import gradio as gr import numpy as np import random import torch import spaces from PIL import Image import math from diffusers import FlowMatchEulerDiscreteScheduler, QwenImageEditPlusPipeline from huggingface_hub import hf_hub_download from safetensors.torch import load_file from briarmbg import BriaRMBG import os import tempfile # --- Model Loading --- dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" 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 = QwenImageEditPlusPipeline.from_pretrained( "Qwen/Qwen-Image-Edit-2509", scheduler=scheduler, torch_dtype=dtype ).to(device) pipe.load_lora_weights( "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-4steps-V2.0.safetensors", adapter_name="fast" ) pipe.load_lora_weights( "dx8152/Qwen-Image-Edit-2509-Fusion", weight_name="溶图.safetensors", adapter_name="fusion" ) pipe.set_adapters(["fast", "fusion"], adapter_weights=[1., 1.]) pipe.fuse_lora(adapter_names=["fast"]) pipe.fuse_lora(adapter_names=["fusion"]) pipe.unload_lora_weights() # ✅ Load background remover rmbg = BriaRMBG.from_pretrained("briaai/RMBG-1.4").to(device, dtype=torch.float32) MAX_SEED = np.iinfo(np.int32).max # --- Background Removal Helpers --- @torch.inference_mode() def numpy2pytorch(imgs): h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0 h = h.movedim(-1, 1) return h @torch.inference_mode() def run_rmbg(img: np.ndarray): H, W, C = img.shape k = (256.0 / float(H * W)) ** 0.5 resized = Image.fromarray(img).resize((int(64 * round(W * k)), int(64 * round(H * k))), Image.LANCZOS) feed = numpy2pytorch([np.array(resized)]).to("cuda", dtype=torch.float32) alpha = rmbg(feed)[0][0] alpha = torch.nn.functional.interpolate(alpha, size=(H, W), mode="bilinear") alpha = alpha.movedim(1, -1)[0].detach().float().cpu().numpy().clip(0, 1) result = 127 + (img.astype(np.float32) - 127) * alpha return result.clip(0, 255).astype(np.uint8), alpha def remove_background(image: Image.Image) -> Image.Image: img_array = np.array(image) result_array, alpha_mask = run_rmbg(img_array) result_image = Image.fromarray(result_array) if result_image.mode != 'RGBA': result_image = result_image.convert('RGBA') alpha = (alpha_mask * 255).astype(np.uint8) alpha_pil = Image.fromarray(alpha, 'L') result_image.putalpha(alpha_pil) return result_image # --- Inference --- @spaces.GPU def infer( gallery_images, image_background, prompt="", seed=42, randomize_seed=True, true_guidance_scale=1, num_inference_steps=4, height=None, width=None, progress=gr.Progress(track_tqdm=True) ): if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator(device=device).manual_seed(seed) processed_subjects = [] if gallery_images: for gimg in gallery_images: pil_img = gimg[0] if isinstance(gimg, list) else gimg processed_subjects.append(remove_background(pil_img)) all_inputs = processed_subjects if image_background is not None: all_inputs.append(image_background) if not all_inputs: raise gr.Error("Please upload at least one image or a background image.") result = pipe( image=all_inputs, prompt=prompt, num_inference_steps=num_inference_steps, generator=generator, true_cfg_scale=true_guidance_scale, num_images_per_prompt=1, ).images[0] return [image_background, result], seed # --- UI --- css = '''#col-container { max-width: 900px; margin: 0 auto; } .dark .progress-text{color: white !important} #examples{max-width: 900px; margin: 0 auto; }''' with gr.Blocks(theme=gr.themes.Citrus(), css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown("## Qwen Image Edit — Fusion") gr.Markdown(""" Qwen Image Edit 2509 ✨ Using [dx8152's Qwen-Image-Edit-2509 Fusion LoRA](https://huggingface.co/dx8152/Qwen-Image-Edit-2509-Fusion) and [lightx2v Qwen-Image-Lightning LoRA]() for 4-step inference 💨 """ ) with gr.Row(): with gr.Column(): gallery = gr.Gallery( label="Upload subject images (background auto removed)", columns=3, rows=2, height="auto", type="pil" ) image_background = gr.Image(label="Background Image", type="pil", visible=True) prompt = gr.Textbox(label="Prompt") run_button = gr.Button("Fuse Images", variant="primary") 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) true_guidance_scale = gr.Slider(label="True Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0) num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=40, step=1, value=4) height = gr.Slider(label="Height", minimum=256, maximum=2048, step=8, value=1024) width = gr.Slider(label="Width", minimum=256, maximum=2048, step=8, value=1024) with gr.Column(): result = gr.ImageSlider(label="Output Image", interactive=False) # gr.Examples( # examples=[ # [["fusion_car.png", "fusion_shoes.png"], "fusion_bg.png", "put the car and shoes in the background"], # [["wednesday_product.png"], "simple_room.png", "put the product in her hand"] # ], # inputs=[gallery, image_background, prompt], # outputs=[result, seed], # fn=infer, # cache_examples="lazy", # elem_id="examples" # ) inputs = [gallery, image_background, prompt, seed, randomize_seed, true_guidance_scale, num_inference_steps, height, width] outputs = [result, seed] run_button.click(fn=infer, inputs=inputs, outputs=outputs) demo.launch(share=True)