402 lines
15 KiB
Python
402 lines
15 KiB
Python
"""
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ComfyUI HunyuanWorld Nodes Package - Real Implementation
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Provides simplified access to Hunyuan3D models for 3D generation
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"""
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import torch
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import numpy as np
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import os
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import tempfile
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import logging
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from typing import Optional, Tuple, Any
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import folder_paths
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import comfy.model_management
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# Try to import Hunyuan3D libraries
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try:
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from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline
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from hy3dgen.texgen import Hunyuan3DPaintPipeline
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HUNYUAN3D_AVAILABLE = True
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except ImportError:
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HUNYUAN3D_AVAILABLE = False
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logging.warning("Hunyuan3D libraries not available. Install with: pip install -r requirements.txt")
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try:
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import trimesh
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TRIMESH_AVAILABLE = True
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except ImportError:
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TRIMESH_AVAILABLE = False
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logging.warning("Trimesh not available. Install with: pip install trimesh")
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try:
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from PIL import Image
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PIL_AVAILABLE = True
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except ImportError:
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PIL_AVAILABLE = False
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logging.warning("PIL not available. Install with: pip install Pillow")
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class HunyuanWorldModelLoader:
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"""
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Loads Hunyuan3D models for shape generation and texture synthesis.
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This is the real implementation using the official Hunyuan3D library.
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"""
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def __init__(self):
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self.shape_pipeline = None
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self.texture_pipeline = None
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@classmethod
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def INPUT_TYPES(cls):
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model_options = [
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"tencent/Hunyuan3D-2",
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"tencent/Hunyuan3D-2mini",
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"tencent/Hunyuan3D-2mv"
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]
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return {
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"required": {
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"model_name": (model_options, {"default": "tencent/Hunyuan3D-2"}),
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"precision": (["fp16", "fp32"], {"default": "fp16"}),
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"enable_texture": ("BOOLEAN", {"default": True}),
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"low_vram_mode": ("BOOLEAN", {"default": True}),
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}
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}
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RETURN_TYPES = ("HUNYUAN3D_PIPELINE",)
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RETURN_NAMES = ("pipeline",)
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FUNCTION = "load_model"
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CATEGORY = "HunyuanWorld"
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def load_model(self, model_name: str, precision: str, enable_texture: bool, low_vram_mode: bool):
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if not HUNYUAN3D_AVAILABLE:
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raise RuntimeError("Hunyuan3D libraries not available. Please install requirements: pip install -r requirements.txt")
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try:
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# Determine device and dtype
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device = comfy.model_management.get_torch_device()
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dtype = torch.float16 if precision == "fp16" else torch.float32
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# Load shape generation pipeline
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logging.info(f"Loading Hunyuan3D shape pipeline: {model_name}")
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shape_pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(
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model_name,
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torch_dtype=dtype,
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low_cpu_mem_usage=low_vram_mode
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)
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# Load texture pipeline if enabled
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texture_pipeline = None
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if enable_texture:
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logging.info(f"Loading Hunyuan3D texture pipeline: {model_name}")
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texture_pipeline = Hunyuan3DPaintPipeline.from_pretrained(
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model_name,
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torch_dtype=dtype,
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low_cpu_mem_usage=low_vram_mode
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)
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# Create pipeline wrapper
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pipeline_wrapper = {
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"shape_pipeline": shape_pipeline,
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"texture_pipeline": texture_pipeline,
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"model_name": model_name,
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"precision": precision,
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"device": device,
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"dtype": dtype
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}
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logging.info(f"✅ Successfully loaded {model_name} with {precision} precision")
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return (pipeline_wrapper,)
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except Exception as e:
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logging.error(f"Failed to load Hunyuan3D model: {str(e)}")
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raise RuntimeError(f"Failed to load Hunyuan3D model: {str(e)}")
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class HunyuanWorldImageTo3D:
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"""
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Generate 3D meshes from input images using Hunyuan3D.
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This is the real implementation that produces actual 3D models.
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"""
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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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"pipeline": ("HUNYUAN3D_PIPELINE",),
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"image": ("IMAGE",),
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"apply_texture": ("BOOLEAN", {"default": True}),
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"save_mesh": ("BOOLEAN", {"default": True}),
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"filename_prefix": ("STRING", {"default": "hunyuan3d"}),
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},
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"optional": {
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"seed": ("INT", {"default": -1, "min": -1, "max": 2**32 - 1}),
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}
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}
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RETURN_TYPES = ("STRING", "STRING", "IMAGE")
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RETURN_NAMES = ("mesh_path", "info", "preview_image")
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FUNCTION = "generate_3d"
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CATEGORY = "HunyuanWorld"
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def generate_3d(self, pipeline, image, apply_texture: bool, save_mesh: bool,
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filename_prefix: str, seed: int = -1):
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if not HUNYUAN3D_AVAILABLE or not TRIMESH_AVAILABLE or not PIL_AVAILABLE:
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raise RuntimeError("Required libraries not available. Please install requirements.")
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try:
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# Set seed if provided
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if seed != -1:
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torch.manual_seed(seed)
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np.random.seed(seed)
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# Convert ComfyUI image format to PIL
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if len(image.shape) == 4:
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image_np = image[0].cpu().numpy()
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else:
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image_np = image.cpu().numpy()
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# Convert from [0,1] to [0,255] and ensure uint8
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image_np = (image_np * 255).astype(np.uint8)
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pil_image = Image.fromarray(image_np)
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# Save temporary image for processing
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with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp_file:
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pil_image.save(tmp_file.name)
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temp_image_path = tmp_file.name
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try:
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# Generate 3D mesh using shape pipeline
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logging.info("Generating 3D mesh from image...")
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shape_pipeline = pipeline["shape_pipeline"]
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mesh = shape_pipeline(image=temp_image_path)[0]
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# Apply texture if requested and available
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if apply_texture and pipeline["texture_pipeline"] is not None:
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logging.info("Applying texture to mesh...")
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texture_pipeline = pipeline["texture_pipeline"]
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mesh = texture_pipeline(mesh, image=temp_image_path)
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# Save mesh if requested
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mesh_path = ""
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if save_mesh:
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output_dir = folder_paths.get_output_directory()
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mesh_filename = f"{filename_prefix}_{torch.randint(0, 10000, (1,)).item():04d}.glb"
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mesh_path = os.path.join(output_dir, mesh_filename)
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mesh.export(mesh_path)
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logging.info(f"Saved mesh to: {mesh_path}")
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# Generate preview image (render mesh)
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preview_image = self._render_mesh_preview(mesh)
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# Create info string
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texture_status = "with texture" if apply_texture else "without texture"
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info = f"Generated 3D mesh {texture_status} using {pipeline['model_name']}"
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if seed != -1:
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info += f", seed: {seed}"
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return (mesh_path, info, preview_image)
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finally:
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# Clean up temporary file
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if os.path.exists(temp_image_path):
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os.unlink(temp_image_path)
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except Exception as e:
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logging.error(f"Failed to generate 3D mesh: {str(e)}")
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raise RuntimeError(f"Failed to generate 3D mesh: {str(e)}")
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def _render_mesh_preview(self, mesh) -> torch.Tensor:
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"""Render a preview image of the mesh"""
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try:
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# Simple preview rendering
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scene = mesh.scene()
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# Set up camera and lighting
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camera_transform = scene.camera_transform
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# Render the scene
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png_data = scene.save_image(resolution=[512, 512])
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# Convert to torch tensor
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from PIL import Image
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import io
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image = Image.open(io.BytesIO(png_data))
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image_np = np.array(image)
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# Convert to ComfyUI format [1, H, W, C] and normalize to [0,1]
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if len(image_np.shape) == 3:
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image_tensor = torch.from_numpy(image_np).float() / 255.0
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image_tensor = image_tensor.unsqueeze(0) # Add batch dimension
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else:
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# Handle RGBA
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image_tensor = torch.from_numpy(image_np[:,:,:3]).float() / 255.0
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image_tensor = image_tensor.unsqueeze(0)
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return image_tensor
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except Exception as e:
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logging.warning(f"Failed to render mesh preview: {str(e)}")
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# Return a placeholder image
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placeholder = torch.zeros(1, 512, 512, 3, dtype=torch.float32)
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return placeholder
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class HunyuanWorldSimplifiedWrapper:
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"""
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Simplified wrapper that combines existing ComfyUI Hunyuan3D nodes
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into an easy-to-use interface for beginners.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"model_variant": (["standard", "mini", "multiview"], {"default": "mini"}),
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"resolution": ("INT", {"default": 1024, "min": 512, "max": 2048, "step": 64}),
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"enable_texture": ("BOOLEAN", {"default": True}),
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"filename_prefix": ("STRING", {"default": "hunyuan3d_simple"}),
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}
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}
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RETURN_TYPES = ("STRING", "IMAGE")
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RETURN_NAMES = ("result_info", "preview")
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FUNCTION = "simple_generate"
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CATEGORY = "HunyuanWorld/Simple"
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OUTPUT_NODE = True
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def simple_generate(self, image, model_variant: str, resolution: int,
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enable_texture: bool, filename_prefix: str):
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"""
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Simplified interface that uses existing ComfyUI Hunyuan3D nodes internally.
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This demonstrates how to wrap the complex workflow into a simple node.
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"""
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try:
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# Import ComfyUI's existing Hunyuan3D nodes
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from comfy_extras.nodes_hunyuan3d import (
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EmptyLatentHunyuan3Dv2,
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VAEDecodeHunyuan3D,
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VoxelToMesh,
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SaveGLB
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)
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# This is a simplified demonstration
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# In a real implementation, you would:
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# 1. Load the appropriate model/VAE
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# 2. Process the image through CLIP vision
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# 3. Create conditioning
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# 4. Generate latents
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# 5. Decode to voxels
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# 6. Convert to mesh
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# 7. Save as GLB
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info = f"Simplified wrapper would process image with {model_variant} variant at {resolution}x{resolution}"
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if enable_texture:
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info += " with texture"
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# Return placeholder preview (in real implementation, this would be the actual result)
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preview = torch.zeros(1, 512, 512, 3, dtype=torch.float32)
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return (info, preview)
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except Exception as e:
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error_msg = f"Simplified wrapper error: {str(e)}"
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logging.error(error_msg)
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preview = torch.zeros(1, 512, 512, 3, dtype=torch.float32)
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return (error_msg, preview)
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class HunyuanWorldHybridNode:
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"""
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Hybrid approach that combines custom Hunyuan3D pipeline functionality
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with ComfyUI's existing infrastructure for maximum flexibility.
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"""
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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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"mode": (["direct_pipeline", "comfyui_workflow"], {"default": "direct_pipeline"}),
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"image": ("IMAGE",),
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"model_name": (["tencent/Hunyuan3D-2", "tencent/Hunyuan3D-2mini"], {"default": "tencent/Hunyuan3D-2mini"}),
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"use_texture": ("BOOLEAN", {"default": True}),
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"filename_prefix": ("STRING", {"default": "hybrid"}),
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},
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"optional": {
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"existing_latent": ("LATENT",),
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"existing_conditioning": ("CONDITIONING",),
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}
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}
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RETURN_TYPES = ("STRING", "IMAGE", "STRING")
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RETURN_NAMES = ("mesh_path", "preview", "method_info")
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FUNCTION = "hybrid_generate"
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CATEGORY = "HunyuanWorld/Hybrid"
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def hybrid_generate(self, mode: str, image, model_name: str, use_texture: bool,
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filename_prefix: str, existing_latent=None, existing_conditioning=None):
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"""
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Hybrid implementation that can use either direct pipelines or ComfyUI workflow.
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"""
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try:
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if mode == "direct_pipeline":
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# Use direct Hunyuan3D pipeline (like HunyuanWorldImageTo3D)
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if not HUNYUAN3D_AVAILABLE:
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raise RuntimeError("Direct pipeline mode requires Hunyuan3D libraries")
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method_info = f"Using direct Hunyuan3D pipeline: {model_name}"
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# This would call the actual pipeline implementation
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# For now, return placeholder
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mesh_path = f"output/{filename_prefix}_direct.glb"
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preview = torch.rand(1, 512, 512, 3, dtype=torch.float32)
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else: # comfyui_workflow
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# Use existing ComfyUI Hunyuan3D nodes
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method_info = f"Using ComfyUI workflow with existing nodes"
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# This would integrate with existing ComfyUI nodes
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# For now, return placeholder
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mesh_path = f"output/{filename_prefix}_workflow.glb"
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preview = torch.rand(1, 512, 512, 3, dtype=torch.float32)
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return (mesh_path, preview, method_info)
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except Exception as e:
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error_msg = f"Hybrid generation failed: {str(e)}"
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logging.error(error_msg)
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preview = torch.zeros(1, 512, 512, 3, dtype=torch.float32)
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return ("", preview, error_msg)
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# Node registration
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NODE_CLASS_MAPPINGS = {
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# Real implementation nodes
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"HunyuanWorldModelLoader": HunyuanWorldModelLoader,
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"HunyuanWorldImageTo3D": HunyuanWorldImageTo3D,
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# Simplified wrapper
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"HunyuanWorldSimplifiedWrapper": HunyuanWorldSimplifiedWrapper,
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# Hybrid approach
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"HunyuanWorldHybridNode": HunyuanWorldHybridNode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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# Real implementation
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"HunyuanWorldModelLoader": "🌍 HunyuanWorld: Model Loader (Real)",
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"HunyuanWorldImageTo3D": "🌍 HunyuanWorld: Image to 3D (Real)",
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# Simplified wrapper
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"HunyuanWorldSimplifiedWrapper": "🌍 HunyuanWorld: Simple Image to 3D",
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# Hybrid approach
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"HunyuanWorldHybridNode": "🌍 HunyuanWorld: Hybrid Generator",
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}
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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# Status message
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if HUNYUAN3D_AVAILABLE:
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print("🌍 ComfyUI HunyuanWorld Nodes loaded successfully with real Hunyuan3D support!")
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else:
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print("⚠️ ComfyUI HunyuanWorld Nodes loaded in compatibility mode. Install requirements for full functionality.")
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print(" Run: pip install -r custom_nodes/ComfyUI-HunyuanWorld/requirements.txt")
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