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import os, math, argparse, random |
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from PIL import Image |
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import torch |
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import numpy as np |
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from diffusers import FlowMatchEulerDiscreteScheduler |
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from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline |
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from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel |
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IMG_EXTS = {".jpg", ".jpeg", ".png", ".webp", ".bmp"} |
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dtype = torch.bfloat16 |
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scheduler_config = { |
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"base_image_seq_len": 256, |
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"base_shift": math.log(3), |
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"invert_sigmas": False, |
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"max_image_seq_len": 8192, |
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"max_shift": math.log(3), |
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"num_train_timesteps": 1000, |
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"shift": 1.0, |
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"shift_terminal": None, |
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"stochastic_sampling": False, |
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"time_shift_type": "exponential", |
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"use_beta_sigmas": False, |
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"use_dynamic_shifting": True, |
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"use_exponential_sigmas": False, |
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"use_karras_sigmas": False, |
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} |
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FIX_PROMPT = ("seamlessly blend the object into the background, remove white sides and artifacts, " |
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"smooth jagged edges, natural lighting and color consistency, photorealistic") |
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def iter_images(root_dir): |
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for root, _, files in os.walk(root_dir): |
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for fn in files: |
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ext = os.path.splitext(fn)[1].lower() |
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if ext in IMG_EXTS: |
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yield os.path.join(root, fn) |
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def load_pipeline(base_model_path, lora_dir, lora_weight_name, device): |
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scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config) |
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pipe = QwenImageEditPlusPipeline.from_pretrained( |
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base_model_path, scheduler=scheduler, torch_dtype=dtype |
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).to(device) |
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pipe.load_lora_weights(lora_dir, weight_name=lora_weight_name) |
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pipe.fuse_lora(lora_scale=1.0) |
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pipe.transformer.__class__ = QwenImageTransformer2DModel |
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return pipe |
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@torch.no_grad() |
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def generate_single(pipe, input_image, prompt, seed, steps, true_cfg, device): |
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generator = torch.Generator(device=device).manual_seed(seed) |
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out = pipe( |
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image=[input_image], |
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prompt=prompt, |
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negative_prompt=" ", |
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num_inference_steps=steps, |
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generator=generator, |
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true_cfg_scale=true_cfg, |
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num_images_per_prompt=1, |
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).images[0] |
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return out |
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def main(): |
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p = argparse.ArgumentParser() |
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p.add_argument("--in_dir", default="/mnt/prev_nas/qhy_1/datasets/flux_gen_images_size_change") |
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p.add_argument("--out_dir", default="/mnt/prev_nas/qhy_1/datasets/flux_gen_images_size_change_fixed") |
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p.add_argument("--base_model_path", default="/mnt/5T_nas/cwl/model/Qwen-Image-Edit-2509") |
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p.add_argument("--lora_dir", default="/mnt/prev_nas/qhy/Qwen-Edit-2509-Multiple-angles") |
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p.add_argument("--lora_weight_name", default="Qwen-Image-Edit-2509-Lightning-4steps-V1.0-bf16_dim1.safetensors") |
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p.add_argument("--steps", type=int, default=4) |
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p.add_argument("--true_cfg", type=float, default=1.0) |
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p.add_argument("--seed", type=int, default=0, help=">=0 固定；<0 随机") |
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p.add_argument("--overwrite", action="store_true") |
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p.add_argument("--rank", type=int, default=0) |
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p.add_argument("--world_size", type=int, default=1) |
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args = p.parse_args() |
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assert 0 <= args.rank < args.world_size |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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os.makedirs(args.out_dir, exist_ok=True) |
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pipe = load_pipeline(args.base_model_path, args.lora_dir, args.lora_weight_name, device) |
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all_imgs = sorted(list(iter_images(args.in_dir))) |
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print(f"rank {args.rank}/{args.world_size} total imgs: {len(all_imgs)}") |
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max_seed = np.iinfo(np.int32).max |
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for i, img_path in enumerate(all_imgs): |
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if (i % args.world_size) != args.rank: |
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continue |
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rel = os.path.relpath(img_path, args.in_dir) |
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out_path = os.path.join(args.out_dir, rel) |
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os.makedirs(os.path.dirname(out_path), exist_ok=True) |
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if (not args.overwrite) and os.path.exists(out_path): |
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continue |
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try: |
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img = Image.open(img_path).convert("RGB") |
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except Exception as e: |
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print("open failed:", img_path, e) |
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continue |
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seed = args.seed if args.seed >= 0 else random.randint(0, max_seed) |
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try: |
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out_img = generate_single(pipe, img, FIX_PROMPT, seed, args.steps, args.true_cfg, device) |
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except Exception as e: |
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print("gen failed:", img_path, e) |
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continue |
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out_img.save(out_path) |
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print(f"rank {args.rank} done.") |
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if __name__ == "__main__": |
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main() |
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