Delete app.py

app.py

@@ -1,415 +0,0 @@
-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()