1416 lines
55 KiB
Python
1416 lines
55 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 asyncio
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import threading
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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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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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import sys
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import os
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try:
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from python.log_system import create_module_logger
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log = create_module_logger(__name__)
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log.info("Logger initialized for canvas_node")
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except ImportError as e:
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print(f"Warning: Logger module not available: {e}")
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class _FallbackLogger:
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@staticmethod
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def debug(*args, **kwargs): print("[DEBUG]", *args)
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@staticmethod
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def info(*args, **kwargs): print("[INFO]", *args)
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@staticmethod
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def warning(*args, **kwargs): print("[WARN]", *args)
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@staticmethod
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def error(*args, **kwargs): print("[ERROR]", *args)
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@staticmethod
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def exception(*args, **kwargs):
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print("[ERROR]", *args)
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traceback.print_exc()
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@staticmethod
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def fatal(*args, **kwargs): print("[FATAL]", *args)
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log = _FallbackLogger()
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torch.set_float32_matmul_precision('high')
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def _get_comfy_birefnet_loader():
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"""Return ComfyUI's native BiRefNet loader when it is available."""
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try:
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from comfy.bg_removal_model import load
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return load
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except Exception as error:
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log.debug(f"Native ComfyUI BiRefNet loader is unavailable: {error}")
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return None
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class LayerForgeNode:
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_canvas_data_storage = {}
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_storage_lock = threading.Lock()
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_canvas_cache = {
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'image': None,
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'mask': None,
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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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_websocket_data = {}
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_websocket_listeners = {}
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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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self.node_id = None # Will be set when node is created
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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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log.info(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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log.info("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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log.info("Restored mask from persistent cache")
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except Exception as e:
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log.error(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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log.error(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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log.debug("Updated persistent cache")
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except Exception as e:
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log.error(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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log.debug(f"Data Flow [{self.flow_id}] - Stage: {stage}, Status: {status}")
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if data_info:
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log.debug(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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"fit_on_add": ("BOOLEAN", {"default": False, "label_on": "Fit on Add/Paste", "label_off": "Default Behavior"}),
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"show_preview": ("BOOLEAN", {"default": False, "label_on": "Show Preview", "label_off": "Hide Preview"}),
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"auto_refresh_after_generation": ("BOOLEAN", {"default": False, "label_on": "True", "label_off": "False"}),
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"trigger": ("INT", {"default": 0, "min": 0, "max": 99999999, "step": 1}),
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"node_id": ("STRING", {"default": "0"}),
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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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"hidden": {
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"prompt": ("PROMPT",),
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"unique_id": ("UNIQUE_ID",),
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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 = "azNodes > LayerForge"
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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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log.error(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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log.error(f"Error in add_mask_to_canvas: {str(e)}")
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return None
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_processing_lock = threading.Lock()
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def process_canvas_image(self, fit_on_add, show_preview, auto_refresh_after_generation, trigger, node_id, input_image=None, input_mask=None, prompt=None, unique_id=None):
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try:
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if not self.__class__._processing_lock.acquire(blocking=False):
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log.warning(f"Process already in progress for node {node_id}, skipping...")
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return self.get_cached_data()
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log.info(f"Lock acquired. Starting process_canvas_image for node_id: {node_id} (fallback unique_id: {unique_id})")
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# Always store fresh input data, even if None, to clear stale data
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log.info(f"Storing input data for node {node_id} - Image: {input_image is not None}, Mask: {input_mask is not None}")
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with self.__class__._storage_lock:
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input_data = {}
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if input_image is not None:
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# Convert image tensor(s) to base64 - handle batch
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if isinstance(input_image, torch.Tensor):
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# Ensure correct shape [B, H, W, C]
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if input_image.dim() == 3:
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input_image = input_image.unsqueeze(0)
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batch_size = input_image.shape[0]
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log.info(f"Processing batch of {batch_size} image(s)")
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if batch_size == 1:
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# Single image - keep backward compatibility
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img_np = (input_image.squeeze(0).cpu().numpy() * 255).astype(np.uint8)
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pil_img = Image.fromarray(img_np, 'RGB')
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# Convert to base64
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buffered = io.BytesIO()
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pil_img.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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input_data['input_image'] = f"data:image/png;base64,{img_str}"
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input_data['input_image_width'] = pil_img.width
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input_data['input_image_height'] = pil_img.height
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log.debug(f"Stored single input image: {pil_img.width}x{pil_img.height}")
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else:
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# Multiple images - store as array
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images_array = []
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for i in range(batch_size):
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img_np = (input_image[i].cpu().numpy() * 255).astype(np.uint8)
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pil_img = Image.fromarray(img_np, 'RGB')
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# Convert to base64
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buffered = io.BytesIO()
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pil_img.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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images_array.append({
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'data': f"data:image/png;base64,{img_str}",
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'width': pil_img.width,
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'height': pil_img.height
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})
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log.debug(f"Stored batch image {i+1}/{batch_size}: {pil_img.width}x{pil_img.height}")
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input_data['input_images_batch'] = images_array
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log.info(f"Stored batch of {batch_size} images")
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if input_mask is not None:
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# Convert mask tensor to base64
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if isinstance(input_mask, torch.Tensor):
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# Ensure correct shape
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if input_mask.dim() == 2:
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input_mask = input_mask.unsqueeze(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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# Convert to numpy and then to PIL
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mask_np = (input_mask.cpu().numpy() * 255).astype(np.uint8)
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pil_mask = Image.fromarray(mask_np, 'L')
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# Convert to base64
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mask_buffered = io.BytesIO()
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pil_mask.save(mask_buffered, format="PNG")
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mask_str = base64.b64encode(mask_buffered.getvalue()).decode()
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input_data['input_mask'] = f"data:image/png;base64,{mask_str}"
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log.debug(f"Stored input mask: {pil_mask.width}x{pil_mask.height}")
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input_data['fit_on_add'] = fit_on_add
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# Store in a special key for input data (overwrites any previous data)
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self.__class__._canvas_data_storage[f"{node_id}_input"] = input_data
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storage_key = node_id
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processed_image = None
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processed_mask = None
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with self.__class__._storage_lock:
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canvas_data = self.__class__._canvas_data_storage.pop(storage_key, None)
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if canvas_data:
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log.info(f"Canvas data found for node {storage_key} from WebSocket")
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if canvas_data.get('image'):
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image_data = canvas_data['image'].split(',')[1]
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image_bytes = base64.b64decode(image_data)
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pil_image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
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image_array = np.array(pil_image).astype(np.float32) / 255.0
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processed_image = torch.from_numpy(image_array)[None,]
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log.debug(f"Image loaded from WebSocket, shape: {processed_image.shape}")
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if canvas_data.get('mask'):
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mask_data = canvas_data['mask'].split(',')[1]
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mask_bytes = base64.b64decode(mask_data)
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pil_mask = Image.open(io.BytesIO(mask_bytes)).convert('L')
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mask_array = np.array(pil_mask).astype(np.float32) / 255.0
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processed_mask = torch.from_numpy(mask_array)[None,]
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log.debug(f"Mask loaded from WebSocket, shape: {processed_mask.shape}")
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else:
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log.warning(f"No canvas data found for node {storage_key} in WebSocket cache.")
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if processed_image is None:
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log.warning(f"Processed image is still None, creating default blank image.")
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processed_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32)
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if processed_mask is None:
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log.warning(f"Processed mask is still None, creating default blank mask.")
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processed_mask = torch.zeros((1, 512, 512), dtype=torch.float32)
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log.debug(f"About to return output - Image shape: {processed_image.shape}, Mask shape: {processed_mask.shape}")
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self.update_persistent_cache()
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log.info(f"Successfully returning processed image and mask")
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return (processed_image, processed_mask)
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except Exception as e:
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log.exception(f"Error in process_canvas_image: {str(e)}")
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return (None, None)
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finally:
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if self.__class__._processing_lock.locked():
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self.__class__._processing_lock.release()
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log.debug(f"Process completed for node {node_id}, lock released")
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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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|
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@classmethod
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def get_latest_images(cls, since_timestamp=0):
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output_dir = folder_paths.get_output_directory()
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files = []
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for f_name in os.listdir(output_dir):
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file_path = os.path.join(output_dir, f_name)
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if os.path.isfile(file_path) and file_path.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')):
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try:
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mtime = os.path.getmtime(file_path)
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if mtime > since_timestamp:
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files.append((mtime, file_path))
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except OSError:
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continue
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files.sort(key=lambda x: x[0])
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|
|
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return [f[1] for f in files]
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|
|
|
@classmethod
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def get_flow_status(cls, flow_id=None):
|
|
|
|
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
|
|
def _cleanup_old_websocket_data(cls):
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"""Clean up old WebSocket data from invalid nodes or data older than 5 minutes"""
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try:
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current_time = time.time()
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cleanup_threshold = 300 # 5 minutes
|
|
|
|
nodes_to_remove = []
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for node_id, data in cls._websocket_data.items():
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|
|
|
if node_id < 0:
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nodes_to_remove.append(node_id)
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continue
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|
|
|
if current_time - data.get('timestamp', 0) > cleanup_threshold:
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nodes_to_remove.append(node_id)
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|
continue
|
|
|
|
for node_id in nodes_to_remove:
|
|
del cls._websocket_data[node_id]
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log.debug(f"Cleaned up old WebSocket data for node {node_id}")
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|
|
|
if nodes_to_remove:
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|
log.info(f"Cleaned up {len(nodes_to_remove)} old WebSocket entries")
|
|
|
|
except Exception as e:
|
|
log.error(f"Error during WebSocket cleanup: {str(e)}")
|
|
|
|
@classmethod
|
|
def setup_routes(cls):
|
|
@PromptServer.instance.routes.get("/layerforge/canvas_ws")
|
|
async def handle_canvas_websocket(request):
|
|
ws = web.WebSocketResponse(max_msg_size=33554432)
|
|
await ws.prepare(request)
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|
|
|
async for msg in ws:
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|
if msg.type == web.WSMsgType.TEXT:
|
|
try:
|
|
data = msg.json()
|
|
node_id = data.get('nodeId')
|
|
if not node_id:
|
|
await ws.send_json({'status': 'error', 'message': 'nodeId is required'})
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continue
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|
|
|
image_data = data.get('image')
|
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mask_data = data.get('mask')
|
|
|
|
with cls._storage_lock:
|
|
cls._canvas_data_storage[node_id] = {
|
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'image': image_data,
|
|
'mask': mask_data,
|
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'timestamp': time.time()
|
|
}
|
|
|
|
log.info(f"Received canvas data for node {node_id} via WebSocket")
|
|
|
|
ack_payload = {
|
|
'type': 'ack',
|
|
'nodeId': node_id,
|
|
'status': 'success'
|
|
}
|
|
await ws.send_json(ack_payload)
|
|
log.debug(f"Sent ACK for node {node_id}")
|
|
|
|
except Exception as e:
|
|
log.error(f"Error processing WebSocket message: {e}")
|
|
await ws.send_json({'status': 'error', 'message': str(e)})
|
|
elif msg.type == web.WSMsgType.ERROR:
|
|
log.error(f"WebSocket connection closed with exception {ws.exception()}")
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|
|
|
log.info("WebSocket connection closed")
|
|
return ws
|
|
|
|
@PromptServer.instance.routes.get("/layerforge/get_input_data/{node_id}")
|
|
async def get_input_data(request):
|
|
try:
|
|
node_id = request.match_info["node_id"]
|
|
log.debug(f"Checking for input data for node: {node_id}")
|
|
|
|
with cls._storage_lock:
|
|
input_key = f"{node_id}_input"
|
|
input_data = cls._canvas_data_storage.get(input_key, None)
|
|
|
|
if input_data:
|
|
log.info(f"Input data found for node {node_id}, sending to frontend")
|
|
return web.json_response({
|
|
'success': True,
|
|
'has_input': True,
|
|
'data': input_data
|
|
})
|
|
else:
|
|
log.debug(f"No input data found for node {node_id}")
|
|
return web.json_response({
|
|
'success': True,
|
|
'has_input': False
|
|
})
|
|
|
|
except Exception as e:
|
|
log.error(f"Error in get_input_data: {str(e)}")
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': str(e)
|
|
}, status=500)
|
|
|
|
@PromptServer.instance.routes.post("/layerforge/clear_input_data/{node_id}")
|
|
async def clear_input_data(request):
|
|
try:
|
|
node_id = request.match_info["node_id"]
|
|
log.info(f"Clearing input data for node: {node_id}")
|
|
|
|
with cls._storage_lock:
|
|
input_key = f"{node_id}_input"
|
|
if input_key in cls._canvas_data_storage:
|
|
del cls._canvas_data_storage[input_key]
|
|
log.info(f"Input data cleared for node {node_id}")
|
|
else:
|
|
log.debug(f"No input data to clear for node {node_id}")
|
|
|
|
return web.json_response({
|
|
'success': True,
|
|
'message': f'Input data cleared for node {node_id}'
|
|
})
|
|
|
|
except Exception as e:
|
|
log.error(f"Error in clear_input_data: {str(e)}")
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': str(e)
|
|
}, status=500)
|
|
|
|
@PromptServer.instance.routes.get("/ycnode/get_canvas_data/{node_id}")
|
|
async def get_canvas_data(request):
|
|
try:
|
|
node_id = request.match_info["node_id"]
|
|
log.debug(f"Received request for node: {node_id}")
|
|
|
|
cache_data = cls._canvas_cache
|
|
log.debug(f"Cache content: {cache_data}")
|
|
log.debug(f"Image in cache: {cache_data['image'] is not None}")
|
|
|
|
response_data = {
|
|
'success': True,
|
|
'data': {
|
|
'image': None,
|
|
'mask': None
|
|
}
|
|
}
|
|
|
|
if cache_data['image'] is not None:
|
|
pil_image = cache_data['image']
|
|
buffered = io.BytesIO()
|
|
pil_image.save(buffered, format="PNG")
|
|
img_str = base64.b64encode(buffered.getvalue()).decode()
|
|
response_data['data']['image'] = f"data:image/png;base64,{img_str}"
|
|
|
|
if cache_data['mask'] is not None:
|
|
pil_mask = cache_data['mask']
|
|
mask_buffer = io.BytesIO()
|
|
pil_mask.save(mask_buffer, format="PNG")
|
|
mask_str = base64.b64encode(mask_buffer.getvalue()).decode()
|
|
response_data['data']['mask'] = f"data:image/png;base64,{mask_str}"
|
|
|
|
return web.json_response(response_data)
|
|
|
|
except Exception as e:
|
|
log.error(f"Error in get_canvas_data: {str(e)}")
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': str(e)
|
|
})
|
|
|
|
@PromptServer.instance.routes.get("/layerforge/get-latest-images/{since}")
|
|
async def get_latest_images_route(request):
|
|
try:
|
|
since_timestamp = float(request.match_info.get('since', 0))
|
|
# JS Timestamps are in milliseconds, Python's are in seconds
|
|
latest_image_paths = cls.get_latest_images(since_timestamp / 1000.0)
|
|
|
|
images_data = []
|
|
for image_path in latest_image_paths:
|
|
with open(image_path, "rb") as f:
|
|
encoded_string = base64.b64encode(f.read()).decode('utf-8')
|
|
images_data.append(f"data:image/png;base64,{encoded_string}")
|
|
|
|
return web.json_response({
|
|
'success': True,
|
|
'images': images_data
|
|
})
|
|
except Exception as e:
|
|
log.error(f"Error in get_latest_images_route: {str(e)}")
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': str(e)
|
|
}, status=500)
|
|
|
|
@PromptServer.instance.routes.get("/ycnode/get_latest_image")
|
|
async def get_latest_image_route(request):
|
|
try:
|
|
latest_image_path = cls.get_latest_image()
|
|
if latest_image_path:
|
|
with open(latest_image_path, "rb") as f:
|
|
encoded_string = base64.b64encode(f.read()).decode('utf-8')
|
|
return web.json_response({
|
|
'success': True,
|
|
'image_data': f"data:image/png;base64,{encoded_string}"
|
|
})
|
|
else:
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': 'No images found in output directory.'
|
|
}, status=404)
|
|
except Exception as e:
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': str(e)
|
|
}, status=500)
|
|
|
|
@PromptServer.instance.routes.post("/ycnode/load_image_from_path")
|
|
async def load_image_from_path_route(request):
|
|
try:
|
|
data = await request.json()
|
|
file_path = data.get('file_path')
|
|
|
|
if not file_path:
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': 'file_path is required'
|
|
}, status=400)
|
|
|
|
log.info(f"Attempting to load image from path: {file_path}")
|
|
|
|
# Check if file exists and is accessible
|
|
if not os.path.exists(file_path):
|
|
log.warning(f"File not found: {file_path}")
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': f'File not found: {file_path}'
|
|
}, status=404)
|
|
|
|
# Check if it's an image file
|
|
valid_extensions = ('.png', '.jpg', '.jpeg', '.gif', '.bmp', '.webp', '.tiff', '.tif', '.ico', '.avif')
|
|
if not file_path.lower().endswith(valid_extensions):
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': f'Invalid image file extension. Supported: {valid_extensions}'
|
|
}, status=400)
|
|
|
|
# Try to load and convert the image
|
|
try:
|
|
with Image.open(file_path) as img:
|
|
# Convert to RGB if necessary
|
|
if img.mode != 'RGB':
|
|
img = img.convert('RGB')
|
|
|
|
# Convert to base64
|
|
buffered = io.BytesIO()
|
|
img.save(buffered, format="PNG")
|
|
img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
|
|
|
|
log.info(f"Successfully loaded image from path: {file_path}")
|
|
return web.json_response({
|
|
'success': True,
|
|
'image_data': f"data:image/png;base64,{img_str}",
|
|
'width': img.width,
|
|
'height': img.height
|
|
})
|
|
|
|
except Exception as img_error:
|
|
log.error(f"Error processing image file {file_path}: {str(img_error)}")
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': f'Error processing image file: {str(img_error)}'
|
|
}, status=500)
|
|
|
|
except Exception as e:
|
|
log.error(f"Error in load_image_from_path_route: {str(e)}")
|
|
return web.json_response({
|
|
'success': False,
|
|
'error': str(e)
|
|
}, status=500)
|
|
|
|
def store_image(self, image_data):
|
|
|
|
if isinstance(image_data, str) and image_data.startswith('data:image'):
|
|
image_data = image_data.split(',')[1]
|
|
image_bytes = base64.b64decode(image_data)
|
|
self.cached_image = Image.open(io.BytesIO(image_bytes))
|
|
else:
|
|
self.cached_image = image_data
|
|
|
|
def get_cached_image(self):
|
|
|
|
if self.cached_image:
|
|
buffered = io.BytesIO()
|
|
self.cached_image.save(buffered, format="PNG")
|
|
img_str = base64.b64encode(buffered.getvalue()).decode()
|
|
return f"data:image/png;base64,{img_str}"
|
|
return None
|
|
|
|
|
|
_BIREFNET_REPOSITORY = "ZhengPeng7/BiRefNet"
|
|
_BIREFNET_FILENAME = "model.safetensors"
|
|
_BIREFNET_REMOTE_PREFIX = "remote:"
|
|
_BIREFNET_REMOTE_DIRECTORY = "layerforge_birefnet"
|
|
_BIREFNET_REQUIRED_KEYS = {
|
|
"bb.layers.1.blocks.0.attn.relative_position_index",
|
|
"bb.layers.2.blocks.17.attn.qkv.weight",
|
|
}
|
|
_BIREFNET_MODEL_CATALOG = (
|
|
{
|
|
"id": "general",
|
|
"label": "BiRefNet — General",
|
|
"description": "General-purpose foreground segmentation.",
|
|
"repo_id": "ZhengPeng7/BiRefNet",
|
|
"filename": "model.safetensors",
|
|
},
|
|
{
|
|
"id": "high_resolution",
|
|
"label": "BiRefNet — High Resolution",
|
|
"description": "High-resolution foreground segmentation.",
|
|
"repo_id": "ZhengPeng7/BiRefNet_HR",
|
|
"filename": "model.safetensors",
|
|
},
|
|
{
|
|
"id": "portrait",
|
|
"label": "BiRefNet — Portrait",
|
|
"description": "Portrait-focused foreground segmentation.",
|
|
"repo_id": "ZhengPeng7/BiRefNet-portrait",
|
|
"filename": "model.safetensors",
|
|
},
|
|
{
|
|
"id": "matting",
|
|
"label": "BiRefNet — Matting",
|
|
"description": "Matting-focused foreground segmentation.",
|
|
"repo_id": "ZhengPeng7/BiRefNet-matting",
|
|
"filename": "model.safetensors",
|
|
},
|
|
{
|
|
"id": "high_resolution_matting",
|
|
"label": "BiRefNet — High Resolution Matting",
|
|
"description": "High-resolution matting-focused segmentation.",
|
|
"repo_id": "ZhengPeng7/BiRefNet_HR-matting",
|
|
"filename": "model.safetensors",
|
|
},
|
|
{
|
|
"id": "dynamic",
|
|
"label": "BiRefNet — Dynamic",
|
|
"description": "Dynamic-resolution foreground segmentation.",
|
|
"repo_id": "ZhengPeng7/BiRefNet_dynamic",
|
|
"filename": "model.safetensors",
|
|
},
|
|
{
|
|
"id": "dynamic_matting",
|
|
"label": "BiRefNet — Dynamic Matting",
|
|
"description": "Dynamic-resolution matting-focused segmentation.",
|
|
"repo_id": "ZhengPeng7/BiRefNet_dynamic-matting",
|
|
"filename": "model.safetensors",
|
|
},
|
|
{
|
|
"id": "hrsod",
|
|
"label": "BiRefNet — HRSOD",
|
|
"description": "High-resolution salient-object segmentation.",
|
|
"repo_id": "ZhengPeng7/BiRefNet-HRSOD",
|
|
"filename": "model.safetensors",
|
|
},
|
|
{
|
|
"id": "dis5k",
|
|
"label": "BiRefNet — DIS5K",
|
|
"description": "Dichotomous image segmentation model.",
|
|
"repo_id": "ZhengPeng7/BiRefNet-DIS5K",
|
|
"filename": "model.safetensors",
|
|
},
|
|
{
|
|
"id": "cod",
|
|
"label": "BiRefNet — COD",
|
|
"description": "Camouflaged-object segmentation model.",
|
|
"repo_id": "ZhengPeng7/BiRefNet-COD",
|
|
"filename": "model.safetensors",
|
|
},
|
|
)
|
|
|
|
|
|
def _get_birefnet_base_paths():
|
|
"""Return native ComfyUI and legacy locations that may contain BiRefNet."""
|
|
paths = []
|
|
|
|
get_folder_paths = getattr(folder_paths, "get_folder_paths", None)
|
|
if callable(get_folder_paths):
|
|
try:
|
|
paths.extend(get_folder_paths("background_removal"))
|
|
except (KeyError, TypeError):
|
|
pass
|
|
|
|
comfy_models_dir = getattr(folder_paths, "models_dir", None)
|
|
if comfy_models_dir:
|
|
paths.extend([
|
|
os.path.join(comfy_models_dir, "background_removal"),
|
|
os.path.join(comfy_models_dir, "RMBG", "BiRefNet"),
|
|
os.path.join(comfy_models_dir, "BiRefNet"),
|
|
])
|
|
|
|
unique_paths = []
|
|
seen = set()
|
|
for path in paths:
|
|
normalized = os.path.normcase(os.path.normpath(path))
|
|
if normalized not in seen:
|
|
seen.add(normalized)
|
|
unique_paths.append(path)
|
|
|
|
return unique_paths
|
|
|
|
|
|
def _is_native_birefnet_checkpoint(path):
|
|
"""Check the checkpoint signature without loading all weights into memory."""
|
|
if not os.path.isfile(path) or not path.lower().endswith(".safetensors"):
|
|
return False
|
|
|
|
try:
|
|
from safetensors import safe_open
|
|
|
|
with safe_open(path, framework="pt") as checkpoint:
|
|
keys = checkpoint.keys()
|
|
return _BIREFNET_REQUIRED_KEYS.issubset(keys)
|
|
except Exception as error:
|
|
log.debug(f"Unable to inspect BiRefNet checkpoint {path}: {error}")
|
|
return False
|
|
|
|
|
|
def _iter_birefnet_checkpoint_paths():
|
|
"""Yield candidate checkpoints from native and legacy model directories."""
|
|
for base_path in _get_birefnet_base_paths():
|
|
if not os.path.isdir(base_path):
|
|
continue
|
|
|
|
for root, directories, files in os.walk(base_path):
|
|
directories[:] = [
|
|
directory for directory in directories
|
|
if directory not in {".git", ".no_exist", "__pycache__"}
|
|
]
|
|
for filename in sorted(files):
|
|
if filename.lower().endswith(".safetensors"):
|
|
yield os.path.join(root, filename)
|
|
|
|
|
|
def _find_local_birefnet_model(model_path=None):
|
|
"""Find a full BiRefNet checkpoint accepted by ComfyUI's native loader."""
|
|
candidates = []
|
|
seen = set()
|
|
for path in _iter_birefnet_checkpoint_paths():
|
|
normalized = os.path.normcase(os.path.normpath(path))
|
|
if normalized in seen:
|
|
continue
|
|
seen.add(normalized)
|
|
if _is_native_birefnet_checkpoint(path):
|
|
candidates.append(path)
|
|
|
|
if model_path and model_path != "auto":
|
|
requested = os.path.normcase(os.path.normpath(os.path.abspath(model_path)))
|
|
for candidate in candidates:
|
|
if os.path.normcase(os.path.normpath(os.path.abspath(candidate))) == requested:
|
|
return candidate
|
|
return None
|
|
|
|
if not candidates:
|
|
return None
|
|
|
|
priority = {
|
|
"birefnet.safetensors": 0,
|
|
"model.safetensors": 1,
|
|
"birefnet-general.safetensors": 2,
|
|
"birefnet-hr.safetensors": 3,
|
|
}
|
|
return min(
|
|
candidates,
|
|
key=lambda path: (priority.get(os.path.basename(path).lower(), 10), path.lower()),
|
|
)
|
|
|
|
|
|
def _get_birefnet_remote_model(model_path):
|
|
"""Resolve a client-facing remote model identifier from the fixed catalog."""
|
|
if not isinstance(model_path, str) or not model_path.startswith(_BIREFNET_REMOTE_PREFIX):
|
|
return None
|
|
|
|
model_id = model_path[len(_BIREFNET_REMOTE_PREFIX):]
|
|
return next((model for model in _BIREFNET_MODEL_CATALOG if model["id"] == model_id), None)
|
|
|
|
|
|
def _get_birefnet_remote_checkpoint_path(model):
|
|
"""Return the managed local path for a catalog model, if ComfyUI exposes storage."""
|
|
base_paths = _get_birefnet_base_paths()
|
|
if not base_paths:
|
|
return None
|
|
|
|
return os.path.join(
|
|
base_paths[0],
|
|
_BIREFNET_REMOTE_DIRECTORY,
|
|
model["id"],
|
|
model["filename"],
|
|
)
|
|
|
|
|
|
def _get_birefnet_model_options():
|
|
"""Return local checkpoints and official models available for download."""
|
|
local_options = []
|
|
seen = set()
|
|
managed_roots = [
|
|
os.path.normcase(os.path.normpath(os.path.join(base_path, _BIREFNET_REMOTE_DIRECTORY)))
|
|
for base_path in _get_birefnet_base_paths()
|
|
]
|
|
|
|
for path in _iter_birefnet_checkpoint_paths():
|
|
normalized = os.path.normcase(os.path.normpath(os.path.abspath(path)))
|
|
if normalized in seen or not _is_native_birefnet_checkpoint(path):
|
|
continue
|
|
if any(normalized == root or normalized.startswith(root + os.sep) for root in managed_roots):
|
|
continue
|
|
|
|
seen.add(normalized)
|
|
label = os.path.basename(path)
|
|
for base_path in _get_birefnet_base_paths():
|
|
try:
|
|
relative_path = os.path.relpath(path, base_path)
|
|
except ValueError:
|
|
continue
|
|
if relative_path == os.pardir or relative_path.startswith(os.pardir + os.sep):
|
|
continue
|
|
label = relative_path.replace(os.sep, "/")
|
|
break
|
|
|
|
local_options.append({
|
|
"path": path,
|
|
"label": label,
|
|
"source": "local",
|
|
"downloaded": True,
|
|
})
|
|
|
|
remote_options = []
|
|
for model in _BIREFNET_MODEL_CATALOG:
|
|
checkpoint_path = _get_birefnet_remote_checkpoint_path(model)
|
|
remote_options.append({
|
|
"path": f"{_BIREFNET_REMOTE_PREFIX}{model['id']}",
|
|
"label": model["label"],
|
|
"description": model["description"],
|
|
"source": "remote",
|
|
"downloaded": bool(checkpoint_path and _is_native_birefnet_checkpoint(checkpoint_path)),
|
|
})
|
|
|
|
local_options.sort(key=lambda option: option["label"].lower())
|
|
return local_options + remote_options
|
|
|
|
|
|
def _get_birefnet_download_dir():
|
|
paths = _get_birefnet_base_paths()
|
|
if not paths:
|
|
raise RuntimeError("ComfyUI did not expose a background_removal model directory")
|
|
|
|
download_dir = paths[0]
|
|
os.makedirs(download_dir, exist_ok=True)
|
|
return download_dir
|
|
|
|
|
|
def _download_birefnet_checkpoint(model=None):
|
|
"""Download and validate a full BiRefNet checkpoint into ComfyUI's model path."""
|
|
try:
|
|
from huggingface_hub import hf_hub_download
|
|
except ImportError as error:
|
|
raise RuntimeError(
|
|
"Automatic BiRefNet download requires the 'huggingface_hub' package. "
|
|
"Install the LayerForge requirements or place a compatible checkpoint in "
|
|
"ComfyUI/models/background_removal/."
|
|
) from error
|
|
|
|
if model is None:
|
|
repository = _BIREFNET_REPOSITORY
|
|
filename = _BIREFNET_FILENAME
|
|
download_dir = _get_birefnet_download_dir()
|
|
model_label = "BiRefNet — General"
|
|
else:
|
|
repository = model["repo_id"]
|
|
filename = model["filename"]
|
|
download_dir = os.path.join(_get_birefnet_download_dir(), _BIREFNET_REMOTE_DIRECTORY, model["id"])
|
|
os.makedirs(download_dir, exist_ok=True)
|
|
model_label = model["label"]
|
|
|
|
log.info(f"Downloading {model_label} from Hugging Face into {download_dir}...")
|
|
|
|
try:
|
|
downloaded_path = hf_hub_download(
|
|
repo_id=repository,
|
|
filename=filename,
|
|
local_dir=download_dir,
|
|
local_dir_use_symlinks=False,
|
|
)
|
|
except TypeError:
|
|
# Older huggingface_hub versions do not accept local_dir_use_symlinks.
|
|
downloaded_path = hf_hub_download(
|
|
repo_id=repository,
|
|
filename=filename,
|
|
local_dir=download_dir,
|
|
)
|
|
|
|
if not _is_native_birefnet_checkpoint(downloaded_path):
|
|
raise RuntimeError(
|
|
f"Downloaded {model_label} is not a ComfyUI-compatible BiRefNet checkpoint: {downloaded_path}"
|
|
)
|
|
|
|
log.info(f"{model_label} checkpoint is ready at {downloaded_path}")
|
|
return downloaded_path
|
|
|
|
|
|
def _ensure_birefnet_checkpoint(model_path=None):
|
|
remote_model = _get_birefnet_remote_model(model_path)
|
|
if remote_model:
|
|
checkpoint_path = _get_birefnet_remote_checkpoint_path(remote_model)
|
|
if checkpoint_path and _is_native_birefnet_checkpoint(checkpoint_path):
|
|
return checkpoint_path
|
|
return _download_birefnet_checkpoint(remote_model)
|
|
|
|
checkpoint_path = _find_local_birefnet_model(model_path)
|
|
if checkpoint_path:
|
|
return checkpoint_path
|
|
|
|
if model_path and model_path != "auto":
|
|
raise RuntimeError("The selected BiRefNet checkpoint is not available or is not compatible with ComfyUI.")
|
|
|
|
return _download_birefnet_checkpoint()
|
|
|
|
|
|
class BiRefNetMatting:
|
|
_model_cache = {}
|
|
_model_cache_lock = threading.Lock()
|
|
|
|
def __init__(self):
|
|
self.model = None
|
|
self.model_path = None
|
|
|
|
def load_model(self, model_path=None):
|
|
loader = _get_comfy_birefnet_loader()
|
|
if loader is None:
|
|
raise RuntimeError(
|
|
"This ComfyUI version does not provide the native BiRefNet background-removal loader."
|
|
)
|
|
|
|
checkpoint_path = _ensure_birefnet_checkpoint(model_path)
|
|
with self._model_cache_lock:
|
|
if checkpoint_path not in self._model_cache:
|
|
log.info(f"Loading BiRefNet with ComfyUI's native loader from {checkpoint_path}")
|
|
model = loader(checkpoint_path)
|
|
if model is None:
|
|
raise RuntimeError(
|
|
f"ComfyUI did not recognize the BiRefNet checkpoint: {checkpoint_path}"
|
|
)
|
|
self._model_cache[checkpoint_path] = model
|
|
else:
|
|
log.debug(f"Using cached native BiRefNet model from {checkpoint_path}")
|
|
|
|
self.model = self._model_cache[checkpoint_path]
|
|
self.model_path = checkpoint_path
|
|
|
|
return self.model
|
|
|
|
def preprocess_image(self, image):
|
|
if not isinstance(image, torch.Tensor):
|
|
image = transforms.ToTensor()(image).unsqueeze(0)
|
|
|
|
if image.dim() == 3:
|
|
if image.shape[0] in (1, 3, 4):
|
|
image = image.movedim(0, -1).unsqueeze(0)
|
|
else:
|
|
image = image.unsqueeze(0)
|
|
elif image.dim() == 4 and image.shape[1] in (1, 3, 4):
|
|
image = image.movedim(1, -1)
|
|
|
|
if image.dim() != 4 or image.shape[-1] not in (1, 3, 4):
|
|
raise ValueError(f"Expected an image tensor in BCHW or BHWC format, got {tuple(image.shape)}")
|
|
|
|
if image.shape[-1] == 1:
|
|
image = image.expand(-1, -1, -1, 3)
|
|
elif image.shape[-1] == 4:
|
|
image = image[..., :3]
|
|
|
|
return image.to(dtype=torch.float32).contiguous()
|
|
|
|
def execute(self, image, model_path, threshold=0.5, refinement=1, mode="remove_background"):
|
|
try:
|
|
PromptServer.instance.send_sync("matting_status", {"status": "processing"})
|
|
|
|
del refinement # Reserved for future edge refinement controls.
|
|
if mode not in {"remove_background", "remove_foreground", "mask_only"}:
|
|
raise ValueError(f"Unsupported matting mode: {mode}")
|
|
|
|
threshold = float(threshold)
|
|
if not 0.0 <= threshold <= 1.0:
|
|
raise ValueError("Matting threshold must be between 0 and 1")
|
|
|
|
image_tensor = self.preprocess_image(image)
|
|
original_size = (image_tensor.shape[1], image_tensor.shape[2])
|
|
log.debug(f"Original size: {original_size}")
|
|
self.load_model(model_path)
|
|
log.debug(f"Processed image shape: {image_tensor.shape}, dtype: {image_tensor.dtype}")
|
|
|
|
with torch.no_grad():
|
|
result = self.model.encode_image(image_tensor)
|
|
if result.dim() == 3:
|
|
result = result.unsqueeze(1)
|
|
elif result.dim() == 2:
|
|
result = result.unsqueeze(0).unsqueeze(0)
|
|
else:
|
|
raise ValueError(f"Unexpected BiRefNet output shape: {tuple(result.shape)}")
|
|
|
|
result = result.to(device=image_tensor.device, dtype=torch.float32)
|
|
if result.shape[-2:] != original_size:
|
|
result = F.interpolate(
|
|
result,
|
|
size=original_size,
|
|
mode="bilinear",
|
|
align_corners=False,
|
|
)
|
|
result = result.clamp(0.0, 1.0)
|
|
log.debug(f"Native BiRefNet output shape: {result.shape}, dtype: {result.dtype}")
|
|
|
|
if threshold > 0:
|
|
result = (result > threshold).to(dtype=torch.float32)
|
|
|
|
alpha_mask = 1.0 - result if mode == "remove_foreground" else result
|
|
if mode == "mask_only":
|
|
masked_image = alpha_mask.expand(-1, 3, -1, -1)
|
|
else:
|
|
masked_image = image_tensor.movedim(-1, 1) * alpha_mask
|
|
|
|
PromptServer.instance.send_sync("matting_status", {"status": "completed"})
|
|
|
|
return (masked_image, alpha_mask)
|
|
|
|
except Exception:
|
|
PromptServer.instance.send_sync("matting_status", {"status": "error"})
|
|
raise
|
|
|
|
@classmethod
|
|
def IS_CHANGED(cls, image, model_path, threshold, refinement, mode="remove_background"):
|
|
|
|
m = hashlib.md5()
|
|
m.update(str(image).encode())
|
|
m.update(str(model_path).encode())
|
|
m.update(str(threshold).encode())
|
|
m.update(str(refinement).encode())
|
|
m.update(str(mode).encode())
|
|
return m.hexdigest()
|
|
|
|
|
|
_matting_lock = None
|
|
|
|
@PromptServer.instance.routes.get("/matting/check-model")
|
|
async def check_matting_model(request):
|
|
"""Check if the matting model is available and ready to use"""
|
|
try:
|
|
if _get_comfy_birefnet_loader() is None:
|
|
return web.json_response({
|
|
"available": False,
|
|
"reason": "unsupported_comfyui",
|
|
"message": "This ComfyUI version does not provide the native BiRefNet background-removal loader."
|
|
})
|
|
|
|
model_options = _get_birefnet_model_options()
|
|
requested_model = request.query.get("model_path") or "auto"
|
|
remote_model = _get_birefnet_remote_model(requested_model)
|
|
|
|
if remote_model:
|
|
local_model_path = _get_birefnet_remote_checkpoint_path(remote_model)
|
|
if local_model_path and _is_native_birefnet_checkpoint(local_model_path):
|
|
log.info(f"Selected BiRefNet model is ready at {local_model_path}")
|
|
return web.json_response({
|
|
"available": True,
|
|
"reason": "ready",
|
|
"message": "Selected model is ready to use",
|
|
"model_path": local_model_path,
|
|
"selected_model": remote_model["label"],
|
|
"models": model_options,
|
|
})
|
|
|
|
log.info(f"Selected BiRefNet model is not downloaded: {remote_model['label']}")
|
|
return web.json_response({
|
|
"available": False,
|
|
"reason": "not_downloaded",
|
|
"message": f"{remote_model['label']} will be downloaded automatically on first use.",
|
|
"model_path": requested_model,
|
|
"selected_model": remote_model["label"],
|
|
"models": model_options,
|
|
})
|
|
|
|
if requested_model != "auto":
|
|
local_model_path = _find_local_birefnet_model(requested_model)
|
|
if local_model_path:
|
|
log.info(f"Selected BiRefNet model is ready at {local_model_path}")
|
|
return web.json_response({
|
|
"available": True,
|
|
"reason": "ready",
|
|
"message": "Selected model is ready to use",
|
|
"model_path": local_model_path,
|
|
"selected_model": local_model_path,
|
|
"models": model_options,
|
|
})
|
|
|
|
return web.json_response({
|
|
"available": False,
|
|
"reason": "selected_model_unavailable",
|
|
"message": "The selected BiRefNet checkpoint is not available or is not compatible with ComfyUI.",
|
|
"model_path": requested_model,
|
|
"models": model_options,
|
|
})
|
|
|
|
local_model_path = _find_local_birefnet_model()
|
|
|
|
if local_model_path:
|
|
log.info(f"BiRefNet model files detected at {local_model_path}")
|
|
return web.json_response({
|
|
"available": True,
|
|
"reason": "ready",
|
|
"message": "Model is ready to use",
|
|
"model_path": local_model_path,
|
|
"models": model_options,
|
|
})
|
|
|
|
searched_paths = _get_birefnet_base_paths()
|
|
log.info(f"BiRefNet model not found in any of: {searched_paths}")
|
|
return web.json_response({
|
|
"available": False,
|
|
"reason": "not_downloaded",
|
|
"message": "The BiRefNet checkpoint will be downloaded automatically on first use (requires internet connection).",
|
|
"model_path": searched_paths[0] if searched_paths else None,
|
|
"models": model_options,
|
|
})
|
|
|
|
except Exception as e:
|
|
log.error(f"Error checking matting model: {str(e)}")
|
|
return web.json_response({
|
|
"available": False,
|
|
"reason": "error",
|
|
"message": f"Error checking model status: {str(e)}"
|
|
}, status=500)
|
|
|
|
@PromptServer.instance.routes.post("/matting")
|
|
async def matting(request):
|
|
global _matting_lock
|
|
|
|
if _matting_lock is not None:
|
|
log.warning("Matting already in progress, rejecting request")
|
|
return web.json_response({
|
|
"error": "Another matting operation is in progress",
|
|
"details": "Please wait for the current operation to complete"
|
|
}, status=429)
|
|
|
|
_matting_lock = True
|
|
try:
|
|
log.info("Received matting request")
|
|
data = await request.json()
|
|
|
|
matting_instance = BiRefNetMatting()
|
|
|
|
image_tensor, original_alpha = convert_base64_to_tensor(data["image"])
|
|
log.debug(f"Input image shape: {image_tensor.shape}")
|
|
|
|
mode = data.get("mode", "remove_background")
|
|
model_path = data.get("model_path") or "auto"
|
|
|
|
matted_image, alpha_mask = matting_instance.execute(
|
|
image_tensor,
|
|
model_path,
|
|
threshold=data.get("threshold", 0.5),
|
|
refinement=data.get("refinement", 1),
|
|
mode=mode,
|
|
)
|
|
|
|
if mode == "mask_only":
|
|
result_image = convert_tensor_to_base64(alpha_mask)
|
|
else:
|
|
result_image = convert_tensor_to_base64(matted_image, alpha_mask, original_alpha)
|
|
result_mask = convert_tensor_to_base64(alpha_mask)
|
|
# LayerForge's Draw Mask uses white pixels as the area to remove,
|
|
# while the matting result uses white pixels as the area to keep.
|
|
draw_mask = 1.0 - alpha_mask
|
|
|
|
return web.json_response({
|
|
"matted_image": result_image,
|
|
"alpha_mask": result_mask,
|
|
"draw_mask": convert_tensor_to_base64(draw_mask),
|
|
"mode": mode,
|
|
"model_path": matting_instance.model_path,
|
|
})
|
|
|
|
except RuntimeError as e:
|
|
log.error(f"Runtime error during matting: {e}")
|
|
return web.json_response({
|
|
"error": "Matting Model Error",
|
|
"details": str(e)
|
|
}, status=500)
|
|
except Exception as e:
|
|
log.exception(f"Error in matting endpoint: {e}")
|
|
error_text = str(e).lower()
|
|
if any(
|
|
marker in error_text
|
|
for marker in ("offline", "connection", "timed out", "huggingface", "localentrynotfound")
|
|
):
|
|
return web.json_response({
|
|
"error": "Network Connection Error",
|
|
"details": "Failed to download the BiRefNet model from Hugging Face. Please check your internet connection."
|
|
}, status=400)
|
|
|
|
return web.json_response({
|
|
"error": "An unexpected error occurred",
|
|
"details": traceback.format_exc()
|
|
}, status=500)
|
|
finally:
|
|
_matting_lock = None
|
|
log.debug("Matting lock released")
|
|
|
|
|
|
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:
|
|
log.error(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:
|
|
log.error(f"Error in convert_tensor_to_base64: {str(e)}")
|
|
log.debug(f"Tensor shape: {tensor.shape}, dtype: {tensor.dtype}")
|
|
raise
|