Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import FluxPipeline, FluxTransformer2DModel, FlowMatchEulerDiscreteScheduler
+from huggingface_hub import hf_hub_download
+from PIL import Image
+import numpy as np
+import random
+import spaces
+
+# Constants
+BASE_MODEL = "black-forest-labs/FLUX.1-dev"
+LORA_MODEL = "MegaTronX/SuicideGirl-FLUX"  # Replace with your LoRA path 
+MAX_SEED = np.iinfo(np.int32).max
+
+# Initialize model and scheduler
+if torch.cuda.is_available():
+    transformer = FluxTransformer2DModel.from_single_file(
+        "https://huggingface.co/MegaTronX/SuicideGirl-FLUX/blob/main/SuicideGirls.safetensors",
+        torch_dtype=torch.bfloat16
+    )
+    pipe = FluxPipeline.from_pretrained(
+        BASE_MODEL,
+        transformer=transformer,
+        torch_dtype=torch.bfloat16
+    )
+    pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(
+        pipe.scheduler.config, use_beta_sigmas=True
+    )
+    pipe.to("cuda")
+    
+    # Load and apply LoRA weights
+    pipe.load_lora_weights(LORA_MODEL)
+
+@spaces.GPU
+def generate_image(
+    prompt,
+    width=768,
+    height=1024,
+    guidance_scale=3.5,
+    num_inference_steps=24,
+    seed=-1,
+    num_images=1,
+    progress=gr.Progress(track_tqdm=True)
+):
+    if seed == -1:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed)
+    
+    images = pipe(
+        prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        output_type="pil",
+        max_sequence_length=512,
+        num_images_per_prompt=num_images,
+    ).images
+    
+    return images, seed
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.HTML("<h1><center>Flux LoRA Image Generator</center></h1>")
+    
+    with gr.Group():
+        prompt = gr.Textbox(label='Enter Your Prompt', lines=3)
+        generate_button = gr.Button("Generate", variant='primary')
+        
+        with gr.Row():
+            image_output = gr.Gallery(label="Generated Images", columns=2, preview=True)
+            seed_output = gr.Number(label="Seed Used")
+        
+        with gr.Accordion("Advanced Options", open=False):
+            width = gr.Slider(label="Width", minimum=512, maximum=1280, step=8, value=768)
+            height = gr.Slider(label="Height", minimum=512, maximum=1280, step=8, value=1024)
+            guidance_scale = gr.Slider(label="Guidance Scale", minimum=0, maximum=50, step=0.1, value=3.5)
+            num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=24)
+            seed = gr.Slider(label="Seed (-1 for random)", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
+            num_images = gr.Slider(label="Number of Images", minimum=1, maximum=4, step=1, value=1)
+    
+    generate_button.click(
+        fn=generate_image,
+        inputs=[prompt, width, height, guidance_scale, num_inference_steps, seed, num_images],
+        outputs=[image_output, seed_output]
+    )
+
+demo.launch()