import gradio as gr import spaces from diffusers import DiffusionPipeline import torch device = "cuda" if torch.cuda.is_available() else "cpu" pipe = DiffusionPipeline.from_pretrained( "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16 ).to(device) pipe.load_lora_weights("MegaTronX/MetartLoRA", weight_name="MetartLoRA.safetensors") @spaces.GPU(duration=75) def generate_image(prompt, num_inference_steps=25, guidance_scale=7.5, seed=None): """Generates an image using the FLUX.1-dev LoRA model.""" generator = torch.Generator("cuda").manual_seed(seed) if seed else None image = pipe( prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=generator, ).images[0] return image # Gradio Interface iface = gr.Interface( fn=generate_image, inputs=[ gr.Textbox(lines=3, label="Prompt"), gr.Slider(minimum=10, maximum=100, value=25, label="Inference Steps"), gr.Slider(minimum=1, maximum=15, value=7.5, label="Guidance Scale"), gr.Number(label="Seed (Optional)"), ], outputs=gr.Image(label="Generated Image"), title="FLUX.1-dev LoRA Demo", description="A demo of your FLUX.1-dev LoRA model.", ) iface.launch()