import gradio as gr import torch d = "cuda" if torch.cuda.is_available() else False if d: import spaces from diffusers import FluxPipeline pipeline = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float16).to(d) #pipeline.enable_model_cpu_offload() @spaces.GPU(duration=70) def generate(prompt, negative_prompt, width, height, sample_steps): return pipeline(prompt=f"{prompt}\nDO NOT INCLUDE {negative_prompt}", width=width, height=height, num_inference_steps=sample_steps, guidance_scale=7).images[0] with gr.Blocks() as demo: with gr.Column(): with gr.Row(): with gr.Column(): 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) 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) with gr.Column(): generate_button = gr.Button("Generate") output = gr.Image() with gr.Row(): with gr.Accordion(label="Advanced Settings", open=False): with gr.Row(): with gr.Column(): width = gr.Slider(label="Width", info="The width in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True) height = gr.Slider(label="Height", info="The height in pixels of the generated image.", value=512, minimum=128, maximum=4096, step=64, interactive=True) with gr.Column(): sampling_steps = gr.Slider(label="Sampling Steps", info="The number of denoising steps.", value=20, minimum=4, maximum=50, step=1, interactive=True) generate_button.click(fn=generate, inputs=[prompt, negative_prompt, width, height, sampling_steps], outputs=[output]) else: def show_message(): return "# This is the legacy space. To access the app, [click here](https://huggingface.co/spaces/nroggendorff/flux-lora-tester)" demo = gr.Interface(fn=show_message, inputs=None, outputs="markdown") if __name__ == "__main__": demo.launch()