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Running
on
Zero
File size: 3,824 Bytes
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# In appTrellis_fileErrorsFIx.py
@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]: # Return type changed for clarity
"""
Generates a 3D model (Gaussian and Mesh) from text and returns a
serializable state dictionary and a video preview path.
"""
print(f"[text_to_3d] Received prompt: '{prompt}', Seed: {seed}")
user_dir = os.path.join(TMP_DIR, str(req.session_hash))
os.makedirs(user_dir, exist_ok=True)
print(f"[text_to_3d] User directory: {user_dir}")
# --- Generation Pipeline ---
try:
print("[text_to_3d] Running Trellis pipeline...")
outputs = pipeline.run(
prompt,
seed=seed,
formats=["gaussian", "mesh"], # Ensure both are generated
sparse_structure_sampler_params={
"steps": int(ss_sampling_steps), # Ensure steps are int
"cfg_strength": float(ss_guidance_strength),
},
slat_sampler_params={
"steps": int(slat_sampling_steps), # Ensure steps are int
"cfg_strength": float(slat_guidance_strength),
},
)
print("[text_to_3d] Pipeline run completed.")
except Exception as e:
print(f"❌ [text_to_3d] Pipeline error: {e}", file=sys.stderr)
traceback.print_exc()
raise gr.Error(f"Trellis pipeline failed: {e}")
# --- Create Serializable State Dictionary ---
try:
state_dict = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
except Exception as e:
print(f"❌ [text_to_3d] pack_state error: {e}", file=sys.stderr)
traceback.print_exc()
raise gr.Error(f"Failed to pack state: {e}")
# --- Render Video Preview (TEMPORARILY DISABLED FOR DEBUGGING) ---
video_path = None # Set path to None
# try:
# print("[text_to_3d] Rendering video preview...")
# 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']
# # Ensure video frames are uint8
# video = [np.concatenate([v.astype(np.uint8), vg.astype(np.uint8)], axis=1) for v, vg in zip(video, video_geo)]
# video_path = os.path.join(user_dir, 'sample.mp4')
# imageio.mimsave(video_path, video, fps=15, quality=8) # Added quality setting
# print(f"[text_to_3d] Video saved to: {video_path}")
# except Exception as e:
# print(f"❌ [text_to_3d] Video rendering/saving error: {e}", file=sys.stderr)
# traceback.print_exc()
# # Still return state_dict, but maybe signal video error? Return None for path.
# video_path = None # Indicate video failure
print("[text_to_3d] Skipping video rendering for debugging.")
# --- Cleanup and Return ---
if torch.cuda.is_available():
torch.cuda.empty_cache()
print("[text_to_3d] Cleared CUDA cache.")
# --- Return Serializable Dictionary and None for Video Path ---
print("[text_to_3d] Returning state dictionary and None video path.")
return state_dict, video_path # Return dict and None video path
# --- Gradio UI Definition ---
# ... (rest of the file is the same, but you might want to adjust the output mapping if needed)
# In the generate_btn.click handler, adjust the outputs if the video component causes issues:
# Option 1: Keep Video component, it will just show nothing.
# outputs=[output_buf, video_output], # This might be fine
# Option 2: Use a dummy hidden component if video_output causes issues receiving None
# outputs=[output_buf, gr.Textbox(visible=False)], # Example dummy |