app.py

import gradio as gr
import torch
from diffusers import FluxPipeline

# Function to load the model and list layers
def list_flux_layers():
    try:
        # Load the FLUX.1-dev model
        # Using torch.bfloat16 to reduce memory usage, offloading to CPU if needed
        pipe = FluxPipeline.from_pretrained(
            "black-forest-labs/FLUX.1-dev",
            torch_dtype=torch.bfloat16
        )
        pipe.enable_model_cpu_offload()  # Offload to CPU to save VRAM

        # Access the transformer (main component of Flux) and get layer names
        model = pipe.transformer  # Flux's core transformer model
        layer_names = []

        # Iterate through all named modules to get layer names
        for name, module in model.named_modules():
            layer_names.append(name)

        # Format the output as a numbered list
        output = "\n".join([f"{i+1}. {name}" for i, name in enumerate(layer_names)])
        return f"Layers of FLUX.1-dev:\n\n{output}"
    except Exception as e:
        return f"Error loading model or listing layers: {str(e)}"

# Create Gradio interface
with gr.Blocks(title="FLUX.1-dev Layer Lister") as demo:
    gr.Markdown("# FLUX.1-dev Layer Lister")
    gr.Markdown("Click the button below to list all layers in the black-forest-labs/FLUX.1-dev model.")
    
    # Button to trigger the layer listing
    btn = gr.Button("List Layers")
    
    # Output area for the layer names
    output = gr.Textbox(label="Model Layers", lines=20, placeholder="Layer names will appear here...")
    
    # Connect the button to the function
    btn.click(fn=list_flux_layers, inputs=None, outputs=output)

# Launch the app
demo.launch()