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import gradio as gr |
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import spaces |
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import random |
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import torch |
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from diffusers import FluxPipeline |
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from huggingface_hub.utils import RepositoryNotFoundError |
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pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16).to("cuda") |
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@spaces.GPU(duration=70) |
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def generate(prompt, negative_prompt, width, height, sample_steps, lora_id): |
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try: |
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pipeline.load_lora_weights(lora_id) |
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except RepositoryNotFoundError: |
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raise ValueError(f"Recieved invalid FLUX LoRA.") |
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return pipeline(prompt=f"{prompt}\n(NOT {negative_prompt}:2)", width=width, height=height, num_inference_steps=sample_steps, generator=torch.Generator("cpu").manual_seed(random.randint(42, 69)), guidance_scale=7).images[0] |
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with gr.Blocks() as interface: |
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with gr.Column(): |
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with gr.Row(): |
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with gr.Column(): |
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prompt = gr.Textbox(label="Prompt", info="What do you want?", value="Keanu Reeves holding a neon sign reading 'Hello, world!', 32k HDR, paparazzi", lines=4, interactive=True) |
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negative_prompt = gr.Textbox(label="Negative Prompt", info="What do you want to exclude from the image?", value="ugly, low quality", lines=4, interactive=True) |
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with gr.Column(): |
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generate_button = gr.Button("Generate") |
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output = gr.Image() |
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with gr.Row(): |
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with gr.Accordion(label="Advanced Settings", open=False): |
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with gr.Row(): |
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with gr.Column(): |
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width = gr.Slider(label="Width", info="The width in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True) |
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height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True) |
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with gr.Column(): |
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sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=20, minimum=4, maximum=50, step=1, interactive=True) |
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lora_id = gr.Textbox(label="Adapter Repository", info="ID of the FLUX LoRA", value="pepper13/fluxfw") |
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generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps, lora_id], outputs=[output]) |
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if __name__ == "__main__": |
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interface.launch() |
