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# This custom node for ComfyUI provides functionality for background removal using various models,
# including RMBG-2.0, INSPYRENET, and BEN. It leverages deep learning techniques
# to process images and generate masks for background removal.
#
# This integration script follows GPL-3.0 License.
# When using or modifying this code, please respect both the original model licenses
# and this integration's license terms.
#
# Source: https://github.com/AILab-AI/ComfyUI-RMBG
import os
import torch
import torch.nn as nn
import numpy as np
from typing import Tuple, Union
from PIL import Image, ImageFilter
from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
import folder_paths
from huggingface_hub import hf_hub_download
import shutil
from torchvision import transforms
def pil2tensor(image: Image.Image) -> torch.Tensor:
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0)[None,]
def tensor2pil(image: torch.Tensor) -> Image.Image:
return Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8))
def image2mask(image: Image.Image) -> torch.Tensor:
if isinstance(image, Image.Image):
image = pil2tensor(image)
return image.squeeze()[..., 0]
def mask2image(mask: torch.Tensor) -> Image.Image:
if len(mask.shape) == 2:
mask = mask.unsqueeze(0)
return tensor2pil(mask)
def RGB2RGBA(image: Image.Image, mask: Union[Image.Image, torch.Tensor]) -> Image.Image:
if isinstance(mask, torch.Tensor):
mask = mask2image(mask)
if mask.size != image.size:
mask = mask.resize(image.size, Image.Resampling.LANCZOS)
return Image.merge('RGBA', (*image.convert('RGB').split(), mask.convert('L')))
device = "cuda" if torch.cuda.is_available() else "cpu"
folder_paths.add_model_folder_path("rmbg", os.path.join(folder_paths.models_dir, "RMBG"))
AVAILABLE_MODELS = {
"segformer_b2_clothes": "1038lab/segformer_clothes"
}
class ClothesSegment:
def __init__(self):
self.processor = None
self.model = None
self.cache_dir = os.path.join(folder_paths.models_dir, "RMBG", "segformer_clothes")
@classmethod
def INPUT_TYPES(cls):
available_classes = ["Hat", "Hair", "Face", "Sunglasses", "Upper-clothes", "Skirt", "Dress", "Belt", "Pants", "Left-arm", "Right-arm", "Left-leg", "Right-leg", "Bag", "Scarf", "Left-shoe", "Right-shoe","Background"]
tooltips = {
"process_res": "Processing resolution (higher = more VRAM)",
"mask_blur": "Blur amount for mask edges",
"mask_offset": "Expand/Shrink mask boundary",
"invert_output": "Invert both image and mask output",
"background": "Choose background type: Alpha (transparent) or Color (custom background color).",
"background_color": "Choose background color (Alpha = transparent)"
}
return {
"required": {
"images": ("IMAGE",),
},
"optional": {
**{cls_name: ("BOOLEAN", {"default": False})
for cls_name in available_classes},
"process_res": ("INT", {"default": 512, "min": 128, "max": 2048, "step": 32, "tooltip": tooltips["process_res"]}),
"mask_blur": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1, "tooltip": tooltips["mask_blur"]}),
"mask_offset": ("INT", {"default": 0, "min": -64, "max": 64, "step": 1, "tooltip": tooltips["mask_offset"]}),
"invert_output": ("BOOLEAN", {"default": False, "tooltip": tooltips["invert_output"]}),
"background": (["Alpha", "Color"], {"default": "Alpha", "tooltip": tooltips["background"]}),
"background_color": ("COLORCODE", {"default": "#222222", "tooltip": tooltips["background_color"]}),
},
}
RETURN_TYPES = ("IMAGE", "MASK", "IMAGE")
RETURN_NAMES = ("IMAGE", "MASK", "MASK_IMAGE")
FUNCTION = "segment_clothes"
CATEGORY = "🧪AILab/🧽RMBG"
def check_model_cache(self):
if not os.path.exists(self.cache_dir):
return False, "Model directory not found"
required_files = [
'config.json',
'model.safetensors',
'preprocessor_config.json'
]
missing_files = [f for f in required_files if not os.path.exists(os.path.join(self.cache_dir, f))]
if missing_files:
return False, f"Required model files missing: {', '.join(missing_files)}"
return True, "Model cache verified"
def clear_model(self):
if self.model is not None:
self.model.cpu()
del self.model
self.model = None
self.processor = None
torch.cuda.empty_cache()
def download_model_files(self):
model_id = AVAILABLE_MODELS["segformer_b2_clothes"]
model_files = {
'config.json': 'config.json',
'model.safetensors': 'model.safetensors',
'preprocessor_config.json': 'preprocessor_config.json'
}
os.makedirs(self.cache_dir, exist_ok=True)
print(f"Downloading Clothes Segformer model files...")
try:
for save_name, repo_path in model_files.items():
print(f"Downloading {save_name}...")
downloaded_path = hf_hub_download(
repo_id=model_id,
filename=repo_path,
local_dir=self.cache_dir,
local_dir_use_symlinks=False
)
if os.path.dirname(downloaded_path) != self.cache_dir:
target_path = os.path.join(self.cache_dir, save_name)
shutil.move(downloaded_path, target_path)
return True, "Model files downloaded successfully"
except Exception as e:
return False, f"Error downloading model files: {str(e)}"
def segment_clothes(self, images, process_res=1024, mask_blur=0, mask_offset=0, background="Alpha", background_color="#222222", invert_output=False, **class_selections):
try:
# Check and download model if needed
cache_status, message = self.check_model_cache()
if not cache_status:
print(f"Cache check: {message}")
download_status, download_message = self.download_model_files()
if not download_status:
raise RuntimeError(download_message)
# Load model if needed
if self.processor is None:
self.processor = SegformerImageProcessor.from_pretrained(self.cache_dir)
self.model = AutoModelForSemanticSegmentation.from_pretrained(self.cache_dir)
self.model.eval()
for param in self.model.parameters():
param.requires_grad = False
self.model.to(device)
# Class mapping for segmentation
class_map = {
"Background": 0, "Hat": 1, "Hair": 2, "Sunglasses": 3,
"Upper-clothes": 4, "Skirt": 5, "Pants": 6, "Dress": 7,
"Belt": 8, "Left-shoe": 9, "Right-shoe": 10, "Face": 11,
"Left-leg": 12, "Right-leg": 13, "Left-arm": 14, "Right-arm": 15,
"Bag": 16, "Scarf": 17
}
# Get selected classes
selected_classes = [name for name, selected in class_selections.items() if selected]
if not selected_classes:
selected_classes = ["Upper-clothes"]
# Image preprocessing
transform_image = transforms.Compose([
transforms.Resize((process_res, process_res)),
transforms.ToTensor(),
])
batch_tensor = []
batch_masks = []
for image in images:
orig_image = tensor2pil(image)
w, h = orig_image.size
input_tensor = transform_image(orig_image)
if input_tensor.shape[0] == 4:
input_tensor = input_tensor[:3]
input_tensor = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(input_tensor)
input_tensor = input_tensor.unsqueeze(0).to(device)
with torch.no_grad():
outputs = self.model(input_tensor)
logits = outputs.logits.cpu()
upsampled_logits = nn.functional.interpolate(
logits,
size=(h, w),
mode="bilinear",
align_corners=False,
)
pred_seg = upsampled_logits.argmax(dim=1)[0]
# Combine selected class masks
combined_mask = None
for class_name in selected_classes:
mask = (pred_seg == class_map[class_name]).float()
if combined_mask is None:
combined_mask = mask
else:
combined_mask = torch.clamp(combined_mask + mask, 0, 1)
# Convert mask to PIL for processing
mask_image = Image.fromarray((combined_mask.numpy() * 255).astype(np.uint8))
if mask_blur > 0:
mask_image = mask_image.filter(ImageFilter.GaussianBlur(radius=mask_blur))
if mask_offset != 0:
if mask_offset > 0:
mask_image = mask_image.filter(ImageFilter.MaxFilter(size=mask_offset * 2 + 1))
else:
mask_image = mask_image.filter(ImageFilter.MinFilter(size=-mask_offset * 2 + 1))
if invert_output:
mask_image = Image.fromarray(255 - np.array(mask_image))
# Handle background color
if background == "Alpha":
rgba_image = RGB2RGBA(orig_image, mask_image)
result_image = pil2tensor(rgba_image)
else:
def hex_to_rgba(hex_color):
hex_color = hex_color.lstrip('#')
if len(hex_color) == 6:
r, g, b = int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16)
a = 255
elif len(hex_color) == 8:
r, g, b, a = int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16), int(hex_color[6:8], 16)
else:
raise ValueError("Invalid color format")
return (r, g, b, a)
rgba_image = RGB2RGBA(orig_image, mask_image)
rgba = hex_to_rgba(background_color)
bg_image = Image.new('RGBA', orig_image.size, rgba)
composite_image = Image.alpha_composite(bg_image, rgba_image)
result_image = pil2tensor(composite_image.convert('RGB'))
batch_tensor.append(result_image)
batch_masks.append(pil2tensor(mask_image))
# Create mask image for visualization
mask_images = []
for mask_tensor in batch_masks:
# Convert mask to RGB image format for visualization
mask_image = mask_tensor.reshape((-1, 1, mask_tensor.shape[-2], mask_tensor.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
mask_images.append(mask_image)
mask_image_output = torch.cat(mask_images, dim=0)
# Prepare final output
batch_tensor = torch.cat(batch_tensor, dim=0)
batch_masks = torch.cat(batch_masks, dim=0)
return (batch_tensor, batch_masks, mask_image_output)
except Exception as e:
self.clear_model()
raise RuntimeError(f"Error in Clothes Segformer processing: {str(e)}")
finally:
if not getattr(self.model, "training", False):
self.clear_model()
NODE_CLASS_MAPPINGS = {
"ClothesSegment": ClothesSegment
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ClothesSegment": "Clothes Segment (RMBG)"
} |