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Create app.py
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app.py
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import gradio as gr
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with gr.Blocks(delete_cache=(600, 600)) as demo:
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gr.Markdown("# π¨ SeqTex: Generate Mesh Textures in Video Sequence")
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gr.Markdown("""
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## π Welcome to SeqTex!
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**SeqTex** is a cutting-edge AI system that generates high-quality textures for 3D meshes using image prompts (here we use image generator to get them from textual prompts).
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Choose to either **try our example models** below or **upload your own 3D mesh** to create stunning textures.
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""")
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gr.Markdown("---")
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gr.Markdown("## π§ Step 1: Upload & Process 3D Mesh")
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gr.Markdown("""
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**π How to prepare your 3D mesh:**
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- Upload your 3D mesh in **.obj** or **.glb** format
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- **π‘ Pro Tip**:
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- For optimal results, ensure your mesh includes only one part with <span style="color:#e74c3c; font-weight:bold;">UV parameterization</span>
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- Otherwise, we'll combine all parts and generate UV parameterization using *xAtlas* (may take longer for high-poly meshes; may also fail for certain meshes)
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- **β οΈ Important**: We recommend adjusting your model using *Mesh Orientation Adjustments* to be **Z-UP oriented** for best results
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""")
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position_map_tensor_path = gr.State()
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normal_map_tensor_path = gr.State()
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position_images_tensor_path = gr.State()
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normal_images_tensor_path = gr.State()
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mask_images_tensor_path = gr.State()
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w2c_tensor_path = gr.State()
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mesh = gr.State()
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mvp_matrix_tensor_path = gr.State()
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# fixed_texture_map = Image.open("image.webp").convert("RGB")
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# Step 1
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with gr.Row():
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with gr.Column():
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mesh_upload = gr.File(label="π Upload 3D Mesh", file_types=[".obj", ".glb"])
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# uv_tool = gr.Radio(["xAtlas", "UVAtlas"], label="UV parameterizer", value="xAtlas")
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gr.Markdown("**π Mesh Orientation Adjustments** (if needed):")
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y2z = gr.Checkbox(label="Y β Z Transform", value=False, info="Rotate: Y becomes Z, -Z becomes Y")
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y2x = gr.Checkbox(label="Y β X Transform", value=False, info="Rotate: Y becomes X, -X becomes Y")
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z2x = gr.Checkbox(label="Z β X Transform", value=False, info="Rotate: Z becomes X, -X becomes Z")
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upside_down = gr.Checkbox(label="π Flip Vertically", value=False, info="Fix upside-down mesh orientation")
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step1_button = gr.Button("π Process Mesh & Generate Views", variant="primary")
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step1_progress = gr.Textbox(label="π Processing Status", interactive=False)
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with gr.Column():
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model_input = gr.Model3D(label="π Processed 3D Model", height=500)
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with gr.Row(equal_height=True):
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rgb_views = gr.Image(label="π· Generated Views", type="pil", scale=3)
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position_map = gr.Image(label="πΊοΈ Position Map", type="pil", scale=1)
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normal_map = gr.Image(label="π§ Normal Map", type="pil", scale=1)
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# Step 2
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gr.Markdown("---")
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gr.Markdown("## ποΈ Step 2: Select View & Generate Image Condition")
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gr.Markdown("""
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**π How to generate image condition:**
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- Your mesh will be rendered from **four viewpoints** (front, back, left, right)
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- Choose **one view** as your image condition
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- Enter a **descriptive text prompt** for the desired texture
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- Select your preferred AI model:
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- <span style="color:#27ae60; font-weight:bold;">π― SDXL</span>: Fast generation with depth + normal control, better details (often suffer from wrong highlights)
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- <span style="color:#3498db; font-weight:bold;">β‘ FLUX</span>: ~~High-quality generation with depth control (slower due to CPU offloading). Better work with **Edge Refinement**~~ (Not supported due to the memory limit of HF Space. You can try it locally)
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""")
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with gr.Row():
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with gr.Column():
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img_condition_seed = gr.Number(label="π² Random Seed", minimum=0, maximum=9999, step=1, value=42, info="Change for different results")
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selected_view = gr.Radio(["First View", "Second View", "Third View", "Fourth View"], label="π Camera View", value="First View", info="Choose which viewpoint to use as reference")
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with gr.Row():
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# model_choice = gr.Radio(["SDXL", "FLUX"], label="π€ AI Model", value="SDXL", info="SDXL: Fast, depth+normal control | FLUX: High-quality, slower processing")
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model_choice = gr.Radio(["SDXL"], label="π€ AI Model", value="SDXL", info="SDXL: Fast, depth+normal control | FLUX: High-quality, slower processing (Not supported due to the memory limit of HF Space)")
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edge_refinement = gr.Checkbox(label="β¨ Edge Refinement", value=True, info="Smooth boundary artifacts (recommended for delightning highlights in the boundary)")
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text_prompt = gr.Textbox(label="π¬ Texture Description", placeholder="Describe the desired texture appearance (e.g., 'rustic wooden surface with weathered paint')", lines=2)
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step2_button = gr.Button("π― Generate Image Condition", variant="primary")
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step2_progress = gr.Textbox(label="π Generation Status", interactive=False)
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with gr.Column():
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condition_image = gr.Image(label="πΌοΈ Generated Image Condition", type="pil") # , interactive=False
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# Step 3
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gr.Markdown("---")
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gr.Markdown("## π¨ Step 3: Generate Final Texture")
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gr.Markdown("""
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**π How to generate final texture:**
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- The **SeqTex pipeline** will create a complete texture map for your model
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- View the results from multiple angles and download your textured 3D model (the viewport is a little bit dark)
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""")
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texture_map_tensor_path = gr.State()
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with gr.Row():
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with gr.Column(scale=1):
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step3_button = gr.Button("π¨ Generate Final Texture", variant="primary")
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step3_progress = gr.Textbox(label="π Texture Generation Status", interactive=False)
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texture_map = gr.Image(label="π Generated Texture Map", interactive=False)
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with gr.Column(scale=2):
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rendered_imgs = gr.Image(label="πΌοΈ Final Rendered Views")
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mv_branch_imgs = gr.Image(label="πΌοΈ SeqTex Direct Output")
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with gr.Column(scale=1.5):
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model_display = gr.Model3D(label="π Final Textured Model", height=500)
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demo.launch()
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