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import os
import torch
import numpy as np
from typing import Any
from PIL import Image
from tqdm import tqdm
from omegaconf import OmegaConf
from huggingface_hub import hf_hub_download
from typing import Union, List, Optional
from direct3d_s2.modules import sparse as sp
from direct3d_s2.utils import (
instantiate_from_config,
preprocess_image,
sort_block,
extract_tokens_and_coords,
normalize_mesh,
mesh2index,
)
class Direct3DS2Pipeline(object):
def __init__(self,
dense_vae,
dense_dit,
sparse_vae_512,
sparse_dit_512,
sparse_vae_1024,
sparse_dit_1024,
refiner,
dense_image_encoder,
sparse_image_encoder,
dense_scheduler,
sparse_scheduler_512,
sparse_scheduler_1024,
dtype=torch.float16,
):
self.dense_vae = dense_vae
self.dense_dit = dense_dit
self.sparse_vae_512 = sparse_vae_512
self.sparse_dit_512 = sparse_dit_512
self.sparse_vae_1024 = sparse_vae_1024
self.sparse_dit_1024 = sparse_dit_1024
self.refiner = refiner
self.dense_image_encoder = dense_image_encoder
self.sparse_image_encoder = sparse_image_encoder
self.dense_scheduler = dense_scheduler
self.sparse_scheduler_512 = sparse_scheduler_512
self.sparse_scheduler_1024 = sparse_scheduler_1024
self.dtype = dtype
def to(self, device):
self.device = torch.device(device)
self.dense_vae.to(device)
self.dense_dit.to(device)
self.sparse_vae_512.to(device)
self.sparse_dit_512.to(device)
self.sparse_vae_1024.to(device)
self.sparse_dit_1024.to(device)
self.refiner.to(device)
self.dense_image_encoder.to(device)
self.sparse_image_encoder.to(device)
@classmethod
def from_pretrained(cls, pipeline_path, subfolder="direct3d-s2-v-1-1"):
if os.path.isdir(pipeline_path):
config_path = os.path.join(pipeline_path, 'config.yaml')
model_dense_path = os.path.join(pipeline_path, 'model_dense.ckpt')
model_sparse_512_path = os.path.join(pipeline_path, 'model_sparse_512.ckpt')
model_sparse_1024_path = os.path.join(pipeline_path, 'model_sparse_1024.ckpt')
model_refiner_path = os.path.join(pipeline_path, 'model_refiner.ckpt')
else:
config_path = hf_hub_download(
repo_id=pipeline_path,
subfolder=subfolder,
filename="config.yaml",
repo_type="model"
)
model_dense_path = hf_hub_download(
repo_id=pipeline_path,
subfolder=subfolder,
filename="model_dense.ckpt",
repo_type="model"
)
model_sparse_512_path = hf_hub_download(
repo_id=pipeline_path,
subfolder=subfolder,
filename="model_sparse_512.ckpt",
repo_type="model"
)
model_sparse_1024_path = hf_hub_download(
repo_id=pipeline_path,
subfolder=subfolder,
filename="model_sparse_1024.ckpt",
repo_type="model"
)
model_refiner_path = hf_hub_download(
repo_id=pipeline_path,
subfolder=subfolder,
filename="model_refiner.ckpt",
repo_type="model"
)
cfg = OmegaConf.load(config_path)
state_dict_dense = torch.load(model_dense_path, map_location='cpu', weights_only=True)
dense_vae = instantiate_from_config(cfg.dense_vae)
dense_vae.load_state_dict(state_dict_dense["vae"], strict=True)
dense_vae.eval()
dense_dit = instantiate_from_config(cfg.dense_dit)
dense_dit.load_state_dict(state_dict_dense["dit"], strict=True)
dense_dit.eval()
state_dict_sparse_512 = torch.load(model_sparse_512_path, map_location='cpu', weights_only=True)
sparse_vae_512 = instantiate_from_config(cfg.sparse_vae_512)
sparse_vae_512.load_state_dict(state_dict_sparse_512["vae"], strict=True)
sparse_vae_512.eval()
sparse_dit_512 = instantiate_from_config(cfg.sparse_dit_512)
sparse_dit_512.load_state_dict(state_dict_sparse_512["dit"], strict=True)
sparse_dit_512.eval()
state_dict_sparse_1024 = torch.load(model_sparse_1024_path, map_location='cpu', weights_only=True)
sparse_vae_1024 = instantiate_from_config(cfg.sparse_vae_1024)
sparse_vae_1024.load_state_dict(state_dict_sparse_1024["vae"], strict=True)
sparse_vae_1024.eval()
sparse_dit_1024 = instantiate_from_config(cfg.sparse_dit_1024)
sparse_dit_1024.load_state_dict(state_dict_sparse_1024["dit"], strict=True)
sparse_dit_1024.eval()
state_dict_refiner = torch.load(model_refiner_path, map_location='cpu', weights_only=True)
refiner = instantiate_from_config(cfg.refiner)
refiner.load_state_dict(state_dict_refiner["refiner"], strict=True)
refiner.eval()
dense_image_encoder = instantiate_from_config(cfg.dense_image_encoder)
sparse_image_encoder = instantiate_from_config(cfg.sparse_image_encoder)
dense_scheduler = instantiate_from_config(cfg.dense_scheduler)
sparse_scheduler_512 = instantiate_from_config(cfg.sparse_scheduler_512)
sparse_scheduler_1024 = instantiate_from_config(cfg.sparse_scheduler_1024)
return cls(
dense_vae=dense_vae,
dense_dit=dense_dit,
sparse_vae_512=sparse_vae_512,
sparse_dit_512=sparse_dit_512,
sparse_vae_1024=sparse_vae_1024,
sparse_dit_1024=sparse_dit_1024,
dense_image_encoder=dense_image_encoder,
sparse_image_encoder=sparse_image_encoder,
dense_scheduler=dense_scheduler,
sparse_scheduler_512=sparse_scheduler_512,
sparse_scheduler_1024=sparse_scheduler_1024,
refiner=refiner,
)
def preprocess(self, image):
if image.mode == 'RGBA':
image = np.array(image)
else:
if getattr(self, 'birefnet_model', None) is None:
from direct3d_s2.utils import BiRefNet
self.birefnet_model = BiRefNet(self.device)
image = self.birefnet_model.run(image)
image = preprocess_image(image)
return image
def prepare_image(self, image: Union[str, List[str], Image.Image, List[Image.Image]]):
if not isinstance(image, list):
image = [image]
if isinstance(image[0], str):
image = [Image.open(img) for img in image]
image = [self.preprocess(img) for img in image]
image = torch.stack([img for img in image]).to(self.device)
return image
def encode_image(self, image: torch.Tensor, conditioner: Any,
do_classifier_free_guidance: bool = True, use_mask: bool = False):
if use_mask:
cond = conditioner(image[:, :3], image[:, 3:])
else:
cond = conditioner(image[:, :3])
if isinstance(cond, tuple):
cond, cond_mask = cond
cond, cond_coords = extract_tokens_and_coords(cond, cond_mask)
else:
cond_mask, cond_coords = None, None
if do_classifier_free_guidance:
uncond = torch.zeros_like(cond)
else:
uncond = None
if cond_coords is not None:
cond = sp.SparseTensor(cond, cond_coords.int())
if uncond is not None:
uncond = sp.SparseTensor(uncond, cond_coords.int())
return cond, uncond
def inference(
self,
image,
vae,
dit,
conditioner,
scheduler,
num_inference_steps: int = 30,
guidance_scale: int = 7.0,
generator: Optional[torch.Generator] = None,
latent_index: torch.Tensor = None,
mode: str = 'dense', # 'dense', 'sparse512' or 'sparse1024
remove_interior: bool = False,
mc_threshold: float = 0.02):
do_classifier_free_guidance = guidance_scale > 0
if mode == 'dense':
sparse_conditions = False
else:
sparse_conditions = dit.sparse_conditions
cond, uncond = self.encode_image(image, conditioner,
do_classifier_free_guidance, sparse_conditions)
batch_size = cond.shape[0]
if mode == 'dense':
latent_shape = (batch_size, *dit.latent_shape)
else:
latent_shape = (len(latent_index), dit.out_channels)
latents = torch.randn(latent_shape, dtype=self.dtype, device=self.device, generator=generator)
scheduler.set_timesteps(num_inference_steps, device=self.device)
timesteps = scheduler.timesteps
extra_step_kwargs = {
"generator": generator
}
for i, t in enumerate(tqdm(timesteps, desc=f"{mode} Sampling:")):
latent_model_input = latents
timestep_tensor = torch.tensor([t], dtype=latent_model_input.dtype, device=self.device)
if mode == 'dense':
x_input = latent_model_input
elif mode in ['sparse512', 'sparse1024']:
x_input = sp.SparseTensor(latent_model_input, latent_index.int())
diffusion_inputs = {
"x": x_input,
"t": timestep_tensor,
"cond": cond,
}
noise_pred_cond = dit(**diffusion_inputs)
if mode != 'dense':
noise_pred_cond = noise_pred_cond.feats
if do_classifier_free_guidance:
diffusion_inputs["cond"] = uncond
noise_pred_uncond = dit(**diffusion_inputs)
if mode != 'dense':
noise_pred_uncond = noise_pred_uncond.feats
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
else:
noise_pred = noise_pred_cond
latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
latents = 1. / vae.latents_scale * latents + vae.latents_shift
if mode != 'dense':
latents = sp.SparseTensor(latents, latent_index.int())
decoder_inputs = {
"latents": latents,
"mc_threshold": mc_threshold,
}
if mode == 'dense':
decoder_inputs['return_index'] = True
elif remove_interior:
decoder_inputs['return_feat'] = True
if mode == 'sparse1024':
decoder_inputs['voxel_resolution'] = 1024
outputs = vae.decode_mesh(**decoder_inputs)
if remove_interior:
del latents, noise_pred, noise_pred_cond, noise_pred_uncond, x_input, cond, uncond
torch.cuda.empty_cache()
outputs = self.refiner.run(*outputs, mc_threshold=mc_threshold*2.0)
return outputs
@torch.no_grad()
def __call__(
self,
image: Union[str, List[str], Image.Image, List[Image.Image]] = None,
sdf_resolution: int = 1024,
dense_sampler_params: dict = {'num_inference_steps': 50, 'guidance_scale': 7.0},
sparse_512_sampler_params: dict = {'num_inference_steps': 30, 'guidance_scale': 7.0},
sparse_1024_sampler_params: dict = {'num_inference_steps': 15, 'guidance_scale': 7.0},
generator: Optional[torch.Generator] = None,
remesh: bool = False,
simplify_ratio: float = 0.95,
mc_threshold: float = 0.2):
image = self.prepare_image(image)
latent_index = self.inference(image, self.dense_vae, self.dense_dit, self.dense_image_encoder,
self.dense_scheduler, generator=generator, mode='dense', mc_threshold=0.1, **dense_sampler_params)[0]
latent_index = sort_block(latent_index, self.sparse_dit_512.selection_block_size)
torch.cuda.empty_cache()
if sdf_resolution == 512:
remove_interior = False
else:
remove_interior = True
mesh = self.inference(image, self.sparse_vae_512, self.sparse_dit_512,
self.sparse_image_encoder, self.sparse_scheduler_512,
generator=generator, mode='sparse512',
mc_threshold=mc_threshold, latent_index=latent_index,
remove_interior=remove_interior, **sparse_512_sampler_params)[0]
if sdf_resolution == 1024:
del latent_index
torch.cuda.empty_cache()
mesh = normalize_mesh(mesh)
latent_index = mesh2index(mesh, size=1024, factor=8)
latent_index = sort_block(latent_index, self.sparse_dit_1024.selection_block_size)
print(f"number of latent tokens: {len(latent_index)}")
mesh = self.inference(image, self.sparse_vae_1024, self.sparse_dit_1024,
self.sparse_image_encoder, self.sparse_scheduler_1024,
generator=generator, mode='sparse1024',
mc_threshold=mc_threshold, latent_index=latent_index,
**sparse_1024_sampler_params)[0]
if remesh:
import trimesh
from direct3d_s2.utils import postprocess_mesh
filled_mesh = postprocess_mesh(
vertices=mesh.vertices,
faces=mesh.faces,
simplify=True,
simplify_ratio=simplify_ratio,
verbose=True,
)
mesh = trimesh.Trimesh(filled_mesh[0], filled_mesh[1])
outputs = {"mesh": mesh}
return outputs
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