Create app.py

app.py

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+"""
+app.py - Mega-Scale Refactor for Celebrity_LoRa_Mix Space
+
+Features:
+- Modular imports and dependency management
+- Advanced error handling with user-facing messages
+- Async-ready pipeline integration with fallback sync support
+- Mobile-first responsive layout with concise UX messaging
+- Leverages helpers.py and lora_manager.py for clarity and reuse
+
+Author: Helios Automation Alchemist
+"""
+
+import os
+import sys
+import json
+import logging
+import random
+import time
+import asyncio
+from typing import List
+
+import torch
+import gradio as gr
+import pandas as pd
+import requests
+
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
+from transformers import CLIPTokenizer, CLIPProcessor, CLIPModel, LongformerTokenizer, LongformerModel
+from PIL import Image
+
+# Custom modules
+import helpers
+from lora_manager import LoRAManager
+
+# === Config ===
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+logging.basicConfig(level=logging.INFO, format='%(asctime)s | %(levelname)s | %(message)s')
+logger = logging.getLogger(__name__)
+
+MAX_SEED = 2**32 - 1
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+DTYPE = torch.bfloat16 if DEVICE.type == 'cuda' else torch.float32
+
+# === Model & tokenizer loading ===
+def load_tokenizers_and_models():
+    try:
+        clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch16")
+        clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
+        clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16")
+        logger.info("CLIP tokenizer & model loaded.")
+        
+        longformer_tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
+        longformer_model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
+        logger.info("Longformer tokenizer & model loaded.")
+        return clip_tokenizer, clip_processor, clip_model, longformer_tokenizer, longformer_model
+    except Exception as e:
+        logger.error(f"Tokenizer/model load failed: {e}")
+        sys.exit(1)
+
+clip_tokenizer, clip_processor, clip_model, longformer_tokenizer, longformer_model = load_tokenizers_and_models()
+
+# === Load prompts and LoRAs ===
+def load_prompts_and_loras():
+    try:
+        prompts = pd.read_csv("prompts.csv", header=None).values.flatten()
+    except FileNotFoundError:
+        logger.warning("prompts.csv missing, defaulting to empty prompts.")
+        prompts = []
+    try:
+        with open("loras.json", "r") as f:
+            loras = json.load(f)
+    except FileNotFoundError:
+        logger.warning("loras.json missing, defaulting to empty LoRA list.")
+        loras = []
+    return prompts, loras
+
+PROMPT_VALUES, LORA_LIST = load_prompts_and_loras()
+
+# === Initialize Diffusion Pipeline with retry and fallback ===
+def initialize_pipeline(base_model="sayakpaul/FLUX.1-merged", max_retries=3):
+    for attempt in range(max_retries):
+        try:
+            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
+            )
+            pipe.flux_pipe_call_that_returns_an_iterable_of_images = helpers.flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
+            
+            logger.info("Diffusion pipeline loaded successfully.")
+            return pipe, pipe_i2i
+        except Exception as e:
+            logger.warning(f"Attempt {attempt + 1} failed: {e}")
+            time.sleep(5)
+    logger.error("Failed to load diffusion pipeline after retries.")
+    sys.exit(1)
+
+pipe, pipe_i2i = initialize_pipeline()
+
+# === LoRA Manager for adapter lifecycle ===
+lora_manager = LoRAManager(LORA_LIST)
+
+# === Core business logic ===
+def process_input(text: str, max_length: int=4096):
+    if not text or not text.strip():
+        raise gr.Error("Prompt cannot be empty.")
+    return longformer_tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=max_length)
+
+@helpers.async_run_if_possible
+def generate_image(prompt, steps, seed, cfg_scale, width, height, progress):
+    pipe.to(DEVICE)
+    generator = torch.Generator(device=DEVICE).manual_seed(seed)
+    with helpers.calculate_duration("Generating image"):
+        for step_idx, img in enumerate(pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+                prompt=prompt,
+                num_inference_steps=steps,
+                guidance_scale=cfg_scale,
+                width=width,
+                height=height,
+                generator=generator,
+                joint_attention_kwargs={"scale": 1.0},
+                output_type="pil",
+                good_vae=pipe.vae,
+            )):
+            yield img, seed, gr.update(value=f"Step {step_idx + 1}/{steps}", visible=True)
+
+@spaces.GPU(duration=75)
+def run_lora(prompt, cfg_scale, steps, selected_loras_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4,
+             randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
+    if not selected_loras_indices:
+        raise gr.Error("Select at least one LoRA.")
+
+    selected_loras = [loras_state[i] for i in selected_loras_indices]
+
+    # Compose prompt with LoRA trigger words
+    prepend_words = []
+    append_words = []
+    for lora in selected_loras:
+        tw = lora.get("trigger_word", "")
+        if tw:
+            if lora.get("trigger_position") == "prepend":
+                prepend_words.append(tw)
+            else:
+                append_words.append(tw)
+    prompt_mash = " ".join(prepend_words + [prompt] + append_words)
+
+    if randomize_seed or seed == 0:
+        seed = random.randint(0, MAX_SEED)
+
+    logger.info(f"Generating with prompt: {prompt_mash} Seed: {seed}")
+
+    try:
+        lora_manager.set_active_loras(pipe, selected_loras, [lora_scale_1, lora_scale_2, lora_scale_3, lora_scale_4])
+    except Exception as e:
+        logger.error(f"LoRA weight loading failed: {e}")
+        raise gr.Error(f"Failed to load LoRA weights: {str(e)}")
+
+    return generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
+
+# === UI Setup ===
+MOBILE_CSS = '''
+@media (max-width: 600px) {
+  .gr-row { flex-direction: column !important; }
+  .button_total { width: 100% !important; }
+}
+'''
+
+font = [gr.themes.GoogleFont("Source Sans Pro"), "Arial", "sans-serif"]
+
+with gr.Blocks(theme=gr.themes.Soft(font=font), css=MOBILE_CSS, delete_cache=(128, 256)) as app:
+    # Title and app state
+    gr.HTML(
+        '<h1><img src="https://huggingface.co/spaces/keltezaa/Celebrity_LoRa_Mix/resolve/main/solo-traveller_16875043.png" alt="LoRA"> Celebrity_LoRa_Mix</h1>',
+        elem_id="title"
+    )
+    loras_state = gr.State(LORA_LIST)
+    selected_lora_indices = gr.State([])
+
+    # Main input prompt box
+    prompt = gr.Textbox(label="Prompt", placeholder="Type a prompt after selecting a LoRA")
+
+    # LoRA selectors, sliders and images - built modularly here...
+
+    # Advanced parameters
+    with gr.Accordion("Advanced Settings", open=True):
+        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=28)
+        width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=768)
+        height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
+        randomize_seed = gr.Checkbox(True, label="Randomize seed")
+        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+
+    generate_button = gr.Button("Generate", variant="primary", elem_classes=["button_total"])
+    output_img = gr.Image(interactive=False, show_share_button=False)
+    progress_bar = gr.Markdown(visible=False)
+
+    # Bind callbacks here (your existing logic, updated variable names)
+
+app.queue(concurrency_count=3).launch()