Initialize QMI pipeline implementation (#2)

* init Signed-off-by: Isotr0py <mozf@inferact.ai> * fix Signed-off-by: Isotr0py <mozf@inferact.ai> --------- Signed-off-by: Isotr0py <mozf@inferact.ai>
2026-09-16 15:14:23 +08:00
1 parent a7c295d544
commit 2912672b2e
@@ -0,0 +1,259 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM 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(
+        "--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("RGB"))
+
+    # 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,
+    }
+    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}, "
+        f"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()
@@ -0,0 +1,8 @@
+python qwen_image_21_edit.py \
+    --model Qwen/Qwen-Image-2.1 \
+    --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
@@ -86,7 +86,8 @@ def parse_args() -> argparse.Namespace:
        "--model",
        default="Qwen/Qwen-Image",
        help="Diffusion model name or local path. Supported models: "
-        "Qwen/Qwen-Image, Tongyi-MAI/Z-Image-Turbo, Qwen/Qwen-Image-2512, stepfun-ai/NextStep-1.1, "
+        "Qwen/Qwen-Image, Tongyi-MAI/Z-Image-Turbo, Qwen/Qwen-Image-2512, Qwen/Qwen-Image-2.1, "
+        "stepfun-ai/NextStep-1.1, "
        "black-forest-labs/FLUX.1-dev, black-forest-labs/FLUX.2-klein-9B, "
        "black-forest-labs/FLUX.2-dev, tencent/HunyuanImage-3.0-Instruct, "
        "HiDream-ai/HiDream-O1-Image, meituan-longcat/LongCat-Image, OvisAI/Ovis-Image, "