Running on CPU 🥶 This demo does not work on CPU.
" IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1" HF_TOKEN = os.getenv("HF_TOKEN") CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1" MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512")) MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048")) USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1" ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1" OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs") MODEL = os.getenv( "MODEL", "https://huggingface.co/AstraliteHeart/pony-diffusion-v6/blob/main/v6.safetensors", ) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") def load_pipeline(model_name): vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, ) pipeline = ( StableDiffusionXLPipeline.from_single_file if MODEL.endswith(".safetensors") else StableDiffusionXLPipeline.from_pretrained ) pipe = pipeline( model_name, vae=vae, torch_dtype=torch.float16, custom_pipeline="lpw_stable_diffusion_xl", use_safetensors=True, add_watermarker=False, use_auth_token=HF_TOKEN, variant="fp16", ) pipe.to(device) return pipe def parse_json_parameters(json_str): try: params = json.loads(json_str) required_keys = ['prompt', 'negative_prompt', 'resolution', 'guidance_scale', 'num_inference_steps', 'seed', 'sampler'] for key in required_keys: if key not in params: raise ValueError(f"Missing required key: {key}") # Parse resolution width, height = map(int, params['resolution'].split(' x ')) return { 'prompt': params['prompt'], 'negative_prompt': params['negative_prompt'], 'seed': params['seed'], 'width': width, 'height': height, 'guidance_scale': params['guidance_scale'], 'num_inference_steps': params['num_inference_steps'], 'sampler': params['sampler'], 'use_upscaler': params.get('use_upscaler', False) } except json.JSONDecodeError: raise ValueError("Invalid JSON format") except Exception as e: raise ValueError(f"Error parsing JSON: {str(e)}") @spaces.GPU def generate( prompt: str, negative_prompt: str = "", seed: int = 0, custom_width: int = 1024, custom_height: int = 1024, guidance_scale: float = 7.0, num_inference_steps: int = 30, sampler: str = "DPM++ 2M SDE Karras", aspect_ratio_selector: str = "1024 x 1024", use_upscaler: bool = False, upscaler_strength: float = 0.55, upscale_by: float = 1.5, json_params: str = "", progress=gr.Progress(track_tqdm=True), ) -> Image: if json_params: try: params = parse_json_parameters(json_params) prompt = params['prompt'] negative_prompt = params['negative_prompt'] seed = params['seed'] custom_width = params['width'] custom_height = params['height'] guidance_scale = params['guidance_scale'] num_inference_steps = params['num_inference_steps'] sampler = params['sampler'] use_upscaler = params['use_upscaler'] except ValueError as e: raise gr.Error(str(e)) generator = utils.seed_everything(seed) width, height = utils.aspect_ratio_handler( aspect_ratio_selector, custom_width, custom_height, ) width, height = utils.preprocess_image_dimensions(width, height) backup_scheduler = pipe.scheduler pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler) if use_upscaler: upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components) metadata = { "prompt": prompt, "negative_prompt": negative_prompt, "resolution": f"{width} x {height}", "guidance_scale": guidance_scale, "num_inference_steps": num_inference_steps, "seed": seed, "sampler": sampler, } if use_upscaler: new_width = int(width * upscale_by) new_height = int(height * upscale_by) metadata["use_upscaler"] = { "upscale_method": "nearest-exact", "upscaler_strength": upscaler_strength, "upscale_by": upscale_by, "new_resolution": f"{new_width} x {new_height}", } else: metadata["use_upscaler"] = None logger.info(json.dumps(metadata, indent=4)) try: if use_upscaler: latents = pipe( prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator, output_type="latent", ).images upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by) images = upscaler_pipe( prompt=prompt, negative_prompt=negative_prompt, image=upscaled_latents, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, strength=upscaler_strength, generator=generator, output_type="pil", ).images else: images = pipe( prompt=prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator, output_type="pil", ).images if images and IS_COLAB: for image in images: filepath = utils.save_image(image, metadata, OUTPUT_DIR) logger.info(f"Image saved as {filepath} with metadata") return images, metadata except Exception as e: logger.exception(f"An error occurred: {e}") raise finally: if use_upscaler: del upscaler_pipe pipe.scheduler = backup_scheduler utils.free_memory() # Initialize an empty list to store the generation history generation_history = [] # Function to update the history list def update_history_list(): return [item["image"] for item in generation_history] # Function to handle image click in history def handle_image_click(evt: gr.SelectData): selected = generation_history[evt.index] return selected["image"], json.dumps(selected["metadata"], indent=2) # Modify the generate function to add results to the history def generate_and_update_history(*args, **kwargs): images, metadata = generate(*args, **kwargs) timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") generation_history.insert(0, { "prompt": metadata["prompt"], "timestamp": timestamp, "image": images[0], "metadata": metadata }) if len(generation_history) > 10: # Limit history to 10 items generation_history.pop() return images[0], json.dumps(metadata, indent=2), update_history_list() if torch.cuda.is_available(): pipe = load_pipeline(MODEL) logger.info("Loaded on Device!") else: pipe = None with gr.Blocks(css="style.css") as demo: gr.HTML("""