[Model] Support Qwen-Image-Edit 2509 ( multi-image input edit) (#330)

Signed-off-by: samithuang <285365963@qq.com>

docs/models/supported_models.md

@@ -21,6 +21,7 @@ th {
 | `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` |
 | `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` |
 | `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` |
+| `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2509 | `Qwen/Qwen-Image-Edit-2509` |
 |`ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` |
 | `WanPipeline` | Wan2.2 | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` |
 

docs/user_guide/diffusion_acceleration.md

@@ -31,11 +31,13 @@ vLLM-Omni also supports the sequence parallelism (SP) for the diffusion model, t
 
 The following table shows which models are currently supported by each acceleration method:
 
+
 | Model | Model Identifier | TeaCache | Cache-DiT | Ulysses-SP |
 |-------|-----------------|----------|-----------|-----------|
 | **Qwen-Image** | `Qwen/Qwen-Image` | ✅ | ✅ | ✅ |
 | **Z-Image** | `Tongyi-MAI/Z-Image-Turbo` | ❌ | ✅ |❌ |
 | **Qwen-Image-Edit** | `Qwen/Qwen-Image-Edit` | ❌ | ✅ |✅ |
+| **Qwen-Image-Edit-2509** | `Qwen/Qwen-Image-Edit-2509` | ❌ | ✅ |✅ |
 
 
 ## Performance Benchmarks

docs/user_guide/examples/offline_inference/image_to_image.md

@@ -7,20 +7,40 @@ This example edits an input image with `Qwen/Qwen-Image-Edit` using the `image_e
 
 ## Local CLI Usage
 
+### Single Image Editing
+
 ```bash
 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
+  --cfg_scale 4.0 \
+  --guidance_scale 1.0
+```
+
+### Multiple Image Editing (Qwen-Image-Edit-2509+)
+
+For multiple image inputs, use `Qwen/Qwen-Image-Edit-2509` or later version:
+
+```bash
+python image_edit.py \
+  --model Qwen/Qwen-Image-Edit-2509 \
+  --image img1.png img2.png img3.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
 ```
 
 Key arguments:
 
-- `--image`: path to the source image (PNG/JPG, converted to RGB).
+- `--model`: model name or path. Use `Qwen/Qwen-Image-Edit-2509` or later for multiple image support.
+- `--image`: path(s) to the source image(s) (PNG/JPG, converted to RGB). Can specify multiple images.
 - `--prompt` / `--negative_prompt`: text description (string).
-- `--cfg_scale`: true CFG scale for Qwen-Image-Edit (quality vs. fidelity).
+- `--cfg_scale`: true classifier-free guidance scale (default: 4.0). 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.
+- `--guidance_scale`: 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.
 - `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
 - `--output`: path to save the generated PNG.
 

examples/offline_inference/image_to_image/image_edit.py

@@ -4,13 +4,23 @@
 """
 Example script for image editing with Qwen-Image-Edit.
 
-Usage:
+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
+        --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
 
 For more options, run:
     python image_edit.py --help
@@ -34,13 +44,18 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--model",
         default="Qwen/Qwen-Image-Edit",
-        help="Diffusion model name or local path.",
+        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 to input image file (PNG, JPG, etc.).",
+        help="Path(s) to input image file(s) (PNG, JPG, etc.). Can specify multiple images.",
     )
     parser.add_argument(
         "--prompt",
@@ -64,7 +79,22 @@ def parse_args() -> argparse.Namespace:
         "--cfg_scale",
         type=float,
         default=4.0,
-        help="True classifier-free guidance scale specific to Qwen-Image-Edit.",
+        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",
@@ -108,13 +138,19 @@ def parse_args() -> argparse.Namespace:
 def main():
     args = parse_args()
 
-    # Validate input image exists
-    if not os.path.exists(args.image):
-        raise FileNotFoundError(f"Input image not found: {args.image}")
+    # 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("RGB")
+        input_images.append(img)
 
-    # Load input image
-    input_image = Image.open(args.image).convert("RGB")
-    print(f"Loaded input image from {args.image} (size: {input_image.size})")
+    # 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)
@@ -153,10 +189,9 @@ def main():
             #       (e.g., QwenImagePipeline or FluxPipeline)
         }
 
-    # Initialize Omni with QwenImageEditPipeline
+    # Initialize Omni with appropriate pipeline
     omni = Omni(
         model=args.model,
-        model_class_name="QwenImageEditPipeline",
         vae_use_slicing=vae_use_slicing,
         vae_use_tiling=vae_use_tiling,
         cache_backend=args.cache_backend,
@@ -171,6 +206,12 @@ def main():
     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"  Input image size: {input_image.size}")
     print(f"{'=' * 60}\n")
@@ -183,6 +224,7 @@ def main():
         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,
     )

examples/offline_inference/image_to_image/image_to_image.md

@@ -4,19 +4,38 @@ This example edits an input image with `Qwen/Qwen-Image-Edit` using the `image_e
 
 ## Local CLI Usage
 
+### Single Image Editing
+
 ```bash
 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
+  --cfg_scale 4.0 \
+```
+
+### Multiple Image Editing (Qwen-Image-Edit-2509+)
+
+For multiple image inputs, use `Qwen/Qwen-Image-Edit-2509` or later version:
+
+```bash
+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
 ```
 
 Key arguments:
 
-- `--image`: path to the source image (PNG/JPG, converted to RGB).
+- `--model`: model name or path. Use `Qwen/Qwen-Image-Edit-2509` or later for multiple image support.
+- `--image`: path(s) to the source image(s) (PNG/JPG, converted to RGB). Can specify multiple images.
 - `--prompt` / `--negative_prompt`: text description (string).
-- `--cfg_scale`: true CFG scale for Qwen-Image-Edit (quality vs. fidelity).
+- `--cfg_scale`: true classifier-free guidance scale (default: 4.0). 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.
+- `--guidance_scale`: 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.
 - `--num_inference_steps`: diffusion sampling steps (more steps = higher quality, slower).
 - `--output`: path to save the generated PNG.

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_edit_plus.py

@@ -0,0 +1,793 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import json
+import logging
+import os
+from collections.abc import Iterable
+from typing import Any
+
+import numpy as np
+import PIL.Image
+import torch
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.autoencoders.autoencoder_kl_qwenimage import (
+    AutoencoderKLQwenImage,
+)
+from diffusers.schedulers.scheduling_flow_match_euler_discrete import (
+    FlowMatchEulerDiscreteScheduler,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from torch import nn
+from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2Tokenizer, Qwen2VLProcessor
+from vllm.model_executor.models.utils import AutoWeightsLoader
+
+from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.distributed.utils import get_local_device
+from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
+from vllm_omni.diffusion.models.qwen_image.pipeline_qwen_image import calculate_shift
+from vllm_omni.diffusion.models.qwen_image.pipeline_qwen_image_edit import (
+    calculate_dimensions,
+    retrieve_latents,
+    retrieve_timesteps,
+)
+from vllm_omni.diffusion.models.qwen_image.qwen_image_transformer import (
+    QwenImageTransformer2DModel,
+)
+from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.model_executor.model_loader.weight_utils import (
+    download_weights_from_hf_specific,
+)
+
+logger = logging.getLogger(__name__)
+
+CONDITION_IMAGE_SIZE = 384 * 384
+VAE_IMAGE_SIZE = 1024 * 1024
+
+
+def get_qwen_image_edit_plus_pre_process_func(
+    od_config: OmniDiffusionConfig,
+):
+    """Pre-processing function for QwenImageEditPlusPipeline."""
+    model_name = od_config.model
+    if os.path.exists(model_name):
+        model_path = model_name
+    else:
+        model_path = download_weights_from_hf_specific(model_name, None, ["*"])
+    vae_config_path = os.path.join(model_path, "vae/config.json")
+    with open(vae_config_path) as f:
+        vae_config = json.load(f)
+        vae_scale_factor = 2 ** len(vae_config["temporal_downsample"]) if "temporal_downsample" in vae_config else 8
+
+    image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor * 2)
+    latent_channels = vae_config.get("z_dim", 16)
+
+    def pre_process_func(
+        requests: list[OmniDiffusionRequest],
+    ):
+        """Pre-process requests for QwenImageEditPlusPipeline."""
+        for req in requests:
+            image = req.pil_image
+
+            # Handle single image or list of images
+            if image is None:
+                continue
+
+            if not isinstance(image, list):
+                image = [image]
+
+            # Calculate dimensions based on first image
+            image_size = image[0].size
+            calculated_width, calculated_height = calculate_dimensions(VAE_IMAGE_SIZE, image_size[0] / image_size[1])
+            height = req.height or calculated_height
+            width = req.width or calculated_width
+
+            # Ensure dimensions are multiples of vae_scale_factor * 2
+            multiple_of = vae_scale_factor * 2
+            height = height // multiple_of * multiple_of
+            width = width // multiple_of * multiple_of
+
+            # Store calculated dimensions in request
+            req.calculated_height = calculated_height
+            req.calculated_width = calculated_width
+            req.height = height
+            req.width = width
+
+            # Preprocess images into condition_images (for prompt encoding) and vae_images (for VAE encoding)
+            condition_images = []
+            vae_images = []
+            condition_image_sizes = []
+            vae_image_sizes = []
+
+            for img in image:
+                if isinstance(img, torch.Tensor) and len(img.shape) > 1 and img.shape[1] == latent_channels:
+                    # Already a latent tensor
+                    continue
+
+                image_width, image_height = img.size
+                condition_width, condition_height = calculate_dimensions(
+                    CONDITION_IMAGE_SIZE, image_width / image_height
+                )
+                vae_width, vae_height = calculate_dimensions(VAE_IMAGE_SIZE, image_width / image_height)
+
+                condition_image_sizes.append((condition_width, condition_height))
+                vae_image_sizes.append((vae_width, vae_height))
+
+                condition_images.append(image_processor.resize(img, condition_height, condition_width))
+                vae_images.append(image_processor.preprocess(img, vae_height, vae_width).unsqueeze(2))
+
+            # Store preprocessed images in request
+            req.condition_images = condition_images
+            req.vae_images = vae_images
+            req.condition_image_sizes = condition_image_sizes
+            req.vae_image_sizes = vae_image_sizes
+
+        return requests
+
+    return pre_process_func
+
+
+def get_qwen_image_edit_plus_post_process_func(
+    od_config: OmniDiffusionConfig,
+):
+    """Post-processing function for QwenImageEditPlusPipeline."""
+    model_name = od_config.model
+    if os.path.exists(model_name):
+        model_path = model_name
+    else:
+        model_path = download_weights_from_hf_specific(model_name, None, ["*"])
+    vae_config_path = os.path.join(model_path, "vae/config.json")
+    with open(vae_config_path) as f:
+        vae_config = json.load(f)
+        vae_scale_factor = 2 ** len(vae_config["temporal_downsample"]) if "temporal_downsample" in vae_config else 8
+
+    image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor * 2)
+
+    def post_process_func(
+        images: torch.Tensor,
+    ):
+        return image_processor.postprocess(images)
+
+    return post_process_func
+
+
+class QwenImageEditPlusPipeline(
+    nn.Module,
+):
+    def __init__(
+        self,
+        *,
+        od_config: OmniDiffusionConfig,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.od_config = od_config
+        self.weights_sources = [
+            DiffusersPipelineLoader.ComponentSource(
+                model_or_path=od_config.model,
+                subfolder="transformer",
+                revision=None,
+                prefix="transformer.",
+                fall_back_to_pt=True,
+            )
+        ]
+        self.device = get_local_device()
+        model = od_config.model
+
+        # Check if model is a local path
+        local_files_only = os.path.exists(model)
+
+        self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
+            model, subfolder="scheduler", local_files_only=local_files_only
+        )
+        self.text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            model, subfolder="text_encoder", local_files_only=local_files_only
+        )
+
+        self.vae = AutoencoderKLQwenImage.from_pretrained(model, subfolder="vae", local_files_only=local_files_only).to(
+            self.device
+        )
+        self.transformer = QwenImageTransformer2DModel(od_config=od_config)
+        self.tokenizer = Qwen2Tokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
+        self.processor = Qwen2VLProcessor.from_pretrained(
+            model, subfolder="processor", local_files_only=local_files_only
+        )
+
+        self.stage = None
+
+        self.vae_scale_factor = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
+        self.latent_channels = self.vae.config.z_dim if getattr(self, "vae", None) else 16
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
+        self.tokenizer_max_length = 1024
+        # Edit prompt template - different from generation template, supports multiple images
+        self.prompt_template_encode = (
+            "<|im_start|>system\nDescribe the key features of the input image "
+            "(color, shape, size, texture, objects, background), then explain how the user's "
+            "text instruction should alter or modify the image. Generate a new image that meets "
+            "the user's requirements while maintaining consistency with the original input where "
+            "appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
+        )
+        self.prompt_template_encode_start_idx = 64
+        self.default_sample_size = 128
+
+    def check_inputs(
+        self,
+        prompt,
+        height,
+        width,
+        image=None,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        prompt_embeds_mask=None,
+        negative_prompt_embeds_mask=None,
+        callback_on_step_end_tensor_inputs=None,
+        max_sequence_length=None,
+    ):
+        if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0:
+            logger.warning(
+                f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} "
+                f"but are {height} and {width}. Dimensions will be resized accordingly"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and prompt_embeds_mask is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `prompt_embeds_mask` also have to be passed. "
+                "Make sure to generate `prompt_embeds_mask` from the same text encoder "
+                "that was used to generate `prompt_embeds`."
+            )
+        if negative_prompt_embeds is not None and negative_prompt_embeds_mask is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_prompt_embeds_mask` also have to be passed. "
+                "Make sure to generate `negative_prompt_embeds_mask` from the same text encoder "
+                "that was used to generate `negative_prompt_embeds`."
+            )
+
+        if max_sequence_length is not None and max_sequence_length > 1024:
+            raise ValueError(f"`max_sequence_length` cannot be greater than 1024 but is {max_sequence_length}")
+
+    def _extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor):
+        bool_mask = mask.bool()
+        valid_lengths = bool_mask.sum(dim=1)
+        selected = hidden_states[bool_mask]
+        split_result = torch.split(selected, valid_lengths.tolist(), dim=0)
+
+        return split_result
+
+    def _get_qwen_prompt_embeds(
+        self,
+        prompt: str | list[str] = None,
+        image: list[torch.Tensor] | torch.Tensor | None = None,
+        dtype: torch.dtype | None = None,
+    ):
+        """Get prompt embeddings with support for multiple images."""
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        # Build image prompt template for multiple images
+        img_prompt_template = "Picture {}: <|vision_start|><|image_pad|><|vision_end|>"
+        if isinstance(image, list):
+            base_img_prompt = ""
+            for i, img in enumerate(image):
+                base_img_prompt += img_prompt_template.format(i + 1)
+        elif image is not None:
+            base_img_prompt = img_prompt_template.format(1)
+        else:
+            base_img_prompt = ""
+
+        template = self.prompt_template_encode
+        drop_idx = self.prompt_template_encode_start_idx
+        txt = [template.format(base_img_prompt + e) for e in prompt]
+
+        # Use processor to handle both text and image inputs
+        model_inputs = self.processor(
+            text=txt,
+            images=image,
+            padding=True,
+            return_tensors="pt",
+        ).to(self.device)
+
+        outputs = self.text_encoder(
+            input_ids=model_inputs.input_ids,
+            attention_mask=model_inputs.attention_mask,
+            pixel_values=model_inputs.pixel_values,
+            image_grid_thw=model_inputs.image_grid_thw,
+            output_hidden_states=True,
+        )
+
+        hidden_states = outputs.hidden_states[-1]
+        split_hidden_states = self._extract_masked_hidden(hidden_states, model_inputs.attention_mask)
+        split_hidden_states = [e[drop_idx:] for e in split_hidden_states]
+        attn_mask_list = [torch.ones(e.size(0), dtype=torch.long, device=e.device) for e in split_hidden_states]
+        max_seq_len = max([e.size(0) for e in split_hidden_states])
+        prompt_embeds = torch.stack(
+            [torch.cat([u, u.new_zeros(max_seq_len - u.size(0), u.size(1))]) for u in split_hidden_states]
+        )
+        encoder_attention_mask = torch.stack(
+            [torch.cat([u, u.new_zeros(max_seq_len - u.size(0))]) for u in attn_mask_list]
+        )
+
+        prompt_embeds = prompt_embeds.to(dtype=dtype)
+
+        return prompt_embeds, encoder_attention_mask
+
+    def encode_prompt(
+        self,
+        prompt: str | list[str],
+        image: list[torch.Tensor] | torch.Tensor | None = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: torch.Tensor | None = None,
+        prompt_embeds_mask: torch.Tensor | None = None,
+        max_sequence_length: int = 1024,
+    ):
+        r"""
+
+        Args:
+            prompt (`str` or `list[str]`, *optional*):
+                prompt to be encoded
+            image (`torch.Tensor` or `list[torch.Tensor]`, *optional*):
+                image(s) to be encoded
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+        """
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt) if prompt_embeds is None else prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds(prompt, image)
+
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+        prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
+
+        return prompt_embeds, prompt_embeds_mask
+
+    @staticmethod
+    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
+        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
+        latents = latents.permute(0, 2, 4, 1, 3, 5)
+        latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
+
+        return latents
+
+    @staticmethod
+    def _unpack_latents(latents, height, width, vae_scale_factor):
+        batch_size, num_patches, channels = latents.shape
+
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // (vae_scale_factor * 2))
+        width = 2 * (int(width) // (vae_scale_factor * 2))
+
+        latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2)
+        latents = latents.permute(0, 3, 1, 4, 2, 5)
+
+        latents = latents.reshape(batch_size, channels // (2 * 2), 1, height, width)
+
+        return latents
+
+    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
+        if isinstance(generator, list):
+            image_latents = [
+                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i], sample_mode="argmax")
+                for i in range(image.shape[0])
+            ]
+            image_latents = torch.cat(image_latents, dim=0)
+        else:
+            image_latents = retrieve_latents(self.vae.encode(image), generator=generator, sample_mode="argmax")
+        latents_mean = (
+            torch.tensor(self.vae.config.latents_mean)
+            .view(1, self.latent_channels, 1, 1, 1)
+            .to(image_latents.device, image_latents.dtype)
+        )
+        latents_std = (
+            torch.tensor(self.vae.config.latents_std)
+            .view(1, self.latent_channels, 1, 1, 1)
+            .to(image_latents.device, image_latents.dtype)
+        )
+        image_latents = (image_latents - latents_mean) / latents_std
+
+        return image_latents
+
+    def prepare_latents(
+        self,
+        images,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        # VAE applies 8x compression on images but we must also account for packing which requires
+        # latent height and width to be divisible by 2.
+        height = 2 * (int(height) // (self.vae_scale_factor * 2))
+        width = 2 * (int(width) // (self.vae_scale_factor * 2))
+
+        shape = (batch_size, 1, num_channels_latents, height, width)
+
+        image_latents = None
+        if images is not None:
+            if not isinstance(images, list):
+                images = [images]
+            all_image_latents = []
+            for image in images:
+                image = image.to(device=device, dtype=dtype)
+                if image.shape[1] != self.latent_channels:
+                    image_latents = self._encode_vae_image(image=image, generator=generator)
+                else:
+                    image_latents = image
+                if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
+                    # expand init_latents for batch_size
+                    additional_image_per_prompt = batch_size // image_latents.shape[0]
+                    image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
+                elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
+                    raise ValueError(
+                        f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
+                    )
+                else:
+                    image_latents = torch.cat([image_latents], dim=0)
+
+                image_latent_height, image_latent_width = image_latents.shape[3:]
+                image_latents = self._pack_latents(
+                    image_latents, batch_size, num_channels_latents, image_latent_height, image_latent_width
+                )
+                all_image_latents.append(image_latents)
+            # Concatenate all image latents along dimension 1
+            image_latents = torch.cat(all_image_latents, dim=1)
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
+        else:
+            latents = latents.to(device=device, dtype=dtype)
+
+        return latents, image_latents
+
+    def prepare_timesteps(self, num_inference_steps, sigmas, image_seq_len):
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.get("base_image_seq_len", 256),
+            self.scheduler.config.get("max_image_seq_len", 4096),
+            self.scheduler.config.get("base_shift", 0.5),
+            self.scheduler.config.get("max_shift", 1.15),
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            sigmas=sigmas,
+            mu=mu,
+        )
+        return timesteps, num_inference_steps
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    def diffuse(
+        self,
+        prompt_embeds,
+        prompt_embeds_mask,
+        negative_prompt_embeds,
+        negative_prompt_embeds_mask,
+        latents,
+        image_latents,
+        img_shapes,
+        txt_seq_lens,
+        negative_txt_seq_lens,
+        timesteps,
+        do_true_cfg,
+        guidance,
+        true_cfg_scale,
+    ):
+        """Diffusion loop with optional image conditioning."""
+        self.scheduler.set_begin_index(0)
+        for i, t in enumerate(timesteps):
+            if self.interrupt:
+                continue
+            self._current_timestep = t
+            # broadcast to batch dimension and place on same device/dtype as latents
+            timestep = t.expand(latents.shape[0]).to(device=latents.device, dtype=latents.dtype)
+
+            # Concatenate image latents with noise latents if available
+            latent_model_input = latents
+            if image_latents is not None:
+                latent_model_input = torch.cat([latents, image_latents], dim=1)
+
+            noise_pred = self.transformer(
+                hidden_states=latent_model_input,
+                timestep=timestep / 1000,
+                guidance=guidance,
+                encoder_hidden_states_mask=prompt_embeds_mask,
+                encoder_hidden_states=prompt_embeds,
+                img_shapes=img_shapes,
+                txt_seq_lens=txt_seq_lens,
+                attention_kwargs=self.attention_kwargs,
+                return_dict=False,
+            )[0]
+            noise_pred = noise_pred[:, : latents.size(1)]
+
+            if do_true_cfg:
+                neg_noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    encoder_hidden_states_mask=negative_prompt_embeds_mask,
+                    encoder_hidden_states=negative_prompt_embeds,
+                    img_shapes=img_shapes,
+                    txt_seq_lens=negative_txt_seq_lens,
+                    attention_kwargs=self.attention_kwargs,
+                    return_dict=False,
+                )[0]
+                neg_noise_pred = neg_noise_pred[:, : latents.size(1)]
+                comb_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
+                cond_norm = torch.norm(noise_pred, dim=-1, keepdim=True)
+                noise_norm = torch.norm(comb_pred, dim=-1, keepdim=True)
+                noise_pred = comb_pred * (cond_norm / noise_norm)
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+        return latents
+
+    def forward(
+        self,
+        req: OmniDiffusionRequest,
+        prompt: str | list[str] = "",
+        negative_prompt: str | list[str] = "",
+        image: PIL.Image.Image | list[PIL.Image.Image] | torch.Tensor | None = None,
+        true_cfg_scale: float = 4.0,
+        height: int | None = None,
+        width: int | None = None,
+        num_inference_steps: int = 50,
+        sigmas: list[float] | None = None,
+        guidance_scale: float = 1.0,
+        num_images_per_prompt: int = 1,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
+        prompt_embeds: torch.Tensor | None = None,
+        prompt_embeds_mask: torch.Tensor | None = None,
+        negative_prompt_embeds: torch.Tensor | None = None,
+        negative_prompt_embeds_mask: torch.Tensor | None = None,
+        output_type: str | None = "pil",
+        attention_kwargs: dict[str, Any] | None = None,
+        callback_on_step_end_tensor_inputs: list[str] = ["latents"],
+        max_sequence_length: int = 512,
+    ) -> DiffusionOutput:
+        """Forward pass for image editing with support for multiple images."""
+        prompt = req.prompt if req.prompt is not None else prompt
+        negative_prompt = req.negative_prompt if req.negative_prompt is not None else negative_prompt
+
+        # Get preprocessed images from request (pre-processing is done in DiffusionEngine)
+        if hasattr(req, "vae_images") and hasattr(req, "condition_images"):
+            condition_images = req.condition_images
+            vae_images = req.vae_images
+            condition_image_sizes = req.condition_image_sizes
+            vae_image_sizes = req.vae_image_sizes
+            calculated_height = req.calculated_height
+            calculated_width = req.calculated_width
+            height = req.height
+            width = req.width
+        else:
+            # fallback to run pre-processing in pipeline (debug only)
+            if image is None:
+                raise ValueError("Image is required for QwenImageEditPlusPipeline")
+
+            if not isinstance(image, list):
+                image = [image]
+
+            image_size = image[0].size
+            calculated_width, calculated_height = calculate_dimensions(VAE_IMAGE_SIZE, image_size[0] / image_size[1])
+            height = height or calculated_height
+            width = width or calculated_width
+
+            multiple_of = self.vae_scale_factor * 2
+            width = width // multiple_of * multiple_of
+            height = height // multiple_of * multiple_of
+
+            condition_images = []
+            vae_images = []
+            condition_image_sizes = []
+            vae_image_sizes = []
+
+            for img in image:
+                image_width, image_height = img.size
+                condition_width, condition_height = calculate_dimensions(
+                    CONDITION_IMAGE_SIZE, image_width / image_height
+                )
+                vae_width, vae_height = calculate_dimensions(VAE_IMAGE_SIZE, image_width / image_height)
+                condition_image_sizes.append((condition_width, condition_height))
+                vae_image_sizes.append((vae_width, vae_height))
+                condition_images.append(self.image_processor.resize(img, condition_height, condition_width))
+                vae_images.append(self.image_processor.preprocess(img, vae_height, vae_width).unsqueeze(2))
+
+        num_inference_steps = req.num_inference_steps or num_inference_steps
+        generator = req.generator or generator
+        true_cfg_scale = req.true_cfg_scale or true_cfg_scale
+        req_num_outputs = getattr(req, "num_outputs_per_prompt", None)
+        if req_num_outputs and req_num_outputs > 0:
+            num_images_per_prompt = req_num_outputs
+
+        # 1. check inputs
+        # 2. encode prompts
+        # 3. prepare latents and timesteps
+        # 4. diffusion process
+        # 5. decode latents
+        # 6. post-process outputs
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            image,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            prompt_embeds_mask,
+            negative_prompt_embeds_mask,
+            callback_on_step_end_tensor_inputs,
+            max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        has_neg_prompt = negative_prompt is not None or (
+            negative_prompt_embeds is not None and negative_prompt_embeds_mask is not None
+        )
+
+        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
+        prompt_embeds, prompt_embeds_mask = self.encode_prompt(
+            prompt=prompt,
+            image=condition_images,  # Use condition images for prompt encoding
+            prompt_embeds=prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+        )
+
+        if do_true_cfg:
+            negative_prompt_embeds, negative_prompt_embeds_mask = self.encode_prompt(
+                prompt=negative_prompt,
+                image=condition_images,  # Use same condition images for negative prompt encoding
+                prompt_embeds=negative_prompt_embeds,
+                prompt_embeds_mask=negative_prompt_embeds_mask,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+            )
+
+        num_channels_latents = self.transformer.in_channels // 4
+        # random noise latents, and image latents encoded by vae
+        latents, image_latents = self.prepare_latents(
+            vae_images,  # Use VAE images for latent preparation
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            self.device,
+            generator,
+            latents,
+        )
+        # img_shapes includes shapes for output image and all input images
+        img_shapes = [
+            [
+                (1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2),
+                *[
+                    (1, vae_height // self.vae_scale_factor // 2, vae_width // self.vae_scale_factor // 2)
+                    for vae_width, vae_height in vae_image_sizes
+                ],
+            ]
+        ] * batch_size
+
+        timesteps, num_inference_steps = self.prepare_timesteps(num_inference_steps, sigmas, latents.shape[1])
+        self._num_timesteps = len(timesteps)
+
+        # handle guidance
+        if self.transformer.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, dtype=torch.float32)
+            guidance = guidance.expand(latents.shape[0])
+        else:
+            guidance = None
+
+        if self.attention_kwargs is None:
+            self._attention_kwargs = {}
+
+        txt_seq_lens = prompt_embeds_mask.sum(dim=1).tolist() if prompt_embeds_mask is not None else None
+        negative_txt_seq_lens = (
+            negative_prompt_embeds_mask.sum(dim=1).tolist() if negative_prompt_embeds_mask is not None else None
+        )
+
+        latents = self.diffuse(
+            prompt_embeds,
+            prompt_embeds_mask,
+            negative_prompt_embeds,
+            negative_prompt_embeds_mask,
+            latents,
+            image_latents,
+            img_shapes,
+            txt_seq_lens,
+            negative_txt_seq_lens,
+            timesteps,
+            do_true_cfg,
+            guidance,
+            true_cfg_scale,
+        )
+
+        self._current_timestep = None
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = latents.to(self.vae.dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+                latents.device, latents.dtype
+            )
+            latents = latents / latents_std + latents_mean
+            image = self.vae.decode(latents, return_dict=False)[0][:, :, 0]
+
+        return DiffusionOutput(output=image)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)

vllm_omni/diffusion/registry.py

@@ -19,6 +19,11 @@ _DIFFUSION_MODELS = {
         "pipeline_qwen_image_edit",
         "QwenImageEditPipeline",
     ),
+    "QwenImageEditPlusPipeline": (
+        "qwen_image",
+        "pipeline_qwen_image_edit_plus",
+        "QwenImageEditPlusPipeline",
+    ),
     "ZImagePipeline": (
         "z_image",
         "pipeline_z_image",
@@ -65,6 +70,7 @@ _DIFFUSION_POST_PROCESS_FUNCS = {
     # where mod_folder and mod_relname are  defined and mapped using `_DIFFUSION_MODELS` via the `arch` key
     "QwenImagePipeline": "get_qwen_image_post_process_func",
     "QwenImageEditPipeline": "get_qwen_image_edit_post_process_func",
+    "QwenImageEditPlusPipeline": "get_qwen_image_edit_plus_post_process_func",
     "ZImagePipeline": "get_post_process_func",
     "WanPipeline": "get_wan22_post_process_func",
 }
@@ -74,6 +80,7 @@ _DIFFUSION_PRE_PROCESS_FUNCS = {
     # `pre_process_func` function must be placed in {mod_folder}/{mod_relname}.py,
     # where mod_folder and mod_relname are  defined and mapped using `_DIFFUSION_MODELS` via the `arch` key
     "QwenImageEditPipeline": "get_qwen_image_edit_pre_process_func",
+    "QwenImageEditPlusPipeline": "get_qwen_image_edit_plus_pre_process_func",
 }