图像到图像¶
来源 https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_image。
此示例使用 image_edit.py CLI 通过 Qwen/Qwen-Image-Edit 编辑输入图像。
本地 CLI 用法¶
单张图像编辑¶
python image_edit.py \
--image qwen_bear.png \
--prompt "Let this mascot dance under the moon, surrounded by floating stars and poetic bubbles such as 'Be Kind'" \
--output output_image_edit.png \
--num_inference_steps 50 \
--cfg_scale 4.0 \
多张图像编辑 (Qwen-Image-Edit-2509)¶
对于多张图像输入，请使用 Qwen/Qwen-Image-Edit-2509 或更高版本
python image_edit.py \
--model Qwen/Qwen-Image-Edit-2509 \
--image img1.png img2.png \
--prompt "Combine these images into a single scene" \
--output output_image_edit.png \
--num_inference_steps 50 \
--cfg_scale 4.0 \
--guidance_scale 1.0
关键参数
--model：模型名称或路径。使用Qwen/Qwen-Image-Edit-2509或更高版本以支持多张图像。--image：源图像的路径（PNG/JPG，已转换为 RGB）。可指定多张图像。--prompt/--negative_prompt：文本描述（字符串）。--cfg_scale：真分类器自由引导尺度（默认值：4.0）。通过设置 cfg_scale > 1 并提供 negative_prompt 来启用分类器自由引导。较高的引导尺度会鼓励生成与文本提示更相关的图像，通常会牺牲较低的图像质量。--guidance_scale：引导蒸馏模型的引导尺度（默认值：1.0，禁用）。与分类器自由引导（--cfg_scale）不同，引导蒸馏模型直接将引导尺度作为输入参数。当 guidance_scale > 1 时启用。使用非引导蒸馏模型时将被忽略。--num_inference_steps：扩散采样步数（步数越多 = 质量越高，速度越慢）。--output：保存生成的 PNG 的路径。
示例材料¶
image_edit.py
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
Example script for image editing with Qwen-Image-Edit.
Usage (single image):
python image_edit.py \
--image input.png \
--prompt "Let this mascot dance under the moon, surrounded by floating stars and poetic bubbles such as 'Be Kind'" \
--output output_image_edit.png \
--num_inference_steps 50 \
--cfg_scale 4.0 \
--guidance_scale 1.0
Usage (multiple images):
python image_edit.py \
--image input1.png input2.png input3.png \
--prompt "Combine these images into a single scene" \
--output output_image_edit.png \
--num_inference_steps 50 \
--cfg_scale 4.0 \
--guidance_scale 1.0
Usage (layered):
python image_edit.py \
--model "Qwen/Qwen-Image-Layered" \
--image input.png \
--prompt "" \
--output "layered" \
--num_inference_steps 50 \
--cfg_scale 4.0 \
--layers 4 \
--color-format "RGBA"
For more options, run:
python image_edit.py --help
"""
import argparse
import os
import time
from pathlib import Path
import torch
from PIL import Image
from vllm_omni.diffusion.data import DiffusionParallelConfig
from vllm_omni.entrypoints.omni import Omni
from vllm_omni.utils.platform_utils import detect_device_type, is_npu
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Edit an image with Qwen-Image-Edit.")
parser.add_argument(
"--model",
default="Qwen/Qwen-Image-Edit",
help=(
"Diffusion model name or local path. "
"For multiple image inputs, use Qwen/Qwen-Image-Edit-2509 or later version "
"which supports QwenImageEditPlusPipeline."
),
)
parser.add_argument(
"--image",
type=str,
nargs="+",
required=True,
help="Path(s) to input image file(s) (PNG, JPG, etc.). Can specify multiple images.",
)
parser.add_argument(
"--prompt",
type=str,
required=True,
help="Text prompt describing the edit to make to the image.",
)
parser.add_argument(
"--negative_prompt",
type=str,
default=" ",
required=False,
)
parser.add_argument(
"--seed",
type=int,
default=0,
help="Random seed for deterministic results.",
)
parser.add_argument(
"--cfg_scale",
type=float,
default=4.0,
help=(
"True classifier-free guidance scale (default: 4.0). Guidance scale as defined in Classifier-Free "
"Diffusion Guidance. Classifier-free guidance is enabled by setting cfg_scale > 1 and providing "
"a negative_prompt. Higher guidance scale encourages images closely linked to the text prompt, "
"usually at the expense of lower image quality."
),
)
parser.add_argument(
"--guidance_scale",
type=float,
default=1.0,
help=(
"Guidance scale for guidance-distilled models (default: 1.0, disabled). "
"Unlike classifier-free guidance (--cfg_scale), guidance-distilled models take the guidance scale "
"directly as an input parameter. Enabled when guidance_scale > 1. Ignored when not using guidance-distilled models."
),
)
parser.add_argument(
"--output",
type=str,
default="output_image_edit.png",
help=("Path to save the edited image (PNG). Or prefix for Qwen-Image-Layered model save images(PNG)."),
)
parser.add_argument(
"--num_outputs_per_prompt",
type=int,
default=1,
help="Number of images to generate for the given prompt.",
)
parser.add_argument(
"--num_inference_steps",
type=int,
default=50,
help="Number of denoising steps for the diffusion sampler.",
)
parser.add_argument(
"--cache_backend",
type=str,
default=None,
choices=["cache_dit", "tea_cache"],
help=(
"Cache backend to use for acceleration. "
"Options: 'cache_dit' (DBCache + SCM + TaylorSeer), 'tea_cache' (Timestep Embedding Aware Cache). "
"Default: None (no cache acceleration)."
),
)
parser.add_argument(
"--ulysses_degree",
type=int,
default=1,
help="Number of GPUs used for ulysses sequence parallelism.",
)
parser.add_argument("--layers", type=int, default=4, help="Number of layers to decompose the input image into.")
parser.add_argument(
"--resolution",
type=int,
default=640,
help="Bucket in (640, 1024) to determine the condition and output resolution",
)
parser.add_argument(
"--color-format",
type=str,
default="RGB",
help="For Qwen-Image-Layered, set to RGBA.",
)
return parser.parse_args()
def main():
args = parse_args()
# Validate input images exist and load them
input_images = []
for image_path in args.image:
if not os.path.exists(image_path):
raise FileNotFoundError(f"Input image not found: {image_path}")
img = Image.open(image_path).convert(args.color_format)
input_images.append(img)
# Use single image or list based on number of inputs
if len(input_images) == 1:
input_image = input_images[0]
else:
input_image = input_images
device = detect_device_type()
generator = torch.Generator(device=device).manual_seed(args.seed)
# Enable VAE memory optimizations on NPU
vae_use_slicing = is_npu()
vae_use_tiling = is_npu()
parallel_config = DiffusionParallelConfig(ulysses_degree=args.ulysses_degree)
# Configure cache based on backend type
cache_config = None
if args.cache_backend == "cache_dit":
# cache-dit configuration: Hybrid DBCache + SCM + TaylorSeer
# All parameters marked with [cache-dit only] in DiffusionCacheConfig
cache_config = {
# DBCache parameters [cache-dit only]
"Fn_compute_blocks": 1, # Optimized for single-transformer models
"Bn_compute_blocks": 0, # Number of backward compute blocks
"max_warmup_steps": 4, # Maximum warmup steps (works for few-step models)
"residual_diff_threshold": 0.24, # Higher threshold for more aggressive caching
"max_continuous_cached_steps": 3, # Limit to prevent precision degradation
# TaylorSeer parameters [cache-dit only]
"enable_taylorseer": False, # Disabled by default (not suitable for few-step models)
"taylorseer_order": 1, # TaylorSeer polynomial order
# SCM (Step Computation Masking) parameters [cache-dit only]
"scm_steps_mask_policy": None, # SCM mask policy: None (disabled), "slow", "medium", "fast", "ultra"
"scm_steps_policy": "dynamic", # SCM steps policy: "dynamic" or "static"
}
elif args.cache_backend == "tea_cache":
# TeaCache configuration
# All parameters marked with [tea_cache only] in DiffusionCacheConfig
cache_config = {
# TeaCache parameters [tea_cache only]
"rel_l1_thresh": 0.2, # Threshold for accumulated relative L1 distance
# Note: coefficients will use model-specific defaults based on model_type
# (e.g., QwenImagePipeline or FluxPipeline)
}
# Initialize Omni with appropriate pipeline
omni = Omni(
model=args.model,
vae_use_slicing=vae_use_slicing,
vae_use_tiling=vae_use_tiling,
cache_backend=args.cache_backend,
cache_config=cache_config,
parallel_config=parallel_config,
)
print("Pipeline loaded")
# Time profiling for generation
print(f"\n{'=' * 60}")
print("Generation Configuration:")
print(f" Model: {args.model}")
print(f" Inference steps: {args.num_inference_steps}")
print(f" Cache backend: {args.cache_backend if args.cache_backend else 'None (no acceleration)'}")
if isinstance(input_image, list):
print(f" Number of input images: {len(input_image)}")
for idx, img in enumerate(input_image):
print(f" Image {idx + 1} size: {img.size}")
else:
print(f" Input image size: {input_image.size}")
print(f" Parallel configuration: ulysses_degree={args.ulysses_degree}")
print(f"{'=' * 60}\n")
generation_start = time.perf_counter()
# Generate edited image
images = omni.generate(
prompt=args.prompt,
pil_image=input_image,
negative_prompt=args.negative_prompt,
generator=generator,
true_cfg_scale=args.cfg_scale,
guidance_scale=args.guidance_scale,
num_inference_steps=args.num_inference_steps,
num_outputs_per_prompt=args.num_outputs_per_prompt,
layers=args.layers,
)
generation_end = time.perf_counter()
generation_time = generation_end - generation_start
# Print profiling results
print(f"Total generation time: {generation_time:.4f} seconds ({generation_time * 1000:.2f} ms)")
# Save output image(s)
output_path = Path(args.output)
output_path.parent.mkdir(parents=True, exist_ok=True)
suffix = output_path.suffix or ".png"
stem = output_path.stem or "output_image_edit"
if args.num_outputs_per_prompt <= 1:
img = images[0]
img = img if isinstance(img, list) else [img]
for sub_idx, sub_img in enumerate(img):
save_path = output_path.parent / f"{stem}_{sub_idx}{suffix}"
sub_img.save(save_path)
print(f"Saved edited image to {os.path.abspath(save_path)}")
else:
for idx, img in enumerate(images):
img = img if isinstance(img, list) else [img]
for sub_idx, sub_img in enumerate(img):
save_path = output_path.parent / f"{stem}_{idx}_{sub_idx}{suffix}"
sub_img.save(save_path)
print(f"Saved edited image to {os.path.abspath(save_path)}")
if __name__ == "__main__":
main()