import gradio as gr import torch from diffusers import StableDiffusionXLPipeline, AutoencoderKL from huggingface_hub import hf_hub_download from share_btn import community_icon_html, loading_icon_html, share_js import lora from time import sleep import copy import json with open("sdxl_loras.json", "r") as file: data = json.load(file) sdxl_loras = [ { "image": item["image"], "title": item["title"], "repo": item["repo"], "trigger_word": item["trigger_word"], "weights": item["weights"], "is_compatible": item["is_compatible"], } for item in data ] saved_names = [ hf_hub_download(item["repo"], item["weights"]) for item in sdxl_loras ] device = "cuda" # replace this to `mps` if on a MacOS Silicon 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(device) last_lora = "" last_merged = False def update_selection(selected_state: gr.SelectData): lora_repo = sdxl_loras[selected_state.index]["repo"] instance_prompt = sdxl_loras[selected_state.index]["trigger_word"] new_placeholder = "Type a prompt! This style works for all prompts without a trigger word" if instance_prompt == "" else "Type a prompt to use your selected LoRA" weight_name = sdxl_loras[selected_state.index]["weights"] updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨" use_with_diffusers = f''' ## Using [`{lora_repo}`](https://huggingface.co/{lora_repo}) ## Use it with diffusers: ```python from diffusers import StableDiffusionXLPipeline import torch model_path = "stabilityai/stable-diffusion-xl-base-1.0" pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16) pipe.to("cuda") pipe.load_lora_weights("{lora_repo}", weight_name={weight_name}) prompt = "{instance_prompt}..." lora_weight = 0.5 image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5, cross_attention_kwargs={{"scale":lora_weight}}).images[0] image.save("image.png") ``` ''' use_with_uis = f''' ## Use it with Comfy UI, Invoke AI, SD.Next, AUTO1111: ### Download the `*.safetensors` weights of [here](https://huggingface.co/{lora_repo}/resolve/main/{weight_name}) - [ComfyUI guide](https://comfyanonymous.github.io/ComfyUI_examples/lora/) - [Invoke AI guide](https://invoke-ai.github.io/InvokeAI/features/CONCEPTS/?h=lora#using-loras) - [SD.Next guide](https://github.com/vladmandic/automatic) - [AUTOMATIC1111 guide](https://stable-diffusion-art.com/lora/) ''' return ( updated_text, instance_prompt, gr.update(placeholder=new_placeholder), selected_state, use_with_diffusers, use_with_uis, ) def check_selected(selected_state): if not selected_state: raise gr.Error("You must select a LoRA") def get_cross_attention_kwargs(scale, repo_name, is_compatible): if repo_name != last_lora and is_compatible: return {"scale": scale} return None def load_lora_model(pipe, repo_name, full_path_lora, lora_scale): if repo_name == last_lora: return if last_merged: pipe = copy.deepcopy(original_pipe) pipe.to(device) else: pipe.unload_lora_weights() is_compatible = sdxl_loras[selected_state.index]["is_compatible"] if is_compatible: pipe.load_lora_weights(full_path_lora) else: load_incompatible_lora(pipe, full_path_lora, lora_scale) def load_incompatible_lora(pipe, full_path_lora, lora_scale): for weights_file in [full_path_lora]: if ";" in weights_file: weights_file, multiplier = weights_file.split(";") multiplier = float(multiplier) else: multiplier = lora_scale 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.merge_to( pipe.text_encoder, pipe.unet, weights_sd, torch.float16, "cuda" ) def generate_image(pipe, prompt, negative, cross_attention_kwargs): return pipe( prompt=prompt, negative_prompt=negative, width=768, height=768, num_inference_steps=20, guidance_scale=7.5, cross_attention_kwargs=cross_attention_kwargs, ).images[0] def run_lora(prompt, negative, lora_scale, selected_state): global last_lora, last_merged, pipe if not selected_state: raise gr.Error("You must select a LoRA") if negative == "": negative = None repo_name = sdxl_loras[selected_state.index]["repo"] full_path_lora = saved_names[selected_state.index] cross_attention_kwargs = get_cross_attention_kwargs( lora_scale, repo_name, sdxl_loras[selected_state.index]["is_compatible"]) load_lora_model(pipe, repo_name, full_path_lora, lora_scale) image = generate_image(pipe, prompt, negative, cross_attention_kwargs) last_lora = repo_name return image, gr.update(visible=True) with gr.Blocks(css="custom.css") as demo: title = gr.HTML( """