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# trellis_model_manager.py
import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from safetensors.torch import load_file
import folder_paths
from huggingface_hub import hf_hub_download, snapshot_download
from typing import Dict, Union
import json
import importlib # Import the importlib module
from trellis.modules.utils import convert_module_to_f16, convert_module_to_f32
logger = logging.getLogger('model_manager')
__attributes = {
'SparseStructureDecoder': 'trellis.models.sparse_structure_vae',
'SparseStructureFlowModel': 'trellis.models.sparse_structure_flow',
'SLatFlowModel': 'trellis.models.structured_latent_flow',
# Add other model mappings here
}
__all__ = list(__attributes.keys())
def __getattr__(name):
if name in __attributes:
module_name = __attributes[name]
module = importlib.import_module(module_name, package=None) # Import the module
return getattr(module, name)
raise AttributeError(f"module {__name__} has no attribute {name}")
class TrellisModelManager:
"""
Basic manager for Trellis models, using ComfyUI's new model path.
"""
def __init__(self, model_dir: str, config=None, device: str = "cuda"):
"""
Initialize the model manager with a specific model directory.
Args:
model_dir (str): Path to model directory (e.g. "models/checkpoints/TRELLIS-image-large")
config (dict or object): Global configuration for Trellis
device (str): Device to load models on (e.g. "cuda")
"""
self.model_dir = model_dir
# Handle config being either a dict or an object
if config is None:
self.device = device
elif isinstance(config, dict):
self.device = config.get('device', device)
self.config = config
else:
self.device = getattr(config, 'device', device)
self.config = config
self.model = None
self.dinov2_model = None
def load(self) -> None:
"""Load model configuration and checkpoints"""
try:
# Ensure directory exists
os.makedirs(self.model_dir, exist_ok=True)
ckpts_folder = os.path.join(self.model_dir, "ckpts")
os.makedirs(ckpts_folder, exist_ok=True)
# Download model files if needed
if not os.path.exists(os.path.join(self.model_dir, "pipeline.json")):
logger.info("Downloading TRELLIS models...")
try:
# Download main pipeline files
snapshot_download(
repo_id="JeffreyXiang/TRELLIS-image-large",
local_dir=self.model_dir,
local_dir_use_symlinks=False,
allow_patterns=["pipeline.json", "README.md"]
)
# Download checkpoint files
snapshot_download(
repo_id="JeffreyXiang/TRELLIS-image-large",
local_dir=ckpts_folder,
local_dir_use_symlinks=False,
allow_patterns=["*.safetensors", "*.json"],
cache_dir=os.path.join(self.model_dir, ".cache")
)
logger.info("Model files downloaded successfully")
except Exception as e:
logger.error(f"Error downloading model files: {str(e)}")
raise
# Load configuration
self.config = self._load_config()
except Exception as e:
logger.error(f"Error in load(): {str(e)}")
raise
def get_checkpoint_path(self, filename: str) -> str:
"""
Returns the full path to a checkpoint file.
"""
ckpts_folder = os.path.join(self.model_dir, "ckpts")
# Add .safetensors extension if not present
if not filename.endswith('.safetensors'):
filename = f"{filename}.safetensors"
full_path = os.path.join(ckpts_folder, filename)
if not os.path.exists(full_path):
raise FileNotFoundError(f"Checkpoint file not found: {full_path}")
return full_path
def _load_config(self) -> Dict:
"""Load model configuration from pipeline.json"""
try:
config_path = os.path.join(self.model_dir, "pipeline.json")
if os.path.exists(config_path):
logger.info(f"Loading config from {config_path}")
with open(config_path, 'r') as f:
config = json.load(f)
else:
logger.info(f"Config not found locally, downloading from HuggingFace")
config_path = hf_hub_download(
repo_id=f"JeffreyXiang/{os.path.basename(self.model_dir)}",
filename="pipeline.json",
cache_dir=os.path.join(self.model_dir, ".cache")
)
with open(config_path, 'r') as f:
config = json.load(f)
# Debug: Print raw config
logger.info("Raw config contents:")
logger.info(json.dumps(config, indent=2))
if not config:
raise ValueError(f"Could not load valid configuration from {self.model_dir}")
if 'name' not in config:
config['name'] = 'TrellisImageTo3DPipeline'
return config
except Exception as e:
logger.error(f"Error loading config from {self.model_dir}: {e}")
return {
'name': 'TrellisImageTo3DPipeline',
'version': '1.0'
}
def load_models(self) -> Dict[str, nn.Module]:
"""Load all required models with current configuration"""
return {
'sparse_structure_flow_model': self.get_checkpoint_path("ss_flow_img_dit_L_16l8_fp16"),
'slat_flow_model': self.get_checkpoint_path("slat_flow_img_dit_L_64l8p2_fp16")
}
def load_model_components(self) -> Dict[str, nn.Module]:
"""Loads individual model components."""
models = {}
model_paths = self.load_models()
for name, path in model_paths.items():
models[name] = models.from_pretrained(path, config=self.config)
# Ensure each model is converted to the desired precision
if self.config.get('use_fp16', True):
convert_module_to_f16(models[name])
else:
convert_module_to_f32(models[name])
# DINOv2 is handled separately
# models['image_cond_model'] = self.load_dinov2(self.config.get("dinov2_model", "dinov2_vitl14"))
return models
def load_dinov2(self, model_name: str):
"""Load DINOv2 model with device and precision management"""
try:
# Get use_fp16 from config dict or object
use_fp16 = (self.config.get('use_fp16', True)
if isinstance(self.config, dict)
else getattr(self.config, 'use_fp16', True))
# Try to load from local path first
model_path = folder_paths.get_full_path("classifiers", f"{model_name}.pth")
if model_path is None:
print(f"Downloading {model_name} from torch hub...")
try:
# Load model architecture
model = torch.hub.load('facebookresearch/dinov2', model_name, pretrained=True)
# Save model for future use
save_dir = os.path.join(folder_paths.models_dir, "classifiers")
os.makedirs(save_dir, exist_ok=True)
save_path = os.path.join(save_dir, f"{model_name}.pth")
# Save on CPU to avoid memory issues
model = model.cpu()
torch.save(model.state_dict(), save_path)
print(f"Saved DINOv2 model to {save_path}")
except Exception as e:
raise RuntimeError(f"Failed to download DINOv2 model: {str(e)}")
else:
# Load from local path
print(f"Loading DINOv2 model from {model_path}")
model = torch.hub.load('facebookresearch/dinov2', model_name, pretrained=False)
model.load_state_dict(torch.load(model_path))
# Move model to specified device and apply precision settings
model = model.to(self.device)
if use_fp16:
model = model.half()
model.eval()
return model
except Exception as e:
raise RuntimeError(f"Error loading DINOv2 model: {str(e)}") |