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import os
import re
import json
from tqdm import tqdm
import torch
from dataclasses import dataclass, field
from diffusers import StableDiffusionPipeline
from .base import Pipeline
from ..models.geometry import StableDiffusionTriplaneDualAttention
from ..utils.mesh_exporter import isosurface, colorize_mesh, DiffMarchingCubeHelper
from diffusers.loaders import AttnProcsLayers
from ..models.networks import get_activation
@dataclass
class TriplaneTurboTextTo3DPipelineConfig:
"""Configuration for TriplaneTurboTextTo3DPipeline"""
# Basic pipeline settings
base_model_name_or_path: str = "stabilityai/stable-diffusion-2-1-base"
# Training/sampling settings
num_steps_sampling: int = 4
# Geometry settings
radius: float = 1.0
normal_type: str = "analytic"
sdf_bias: str = "sphere"
sdf_bias_params: float = 0.5
rotate_planes: str = "v1"
split_channels: str = "v1"
geo_interpolate: str = "v1"
tex_interpolate: str = "v2"
n_feature_dims: int = 3
sample_scheduler: str = "ddim" # any of "ddpm", "ddim"
# Network settings
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 2,
}
)
# Adapter settings
space_generator_config: dict = field(
default_factory=lambda: {
"training_type": "self_lora_rank_16-cross_lora_rank_16-locon_rank_16" ,
"output_dim": 64, # 32 * 2 for v1
"self_lora_type": "hexa_v1",
"cross_lora_type": "vanilla",
"locon_type": "vanilla_v1",
"prompt_bias": False,
"vae_attn_type": "basic", # "basic", "vanilla"
}
)
isosurface_deformable_grid: bool = True
isosurface_resolution: int = 160
color_activation: str = "sigmoid-mipnerf"
@classmethod
def from_pretrained(cls, pretrained_path):
"""Load config from pretrained path"""
config_path = os.path.join(pretrained_path, "config.json")
if os.path.exists(config_path):
with open(config_path, "r") as f:
config_dict = json.load(f)
return cls(**config_dict)
else:
print(f"No config file found at {pretrained_path}, using default config")
return cls() # Return default config if no config file found
class TriplaneTurboTextTo3DPipeline(Pipeline):
"""
A pipeline for converting text to 3D models using triplane representation.
"""
config_name = "config.json"
def __init__(
self,
geometry,
material,
base_pipeline,
sample_scheduler,
isosurface_helper,
**kwargs,
):
super().__init__()
self.geometry = geometry
self.material = material
self.base_pipeline = base_pipeline
self.sample_scheduler = sample_scheduler
self.isosurface_helper = isosurface_helper
self.models = {
"geometry": geometry,
"base_pipeline": base_pipeline,
}
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path,
**kwargs,
):
"""
Load pretrained adapter weights, config and update pipeline components.
Args:
pretrained_model_name_or_path: Path to pretrained adapter weights
base_pipeline: Optional base pipeline instance
**kwargs: Additional arguments to override config values
Returns:
pipeline: Updated pipeline instance
"""
# Load config from pretrained path
config = TriplaneTurboTextTo3DPipelineConfig.from_pretrained(
pretrained_model_name_or_path,
**kwargs,
)
# load base pipeline
base_pipeline = StableDiffusionPipeline.from_pretrained(
config.base_model_name_or_path,
**kwargs,
)
# load sample scheduler
if config.sample_scheduler == "ddim":
from diffusers import DDIMScheduler
sample_scheduler = DDIMScheduler.from_pretrained(
config.base_model_name_or_path,
subfolder="scheduler",
)
else:
raise ValueError(f"Unknown sample scheduler: {config.sample_scheduler}")
# load geometry
geometry = StableDiffusionTriplaneDualAttention(
config=config,
vae=base_pipeline.vae,
unet=base_pipeline.unet,
)
# no gradient for geometry
for param in geometry.parameters():
param.requires_grad = False
# and load adapter weights
if pretrained_model_name_or_path.endswith(".pth"):
state_dict = torch.load(pretrained_model_name_or_path)["state_dict"]
new_state_dict = {}
for key, value in state_dict.items():
new_key = key.replace("geometry.", "")
new_state_dict[new_key] = value
_, unused = geometry.load_state_dict(new_state_dict, strict=False)
if len(unused) > 0:
print(f"Unused keys: {unused}")
else:
raise ValueError(f"Unknown pretrained model name or path: {pretrained_model_name_or_path}")
# load material, convert to int
# material = lambda x: (256 * get_activation(config.color_activation)(x)).int()
material = get_activation(config.color_activation)
# Load geometry model
pipeline = cls(
base_pipeline=base_pipeline,
geometry=geometry,
sample_scheduler=sample_scheduler,
material=material,
isosurface_helper=DiffMarchingCubeHelper(
resolution=config.isosurface_resolution,
),
**kwargs,
)
return pipeline
def encode_prompt(
self,
prompt,
device,
num_results_per_prompt = 1,
):
"""
Encodes the prompt into text encoder hidden states.
Args:
prompt: The prompt to encode.
device: The device to use for encoding.
num_results_per_prompt: Number of results to generate per prompt.
do_classifier_free_guidance: Whether to use classifier-free guidance.
negative_prompt: The negative prompt to encode.
Returns:
text_embeddings: Text embeddings tensor.
"""
# Use base_pipeline to encode prompt
text_embeddings = self.base_pipeline.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_results_per_prompt,
do_classifier_free_guidance=False,
negative_prompt=None
)
return text_embeddings
@torch.no_grad()
def __call__(
self,
prompt,
num_results_per_prompt=1,
generator=None,
device=None,
return_dict=True,
num_inference_steps=4,
colorize = True,
):
# Implementation similar to Zero123Pipeline
# Reference code from: https://github.com/zero123/zero123-diffusers
# Validate inputs
if isinstance(prompt, str):
batch_size = 1
prompt = [prompt]
elif isinstance(prompt, list):
batch_size = len(prompt)
else:
raise ValueError(f"Prompt must be a string or list of strings, got {type(prompt)}")
# Get the device from the first available module
# Generate latents if not provided
if device is None:
device = self.device
if generator is None:
generator = torch.Generator(device=device)
latents = torch.randn(
(batch_size * 6, 4, 32, 32), # hard-coded for now
generator=generator,
device=device,
)
# Process text prompt through geometry module
text_embed, _ = self.encode_prompt(prompt, device, num_results_per_prompt)
# Run diffusion process
# Set up timesteps for sampling
timesteps = self._set_timesteps(
self.sample_scheduler,
num_inference_steps
)
with torch.no_grad():
# Run diffusion process
for i, t in tqdm(enumerate(timesteps)):
# Scale model input
noisy_latent_input = self.sample_scheduler.scale_model_input(
latents,
t
)
# Predict noise/sample
pred = self.geometry.denoise(
noisy_input=noisy_latent_input,
text_embed=text_embed,
timestep=t.to(device),
)
# Update latents
results = self.sample_scheduler.step(pred, t, latents)
latents = results.prev_sample
latents_denoised = results.pred_original_sample
# Use final denoised latents
latents = latents_denoised
# Generate final 3D representation
space_cache = self.geometry.decode(latents)
# Extract mesh from space cache
mesh_list = isosurface(
space_cache,
self.geometry.forward_field,
self.isosurface_helper,
)
if colorize:
mesh_list = colorize_mesh(
space_cache,
self.geometry.export,
mesh_list,
activation=self.material,
)
if return_dict:
return {
"space_cache": space_cache,
"latents": latents,
"mesh": mesh_list,
}
else:
return mesh_list
def _set_timesteps(
self,
scheduler,
num_steps,
):
"""Set up timesteps for sampling.
Args:
scheduler: The scheduler to use for timestep generation
num_steps: Number of diffusion steps
Returns:
timesteps: Tensor of timesteps to use for sampling
"""
scheduler.set_timesteps(num_steps)
timesteps_orig = scheduler.timesteps
# Shift timesteps to start from T
timesteps_delta = scheduler.config.num_train_timesteps - 1 - timesteps_orig.max()
timesteps = timesteps_orig + timesteps_delta
return timesteps
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