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| import gradio as gr | |
| from time import sleep, time | |
| from diffusers import DiffusionPipeline, StableDiffusionXLPipeline | |
| from huggingface_hub import hf_hub_download, CommitScheduler | |
| from safetensors.torch import load_file | |
| from share_btn import community_icon_html, loading_icon_html, share_js | |
| from uuid import uuid4 | |
| from pathlib import Path | |
| from PIL import Image | |
| import torch | |
| import json | |
| import random | |
| import copy | |
| import gc | |
| import pickle | |
| import spaces | |
| lora_list = hf_hub_download(repo_id="multimodalart/LoraTheExplorer", filename="sdxl_loras.json", repo_type="space") | |
| IMAGE_DATASET_DIR = Path("image_dataset") / f"train-{uuid4()}" | |
| IMAGE_DATASET_DIR.mkdir(parents=True, exist_ok=True) | |
| IMAGE_JSONL_PATH = IMAGE_DATASET_DIR / "metadata.jsonl" | |
| scheduler = CommitScheduler( | |
| repo_id="multimodalart/lora-fusing-preferences", | |
| repo_type="dataset", | |
| folder_path=IMAGE_DATASET_DIR, | |
| path_in_repo=IMAGE_DATASET_DIR.name, | |
| every=10 | |
| ) | |
| with open(lora_list, "r") as file: | |
| data = json.load(file) | |
| sdxl_loras = [ | |
| { | |
| "image": item["image"] if item["image"].startswith("https://") else f'https://huggingface.co/spaces/multimodalart/LoraTheExplorer/resolve/main/{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 | |
| ] | |
| state_dicts = {} | |
| 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, map_location=torch.device('cpu')) | |
| else: | |
| state_dict = load_file(saved_name, device="cpu") | |
| state_dicts[item["repo"]] = { | |
| "saved_name": saved_name, | |
| "state_dict": state_dict | |
| } | |
| css = ''' | |
| .gradio-container{max-width: 650px! important} | |
| #title{text-align:center;} | |
| #title h1{font-size: 250%} | |
| .selected_random img{object-fit: cover} | |
| .selected_random [data-testid="block-label"] span{display: none} | |
| .plus_column{align-self: center} | |
| .plus_button{font-size: 235% !important; text-align: center;margin-bottom: 19px} | |
| #prompt{padding: 0 0 1em 0} | |
| #prompt input{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;} | |
| #run_button{position: absolute;margin-top: 25.8px;right: 0;margin-right: 0.75em;border-bottom-left-radius: 0px;border-top-left-radius: 0px} | |
| .random_column{align-self: center; align-items: center;gap: 0.5em !important} | |
| #share-btn-container{padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;margin-top: 0.35em;} | |
| div#share-btn-container > div {flex-direction: row;background: black;align-items: center} | |
| #share-btn-container:hover {background-color: #060606} | |
| #share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;font-size: 15px;} | |
| #share-btn * {all: unset} | |
| #share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;} | |
| #share-btn-container .wrap {display: none !important} | |
| #share-btn-container.hidden {display: none!important} | |
| #post_gen_info{margin-top: .5em} | |
| #thumbs_up_clicked{background:green} | |
| #thumbs_down_clicked{background:red} | |
| .title_lora a{color: var(--body-text-color) !important; opacity:0.6} | |
| #prompt_area .form{border:0} | |
| #reroll_button{position: absolute;right: 0;flex-grow: 1;min-width: 75px;padding: .1em} | |
| .pending .min {min-height: auto} | |
| ''' | |
| original_pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16) | |
| def merge_and_run(prompt, negative_prompt, shuffled_items, lora_1_scale=0.5, lora_2_scale=0.5, seed=-1): | |
| repo_id_1 = shuffled_items[0]['repo'] | |
| repo_id_2 = shuffled_items[1]['repo'] | |
| print("Loading state dicts...") | |
| start_time = time() | |
| state_dict_1 = copy.deepcopy(state_dicts[repo_id_1]["state_dict"]) | |
| state_dict_1 = {k: v.to(device="cuda", dtype=torch.float16) for k,v in state_dict_1.items() if torch.is_tensor(v)} | |
| state_dict_2 = copy.deepcopy(state_dicts[repo_id_2]["state_dict"]) | |
| state_dict_2 = {k: v.to(device="cuda", dtype=torch.float16) for k,v in state_dict_2.items() if torch.is_tensor(v)} | |
| state_dict_time = time() - start_time | |
| print(f"State Dict time: {state_dict_time}") | |
| start_time = time() | |
| unet = copy.deepcopy(original_pipe.unet) | |
| text_encoder=copy.deepcopy(original_pipe.text_encoder) | |
| text_encoder_2=copy.deepcopy(original_pipe.text_encoder_2) | |
| pipe = StableDiffusionXLPipeline(vae=original_pipe.vae, | |
| text_encoder=text_encoder, | |
| text_encoder_2=text_encoder_2, | |
| scheduler=original_pipe.scheduler, | |
| tokenizer=original_pipe.tokenizer, | |
| tokenizer_2=original_pipe.tokenizer_2, | |
| unet=unet) | |
| pickle_time = time() - start_time | |
| print(f"copy time: {pickle_time}") | |
| pipe.to("cuda") | |
| start_time = time() | |
| print("Loading LoRA weights...") | |
| pipe.load_lora_weights(state_dict_1, low_cpu_mem_usage=True) | |
| pipe.fuse_lora(lora_1_scale) | |
| pipe.load_lora_weights(state_dict_2, low_cpu_mem_usage=True) | |
| pipe.fuse_lora(lora_2_scale) | |
| lora_time = time() - start_time | |
| print(f"Loaded LoRAs time: {lora_time}") | |
| if negative_prompt == "": | |
| negative_prompt = None | |
| if(seed < 0): | |
| seed = random.randint(0, 2147483647) | |
| generator = torch.Generator(device="cuda").manual_seed(seed) | |
| image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=20, width=768, height=768, generator=generator).images[0] | |
| return image, gr.update(visible=True), seed, gr.update(visible=True, interactive=True), gr.update(visible=False), gr.update(visible=True, interactive=True), gr.update(visible=False) | |
| def get_description(item): | |
| trigger_word = item["trigger_word"] | |
| return f"Trigger: `{trigger_word}`" if trigger_word else "No trigger, applied automatically", trigger_word | |
| def truncate_string(s, max_length=29): | |
| return s[:max_length - 3] + "..." if len(s) > max_length else s | |
| def shuffle_images(): | |
| compatible_items = [item for item in sdxl_loras if item['is_compatible']] | |
| random.shuffle(compatible_items) | |
| two_shuffled_items = compatible_items[:2] | |
| title_1 = gr.update(label=two_shuffled_items[0]['title'], value=two_shuffled_items[0]['image']) | |
| title_2 = gr.update(label=two_shuffled_items[1]['title'], value=two_shuffled_items[1]['image']) | |
| repo_id_1 = gr.update(value=two_shuffled_items[0]['repo']) | |
| repo_id_2 = gr.update(value=two_shuffled_items[1]['repo']) | |
| description_1, trigger_word_1 = get_description(two_shuffled_items[0]) | |
| description_2, trigger_word_2 = get_description(two_shuffled_items[1]) | |
| lora_1_link = f"[{truncate_string(two_shuffled_items[0]['repo'])}](https://huggingface.co/{two_shuffled_items[0]['repo']}) ✨" | |
| lora_2_link = f"[{truncate_string(two_shuffled_items[1]['repo'])}](https://huggingface.co/{two_shuffled_items[1]['repo']}) ✨" | |
| prompt_description_1 = gr.update(value=description_1, visible=True) | |
| prompt_description_2 = gr.update(value=description_2, visible=True) | |
| prompt = gr.update(value=f"{trigger_word_1} {trigger_word_2}") | |
| scale = gr.update(value=0.7) | |
| return lora_1_link, title_1, prompt_description_1, repo_id_1, lora_2_link, title_2, prompt_description_2, repo_id_2, prompt, two_shuffled_items, scale, scale | |
| def save_preferences(lora_1_id, lora_1_scale, lora_2_id, lora_2_scale, prompt, generated_image, thumbs_direction, seed): | |
| image_path = IMAGE_DATASET_DIR / f"{uuid4()}.png" | |
| with scheduler.lock: | |
| Image.fromarray(generated_image).save(image_path) | |
| with IMAGE_JSONL_PATH.open("a") as f: | |
| json.dump({"prompt": prompt, "file_name":image_path.name, "lora_1_id": lora_1_id, "lora_1_scale": float(lora_1_scale), "lora_2_id": lora_2_id, "lora_2_scale": float(lora_2_scale), "thumbs_direction": thumbs_direction, "seed": int(seed)}, f) | |
| f.write("\n") | |
| return gr.update(visible=False), gr.update(visible=True), gr.update(interactive=False) | |
| def hide_post_gen_info(): | |
| return gr.update(visible=False) | |
| with gr.Blocks(css=css) as demo: | |
| shuffled_items = gr.State() | |
| title = gr.HTML( | |
| '''<h1>LoRA Roulette 🎰</h1> | |
| <p>This random LoRAs are loaded into SDXL, can you find a fun way to combine them? 🎨</p> | |
| ''', | |
| elem_id="title" | |
| ) | |
| with gr.Column(): | |
| with gr.Column(min_width=10, scale=16, elem_classes="plus_column"): | |
| with gr.Row(): | |
| with gr.Column(min_width=10, scale=4, elem_classes="random_column"): | |
| lora_1_link = gr.Markdown(elem_classes="title_lora") | |
| lora_1 = gr.Image(interactive=False, height=150, elem_classes="selected_random", elem_id="randomLoRA_1", show_share_button=False, show_download_button=False) | |
| lora_1_id = gr.Textbox(visible=False, elem_id="random_lora_1_id") | |
| lora_1_prompt = gr.Markdown(visible=False) | |
| with gr.Column(min_width=10, scale=1, elem_classes="plus_column"): | |
| plus = gr.HTML("+", elem_classes="plus_button") | |
| with gr.Column(min_width=10, scale=4, elem_classes="random_column"): | |
| lora_2_link = gr.Markdown(elem_classes="title_lora") | |
| lora_2 = gr.Image(interactive=False, height=150, elem_classes="selected_random", elem_id="randomLoRA_2", show_share_button=False, show_download_button=False) | |
| lora_2_id = gr.Textbox(visible=False, elem_id="random_lora_2_id") | |
| lora_2_prompt = gr.Markdown(visible=False) | |
| with gr.Column(min_width=10, scale=2, elem_classes="plus_column"): | |
| equal = gr.HTML("=", elem_classes="plus_button") | |
| shuffle_button = gr.Button("🎲 reroll", elem_id="reroll_button") | |
| with gr.Column(min_width=10, scale=14): | |
| with gr.Box(elem_id="generate_area"): | |
| with gr.Row(elem_id="prompt_area"): | |
| prompt = gr.Textbox(label="Your prompt", info="Rearrange the trigger words into a coherent prompt", show_label=False, interactive=True, elem_id="prompt") | |
| run_btn = gr.Button("Run", elem_id="run_button") | |
| output_image = gr.Image(label="Output", height=460, elem_id="output_image", interactive=False) | |
| with gr.Column(visible=False, elem_id="post_gen_info") as post_gen_info: | |
| with gr.Row(): | |
| with gr.Column(min_width=10): | |
| thumbs_up = gr.Button("👍", elem_id="thumbs_up_unclicked") | |
| thumbs_up_clicked = gr.Button("👍", elem_id="thumbs_up_clicked", interactive=False, visible=False) | |
| with gr.Column(min_width=10): | |
| thumbs_down = gr.Button("👎", elem_id="thumbs_down_unclicked") | |
| thumbs_down_clicked = gr.Button("👎", elem_id="thumbs_down_clicked", interactive=False, visible=False) | |
| with gr.Column(min_width=10): | |
| with gr.Group(elem_id="share-btn-container") 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") | |
| gr.Markdown('<p style="font-size: 90%;opacity: 0.8;">Rating helps finding the most compatible LoRAs 🤗, results are shared annonymously <a href="https://huggingface.co/datasets/multimodalart/lora-fusing-preferences" target="_blank" rel="noopener noreferrer">here</a></p>') | |
| with gr.Accordion("Advanced settings", open=False): | |
| with gr.Row(): | |
| lora_1_scale = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=1, step=0.1, value=0.7) | |
| lora_2_scale = gr.Slider(label="LoRa 2 Scale", minimum=0, maximum=1, step=0.1, value=0.7) | |
| negative_prompt = gr.Textbox(label="Negative prompt") | |
| seed = gr.Slider(label="Seed", info="-1 denotes a random seed", minimum=-1, maximum=2147483647, value=-1) | |
| last_used_seed = gr.Number(label="Last used seed", info="The seed used in the last generation", minimum=0, maximum=2147483647, value=-1, interactive=False) | |
| gr.Markdown("Generate with intent in [LoRA the Explorer](https://huggingface.co/spaces/multimodalart/LoraTheExplorer), but remember: sometimes restrictions flourish creativity 🌸") | |
| demo.load(shuffle_images, inputs=[], outputs=[lora_1_link, lora_1, lora_1_prompt, lora_1_id, lora_2_link, lora_2, lora_2_prompt, lora_2_id, prompt, shuffled_items, lora_1_scale, lora_2_scale], queue=False, show_progress="hidden") | |
| shuffle_button.click(shuffle_images, outputs=[lora_1_link, lora_1, lora_1_prompt, lora_1_id, lora_2_link, lora_2, lora_2_prompt, lora_2_id, prompt, shuffled_items, lora_1_scale, lora_2_scale], queue=False, show_progress="hidden") | |
| run_btn.click(hide_post_gen_info, outputs=[post_gen_info], queue=False).then(merge_and_run, | |
| inputs=[prompt, negative_prompt, shuffled_items, lora_1_scale, lora_2_scale, seed], | |
| outputs=[output_image, post_gen_info, last_used_seed, thumbs_up, thumbs_up_clicked, thumbs_down, thumbs_down_clicked]) | |
| prompt.submit(hide_post_gen_info, outputs=[post_gen_info], queue=False).then(merge_and_run, | |
| inputs=[prompt, negative_prompt, shuffled_items, lora_1_scale, lora_2_scale, seed], | |
| outputs=[output_image, post_gen_info, last_used_seed, thumbs_up, thumbs_up_clicked, thumbs_down, thumbs_down_clicked]) | |
| thumbs_up.click(save_preferences, inputs=[lora_1_id, lora_1_scale, lora_2_id, lora_2_scale, prompt, output_image, gr.State("up"), last_used_seed], outputs=[thumbs_up, thumbs_up_clicked, thumbs_down]) | |
| thumbs_down.click(save_preferences, inputs=[lora_1_id, lora_1_scale, lora_2_id, lora_2_scale, prompt, output_image, gr.State("down"), last_used_seed], outputs=[thumbs_down, thumbs_down_clicked, thumbs_up]) | |
| share_button.click(None, [], [], _js=share_js) | |
| demo.queue(concurrency_count=2) | |
| demo.launch() |