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Optimize Qwen-Image 2.1 attention and decode graph reuse (#7790)

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Signed-off-by: Tianyu Guo <guoty9@mail2.sysu.edu.cn>
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Tianyu Guo authored and GitHub committed 2026-09-18 19:37:19 +08:00
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commit ab4cf24b3e
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examples/offline_inference/image_to_image/image_edit.py
+6 -5
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@@ -1,5 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
Example script for image editing with OmniGen2.
@@ -121,14 +121,14 @@ def parse_profiler_config(value: str) -> dict[str, Any]:
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Edit an image with Qwen-Image-Edit.")
parser = argparse.ArgumentParser(description="Edit images with vLLM-Omni.")
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 Qwen/Qwen-Image-Edit-2511"
"which supports QwenImageEditPlusPipeline."
"For multiple image inputs, use Qwen/Qwen-Image-Edit-2509, "
"Qwen/Qwen-Image-Edit-2511, or Qwen/Qwen-Image-2.1."
),
)
parser.add_argument(
@@ -280,7 +280,7 @@ def parse_args() -> argparse.Namespace:
"--color-format",
type=str,
default="RGB",
help="For Qwen-Image-Layered, set to RGBA.",
help="For Qwen-Image-Layered or Qwen-Image-2.1, set to RGBA to preserve transparency.",
)
# Cache-DiT specific parameters
@@ -541,6 +541,7 @@ def main():
diffusion_params = OmniDiffusionSamplingParams(
generator=generator,
seed=args.seed,
true_cfg_scale=args.cfg_scale,
guidance_scale=args.guidance_scale,
guidance_scale_2=args.guidance_scale_2,
examples/offline_inference/image_to_image/qwen_image_21_edit.py
-262
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@@ -1,262 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""
Example script for image-conditioned generation with Qwen-Image 2.1.
Qwen-Image 2.1 uses a single pipeline (QwenImage21Pipeline) for both
text-to-image and image-conditioned generation. Pass one or more condition
images (at most 4) via --image to run the image-conditioned path.
Usage (single image):
python qwen_image_21_edit.py \
--image input.png \
--prompt "Let this mascot dance under the moon, surrounded by floating stars" \
--negative-prompt "blurry, low quality, text, watermark" \
--output qwen_image_21_edit.png \
--num-inference-steps 50 \
--cfg-scale 4.0
Usage (multiple images, up to 4):
python qwen_image_21_edit.py \
--image input1.png input2.png \
--prompt "Combine these images into a single scene" \
--negative-prompt "blurry, low quality" \
--output qwen_image_21_edit.png \
--num-inference-steps 50 \
--cfg-scale 4.0
Notes:
- Qwen-Image 2.1 only supports true classifier-free guidance
(--cfg-scale); there is no guidance-distilled guidance_scale.
--cfg-scale > 1 only takes effect when --negative-prompt is provided.
- --height/--width must be multiples of 32. When omitted, the output size
is derived from the last condition image's aspect ratio at ~1024x1024.
- Cache acceleration backends (cache_dit / tea_cache) are not supported.
For more options, run:
python qwen_image_21_edit.py --help
"""
import argparse
import os
import time
from pathlib import Path
import torch
from PIL import Image
from vllm_omni.diffusion.utils.image_output import extract_images_from_outputs
from vllm_omni.entrypoints.omni import Omni
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
from vllm_omni.model_extras import (
build_image_to_image_prompt,
get_model_class_name,
)
from vllm_omni.platforms import current_omni_platform
MAX_INPUT_IMAGES = 4
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Image-conditioned generation with Qwen-Image 2.1.")
parser.add_argument(
"--model",
default="Qwen/Qwen-Image-2.1",
help="Diffusion model name or local path.",
)
parser.add_argument(
"--image",
type=str,
nargs="+",
required=True,
help="Path(s) to input condition image file(s) (PNG, JPG, etc.). At most 4 images.",
)
parser.add_argument(
"--prompt",
type=str,
required=True,
help="Text prompt describing the desired generation.",
)
parser.add_argument(
"--negative-prompt",
type=str,
default=None,
help="Negative prompt. Required for true classifier-free guidance (--cfg-scale > 1) to take effect.",
)
parser.add_argument(
"--width",
type=int,
default=None,
metavar="W",
help="Output image width in pixels (multiple of 32). Default: None (derived from the condition image).",
)
parser.add_argument(
"--height",
type=int,
default=None,
metavar="H",
help="Output image height in pixels (multiple of 32). Default: None (derived from the condition image).",
)
parser.add_argument(
"--seed",
type=int,
default=42,
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). Enabled by setting cfg_scale > 1 and providing "
"a negative prompt. Qwen-Image 2.1 supports true CFG only; there is no guidance_scale."
),
)
parser.add_argument(
"--output",
type=str,
default="qwen_image_21_edit.png",
help="Path to save the generated image (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(
"--ulysses-degree",
type=int,
default=1,
help="Number of GPUs used for ulysses sequence parallelism (ring attention is not supported).",
)
parser.add_argument(
"--cfg-parallel-size",
type=int,
default=1,
choices=[1, 2],
help="Number of GPUs used for classifier free guidance parallel size.",
)
parser.add_argument(
"--vae-use-tiling",
action="store_true",
help="Enable VAE tiling for memory optimization (also tiles condition-image encoding).",
)
parser.add_argument(
"--enforce-eager",
action="store_true",
default=None,
help="Disable torch.compile and force eager execution.",
)
return parser.parse_args()
def main():
args = parse_args()
if len(args.image) > MAX_INPUT_IMAGES:
raise ValueError(f"Qwen-Image 2.1 supports at most {MAX_INPUT_IMAGES} input images, got {len(args.image)}")
input_images = []
for image_path in args.image:
if not os.path.exists(image_path):
raise FileNotFoundError(f"Input image not found: {image_path}")
input_images.append(Image.open(image_path).convert("RGBA"))
# Use single image or list based on number of inputs
input_image = input_images[0] if len(input_images) == 1 else input_images
generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed)
omni_kwargs = {
"model": args.model,
"ulysses_degree": args.ulysses_degree,
"cfg_parallel_size": args.cfg_parallel_size,
"vae_use_tiling": args.vae_use_tiling,
}
if args.enforce_eager is not None:
omni_kwargs["enforce_eager"] = args.enforce_eager
omni = Omni(**omni_kwargs)
model_class_name = get_model_class_name(omni)
print("Pipeline loaded")
print(f"\n{'=' * 60}")
print("Generation Configuration:")
print(f" Model: {args.model}")
print(f" Inference steps: {args.num_inference_steps}")
print(f" CFG scale (true CFG): {args.cfg_scale}")
if args.height is not None or args.width is not None:
print(f" Output size: {args.width or 'auto'}x{args.height or 'auto'}")
if isinstance(input_image, list):
print(f" Number of input images: {len(input_image)}")
else:
print(f" Input image size: {input_image.size}")
print(f" Parallel configuration: ulysses_degree={args.ulysses_degree}, cfg_parallel_size={args.cfg_parallel_size}")
print(f"{'=' * 60}\n")
generation_start = time.perf_counter()
prompt_dict = build_image_to_image_prompt(
model_class_name=model_class_name,
prompt=args.prompt,
negative_prompt=args.negative_prompt,
input_image=input_image,
height=args.height,
width=args.width,
)
diffusion_params = OmniDiffusionSamplingParams(
generator=generator,
seed=args.seed,
true_cfg_scale=args.cfg_scale,
num_inference_steps=args.num_inference_steps,
num_outputs_per_prompt=args.num_outputs_per_prompt,
height=args.height,
width=args.width,
)
outputs = omni.generate(prompt_dict, sampling_params_list=[diffusion_params])
generation_end = time.perf_counter()
generation_time = generation_end - generation_start
print(f"Total generation time: {generation_time:.4f} seconds ({generation_time * 1000:.2f} ms)")
if not outputs:
raise ValueError("No output generated from omni.generate()")
images = None
for output in outputs:
images = getattr(output, "images", None)
if images:
break
if not images:
images = extract_images_from_outputs(outputs)
if not images:
raise ValueError("No images found in request_output")
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 "qwen_image_21_edit"
if len(images) <= 1:
images[0].save(output_path)
print(f"Saved generated image to {os.path.abspath(output_path)}")
else:
for idx, img in enumerate(images):
save_path = output_path.parent / f"{stem}_{idx}{suffix}"
img.save(save_path)
print(f"Saved generated image to {os.path.abspath(save_path)}")
if __name__ == "__main__":
main()
examples/offline_inference/image_to_image/run_qwen_image_21_edit.sh
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@@ -1,8 +1,17 @@
python qwen_image_21_edit.py \
#!/usr/bin/env bash
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
set -euo pipefail
python "$(dirname "${BASH_SOURCE[0]}")/image_edit.py" \
--model Qwen/Qwen-Image-2.1 \
--color-format RGBA \
--seed 42 \
--image qwen_bear.png \
--prompt "Let this mascot dance under the moon, surrounded by floating stars and poetic bubbles such as 'Be Kind'" \
--negative-prompt "blurry, low quality, text, watermark" \
--output qwen_image_21_edit.png \
--num-inference-steps 50 \
--cfg-scale 4.0
--cfg-scale 4.0 \
"$@"