import gradio as gr import torch from diffusers import StableDiffusionXLPipeline, AutoencoderKL from huggingface_hub import hf_hub_download import lora from time import sleep import copy import json with open("sdxl_loras.json", "r") as file: sdxl_loras = [ ( item["image"], item["title"], item["repo"], item["trigger_word"], item["weights"], item["is_compatible"], ) for item in json.load(file) ] saved_names = [ hf_hub_download(repo_id, filename) for _, _, repo_id, _, filename, _ in sdxl_loras ] def update_selection(selected_state: gr.SelectData): lora_repo = sdxl_loras[selected_state.index][2] instance_prompt = sdxl_loras[selected_state.index][3] updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo})" return updated_text, instance_prompt, selected_state vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 ) pipe = StableDiffusionXLPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, ).to("cpu") original_pipe = copy.deepcopy(pipe) pipe.to("cuda") last_lora = "" last_merged = False def run_lora(prompt, negative, weight, selected_state): global last_lora, last_merged, pipe if not selected_state: raise gr.Error("You must select a LoRA") repo_name = sdxl_loras[selected_state.index][2] weight_name = sdxl_loras[selected_state.index][4] full_path_lora = saved_names[selected_state.index] cross_attention_kwargs = None if last_lora != repo_name: if last_merged: pipe = copy.deepcopy(original_pipe) pipe.to("cuda") else: pipe.unload_lora_weights() is_compatible = sdxl_loras[selected_state.index][5] if is_compatible: pipe.load_lora_weights(full_path_lora) cross_attention_kwargs = {"scale": weight} else: for weights_file in [full_path_lora]: if ";" in weights_file: weights_file, multiplier = weights_file.split(";") multiplier = float(weight) else: multiplier = 1.0 lora_model, weights_sd = lora.create_network_from_weights( multiplier, full_path_lora, pipe.vae, pipe.text_encoder, pipe.unet, for_inference=True, ) lora_model.apply_to(pipe.text_encoder, pipe.unet) lora_model.merge_to( pipe.text_encoder, pipe.unet, weights_sd, torch.float16, "cuda" ) last_merged = True image = pipe( prompt=prompt, negative_prompt=negative, num_inference_steps=20, guidance_scale=7.5, cross_attention_kwargs=cross_attention_kwargs, ).images[0] last_lora = repo_name return image css = """ #title{text-align: center;margin-bottom: 0.5em} #title h1{font-size: 3em} #prompt textarea{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;} #run_button{position:absolute;margin-top: 38px;right: 0;margin-right: 0.8em;border-bottom-left-radius: 0px; border-top-left-radius: 0px;} #gallery{display:flex} #gallery .grid-wrap{min-height: 100%;} """ with gr.Blocks(css=css) as demo: title = gr.Markdown("# LoRA the Explorer ๐Ÿ”Ž", elem_id="title") with gr.Row(): gallery = gr.Gallery( value=[(a, b) for a, b, _, _, _, _ in sdxl_loras], label="SDXL LoRA Gallery", allow_preview=False, columns=3, elem_id="gallery", ) with gr.Column(): prompt_title = gr.Markdown( value="### Click on a LoRA in the gallery to select it", visible=True ) with gr.Row(): prompt = gr.Textbox(label="Prompt", elem_id="prompt") button = gr.Button("Run", elem_id="run_button") result = gr.Image(interactive=False, label="result") with gr.Accordion("Advanced options", open=False): negative = gr.Textbox(label="Negative Prompt") weight = gr.Slider(0, 1, value=1, step=0.1, label="LoRA weight") with gr.Column(): gr.Markdown("Use it with:") with gr.Row(): with gr.Accordion("๐Ÿงจ diffusers", open=False): gr.Markdown("") with gr.Accordion("ComfyUI", open=False): gr.Markdown("") with gr.Accordion("Invoke AI", open=False): gr.Markdown("") with gr.Accordion("SD.Next (AUTO1111 fork)", open=False): gr.Markdown("") selected_state = gr.State() gallery.select( update_selection, outputs=[prompt_title, prompt, selected_state], queue=False, show_progress=False, ) prompt.submit( fn=run_lora, inputs=[prompt, negative, weight, selected_state], outputs=result ) button.click( fn=run_lora, inputs=[prompt, negative, weight, selected_state], outputs=result ) demo.launch()