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import os
import torch
from .model.cloth_masker import AutoMasker as AM
from .model.cloth_masker import vis_mask
from .model.pipeline import CatVTONPipeline
from .utils import resize_and_crop, resize_and_padding
from diffusers.image_processor import VaeImageProcessor
from huggingface_hub import snapshot_download
from PIL import Image
import numpy as np
from torchvision.transforms.functional import to_pil_image, to_tensor
class LoadCatVTONPipeline:
display_name = "Load CatVTON Pipeline"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"sd15_inpaint_path": ("STRING", {"default": "runwayml/stable-diffusion-inpainting"}),
"catvton_path": ("STRING", {"default": "zhengchong/CatVTON"}),
"mixed_precision": (["fp32", "fp16", "bf16"],),
}
}
RETURN_TYPES = ("MODEL",)
RETURN_NAMES = ("pipe",)
FUNCTION = "load"
CATEGORY = "CatVTON"
def load(self, sd15_inpaint_path, catvton_path, mixed_precision):
mixed_precision = {
"fp32": torch.float32,
"fp16": torch.float16,
"bf16": torch.bfloat16,
}[mixed_precision]
pipeline = CatVTONPipeline(
base_ckpt=sd15_inpaint_path,
attn_ckpt=catvton_path,
attn_ckpt_version="mix",
weight_dtype=mixed_precision,
use_tf32=True,
device='cuda'
)
return (pipeline,)
class LoadAutoMasker:
display_name = "Load AutoMask Generator"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"catvton_path": ("STRING", {"default": "zhengchong/CatVTON"}),
}
}
RETURN_TYPES = ("MODEL",)
RETURN_NAMES = ("pipe",)
FUNCTION = "load"
CATEGORY = "CatVTON"
def load(self, catvton_path):
catvton_path = snapshot_download(repo_id=catvton_path)
automasker = AM(
densepose_ckpt=os.path.join(catvton_path, "DensePose"),
schp_ckpt=os.path.join(catvton_path, "SCHP"),
device='cuda',
)
return (automasker,)
class CatVTON:
display_name = "TryOn by CatVTON"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"pipe": ("MODEL",),
"target_image": ("IMAGE",),
"refer_image": ("IMAGE",),
"mask_image": ("IMAGE",),
"seed": ("INT", {"default": 42, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
"steps": ("INT", {"default": 50, "min": 1, "max": 10000}),
"cfg": (
"FLOAT",
{
"default": 2.5,
"min": 0.0,
"max": 14.0,
"step": 0.1,
"round": 0.01,
},
),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "generate"
CATEGORY = "CatVTON"
def generate(
self, pipe: CatVTONPipeline, target_image, refer_image, mask_image, seed, steps, cfg
):
target_image, refer_image, mask_image = [_.squeeze(0).permute(2, 0, 1) for _ in [target_image, refer_image, mask_image]]
target_image = to_pil_image(target_image)
refer_image = to_pil_image(refer_image)
mask_image = mask_image[0]
mask_image = to_pil_image(mask_image)
generator = torch.Generator(device='cuda').manual_seed(seed)
person_image = resize_and_crop(target_image, (768, 1024))
cloth_image = resize_and_padding(refer_image, (768, 1024))
mask = resize_and_crop(mask_image, (768, 1024))
mask_processor = VaeImageProcessor(vae_scale_factor=8, do_normalize=False, do_binarize=True, do_convert_grayscale=True)
mask = mask_processor.blur(mask, blur_factor=9)
# Inference
result_image = pipe(
image=person_image,
condition_image=cloth_image,
mask=mask,
num_inference_steps=steps,
guidance_scale=cfg,
generator=generator
)[0]
result_image = to_tensor(result_image).permute(1, 2, 0).unsqueeze(0)
return (result_image,)
class AutoMasker:
display_name = "Auto Mask Generation"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"pipe": ("MODEL",),
"target_image": ("IMAGE",),
"cloth_type": (["upper", "lower", 'overall'],),
}
}
RETURN_TYPES = ("IMAGE", "IMAGE")
RETURN_NAMES = ("image", "image_masked")
FUNCTION = "generate"
CATEGORY = "CatVTON"
def generate(
self, pipe, target_image, cloth_type
):
target_image = target_image.squeeze(0).permute(2, 0, 1)
target_image = to_pil_image(target_image)
person_image = resize_and_crop(target_image, (768, 1024))
mask = pipe(
person_image,
cloth_type
)['mask']
masked_image = vis_mask(person_image, mask)
mask = to_tensor(mask).permute(1, 2, 0).repeat(1, 1, 3).unsqueeze(0)
masked_image = to_tensor(masked_image).permute(1, 2, 0).unsqueeze(0)
return (mask, masked_image)
_export_classes = [
LoadCatVTONPipeline,
LoadAutoMasker,
CatVTON,
AutoMasker,
]
NODE_CLASS_MAPPINGS = {c.__name__: c for c in _export_classes}
NODE_DISPLAY_NAME_MAPPINGS = {
c.__name__: getattr(c, "display_name", c.__name__) for c in _export_classes
} |