[Model] Add Qwen-Image-Edit (#196)

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

docs/models/supported_models.md

@@ -20,6 +20,7 @@ th {
 | `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` |
 | `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` |
 |`ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` |
 
 

examples/offline_inference/qwen_image/image_edit.py

@@ -0,0 +1,143 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""
+Example script for image editing with Qwen-Image-Edit.
+
+Usage:
+    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
+
+For more options, run:
+    python image_edit.py --help
+"""
+
+import argparse
+import os
+from pathlib import Path
+
+import torch
+from PIL import Image
+
+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.",
+    )
+    parser.add_argument(
+        "--image",
+        type=str,
+        required=True,
+        help="Path to input image file (PNG, JPG, etc.).",
+    )
+    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 specific to Qwen-Image-Edit.",
+    )
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="output_image_edit.png",
+        help="Path to save the edited 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.",
+    )
+    return parser.parse_args()
+
+
+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}")
+
+    # Load input image
+    input_image = Image.open(args.image).convert("RGB")
+    print(f"Loaded input image from {args.image} (size: {input_image.size})")
+
+    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()
+
+    # Initialize Omni with QwenImageEditPipeline
+    omni = Omni(
+        model=args.model,
+        model_class_name="QwenImageEditPipeline",
+        vae_use_slicing=vae_use_slicing,
+        vae_use_tiling=vae_use_tiling,
+    )
+    print("Pipeline loaded")
+
+    # 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,
+        num_inference_steps=args.num_inference_steps,
+        num_outputs_per_prompt=args.num_outputs_per_prompt,
+    )
+
+    # 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:
+        images[0].save(output_path)
+        print(f"Saved edited 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 edited image to {os.path.abspath(save_path)}")
+
+
+if __name__ == "__main__":
+    main()

vllm_omni/diffusion/diffusion_engine.py

@@ -2,11 +2,12 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import multiprocessing as mp
+import time
 
 from vllm.logger import init_logger
 
 from vllm_omni.diffusion.data import SHUTDOWN_MESSAGE, OmniDiffusionConfig
-from vllm_omni.diffusion.registry import get_diffusion_post_process_func
+from vllm_omni.diffusion.registry import get_diffusion_post_process_func, get_diffusion_pre_process_func
 from vllm_omni.diffusion.request import OmniDiffusionRequest
 from vllm_omni.diffusion.scheduler import scheduler
 from vllm_omni.utils.platform_utils import get_diffusion_worker_class
@@ -26,6 +27,7 @@ class DiffusionEngine:
         self.od_config = od_config
 
         self.post_process_func = get_diffusion_post_process_func(od_config)
+        self.pre_process_func = get_diffusion_pre_process_func(od_config)
 
         self._processes: list[mp.Process] = []
         self._closed = False
@@ -33,11 +35,24 @@ class DiffusionEngine:
 
     def step(self, requests: list[OmniDiffusionRequest]):
         try:
+            # Apply pre-processing if available
+            if self.pre_process_func is not None:
+                preprocess_start_time = time.time()
+                requests = self.pre_process_func(requests)
+                preprocess_time = time.time() - preprocess_start_time
+                logger.info(f"Pre-processing completed in {preprocess_time:.4f} seconds")
+
             output = self.add_req_and_wait_for_response(requests)
             if output.error:
                 raise Exception(f"{output.error}")
             logger.info("Generation completed successfully.")
-            return self.post_process_func(output.output)
+
+            postprocess_start_time = time.time()
+            result = self.post_process_func(output.output)
+            postprocess_time = time.time() - postprocess_start_time
+            logger.info(f"Post-processing completed in {postprocess_time:.4f} seconds")
+
+            return result
         except Exception as e:
             logger.error(f"Generation failed: {e}")
             return None

vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image_edit.py

@@ -0,0 +1,829 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import inspect
+import json
+import logging
+import math
+import os
+from collections.abc import Iterable
+from typing import Any, Optional, Union
+
+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.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__)
+
+
+def get_qwen_image_edit_pre_process_func(
+    od_config: OmniDiffusionConfig,
+):
+    """Pre-processing function for QwenImageEditPipeline."""
+    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 QwenImageEditPipeline."""
+        for req in requests:
+            image = req.pil_image
+
+            image_size = image[0].size if isinstance(image, list) else image.size
+            calculated_width, calculated_height = calculate_dimensions(1024 * 1024, 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 image
+            if image is not None and not (
+                isinstance(image, torch.Tensor) and len(image.shape) > 1 and image.shape[1] == latent_channels
+            ):
+                image = image_processor.resize(image, height, width)
+                prompt_image = image
+                image = image_processor.preprocess(image, height, width)
+                image = image.unsqueeze(2)
+
+                # Store preprocessed image and prompt image in request
+                req.preprocessed_image = image
+                req.prompt_image = prompt_image
+
+        return requests
+
+    return pre_process_func
+
+
+def get_qwen_image_edit_post_process_func(
+    od_config: OmniDiffusionConfig,
+):
+    """Post-processing function for QwenImageEditPipeline."""
+    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
+
+
+def calculate_dimensions(target_area: float, ratio: float):
+    """Calculate width and height from target area and aspect ratio."""
+    width = math.sqrt(target_area * ratio)
+    height = width / ratio
+
+    width = round(width / 32) * 32
+    height = round(height / 32) * 32
+
+    return width, height
+
+
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[list[int]] = None,
+    sigmas: Optional[list[float]] = None,
+    **kwargs,
+) -> tuple[torch.Tensor, int]:
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "argmax"
+):
+    """Retrieve latents from VAE encoder output."""
+    if hasattr(encoder_output, "latent_dist"):
+        return (
+            encoder_output.latent_dist.mode()
+            if sample_mode == "argmax"
+            else encoder_output.latent_dist.sample(generator)
+        )
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+class QwenImageEditPipeline(
+    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()
+        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
+        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<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"  # noqa: E501
+        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: Union[str, list[str]] = None,
+        image: Optional[torch.Tensor] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        template = self.prompt_template_encode
+        drop_idx = self.prompt_template_encode_start_idx
+        txt = [template.format(e) for e in prompt]
+
+        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, device=self.device)
+
+        return prompt_embeds, encoder_attention_mask
+
+    def _get_qwen_prompt_embeds(
+        self,
+        prompt: Union[str, list[str]] = None,
+        image: Optional[Union[PIL.Image.Image, torch.Tensor]] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        """Get prompt embeddings with image support for editing."""
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        template = self.prompt_template_encode
+        drop_idx = self.prompt_template_encode_start_idx
+        txt = [template.format(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: Union[str, list[str]],
+        image: Optional[torch.Tensor] = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 1024,
+    ):
+        r"""
+
+        Args:
+            prompt (`str` or `list[str]`, *optional*):
+                prompt to be encoded
+            image (`torch.Tensor`, *optional*):
+                image 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,
+        image,
+        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 image is not None:
+            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
+            )
+
+        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: Union[str, list[str]] = "",
+        negative_prompt: Union[str, list[str]] = "",
+        image: Optional[Union[PIL.Image.Image, torch.Tensor]] = None,
+        true_cfg_scale: float = 4.0,
+        height: int | None = None,
+        width: int | None = None,
+        num_inference_steps: int = 50,
+        sigmas: Optional[list[float]] = None,
+        guidance_scale: float = 1.0,
+        num_images_per_prompt: int = 1,
+        generator: Optional[Union[torch.Generator, list[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_embeds_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds_mask: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        attention_kwargs: Optional[dict[str, Any]] = None,
+        callback_on_step_end_tensor_inputs: list[str] = ["latents"],
+        max_sequence_length: int = 512,
+    ) -> DiffusionOutput:
+        """Forward pass for image editing."""
+        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 image from request (pre-processing is done in DiffusionEngine)
+        if hasattr(req, "preprocessed_image"):
+            prompt_image = req.prompt_image
+            image = req.preprocessed_image
+            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)
+            image_size = image[0].size if isinstance(image, list) else image.size
+            calculated_width, calculated_height, _ = calculate_dimensions(1024 * 1024, 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
+
+            if image is not None and not (isinstance(image, torch.Tensor) and image.size(1) == self.latent_channels):
+                image = self.image_processor.resize(image, calculated_height, calculated_width)
+                prompt_image = image
+                image = self.image_processor.preprocess(image, calculated_height, calculated_width)
+                image = image.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=prompt_image,  # Use resized image 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=prompt_image,  # Use same resized image 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(
+            image,
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            self.device,
+            generator,
+            latents,
+        )
+        img_shapes = [
+            [
+                (1, height // self.vae_scale_factor // 2, width // self.vae_scale_factor // 2),
+                (1, calculated_height // self.vae_scale_factor // 2, calculated_width // self.vae_scale_factor // 2),
+            ]
+        ] * 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

@@ -14,6 +14,11 @@ _DIFFUSION_MODELS = {
         "pipeline_qwen_image",
         "QwenImagePipeline",
     ),
+    "QwenImageEditPipeline": (
+        "qwen_image",
+        "pipeline_qwen_image_edit",
+        "QwenImageEditPipeline",
+    ),
     "ZImagePipeline": (
         "z_image",
         "pipeline_z_image",
@@ -54,19 +59,38 @@ _DIFFUSION_POST_PROCESS_FUNCS = {
     # `post_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
     "QwenImagePipeline": "get_qwen_image_post_process_func",
+    "QwenImageEditPipeline": "get_qwen_image_edit_post_process_func",
     "ZImagePipeline": "get_post_process_func",
 }
 
+_DIFFUSION_PRE_PROCESS_FUNCS = {
+    # arch: pre_process_func
+    # `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",
+}
+
+
+def _load_process_func(od_config: OmniDiffusionConfig, func_name: str):
+    """Load and return a process function from the appropriate module."""
+    mod_folder, mod_relname, _ = _DIFFUSION_MODELS[od_config.model_class_name]
+    module_name = f"vllm_omni.diffusion.models.{mod_folder}.{mod_relname}"
+    module = importlib.import_module(module_name)
+    func = getattr(module, func_name)
+    return func(od_config)
+
 
 def get_diffusion_post_process_func(od_config: OmniDiffusionConfig):
-    if od_config.model_class_name in _DIFFUSION_POST_PROCESS_FUNCS:
-        mod_folder, mod_relname, _ = _DIFFUSION_MODELS[od_config.model_class_name]
-        func_name = _DIFFUSION_POST_PROCESS_FUNCS[od_config.model_class_name]
-        module_name = f"vllm_omni.diffusion.models.{mod_folder}.{mod_relname}"
-        module = importlib.import_module(module_name)
-        post_process_func = getattr(module, func_name)
-        return post_process_func(od_config)
-    else:
+    if od_config.model_class_name not in _DIFFUSION_POST_PROCESS_FUNCS:
         raise ValueError(
             f"Post process function for model class {od_config.model_class_name} not found in diffusion model registry."
         )
+    func_name = _DIFFUSION_POST_PROCESS_FUNCS[od_config.model_class_name]
+    return _load_process_func(od_config, func_name)
+
+
+def get_diffusion_pre_process_func(od_config: OmniDiffusionConfig):
+    if od_config.model_class_name not in _DIFFUSION_PRE_PROCESS_FUNCS:
+        return None  # Return None if no pre-processing function is registered (for backward compatibility)
+    func_name = _DIFFUSION_PRE_PROCESS_FUNCS[od_config.model_class_name]
+    return _load_process_func(od_config, func_name)