app.py

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
from share_btn import community_icon_html, loading_icon_html, share_js

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! 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 input{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;}
#run_button{position:absolute;margin-top: 36px;right: 0;margin-right: 1.5em;border-bottom-left-radius: 0px;
    border-top-left-radius: 0px;}
.random_column{align-self: center; align-items: center}
#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}
'''

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, gr.update(visible=True)

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 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>
        <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 = gr.Image(interactive=False, height=150, elem_classes="selected_random", show_share_button=False, show_download_button=False)
              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", show_share_button=False, show_download_button=False)
              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")
    with gr.Column(min_width=10, scale=14):
        with gr.Box():
            with gr.Row():
                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=355)
            with gr.Row(visible=False) as post_gen_info:
                with gr.Column(min_width=10):
                    thumbs_up = gr.Button("👍")
                with gr.Column(min_width=10):
                    thumbs_down = gr.Button("👎")
                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")

  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, post_gen_info])
  prompt.submit(merge_and_run, inputs=[prompt, negative_prompt, shuffled_items, lora_1_scale, lora_2_scale], outputs=[output_image, post_gen_info])
  share_button.click(None, [], [], _js=share_js)
demo.queue()
demo.launch()