(latent),
|
|
latent_image,
|
|
sampler,
|
|
sigmas,
|
|
denoise_mask=noise_mask,
|
|
callback=callback,
|
|
disable_pbar=disable_pbar,
|
|
seed=noise.seed
|
|
)
|
|
samples = samples.to(comfy.model_management.intermediate_device())
|
|
|
|
finally:
|
|
# 恢复原始的 forward 方法
|
|
transformer.forward = transformer.original_forward
|
|
delattr(transformer, 'original_forward')
|
|
transformer.enable_teacache = False
|
|
|
|
out = latent.copy()
|
|
out["samples"] = samples
|
|
if "x0" in x0_output:
|
|
out_denoised = latent.copy()
|
|
out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
|
|
else:
|
|
out_denoised = out
|
|
|
|
return (out, out_denoised)
|
|
|
|
except Exception as e:
|
|
raise RuntimeError(f"Sampling failed: {str(e)}")
|
|
|
|
|
|
class TTP_Iterative_Upscale:
|
|
def __init__(self, *args, **kwargs):
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"iterations": ("INT", {"default": 1, "min": 1, "max": 10, "step": 1}),
|
|
"scale_factor": ("FLOAT", {"default": 2.0, "min": 1.0, "max": 4.0, "step": 0.05}),
|
|
},
|
|
"optional": {
|
|
"crop_x": ("INT", {"default": 0, "min": 0}),
|
|
"crop_y": ("INT", {"default": 0, "min": 0}),
|
|
"crop_width": ("INT", {"default": 0, "min": 0}),
|
|
"crop_height": ("INT", {"default": 0, "min": 0}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("IMAGE",)
|
|
FUNCTION = "iter_upscale"
|
|
CATEGORY = "TTP/Image"
|
|
|
|
def iter_upscale(self, image, iterations=1, scale_factor=2.0, crop_x=0, crop_y=0, crop_width=0, crop_height=0):
|
|
pil_img = tensor2pil(image)
|
|
|
|
for _ in range(iterations):
|
|
if crop_width > 0 and crop_height > 0:
|
|
left = max(0, crop_x)
|
|
top = max(0, crop_y)
|
|
right = min(pil_img.width, crop_x + crop_width)
|
|
bottom = min(pil_img.height, crop_y + crop_height)
|
|
pil_img = pil_img.crop((left, top, right, bottom))
|
|
|
|
new_size = (int(pil_img.width * scale_factor), int(pil_img.height * scale_factor))
|
|
pil_img = pil_img.resize(new_size, Image.LANCZOS)
|
|
|
|
return (pil2tensor(pil_img),)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"TTPlanet_Tile_Preprocessor_Simple": TTPlanet_Tile_Preprocessor_Simple,
|
|
"TTP_Image_Tile_Batch": TTP_Image_Tile_Batch,
|
|
"TTP_Image_Assy": TTP_Image_Assy,
|
|
"TTP_CoordinateSplitter": TTP_CoordinateSplitter,
|
|
"TTP_condtobatch": TTP_condtobatch,
|
|
"TTP_condsetarea_merge": TTP_condsetarea_merge,
|
|
"TTP_Tile_image_size": Tile_imageSize,
|
|
"TTP_condsetarea_merge_test": TTP_condsetarea_merge_test,
|
|
"TTP_Expand_And_Mask": TTP_Expand_And_Mask,
|
|
"TTP_Iterative_Upscale": TTP_Iterative_Upscale,
|
|
"TTP_text_mix": TTP_text_mix,
|
|
"TeaCacheHunyuanVideoSampler": TeaCacheHunyuanVideoSampler
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"TTPlanet_Tile_Preprocessor_Simple": "TTP Tile Preprocessor Simple",
|
|
"TTP_Image_Tile_Batch": "TTP_Image_Tile_Batch",
|
|
"TTP_Image_Assy": "TTP_Image_Assy",
|
|
"TTP_CoordinateSplitter": "TTP_CoordinateSplitter",
|
|
"TTP_condtobatch": "TTP_cond to batch",
|
|
"TTP_condsetarea_merge": "TTP_condsetarea_merge",
|
|
"TTP_Tile_image_size": "TTP_Tile_image_size",
|
|
"TTP_condsetarea_merge_test": "TTP_condsetarea_merge_test",
|
|
"TTP_Expand_And_Mask": "TTP_Expand_And_Mask",
|
|
"TTP_Iterative_Upscale": "TTP_Iterative_Upscale",
|
|
"TTP_text_mix": "TTP_text_mix",
|
|
"TeaCacheHunyuanVideoSampler": "TTP_TeaCache HunyuanVideo Sampler"
|
|
}
|