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# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.
# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import os
# import ipdb
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as pl
from tqdm import tqdm
from torchvision.transforms import v2
from torchvision.utils import make_grid, save_image
from einops import rearrange
from diffusers import (
DiffusionPipeline,
EulerAncestralDiscreteScheduler,
DDPMScheduler,
UNet2DConditionModel,
ControlNetModel,
)
from .modules import Dino_v2, UNet2p5DConditionModel
import math
def extract_into_tensor(a, t, x_shape):
b, *_ = t.shape
out = a.gather(-1, t)
return out.reshape(b, *((1,) * (len(x_shape) - 1)))
class HunyuanPaint(pl.LightningModule):
def __init__(
self,
stable_diffusion_config,
control_net_config=None,
num_view=6,
view_size=320,
drop_cond_prob=0.1,
with_normal_map=None,
with_position_map=None,
pbr_settings=["albedo", "mr"],
**kwargs,
):
"""Initializes the HunyuanPaint Lightning Module.
Args:
stable_diffusion_config: Configuration for loading the Stable Diffusion pipeline
control_net_config: Configuration for ControlNet (optional)
num_view: Number of views to process
view_size: Size of input views (height/width)
drop_cond_prob: Probability of dropping conditioning input during training
with_normal_map: Flag indicating whether normal maps are used
with_position_map: Flag indicating whether position maps are used
pbr_settings: List of PBR materials to generate (e.g., albedo, metallic-roughness)
**kwargs: Additional keyword arguments
"""
super(HunyuanPaint, self).__init__()
self.num_view = num_view
self.view_size = view_size
self.drop_cond_prob = drop_cond_prob
self.pbr_settings = pbr_settings
# init modules
pipeline = DiffusionPipeline.from_pretrained(**stable_diffusion_config)
pipeline.set_pbr_settings(self.pbr_settings)
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
pipeline.scheduler.config, timestep_spacing="trailing"
)
self.with_normal_map = with_normal_map
self.with_position_map = with_position_map
self.pipeline = pipeline
self.pipeline.vae.use_slicing = True
train_sched = DDPMScheduler.from_config(self.pipeline.scheduler.config)
if isinstance(self.pipeline.unet, UNet2DConditionModel):
self.pipeline.unet = UNet2p5DConditionModel(
self.pipeline.unet, train_sched, self.pipeline.scheduler, self.pbr_settings
)
self.train_scheduler = train_sched # use ddpm scheduler during training
self.register_schedule()
pipeline.set_learned_parameters()
if control_net_config is not None:
pipeline.unet = pipeline.unet.bfloat16().requires_grad_(control_net_config.train_unet)
self.pipeline.add_controlnet(
ControlNetModel.from_pretrained(control_net_config.pretrained_model_name_or_path),
conditioning_scale=0.75,
)
self.unet = pipeline.unet
self.pipeline.set_progress_bar_config(disable=True)
self.pipeline.vae = self.pipeline.vae.bfloat16()
self.pipeline.text_encoder = self.pipeline.text_encoder.bfloat16()
if self.unet.use_dino:
self.dino_v2 = Dino_v2("facebook/dinov2-giant")
self.dino_v2 = self.dino_v2.bfloat16()
self.validation_step_outputs = []
def register_schedule(self):
self.num_timesteps = self.train_scheduler.config.num_train_timesteps
betas = self.train_scheduler.betas.detach().cpu()
alphas = 1.0 - betas
alphas_cumprod = torch.cumprod(alphas, dim=0)
alphas_cumprod_prev = torch.cat([torch.ones(1, dtype=torch.float64), alphas_cumprod[:-1]], 0)
self.register_buffer("betas", betas.float())
self.register_buffer("alphas_cumprod", alphas_cumprod.float())
self.register_buffer("alphas_cumprod_prev", alphas_cumprod_prev.float())
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer("sqrt_alphas_cumprod", torch.sqrt(alphas_cumprod).float())
self.register_buffer("sqrt_one_minus_alphas_cumprod", torch.sqrt(1 - alphas_cumprod).float())
self.register_buffer("sqrt_recip_alphas_cumprod", torch.sqrt(1.0 / alphas_cumprod).float())
self.register_buffer("sqrt_recipm1_alphas_cumprod", torch.sqrt(1.0 / alphas_cumprod - 1).float())
def on_fit_start(self):
device = torch.device(f"cuda:{self.local_rank}")
self.pipeline.to(device)
if self.global_rank == 0:
os.makedirs(os.path.join(self.logdir, "images_val"), exist_ok=True)
def prepare_batch_data(self, batch):
"""Preprocesses a batch of input data for training/inference.
Args:
batch: Raw input batch dictionary
Returns:
tuple: Contains:
- cond_imgs: Primary conditioning images (B, 1, C, H, W)
- cond_imgs_another: Secondary conditioning images (B, 1, C, H, W)
- target_imgs: Dictionary of target PBR images resized and clamped
- images_normal: Preprocessed normal maps (if available)
- images_position: Preprocessed position maps (if available)
"""
images_cond = batch["images_cond"].to(self.device) # (B, M, C, H, W), where M is the number of reference images
cond_imgs, cond_imgs_another = images_cond[:, 0:1, ...], images_cond[:, 1:2, ...]
cond_size = self.view_size
cond_imgs = v2.functional.resize(cond_imgs, cond_size, interpolation=3, antialias=True).clamp(0, 1)
cond_imgs_another = v2.functional.resize(cond_imgs_another, cond_size, interpolation=3, antialias=True).clamp(
0, 1
)
target_imgs = {}
for pbr_token in self.pbr_settings:
target_imgs[pbr_token] = batch[f"images_{pbr_token}"].to(self.device)
target_imgs[pbr_token] = v2.functional.resize(
target_imgs[pbr_token], self.view_size, interpolation=3, antialias=True
).clamp(0, 1)
images_normal = None
if "images_normal" in batch:
images_normal = batch["images_normal"] # (B, N, C, H, W)
images_normal = v2.functional.resize(images_normal, self.view_size, interpolation=3, antialias=True).clamp(
0, 1
)
images_normal = [images_normal]
images_position = None
if "images_position" in batch:
images_position = batch["images_position"] # (B, N, C, H, W)
images_position = v2.functional.resize(
images_position, self.view_size, interpolation=3, antialias=True
).clamp(0, 1)
images_position = [images_position]
return cond_imgs, cond_imgs_another, target_imgs, images_normal, images_position
@torch.no_grad()
def forward_text_encoder(self, prompts):
device = next(self.pipeline.vae.parameters()).device
text_embeds = self.pipeline.encode_prompt(prompts, device, 1, False)[0]
return text_embeds
@torch.no_grad()
def encode_images(self, images):
"""Encodes input images into latent representations using the VAE.
Handles both standard input (B, N, C, H, W) and PBR input (B, N_pbrs, N, C, H, W)
Maintains original batch structure in output latents.
Args:
images: Input images tensor
Returns:
torch.Tensor: Latent representations with original batch dimensions preserved
"""
B = images.shape[0]
image_ndims = images.ndim
if image_ndims != 5:
N_pbrs, N = images.shape[1:3]
images = (
rearrange(images, "b n c h w -> (b n) c h w")
if image_ndims == 5
else rearrange(images, "b n_pbrs n c h w -> (b n_pbrs n) c h w")
)
dtype = next(self.pipeline.vae.parameters()).dtype
images = (images - 0.5) * 2.0
posterior = self.pipeline.vae.encode(images.to(dtype)).latent_dist
latents = posterior.sample() * self.pipeline.vae.config.scaling_factor
latents = (
rearrange(latents, "(b n) c h w -> b n c h w", b=B)
if image_ndims == 5
else rearrange(latents, "(b n_pbrs n) c h w -> b n_pbrs n c h w", b=B, n_pbrs=N_pbrs)
)
return latents
def forward_unet(self, latents, t, **cached_condition):
"""Runs the UNet model to predict noise/latent residuals.
Args:
latents: Noisy latent representations (B, C, H, W)
t: Timestep tensor (B,)
**cached_condition: Dictionary of conditioning inputs (text embeds, reference images, etc)
Returns:
torch.Tensor: UNet output (predicted noise or velocity)
"""
dtype = next(self.unet.parameters()).dtype
latents = latents.to(dtype)
shading_embeds = cached_condition["shading_embeds"]
pred_noise = self.pipeline.unet(latents, t, encoder_hidden_states=shading_embeds, **cached_condition)
return pred_noise[0]
def predict_start_from_z_and_v(self, x_t, t, v):
"""
Predicts clean image (x0) from noisy latents (x_t) and
velocity prediction (v) using the v-prediction formula.
Args:
x_t: Noisy latents at timestep t
t: Current timestep
v: Predicted velocity (v) from UNet
Returns:
torch.Tensor: Predicted clean image (x0)
"""
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t
- extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
)
def get_v(self, x, noise, t):
"""Computes the target velocity (v) for v-prediction training.
Args:
x: Clean latents (x0)
noise: Added noise
t: Current timestep
Returns:
torch.Tensor: Target velocity
"""
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise
- extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
)
def training_step(self, batch, batch_idx):
"""Performs a single training step with both conditioning paths.
Implements:
1. Dual-conditioning path training (main ref + secondary ref)
2. Velocity-prediction with consistency loss
3. Conditional dropout for robust learning
4. PBR-specific losses (albedo/metallic-roughness)
Args:
batch: Input batch from dataloader
batch_idx: Index of current batch
Returns:
torch.Tensor: Combined loss value
"""
cond_imgs, cond_imgs_another, target_imgs, normal_imgs, position_imgs = self.prepare_batch_data(batch)
B, N_ref = cond_imgs.shape[:2]
_, N_gen, _, H, W = target_imgs["albedo"].shape
N_pbrs = len(self.pbr_settings)
t = torch.randint(0, self.num_timesteps, size=(B,)).long().to(self.device)
t = t.unsqueeze(-1).repeat(1, N_pbrs, N_gen)
t = rearrange(t, "b n_pbrs n -> (b n_pbrs n)")
all_target_pbrs = []
for pbr_token in self.pbr_settings:
all_target_pbrs.append(target_imgs[pbr_token])
all_target_pbrs = torch.stack(all_target_pbrs, dim=0).transpose(1, 0)
gen_latents = self.encode_images(all_target_pbrs) #! B, N_pbrs N C H W
ref_latents = self.encode_images(cond_imgs) #! B, M, C, H, W
ref_latents_another = self.encode_images(cond_imgs_another) #! B, M, C, H, W
all_shading_tokens = []
for token in self.pbr_settings:
if token in ["albedo", "mr"]:
all_shading_tokens.append(
getattr(self.unet, f"learned_text_clip_{token}").unsqueeze(dim=0).repeat(B, 1, 1)
)
shading_embeds = torch.stack(all_shading_tokens, dim=1)
if self.unet.use_dino:
dino_hidden_states = self.dino_v2(cond_imgs[:, :1, ...])
dino_hidden_states_another = self.dino_v2(cond_imgs_another[:, :1, ...])
gen_latents = rearrange(gen_latents, "b n_pbrs n c h w -> (b n_pbrs n) c h w")
noise = torch.randn_like(gen_latents).to(self.device)
latents_noisy = self.train_scheduler.add_noise(gen_latents, noise, t).to(self.device)
latents_noisy = rearrange(latents_noisy, "(b n_pbrs n) c h w -> b n_pbrs n c h w", b=B, n_pbrs=N_pbrs)
cached_condition = {}
if normal_imgs is not None:
normal_embeds = self.encode_images(normal_imgs[0])
cached_condition["embeds_normal"] = normal_embeds #! B, N, C, H, W
if position_imgs is not None:
position_embeds = self.encode_images(position_imgs[0])
cached_condition["embeds_position"] = position_embeds #! B, N, C, H, W
cached_condition["position_maps"] = position_imgs[0] #! B, N, C, H, W
for b in range(B):
prob = np.random.rand()
if prob < self.drop_cond_prob:
if "normal_imgs" in cached_condition:
cached_condition["embeds_normal"][b, ...] = torch.zeros_like(
cached_condition["embeds_normal"][b, ...]
)
if "position_imgs" in cached_condition:
cached_condition["embeds_position"][b, ...] = torch.zeros_like(
cached_condition["embeds_position"][b, ...]
)
prob = np.random.rand()
if prob < self.drop_cond_prob:
if "position_maps" in cached_condition:
cached_condition["position_maps"][b, ...] = torch.zeros_like(
cached_condition["position_maps"][b, ...]
)
prob = np.random.rand()
if prob < self.drop_cond_prob:
dino_hidden_states[b, ...] = torch.zeros_like(dino_hidden_states[b, ...])
prob = np.random.rand()
if prob < self.drop_cond_prob:
dino_hidden_states_another[b, ...] = torch.zeros_like(dino_hidden_states_another[b, ...])
# MVA & Ref Attention
prob = np.random.rand()
cached_condition["mva_scale"] = 1.0
cached_condition["ref_scale"] = 1.0
if prob < self.drop_cond_prob:
cached_condition["mva_scale"] = 0.0
cached_condition["ref_scale"] = 0.0
elif prob > 1.0 - self.drop_cond_prob:
prob = np.random.rand()
if prob < 0.5:
cached_condition["mva_scale"] = 0.0
else:
cached_condition["ref_scale"] = 0.0
else:
pass
if self.train_scheduler.config.prediction_type == "v_prediction":
cached_condition["shading_embeds"] = shading_embeds
cached_condition["ref_latents"] = ref_latents
cached_condition["dino_hidden_states"] = dino_hidden_states
v_pred = self.forward_unet(latents_noisy, t, **cached_condition)
v_pred_albedo, v_pred_mr = torch.split(
rearrange(
v_pred, "(b n_pbr n) c h w -> b n_pbr n c h w", n_pbr=len(self.pbr_settings), n=self.num_view
),
1,
dim=1,
)
v_target = self.get_v(gen_latents, noise, t)
v_target_albedo, v_target_mr = torch.split(
rearrange(
v_target, "(b n_pbr n) c h w -> b n_pbr n c h w", n_pbr=len(self.pbr_settings), n=self.num_view
),
1,
dim=1,
)
albedo_loss_1, _ = self.compute_loss(v_pred_albedo, v_target_albedo)
mr_loss_1, _ = self.compute_loss(v_pred_mr, v_target_mr)
cached_condition["ref_latents"] = ref_latents_another
cached_condition["dino_hidden_states"] = dino_hidden_states_another
v_pred_another = self.forward_unet(latents_noisy, t, **cached_condition)
v_pred_another_albedo, v_pred_another_mr = torch.split(
rearrange(
v_pred_another,
"(b n_pbr n) c h w -> b n_pbr n c h w",
n_pbr=len(self.pbr_settings),
n=self.num_view,
),
1,
dim=1,
)
albedo_loss_2, _ = self.compute_loss(v_pred_another_albedo, v_target_albedo)
mr_loss_2, _ = self.compute_loss(v_pred_another_mr, v_target_mr)
consistency_loss, _ = self.compute_loss(v_pred_another, v_pred)
albedo_loss = (albedo_loss_1 + albedo_loss_2) * 0.5
mr_loss = (mr_loss_1 + mr_loss_2) * 0.5
log_loss_dict = {}
log_loss_dict.update({f"train/albedo_loss": albedo_loss})
log_loss_dict.update({f"train/mr_loss": mr_loss})
log_loss_dict.update({f"train/cons_loss": consistency_loss})
loss_dict = log_loss_dict
elif self.train_scheduler.config.prediction_type == "epsilon":
e_pred = self.forward_unet(latents_noisy, t, **cached_condition)
loss, loss_dict = self.compute_loss(e_pred, noise)
else:
raise f"No {self.train_scheduler.config.prediction_type}"
# logging
self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log("global_step", self.global_step, prog_bar=True, logger=True, on_step=True, on_epoch=False)
lr = self.optimizers().param_groups[0]["lr"]
self.log("lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
return 0.85 * (albedo_loss + mr_loss) + 0.15 * consistency_loss
def compute_loss(self, noise_pred, noise_gt):
loss = F.mse_loss(noise_pred, noise_gt)
prefix = "train"
loss_dict = {}
loss_dict.update({f"{prefix}/loss": loss})
return loss, loss_dict
@torch.no_grad()
def validation_step(self, batch, batch_idx):
"""Performs validation on a single batch.
Generates predicted images using:
1. Reference conditioning images
2. Optional normal/position maps
3. Frozen DINO features (if enabled)
4. Text prompt conditioning
Compares predictions against ground truth targets and prepares visualization.
Stores results for epoch-level aggregation.
Args:
batch: Input batch from validation dataloader
batch_idx: Index of current batch
"""
# [Validation image generation and comparison logic...]
# Key steps:
# 1. Preprocess conditioning images to PIL format
# 2. Set up conditioning inputs (normal maps, position maps, DINO features)
# 3. Run pipeline inference with fixed prompt ("high quality")
# 4. Decode latent outputs to image space
# 5. Arrange predictions and ground truths for visualization
cond_imgs_tensor, _, target_imgs, normal_imgs, position_imgs = self.prepare_batch_data(batch)
resolution = self.view_size
image_pils = []
for i in range(cond_imgs_tensor.shape[0]):
image_pils.append([])
for j in range(cond_imgs_tensor.shape[1]):
image_pils[-1].append(v2.functional.to_pil_image(cond_imgs_tensor[i, j, ...]))
outputs, gts = [], []
for idx in range(len(image_pils)):
cond_imgs = image_pils[idx]
cached_condition = dict(num_in_batch=self.num_view, N_pbrs=len(self.pbr_settings))
if normal_imgs is not None:
cached_condition["images_normal"] = normal_imgs[0][idx, ...].unsqueeze(0)
if position_imgs is not None:
cached_condition["images_position"] = position_imgs[0][idx, ...].unsqueeze(0)
if self.pipeline.unet.use_dino:
dino_hidden_states = self.dino_v2([cond_imgs][0])
cached_condition["dino_hidden_states"] = dino_hidden_states
latent = self.pipeline(
cond_imgs,
prompt="high quality",
num_inference_steps=30,
output_type="latent",
height=resolution,
width=resolution,
**cached_condition,
).images
image = self.pipeline.vae.decode(latent / self.pipeline.vae.config.scaling_factor, return_dict=False)[
0
] # [-1, 1]
image = (image * 0.5 + 0.5).clamp(0, 1)
image = rearrange(
image, "(b n_pbr n) c h w -> b n_pbr n c h w", n_pbr=len(self.pbr_settings), n=self.num_view
)
image = torch.cat((torch.ones_like(image[:, :, :1, ...]) * 0.5, image), dim=2)
image = rearrange(image, "b n_pbr n c h w -> (b n_pbr n) c h w")
image = rearrange(
image,
"(b n_pbr n) c h w -> b c (n_pbr h) (n w)",
b=1,
n_pbr=len(self.pbr_settings),
n=self.num_view + 1,
)
outputs.append(image)
all_target_pbrs = []
for pbr_token in self.pbr_settings:
all_target_pbrs.append(target_imgs[pbr_token])
all_target_pbrs = torch.stack(all_target_pbrs, dim=0).transpose(1, 0)
all_target_pbrs = torch.cat(
(cond_imgs_tensor.unsqueeze(1).repeat(1, len(self.pbr_settings), 1, 1, 1, 1), all_target_pbrs), dim=2
)
all_target_pbrs = rearrange(all_target_pbrs, "b n_pbrs n c h w -> b c (n_pbrs h) (n w)")
gts = all_target_pbrs
outputs = torch.cat(outputs, dim=0).to(self.device)
images = torch.cat([gts, outputs], dim=-2)
self.validation_step_outputs.append(images)
@torch.no_grad()
def on_validation_epoch_end(self):
"""Aggregates validation results at epoch end.
Gathers outputs from all GPUs (if distributed training),
creates a unified visualization grid, and saves to disk.
Only rank 0 process performs saving.
"""
# [Result aggregation and visualization...]
# Key steps:
# 1. Gather validation outputs from all processes
# 2. Create image grid combining ground truths and predictions
# 3. Save visualization with step-numbered filename
# 4. Clear memory for next validation cycle
images = torch.cat(self.validation_step_outputs, dim=0)
all_images = self.all_gather(images)
all_images = rearrange(all_images, "r b c h w -> (r b) c h w")
if self.global_rank == 0:
grid = make_grid(all_images, nrow=8, normalize=True, value_range=(0, 1))
save_image(grid, os.path.join(self.logdir, "images_val", f"val_{self.global_step:07d}.png"))
self.validation_step_outputs.clear() # free memory
def configure_optimizers(self):
lr = self.learning_rate
optimizer = torch.optim.AdamW(self.unet.parameters(), lr=lr)
def lr_lambda(step):
warm_up_step = 1000
T_step = 9000
gamma = 0.9
min_lr = 0.1 if step >= warm_up_step else 0.0
max_lr = 1.0
normalized_step = step % (warm_up_step + T_step)
current_max_lr = max_lr * gamma ** (step // (warm_up_step + T_step))
if current_max_lr < min_lr:
current_max_lr = min_lr
if normalized_step < warm_up_step:
lr_step = min_lr + (normalized_step / warm_up_step) * (current_max_lr - min_lr)
else:
step_wc_wp = normalized_step - warm_up_step
ratio = step_wc_wp / T_step
lr_step = min_lr + 0.5 * (current_max_lr - min_lr) * (1 + math.cos(math.pi * ratio))
return lr_step
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
lr_scheduler_config = {
"scheduler": lr_scheduler,
"interval": "step",
"frequency": 1,
"monitor": "val_loss",
"strict": False,
"name": None,
}
return {"optimizer": optimizer, "lr_scheduler": lr_scheduler_config}