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import gradio as gr |
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import os |
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import tempfile |
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from huggingface_hub import InferenceClient |
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from gradio_client import Client, handle_file |
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def infer(text_prompt, action_prompt, oauth_token: gr.OAuthToken): |
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gradio_auth_token = oauth_token.token |
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inf_client = InferenceClient( |
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provider="auto", |
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api_key=gradio_auth_token |
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) |
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image = inf_client.text_to_image( |
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text_prompt, |
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model="multimodalart/isometric-skeumorphic-3d-bnb", |
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) |
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with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_file: |
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temp_image_path = tmp_file.name |
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image.save(temp_image_path, format="PNG") |
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print(f"✅ Image saved temporarily at: {temp_image_path}") |
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gr_client = Client("Lightricks/ltx-video-distilled", hf_token=gradio_auth_token) |
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action_prompt = f"{action_prompt}, RBNBICN, icon, white background, isometric perspective" |
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result = gr_client.predict( |
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prompt=action_prompt, |
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negative_prompt="worst quality, inconsistent motion, blurry, jittery, distorted", |
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input_image_filepath=handle_file(temp_image_path), |
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input_video_filepath=None, |
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height_ui=512, |
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width_ui=704, |
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mode="image-to-video", |
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duration_ui=2, |
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ui_frames_to_use=9, |
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seed_ui=42, |
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randomize_seed=True, |
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ui_guidance_scale=1, |
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improve_texture_flag=True, |
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api_name="/image_to_video" |
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) |
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print(result) |
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video_path = result[0]['video'] |
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return temp_image_path, video_path |
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css = """ |
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div#col-container{ |
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margin: 0 auto; |
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max-width: 800px; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown("# AirBnb's style Isometric Skeuomorphic 3D icons generator") |
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with gr.Row(): |
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with gr.Column(): |
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gr.LoginButton() |
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text_prompt = gr.Textbox(label="Icon label", info="a very few words to describe the icon you want to generate") |
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action_prompt = gr.Textbox(label="Action label", info="describe how the subject will act/move") |
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submit_btn = gr.Button("Submit") |
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with gr.Column(): |
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image_out = gr.Image() |
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video_out = gr.Video() |
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submit_btn.click( |
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fn = infer, |
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inputs = [text_prompt, action_prompt], |
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outputs = [image_out, video_out] |
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) |
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demo.launch(show_error=True, show_api=False) |
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