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import torch
import gradio as gr
from diffusers import ShapEPipeline, ShapEImg2ImgPipeline
from diffusers.utils import export_to_gif
import os
from huggingface_hub import HfApi, login
from PIL import Image
import numpy as np
import gc
# Force CPU usage
device = "cpu"
torch.set_num_threads(4)
print(f"Using device: {device}")
def validate_token(token):
try:
login(token=token)
return True
except Exception as e:
print(f"Token validation error: {str(e)}")
return False
def generate_3d_from_text(prompt, token, guidance_scale=7.0, export_format="obj", progress=gr.Progress()):
try:
if not validate_token(token):
return gr.update(value="Invalid Hugging Face token"), None, None
print(f"Starting generation: {prompt}")
progress(0.1, desc="Loading model...")
pipe = ShapEPipeline.from_pretrained(
"openai/shap-e",
torch_dtype=torch.float32,
token=token,
revision="main",
low_cpu_mem_usage=True
)
os.makedirs("outputs", exist_ok=True)
safe_prompt = "".join(x for x in prompt if x.isalnum() or x in (" ", "-", "_"))
base_filename = f"outputs/{safe_prompt}"
try:
progress(0.3, desc="Creating 3D model...")
with torch.no_grad():
output = pipe(
prompt,
guidance_scale=min(guidance_scale, 10.0),
num_inference_steps=16
)
progress(0.5, desc="Creating GIF...")
gif_path = export_to_gif(output.images, f"{base_filename}.gif")
progress(0.7, desc="Creating 3D mesh...")
mesh_output = pipe(
prompt,
guidance_scale=min(guidance_scale, 10.0),
num_inference_steps=16,
output_type="mesh"
)
progress(0.9, desc="Saving files...")
output_path = f"{base_filename}.{export_format}"
mesh_output.meshes[0].export(output_path)
del pipe
del output
del mesh_output
gc.collect()
print(f"Generation completed: {output_path}")
progress(1.0, desc="Completed!")
return gr.update(value="Generation successful!"), gr.update(value=gif_path), gr.update(value=output_path)
except Exception as model_error:
error_msg = f"Model execution error: {str(model_error)}"
print(error_msg)
return gr.update(value=error_msg), None, None
except Exception as e:
error_msg = f"General error: {str(e)}"
print(error_msg)
return gr.update(value=error_msg), None, None
def generate_3d_from_image(image, token, guidance_scale=7.0, export_format="obj", progress=gr.Progress()):
try:
if not validate_token(token):
return gr.update(value="Invalid Hugging Face token"), None, None
print("Starting image to 3D generation")
progress(0.1, desc="Loading model...")
pipe = ShapEImg2ImgPipeline.from_pretrained(
"openai/shap-e-img2img",
torch_dtype=torch.float32,
token=token,
revision="main",
low_cpu_mem_usage=True
)
os.makedirs("outputs", exist_ok=True)
import time
timestamp = int(time.time())
base_filename = f"outputs/image_to_3d_{timestamp}"
try:
progress(0.3, desc="Preparing image...")
if isinstance(image, str):
image = Image.open(image)
elif isinstance(image, np.ndarray):
image = Image.fromarray(image)
image = image.resize((128, 128))
progress(0.5, desc="Creating 3D model...")
with torch.no_grad():
output = pipe(
image=image,
guidance_scale=min(guidance_scale, 10.0),
num_inference_steps=16
)
progress(0.7, desc="Creating GIF...")
gif_path = export_to_gif(output.images, f"{base_filename}.gif")
progress(0.8, desc="Creating 3D mesh...")
mesh_output = pipe(
image=image,
guidance_scale=min(guidance_scale, 10.0),
num_inference_steps=16,
output_type="mesh"
)
progress(0.9, desc="Saving files...")
output_path = f"{base_filename}.{export_format}"
mesh_output.meshes[0].export(output_path)
del pipe
del output
del mesh_output
gc.collect()
print(f"Generation completed: {output_path}")
progress(1.0, desc="Completed!")
return gr.update(value="Generation successful!"), gr.update(value=gif_path), gr.update(value=output_path)
except Exception as model_error:
error_msg = f"Model execution error: {str(model_error)}"
print(error_msg)
return gr.update(value=error_msg), None, None
except Exception as e:
error_msg = f"General error: {str(e)}"
print(error_msg)
return gr.update(value=error_msg), None, None
with gr.Blocks(theme=gr.themes.Soft()) as interface:
gr.Markdown("# SORA-3D - Text/Image to 3D Model Generator")
gr.Markdown("Create 3D models from text or image input. You need a Hugging Face token to use this app.")
gr.Markdown("""
> **Important Notes**:
> - Processing time may be longer on CPU
> - Keep guidance scale under 10 for faster results
> - Number of steps is fixed at 16
> - Image size is optimized for quality/speed
""")
with gr.Tab("Text → 3D"):
with gr.Row():
with gr.Column():
text_input = gr.Textbox(label="Enter description for 3D model", scale=2)
text_token = gr.Textbox(label="Hugging Face Token", type="password", scale=2)
with gr.Row():
text_guidance = gr.Slider(minimum=1, maximum=10, value=7, label="Guidance Scale", scale=1)
text_format = gr.Radio(["obj", "glb"], label="Export Format", value="obj", scale=1)
text_button = gr.Button("Generate", variant="primary")
with gr.Column():
text_status = gr.Textbox(label="Status", interactive=False)
text_preview = gr.Image(label="3D Preview (GIF)", interactive=False)
text_file = gr.File(label="3D Model File")
with gr.Tab("Image → 3D"):
with gr.Row():
with gr.Column():
image_input = gr.Image(label="Image to convert to 3D", type="pil", scale=2)
image_token = gr.Textbox(label="Hugging Face Token", type="password", scale=2)
with gr.Row():
image_guidance = gr.Slider(minimum=1, maximum=10, value=7, label="Guidance Scale", scale=1)
image_format = gr.Radio(["obj", "glb"], label="Export Format", value="obj", scale=1)
image_button = gr.Button("Generate", variant="primary")
with gr.Column():
image_status = gr.Textbox(label="Status", interactive=False)
image_preview = gr.Image(label="3D Preview (GIF)", interactive=False)
image_file = gr.File(label="3D Model File")
text_button.click(
generate_3d_from_text,
inputs=[text_input, text_token, text_guidance, text_format],
outputs=[text_status, text_preview, text_file]
)
image_button.click(
generate_3d_from_image,
inputs=[image_input, image_token, image_guidance, image_format],
outputs=[image_status, image_preview, image_file]
)
if __name__ == "__main__":
interface.launch() |