import gradio as gr import torch from diffusers import StableDiffusionXLPipeline, AutoencoderKL from huggingface_hub import hf_hub_download from safetensors.torch import load_file from share_btn import community_icon_html, loading_icon_html, share_js from cog_sdxl_dataset_and_utils import TokenEmbeddingsHandler import lora from time import sleep import copy import json import gc 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"], "is_pivotal": item.get("is_pivotal", False), "text_embedding_weights": item.get("text_embedding_weights", None), "is_nc": item.get("is_nc", False) } for item in data ] device = "cuda" for item in sdxl_loras: saved_name = hf_hub_download(item["repo"], item["weights"]) if not saved_name.endswith('.safetensors'): state_dict = torch.load(saved_name) else: state_dict = load_file(saved_name) item["saved_name"] = saved_name item["state_dict"] = state_dict #{k: v.to(device=device, dtype=torch.float16) for k, v in state_dict.items() if torch.is_tensor(v)} 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, ) original_pipe = copy.deepcopy(pipe) pipe.to(device) last_lora = "" last_merged = False last_fused = 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 LoRA applies for all prompts, no need for 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}) ✨ {'(non-commercial LoRA, `cc-by-nc`)' if sdxl_loras[selected_state.index]['is_nc'] else '' }" is_compatible = sdxl_loras[selected_state.index]["is_compatible"] is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"] use_with_diffusers = f''' ## Using [`{lora_repo}`](https://huggingface.co/{lora_repo}) ## Use it with diffusers: ''' if is_compatible: use_with_diffusers += f''' 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_scale= 0.9 image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5, cross_attention_kwargs={{"scale": lora_scale}}).images[0] image.save("image.png") ''' elif not is_pivotal: use_with_diffusers += "This LoRA is not compatible with diffusers natively yet. But you can still use it on diffusers with `bmaltais/kohya_ss` LoRA class, check out this [Google Colab](https://colab.research.google.com/drive/14aEJsKdEQ9_kyfsiV6JDok799kxPul0j )" else: use_with_diffusers += f"This LoRA is not compatible with diffusers natively yet. But you can still use it on diffusers with sdxl-cog `TokenEmbeddingsHandler` class, check out the [model repo](https://huggingface.co/{lora_repo}#inference-with-🧨-diffusers)" 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 merge_incompatible_lora(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" ) del weights_sd del lora_model gc.collect() def run_lora(prompt, negative, lora_scale, selected_state, progress=gr.Progress(track_tqdm=True)): global last_lora, last_merged, last_fused, pipe if negative == "": negative = None if not selected_state: raise gr.Error("You must select a LoRA") repo_name = sdxl_loras[selected_state.index]["repo"] weight_name = sdxl_loras[selected_state.index]["weights"] full_path_lora = sdxl_loras[selected_state.index]["saved_name"] loaded_state_dict = sdxl_loras[selected_state.index]["state_dict"] cross_attention_kwargs = None if last_lora != repo_name: pipe = copy.deepcopy(original_pipe) pipe.to(device) #if last_merged: # del pipe # gc.collect() #elif(last_fused): # pipe.unload_lora_weights() # pipe.unfuse_lora() is_compatible = sdxl_loras[selected_state.index]["is_compatible"] if is_compatible: pipe.load_lora_weights(loaded_state_dict) pipe.fuse_lora(lora_scale) last_fused = True else: is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"] if(is_pivotal): pipe.load_lora_weights(loaded_state_dict) pipe.fuse_lora(lora_scale) last_fused = True #Add the textual inversion embeddings from pivotal tuning models text_embedding_name = sdxl_loras[selected_state.index]["text_embedding_weights"] text_encoders = [pipe.text_encoder, pipe.text_encoder_2] tokenizers = [pipe.tokenizer, pipe.tokenizer_2] embedding_path = hf_hub_download(repo_id=repo_name, filename=text_embedding_name, repo_type="model") embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers) embhandler.load_embeddings(embedding_path) else: merge_incompatible_lora(full_path_lora, lora_scale) last_merged = True last_fused=False image = pipe( prompt=prompt, negative_prompt=negative, width=768, height=768, num_inference_steps=20, guidance_scale=7.5, ).images[0] last_lora = repo_name gc.collect() return image, gr.update(visible=True) with gr.Blocks(css="custom.css") as demo: title = gr.HTML( """

LoRA LoRA the Explorer

""", elem_id="title", ) selected_state = gr.State() with gr.Row(): gallery = gr.Gallery( value=[(item["image"], item["title"]) for item in sdxl_loras], label="SDXL LoRA Gallery", allow_preview=False, columns=3, elem_id="gallery", show_share_button=False ) with gr.Column(): prompt_title = gr.Markdown( value="### Click on a LoRA in the gallery to select it", visible=True, elem_id="selected_lora", ) with gr.Row(): prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="Type a prompt after selecting a LoRA", elem_id="prompt") button = gr.Button("Run", elem_id="run_button") with gr.Group(elem_id="share-btn-container", visible=False) as share_group: community_icon = gr.HTML(community_icon_html) loading_icon = gr.HTML(loading_icon_html) share_button = gr.Button("Share to community", elem_id="share-btn") result = gr.Image( interactive=False, label="Generated Image", elem_id="result-image" ) with gr.Accordion("Advanced options", open=False): negative = gr.Textbox(label="Negative Prompt") weight = gr.Slider(0, 10, value=1, step=0.1, label="LoRA weight") with gr.Column(elem_id="extra_info"): with gr.Accordion( "Use it with: 🧨 diffusers, ComfyUI, Invoke AI, SD.Next, AUTO1111", open=False, elem_id="accordion", ): with gr.Row(): use_diffusers = gr.Markdown("""## Select a LoRA first 🤗""") use_uis = gr.Markdown() with gr.Accordion("Submit a LoRA! 📥", open=False): submit_title = gr.Markdown( "### Streamlined submission coming soon! Until then [suggest your LoRA in the community tab](https://huggingface.co/spaces/multimodalart/LoraTheExplorer/discussions) 🤗" ) with gr.Box(elem_id="soon"): submit_source = gr.Radio( ["Hugging Face", "CivitAI"], label="LoRA source", value="Hugging Face", ) with gr.Row(): submit_source_hf = gr.Textbox( label="Hugging Face Model Repo", info="In the format `username/model_id`", ) submit_safetensors_hf = gr.Textbox( label="Safetensors filename", info="The filename `*.safetensors` in the model repo", ) with gr.Row(): submit_trigger_word_hf = gr.Textbox(label="Trigger word") submit_image = gr.Image( label="Example image (optional if the repo already contains images)" ) submit_button = gr.Button("Submit!") submit_disclaimer = gr.Markdown( "This is a curated gallery by me, [apolinário (multimodal.art)](https://twitter.com/multimodalart). I'll try to include as many cool LoRAs as they are submitted! You can [duplicate this Space](https://huggingface.co/spaces/multimodalart/LoraTheExplorer?duplicate=true) to use it privately, and add your own LoRAs by editing `sdxl_loras.json` in the Files tab of your private space." ) gallery.select( update_selection, outputs=[prompt_title, prompt, prompt, selected_state, use_diffusers, use_uis], queue=False, show_progress=False, ) prompt.submit( fn=check_selected, inputs=[selected_state], queue=False, show_progress=False ).success( fn=run_lora, inputs=[prompt, negative, weight, selected_state], outputs=[result, share_group], ) button.click( fn=check_selected, inputs=[selected_state], queue=False, show_progress=False ).success( fn=run_lora, inputs=[prompt, negative, weight, selected_state], outputs=[result, share_group], ) share_button.click(None, [], [], _js=share_js) demo.queue(max_size=20) demo.launch()