mirror of
https://github.com/Azornes/Comfyui-LayerForge.git
synced 2026-03-22 05:02:11 -03:00
691 lines
25 KiB
Python
691 lines
25 KiB
Python
from PIL import Image, ImageOps
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import hashlib
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import torch
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import numpy as np
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import folder_paths
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from server import PromptServer
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from aiohttp import web
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import os
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from tqdm import tqdm
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from torchvision import transforms
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from transformers import AutoModelForImageSegmentation, PretrainedConfig
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import torch.nn.functional as F
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import traceback
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import uuid
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import time
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import base64
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from PIL import Image
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import io
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torch.set_float32_matmul_precision('high')
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class BiRefNetConfig(PretrainedConfig):
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model_type = "BiRefNet"
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def __init__(self, bb_pretrained=False, **kwargs):
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self.bb_pretrained = bb_pretrained
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super().__init__(**kwargs)
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class BiRefNet(torch.nn.Module):
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def __init__(self, config):
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super().__init__()
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self.encoder = torch.nn.Sequential(
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torch.nn.Conv2d(3, 64, kernel_size=3, padding=1),
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torch.nn.ReLU(inplace=True),
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torch.nn.Conv2d(64, 64, kernel_size=3, padding=1),
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torch.nn.ReLU(inplace=True)
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)
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self.decoder = torch.nn.Sequential(
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torch.nn.Conv2d(64, 32, kernel_size=3, padding=1),
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torch.nn.ReLU(inplace=True),
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torch.nn.Conv2d(32, 1, kernel_size=1)
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)
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def forward(self, x):
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features = self.encoder(x)
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output = self.decoder(features)
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return [output]
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class CanvasNode:
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_canvas_cache = {
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'image': None,
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'mask': None,
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'cache_enabled': True,
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'data_flow_status': {},
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'persistent_cache': {},
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'last_execution_id': None
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}
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def __init__(self):
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super().__init__()
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self.flow_id = str(uuid.uuid4())
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if self.__class__._canvas_cache['persistent_cache']:
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self.restore_cache()
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def restore_cache(self):
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try:
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persistent = self.__class__._canvas_cache['persistent_cache']
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current_execution = self.get_execution_id()
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if current_execution != self.__class__._canvas_cache['last_execution_id']:
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print(f"New execution detected: {current_execution}")
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self.__class__._canvas_cache['image'] = None
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self.__class__._canvas_cache['mask'] = None
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self.__class__._canvas_cache['last_execution_id'] = current_execution
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else:
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if persistent.get('image') is not None:
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self.__class__._canvas_cache['image'] = persistent['image']
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print("Restored image from persistent cache")
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if persistent.get('mask') is not None:
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self.__class__._canvas_cache['mask'] = persistent['mask']
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print("Restored mask from persistent cache")
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except Exception as e:
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print(f"Error restoring cache: {str(e)}")
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def get_execution_id(self):
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try:
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return str(int(time.time() * 1000))
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except Exception as e:
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print(f"Error getting execution ID: {str(e)}")
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return None
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def update_persistent_cache(self):
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try:
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self.__class__._canvas_cache['persistent_cache'] = {
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'image': self.__class__._canvas_cache['image'],
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'mask': self.__class__._canvas_cache['mask']
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}
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print("Updated persistent cache")
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except Exception as e:
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print(f"Error updating persistent cache: {str(e)}")
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def track_data_flow(self, stage, status, data_info=None):
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flow_status = {
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'timestamp': time.time(),
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'stage': stage,
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'status': status,
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'data_info': data_info
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}
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print(f"Data Flow [{self.flow_id}] - Stage: {stage}, Status: {status}")
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if data_info:
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print(f"Data Info: {data_info}")
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self.__class__._canvas_cache['data_flow_status'][self.flow_id] = flow_status
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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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"canvas_image": ("STRING", {"default": "canvas_image.png"}),
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"trigger": ("INT", {"default": 0, "min": 0, "max": 99999999, "step": 1, "hidden": True}),
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"output_switch": ("BOOLEAN", {"default": True}),
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"cache_enabled": ("BOOLEAN", {"default": True, "label": "Enable Cache"})
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},
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"optional": {
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"input_image": ("IMAGE",),
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"input_mask": ("MASK",)
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK")
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RETURN_NAMES = ("image", "mask")
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FUNCTION = "process_canvas_image"
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CATEGORY = "Ycanvas"
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def add_image_to_canvas(self, input_image):
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try:
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if not isinstance(input_image, torch.Tensor):
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raise ValueError("Input image must be a torch.Tensor")
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if input_image.dim() == 4:
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input_image = input_image.squeeze(0)
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if input_image.dim() == 3 and input_image.shape[0] in [1, 3]:
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input_image = input_image.permute(1, 2, 0)
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return input_image
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except Exception as e:
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print(f"Error in add_image_to_canvas: {str(e)}")
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return None
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def add_mask_to_canvas(self, input_mask, input_image):
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try:
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if not isinstance(input_mask, torch.Tensor):
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raise ValueError("Input mask must be a torch.Tensor")
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if input_mask.dim() == 4:
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input_mask = input_mask.squeeze(0)
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if input_mask.dim() == 3 and input_mask.shape[0] == 1:
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input_mask = input_mask.squeeze(0)
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if input_image is not None:
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expected_shape = input_image.shape[:2]
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if input_mask.shape != expected_shape:
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input_mask = F.interpolate(
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input_mask.unsqueeze(0).unsqueeze(0),
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size=expected_shape,
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mode='bilinear',
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align_corners=False
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).squeeze()
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return input_mask
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except Exception as e:
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print(f"Error in add_mask_to_canvas: {str(e)}")
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return None
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def process_canvas_image(self, canvas_image, trigger, output_switch, cache_enabled, input_image=None,
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input_mask=None):
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try:
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current_execution = self.get_execution_id()
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print(f"Processing canvas image, execution ID: {current_execution}")
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if current_execution != self.__class__._canvas_cache['last_execution_id']:
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print(f"New execution detected: {current_execution}")
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self.__class__._canvas_cache['image'] = None
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self.__class__._canvas_cache['mask'] = None
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self.__class__._canvas_cache['last_execution_id'] = current_execution
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if input_image is not None:
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print("Input image received, converting to PIL Image...")
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if isinstance(input_image, torch.Tensor):
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if input_image.dim() == 4:
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input_image = input_image.squeeze(0) # 移除batch维度
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if input_image.shape[0] == 3: # 如果是[C, H, W]格式
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input_image = input_image.permute(1, 2, 0)
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image_array = (input_image.cpu().numpy() * 255).astype(np.uint8)
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if len(image_array.shape) == 2: # 如果是灰度图
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image_array = np.stack([image_array] * 3, axis=-1)
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elif len(image_array.shape) == 3 and image_array.shape[-1] != 3:
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image_array = np.transpose(image_array, (1, 2, 0))
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try:
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pil_image = Image.fromarray(image_array, 'RGB')
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print("Successfully converted to PIL Image")
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self.__class__._canvas_cache['image'] = pil_image
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print(f"Image stored in cache with size: {pil_image.size}")
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except Exception as e:
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print(f"Error converting to PIL Image: {str(e)}")
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print(f"Array shape: {image_array.shape}, dtype: {image_array.dtype}")
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raise
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if input_mask is not None:
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print("Input mask received, converting to PIL Image...")
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if isinstance(input_mask, torch.Tensor):
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if input_mask.dim() == 4:
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input_mask = input_mask.squeeze(0)
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if input_mask.dim() == 3 and input_mask.shape[0] == 1:
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input_mask = input_mask.squeeze(0)
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mask_array = (input_mask.cpu().numpy() * 255).astype(np.uint8)
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pil_mask = Image.fromarray(mask_array, 'L')
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print("Successfully converted mask to PIL Image")
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self.__class__._canvas_cache['mask'] = pil_mask
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print(f"Mask stored in cache with size: {pil_mask.size}")
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self.__class__._canvas_cache['cache_enabled'] = cache_enabled
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try:
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path_image = folder_paths.get_annotated_filepath(canvas_image)
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i = Image.open(path_image)
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i = ImageOps.exif_transpose(i)
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if i.mode not in ['RGB', 'RGBA']:
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i = i.convert('RGB')
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image = np.array(i).astype(np.float32) / 255.0
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if i.mode == 'RGBA':
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rgb = image[..., :3]
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alpha = image[..., 3:]
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image = rgb * alpha + (1 - alpha) * 0.5
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processed_image = torch.from_numpy(image)[None,]
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except Exception as e:
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processed_image = torch.ones((1, 512, 512, 3), dtype=torch.float32)
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try:
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path_mask = path_image.replace('.png', '_mask.png')
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if os.path.exists(path_mask):
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mask = Image.open(path_mask).convert('L')
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mask = np.array(mask).astype(np.float32) / 255.0
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processed_mask = torch.from_numpy(mask)[None,]
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else:
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processed_mask = torch.ones((1, processed_image.shape[1], processed_image.shape[2]),
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dtype=torch.float32)
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except Exception as e:
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print(f"Error loading mask: {str(e)}")
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processed_mask = torch.ones((1, processed_image.shape[1], processed_image.shape[2]),
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dtype=torch.float32)
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if not output_switch:
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return ()
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self.update_persistent_cache()
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return (processed_image, processed_mask)
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except Exception as e:
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print(f"Error in process_canvas_image: {str(e)}")
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traceback.print_exc()
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return ()
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def get_cached_data(self):
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return {
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'image': self.__class__._canvas_cache['image'],
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'mask': self.__class__._canvas_cache['mask']
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}
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@classmethod
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def api_get_data(cls, node_id):
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try:
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return {
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'success': True,
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'data': cls._canvas_cache
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}
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except Exception as e:
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return {
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'success': False,
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'error': str(e)
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}
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@classmethod
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def get_latest_image(cls):
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output_dir = folder_paths.get_output_directory()
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files = [os.path.join(output_dir, f) for f in os.listdir(output_dir) if
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os.path.isfile(os.path.join(output_dir, f))]
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image_files = [f for f in files if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif'))]
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if not image_files:
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return None
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latest_image_path = max(image_files, key=os.path.getctime)
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return latest_image_path
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@classmethod
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def get_flow_status(cls, flow_id=None):
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if flow_id:
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return cls._canvas_cache['data_flow_status'].get(flow_id)
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return cls._canvas_cache['data_flow_status']
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@classmethod
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def setup_routes(cls):
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@PromptServer.instance.routes.get("/ycnode/get_canvas_data/{node_id}")
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async def get_canvas_data(request):
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try:
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node_id = request.match_info["node_id"]
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print(f"Received request for node: {node_id}")
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cache_data = cls._canvas_cache
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print(f"Cache content: {cache_data}")
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print(f"Image in cache: {cache_data['image'] is not None}")
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response_data = {
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'success': True,
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'data': {
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'image': None,
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'mask': None
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}
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}
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if cache_data['image'] is not None:
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pil_image = cache_data['image']
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buffered = io.BytesIO()
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pil_image.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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response_data['data']['image'] = f"data:image/png;base64,{img_str}"
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if cache_data['mask'] is not None:
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pil_mask = cache_data['mask']
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mask_buffer = io.BytesIO()
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pil_mask.save(mask_buffer, format="PNG")
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mask_str = base64.b64encode(mask_buffer.getvalue()).decode()
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response_data['data']['mask'] = f"data:image/png;base64,{mask_str}"
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return web.json_response(response_data)
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except Exception as e:
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print(f"Error in get_canvas_data: {str(e)}")
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return web.json_response({
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'success': False,
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'error': str(e)
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})
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@PromptServer.instance.routes.get("/ycnode/get_latest_image")
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async def get_latest_image_route(request):
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try:
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latest_image_path = cls.get_latest_image()
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if latest_image_path:
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with open(latest_image_path, "rb") as f:
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encoded_string = base64.b64encode(f.read()).decode('utf-8')
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return web.json_response({
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'success': True,
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'image_data': f"data:image/png;base64,{encoded_string}"
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})
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else:
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return web.json_response({
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'success': False,
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'error': 'No images found in output directory.'
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}, status=404)
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except Exception as e:
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return web.json_response({
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'success': False,
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'error': str(e)
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}, status=500)
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def store_image(self, image_data):
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if isinstance(image_data, str) and image_data.startswith('data:image'):
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image_data = image_data.split(',')[1]
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image_bytes = base64.b64decode(image_data)
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self.cached_image = Image.open(io.BytesIO(image_bytes))
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else:
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self.cached_image = image_data
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def get_cached_image(self):
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if self.cached_image:
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buffered = io.BytesIO()
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self.cached_image.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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return f"data:image/png;base64,{img_str}"
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return None
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class BiRefNetMatting:
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def __init__(self):
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self.model = None
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self.model_path = None
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self.model_cache = {}
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self.base_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))),
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"models")
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def load_model(self, model_path):
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try:
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if model_path not in self.model_cache:
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full_model_path = os.path.join(self.base_path, "BiRefNet")
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print(f"Loading BiRefNet model from {full_model_path}...")
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try:
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self.model = AutoModelForImageSegmentation.from_pretrained(
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"ZhengPeng7/BiRefNet",
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trust_remote_code=True,
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cache_dir=full_model_path
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)
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self.model.eval()
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if torch.cuda.is_available():
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self.model = self.model.cuda()
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self.model_cache[model_path] = self.model
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print("Model loaded successfully from Hugging Face")
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print(f"Model type: {type(self.model)}")
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print(f"Model device: {next(self.model.parameters()).device}")
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except Exception as e:
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print(f"Failed to load model: {str(e)}")
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raise
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else:
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self.model = self.model_cache[model_path]
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print("Using cached model")
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return True
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except Exception as e:
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print(f"Error loading model: {str(e)}")
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traceback.print_exc()
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return False
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def preprocess_image(self, image):
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try:
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if isinstance(image, torch.Tensor):
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if image.dim() == 4:
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image = image.squeeze(0)
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if image.dim() == 3:
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image = transforms.ToPILImage()(image)
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transform_image = transforms.Compose([
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transforms.Resize((1024, 1024)),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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image_tensor = transform_image(image).unsqueeze(0)
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if torch.cuda.is_available():
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image_tensor = image_tensor.cuda()
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return image_tensor
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except Exception as e:
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print(f"Error preprocessing image: {str(e)}")
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return None
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def execute(self, image, model_path, threshold=0.5, refinement=1):
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try:
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PromptServer.instance.send_sync("matting_status", {"status": "processing"})
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if not self.load_model(model_path):
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raise RuntimeError("Failed to load model")
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if isinstance(image, torch.Tensor):
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original_size = image.shape[-2:] if image.dim() == 4 else image.shape[-2:]
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else:
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original_size = image.size[::-1]
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print(f"Original size: {original_size}")
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processed_image = self.preprocess_image(image)
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if processed_image is None:
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raise Exception("Failed to preprocess image")
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|
print(f"Processed image shape: {processed_image.shape}")
|
|
|
|
with torch.no_grad():
|
|
outputs = self.model(processed_image)
|
|
result = outputs[-1].sigmoid().cpu()
|
|
print(f"Model output shape: {result.shape}")
|
|
|
|
if result.dim() == 3:
|
|
result = result.unsqueeze(1) # 添加通道维度
|
|
elif result.dim() == 2:
|
|
result = result.unsqueeze(0).unsqueeze(0) # 添加batch和通道维度
|
|
|
|
print(f"Reshaped result shape: {result.shape}")
|
|
|
|
result = F.interpolate(
|
|
result,
|
|
size=(original_size[0], original_size[1]), # 明确指定高度和宽度
|
|
mode='bilinear',
|
|
align_corners=True
|
|
)
|
|
print(f"Resized result shape: {result.shape}")
|
|
|
|
result = result.squeeze() # 移除多余的维度
|
|
ma = torch.max(result)
|
|
mi = torch.min(result)
|
|
result = (result - mi) / (ma - mi)
|
|
|
|
if threshold > 0:
|
|
result = (result > threshold).float()
|
|
|
|
alpha_mask = result.unsqueeze(0).unsqueeze(0) # 确保mask是 [1, 1, H, W]
|
|
if isinstance(image, torch.Tensor):
|
|
if image.dim() == 3:
|
|
image = image.unsqueeze(0)
|
|
masked_image = image * alpha_mask
|
|
else:
|
|
image_tensor = transforms.ToTensor()(image).unsqueeze(0)
|
|
masked_image = image_tensor * alpha_mask
|
|
|
|
PromptServer.instance.send_sync("matting_status", {"status": "completed"})
|
|
|
|
return (masked_image, alpha_mask)
|
|
|
|
except Exception as e:
|
|
|
|
PromptServer.instance.send_sync("matting_status", {"status": "error"})
|
|
raise e
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, image, model_path, threshold, refinement):
|
|
|
|
m = hashlib.md5()
|
|
m.update(str(image).encode())
|
|
m.update(str(model_path).encode())
|
|
m.update(str(threshold).encode())
|
|
m.update(str(refinement).encode())
|
|
return m.hexdigest()
|
|
|
|
|
|
@PromptServer.instance.routes.post("/matting")
|
|
async def matting(request):
|
|
try:
|
|
print("Received matting request")
|
|
data = await request.json()
|
|
|
|
matting = BiRefNetMatting()
|
|
|
|
image_tensor, original_alpha = convert_base64_to_tensor(data["image"])
|
|
print(f"Input image shape: {image_tensor.shape}")
|
|
|
|
matted_image, alpha_mask = matting.execute(
|
|
image_tensor,
|
|
"BiRefNet/model.safetensors",
|
|
threshold=data.get("threshold", 0.5),
|
|
refinement=data.get("refinement", 1)
|
|
)
|
|
|
|
result_image = convert_tensor_to_base64(matted_image, alpha_mask, original_alpha)
|
|
result_mask = convert_tensor_to_base64(alpha_mask)
|
|
|
|
return web.json_response({
|
|
"matted_image": result_image,
|
|
"alpha_mask": result_mask
|
|
})
|
|
|
|
except Exception as e:
|
|
print(f"Error in matting endpoint: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return web.json_response({
|
|
"error": str(e),
|
|
"details": traceback.format_exc()
|
|
}, status=500)
|
|
|
|
|
|
def convert_base64_to_tensor(base64_str):
|
|
import base64
|
|
import io
|
|
|
|
try:
|
|
|
|
img_data = base64.b64decode(base64_str.split(',')[1])
|
|
img = Image.open(io.BytesIO(img_data))
|
|
|
|
has_alpha = img.mode == 'RGBA'
|
|
alpha = None
|
|
if has_alpha:
|
|
alpha = img.split()[3]
|
|
|
|
background = Image.new('RGB', img.size, (255, 255, 255))
|
|
background.paste(img, mask=alpha)
|
|
img = background
|
|
elif img.mode != 'RGB':
|
|
img = img.convert('RGB')
|
|
|
|
transform = transforms.ToTensor()
|
|
img_tensor = transform(img).unsqueeze(0) # [1, C, H, W]
|
|
|
|
if has_alpha:
|
|
alpha_tensor = transforms.ToTensor()(alpha).unsqueeze(0) # [1, 1, H, W]
|
|
return img_tensor, alpha_tensor
|
|
|
|
return img_tensor, None
|
|
|
|
except Exception as e:
|
|
print(f"Error in convert_base64_to_tensor: {str(e)}")
|
|
raise
|
|
|
|
|
|
def convert_tensor_to_base64(tensor, alpha_mask=None, original_alpha=None):
|
|
import base64
|
|
import io
|
|
|
|
try:
|
|
|
|
tensor = tensor.cpu()
|
|
|
|
if tensor.dim() == 4:
|
|
tensor = tensor.squeeze(0) # 移除batch维度
|
|
if tensor.dim() == 3 and tensor.shape[0] in [1, 3]:
|
|
tensor = tensor.permute(1, 2, 0)
|
|
|
|
img_array = (tensor.numpy() * 255).astype(np.uint8)
|
|
|
|
if alpha_mask is not None and original_alpha is not None:
|
|
|
|
alpha_mask = alpha_mask.cpu().squeeze().numpy()
|
|
alpha_mask = (alpha_mask * 255).astype(np.uint8)
|
|
|
|
original_alpha = original_alpha.cpu().squeeze().numpy()
|
|
original_alpha = (original_alpha * 255).astype(np.uint8)
|
|
|
|
combined_alpha = np.minimum(alpha_mask, original_alpha)
|
|
|
|
img = Image.fromarray(img_array, mode='RGB')
|
|
alpha_img = Image.fromarray(combined_alpha, mode='L')
|
|
img.putalpha(alpha_img)
|
|
else:
|
|
|
|
if img_array.shape[-1] == 1:
|
|
img_array = img_array.squeeze(-1)
|
|
img = Image.fromarray(img_array, mode='L')
|
|
else:
|
|
img = Image.fromarray(img_array, mode='RGB')
|
|
|
|
buffer = io.BytesIO()
|
|
img.save(buffer, format='PNG')
|
|
img_str = base64.b64encode(buffer.getvalue()).decode()
|
|
|
|
return f"data:image/png;base64,{img_str}"
|
|
|
|
except Exception as e:
|
|
print(f"Error in convert_tensor_to_base64: {str(e)}")
|
|
print(f"Tensor shape: {tensor.shape}, dtype: {tensor.dtype}")
|
|
raise
|