import gradio as gr import torch import os from diffusers import StableDiffusionXLPipeline, AutoencoderKL, LCMScheduler 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 import copy import json import gc import random from urllib.parse import quote lora_list = hf_hub_download(repo_id="multimodalart/LoraTheExplorer", filename="sdxl_loras.json", repo_type="space") with open(lora_list, "r") as file: data = json.load(file) sdxl_loras_raw = [ { "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), "likes": item.get("likes", 0), "downloads": item.get("downloads", 0), "is_nc": item.get("is_nc", False), "new": item.get("new", False), } for item in data ] device = "cuda" state_dicts = {} for item in sdxl_loras_raw: 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) state_dicts[item["repo"]] = { "saved_name": saved_name, "state_dict": state_dict } sdxl_loras_raw_new = [item for item in sdxl_loras_raw if item.get("new") == True] sdxl_loras_raw = [item for item in sdxl_loras_raw if item.get("new") != True] lcm_lora_id = "latent-consistency/lcm-lora-sdxl" 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, ) pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.to(device) pipe.load_lora_weights(lcm_lora_id, weight_name="pytorch_lora_weights.safetensors", adapter_name="lcm_lora", use_auth_token=os.getenv('HF_TOKEN')) last_lora = "" last_merged = False last_fused = False js = ''' var button = document.getElementById('button'); // Add a click event listener to the button button.addEventListener('click', function() { element.classList.add('selected'); }); ''' def update_selection(selected_state: gr.SelectData, sdxl_loras, is_new=False): 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/) ''' if(is_new): if(selected_state.index == 0): selected_state.index = -9999 else: selected_state.index *= -1 return ( updated_text, instance_prompt, gr.update(placeholder=new_placeholder), selected_state, use_with_diffusers, use_with_uis, gr.Gallery(selected_index=None) ) def check_selected(selected_state): if not selected_state: raise gr.Error("You must select a LoRA") def run_lora(prompt, negative, lora_scale, steps, selected_state, sdxl_loras, sdxl_loras_new, progress=gr.Progress(track_tqdm=True)): global last_lora, last_merged, last_fused, pipe print("Index when running ", selected_state.index) if(selected_state.index < 0): if(selected_state.index == -9999): selected_state.index = 0 else: selected_state.index *= -1 sdxl_loras = sdxl_loras_new print("Selected State: ", selected_state.index) print(sdxl_loras[selected_state.index]["repo"]) 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 = state_dicts[repo_name]["saved_name"] loaded_state_dict = copy.deepcopy(state_dicts[repo_name]["state_dict"]) #cross_attention_kwargs = None if last_lora != repo_name: #if(last_fused): #pipe.unfuse_lora() try: pipe.load_lora_weights(loaded_state_dict, adapter_name=sdxl_loras[selected_state.index]["repo"]) except: print(sdxl_loras[selected_state.index]["repo"], " was loaded") pipe.set_adapters([sdxl_loras[selected_state.index]["repo"], "lcm_lora"], adapter_weights=[lora_scale, 1.0]) #last_fused = True is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"] if(is_pivotal): #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) image = pipe( prompt=prompt, negative_prompt=negative, guidance_scale=0.5, num_inference_steps=steps ).images[0] last_lora = repo_name gc.collect() return image, gr.update(visible=True) def shuffle_gallery(sdxl_loras): random.shuffle(sdxl_loras) return [(item["image"], item["title"]) for item in sdxl_loras], sdxl_loras def swap_gallery(order, sdxl_loras): if(order == "random"): return shuffle_gallery(sdxl_loras) else: sorted_gallery = sorted(sdxl_loras, key=lambda x: x.get(order, 0), reverse=True) return [(item["image"], item["title"]) for item in sorted_gallery], sorted_gallery def deselect(): return gr.Gallery(selected_index=None) with gr.Blocks(css="custom.css") as demo: gr_sdxl_loras = gr.State(value=sdxl_loras_raw) gr_sdxl_loras_new = gr.State(value=sdxl_loras_raw_new) title = gr.HTML( """

LoRAExperimental  LCM LoRA the Explorer


Combine loading an LCM-LoRA with your favorite SDXL LoRa and run LoRAs in only 8 steps. Check out our blogon how this works. Try experimenting with step count and LoRA strength in advanced settings!Not all LoRAs may combine well with the LCM-LoRA """, elem_id="title", ) selected_state = gr.State() with gr.Row(elem_id="main_app"): with gr.Group(elem_id="gallery_box"): order_gallery = gr.Radio(choices=["random", "likes"], value="random", label="Order by", elem_id="order_radio") selected_loras = gr.Gallery(label="Selected LoRAs", height=80, show_share_button=False, visible=False, elem_id="gallery_selected", ) new_gallery = gr.Gallery(label="New LoRAs", elem_id="gallery_new", columns=3, value=[(item["image"], item["title"]) for item in sdxl_loras_raw_new], allow_preview=False, show_share_button=False) 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, height=784 ) 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", visible=False) weight = gr.Slider(0, 10, value=1.2, step=0.1, label="LoRA weight") steps = gr.Slider(0,8, value=4, step=1, label="Steps") 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.Group(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." ) order_gallery.change( fn=swap_gallery, inputs=[order_gallery, gr_sdxl_loras], outputs=[gallery, gr_sdxl_loras], queue=False ) gallery.select( fn=update_selection, inputs=[gr_sdxl_loras], outputs=[prompt_title, prompt, prompt, selected_state, use_diffusers, use_uis, new_gallery], queue=False, show_progress=False ) new_gallery.select( fn=update_selection, inputs=[gr_sdxl_loras_new, gr.State(True)], outputs=[prompt_title, prompt, prompt, selected_state, use_diffusers, use_uis, gallery], 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, steps, selected_state, gr_sdxl_loras, gr_sdxl_loras_new], 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, steps, selected_state, gr_sdxl_loras, gr_sdxl_loras_new], outputs=[result, share_group], ) share_button.click(None, [], [], js=share_js) demo.load(fn=shuffle_gallery, inputs=[gr_sdxl_loras], outputs=[gallery, gr_sdxl_loras], queue=False, js=js) demo.queue(max_size=20) demo.launch(share=True)