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Update app.py
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app.py
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@@ -1,20 +1,24 @@
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import gradio as gr
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import random
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#
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def
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if seed is not None:
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random.seed(seed)
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print(f"Width: {width}, Height: {height}, Guidance Scale: {guidance_scale}, Inference Steps: {num_inference_steps}")
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return
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def randomize_parameters():
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seed = random.randint(0, 999999)
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return seed, width, height, guidance_scale, num_inference_steps
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interface = gr.Interface(
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fn=
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inputs=[
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gr.Textbox(label="Type here your imagination:", placeholder="Type or click an example..."),
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gr.Slider(label="Seed", minimum=0, maximum=999999, step=1),
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gr.Slider(label="Guidance Scale", minimum=0.1, maximum=20.0, step=0.1, value=3.0),
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gr.Slider(label="Number of inference steps", minimum=1, maximum=40, step=1, value=28),
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],
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outputs=
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)
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interface.launch()
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import gradio as gr
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import random
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# Load each model separately
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model1 = gr.load("models/pimpilikipilapi1/NSFW_master")
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model2 = gr.load("models/prashanth970/flux-lora-uncensored")
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model3 = gr.load("models/DiegoJR1973/NSFW-TrioHMH-Flux")
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def generate_images(text, seed, width, height, guidance_scale, num_inference_steps):
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if seed is not None:
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random.seed(seed)
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# Generate images using each model
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result_image1 = model1(text)
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result_image2 = model2(text)
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result_image3 = model3(text)
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# Print parameters for debugging
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print(f"Width: {width}, Height: {height}, Guidance Scale: {guidance_scale}, Inference Steps: {num_inference_steps}")
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return result_image1, result_image2, result_image3
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def randomize_parameters():
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seed = random.randint(0, 999999)
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return seed, width, height, guidance_scale, num_inference_steps
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interface = gr.Interface(
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fn=generate_images,
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inputs=[
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gr.Textbox(label="Type here your imagination:", placeholder="Type or click an example..."),
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gr.Slider(label="Seed", minimum=0, maximum=999999, step=1),
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gr.Slider(label="Guidance Scale", minimum=0.1, maximum=20.0, step=0.1, value=3.0),
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gr.Slider(label="Number of inference steps", minimum=1, maximum=40, step=1, value=28),
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],
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outputs=[
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gr.Image(label="Output 01"),
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gr.Image(label="Output 02"),
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gr.Image(label="Output 03")
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],
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description="Generate images with three different models. Please note that the models are running on the CPU, which might affect performance. Thank you for your patience!",
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)
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interface.launch()
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