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import os, math, argparse, random
from PIL import Image
import torch
import numpy as np
from diffusers import FlowMatchEulerDiscreteScheduler
from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
IMG_EXTS = {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
dtype = torch.bfloat16
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,
}
FIX_PROMPT = ("seamlessly blend the object into the background, remove white sides and artifacts, "
"smooth jagged edges, natural lighting and color consistency, photorealistic")
def iter_images(root_dir):
for root, _, files in os.walk(root_dir):
for fn in files:
ext = os.path.splitext(fn)[1].lower()
if ext in IMG_EXTS:
yield os.path.join(root, fn)
def load_pipeline(base_model_path, lora_dir, lora_weight_name, device):
scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
pipe = QwenImageEditPlusPipeline.from_pretrained(
base_model_path, scheduler=scheduler, torch_dtype=dtype
).to(device)
pipe.load_lora_weights(lora_dir, weight_name=lora_weight_name)
pipe.fuse_lora(lora_scale=1.0)
pipe.transformer.__class__ = QwenImageTransformer2DModel
return pipe
@torch.no_grad()
def generate_single(pipe, input_image, prompt, seed, steps, true_cfg, device):
generator = torch.Generator(device=device).manual_seed(seed)
out = pipe(
image=[input_image],
prompt=prompt,
negative_prompt=" ",
num_inference_steps=steps,
generator=generator,
true_cfg_scale=true_cfg,
num_images_per_prompt=1,
).images[0]
return out
def main():
p = argparse.ArgumentParser()
p.add_argument("--in_dir", default="/mnt/prev_nas/qhy_1/datasets/flux_gen_images_size_change")
p.add_argument("--out_dir", default="/mnt/prev_nas/qhy_1/datasets/flux_gen_images_size_change_fixed")
p.add_argument("--base_model_path", default="/mnt/5T_nas/cwl/model/Qwen-Image-Edit-2509")
p.add_argument("--lora_dir", default="/mnt/prev_nas/qhy/Qwen-Edit-2509-Multiple-angles")
p.add_argument("--lora_weight_name", default="Qwen-Image-Edit-2509-Lightning-4steps-V1.0-bf16_dim1.safetensors")
p.add_argument("--steps", type=int, default=4)
p.add_argument("--true_cfg", type=float, default=1.0)
p.add_argument("--seed", type=int, default=0, help=">=0 固定；<0 随机")
p.add_argument("--overwrite", action="store_true")
# 分卡参数
p.add_argument("--rank", type=int, default=0)
p.add_argument("--world_size", type=int, default=1)
args = p.parse_args()
assert 0 <= args.rank < args.world_size
device = "cuda" if torch.cuda.is_available() else "cpu"
os.makedirs(args.out_dir, exist_ok=True)
pipe = load_pipeline(args.base_model_path, args.lora_dir, args.lora_weight_name, device)
all_imgs = sorted(list(iter_images(args.in_dir)))
print(f"rank {args.rank}/{args.world_size} total imgs: {len(all_imgs)}")
max_seed = np.iinfo(np.int32).max
for i, img_path in enumerate(all_imgs):
if (i % args.world_size) != args.rank:
continue
rel = os.path.relpath(img_path, args.in_dir)
out_path = os.path.join(args.out_dir, rel)
os.makedirs(os.path.dirname(out_path), exist_ok=True)
if (not args.overwrite) and os.path.exists(out_path):
continue
try:
img = Image.open(img_path).convert("RGB")
except Exception as e:
print("open failed:", img_path, e)
continue
seed = args.seed if args.seed >= 0 else random.randint(0, max_seed)
try:
out_img = generate_single(pipe, img, FIX_PROMPT, seed, args.steps, args.true_cfg, device)
except Exception as e:
print("gen failed:", img_path, e)
continue
out_img.save(out_path)
print(f"rank {args.rank} done.")
if __name__ == "__main__":
main()
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