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import gradio as gr
def infer(text_prompt, action_prompt, login):
from huggingface_hub import InferenceClient
inf_client = InferenceClient(
provider="fal-ai",
api_key=login
)
# output is a PIL.Image object
image = inf_client.text_to_image(
text_prompt,
model="multimodalart/isometric-skeumorphic-3d-bnb",
)
import tempfile
# Create a temporary PNG file
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_file:
temp_image_path = tmp_file.name
image.save(temp_image_path, format="PNG")
print(f"✅ Image saved temporarily at: {temp_image_path}")
from gradio_client import Client, handle_file
gr_client = Client("Lightricks/ltx-video-distilled")
result = gr_client.predict(
prompt=action_prompt,
negative_prompt="worst quality, inconsistent motion, blurry, jittery, distorted",
input_image_filepath=handle_file(temp_image_path),
input_video_filepath=None,
height_ui=512,
width_ui=704,
mode="image-to-video",
duration_ui=2,
ui_frames_to_use=9,
seed_ui=42,
randomize_seed=True,
ui_guidance_scale=1,
improve_texture_flag=True,
api_name="/image_to_video"
)
print(result)
return temp_image_path, result
with gr.Blocks() as demo:
with gr.Column():
login = gr.LoginButton()
text_prompt = gr.Textbox(label="Icon label")
action_prompt = gr.Textbox(label="Action label")
submit_btn = gr.Button("Submit")
image_out = gr.Image()
video_out = gr.Video()
submit_btn.click(
fn = infer,
inputs = [text_prompt, action_prompt, login],
outputs = [image_out, video_out]
)
demo.launch(show_error=True)
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