import gradio as gr import numpy as np import random import torch import spaces import math import os import yaml import io import tempfile import shutil import uuid import time import json from typing import List, Tuple, Dict, Optional from datetime import datetime, timedelta from pathlib import Path from PIL import Image from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler from huggingface_hub import InferenceClient from reportlab.lib.pagesizes import A4 from reportlab.pdfgen import canvas from reportlab.pdfbase import pdfmetrics from reportlab.lib.utils import ImageReader from PyPDF2 import PdfReader, PdfWriter # --- Style Presets Loading --- def load_style_presets(): """Load style presets from YAML file.""" try: with open('style_presets.yaml', 'r') as f: data = yaml.safe_load(f) # Filter only enabled presets presets = {k: v for k, v in data['presets'].items() if v.get('enabled', True)} return presets except Exception as e: print(f"Error loading style presets: {e}") return {"no_style": {"id": "no_style", "label": "No style (custom)", "prompt_prefix": "", "prompt_suffix": "", "negative_prompt": ""}} # Load presets at startup STYLE_PRESETS = load_style_presets() # --- Page Layouts Loading --- def load_page_layouts(): """Load page layouts from YAML file.""" try: with open('page_layouts.yaml', 'r') as f: data = yaml.safe_load(f) return data['layouts'] except Exception as e: print(f"Error loading page layouts: {e}") # Fallback to basic layouts return { 1: [{"id": "full_page", "label": "Full Page", "positions": [[0.05, 0.05, 0.9, 0.9]]}], 2: [{"id": "horizontal_split", "label": "Horizontal Split", "positions": [[0.05, 0.05, 0.425, 0.9], [0.525, 0.05, 0.425, 0.9]]}], 3: [{"id": "grid", "label": "Grid", "positions": [[0.05, 0.05, 0.283, 0.5], [0.358, 0.05, 0.283, 0.5], [0.666, 0.05, 0.283, 0.5]]}], 4: [{"id": "grid_2x2", "label": "2x2 Grid", "positions": [[0.05, 0.05, 0.425, 0.425], [0.525, 0.05, 0.425, 0.425], [0.05, 0.525, 0.425, 0.425], [0.525, 0.525, 0.425, 0.425]]}] } # Load layouts at startup PAGE_LAYOUTS = load_page_layouts() def get_layout_choices(num_images: int) -> List[Tuple[str, str]]: """Get available layout choices for a given number of images.""" key = f"{num_images}_image" if num_images == 1 else f"{num_images}_images" if key in PAGE_LAYOUTS: return [(layout["label"], layout["id"]) for layout in PAGE_LAYOUTS[key]] # Return empty list if no layouts found (shouldn't happen with our config) return [("Default", "default")] def get_random_style_preset(): """Get a random style preset (excluding 'no_style' and 'random').""" eligible_keys = [k for k in STYLE_PRESETS.keys() if k not in ['no_style', 'random']] if eligible_keys: return random.choice(eligible_keys) return 'no_style' def apply_style_preset(prompt, style_preset_key, custom_style_text=""): """ Apply style preset to the prompt. Args: prompt: The user's base prompt style_preset_key: The key of the selected style preset custom_style_text: Custom style text when 'no_style' is selected Returns: tuple: (styled_prompt, negative_prompt) """ if style_preset_key == 'no_style': # Use custom style text if provided if custom_style_text and custom_style_text.strip(): styled_prompt = f"{custom_style_text}, {prompt}" else: styled_prompt = prompt return styled_prompt, "" if style_preset_key == 'random': # Select a random style style_preset_key = get_random_style_preset() if style_preset_key in STYLE_PRESETS: preset = STYLE_PRESETS[style_preset_key] prefix = preset.get('prompt_prefix', '') suffix = preset.get('prompt_suffix', '') negative = preset.get('negative_prompt', '') # Build the styled prompt parts = [] if prefix: parts.append(prefix) parts.append(prompt) if suffix: parts.append(suffix) styled_prompt = ', '.join(parts) return styled_prompt, negative # Fallback to original prompt if preset not found return prompt, "" # --- Story Generation using Hugging Face InferenceClient --- def generate_story_scenes(story_prompt, num_scenes, style_context=""): """ Generates a sequence of scene descriptions with captions and dialogues. Args: story_prompt: The user's story prompt num_scenes: Number of scenes to generate style_context: Optional style context to consider Returns: List of dicts with 'caption' and 'dialogue' keys """ # Ensure HF_TOKEN is set api_key = os.environ.get("HF_TOKEN") if not api_key: print("HF_TOKEN not set, using fallback scene generation") # Simple fallback - just split the prompt into scenes fallback_scenes = [] for i in range(num_scenes): fallback_scenes.append({ "caption": f"{story_prompt} (scene {i+1} of {num_scenes})", "dialogue": "" }) return fallback_scenes # Initialize the client client = InferenceClient( provider="cerebras", api_key=api_key, ) # Create system prompt for story generation system_prompt = f"""You are a comic book story writer. Generate exactly {num_scenes} scenes for a comic page based on the user's story prompt. IMPORTANT INSTRUCTIONS: 1. Output ONLY a YAML list with exactly {num_scenes} items 2. Each item must have exactly two fields: - caption: A detailed visual description of the scene (describe characters, clothing, location, action, expressions) - dialogue: What characters are saying or thinking (can be empty string if no dialogue) 3. For captions: Be very descriptive. Repeat character descriptions in each scene (appearance, clothes, etc.) 4. For dialogue: Format as 'CHARACTER: "What they say"' or describe sounds/thoughts 5. Keep continuity between scenes to tell a coherent story 6. Make each scene visually distinct but connected to the narrative Example output format: - caption: "A young woman with long red hair wearing a blue detective coat stands in a dark alley, holding a magnifying glass up to examine mysterious glowing footprints on the wet pavement" dialogue: 'DETECTIVE SARAH: "These tracks... they\'re not human!"' - caption: "The same red-haired woman in the blue coat backs away in shock as a massive shark fin emerges from a puddle in the alley, water splashing everywhere" dialogue: 'DETECTIVE SARAH: "OH NO, SHARKS IN THE CITY!"' Generate exactly {num_scenes} scenes. Output ONLY the YAML list, no other text.""" # Format the messages messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": f"Create {num_scenes} comic scenes for this story: {story_prompt}"} ] try: # Call the API completion = client.chat.completions.create( model="Qwen/Qwen3-235B-A22B-Instruct-2507", messages=messages, temperature=0.7, max_tokens=2000, ) response = completion.choices[0].message.content # Parse the YAML response scenes = parse_yaml_scenes(response, num_scenes) return scenes except Exception as e: print(f"Error during story generation: {e}") # Fallback to simple scene splitting fallback_scenes = [] for i in range(num_scenes): fallback_scenes.append({ "caption": f"{story_prompt} (part {i+1} of {num_scenes})", "dialogue": "" }) return fallback_scenes def parse_yaml_scenes(yaml_text, expected_count): """ Parse YAML text to extract scene captions and dialogues. """ try: # Clean up the text - remove markdown code blocks if present yaml_text = yaml_text.strip() if yaml_text.startswith("```yaml"): yaml_text = yaml_text[7:] if yaml_text.startswith("```"): yaml_text = yaml_text[3:] if yaml_text.endswith("```"): yaml_text = yaml_text[:-3] # Parse YAML scenes = yaml.safe_load(yaml_text) if not isinstance(scenes, list): raise ValueError("Expected a list of scenes") # Validate and clean scenes valid_scenes = [] for scene in scenes: if isinstance(scene, dict) and 'caption' in scene: valid_scenes.append({ 'caption': str(scene.get('caption', '')), 'dialogue': str(scene.get('dialogue', '')) }) # Ensure we have the expected number of scenes while len(valid_scenes) < expected_count: valid_scenes.append({ 'caption': 'continuation of the story', 'dialogue': '' }) return valid_scenes[:expected_count] except Exception as e: print(f"Error parsing YAML scenes: {e}") # Return fallback scenes return [{'caption': 'scene description', 'dialogue': ''} for _ in range(expected_count)] def get_caption_language(prompt): """Detects if the prompt contains Chinese characters.""" ranges = [ ('\u4e00', '\u9fff'), # CJK Unified Ideographs ] for char in prompt: if any(start <= char <= end for start, end in ranges): return 'zh' return 'en' # --- Model Loading --- # Use the new lightning-fast model setup ckpt_id = "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( ckpt_id, scheduler=scheduler, torch_dtype=torch.bfloat16 ).to("cuda") # Load LoRA weights for acceleration pipe.load_lora_weights( "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.1.safetensors" ) pipe.fuse_lora() #pipe.unload_lora_weights() #pipe.load_lora_weights("flymy-ai/qwen-image-realism-lora") #pipe.fuse_lora() #pipe.unload_lora_weights() # --- UI Constants and Helpers --- MAX_SEED = np.iinfo(np.int32).max def get_image_size_for_position(position_data, image_index, num_images): """Determines optimal image size based on its position in the layout. Args: position_data: Layout position data [x, y, width, height] in relative units image_index: Index of the current image (0-based) num_images: Total number of images in the layout Returns: tuple: (width, height) optimized for the position's aspect ratio, max 1024 in any dimension """ if not position_data: return 1024, 1024 # Default square x_rel, y_rel, w_rel, h_rel = position_data aspect_ratio = w_rel / h_rel if h_rel > 0 else 1.0 # Max dimension is 1024 max_dim = 1024 # Calculate dimensions maintaining aspect ratio with max of 1024 if aspect_ratio >= 1: # Wider than tall width = max_dim height = int(max_dim / aspect_ratio) # Ensure height is at least 256 for quality if height < 256: height = 256 width = int(256 * aspect_ratio) else: # Taller than wide height = max_dim width = int(max_dim * aspect_ratio) # Ensure width is at least 256 for quality if width < 256: width = 256 height = int(256 / aspect_ratio) # Round to nearest 64 for better compatibility width = (width // 64) * 64 height = (height // 64) * 64 # Ensure we don't exceed max_dim after rounding if width > max_dim: width = max_dim if height > max_dim: height = max_dim # Minimum size check width = max(width, 256) height = max(height, 256) return width, height def get_layout_position_for_image(layout_id, num_images, image_index): """Get the position data for a specific image in a layout. Args: layout_id: ID of the selected layout num_images: Total number of images image_index: Index of the current image (0-based) Returns: Position data [x, y, width, height] or None """ key = f"{num_images}_image" if num_images == 1 else f"{num_images}_images" layouts = PAGE_LAYOUTS.get(key, []) layout = next((l for l in layouts if l["id"] == layout_id), None) if layout and "positions" in layout: positions = layout["positions"] if image_index < len(positions): return positions[image_index] # Fallback positions for each number of images fallback_positions = { 1: [[0.05, 0.05, 0.9, 0.9]], 2: [[0.05, 0.05, 0.425, 0.9], [0.525, 0.05, 0.425, 0.9]], 3: [[0.05, 0.25, 0.283, 0.5], [0.358, 0.25, 0.283, 0.5], [0.666, 0.25, 0.283, 0.5]], 4: [[0.05, 0.05, 0.425, 0.425], [0.525, 0.05, 0.425, 0.425], [0.05, 0.525, 0.425, 0.425], [0.525, 0.525, 0.425, 0.425]], 5: [[0.05, 0.05, 0.9, 0.3], [0.05, 0.4, 0.283, 0.55], [0.358, 0.4, 0.283, 0.55], [0.666, 0.4, 0.283, 0.275], [0.666, 0.7, 0.283, 0.275]], 6: [[0.05, 0.05, 0.425, 0.283], [0.525, 0.05, 0.425, 0.283], [0.05, 0.358, 0.425, 0.283], [0.525, 0.358, 0.425, 0.283], [0.05, 0.666, 0.425, 0.283], [0.525, 0.666, 0.425, 0.283]] } positions = fallback_positions.get(num_images, fallback_positions[1]) if image_index < len(positions): return positions[image_index] return [0.05, 0.05, 0.9, 0.9] # Ultimate default # --- Session Management Functions --- class SessionManager: """Manages user session data and temporary file storage.""" def __init__(self, session_id: str = None): self.session_id = session_id or str(uuid.uuid4()) self.base_dir = Path(tempfile.gettempdir()) / "gradio_comic_sessions" self.session_dir = self.base_dir / self.session_id self.session_dir.mkdir(parents=True, exist_ok=True) self.metadata_file = self.session_dir / "metadata.json" self.pdf_path = self.session_dir / "comic.pdf" self.load_or_create_metadata() def load_or_create_metadata(self): """Load existing metadata or create new.""" if self.metadata_file.exists(): with open(self.metadata_file, 'r') as f: self.metadata = json.load(f) else: self.metadata = { "created_at": datetime.now().isoformat(), "pages": [], "total_pages": 0 } self.save_metadata() def save_metadata(self): """Save metadata to file.""" with open(self.metadata_file, 'w') as f: json.dump(self.metadata, f, indent=2) def add_page(self, images: List[Image.Image], layout_id: str, seeds: List[int]): """Add a new page to the session.""" page_num = self.metadata["total_pages"] + 1 page_dir = self.session_dir / f"page_{page_num}" page_dir.mkdir(exist_ok=True) # Save images image_paths = [] for i, img in enumerate(images): img_path = page_dir / f"image_{i+1}.jpg" img.save(img_path, 'JPEG', quality=95) image_paths.append(str(img_path)) # Update metadata self.metadata["pages"].append({ "page_num": page_num, "layout_id": layout_id, "num_images": len(images), "image_paths": image_paths, "seeds": seeds, "created_at": datetime.now().isoformat() }) self.metadata["total_pages"] = page_num self.save_metadata() return page_num def get_all_pages_images(self) -> List[Tuple[List[Image.Image], str, int]]: """Get all images from all pages.""" pages_data = [] for page in self.metadata["pages"]: images = [] for img_path in page["image_paths"]: if Path(img_path).exists(): images.append(Image.open(img_path)) if images: pages_data.append((images, page["layout_id"], page["num_images"])) return pages_data def cleanup_old_sessions(self, max_age_hours: int = 24): """Clean up sessions older than max_age_hours.""" if not self.base_dir.exists(): return cutoff_time = datetime.now() - timedelta(hours=max_age_hours) for session_dir in self.base_dir.iterdir(): if session_dir.is_dir(): metadata_file = session_dir / "metadata.json" if metadata_file.exists(): try: with open(metadata_file, 'r') as f: metadata = json.load(f) created_at = datetime.fromisoformat(metadata["created_at"]) if created_at < cutoff_time: shutil.rmtree(session_dir) print(f"Cleaned up old session: {session_dir.name}") except Exception as e: print(f"Error cleaning session {session_dir.name}: {e}") # --- PDF Generation Functions --- def create_single_page_pdf(images: List[Image.Image], layout_id: str, num_images: int) -> bytes: """ Create a single PDF page with images arranged according to the selected layout. Args: images: List of PIL images layout_id: ID of the selected layout num_images: Number of images to include Returns: PDF page as bytes """ # Create a bytes buffer for the PDF pdf_buffer = io.BytesIO() # Create canvas with A4 size pdf = canvas.Canvas(pdf_buffer, pagesize=A4) page_width, page_height = A4 # Get the layout configuration key = f"{num_images}_image" if num_images == 1 else f"{num_images}_images" layouts = PAGE_LAYOUTS.get(key, []) layout = next((l for l in layouts if l["id"] == layout_id), None) if not layout: # Fallback to default grid layout if num_images == 1: positions = [[0.05, 0.05, 0.9, 0.9]] elif num_images == 2: positions = [[0.05, 0.05, 0.425, 0.9], [0.525, 0.05, 0.425, 0.9]] elif num_images == 3: positions = [[0.05, 0.05, 0.283, 0.9], [0.358, 0.05, 0.283, 0.9], [0.666, 0.05, 0.283, 0.9]] elif num_images == 4: positions = [[0.05, 0.05, 0.425, 0.425], [0.525, 0.05, 0.425, 0.425], [0.05, 0.525, 0.425, 0.425], [0.525, 0.525, 0.425, 0.425]] elif num_images == 5: positions = [[0.05, 0.05, 0.9, 0.3], [0.05, 0.4, 0.283, 0.55], [0.358, 0.4, 0.283, 0.55], [0.666, 0.4, 0.283, 0.275], [0.666, 0.7, 0.283, 0.275]] elif num_images == 6: positions = [[0.05, 0.05, 0.425, 0.283], [0.525, 0.05, 0.425, 0.283], [0.05, 0.358, 0.425, 0.283], [0.525, 0.358, 0.425, 0.283], [0.05, 0.666, 0.425, 0.283], [0.525, 0.666, 0.425, 0.283]] else: # For more than 6, create a simple grid positions = [[0.05, 0.05, 0.9, 0.9]] else: positions = layout["positions"] # Draw each image according to the layout for i, (image, pos) in enumerate(zip(images[:num_images], positions)): if i >= len(images): break x_rel, y_rel, w_rel, h_rel = pos # Pack images more tightly - significantly reduce empty space # Minimal padding between panels (0.5% of page dimensions) padding = 0.005 # Scale up positions and sizes to fill more of the page # This brings everything closer to the edges and each other scale_factor = 1.15 # Increase overall scale by 15% # Calculate centered scaling to maintain layout proportions center_x = 0.5 center_y = 0.5 # Scale positions relative to center x_rel = center_x + (x_rel - center_x) * scale_factor y_rel = center_y + (y_rel - center_y) * scale_factor # Scale sizes w_rel = w_rel * scale_factor h_rel = h_rel * scale_factor # Apply bounds checking to prevent overflow if x_rel < padding: x_rel = padding if y_rel < padding: y_rel = padding if x_rel + w_rel > 1 - padding: w_rel = 1 - padding - x_rel if y_rel + h_rel > 1 - padding: h_rel = 1 - padding - y_rel # Convert relative positions to absolute positions # Note: In ReportLab, y=0 is at the bottom x = x_rel * page_width y = (1 - y_rel - h_rel) * page_height # Flip Y coordinate width = w_rel * page_width height = h_rel * page_height # Calculate image aspect ratio and layout aspect ratio img_aspect = image.width / image.height layout_aspect = width / height # Preserve aspect ratio while fitting in the allocated space if img_aspect > layout_aspect: # Image is wider than the layout space new_height = width / img_aspect y_offset = (height - new_height) / 2 actual_width = width actual_height = new_height actual_x = x actual_y = y + y_offset else: # Image is taller than the layout space new_width = height * img_aspect x_offset = (width - new_width) / 2 actual_width = new_width actual_height = height actual_x = x + x_offset actual_y = y # Convert PIL image to format suitable for ReportLab img_buffer = io.BytesIO() # Save with good quality image.save(img_buffer, format='JPEG', quality=95) img_buffer.seek(0) # Draw the image on the PDF preserving aspect ratio pdf.drawImage(ImageReader(img_buffer), actual_x, actual_y, width=actual_width, height=actual_height, preserveAspectRatio=True, mask='auto') # Save the PDF pdf.save() # Get the PDF bytes pdf_buffer.seek(0) pdf_bytes = pdf_buffer.read() return pdf_bytes def create_multi_page_pdf(session_manager: SessionManager) -> str: """ Create a multi-page PDF from all pages in the session. Args: session_manager: SessionManager instance with page data Returns: Path to the created PDF file """ pages_data = session_manager.get_all_pages_images() if not pages_data: return None # Create PDF writer pdf_writer = PdfWriter() # Create each page for images, layout_id, num_images in pages_data: page_pdf_bytes = create_single_page_pdf(images, layout_id, num_images) # Read the single page PDF page_pdf_reader = PdfReader(io.BytesIO(page_pdf_bytes)) # Add the page to the writer for page in page_pdf_reader.pages: pdf_writer.add_page(page) # Write to file pdf_path = session_manager.pdf_path with open(pdf_path, 'wb') as f: pdf_writer.write(f) return str(pdf_path) # --- Main Inference Function (with session support) --- @spaces.GPU(duration=180) # Increased duration for up to 6 images def infer_page( prompt, seed=42, randomize_seed=False, guidance_scale=1.0, num_inference_steps=8, style_preset="no_style", custom_style_text="", num_images=1, layout="default", session_state=None, progress=gr.Progress(track_tqdm=True), ): """ Generates images for a new page and adds them to the PDF. Args: prompt (str): The text prompt to generate images from. seed (int): The seed for the random number generator for reproducibility. randomize_seed (bool): If True, a random seed is used for each image. guidance_scale (float): Corresponds to `true_cfg_scale`. num_inference_steps (int): The number of denoising steps. style_preset (str): The key of the style preset to apply. custom_style_text (str): Custom style text when 'no_style' is selected. num_images (int): Number of images to generate (1-6). layout (str): The layout ID for arranging images in the PDF. session_state: Current session state dictionary. progress (gr.Progress): A Gradio Progress object to track generation. Returns: tuple: Updated session state, PDF path, preview image, page info, and updated button label. """ # Initialize or retrieve session if session_state is None or "session_id" not in session_state: session_state = {"session_id": str(uuid.uuid4()), "page_count": 0} session_manager = SessionManager(session_state["session_id"]) # Clean up old sessions periodically if random.random() < 0.1: # 10% chance to cleanup on each request session_manager.cleanup_old_sessions() # Check page limit if session_manager.metadata["total_pages"] >= 128: return session_state, None, None, "Maximum page limit (128) reached!", f"Page limit reached" generated_images = [] used_seeds = [] # Generate story scenes progress(0, f"Generating story with {num_images} scenes...") scenes = generate_story_scenes(prompt, int(num_images), style_preset) # Generate the requested number of images for i in range(int(num_images)): progress((i + 0.5) / num_images, f"Generating image {i+1} of {num_images} for page {session_manager.metadata['total_pages'] + 1}") current_seed = seed + i if not randomize_seed else random.randint(0, MAX_SEED) # Get optimal aspect ratio based on position in layout position_data = get_layout_position_for_image(layout, int(num_images), i) # Use scene caption and dialogue for this image scene_prompt = scenes[i]['caption'] scene_dialogue = scenes[i]['dialogue'] # Generate single image with automatic aspect ratio image, used_seed = infer_single_auto( prompt=scene_prompt, seed=current_seed, randomize_seed=False, # We handle randomization here position_data=position_data, image_index=i, num_images=int(num_images), guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, dialogue=scene_dialogue, # Pass dialogue separately style_preset=style_preset, custom_style_text=custom_style_text, ) generated_images.append(image) used_seeds.append(used_seed) # Add page to session progress(0.8, "Adding page to document...") page_num = session_manager.add_page(generated_images, layout, used_seeds) # Create multi-page PDF progress(0.9, "Creating PDF...") pdf_path = create_multi_page_pdf(session_manager) progress(1.0, "Done!") # Update session state session_state["page_count"] = page_num # Prepare page info seeds_str = ", ".join(str(s) for s in used_seeds) page_info = f"Page {page_num} added\nSeeds: {seeds_str}\nTotal pages: {page_num}" # Next button label next_page_num = page_num + 1 button_label = f"Generate page {next_page_num}" if next_page_num <= 128 else "Page limit reached" return session_state, pdf_path, generated_images[0] if generated_images else None, page_info, button_label # New inference function with automatic aspect ratio def infer_single_auto( prompt, seed=42, randomize_seed=False, position_data=None, image_index=0, num_images=1, guidance_scale=1.0, num_inference_steps=8, dialogue="", # New parameter for dialogue style_preset="no_style", custom_style_text="", ): """ Generates an image with automatically determined aspect ratio based on layout position. """ if randomize_seed: seed = random.randint(0, MAX_SEED) # Automatically determine image size based on position width, height = get_image_size_for_position(position_data, image_index, num_images) # Set up the generator for reproducibility generator = torch.Generator(device="cuda").manual_seed(seed) print(f"Original prompt: '{prompt}'") print(f"Style preset: '{style_preset}'") print(f"Auto-selected size based on layout: {width}x{height}") # Apply style preset first styled_prompt, style_negative_prompt = apply_style_preset(prompt, style_preset, custom_style_text) # Add dialogue to the prompt if present if dialogue and dialogue.strip(): # Format dialogue for image generation dialogue_formatted = dialogue.replace('"', '').replace("'", '') styled_prompt = f"{styled_prompt}, speech bubble saying {dialogue_formatted}" # Use style negative prompt if available, otherwise default negative_prompt = style_negative_prompt if style_negative_prompt else " " print(f"Final Prompt: '{styled_prompt}'") print(f"Negative Prompt: '{negative_prompt}'") print(f"Seed: {seed}, Size: {width}x{height}, Steps: {num_inference_steps}, True CFG Scale: {guidance_scale}") # Generate the image image = pipe( prompt=styled_prompt, negative_prompt=negative_prompt, width=width, height=height, num_inference_steps=num_inference_steps, generator=generator, true_cfg_scale=guidance_scale, # Use true_cfg_scale for this model ).images[0] # Convert to grayscale if using manga_no_color style if style_preset == "manga_no_color": # Convert to grayscale while preserving quality image = image.convert('L').convert('RGB') return image, seed # Keep the old infer function for backward compatibility (simplified) infer = infer_single_auto # --- Examples and UI Layout --- examples = [ "A capybara wearing a suit holding a sign that reads Hello World", "一幅精致细腻的工笔画，画面中心是一株蓬勃生长的红色牡丹，花朵繁茂，既有盛开的硕大花瓣，也有含苞待放的花蕾，层次丰富，色彩艳丽而不失典雅。牡丹枝叶舒展，叶片浓绿饱满，脉络清晰可见，与红花相映成趣。一只蓝紫色蝴蝶仿佛被画中花朵吸引，停驻在画面中央的一朵盛开牡丹上，流连忘返，蝶翼轻展，细节逼真，仿佛随时会随风飞舞。整幅画作笔触工整严谨，色彩浓郁鲜明，展现出中国传统工笔画的精妙与神韵，画面充满生机与灵动之感。", "一位身着淡雅水粉色交领襦裙的年轻女子背对镜头而坐，俯身专注地手持毛笔在素白宣纸上书写“通義千問”四个遒劲汉字。古色古香的室内陈设典雅考究，案头错落摆放着青瓷茶盏与鎏金香炉，一缕熏香轻盈升腾；柔和光线洒落肩头，勾勒出她衣裙的柔美质感与专注神情，仿佛凝固了一段宁静温润的旧时光。", " 一个可抽取式的纸巾盒子，上面写着'Face, CLEAN & SOFT TISSUE'下面写着'亲肤可湿水'，左上角是品牌名'洁柔'，整体是白色和浅黄色的色调", "手绘风格的水循环示意图，整体画面呈现出一幅生动形象的水循环过程图解。画面中央是一片起伏的山脉和山谷，山谷中流淌着一条清澈的河流，河流最终汇入一片广阔的海洋。山体和陆地上绘制有绿色植被。画面下方为地下水层，用蓝色渐变色块表现，与地表水形成层次分明的空间关系。太阳位于画面右上角，促使地表水蒸发，用上升的曲线箭头表示蒸发过程。云朵漂浮在空中，由白色棉絮状绘制而成，部分云层厚重，表示水汽凝结成雨，用向下箭头连接表示降雨过程。雨水以蓝色线条和点状符号表示，从云中落下，补充河流与地下水。整幅图以卡通手绘风格呈现，线条柔和，色彩明亮，标注清晰。背景为浅黄色纸张质感，带有轻微的手绘纹理。", '一个会议室，墙上写着"3.14159265-358979-32384626-4338327950"，一个小陀螺在桌上转动', '一个咖啡店门口有一个黑板，上面写着通义千问咖啡，2美元一杯，旁边有个霓虹灯，写着阿里巴巴，旁边有个海报，海报上面是一个中国美女，海报下方写着qwen newbee', """A young girl wearing school uniform stands in a classroom, writing on a chalkboard. The text "Introducing Qwen-Image, a foundational image generation model that excels in complex text rendering and precise image editing" appears in neat white chalk at the center of the blackboard. Soft natural light filters through windows, casting gentle shadows. The scene is rendered in a realistic photography style with fine details, shallow depth of field, and warm tones. The girl's focused expression and chalk dust in the air add dynamism. Background elements include desks and educational posters, subtly blurred to emphasize the central action. Ultra-detailed 32K resolution, DSLR-quality, soft bokeh effect, documentary-style composition""", "Realistic still life photography style: A single, fresh apple resting on a clean, soft-textured surface. The apple is slightly off-center, softly backlit to highlight its natural gloss and subtle color gradients—deep crimson red blending into light golden hues. Fine details such as small blemishes, dew drops, and a few light highlights enhance its lifelike appearance. A shallow depth of field gently blurs the neutral background, drawing full attention to the apple. Hyper-detailed 8K resolution, studio lighting, photorealistic render, emphasizing texture and form." ] css = """ #col-container { margin: 0 auto; max-width: 1024px; } #logo-title { text-align: center; } #logo-title img { width: 400px; } """ with gr.Blocks(css=css) as demo: # Session state session_state = gr.State(value={"session_id": str(uuid.uuid4()), "page_count": 0}) with gr.Column(elem_id="col-container"): gr.HTML("""
Qwen-Image Logo

Fast, 8-steps with Lightining LoRA

""") gr.Markdown("[Learn more](https://github.com/QwenLM/Qwen-Image) about the Qwen-Image series. This demo uses the [Qwen-Image-Lightning](https://huggingface.co/lightx2v/Qwen-Image-Lightning) LoRA for accelerated inference. [Download model](https://huggingface.co/Qwen/Qwen-Image) to run locally with ComfyUI or diffusers.") with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, placeholder="Enter your prompt", container=False, ) with gr.Column(scale=0): run_button = gr.Button("Generate page 1", variant="primary") reset_button = gr.Button("Start New Document", variant="secondary") # New row for Style Preset and Page Layout with gr.Row(): with gr.Column(scale=1): # Number of images slider (affects layout choices) num_images_slider = gr.Slider( label="Images per page", minimum=1, maximum=6, step=1, value=1, info="Number of images to generate for the PDF (1-6)" ) with gr.Column(scale=2): layout_dropdown = gr.Dropdown( label="Page Layout", choices=[("Full Page", "full_page")], value="full_page", interactive=True, info="How images are arranged on the page" ) with gr.Column(scale=2): # Create dropdown choices from loaded presets style_choices = [(preset["label"], key) for key, preset in STYLE_PRESETS.items()] style_preset = gr.Dropdown( label="Style Preset", choices=style_choices, value="no_style", interactive=True ) with gr.Column(scale=2): custom_style_text = gr.Textbox( label="Custom Style Text", placeholder="Enter custom style (e.g., 'oil painting')", visible=False, lines=1 ) with gr.Row(): with gr.Column(scale=1): result_preview = gr.Image(label="Preview", show_label=True, type="pil") with gr.Column(scale=1): pdf_output = gr.File(label="Download PDF", show_label=True) page_info = gr.Textbox(label="Page Info", show_label=True, interactive=False, lines=3) gr.Markdown("""**Note:** Your images and PDF are saved for up to 24 hours. You can continue adding pages (up to 128) by clicking the generate button.""") with gr.Accordion("Advanced Settings", open=False): seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) # Removed prompt_enhance checkbox - story generation is now always enabled with gr.Row(): guidance_scale = gr.Slider( label="Guidance scale (True CFG Scale)", minimum=1.0, maximum=5.0, step=0.1, value=1.0, ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=4, maximum=28, step=1, value=8, ) # Add interaction to show/hide custom style text field def toggle_custom_style(style_value): return gr.update(visible=(style_value == "no_style")) style_preset.change( fn=toggle_custom_style, inputs=[style_preset], outputs=[custom_style_text] ) # Update layout dropdown when number of images changes def update_layout_choices(num_images): choices = get_layout_choices(int(num_images)) return gr.update(choices=choices, value=choices[0][1] if choices else "default") num_images_slider.change( fn=update_layout_choices, inputs=[num_images_slider], outputs=[layout_dropdown] ) # Update examples to show some with different styles and image counts styled_examples = [ ["A capybara wearing a suit holding a sign that reads Hello World", "no_style", "", 1], ["sharks raining down on san francisco", "anime", "", 2], ["A beautiful landscape with mountains and a lake", "watercolor", "", 3], ["A knight fighting a dragon", "medieval", "", 4], ["Space battle with laser beams", "sci-fi", "", 5], ["Detective investigating a mystery", "noir", "", 6], ] gr.Examples( examples=styled_examples, inputs=[prompt, style_preset, custom_style_text, num_images_slider], outputs=None, # Don't show outputs for examples fn=None, cache_examples=False ) # Define the main generation event generation_event = gr.on( triggers=[run_button.click, prompt.submit], fn=infer_page, inputs=[ prompt, seed, randomize_seed, guidance_scale, num_inference_steps, style_preset, custom_style_text, num_images_slider, layout_dropdown, session_state, ], outputs=[session_state, pdf_output, result_preview, page_info, run_button], ) # Reset button functionality def reset_session(): new_state = {"session_id": str(uuid.uuid4()), "page_count": 0} return new_state, None, None, "", "Generate page 1" # Connect the reset button reset_button.click( fn=reset_session, inputs=[], outputs=[session_state, pdf_output, result_preview, page_info, run_button] ) if __name__ == "__main__": demo.launch(mcp_server=True)