Create app.py

app.py

@@ -0,0 +1,107 @@
+import gradio as gr
+import json
+import logging
+import torch
+from PIL import Image
+from diffusers import DiffusionPipeline
+import spaces
+
+# Load LoRAs from JSON file
+with open('loras.json', 'r') as f:
+    loras = json.load(f)
+
+# Initialize the base model
+base_model = "black-forest-labs/FLUX.1-dev"
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+def update_selection(evt: gr.SelectData):
+    selected_lora = loras[evt.index]
+    new_placeholder = f"Type a prompt for {selected_lora['title']}"
+    lora_repo = selected_lora["repo"]
+    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
+    return (
+        gr.update(placeholder=new_placeholder),
+        updated_text,
+        evt.index
+    )
+
+@spaces.GPU
+def run_lora(prompt, negative_prompt, cfg_scale, steps, selected_index, seed, width, height, lora_scale):
+    if selected_index is None:
+        raise gr.Error("You must select a LoRA before proceeding.")
+
+    selected_lora = loras[selected_index]
+    lora_path = selected_lora["repo"]
+    trigger_word = selected_lora["trigger_word"]
+
+    # Load LoRA weights
+    pipe.load_lora_weights(lora_path)
+
+    # Set random seed for reproducibility
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+
+    # Generate image
+    image = pipe(
+        prompt=f"{prompt} {trigger_word}",
+        negative_prompt=negative_prompt,
+        num_inference_steps=steps,
+        guidance_scale=cfg_scale,
+        width=width,
+        height=height,
+        generator=generator,
+        cross_attention_kwargs={"scale": lora_scale},
+    ).images[0]
+
+    # Unload LoRA weights
+    pipe.unload_lora_weights()
+
+    return image
+
+with gr.Blocks(theme=gr.themes.Soft()) as app:
+    gr.Markdown("# FLUX.1 LoRA the Explorer")
+    selected_index = gr.State(None)
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            result = gr.Image(label="Generated Image", height=768)
+            generate_button = gr.Button("Generate", variant="primary")
+
+        with gr.Column(scale=1):
+            gallery = gr.Gallery(
+                [(item["image"], item["title"]) for item in loras],
+                label="LoRA Gallery",
+                allow_preview=False,
+                columns=2
+            )
+
+    with gr.Row():
+        with gr.Column():
+            prompt_title = gr.Markdown("### Click on a LoRA in the gallery to select it")
+            selected_info = gr.Markdown("")
+            prompt = gr.Textbox(label="Prompt", lines=3, placeholder="Type a prompt after selecting a LoRA")
+            negative_prompt = gr.Textbox(label="Negative Prompt", lines=2, value="low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry")
+
+        with gr.Column():
+            with gr.Row():
+                cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=7.5)
+                steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=30)
+            
+            with gr.Row():
+                width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
+                height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
+            
+            with gr.Row():
+                seed = gr.Slider(label="Seed", minimum=0, maximum=2**32-1, step=1, value=0, randomize=True)
+                lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=1)
+
+    gallery.select(update_selection, outputs=[prompt, selected_info, selected_index])
+    
+    generate_button.click(
+        fn=run_lora,
+        inputs=[prompt, negative_prompt, cfg_scale, steps, selected_index, seed, width, height, lora_scale],
+        outputs=[result]
+    )
+
+app.queue()
+app.launch()