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app.py
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@@ -1,13 +1,6 @@
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import gradio as gr
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from gradio_client import Client, handle_file
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def set_client_for_session(request: gr.Request):
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x_ip_token = request.headers['x-ip-token']
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print(x_ip_token)
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flux_client = Client("black-forest-labs/FLUX.1-Kontext-Dev", headers={"x-ip-token": x_ip_token})
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ltx_client = Client("KingNish/ltx-video-distilled", headers={"x-ip-token": x_ip_token})
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return flux_client, ltx_client
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def image_to_image(prompt, image, flux_client):
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prompt = "You are an AI image-to-image editor that transforms a user-provided input image based on their prompt while maintaining consistency and fidelity to the original image. Generate an output image that accurately reflects the user's requested modifications, preserving key elements like style, composition, and context from the input image unless explicitly instructed otherwise. " + prompt
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image = flux_client.predict(
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@@ -34,22 +27,24 @@ def image_to_video(prompt, image, ltx_client):
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print(result)
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return result
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def personalized_video(prompt, image, flux_client, ltx_client):
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image = image_to_image(prompt, image, flux_client)
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yield image, None
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video = image_to_video(prompt, image, ltx_client)
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yield image, video
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with gr.Blocks() as demo:
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flux_client = gr.State(None)
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ltx_client = gr.State(None)
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input_image = gr.Image(type="filepath")
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prompt = gr.Textbox(max_lines=1)
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edited_image = gr.Image()
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output_video = gr.Video()
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submit_button = gr.Button("Submit")
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submit_button.click(personalized_video, [prompt, input_image
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prompt.submit(personalized_video, [prompt, input_image
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# gr.Examples(
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# examples=[
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@@ -62,7 +57,5 @@ with gr.Blocks() as demo:
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# cache_mode = "eager"
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# )
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demo.load(set_client_for_session, None, [flux_client, ltx_client])
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demo.launch()
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import gradio as gr
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from gradio_client import Client, handle_file
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def image_to_image(prompt, image, flux_client):
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prompt = "You are an AI image-to-image editor that transforms a user-provided input image based on their prompt while maintaining consistency and fidelity to the original image. Generate an output image that accurately reflects the user's requested modifications, preserving key elements like style, composition, and context from the input image unless explicitly instructed otherwise. " + prompt
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image = flux_client.predict(
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print(result)
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return result
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def personalized_video(prompt, image, flux_client, ltx_client, request: gr.Request):
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x_ip_token = request.headers['x-ip-token']
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flux_client = Client("black-forest-labs/FLUX.1-Kontext-Dev", headers={"x-ip-token": x_ip_token})
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ltx_client = Client("KingNish/ltx-video-distilled", headers={"x-ip-token": x_ip_token})
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print(x_ip_token)
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image = image_to_image(prompt, image, flux_client)
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yield image, None
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video = image_to_video(prompt, image, ltx_client)
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yield image, video
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with gr.Blocks() as demo:
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input_image = gr.Image(type="filepath")
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prompt = gr.Textbox(max_lines=1)
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edited_image = gr.Image()
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output_video = gr.Video()
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submit_button = gr.Button("Submit")
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submit_button.click(personalized_video, [prompt, input_image], [edited_image, output_video])
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prompt.submit(personalized_video, [prompt, input_image], [edited_image, output_video])
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# gr.Examples(
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# examples=[
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# cache_mode = "eager"
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# )
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demo.launch()
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