Delete app.py

app.py

@@ -1,544 +0,0 @@
-import os
-import json
-import copy
-import math
-import time
-import random
-import logging
-import numpy as np
-from typing import Any, Dict, List, Optional, Union
-import torch
-from PIL import Image
-import gradio as gr
-import spaces
-from diffusers import (
-    DiffusionPipeline,
-    FlowMatchEulerDiscreteScheduler)
-from huggingface_hub import (
-    hf_hub_download,
-    HfFileSystem,
-    ModelCard,
-    snapshot_download)
-from diffusers.utils import load_image
-import requests
-from urllib.parse import urlparse
-import tempfile
-import shutil
-import uuid
-import zipfile
-
-
-# META: CUDA_CHECK / GPU_INFO
-device = "cuda" if torch.cuda.is_available() else "cpu"
-print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
-print("torch.__version__ =", torch.__version__)
-print("torch.version.cuda =", torch.version.cuda)
-print("cuda available:", torch.cuda.is_available())
-print("cuda device count:", torch.cuda.device_count())
-if torch.cuda.is_available():
-    print("current device:", torch.cuda.current_device())
-    print("device name:", torch.cuda.get_device_name(torch.cuda.current_device()))
-
-print("Using device:", device)
-
-loras = [
-    # Sample Qwen-compatible LoRAs
-    {
-        "image": "https://huggingface.co/damnthatai/Game_Boy_Camera_Pixel_Style_Qwen/resolve/main/images/20250818090201_Qwen8s_00001_.jpg",
-        "title": "Camera Pixel Style",
-        "repo": "damnthatai/Game_Boy_Camera_Pixel_Style_Qwen",
-        "weights": "g4m3b0yc4m3r4_qwen.safetensors",
-        "trigger_word": "g4m3b0yc4m3r4, grayscale, pixel photo, color photo"
-    },
-    {
-        "image": "https://huggingface.co/prithivMLmods/Qwen-Image-Studio-Realism/resolve/main/images/2.png",
-        "title": "Studio Realism",
-        "repo": "prithivMLmods/Qwen-Image-Studio-Realism",
-        "weights": "qwen-studio-realism.safetensors",
-        "trigger_word": "Studio Realism"
-    },
-    {
-        "image": "https://huggingface.co/prithivMLmods/Qwen-Image-Sketch-Smudge/resolve/main/images/1.png",
-        "title": "Sketch Smudge",
-        "repo": "prithivMLmods/Qwen-Image-Sketch-Smudge",
-        "weights": "qwen-sketch-smudge.safetensors",
-        "trigger_word": "Sketch Smudge"
-    },
-    {
-        "image": "https://huggingface.co/prithivMLmods/Qwen-Image-Anime-LoRA/resolve/main/images/1.png",
-        "title": "Qwen Anime",
-        "repo": "prithivMLmods/Qwen-Image-Anime-LoRA",
-        "weights": "qwen-anime.safetensors",
-        "trigger_word": "Qwen Anime"
-    },
-    {
-        "image": "https://huggingface.co/damnthatai/Apple_QuickTake_150_Digital_Camera_Qwen/resolve/main/images/20250817084713_Qwen.jpg",
-        "title": "Apple QuickTake 150 Digital Camera",
-        "repo": "damnthatai/Apple_QuickTake_150_Digital_Camera_Qwen",
-        "weights": "quicktake150style_qwen.safetensors",
-        "trigger_word": "quicktake150style"
-    },
-    {
-        "image": "https://huggingface.co/prithivMLmods/Qwen-Image-Synthetic-Face/resolve/main/images/2.png",
-        "title": "Synthetic Face",
-        "repo": "prithivMLmods/Qwen-Image-Synthetic-Face",
-        "weights": "qwen-synthetic-face.safetensors",
-        "trigger_word": "Synthetic Face"
-    },
-    {
-        "image": "https://huggingface.co/prithivMLmods/Qwen-Image-Fragmented-Portraiture/resolve/main/images/3.png",
-        "title": "Fragmented Portraiture",
-        "repo": "prithivMLmods/Qwen-Image-Fragmented-Portraiture",
-        "weights": "qwen-fragmented-portraiture.safetensors",
-        "trigger_word": "Fragmented Portraiture"
-    },
-    {
-        "image": "https://huggingface.co/Tomechi02/Macne_style_enahncer/resolve/main/images/pixai-1913880604374308947-2.png",
-        "title": "Macne Style Enahncer",
-        "repo": "Tomechi02/Macne_style_enahncer",
-        "weights": "Macne_Style_enhancer.safetensors",
-        "trigger_word": "macloid, gomoku"
-    },
-    {
-        "image": "https://huggingface.co/itspoidaman/qwenglitch/resolve/main/images/GyZTwJIbkAAhS4h.jpeg",
-        "title": "Qwen Glitch",
-        "repo": "itspoidaman/qwenglitch",
-        "weights": "qwenglitch1.safetensors",
-        "trigger_word": "qwenglitch"
-    },
-    {
-        "image": "https://huggingface.co/alfredplpl/qwen-image-modern-anime-lora/resolve/main/sample1.jpg",
-        "title": "Modern Anime Lora",
-        "repo": "alfredplpl/qwen-image-modern-anime-lora",
-        "weights": "lora.safetensors",
-        "trigger_word": "Japanese modern anime style"
-    },
-]
-
-# Initialize the base model
-dtype = torch.bfloat16
-base_model = "Qwen/Qwen-Image"
-
-# Scheduler configuration from the Qwen-Image-Lightning repository
-scheduler_config = {
-    "base_image_seq_len": 256,
-    "base_shift": math.log(3),
-    "invert_sigmas": False,
-    "max_image_seq_len": 8192,
-    "max_shift": math.log(3),
-    "num_train_timesteps": 1000,
-    "shift": 1.0,
-    "shift_terminal": None,
-    "stochastic_sampling": False,
-    "time_shift_type": "exponential",
-    "use_beta_sigmas": False,
-    "use_dynamic_shifting": True,
-    "use_exponential_sigmas": False,
-    "use_karras_sigmas": False,
-}
-
-scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
-pipe = DiffusionPipeline.from_pretrained(
-    base_model, scheduler=scheduler, torch_dtype=dtype
-).to(device)
-
-# Lightning LoRA info (no global state)
-LIGHTNING_LORA_REPO = "lightx2v/Qwen-Image-Lightning"
-LIGHTNING_LORA_WEIGHT = "Qwen-Image-Lightning-8steps-V1.0.safetensors"
-
-MAX_SEED = np.iinfo(np.int32).max
-
-class Timer:
-    def __init__(self, task_name=""):
-        self.task_name = task_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.task_name:
-            print(f"Elapsed time for {self.task_name}: {self.elapsed_time:.6f} seconds")
-        else:
-            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
-
-def compute_image_dimensions(aspect_ratio):
-    """Converts aspect ratio string to width, height tuple."""
-    if aspect_ratio == "1:1":
-        return 1024, 1024
-    elif aspect_ratio == "16:9":
-        return 1152, 640
-    elif aspect_ratio == "9:16":
-        return 640, 1152
-    elif aspect_ratio == "4:3":
-        return 1024, 768
-    elif aspect_ratio == "3:4":
-        return 768, 1024
-    elif aspect_ratio == "3:2":
-        return 1024, 688
-    elif aspect_ratio == "2:3":
-        return 688, 1024
-    else:
-        return 1024, 1024
-
-def handle_lora_selection(evt: gr.SelectData, aspect_ratio):
-    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}) ✨"
-    
-    # Update aspect ratio if specified in LoRA config
-    if "aspect" in selected_lora:
-        if selected_lora["aspect"] == "portrait":
-            aspect_ratio = "9:16"
-        elif selected_lora["aspect"] == "landscape":
-            aspect_ratio = "16:9"
-        else:
-            aspect_ratio = "1:1"
-    
-    return (
-        gr.update(placeholder=new_placeholder),
-        updated_text,
-        evt.index,
-        aspect_ratio,
-    )
-
-def adjust_generation_mode(speed_mode):
-    """Update UI based on speed/quality toggle."""
-    if speed_mode == "Speed (8 steps)":
-        return gr.update(value="Speed mode selected - 8 steps with Lightning LoRA"), 8, 1.0
-    else: 
-        return gr.update(value="Quality mode selected - 48 steps for best quality"), 48, 3.5
-
-@spaces.GPU(duration=108)
-def create_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, negative_prompt=""):
-    pipe.to("cuda")
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    
-    with Timer("Generating image"):
-        # Generate image
-        image = pipe(
-            prompt=prompt_mash,
-            negative_prompt=negative_prompt,
-            num_inference_steps=steps,
-            true_cfg_scale=cfg_scale,  # Use true_cfg_scale for Qwen-Image
-            width=width,
-            height=height,
-            generator=generator,
-        ).images[0]
-        
-    return image
-
-@spaces.GPU(duration=108)
-def process_adapter_generation(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, aspect_ratio, lora_scale, speed_mode, 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"]
-    
-    # Prepare prompt with trigger word
-    if trigger_word:
-        if "trigger_position" in selected_lora:
-            if selected_lora["trigger_position"] == "prepend":
-                prompt_mash = f"{trigger_word} {prompt}"
-            else:
-                prompt_mash = f"{prompt} {trigger_word}"
-        else:
-            prompt_mash = f"{trigger_word} {prompt}"
-    else:
-        prompt_mash = prompt
-
-    # Always unload any existing LoRAs first to avoid conflicts
-    with Timer("Unloading existing LoRAs"):
-        pipe.unload_lora_weights()
-
-    # Load LoRAs based on speed mode
-    if speed_mode == "Speed (8 steps)":
-        with Timer("Loading Lightning LoRA and style LoRA"):
-            # Load Lightning LoRA first
-            pipe.load_lora_weights(
-                LIGHTNING_LORA_REPO, 
-                weight_name=LIGHTNING_LORA_WEIGHT,
-                adapter_name="lightning"
-            )
-            
-            # Load the selected style LoRA
-            weight_name = selected_lora.get("weights", None)
-            pipe.load_lora_weights(
-                lora_path, 
-                weight_name=weight_name, 
-                low_cpu_mem_usage=True,
-                adapter_name="style"
-            )
-            
-            # Set both adapters active with their weights
-            pipe.set_adapters(["lightning", "style"], adapter_weights=[1.0, lora_scale])
-    else:
-        # Quality mode - only load the style LoRA
-        with Timer(f"Loading LoRA weights for {selected_lora['title']}"):
-            weight_name = selected_lora.get("weights", None)
-            pipe.load_lora_weights(
-                lora_path, 
-                weight_name=weight_name, 
-                low_cpu_mem_usage=True
-            )
-                
-    # Set random seed for reproducibility
-    with Timer("Randomizing seed"):
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
-    
-    # Get image dimensions from aspect ratio
-    width, height = compute_image_dimensions(aspect_ratio)
-    
-    # Generate the image
-    final_image = create_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale)
-    
-    return final_image, seed
-
-def fetch_hf_adapter_files(link):
-    split_link = link.split("/")
-    if len(split_link) != 2:
-        raise Exception("Invalid Hugging Face repository link format.")
-
-    print(f"Repository attempted: {split_link}")
-    
-    # Load model card
-    model_card = ModelCard.load(link)
-    base_model = model_card.data.get("base_model")
-    print(f"Base model: {base_model}")
-
-    # Validate model type (for Qwen-Image)
-    acceptable_models = {"Qwen/Qwen-Image"}
-    
-    models_to_check = base_model if isinstance(base_model, list) else [base_model]
-    
-    if not any(model in acceptable_models for model in models_to_check):
-        raise Exception("Not a Qwen-Image 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)
-        
-        # Find safetensors file
-        safetensors_name = None
-        for file in list_of_files:
-            filename = file.split("/")[-1]
-            if filename.endswith(".safetensors"):
-                safetensors_name = filename
-                break
-
-        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 validate_custom_adapter(link):
-    print(f"Checking a custom model on: {link}")
-    
-    if link.endswith('.safetensors'):
-        if 'huggingface.co' in link:
-            parts = link.split('/')
-            try:
-                hf_index = parts.index('huggingface.co')
-                username = parts[hf_index + 1]
-                repo_name = parts[hf_index + 2]
-                repo = f"{username}/{repo_name}"
-                
-                safetensors_name = parts[-1]
-                
-                try:
-                    model_card = ModelCard.load(repo)
-                    trigger_word = model_card.data.get("instance_prompt", "")
-                    image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
-                    image_url = f"https://huggingface.co/{repo}/resolve/main/{image_path}" if image_path else None
-                except:
-                    trigger_word = ""
-                    image_url = None
-                
-                return repo_name, repo, safetensors_name, trigger_word, image_url
-            except:
-                raise Exception("Invalid safetensors URL format")
-    
-    if link.startswith("https://"):
-        if link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co"):
-            link_split = link.split("huggingface.co/")
-            return fetch_hf_adapter_files(link_split[1])
-    else: 
-        return fetch_hf_adapter_files(link)
-
-def incorporate_custom_adapter(custom_lora):
-    global loras
-    if custom_lora:
-        try:
-            title, repo, path, trigger_word, image = validate_custom_adapter(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 existing_item_index is None:
-                new_item = {
-                    "image": image,
-                    "title": title,
-                    "repo": repo,
-                    "weights": path,
-                    "trigger_word": trigger_word
-                }
-                print(new_item)
-                loras.append(new_item)
-                existing_item_index = len(loras) - 1  # Get the actual index after adding
-        
-            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-Qwen-Image LoRA, this was the issue: {e}")
-            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-Qwen-Image LoRA"), gr.update(visible=True), gr.update(), "", None, ""
-    else:
-        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
-
-def discard_custom_adapter():
-    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
-
-process_adapter_generation.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}
-#speed_status{padding: .5em; border-radius: 5px; margin: 1em 0}
-'''
-
-with gr.Blocks(theme="bethecloud/storj_theme", css=css, delete_cache=(120, 120)) as app:
-    title = gr.HTML("""<h1>Qwen Image 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/qwen-image-custom-lora")
-                gr.Markdown("[Check Qwen-Image LoRAs](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image)", elem_id="lora_list")
-            custom_lora_info = gr.HTML(visible=False)
-            custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
-        
-        with gr.Column():
-            result = gr.Image(label="Generated Image")
-
-            with gr.Row():
-                aspect_ratio = gr.Dropdown(
-                    label="Aspect Ratio",
-                    choices=["1:1", "16:9", "9:16", "4:3", "3:4", "3:2", "2:3"],
-                    value="1:1"
-                    )
-            with gr.Row():
-                speed_mode = gr.Dropdown(
-                    label="Generation Mode",
-                    choices=["Speed (8 steps)", "Quality (45 steps)"],
-                    value="Quality (45 steps)",
-                )
-            
-            speed_status = gr.Markdown("Quality mode active", elem_id="speed_status")
-
-    with gr.Row():
-        with gr.Accordion("Advanced Settings", open=False):
-            with gr.Column():
-                with gr.Row():
-                    cfg_scale = gr.Slider(
-                        label="Guidance Scale (True CFG)", 
-                        minimum=1.0, 
-                        maximum=5.0, 
-                        step=0.1, 
-                        value=3.5,
-                        info="Lower for speed mode, higher for quality"
-                    )
-                    steps = gr.Slider(
-                        label="Steps", 
-                        minimum=4, 
-                        maximum=50, 
-                        step=1, 
-                        value=45,
-                        info="Automatically set by speed mode"
-                    )
-                
-                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=2, step=0.01, value=1.0)
-
-    # Event handlers
-    gallery.select(
-        handle_lora_selection,
-        inputs=[aspect_ratio],
-        outputs=[prompt, selected_info, selected_index, aspect_ratio]
-    )
-    
-    speed_mode.change(
-        adjust_generation_mode,
-        inputs=[speed_mode],
-        outputs=[speed_status, steps, cfg_scale]
-    )
-    
-    custom_lora.input(
-        incorporate_custom_adapter,
-        inputs=[custom_lora],
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
-    )
-    
-    custom_lora_button.click(
-        discard_custom_adapter,
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
-    )
-    
-    gr.on(
-        triggers=[generate_button.click, prompt.submit],
-        fn=process_adapter_generation,
-        inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, aspect_ratio, lora_scale, speed_mode],
-        outputs=[result, seed]
-    )
-
-app.queue()
-app.launch(share=False, ssr_mode=False, show_error=True)