app.py

import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline

# 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)
original_load_lora = copy.deepcopy(pipe.load_lora_into_transformer)
pipe.to("cuda")

def load_lora_into_transformer_patched(cls, state_dict, transformer, adapter_name=None, alpha=None, _pipeline=None):
    from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict

    keys = list(state_dict.keys())

    transformer_keys = [k for k in keys if k.startswith(cls.transformer_name)]
    state_dict = {
        k.replace(f"{cls.transformer_name}.", ""): v for k, v in state_dict.items() if k in transformer_keys
    }

    if len(state_dict.keys()) > 0:
        # check with first key if is not in peft format
        first_key = next(iter(state_dict.keys()))
        if "lora_A" not in first_key:
            state_dict = convert_unet_state_dict_to_peft(state_dict)

        if adapter_name in getattr(transformer, "peft_config", {}):
            raise ValueError(
                f"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name."
            )

        rank = {}
        for key, val in state_dict.items():
            if "lora_B" in key:
                rank[key] = val.shape[1]

        lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=None, peft_state_dict=state_dict)
        if "use_dora" in lora_config_kwargs:
            if lora_config_kwargs["use_dora"] and is_peft_version("<", "0.9.0"):
                raise ValueError(
                    "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
                )
            else:
                lora_config_kwargs.pop("use_dora")

        
        lora_config_kwargs["lora_alpha"] = 32
        lora_config = LoraConfig(**lora_config_kwargs)

        # adapter_name
        if adapter_name is None:
            adapter_name = get_adapter_name(transformer)

        # In case the pipeline has been already offloaded to CPU - temporarily remove the hooks
        # otherwise loading LoRA weights will lead to an error
        is_model_cpu_offload, is_sequential_cpu_offload = cls._optionally_disable_offloading(_pipeline)

        inject_adapter_in_model(lora_config, transformer, adapter_name=adapter_name)
        incompatible_keys = set_peft_model_state_dict(transformer, state_dict, adapter_name)

        if incompatible_keys is not None:
            # check only for unexpected keys
            unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
            if unexpected_keys:
                logger.warning(
                    f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
                    f" {unexpected_keys}. "
                )

        # Offload back.
        if is_model_cpu_offload:
            _pipeline.enable_model_cpu_offload()
        elif is_sequential_cpu_offload:
            _pipeline.enable_sequential_cpu_offload()
        # Unsafe code />

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(duration=90)
def run_lora(prompt, cfg_scale, steps, selected_index, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
    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
    if "weights" in selected_lora:
        pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
    else:
        pipe.load_lora_weights(lora_path)

    if "custom_alpha" in selected_lora:
        pipe.load_lora_into_transformer = load_lora_into_transformer_patched
    else:
        pipe.load_lora_into_transformer = original_load_lora
        
    # 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=3):
            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
        with gr.Column(scale=1):
            generate_button = gr.Button("Generate", variant="primary")
    with gr.Row():
        with gr.Column(scale=1):
            selected_info = gr.Markdown("")
            gallery = gr.Gallery(
                [(item["image"], item["title"]) for item in loras],
                label="LoRA Gallery",
                allow_preview=False,
                columns=2
            )
            
        with gr.Column(scale=2):
            result = gr.Image(label="Generated Image")

    with gr.Row():
        #with gr.Column():
            #prompt_title = gr.Markdown("### Click on a LoRA in the gallery to select it")
            #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=3.5)
                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, cfg_scale, steps, selected_index, seed, width, height, lora_scale],
        outputs=[result]
    )

app.queue()
app.launch()