app.py

import gradio as gr
from huggingface_hub import login
from diffusers import DiffusionPipeline, StableDiffusionXLPipeline
import torch
import copy
import os
import spaces
import random

hf_token = os.environ.get("HF_TOKEN")
login(token = hf_token)

original_pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)

@spaces.GPU
def infer(lora_1_id, lora_1_sfts, lora_2_id, lora_2_sfts, prompt, negative_prompt, lora_1_scale, lora_2_scale, seed):

    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
    )

    pipe.to("cuda")

    pipe.load_lora_weights(
        lora_1_id,
        weight_name = lora_1_sfts,        
        low_cpu_mem_usage = True,
        use_auth_token = True
    )

    pipe.fuse_lora(lora_1_scale)

    pipe.load_lora_weights(
        lora_2_id,
        weight_name = lora_2_sfts,        
        low_cpu_mem_usage = True,
        use_auth_token = True
    )

    pipe.fuse_lora(lora_2_scale)

    if negative_prompt == "" :
        negative_prompt = None
    
    if seed < 0 :
        seed = random.randit(0, 423538377342)
    
    generator = torch.Generator(device="cuda").manual_seed(seed)

    image = pipe(
        prompt = prompt,
        negative_prompt = negative_prompt,
        num_inference_steps = 25,
        width = 1024,
        height = 1024,
        generator = generator
    ).images[0]
    
    return image, seed

with gr.Blocks() as demo:
    with gr.Column(elem_id="col-container"):

        title = gr.HTML(
        '''
        <h1 style="text-align: center;">LoRA Fusion</h1>
        <p style="text-align: center;">Fuse 2 custom LoRa models</p>
        '''
        )
        
        # PART 1 • MODELS
        with gr.Row():
            
            with gr.Column():
                
                lora_1_id = gr.Textbox(
                    label = "LoRa 1 ID",
                    placeholder = "username/model_id"
                )

                lora_1_sfts = gr.Textbox(
                    label = "Safetensors file",
                    placeholder = "specific_chosen.safetensors"
                )
            
            with gr.Column():
                
                lora_2_id = gr.Textbox(
                    label = "LoRa 2 ID",
                    placeholder = "username/model_id"
                )

                lora_2_sfts = gr.Textbox(
                    label = "Safetensors file",
                    placeholder = "specific_chosen.safetensors"
                )

        # PART 2 • INFERENCE
        with gr.Row():
            
            prompt = gr.Textbox(
                label = "Your prompt",
                info = "Use your trigger words into a coherent prompt",
                placeholde = "e.g: a triggerWordOne portrait in triggerWord2 style"
            )

            run_btn = gr.Button("Run")
        
        output_image = gr.Image(
            label = "Output"
        )

        # Advanced Settings
        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 = 423538377342,
                value = -1
            )

            last_used_seed = gr.Number(
                label = "Last used seed",
                info = "the seed used in the last generation",
            )
    
    # ACTIONS
    run_btn.click(
        fn = infer,
        inputs = [
            lora_1_id,
            lora_1_sfts,
            lora_2_id,
            lora_2_sfts,
            prompt,
            negative_prompt,
            lora_1_scale,
            lora_2_scale,
            seed
        ],
        outputs = [
            output_image, 
            last_used_seed
        ]
    )

demo.queue(concurrency_count=2).launch()