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import gradio as gr |
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import random |
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import os |
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models = { |
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"Face Projection": gr.load("models/Purz/face-projection"), |
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"Flux LoRA Uncensored": gr.load("models/prashanth970/flux-lora-uncensored"), |
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"NSFW TrioHMH Flux": gr.load("models/DiegoJR1973/NSFW-TrioHMH-Flux"), |
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"NSFW Master": gr.load("models/pimpilikipilapi1/NSFW_master") |
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} |
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def generate_image(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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result_images = {} |
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for model_name, model in models.items(): |
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result_images[model_name] = model(text) |
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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_images[model_name] for model_name in models] |
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def randomize_parameters(): |
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seed = random.randint(0, 999999) |
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width = random.randint(512, 2048) |
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height = random.randint(512, 2048) |
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guidance_scale = round(random.uniform(0.1, 20.0), 1) |
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num_inference_steps = random.randint(1, 40) |
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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_image, |
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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="Width", minimum=512, maximum=2048, step=64, value=1024), |
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gr.Slider(label="Height", minimum=512, maximum=2048, step=64, value=1024), |
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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=[gr.Image(label=model_name) for model_name in models], |
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theme="NoCrypt/miku", |
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description="Sorry for the inconvenience. The model is currently running on the CPU, which might affect performance. We appreciate your understanding.", |
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) |
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interface.launch() |
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