Delete app.py

app.py

@@ -1,407 +0,0 @@
-#!/usr/bin/env python
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in
-# all copies or substantial portions of the Software.
-
-import os
-import gradio as gr
-import json
-import logging
-import torch
-from PIL import Image
-import spaces
-from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
-from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
-from diffusers.utils import load_image
-from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
-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
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-base_model = "black-forest-labs/FLUX.1-dev"
-
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
-pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
-pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model,
-                                                      vae=good_vae,
-                                                      transformer=pipe.transformer,
-                                                      text_encoder=pipe.text_encoder,
-                                                      tokenizer=pipe.tokenizer,
-                                                      text_encoder_2=pipe.text_encoder_2,
-                                                      tokenizer_2=pipe.tokenizer_2,
-                                                      torch_dtype=dtype
-                                                     )
-
-MAX_SEED = 2**32-1
-
-pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
-
-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=100)
-def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
-    pipe.to("cuda")
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    with calculateDuration("Generating image"):
-        # Generate image
-        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-            prompt=prompt_mash,
-            num_inference_steps=steps,
-            guidance_scale=cfg_scale,
-            width=width,
-            height=height,
-            generator=generator,
-            joint_attention_kwargs={"scale": lora_scale},
-            output_type="pil",
-            good_vae=good_vae,
-        ):
-            yield img
-
-def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, 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,
-        joint_attention_kwargs={"scale": lora_scale},
-        output_type="pil",
-    ).images[0]
-    return final_image 
-
-@spaces.GPU(duration=100)
-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:
-            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
-
-    with calculateDuration("Unloading LoRA"):
-        pipe.unload_lora_weights()
-        pipe_i2i.unload_lora_weights()
-        
-    # Load LoRA weights
-    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
-        pipe_to_use = pipe_i2i if image_input is not None else pipe
-        weight_name = selected_lora.get("weights", None)
-        
-        pipe_to_use.load_lora_weights(
-            lora_path, 
-            weight_name=weight_name, 
-            low_cpu_mem_usage=True
-        )
-                
-    # Set random seed for reproducibility
-    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, lora_scale, seed)
-        yield final_image, seed, gr.update(visible=False)
-    else:
-        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
-    
-        # Consume the generator to get the final image
-        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.")
-
-    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
-    acceptable_models = {"black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-schnell"}
-    
-    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 FLUX LoRA!")
-        
-    # Extract image and trigger word
-    print("Before trying to get image")
-    image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
-    print(f"Image path {image_path}")
-    trigger_word = model_card.data.get("instance_prompt", "")
-    print(f"Image path {trigger_word}")
-    image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
-    print(f"Image URL {image_url}")
-
-        
-    # Initialize Hugging Face file system
-    fs = HfFileSystem()
-    try:
-        list_of_files = fs.ls(link, detail=False)
-        
-        # Initialize variables for safetensors selection
-        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]
-
-            # Select safetensors file
-            if filename.endswith(".safetensors"):
-                last_safetensors_file = filename  # Track last encountered file
-                
-                match = step_pattern.search(filename)
-                if not match:
-                    # Found a full model without step numbers, return immediately
-                    safetensors_name = filename
-                    break
-                else:
-                    # Extract step count and track highest
-                    steps = int(match.group().lstrip("_"))
-                    if steps > highest_steps:
-                        highest_trained_file = filename
-                        highest_steps = steps
-
-            # Select an image file if not found in model card
-            if not image_url and filename.lower().endswith((".jpg", ".jpeg", ".png", ".webp")):
-                image_url = f"https://huggingface.co/{link}/resolve/main/{filename}"
-
-        # If no full model found, fall back to the most trained safetensors file
-        if not safetensors_name:
-            safetensors_name = highest_trained_file if highest_trained_file else last_safetensors_file
-
-        # If still no safetensors file found, raise an exception
-        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):
-    print(f"Checking a custom model on: {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-FLUX LoRA, this was the issue: {e}")
-            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX 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}
-'''
-
-with gr.Blocks(theme="bethecloud/storj_theme", css=css, delete_cache=(60, 60)) as app:
-    title = gr.HTML(
-        """<h1>FLUX 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=":/ choose the LoRA and type the prompt ")
-        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", placeholder="prithivMLmods/Canopus-LoRA-Flux-Anime")
-                gr.Markdown("[Check the list of FLUX LoRA's](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", 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=3.5)
-                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
-                
-                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=0.95)
-
-    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()