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import gradio as gr
import spaces
import os
import shutil
os.environ['TOKENIZERS_PARALLELISM'] = 'true'
os.environ['SPCONV_ALGO'] = 'native'
from typing import *
import torch
import numpy as np
import imageio
from easydict import EasyDict as edict
from trellis.pipelines import TrellisTextTo3DPipeline
from trellis.representations import Gaussian, MeshExtractResult
from trellis.utils import render_utils, postprocessing_utils
import traceback
import sys
# --- FastAPI / Threading Imports ---
import threading
import uvicorn
import logging
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
MAX_SEED = np.iinfo(np.int32).max
TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
os.makedirs(TMP_DIR, exist_ok=True)
# --- Global Pipeline Variable ---
pipeline = None
# --- Logging Setup ---
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def start_session(req: gr.Request):
user_dir = os.path.join(TMP_DIR, str(req.session_hash))
os.makedirs(user_dir, exist_ok=True)
def end_session(req: gr.Request):
user_dir = os.path.join(TMP_DIR, str(req.session_hash))
shutil.rmtree(user_dir)
def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
return {
'gaussian': {
**gs.init_params,
'_xyz': gs._xyz.cpu().numpy(),
'_features_dc': gs._features_dc.cpu().numpy(),
'_scaling': gs._scaling.cpu().numpy(),
'_rotation': gs._rotation.cpu().numpy(),
'_opacity': gs._opacity.cpu().numpy(),
},
'mesh': {
'vertices': mesh.vertices.cpu().numpy(),
'faces': mesh.faces.cpu().numpy(),
},
}
def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
gs = 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'],
)
gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
mesh = edict(
vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
faces=torch.tensor(state['mesh']['faces'], device='cuda'),
)
return gs, mesh
def get_seed(randomize_seed: bool, seed: int) -> int:
"""
Get the random seed.
"""
return np.random.randint(0, MAX_SEED) if randomize_seed else seed
@spaces.GPU
def text_to_3d(
prompt: str,
seed: int,
ss_guidance_strength: float,
ss_sampling_steps: int,
slat_guidance_strength: float,
slat_sampling_steps: int,
req: gr.Request,
) -> Tuple[dict, str]:
"""
Convert an text prompt to a 3D model.
Args:
prompt (str): The text prompt.
seed (int): The random seed.
ss_guidance_strength (float): The guidance strength for sparse structure generation.
ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
slat_guidance_strength (float): The guidance strength for structured latent generation.
slat_sampling_steps (int): The number of sampling steps for structured latent generation.
Returns:
dict: The information of the generated 3D model.
str: The path to the video of the 3D model.
"""
# --- Determine user_dir robustly ---
session_hash_str = str(req.session_hash) if hasattr(req, 'session_hash') and req.session_hash else f"api_call_{np.random.randint(10000)}"
user_dir = os.path.join(TMP_DIR, session_hash_str)
os.makedirs(user_dir, exist_ok=True) # Ensure directory exists
outputs = pipeline.run(
prompt,
seed=seed,
formats=["gaussian", "mesh"],
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,
},
)
video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
video_path = os.path.join(user_dir, 'sample.mp4')
try:
imageio.mimsave(video_path, video, fps=15) # Now the directory should exist
except FileNotFoundError:
print(f"ERROR: Directory {user_dir} still not found before mimsave!", file=sys.stderr)
# Decide if we should raise or return an error state?
# Returning a dummy path might hide the error, so let's raise for now
raise
state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
torch.cuda.empty_cache()
return state, video_path
@spaces.GPU(duration=90)
def extract_glb(
state: dict,
mesh_simplify: float,
texture_size: int,
req: gr.Request,
) -> Tuple[str, str]:
"""
Extract a GLB file from the 3D model.
Args:
state (dict): The state of the generated 3D model.
mesh_simplify (float): The mesh simplification factor.
texture_size (int): The texture resolution.
Returns:
str: The path to the extracted GLB file.
"""
# --- Determine user_dir robustly ---
session_hash_str = str(req.session_hash) if hasattr(req, 'session_hash') and req.session_hash else f"api_call_{np.random.randint(10000)}"
user_dir = os.path.join(TMP_DIR, session_hash_str)
os.makedirs(user_dir, exist_ok=True) # Ensure directory exists
gs, mesh = unpack_state(state)
glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
glb_path = os.path.join(user_dir, 'sample.glb')
try:
glb.export(glb_path) # Now the directory should exist
except FileNotFoundError:
print(f"ERROR: Directory {user_dir} still not found before glb.export!", file=sys.stderr)
raise
torch.cuda.empty_cache()
return glb_path, glb_path
@spaces.GPU
def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
"""
Extract a Gaussian file from the 3D model.
Args:
state (dict): The state of the generated 3D model.
Returns:
str: The path to the extracted Gaussian file.
"""
# --- Determine user_dir robustly ---
session_hash_str = str(req.session_hash) if hasattr(req, 'session_hash') and req.session_hash else f"api_call_{np.random.randint(10000)}"
user_dir = os.path.join(TMP_DIR, session_hash_str)
os.makedirs(user_dir, exist_ok=True) # Ensure directory exists
gs, _ = unpack_state(state)
gaussian_path = os.path.join(user_dir, 'sample.ply')
try:
gs.save_ply(gaussian_path) # Now the directory should exist
except FileNotFoundError:
print(f"ERROR: Directory {user_dir} still not found before gs.save_ply!", file=sys.stderr)
raise
torch.cuda.empty_cache()
return gaussian_path, gaussian_path
# --- FastAPI App Setup ---
api_app = FastAPI()
class GenerateRequest(BaseModel):
prompt: str
seed: int = 0 # Default seed
mesh_simplify: float = 0.95 # Default simplify factor
texture_size: int = 1024 # Default texture size
# Add other generation parameters if needed
@api_app.post("/api/generate-sync")
@spaces.GPU(duration=300) # Allow longer duration for API calls
async def generate_sync_api(request_data: GenerateRequest):
global pipeline # Access the globally initialized pipeline
if pipeline is None:
logger.error("API Error: Pipeline not initialized")
raise HTTPException(status_code=503, detail="Pipeline not ready")
prompt = request_data.prompt
seed = request_data.seed
mesh_simplify = request_data.mesh_simplify
texture_size = request_data.texture_size
# Extract other params if added to GenerateRequest
logger.info(f"API /generate-sync received prompt: {prompt}")
try:
# --- Determine a unique temporary directory for this API call ---
api_call_hash = f"api_sync_{np.random.randint(100000)}"
user_dir = os.path.join(TMP_DIR, api_call_hash)
os.makedirs(user_dir, exist_ok=True)
logger.info(f"API using temp dir: {user_dir}")
# --- Stage 1: Run the text-to-3D pipeline ---
logger.info("API running pipeline...")
# Use default values for parameters not exposed in the simple API for now
outputs = pipeline.run(
prompt,
seed=seed,
formats=["gaussian", "mesh"],
sparse_structure_sampler_params={"steps": 25, "cfg_strength": 7.5},
slat_sampler_params={"steps": 25, "cfg_strength": 7.5},
)
gs = outputs['gaussian'][0]
mesh = outputs['mesh'][0]
logger.info("API pipeline finished.")
torch.cuda.empty_cache()
# --- Stage 2: Extract GLB ---
logger.info("API extracting GLB...")
glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
glb_path = os.path.join(user_dir, 'generated_sync.glb')
glb.export(glb_path)
logger.info(f"API GLB exported to: {glb_path}")
torch.cuda.empty_cache()
# Return the absolute path within the container
return {"status": "success", "glb_path": os.path.abspath(glb_path)}
except Exception as e:
logger.error(f"API /generate-sync error: {str(e)}", exc_info=True)
# Clean up temp dir on error if it exists
if os.path.exists(user_dir):
try:
shutil.rmtree(user_dir)
except Exception as cleanup_e:
logger.error(f"API Error cleaning up dir {user_dir}: {cleanup_e}")
raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")
# Note: We don't automatically clean up the user_dir on success,
# as the file needs to be accessible for download by the calling server.
# A separate cleanup mechanism might be needed eventually.
# --- Gradio Blocks Definition ---
with gr.Blocks(delete_cache=(600, 600)) as demo:
gr.Markdown("""
## Text to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
* Type a text prompt and click "Generate" to create a 3D asset.
* If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
""")
with gr.Row():
with gr.Column():
text_prompt = gr.Textbox(label="Text Prompt", lines=5)
with gr.Accordion(label="Generation Settings", open=False):
seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
gr.Markdown("Stage 1: Sparse Structure Generation")
with gr.Row():
ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=25, step=1)
gr.Markdown("Stage 2: Structured Latent Generation")
with gr.Row():
slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=25, step=1)
generate_btn = gr.Button("Generate")
with gr.Accordion(label="GLB Extraction Settings", open=False):
mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
with gr.Row():
extract_glb_btn = gr.Button("Extract GLB", interactive=False)
extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
gr.Markdown("""
*NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.*
""")
with gr.Column():
video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
model_output = gr.Model3D(label="Extracted GLB/Gaussian", height=300)
with gr.Row():
download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)
output_buf = gr.State()
# Handlers
demo.load(start_session)
demo.unload(end_session)
generate_btn.click(
get_seed,
inputs=[randomize_seed, seed],
outputs=[seed],
).then(
text_to_3d,
inputs=[text_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
outputs=[output_buf, video_output],
).then(
lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
outputs=[extract_glb_btn, extract_gs_btn],
)
video_output.clear(
lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
outputs=[extract_glb_btn, extract_gs_btn],
)
extract_glb_btn.click(
extract_glb,
inputs=[output_buf, mesh_simplify, texture_size],
outputs=[model_output, download_glb],
).then(
lambda: gr.Button(interactive=True),
outputs=[download_glb],
)
extract_gs_btn.click(
extract_gaussian,
inputs=[output_buf],
outputs=[model_output, download_gs],
).then(
lambda: gr.Button(interactive=True),
outputs=[download_gs],
)
model_output.clear(
lambda: gr.Button(interactive=False),
outputs=[download_glb],
)
# --- Functions to Run FastAPI in Background ---
def run_api():
"""Run the FastAPI server."""
uvicorn.run(api_app, host="0.0.0.0", port=8000) # Run on port 8000
def start_api_thread():
"""Start the API server in a background thread."""
api_thread = threading.Thread(target=run_api, daemon=True)
api_thread.start()
logger.info("Started FastAPI server thread on port 8000")
return api_thread
# Launch the Gradio app and FastAPI server
if __name__ == "__main__":
logger.info("Initializing Trellis Pipeline...")
# Make pipeline global so API endpoint can access it
pipeline = TrellisTextTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-text-xlarge")
pipeline.cuda()
logger.info("Trellis Pipeline Initialized.")
# Start the background API server
start_api_thread()
# Launch the Gradio interface (blocking call)
logger.info("Launching Gradio Demo...")
demo.launch()
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