import gradio as gr import json import logging import torch from PIL import Image import spaces from diffusers import DiffusionPipeline import copy # 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 ''' #gen_btn{height: 100%} ''' 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", elem_id="gen_btn") with gr.Row(): with gr.Column(scale=2): 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=3): 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]) gr.on( triggers=[generate_button.click, prompt.submit], fn=run_lora, inputs=[prompt, cfg_scale, steps, selected_index, seed, width, height, lora_scale], outputs=[result] ) app.queue() app.launch()