import os import uuid import numpy as np import random import tempfile import spaces import zipfile from PIL import Image from diffusers import QwenImageLayeredPipeline import torch from pptx import Presentation import gradio as gr LOG_DIR = "/tmp/local" MAX_SEED = np.iinfo(np.int32).max from huggingface_hub import login login(token=os.environ.get('hf')) dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" pipeline = QwenImageLayeredPipeline.from_pretrained("Qwen/Qwen-Image-Layered", torch_dtype=dtype).to(device) # pipeline.set_progress_bar_config(disable=None) def ensure_dirname(path: str): if path and not os.path.exists(path): os.makedirs(path, exist_ok=True) def random_str(length=8): return uuid.uuid4().hex[:length] def imagelist_to_pptx(img_files): with Image.open(img_files[0]) as img: img_width_px, img_height_px = img.size def px_to_emu(px, dpi=96): inch = px / dpi emu = inch * 914400 return int(emu) prs = Presentation() prs.slide_width = px_to_emu(img_width_px) prs.slide_height = px_to_emu(img_height_px) slide = prs.slides.add_slide(prs.slide_layouts[6]) left = top = 0 for img_path in img_files: slide.shapes.add_picture(img_path, left, top, width=px_to_emu(img_width_px), height=px_to_emu(img_height_px)) with tempfile.NamedTemporaryFile(suffix=".pptx", delete=False) as tmp: prs.save(tmp.name) return tmp.name def export_gallery(images): # images: list of image file paths images = [e[0] for e in images] pptx_path = imagelist_to_pptx(images) return pptx_path def export_gallery_zip(images): # images: list of tuples (file_path, caption) images = [e[0] for e in images] with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as tmp: with zipfile.ZipFile(tmp.name, 'w', zipfile.ZIP_DEFLATED) as zipf: for i, img_path in enumerate(images): # Get the file extension from original file ext = os.path.splitext(img_path)[1] or '.png' # Add each image to the zip with a numbered filename zipf.write(img_path, f"layer_{i+1}{ext}") return tmp.name @spaces.GPU(duration=180) def infer(input_image, seed=777, randomize_seed=False, prompt=None, neg_prompt=" ", true_guidance_scale=4.0, num_inference_steps=50, layer=4, cfg_norm=True, use_en_prompt=True): if randomize_seed: seed = random.randint(0, MAX_SEED) if isinstance(input_image, list): input_image = input_image[0] if isinstance(input_image, str): pil_image = Image.open(input_image).convert("RGB").convert("RGBA") elif isinstance(input_image, Image.Image): pil_image = input_image.convert("RGB").convert("RGBA") elif isinstance(input_image, np.ndarray): pil_image = Image.fromarray(input_image).convert("RGB").convert("RGBA") else: raise ValueError("Unsupported input_image type: %s" % type(input_image)) inputs = { "image": pil_image, "generator": torch.Generator(device='cuda').manual_seed(seed), "true_cfg_scale": true_guidance_scale, "prompt": prompt, "negative_prompt": neg_prompt, "num_inference_steps": num_inference_steps, "num_images_per_prompt": 1, "layers": layer, "resolution": 640, # Using different bucket (640, 1024) to determine the resolution. For this version, 640 is recommended "cfg_normalize": cfg_norm, # Whether enable cfg normalization. "use_en_prompt": use_en_prompt, } print(inputs) with torch.inference_mode(): output = pipeline(**inputs) output_images = output.images[0] output = [] temp_files = [] for i, image in enumerate(output_images): output.append(image) # Save to temp file for export tmp = tempfile.NamedTemporaryFile(suffix=".png", delete=False) image.save(tmp.name) temp_files.append(tmp.name) # Generate PPTX pptx_path = imagelist_to_pptx(temp_files) # Generate ZIP with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as tmp: with zipfile.ZipFile(tmp.name, 'w', zipfile.ZIP_DEFLATED) as zipf: for i, img_path in enumerate(temp_files): zipf.write(img_path, f"layer_{i+1}.png") zip_path = tmp.name return output, pptx_path, zip_path ensure_dirname(LOG_DIR) examples = [ "assets/test_images/1.png", "assets/test_images/2.png", "assets/test_images/3.png", "assets/test_images/4.png", "assets/test_images/5.png", "assets/test_images/6.png", "assets/test_images/7.png", "assets/test_images/8.png", "assets/test_images/9.png", "assets/test_images/10.png", "assets/test_images/11.png", "assets/test_images/12.png", "assets/test_images/13.png", ] with gr.Blocks() as demo: with gr.Column(elem_id="col-container"): gr.HTML('Qwen-Image-Layered Logo') gr.Markdown(""" The text prompt is intended to describe the overall content of the input image—including elements that may be partially occluded (e.g., you may specify the text hidden behind a foreground object). It is not designed to control the semantic content of individual layers explicitly. """) with gr.Row(): with gr.Column(scale=1): input_image = gr.Image(label="Input Image", image_mode="RGBA") with gr.Accordion("Advanced Settings", open=False): prompt = gr.Textbox( label="Prompt (Optional)", placeholder="Please enter the prompt to descibe the image. （Optional）", value="", lines=2, ) neg_prompt = gr.Textbox( label="Negative Prompt (Optional)", placeholder="Please enter the negative prompt", value=" ", lines=2, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) true_guidance_scale = gr.Slider( label="True guidance scale", minimum=1.0, maximum=10.0, step=0.1, value=4.0 ) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=1, maximum=50, step=1, value=50, ) layer = gr.Slider( label="Layers", minimum=2, maximum=10, step=1, value=4, ) cfg_norm = gr.Checkbox(label="Whether enable CFG normalization", value=True) use_en_prompt = gr.Checkbox(label="Automatic caption language if no prompt provided, True for EN, False for ZH", value=True) run_button = gr.Button("Decompose!", variant="primary") with gr.Column(scale=2): gallery = gr.Gallery(label="Layers", columns=4, rows=1, format="png") with gr.Row(): export_file = gr.File(label="Download PPTX") export_zip_file = gr.File(label="Download ZIP") gr.Examples(examples=examples, inputs=[input_image], outputs=[gallery, export_file, export_zip_file], fn=infer, examples_per_page=14, cache_examples=False, run_on_click=True ) run_button.click( fn=infer, inputs=[ input_image, seed, randomize_seed, prompt, neg_prompt, true_guidance_scale, num_inference_steps, layer, cfg_norm, use_en_prompt, ], outputs=[gallery, export_file, export_zip_file], ) if __name__ == "__main__": demo.launch()