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| import gradio as gr | |
| from time import sleep | |
| from diffusers import DiffusionPipeline | |
| from huggingface_hub import hf_hub_download | |
| from safetensors.torch import load_file | |
| import torch | |
| import json | |
| import random | |
| import copy | |
| import gc | |
| 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 = [ | |
| { | |
| "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 | |
| ] | |
| for item in sdxl_loras: | |
| saved_name = hf_hub_download(item["repo"], item["weights"]) | |
| if saved_name.endswith('.safetensors'): | |
| state_dict = load_file(saved_name) | |
| else: | |
| state_dict = torch.load(saved_name) | |
| item["saved_name"] = saved_name | |
| item["state_dict"] = state_dict #{k: v.to(device="cuda", dtype=torch.float16) for k, v in state_dict.items() if torch.is_tensor(v)} | |
| css = ''' | |
| .gradio-container{max-width: 650px} | |
| #title{text-align:center;} | |
| #title h1{font-size: 250%} | |
| .selected_random img{object-fit: cover} | |
| .plus_column{align-self: center} | |
| .plus_button{font-size: 235% !important; text-align: center;margin-bottom: 19px} | |
| #prompt input{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;} | |
| #run_button{position:absolute;margin-top: 12px;right: 0;margin-right: 1.5em;border-bottom-left-radius: 0px; | |
| border-top-left-radius: 0px;} | |
| .random_column{align-self: center; align-items: center} | |
| @media (max-width: 1024px) { | |
| .roulette_group{flex-direction: column} | |
| } | |
| ''' | |
| 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, progress=gr.Progress(track_tqdm=True)): | |
| state_dict_1 = copy.deepcopy(shuffled_items[0]['state_dict']) | |
| state_dict_2 = copy.deepcopy(shuffled_items[1]['state_dict']) | |
| pipe = copy.deepcopy(original_pipe) | |
| pipe.to("cuda") | |
| pipe.load_lora_weights(state_dict_1) | |
| pipe.fuse_lora(lora_1_scale) | |
| pipe.load_lora_weights(state_dict_2) | |
| pipe.fuse_lora(lora_2_scale) | |
| if negative_prompt == "": | |
| negative_prompt = None | |
| image = pipe(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=20, width=768, height=768).images[0] | |
| del pipe | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| return image | |
| def get_description(item): | |
| trigger_word = item["trigger_word"] | |
| return f"Trigger: `{trigger_word}`" if trigger_word else "No trigger word, will be applied automatically", trigger_word | |
| 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']) | |
| description_1, trigger_word_1 = get_description(two_shuffled_items[0]) | |
| description_2, trigger_word_2 = get_description(two_shuffled_items[1]) | |
| 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 title_1, prompt_description_1, title_2, prompt_description_2, prompt, two_shuffled_items, scale, scale | |
| with gr.Blocks(css=css) as demo: | |
| shuffled_items = gr.State() | |
| title = gr.HTML( | |
| '''<h1>LoRA Roulette 🎲</h1> | |
| ''', | |
| elem_id="title" | |
| ) | |
| with gr.Column(): | |
| with gr.Column(min_width=10, scale=16, elem_classes="plus_column"): | |
| gr.HTML("<p>This 2 random LoRAs are loaded to SDXL, find a fun way to combine them 🎨</p>") | |
| with gr.Row(): | |
| with gr.Column(min_width=10, scale=4, elem_classes="random_column"): | |
| lora_1 = gr.Image(interactive=False, height=150, elem_classes="selected_random") | |
| 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 = gr.Image(interactive=False, height=150, elem_classes="selected_random") | |
| lora_2_prompt = gr.Markdown(visible=False) | |
| with gr.Column(min_width=10, scale=1, elem_classes="plus_column"): | |
| equal = gr.HTML("=", elem_classes="plus_button") | |
| with gr.Column(min_width=10, scale=14): | |
| with gr.Box(): | |
| with gr.Row(): | |
| prompt = gr.Textbox(label="Your 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=355) | |
| with gr.Accordion("Advanced settings", open=False): | |
| negative_prompt = gr.Textbox(label="Negative prompt") | |
| 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) | |
| shuffle_button = gr.Button("Reshuffle!") | |
| demo.load(shuffle_images, inputs=[], outputs=[lora_1, lora_1_prompt, lora_2, lora_2_prompt, prompt, shuffled_items, lora_1_scale, lora_2_scale], queue=False, show_progress="hidden") | |
| shuffle_button.click(shuffle_images, outputs=[lora_1, lora_1_prompt, lora_2, lora_2_prompt, prompt, shuffled_items, lora_1_scale, lora_2_scale], queue=False, show_progress="hidden") | |
| run_btn.click(merge_and_run, inputs=[prompt, negative_prompt, shuffled_items, lora_1_scale, lora_2_scale], outputs=[output_image]) | |
| prompt.submit(merge_and_run, inputs=[prompt, negative_prompt, shuffled_items, lora_1_scale, lora_2_scale], outputs=[output_image]) | |
| demo.queue() | |
| demo.launch() |