app.py

import os
import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
from diffusers.utils import load_image
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard
import copy
import random
import time
import re

# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
    loras = json.load(f)

# Initialize the base model for SDXL
dtype = torch.float16 if torch.cuda.is_available() else torch.float32
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model = "stabilityai/stable-diffusion-xl-base-1.0"

# Load SDXL pipelines
pipe = StableDiffusionXLPipeline.from_pretrained(
    base_model,
    torch_dtype=dtype,
    use_safetensors=True
).to(device)

pipe_i2i = StableDiffusionXLImg2ImgPipeline.from_pretrained(
    base_model,
    torch_dtype=dtype,
    use_safetensors=True
).to(device)

MAX_SEED = 2**32 - 1

# Custom SDXL generation function for live preview
@torch.inference_mode()
def generate_sdxl_images(
    pipe,
    prompt: str,
    height: int = 1024,
    width: int = 1024,
    num_inference_steps: int = 50,
    guidance_scale: float = 7.5,
    generator: Optional[torch.Generator] = None,
    output_type: str = "pil",
):
    # Encode prompt
    prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = pipe.encode_prompt(
        prompt=prompt,
        num_images_per_prompt=1,
        do_classifier_free_guidance=True,
    )
    # Prepare latents
    latents = pipe.prepare_latents(
        batch_size=1,
        num_channels_latents=pipe.unet.config.in_channels,
        height=height,
        width=width,
        dtype=prompt_embeds.dtype,
        device=pipe.device,
        generator=generator,
    )
    # Prepare timesteps
    pipe.scheduler.set_timesteps(num_inference_steps, device=pipe.device)
    timesteps = pipe.scheduler.timesteps
    # Prepare guidance
    do_classifier_free_guidance = guidance_scale > 1.0
    if do_classifier_free_guidance:
        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
        pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds])
    # Denoising loop
    for i, t in enumerate(timesteps):
        # Expand latents for guidance
        latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
        # Predict noise
        noise_pred = pipe.unet(
            latent_model_input,
            t,
            encoder_hidden_states=prompt_embeds,
            added_cond_kwargs={"text_embeds": pooled_prompt_embeds},
        ).sample
        # Perform guidance
        if do_classifier_free_guidance:
            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
        # Step scheduler
        latents = pipe.scheduler.step(noise_pred, t, latents).prev_sample
        # Decode latents to image every step
        image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]
        yield pipe.image_processor.postprocess(image, output_type=output_type)[0]
    # Final image
    image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]
    yield pipe.image_processor.postprocess(image, output_type=output_type)[0]

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self
    
    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")

def update_selection(evt: gr.SelectData, width, height):
    selected_lora = loras[evt.index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
    if "aspect" in selected_lora:
        if selected_lora["aspect"] == "portrait":
            width = 768
            height = 1024
        elif selected_lora["aspect"] == "landscape":
            width = 1024
            height = 768
        else:
            width = 1024
            height = 1024
    return (
        gr.update(placeholder=new_placeholder),
        updated_text,
        evt.index,
        width,
        height,
    )

@spaces.GPU(duration=70)
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress):
    pipe.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    with calculateDuration("Generating image"):
        for img in generate_sdxl_images(
            pipe,
            prompt=prompt_mash,
            num_inference_steps=steps,
            guidance_scale=cfg_scale,
            width=width,
            height=height,
            generator=generator,
            output_type="pil",
        ):
            yield img

def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
    generator = torch.Generator(device="cuda").manual_seed(seed)
    pipe_i2i.to("cuda")
    image_input = load_image(image_input_path)
    final_image = pipe_i2i(
        prompt=prompt_mash,
        image=image_input,
        strength=image_strength,
        num_inference_steps=steps,
        guidance_scale=cfg_scale,
        width=width,
        height=height,
        generator=generator,
        output_type="pil",
    ).images[0]
    return final_image 

@spaces.GPU(duration=70)
def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
    if selected_index is None:
        raise gr.Error("You must select a LoRA before proceeding.")
    selected_lora = loras[selected_index]
    lora_path = selected_lora["repo"]
    trigger_word = selected_lora["trigger_word"]
    if trigger_word:
        if "trigger_position" in selected_lora and selected_lora["trigger_position"] == "prepend":
            prompt_mash = f"{trigger_word} {prompt}"
        else:
            prompt_mash = f"{prompt} {trigger_word}"
    else:
        prompt_mash = prompt

    # Unload previous LoRA weights
    with calculateDuration("Unloading LoRA"):
        pipe.unload_lora_weights()
        pipe_i2i.unload_lora_weights()
        
    # Load LoRA weights and set adapter scale
    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
        weight_name = selected_lora.get("weights", None)
        adapter_name = "lora"
        pipe.load_lora_weights(lora_path, weight_name=weight_name, adapter_name=adapter_name)
        pipe.set_adapters([adapter_name], [lora_scale])
        pipe_i2i.load_lora_weights(lora_path, weight_name=weight_name, adapter_name=adapter_name)
        pipe_i2i.set_adapters([adapter_name], [lora_scale])
                
    # Set random seed
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)
            
    if image_input is not None:
        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed)
        yield final_image, seed, gr.update(visible=False)
    else:
        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
        final_image = None
        step_counter = 0
        for image in image_generator:
            step_counter += 1
            final_image = image
            progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
            yield image, seed, gr.update(value=progress_bar, visible=True)
        yield final_image, seed, gr.update(value=progress_bar, visible=False)

def get_huggingface_safetensors(link):
    split_link = link.split("/")
    if len(split_link) != 2:
        raise Exception("Invalid Hugging Face repository link format.")

    # Load model card
    model_card = ModelCard.load(link)
    base_model = model_card.data.get("base_model")
    print(base_model)

    # Validate model type for SDXL
    if base_model != "stabilityai/stable-diffusion-xl-base-1.0":
        raise Exception("Not an SDXL LoRA!")

    # Extract image and trigger word
    image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
    trigger_word = model_card.data.get("instance_prompt", "")
    image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None

    # Initialize Hugging Face file system
    fs = HfFileSystem()
    try:
        list_of_files = fs.ls(link, detail=False)
        safetensors_name = None
        highest_trained_file = None
        highest_steps = -1
        last_safetensors_file = None
        step_pattern = re.compile(r"_0{3,}\d+")  # Detects step count `_000...`

        for file in list_of_files:
            filename = file.split("/")[-1]
            if filename.endswith(".safetensors"):
                last_safetensors_file = filename
                match = step_pattern.search(filename)
                if not match:
                    safetensors_name = filename
                    break
                else:
                    steps = int(match.group().lstrip("_"))
                    if steps > highest_steps:
                        highest_trained_file = filename
                        highest_steps = steps
            if not image_url and filename.lower().endswith((".jpg", ".jpeg", ".png", ".webp")):
                image_url = f"https://huggingface.co/{link}/resolve/main/{filename}"

        if not safetensors_name:
            safetensors_name = highest_trained_file if highest_trained_file else last_safetensors_file
        if not safetensors_name:
            raise Exception("No valid *.safetensors file found in the repository.")
    except Exception as e:
        print(e)
        raise Exception("You didn't include a valid Hugging Face repository with a *.safetensors LoRA")

    return split_link[1], link, safetensors_name, trigger_word, image_url

def check_custom_model(link):
    if link.startswith("https://"):
        if link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co"):
            link_split = link.split("huggingface.co/")
            return get_huggingface_safetensors(link_split[1])
    else: 
        return get_huggingface_safetensors(link)

def add_custom_lora(custom_lora):
    global loras
    if custom_lora:
        try:
            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
            print(f"Loaded custom LoRA: {repo}")
            card = f'''
            <div class="custom_lora_card">
              <span>Loaded custom LoRA:</span>
              <div class="card_internal">
                <img src="{image}" />
                <div>
                    <h3>{title}</h3>
                    <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
                </div>
              </div>
            </div>
            '''
            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
            if not existing_item_index:
                new_item = {
                    "image": image,
                    "title": title,
                    "repo": repo,
                    "weights": path,
                    "trigger_word": trigger_word
                }
                print(new_item)
                existing_item_index = len(loras)
                loras.append(new_item)
            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word
        except Exception as e:
            gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-SDXL LoRA")
            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-SDXL LoRA"), gr.update(visible=True), gr.update(), "", None, ""
    else:
        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""

def remove_custom_lora():
    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""

run_lora.zerogpu = True

css = '''
#gen_btn{height: 100%}
#gen_column{align-self: stretch}
#title{text-align: center}
#title h1{font-size: 3em; display:inline-flex; align-items:center}
#title img{width: 100px; margin-right: 0.5em}
#gallery .grid-wrap{height: 10vh}
#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
.card_internal{display: flex;height: 100px;margin-top: .5em}
.card_internal img{margin-right: 1em}
.styler{--form-gap-width: 0px !important}
#progress{height:30px}
#progress .generating{display:none}
.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}
.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}
'''
font = [gr.themes.GoogleFont("Source Sans Pro"), "Arial", "sans-serif"]
with gr.Blocks(theme=gr.themes.Soft(font=font), css=css, delete_cache=(60, 60)) as app:
    title = gr.HTML(
        """<h1>SDXL LoRA DLC</h1>""",
        elem_id="title",
    )
    selected_index = gr.State(None)
    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
        with gr.Column(scale=1, elem_id="gen_column"):
            generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
    with gr.Row():
        with gr.Column():
            selected_info = gr.Markdown("")
            gallery = gr.Gallery(
                [(item["image"], item["title"]) for item in loras],
                label="LoRA Gallery",
                allow_preview=False,
                columns=3,
                elem_id="gallery",
                show_share_button=False
            )
            with gr.Group():
                custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="username/sdxl-lora-model")
                gr.Markdown("[Check the list of SDXL LoRAs](https://huggingface.co/models?other=base_model:stabilityai/stable-diffusion-xl-base-1.0)", elem_id="lora_list")
            custom_lora_info = gr.HTML(visible=False)
            custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
        with gr.Column():
            progress_bar = gr.Markdown(elem_id="progress", visible=False)
            result = gr.Image(label="Generated Image")

    with gr.Row():
        with gr.Accordion("Advanced Settings", open=False):
            with gr.Row():
                input_image = gr.Image(label="Input image", type="filepath")
                image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
            with gr.Column():
                with gr.Row():
                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=7.5)
                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=30)
                
                with gr.Row():
                    width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
                    height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
                
                with gr.Row():
                    randomize_seed = gr.Checkbox(True, label="Randomize seed")
                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=1.0)

    gallery.select(
        update_selection,
        inputs=[width, height],
        outputs=[prompt, selected_info, selected_index, width, height]
    )
    custom_lora.input(
        add_custom_lora,
        inputs=[custom_lora],
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
    )
    custom_lora_button.click(
        remove_custom_lora,
        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
    )
    gr.on(
        triggers=[generate_button.click, prompt.submit],
        fn=run_lora,
        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
        outputs=[result, seed, progress_bar]
    )

app.queue()
app.launch()