custom_nodes/ComfyUI-IF_Trellis/IF_Trellis.py

# IF_Trellis.py
import os
import torch
import imageio
import numpy as np
import logging
import traceback
from PIL import Image
import folder_paths
from typing import List, Union, Tuple, Literal, Optional, Dict
from easydict import EasyDict as edict
import gc
import comfy.model_management
import trimesh
import trimesh.exchange.export

from trellis.pipelines import TrellisImageTo3DPipeline
from trellis.utils import render_utils, postprocessing_utils
from trellis.representations import Gaussian, MeshExtractResult

logger = logging.getLogger("IF_Trellis")

def get_subpath_after_dir(full_path: str, target_dir: str) -> str:
    try:
        full_path = os.path.normpath(full_path)
        full_path = full_path.replace('\\', '/')
        path_parts = full_path.split('/')
        try:
            index = path_parts.index(target_dir)
            subpath = '/'.join(path_parts[index + 1:])
            return subpath
        except ValueError:
            return path_parts[-1]
    except Exception as e:
        print(f"Error processing path in get_subpath_after_dir: {str(e)}")
        return os.path.basename(full_path)

class IF_TrellisImageTo3D:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model": ("TRELLIS_MODEL",),
                "mode": (["single", "multi"], {"default": "single", "tooltip": "Mode. single is a single image. with multi you can provide multiple reference angles for the 3D model"}),
                "images": ("IMAGE", {"list": True}),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0x7FFFFFFF}),
                "ss_guidance_strength": ("FLOAT", {"default": 7.5, "min": 0.0, "max": 12.0, "step": 0.1}),
                "ss_sampling_steps": ("INT", {"default": 12, "min": 1, "max": 100}),
                "slat_guidance_strength": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 12.0, "step": 0.1}),
                "slat_sampling_steps": ("INT", {"default": 12, "min": 1, "max": 100}),
                "mesh_simplify": ("FLOAT", {"default": 0.95, "min": 0.9, "max": 1.0, "step": 0.01, "tooltip": "Simplify the mesh. the lower the value more polygons the mesh will have"}),
                "texture_size": ("INT", {"default": 1024, "min": 512, "max": 2048, "step": 512, "tooltip": "Texture size. the higher the value the more detailed the texture will be"}),
                "texture_mode": (["blank", "fast", "opt"], {"default": "fast", "tooltip": "Texture mode. blank is no texture. fast is a fast texture. opt is a high quality texture"}),
                "fps": ("INT", {"default": 15, "min": 1, "max": 60, "tooltip": "FPS. the higher the value the smoother the video will be"}),
                "multimode": (["stochastic", "multidiffusion"], {"default": "stochastic"}),
                "project_name": ("STRING", {"default": "trellis_output"}),
                "save_glb": ("BOOLEAN", {"default": True, "tooltip": "Save the GLB file this is the 3D model"}),
                "render_video": ("BOOLEAN", {"default": False, "tooltip": "Render a video"}),
                "save_gaussian": ("BOOLEAN", {"default": False, "tooltip": "Save the Gaussian file this is a ply file of the 3D model"}),
                "save_texture": ("BOOLEAN", {"default": False, "tooltip": "Save the texture file"}),
                "save_wireframe": ("BOOLEAN", {"default": False, "tooltip": "Save the wireframe file"}),
            },
            "optional": {
                "masks": ("MASK", {"list": True}),
            }
        }

    RETURN_TYPES = ("STRING", "STRING", "IMAGE")
    RETURN_NAMES = ("model_file", "video_path", "texture_image")
    FUNCTION = "image_to_3d"
    CATEGORY = "ImpactFrames💥🎞️/Trellis"
    OUTPUT_NODE = True

    def __init__(self, vertices=None, faces=None, uvs=None, face_uvs=None, albedo=None):
        self.logger = logger
        self.output_dir = folder_paths.get_output_directory()
        self.temp_dir = folder_paths.get_temp_directory()
        self.device = None
        self.vertices = vertices
        self.faces = faces
        self.uvs = uvs
        self.face_uvs = face_uvs
        self.albedo = albedo
        self.normals = None

    def torch_to_pil_batch(self, images: Union[torch.Tensor, List[torch.Tensor]],

                          masks: Optional[torch.Tensor] = None,

                          alpha_min: float = 0.1) -> List[Image.Image]:
        if isinstance(images, list):
            processed_tensors = []
            for img in images:
                if img.ndim == 3:
                    processed_tensors.append(img)
                elif img.ndim == 4:
                    processed_tensors.extend([t for t in img])
            images = torch.stack(processed_tensors, dim=0)

        logger.info(f"torch_to_pil_batch input shape: {images.shape}")
        if images.ndim == 3:
            images = images.unsqueeze(0)

        if images.shape[-1] != 3:
            if images.shape[1] == 3:
                images = images.permute(0, 2, 3, 1)

        processed_images = []
        for i in range(images.shape[0]):
            img = images[i].detach().cpu()
            if masks is not None:
                if isinstance(masks, torch.Tensor):
                    mask = masks[i] if i < masks.shape[0] else masks[0]
                    if mask.ndim > 2:
                        mask = mask.squeeze()
                    if mask.shape != img.shape[:2]:
                        import torch.nn.functional as F
                        mask = F.interpolate(
                            mask.unsqueeze(0).unsqueeze(0),
                            size=img.shape[:2],
                            mode='bilinear',
                            align_corners=False
                        ).squeeze()
                    if torch.any(mask > alpha_min):
                        mask = mask.to(dtype=img.dtype)
                        mask = mask.unsqueeze(-1) if mask.ndim == 2 else mask
                        img = torch.cat([img, mask], dim=-1)
                        mode = "RGBA"
                    else:
                        mode = "RGB"
                else:
                    mode = "RGB"
            else:
                mode = "RGB"
            img_np = (img.numpy() * 255).astype(np.uint8)
            processed_images.append(Image.fromarray(img_np, mode=mode))
            logger.info(f"Processed image {i}, shape: {img_np.shape}, mode: {mode}")

        return processed_images

    def pack_state(self, gaussian, mesh) -> Dict[str, Dict[str, np.ndarray]]:
        return {
            'gaussian': {
                **gaussian.init_params,
                '_xyz': gaussian._xyz.cpu().numpy(),
                '_features_dc': gaussian._features_dc.cpu().numpy(),
                '_scaling': gaussian._scaling.cpu().numpy(),
                '_rotation': gaussian._rotation.cpu().numpy(),
                '_opacity': gaussian._opacity.cpu().numpy(),
            },
            'mesh': {
                'vertices': mesh.vertices.cpu().numpy(),
                'faces': mesh.faces.cpu().numpy(),
            },
        }

    def unpack_state(self, state: dict) -> Tuple[Gaussian, MeshExtractResult]:
        gaussian = Gaussian(
            aabb=state['gaussian']['aabb'],
            sh_degree=state['gaussian']['sh_degree'],
            mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
            scaling_bias=state['gaussian']['scaling_bias'],
            opacity_bias=state['gaussian']['opacity_bias'],
            scaling_activation=state['gaussian']['scaling_activation'],
        )
        gaussian._xyz = torch.tensor(state['gaussian']['_xyz'], device=self.device)
        gaussian._features_dc = torch.tensor(state['gaussian']['_features_dc'], device=self.device)
        gaussian._scaling = torch.tensor(state['gaussian']['_scaling'], device=self.device)
        gaussian._rotation = torch.tensor(state['gaussian']['_rotation'], device=self.device)
        gaussian._opacity = torch.tensor(state['gaussian']['_opacity'], device=self.device)

        mesh = edict(
            vertices=torch.tensor(state['mesh']['vertices'], device=self.device),
            faces=torch.tensor(state['mesh']['faces'], device=self.device),
        )
        return gaussian, mesh

    def generate_outputs(self, outputs, project_name, fps=15, render_video=True, save_glb=True):
        out_dir = os.path.join(self.output_dir, project_name)
        os.makedirs(out_dir, exist_ok=True)

        video_path = glb_path = ""
        texture_path = wireframe_path = ""
        texture_image = wireframe_image = None

        # Extract the first (and usually only) result
        gaussian_output = outputs['gaussian'][0]
        mesh_output = outputs['mesh'][0]

        if render_video:
            video_gs = render_utils.render_video(gaussian_output)['color']
            video_mesh = render_utils.render_video(mesh_output)['normal']
            video = [np.concatenate([frame_gs, frame_mesh], axis=1)
                     for frame_gs, frame_mesh in zip(video_gs, video_mesh)]
            video_path = os.path.join(out_dir, f"{project_name}_preview.mp4")
            imageio.mimsave(video_path, video, fps=fps)
            full_video_path = os.path.abspath(video_path)
            video_path = get_subpath_after_dir(full_video_path, "output")
            logger.info(f"Full video path: {full_video_path}, Processed video path: {video_path}")

        if save_glb:
            texture_path = os.path.join(out_dir, f"{project_name}_texture.png") if self.save_texture else None
            wireframe_path = os.path.join(out_dir, f"{project_name}_wireframe.png") if self.save_wireframe else None
            glb_path = os.path.join(out_dir, f"{project_name}.glb")

            glb = postprocessing_utils.to_glb(
                gaussian_output,
                mesh_output,
                simplify=self.mesh_simplify,
                texture_size=self.texture_size,
                texture_mode=self.texture_mode,
                fill_holes=True,
                save_texture=self.save_texture and self.texture_mode != 'blank',
                texture_path=texture_path,
                save_wireframe=self.save_wireframe and self.texture_mode != 'blank',
                wireframe_path=wireframe_path,
                verbose=True
            )
            glb.export(glb_path)
            glb_path = get_subpath_after_dir(glb_path, "output")
            full_glb_path = os.path.abspath(glb_path)
            logger.info(f"Full GLB path: {full_glb_path}, Processed GLB path: {glb_path}")

            # Handle texture image creation
            if self.save_texture and self.texture_mode != 'blank' and texture_path and os.path.exists(texture_path):
                try:
                    texture_image = Image.open(texture_path).convert('RGB')
                    texture_image = np.array(texture_image)
                except Exception as e:
                    logger.warning(f"Failed to load texture image: {str(e)}")
                    texture_image = np.zeros((self.texture_size, self.texture_size, 3), dtype=np.uint8)
            else:
                # Create a blank texture if not saving or if texture mode is blank
                texture_image = np.zeros((self.texture_size, self.texture_size, 3), dtype=np.uint8)

            # Handle wireframe image
            if wireframe_path and os.path.exists(wireframe_path):
                wireframe_image = Image.open(wireframe_path).convert('RGB')
                wireframe_image = np.array(wireframe_image)
            else:
                wireframe_image = None

        # Clean up the large tensors after we're done using them
        del gaussian_output
        del mesh_output
        torch.cuda.empty_cache()
        
        logger.info(f"Texture image shape: {texture_image.shape}")

        return video_path, glb_path, texture_path, wireframe_path, texture_image, wireframe_image

    def get_pipeline_params(self, seed, ss_sampling_steps, ss_guidance_strength,

                            slat_sampling_steps, slat_guidance_strength):
        if ss_sampling_steps < 1:
            raise ValueError("ss_sampling_steps must be >= 1")
        if slat_sampling_steps < 1:
            raise ValueError("slat_sampling_steps must be >= 1")
        if ss_guidance_strength < 0:
            raise ValueError("ss_guidance_strength must be >= 0")
        if slat_guidance_strength < 0:
            raise ValueError("slat_guidance_strength must be >= 0")

        return {
            "seed": seed,
            "formats": ["gaussian", "mesh"],
            "preprocess_image": True,
            "sparse_structure_sampler_params": {
                "steps": ss_sampling_steps,
                "cfg_strength": ss_guidance_strength,
            },
            "slat_sampler_params": {
                "steps": slat_sampling_steps,
                "cfg_strength": slat_guidance_strength,
            }
        }

    @torch.inference_mode()
    def image_to_3d(

        self,

        model: TrellisImageTo3DPipeline,

        mode: str,

        images: torch.Tensor,

        seed: int,

        ss_guidance_strength: float,

        ss_sampling_steps: int,

        slat_guidance_strength: float,

        slat_sampling_steps: int,

        mesh_simplify: float,

        texture_size: int,

        texture_mode: str,

        fps: int,

        multimode: str,

        project_name: str,

        render_video: bool,

        save_glb: bool,

        save_gaussian: bool,

        save_texture: bool,

        save_wireframe: bool,

        masks: Optional[torch.Tensor] = None,

    ) -> Tuple[str, str, torch.Tensor]:
        try:
            logger.info(f"Input images tensor initial shape: {images.shape}")
            with model.inference_context():
                self.mesh_simplify = mesh_simplify
                self.texture_size = texture_size
                self.texture_mode = texture_mode
                self.save_texture = save_texture
                self.save_wireframe = save_wireframe
                self.device = model.device

                pipeline_params = self.get_pipeline_params(
                    seed, ss_sampling_steps, ss_guidance_strength,
                    slat_sampling_steps, slat_guidance_strength
                )

                # Handle single vs multi mode differently
                if mode == "single":
                    # Take just the first image regardless of how many were input
                    images = images[0:1]
                    pil_imgs = self.torch_to_pil_batch(images, masks)
                    outputs = model.run(pil_imgs[0], **pipeline_params)
                else:
                    # In multi mode, treat the whole list as a batch
                    pil_imgs = self.torch_to_pil_batch(images, masks)
                    logger.info(f"Processing {len(pil_imgs)} views for multi-view reconstruction")
                    outputs = model.run_multi_image(
                        pil_imgs,
                        mode=multimode,
                        **pipeline_params
                    )

                video_path, glb_path, _, _, texture_image, _ = self.generate_outputs(
                    outputs,
                    project_name,
                    fps,
                    render_video=render_video,
                    save_glb=save_glb
                )

                if save_gaussian:
                    gaussian_path = os.path.join(self.output_dir, project_name, f"{project_name}.ply")
                    outputs['gaussian'][0].save_ply(gaussian_path)

                # Convert texture image to tensor
                if isinstance(texture_image, np.ndarray):
                    # Ensure proper shape and type
                    if texture_image.ndim == 2:  # Grayscale
                        texture_image = np.stack([texture_image]*3, axis=-1)
                    elif texture_image.shape[-1] == 4:  # RGBA
                        texture_image = texture_image[..., :3]  # Drop alpha channel
                    
                    texture_tensor = torch.from_numpy(texture_image).float() / 255.0
                    texture_tensor = texture_tensor.unsqueeze(0)  # [1, H, W, 3]
                    logger.info(f"Texture tensor shape after unsqueeze: {texture_tensor.shape}")
                else:
                    # Fallback to black texture
                    texture_tensor = torch.zeros((1, self.texture_size, self.texture_size, 3), dtype=torch.float32)

                self.cleanup_outputs(outputs)
                return glb_path, video_path, texture_tensor

        except Exception as e:
            logger.error(f"Error in image_to_3d: {str(e)}")
            logger.error(traceback.format_exc())
            raise
        finally:
            torch.cuda.empty_cache()
            gc.collect()

    def cleanup_outputs(self, outputs):
        # Now we only need to clean up the dictionary itself
        del outputs
        gc.collect()