docs: update README for v0.24.0

Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>

.buildkite/test-merge.yml

@@ -108,10 +108,7 @@ steps:
     depends_on: upload-merge-pipeline
     steps:
       - label: "Entrypoint Test with H100"
-        # The suite relaunches an OmniServer per test class (~30 model loads,
-        # incl. ten ~27.5 GiB Bagel loads); 60 min passed only ~1 in 3 runs
-        # even on main, and vLLM 0.25 weight loading needs ~10 min more.
-        timeout_in_minutes: 90
+        timeout_in_minutes: 60
         commands:
           - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and H100" --run-level "advanced_model"
         agents:
@@ -149,9 +146,7 @@ steps:
                       type: DirectoryOrCreate
 
       - label: "Entrypoint Test with L4"
-        # 30 min left no headroom for a cold image pull (~11 min for new base
-        # layers) plus per-class engine relaunches; main passed at 26/30.
-        timeout_in_minutes: 45
+        timeout_in_minutes: 30
         commands:
           - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and L4" --run-level "advanced_model"
         agents:

.buildkite/test-nightly.yml

@@ -650,6 +650,8 @@ steps:
             tests/e2e/online_serving/test_qwen_image_edit_expansion.py
             tests/e2e/online_serving/test_qwen_image_layered_expansion.py
             tests/e2e/online_serving/test_bagel_expansion.py
+            tests/e2e/online_serving/test_krea2_expansion.py
+            tests/e2e/offline_inference/test_krea2_expansion.py
             -m "full_model and diffusion and H100 and not distributed_cuda"
             --run-level "full_model"
         agents:
@@ -693,6 +695,7 @@ steps:
             pytest -sv
             tests/e2e/online_serving/test_qwen_image_expansion.py
             tests/e2e/online_serving/test_qwen_image_edit_expansion.py
+            tests/e2e/offline_inference/test_krea2_expansion.py
             -m "full_model and diffusion and H100 and distributed_cuda"
             --run-level "full_model"
         agents:

.buildkite/test-ready.yml

@@ -684,3 +684,24 @@ steps:
                     hostPath:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
+
+      - label: "Diffusion · GGUF Plugin Test"
+        soft_fail: true
+        source_file_dependencies:
+          - tests/plugin/test_gguf_diffusion.py
+          - vllm_omni/diffusion/model_loader
+        commands:
+          - pip install "vllm-gguf-plugin>=0.0.3"
+          - timeout 30m pytest -sv tests/plugin/test_gguf_diffusion.py
+        agents:
+          queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+                - "HF_TOKEN"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"

.gitignore

@@ -275,8 +275,3 @@ tests/.ci_generated/
 
 # Local Lance precision-matching dump scripts (not part of the repo)
 lance_compare/
-
-# FP8 quantization test sample images: tests/diffusion/quantization/
-# test_quantization_fp8.py saves these into the repo root for manual
-# inspection; they are byproducts and must never be committed.
-test_*_fp8.png

README.md

@@ -16,6 +16,7 @@ Easy, fast, and cheap omni-modality model serving for everyone
 ---
 
 *Latest News* 🔥
+- [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support.
 - [2026/06] We released [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) - an **omnimodal world-model** release aligned with vLLM 0.22, featuring [Nvidia Cosmos3](recipes/cosmos3/Cosmos3-Nano.md)/DreamZero world model support, expanded quantization coverage across Blackwell/NPU/XPU, TTS production improvements, new models including MiniCPM-o 4.5, MOSS-TTS, and Lance, plus RL integration with [VeRL-Omni](https://github.com/verl-project/verl-omni).
 - [2026/05] We released [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0) - refreshes the serving/runtime stack for large-scale omni workloads, and improves diffusion model performance, quantization, and hardware readiness across CUDA, ROCm, MUSA, NPU, and XPU backends.
 - [2026/03] We released [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0) - strengthens the core runtime through a large entrypoint refactor and scheduler/runtime cleanups, expands unified quantization and diffusion execution, broadens multimodal model coverage, and improves production readiness across audio, omni, image, video, RL, and multi-platform deployments.
@@ -31,9 +32,9 @@ Easy, fast, and cheap omni-modality model serving for everyone
 
 [vLLM](https://github.com/vllm-project/vllm) was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving:
 
-- **Omni-modality**: Text, image, video, and audio data processing
+- **Omni-modality**: Text, image, audio, video, and action data processing
 - **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
-- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
+- **Heterogeneous outputs**: from traditional text generation to multimodal and action outputs
 
 <p align="center">
   <picture>
@@ -57,9 +58,10 @@ vLLM-Omni is flexible and easy to use with:
 
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
 
-- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos, HunyuanImage, BAGEL)
+- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
 - **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
 - **Diffusion models** — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
+- **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
 
 ## Getting Started
 

docker/Dockerfile.ci

@@ -19,4 +19,4 @@ RUN uv pip install --system ".[dev]"
 
 RUN ln -sf /usr/bin/python3 /usr/bin/python
 
-ENTRYPOINT []
+ENTRYPOINT []

docs/README.md

@@ -28,9 +28,9 @@ Easy, fast, and cheap omni-modality model serving for everyone
 
 [vLLM](https://github.com/vllm-project/vllm) was originally designed to support large language models for text-based autoregressive generation tasks. vLLM-Omni is a framework that extends its support for omni-modality model inference and serving:
 
-- **Omni-modality**: Text, image, video, and audio data processing
+- **Omni-modality**: Text, image, audio, video, and action data processing
 - **Non-autoregressive Architectures**: extend the AR support of vLLM to Diffusion Transformers (DiT) and other parallel generation models
-- **Heterogeneous outputs**: from traditional text generation to multimodal outputs
+- **Heterogeneous outputs**: from traditional text generation to multimodal and action outputs
 
 <p align="center">
   <picture>
@@ -55,8 +55,10 @@ vLLM-Omni is flexible and easy to use with:
 
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
 
-- Omni-modality models (e.g. Qwen2.5-Omni, Qwen3-Omni)
-- Multi-modality generation models (e.g. Qwen-Image)
+- Omni-modality models (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
+- TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
+- Diffusion models — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
+- Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
 
 For more information, checkout the following:
 

docs/design/architecture_overview.md

@@ -13,7 +13,7 @@ This document outlines the architectural design for vLLM-Omni.
 
 The primary goal of the vLLM-Omni project is to build the fastest and easiest-to-use open-source Omni-Modality model inference & serving engine. vLLM-Omni extends the original vLLM, which was created to support large language models for text-based autoregressive (AR) generation tasks. vLLM-Omni is designed to support:
 
-* **Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, and video, alongside text.
+* **Non-textual Output:** Enables the integration, efficient processing and output of various data types, including but not limited to, images, audio, video, and action trajectories, alongside text.
 * **Non-Autoregressive Structure:** Support model structure beyond autoregressive, especially Diffusion Transformer (DiT), which is widely used in visual and audio generation.
 * **Integration with vLLM Core:** Maintain compatibility and leverage existing vLLM key modules and optimizations where applicable.
 * **Extensibility:** Design a modular and flexible architecture that can easily accommodate new modalities, model architectures, and output formats.

docs/models/supported_models.md

@@ -34,6 +34,7 @@ th {
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2511 | `Qwen/Qwen-Image-Edit-2511` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `GlmImagePipeline` | GLM-Image | `zai-org/GLM-Image` | ✅︎ | ✅︎ | | |
 | `ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
+| `Krea2Pipeline` | Krea 2 (Raw + Turbo) | `krea/Krea-2-Raw`, `krea/Krea-2-Turbo` | ✅︎ | | | |
 | `WanPipeline` | Wan2.1-T2V, Wan2.2-T2V, Wan2.2-TI2V | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`, `Wan-AI/Wan2.1-T2V-14B-Diffusers`, `Wan-AI/Wan2.2-T2V-A14B-Diffusers`, `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `WanImageToVideoPipeline` | Wan2.2-I2V | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `Cosmos3OmniDiffusersPipeline` | Cosmos3 T2I, T2V, I2V, V2V, T2V with sound, action policy | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |

docs/user_guide/diffusion_features.md

@@ -121,6 +121,7 @@ The following tables show which models support each feature:
 | **GLM-Image**            |     ❌     |     ❌      |           ❌           |       ✅        |         ✅         |          ❌          |   ✅    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Hidream-I1-Full**        |     ❌     |     ❌      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **HunyuanImage3**        |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ✅        |        ❌         |
+| **Krea 2**               |     ❌     |     ✅      |           ❌           |       ❌        |         ❌         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **LongCat-Image**        |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **LongCat-Image-Edit**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **MagiHuman**            |     ❌     |     ❌      |           ❌           |       ❓        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
@@ -144,6 +145,7 @@ The following tables show which models support each feature:
 > 1. Nextstep_1(T2I) does not support cache acceleration methods such as TeaCache or Cache-DiT.
 > 2. `Tongyi-MAI/Z-Image-Turbo` and `SII-GAIR/daVinci-MagiHuman-Base-1080p` are distilled models with minimal NFEs; CFG-Parallel is not necessary.
 > 3. Cosmos3 T2I uses `Cosmos3OmniDiffusersPipeline` with `modalities=["image"]`. Model-level CPU offload is not supported; use layerwise offload.
+> 4. Krea 2 currently supports single-GPU inference plus LoRA, Cache-DiT, HSDP, CPU/layerwise offload, and VAE-patch-parallel (decode). TP/SP/CFG-Parallel are not yet wired. The few-step distilled (Turbo) checkpoint uses `is_distilled=true` (fixed timestep shift `mu=1.15`); generate at 2048x2048 by default with `num_inference_steps≈8` and `guidance_scale=0`. The Raw checkpoint uses 1024x1024, `num_inference_steps=28`, and `guidance_scale=4.5`.
 
 ### VideoGen
 

docs/user_guide/quantization/gguf.md

@@ -9,6 +9,15 @@ for the transformer.
 
 GGUF is static quantization: the quantized weights are produced before serving.
 
+!!! note
+    GGUF support has migrated to OOT [vllm-gguf-plugin](https://github.com/vllm-project/vllm-gguf-plugin). Make sure you have GGUF plugin installed before serving a GGUF model.
+
+Before serving a GGUF model, make sure to install the [vllm-gguf-plugin](https://github.com/vllm-project/vllm-gguf-plugin):
+
+```bash
+uv pip install vllm-gguf-plugin>=0.0.3
+```
+
 ## Hardware Support
 
 | Device | Support |

examples/offline_inference/image_to_video/image_to_video.py

@@ -220,7 +220,7 @@ def parse_args() -> argparse.Namespace:
         "--quantization",
         type=str,
         default=None,
-        choices=["fp8", "mxfp8", "mxfp4", "mxfp4_dualscale", "int8", "gguf"],
+        choices=["fp8", "mxfp8", "mxfp4", "mxfp4_dualscale", "int8"],
         help="Quantization method for the transformer. mxfp8: W8A8 MXFP8 (NPU). mxfp4: W4A4 MXFP4 (NPU). mxfp4_dualscale: W4A4 MXFP4 dual-scale + BF16 fallback mixed (NPU). fp8: online FP8 (GPU).",
     )
 

examples/offline_inference/text_to_image/README.md

@@ -36,6 +36,7 @@ This folder provides several entrypoints for experimenting with text-to-image di
 | `black-forest-labs/FLUX.2-dev` | 1024 x 1024 | 65.7 | >80 (CPU offload required) |
 | `HunyuanImage-3.0` | 1024 x 1024 | 80.0 (TP≥3)  | 160 |
 | `HiDream-I1-Full` | 1024 x 1024 | 63.7  | 57.7 |
+| `krea/Krea-2-Raw`, `krea/Krea-2-Turbo` | 1024 (Raw) / 2048 (Turbo) | — | ~30 |
 
 !!! info
 *Peak VRAM:  based on basic single-card usage, batch size =1, without any acceleration/optimization features. FLUX.2-dev requires `--enable-cpu-offload` on a single 80 GiB GPU.
@@ -196,6 +197,37 @@ python examples/offline_inference/text_to_image/text_to_image.py \
   --output /output.png
 ```
 
+### Krea 2
+
+Krea 2 is published as diffusers-format checkpoints: `krea/Krea-2-Turbo` (few-step distilled) and `krea/Krea-2-Raw`.
+The pipeline class (`Krea2Pipeline`) is auto-detected from `model_index.json`. The distilled Turbo checkpoint sets
+`is_distilled=true` and is best run at 2048x2048 with few steps and guidance disabled:
+
+```bash
+# Few-step distilled (Turbo) checkpoint
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model krea/Krea-2-Turbo \
+  --prompt "a fox in the snow" \
+  --num-inference-steps 8 \
+  --guidance-scale 0.0 \
+  --height 2048 --width 2048 \
+  --output krea2.png
+```
+
+For the Raw checkpoint, use more steps with CFG enabled:
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model krea/Krea-2-Raw \
+  --prompt "a fox in the snow" \
+  --num-inference-steps 28 \
+  --guidance-scale 4.5 \
+  --output krea2.png
+```
+
+`guidance-scale` follows the Krea 2 convention (`velocity = cond + guidance_scale * (cond - uncond)`); CFG is enabled
+whenever `guidance-scale > 0`.
+
 ### Batch Requests (Multiple Prompts)
 
 You can pass multiple prompts in a single `Omni.generate` call. `Omni`

examples/offline_inference/text_to_image/text_to_image.py

@@ -202,17 +202,11 @@ def parse_args() -> argparse.Namespace:
         "--quantization",
         type=str,
         default=None,
-        choices=["fp8", "int8", "gguf"],
+        choices=["fp8", "int8"],
         help="Quantization method for the transformer. "
-        "Options: 'fp8' (FP8 W8A8 on Ada/Hopper, weight-only on older GPUs), 'int8' (Int8 W8A8), 'gguf' (GGUF quantized weights). "
+        "Options: 'fp8' (FP8 W8A8 on Ada/Hopper, weight-only on older GPUs), 'int8' (Int8 W8A8). "
         "Default: None (no quantization, uses BF16).",
     )
-    parser.add_argument(
-        "--gguf-model",
-        type=str,
-        default=None,
-        help=("GGUF file path or HF reference for transformer weights. Required when --quantization gguf is set."),
-    )
     parser.add_argument(
         "--ignored-layers",
         type=str,
@@ -402,14 +396,7 @@ def main():
     # ignored_layers is specified so the list flows through OmniDiffusionConfig
     quant_kwargs: dict[str, Any] = {}
     ignored_layers = [s.strip() for s in args.ignored_layers.split(",") if s.strip()] if args.ignored_layers else None
-    if args.quantization == "gguf":
-        if not args.gguf_model:
-            raise ValueError("--gguf-model is required when --quantization gguf is set.")
-        quant_kwargs["quantization_config"] = {
-            "method": "gguf",
-            "gguf_model": args.gguf_model,
-        }
-    elif args.quantization and ignored_layers:
+    if args.quantization and ignored_layers:
         quant_kwargs["quantization_config"] = {
             "method": args.quantization,
             "ignored_layers": ignored_layers,

examples/offline_inference/text_to_video/text_to_video.md

@@ -231,7 +231,7 @@ python text_to_video.py \
 - `--frame-rate`: generation FPS for pipelines that require it (e.g., LTX2).
 - `--audio-sample-rate`: audio sample rate for embedded audio (when the
   pipeline returns audio; LTX-2.3 outputs 48kHz audio).
-- `--quantization`: quantization method (`fp8` for FP8, `gguf` for GGUF).
+- `--quantization`: quantization method (such as `fp8` for FP8).
 - `--flow-shift`: scheduler flow_shift parameter.
 - `--extra-body`: JSON object of model-specific generation params, filtered against the model's declared `extra_body_params` (see [`vllm_omni/model_extras`](../../../vllm_omni/model_extras)). Used by Cosmos3 (see above).
 

examples/offline_inference/text_to_video/text_to_video.py

@@ -227,7 +227,7 @@ def parse_args() -> argparse.Namespace:
         "--quantization",
         type=str,
         default=None,
-        choices=["fp8", "mxfp8", "mxfp4", "mxfp4_dualscale", "int8", "gguf"],
+        choices=["fp8", "mxfp8", "mxfp4", "mxfp4_dualscale", "int8"],
         help="Quantization method for the transformer. mxfp8: W8A8 MXFP8 (NPU). mxfp4: W4A4 MXFP4 (NPU). mxfp4_dualscale: W4A4 MXFP4 dual-scale + BF16 fallback mixed (NPU). fp8: online FP8 (GPU).",
     )
 

recipes/README.md

@@ -44,6 +44,7 @@ recipes/
 | [`inclusionAI/Ming-flash-omni-2.0.md`](./inclusionAI/Ming-flash-omni-2.0.md) | Online serving for multimodal chat + standalone TTS | 4x H100 / 1x H100 80GB |
 | [`inclusionAI/Ming-omni-tts.md`](./inclusionAI/Ming-omni-tts.md) | Offline + online dense Ming TTS/audio generation | 1x H100 80GB / 1x AMD MI300X (ROCm 7.2) |
 | [`IndexTeam/IndexTTS-2.md`](./IndexTeam/IndexTTS-2.md) | Online serving for voice-cloned TTS with optional emotion control | 1x L4 24GB or larger CUDA GPU |
+| [`krea/Krea-2.md`](./krea/Krea-2.md) | Text-to-image (Turbo + Raw), offline + online, with LoRA | 1x H100 80GB |
 | [`LTX/LTX-2.md`](./LTX/LTX-2.md) | Text-to-video and image-to-video serving | 1x H200 141GB |
 | [`LTX/LTX-2.3.md`](./LTX/LTX-2.3.md) | Text-to-video with audio generation (22B) | 1x GPU (96GB VRAM) |
 | [`MammothModa2/MammothModa2-Preview.md`](./MammothModa2/MammothModa2-Preview.md) | Text-to-image with the shared offline image example (AR → DiT) | 1x L40S 48GB / 1x ≥40GB GPU |

recipes/krea/Krea-2.md

@@ -0,0 +1,143 @@
+# Krea 2 for text-to-image (base + LoRA)
+
+## Summary
+
+- Vendor: Krea
+- Model: `krea/Krea-2-Turbo` (few-step distilled) and `krea/Krea-2-Raw`
+- Task: Text-to-image (T2I)
+- Mode: Offline engine and online OpenAI-compatible serving
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe for a known-good starting point to run the Krea 2 single-stream
+MMDiT with vLLM-Omni on a **single 80 GB NVIDIA H100** — both the offline
+`text_to_image.py` entrypoint and the online `/v1/images/generations` route,
+including PEFT LoRA adapters. `Krea2Pipeline` is auto-detected from
+`model_index.json`; guidance is checkpoint-aware (distilled Turbo runs no-CFG,
+Raw runs CFG).
+
+## References
+
+- Related example under `examples/`:
+  [`examples/offline_inference/text_to_image/README.md`](../../examples/offline_inference/text_to_image/README.md)
+  (see the "Krea 2" section)
+- Feature support:
+  [`docs/user_guide/diffusion_features.md`](../../docs/user_guide/diffusion_features.md)
+- Related PR: [#4730](https://github.com/vllm-project/vllm-omni/pull/4730)
+
+## Hardware Support
+
+This recipe documents a **single-GPU** CUDA layout on H100 80 GB. Add more
+platforms (ROCm / NPU) or multi-GPU layouts as community validation lands.
+
+## GPU
+
+### 1× H100 80GB
+
+#### Environment
+
+Versions from a working editable install (activate `vllm-omni/.venv` or your equivalent):
+
+- OS: Linux
+- Python: 3.12
+- Driver / runtime: NVIDIA driver **595.71.05** (CUDA 13.x)
+- torch: **2.11.0+cu130**
+- vLLM: **0.24.0**
+- vLLM-Omni: editable install from this repo (Git **`960ce8f6`**)
+- Transformers: **5.12.1**
+
+Krea 2 weights are ~30 GB and fit comfortably on a single 80 GB H100.
+
+#### Command
+
+Offline — few-step distilled (Turbo) checkpoint (2048×2048, no CFG):
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model krea/Krea-2-Turbo \
+  --prompt "a fox in the snow" \
+  --num-inference-steps 8 \
+  --guidance-scale 0.0 \
+  --height 2048 --width 2048 \
+  --output krea2_turbo.png
+```
+
+Offline — Raw checkpoint (1024×1024, CFG enabled):
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model krea/Krea-2-Raw \
+  --prompt "a fox in the snow" \
+  --num-inference-steps 28 \
+  --guidance-scale 4.5 \
+  --height 1024 --width 1024 \
+  --output krea2_raw.png
+```
+
+LoRA (offline) — pass a PEFT adapter with `--lora-path` / `--lora-scale`.
+`NagaSaiAbhinay/Krea-2-vllm-darkbrush-LoRA` is a vLLM-Omni-compatible repackaging
+of `krea/Krea-2-LoRA-darkbrush`; its trigger word is `monochrome ink wash style`:
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model krea/Krea-2-Turbo \
+  --prompt "a fox in the snow, monochrome ink wash style" \
+  --num-inference-steps 8 --guidance-scale 0.0 \
+  --lora-path NagaSaiAbhinay/Krea-2-vllm-darkbrush-LoRA --lora-scale 1.0 \
+  --output krea2_darkbrush.png
+```
+
+Online — serve and query the OpenAI-compatible image route:
+
+```bash
+vllm serve krea/Krea-2-Turbo --omni --enforce-eager --port 8091
+```
+
+```bash
+curl -s http://localhost:8091/v1/images/generations \
+  -H "Content-Type: application/json" \
+  -d '{
+    "prompt": "a fox in the snow, monochrome ink wash style",
+    "size": "1024x1024",
+    "num_inference_steps": 8,
+    "guidance_scale": 0.0,
+    "seed": 42,
+    "lora": {"name": "darkbrush", "path": "NagaSaiAbhinay/Krea-2-vllm-darkbrush-LoRA", "scale": 1.0}
+  }' | jq -r '.data[0].b64_json' | base64 -d > krea2_online.png
+```
+
+Cache-DiT acceleration (offline) — add `--cache-backend cache_dit`:
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model krea/Krea-2-Turbo --prompt "a fox in the snow" \
+  --num-inference-steps 8 --guidance-scale 0.0 \
+  --cache-backend cache_dit --output krea2_cachedit.png
+```
+
+#### Verification
+
+```bash
+# Offline: the script prints "Saved generated image to <path>"; confirm the file exists.
+ls -lh krea2_turbo.png
+```
+
+For the online route, decode the returned `b64_json` (see the `curl` above) and
+confirm a valid 1024×1024 PNG is written.
+
+#### Notes
+
+- Key flags: distilled Turbo → few steps (~8) with `guidance_scale=0.0`; Raw →
+  more steps (~28) with `guidance_scale>0` (CFG). `guidance-scale` follows the
+  Krea 2 convention `velocity = cond + guidance_scale * (cond - uncond)`.
+- LoRA: adapters must be PEFT format (`adapter_config.json` +
+  `adapter_model.safetensors`); the projections are `ReplicatedLinear`
+  (264 modules). Offline uses `--lora-path` (which sets
+  `OmniDiffusionSamplingParams.lora_request` / `lora_scale`); online uses the
+  top-level `lora` object. Style LoRAs like darkbrush need their trigger word in
+  the prompt.
+- Cache-DiT is supported; `has_separate_cfg` is checkpoint-aware (False for Turbo
+  no-CFG, True for Raw CFG).
+- Known limitations: TP / SP / CFG-Parallel are not yet wired for Krea 2. HSDP,
+  layerwise CPU offload, and VAE-patch-parallel (decode) are supported.

tests/config/test_omni_config.py

@@ -751,3 +751,14 @@ def test_diffusion_config_field_classification_covers_current_fields():
         "distributed_executor_backend",
     } <= omni_config_module._DIFFUSION_SHARED_CONFIG_FIELDS
     assert "prompt_file_path" in omni_config_module._DIFFUSION_RUNTIME_CONFIG_FIELDS
+
+
+def test_diffusion_config_projection_keeps_mapping_quantization_config_serializable():
+    quantization_config = {
+        "method": "example_quant",
+        "weights": "weights.bin",
+    }
+
+    cfg = omni_config_module._DiffusionConfigProjection.from_kwargs(quantization_config=quantization_config)
+
+    assert cfg.quantization_config == quantization_config

tests/dfx/reliability/invalid_param_test/test_invalid_image_editing.py

@@ -62,8 +62,8 @@ def _finalize_edits_form_data(template: dict[str, Any], server_model: str) -> di
         ),
         pytest.param(
             True,
-            {"prompt": "edit", "model": _IMAGE_EDITS_SERVER_MODEL, "layers": "2"},
-            ("Invalid layers", "3", "1"),
+            {"prompt": "edit", "model": _IMAGE_EDITS_SERVER_MODEL, "layers": "1"},
+            ("Invalid layers", "2", "10"),
             400,
             id="invalid_layers",
         ),

tests/dfx/reliability/invalid_param_test/test_invalid_image_generation.py

@@ -87,8 +87,8 @@ def _minimal_images_gen_json(omni_server: OmniServer) -> dict[str, object]:
         pytest.param(
             {"true_cfg_scale": 21.0}, ("true_cfg_scale", "less_than_equal", "20"), id="true_cfg_scale_above_max"
         ),
-        pytest.param({"layers": 2}, ("layers", "value_error", "3", "10"), id="layers_below_min"),
-        pytest.param({"layers": 11}, ("layers", "value_error", "3", "10"), id="layers_above_max"),
+        pytest.param({"layers": 1}, ("layers", "value_error", "2", "10"), id="layers_below_min"),
+        pytest.param({"layers": 11}, ("layers", "value_error", "2", "10"), id="layers_above_max"),
         pytest.param({"seed": -1}, ("seed", "greater_than_equal", "0"), id="seed_negative", marks=_SKIP_ISSUE_3649),
         pytest.param(
             {"seed": 2**32},

tests/diffusion/model_loader/gguf_adapters/test_flux2_klein_gguf_adapter.py

@@ -1,112 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Unit tests for the Flux2-Klein GGUF adapter."""
-
-from __future__ import annotations
-
-from types import SimpleNamespace
-
-import pytest
-import torch
-
-from vllm_omni.diffusion.model_loader.gguf_adapters import get_gguf_adapter
-from vllm_omni.diffusion.model_loader.gguf_adapters.flux2_klein import (
-    Flux2KleinGGUFAdapter,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
-
-
-def _make_od_config(
-    model_class_name: str = "Flux2Pipeline",
-    model_type: str = "flux",
-):
-    return SimpleNamespace(
-        model_class_name=model_class_name,
-        tf_model_config={"model_type": model_type},
-    )
-
-
-def _make_source(prefix: str = "", subfolder: str = "transformer"):
-    return SimpleNamespace(prefix=prefix, subfolder=subfolder)
-
-
-def test_flux2_klein_adapter_selected_for_flux_family():
-    adapter = get_gguf_adapter(
-        "dummy.gguf",
-        object(),
-        _make_source(),
-        _make_od_config(),
-    )
-
-    assert isinstance(adapter, Flux2KleinGGUFAdapter)
-
-
-def test_flux2_klein_adapter_matches_flux_model_type():
-    assert Flux2KleinGGUFAdapter.is_compatible(
-        _make_od_config(model_class_name="OtherPipeline", model_type="flux-dev"),
-        object(),
-        _make_source(),
-    )
-
-
-def test_flux2_klein_adapter_renames_core_projection_paths(monkeypatch: pytest.MonkeyPatch):
-    import vllm_omni.diffusion.model_loader.gguf_adapters.flux2_klein as flux_module
-
-    monkeypatch.setattr(
-        flux_module,
-        "gguf_quant_weights_iterator",
-        lambda _path: iter(
-            [
-                ("img_in.qweight", torch.ones((1, 1))),
-                ("time_in.in_layer.qweight_type", torch.tensor(1)),
-                ("double_blocks.0.img_attn.norm.query_norm.weight", torch.full((1, 1), 2.0)),
-                ("final_layer.linear.qweight", torch.full((1, 1), 3.0)),
-            ]
-        ),
-    )
-
-    adapter = Flux2KleinGGUFAdapter(
-        "dummy.gguf",
-        object(),
-        _make_source(),
-        _make_od_config(),
-    )
-
-    weights = list(adapter.weights_iterator())
-    names = [name for name, _ in weights]
-
-    assert "x_embedder.qweight" in names
-    assert "time_guidance_embed.timestep_embedder.linear_1.qweight_type" in names
-    assert "transformer_blocks.0.attn.norm_q.weight" in names
-    assert "proj_out.qweight" in names
-
-
-def test_flux2_klein_adapter_swaps_final_adaln_shift_and_scale(monkeypatch: pytest.MonkeyPatch):
-    import vllm_omni.diffusion.model_loader.gguf_adapters.flux2_klein as flux_module
-
-    monkeypatch.setattr(
-        flux_module,
-        "gguf_quant_weights_iterator",
-        lambda _path: iter(
-            [
-                (
-                    "final_layer.adaLN_modulation.1.scale",
-                    torch.tensor([1.0, 2.0, 3.0, 4.0]),
-                )
-            ]
-        ),
-    )
-
-    adapter = Flux2KleinGGUFAdapter(
-        "dummy.gguf",
-        object(),
-        _make_source(),
-        _make_od_config(),
-    )
-
-    weights = list(adapter.weights_iterator())
-
-    assert len(weights) == 1
-    assert weights[0][0] == "norm_out.linear.weight"
-    assert torch.equal(weights[0][1], torch.tensor([3.0, 4.0, 1.0, 2.0]))

tests/diffusion/model_loader/gguf_adapters/test_qwen_image_gguf_adapter.py

@@ -1,184 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from types import SimpleNamespace
-
-import pytest
-
-from vllm_omni.diffusion.model_loader.gguf_adapters import get_gguf_adapter
-from vllm_omni.diffusion.model_loader.gguf_adapters.qwen_image import (
-    QwenImageGGUFAdapter,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
-
-
-def _make_od_config(
-    model_class_name: str = "QwenImagePipeline",
-    model_type: str = "qwen_image",
-):
-    return SimpleNamespace(
-        model_class_name=model_class_name,
-        tf_model_config={"model_type": model_type},
-    )
-
-
-def _make_source(prefix: str = "", subfolder: str = "transformer"):
-    return SimpleNamespace(prefix=prefix, subfolder=subfolder)
-
-
-class _FakeTransformer:
-    def __init__(self) -> None:
-        self._params = {
-            "img_in.qweight": 1,
-            "img_in.qweight_type": 1,
-            "txt_in.qweight": 1,
-            "txt_in.qweight_type": 1,
-            "proj_out.qweight": 1,
-            "proj_out.qweight_type": 1,
-            "time_text_embed.timestep_embedder.linear_1.qweight": 1,
-            "time_text_embed.timestep_embedder.linear_1.qweight_type": 1,
-            "time_text_embed.timestep_embedder.linear_2.qweight": 1,
-            "time_text_embed.timestep_embedder.linear_2.qweight_type": 1,
-            "transformer_blocks.0.img_mod.1.qweight": 1,
-            "transformer_blocks.0.img_mod.1.qweight_type": 1,
-            "transformer_blocks.0.txt_mod.1.qweight": 1,
-            "transformer_blocks.0.txt_mod.1.qweight_type": 1,
-            "transformer_blocks.0.attn.to_qkv.qweight": 1,
-            "transformer_blocks.0.attn.to_qkv.qweight_type": 1,
-            "transformer_blocks.0.attn.add_kv_proj.qweight": 1,
-            "transformer_blocks.0.attn.add_kv_proj.qweight_type": 1,
-            "transformer_blocks.0.attn.to_out.weight": 1,
-            "transformer_blocks.0.img_mlp.net.0.proj.qweight": 1,
-            "transformer_blocks.0.img_mlp.net.0.proj.qweight_type": 1,
-        }
-
-    def named_parameters(self):
-        return list(self._params.items())
-
-    def named_buffers(self):
-        return []
-
-    def get_submodule(self, _prefix: str):
-        return self
-
-
-def test_qwen_adapter_selected_for_qwen_image_family():
-    adapter = get_gguf_adapter(
-        "dummy.gguf",
-        _FakeTransformer(),
-        _make_source(),
-        _make_od_config(),
-    )
-    assert isinstance(adapter, QwenImageGGUFAdapter)
-
-
-def test_qwen_adapter_matches_multiple_pipeline_variants():
-    for model_class_name in (
-        "QwenImagePipeline",
-        "QwenImageEditPipeline",
-        "QwenImageEditPlusPipeline",
-        "QwenImageLayeredPipeline",
-    ):
-        assert QwenImageGGUFAdapter.is_compatible(
-            _make_od_config(model_class_name=model_class_name),
-            _FakeTransformer(),
-            _make_source(),
-        )
-
-
-def test_qwen_adapter_preserves_split_projection_names(monkeypatch: pytest.MonkeyPatch):
-    import vllm_omni.diffusion.model_loader.gguf_adapters.qwen_image as qwen_image_module
-
-    monkeypatch.setattr(
-        qwen_image_module,
-        "gguf_quant_weights_iterator",
-        lambda _path: iter(
-            [
-                ("transformer_blocks.0.attn.to_q.qweight_type", 1),
-                ("transformer_blocks.0.attn.to_q.qweight", 2),
-                ("transformer_blocks.0.attn.to_k.qweight_type", 3),
-                ("transformer_blocks.0.attn.to_k.qweight", 4),
-                ("transformer_blocks.0.attn.to_out.0.qweight_type", 5),
-                ("transformer_blocks.0.attn.to_out.0.qweight", 6),
-            ]
-        ),
-    )
-
-    adapter = QwenImageGGUFAdapter(
-        "dummy.gguf",
-        _FakeTransformer(),
-        _make_source(),
-        _make_od_config(),
-    )
-
-    weights = list(adapter.weights_iterator())
-
-    assert ("transformer_blocks.0.attn.to_q.qweight_type", 1) in weights
-    assert ("transformer_blocks.0.attn.to_q.qweight", 2) in weights
-    assert ("transformer_blocks.0.attn.to_k.qweight_type", 3) in weights
-    assert ("transformer_blocks.0.attn.to_k.qweight", 4) in weights
-    assert ("transformer_blocks.0.attn.to_out.0.qweight_type", 5) in weights
-    assert ("transformer_blocks.0.attn.to_out.0.qweight", 6) in weights
-
-
-def test_qwen_adapter_keeps_top_level_quantized_weights(monkeypatch: pytest.MonkeyPatch):
-    import vllm_omni.diffusion.model_loader.gguf_adapters.qwen_image as qwen_image_module
-
-    monkeypatch.setattr(
-        qwen_image_module,
-        "gguf_quant_weights_iterator",
-        lambda _path: iter(
-            [
-                ("img_in.qweight_type", 1),
-                ("img_in.qweight", 2),
-                ("time_text_embed.timestep_embedder.linear_1.qweight_type", 3),
-                ("time_text_embed.timestep_embedder.linear_1.qweight", 4),
-            ]
-        ),
-    )
-
-    adapter = QwenImageGGUFAdapter(
-        "dummy.gguf",
-        _FakeTransformer(),
-        _make_source(),
-        _make_od_config(),
-    )
-
-    weights = list(adapter.weights_iterator())
-
-    assert ("img_in.qweight_type", 1) in weights
-    assert ("img_in.qweight", 2) in weights
-    assert ("time_text_embed.timestep_embedder.linear_1.qweight_type", 3) in weights
-    assert ("time_text_embed.timestep_embedder.linear_1.qweight", 4) in weights
-
-
-def test_qwen_adapter_keeps_modulation_quantized_weights(monkeypatch: pytest.MonkeyPatch):
-    import vllm_omni.diffusion.model_loader.gguf_adapters.qwen_image as qwen_image_module
-
-    monkeypatch.setattr(
-        qwen_image_module,
-        "gguf_quant_weights_iterator",
-        lambda _path: iter(
-            [
-                ("transformer_blocks.0.img_mod.1.qweight_type", 1),
-                ("transformer_blocks.0.img_mod.1.qweight", 2),
-                ("transformer_blocks.0.txt_mod.1.qweight_type", 3),
-                ("transformer_blocks.0.txt_mod.1.qweight", 4),
-            ]
-        ),
-    )
-
-    adapter = QwenImageGGUFAdapter(
-        "dummy.gguf",
-        _FakeTransformer(),
-        _make_source(),
-        _make_od_config(),
-    )
-
-    weights = list(adapter.weights_iterator())
-
-    assert ("transformer_blocks.0.img_mod.1.qweight_type", 1) in weights
-    assert ("transformer_blocks.0.img_mod.1.qweight", 2) in weights
-    assert ("transformer_blocks.0.txt_mod.1.qweight_type", 3) in weights
-    assert ("transformer_blocks.0.txt_mod.1.qweight", 4) in weights

tests/diffusion/model_loader/gguf_adapters/test_z_image_gguf_adapter.py

@@ -1,83 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Unit tests for the Z-Image GGUF adapter."""
-
-from __future__ import annotations
-
-from types import SimpleNamespace
-
-import pytest
-import torch
-
-from vllm_omni.diffusion.model_loader.gguf_adapters import get_gguf_adapter
-from vllm_omni.diffusion.model_loader.gguf_adapters.z_image import (
-    ZImageGGUFAdapter,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
-
-
-def _make_od_config(
-    model_class_name: str = "ZImagePipeline",
-    model_type: str = "z_image",
-):
-    return SimpleNamespace(
-        model_class_name=model_class_name,
-        tf_model_config={"model_type": model_type},
-    )
-
-
-def _make_source(prefix: str = "", subfolder: str = "transformer"):
-    return SimpleNamespace(prefix=prefix, subfolder=subfolder)
-
-
-def test_z_image_adapter_selected_for_z_image_family():
-    adapter = get_gguf_adapter(
-        "dummy.gguf",
-        object(),
-        _make_source(),
-        _make_od_config(),
-    )
-
-    assert isinstance(adapter, ZImageGGUFAdapter)
-
-
-def test_z_image_adapter_matches_model_type_variants():
-    for model_type in ("z_image", "zimage", "z-image"):
-        assert ZImageGGUFAdapter.is_compatible(
-            _make_od_config(model_class_name="OtherPipeline", model_type=model_type),
-            object(),
-            _make_source(),
-        )
-
-
-def test_z_image_adapter_renames_known_gguf_tensor_paths(monkeypatch: pytest.MonkeyPatch):
-    import vllm_omni.diffusion.model_loader.gguf_adapters.z_image as z_image_module
-
-    monkeypatch.setattr(
-        z_image_module,
-        "gguf_quant_weights_iterator",
-        lambda _path: iter(
-            [
-                ("model.diffusion_model.final_layer.qweight", torch.ones((1, 1))),
-                ("model.diffusion_model.x_embedder.qweight_type", torch.tensor(1)),
-                ("transformer_blocks.0.attention.out.weight", torch.full((1, 1), 2.0)),
-                ("transformer_blocks.0.attention.qkv.qweight", torch.full((1, 1), 3.0)),
-            ]
-        ),
-    )
-
-    adapter = ZImageGGUFAdapter(
-        "dummy.gguf",
-        object(),
-        _make_source(),
-        _make_od_config(),
-    )
-
-    weights = list(adapter.weights_iterator())
-    names = [name for name, _ in weights]
-
-    assert "all_final_layer.2-1.qweight" in names
-    assert "all_x_embedder.2-1.qweight_type" in names
-    assert "transformer_blocks.0.attention.to_out.0.weight" in names
-    assert "transformer_blocks.0.attention.to_qkv.qweight" in names

tests/diffusion/model_loader/test_diffusers_loader.py

@@ -16,7 +16,6 @@ from vllm.config.load import LoadConfig
 from vllm_omni.diffusion.config import get_current_diffusion_config, get_current_diffusion_config_or_none
 from vllm_omni.diffusion.data import OmniDiffusionConfig
 from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
-from vllm_omni.diffusion.model_loader.gguf_adapters import get_gguf_adapter
 from vllm_omni.diffusion.models.helios import HeliosPipeline
 from vllm_omni.diffusion.registry import initialize_model
 
@@ -111,27 +110,6 @@ def test_empty_source_prefix_keeps_full_model_strict_check():
         loader.load_weights(model)
 
 
-def test_qwen_model_class_selects_qwen_gguf_adapter():
-    od_config = type(
-        "Config",
-        (),
-        {
-            "model_class_name": "QwenImagePipeline",
-            "tf_model_config": {"model_type": "qwen_image"},
-        },
-    )()
-    source = DiffusersPipelineLoader.ComponentSource(
-        model_or_path="dummy",
-        subfolder="transformer",
-        revision=None,
-        prefix="transformer.",
-    )
-
-    adapter = get_gguf_adapter("dummy.gguf", object(), source, od_config)
-
-    assert adapter.__class__.__name__ == "QwenImageGGUFAdapter"
-
-
 class _ConfigAwareModel(nn.Module):
     def __init__(self, *, od_config):
         super().__init__()
@@ -186,7 +164,6 @@ def test_load_model_custom_pipeline_sets_current_diffusion_config(monkeypatch):
     loader = DiffusersPipelineLoader(LoadConfig(), od_config)
     loader.load_weights = lambda model: None  # type: ignore[assignment]
     loader._process_weights_after_loading = lambda model, target_device: None  # type: ignore[assignment]
-    loader._is_gguf_quantization = lambda: False  # type: ignore[assignment]
 
     monkeypatch.setattr(loader_mod, "resolve_obj_by_qualname", lambda _name: _ConfigAwareModel)
     monkeypatch.setattr(loader_mod.torch, "device", lambda _name: _DeviceContext("cpu"))

tests/diffusion/model_loader/test_diffusers_loader_gguf.py

@@ -1,259 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Unit tests for GGUF-specific DiffusersPipelineLoader behavior."""
-
-from __future__ import annotations
-
-from types import SimpleNamespace
-
-import pytest
-import torch
-import torch.nn as nn
-
-import vllm_omni.diffusion.model_loader.diffusers_loader as loader_module
-from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
-
-pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
-
-
-class _DummyModel(nn.Module):
-    def __init__(self) -> None:
-        super().__init__()
-        self.transformer = nn.Linear(2, 2, bias=True)
-        self.vae = nn.Linear(2, 2, bias=False)
-        self.register_buffer("transformer_buffer", torch.ones(1))
-        self.calls: list[list[str]] = []
-        self.weights_sources = [
-            DiffusersPipelineLoader.ComponentSource(
-                model_or_path="dummy",
-                subfolder="transformer",
-                revision=None,
-                prefix="transformer.",
-                fall_back_to_pt=True,
-            ),
-            DiffusersPipelineLoader.ComponentSource(
-                model_or_path="dummy",
-                subfolder="vae",
-                revision=None,
-                prefix="vae.",
-                fall_back_to_pt=True,
-            ),
-        ]
-
-    def load_weights(self, weights):
-        loadable = dict(self.named_parameters())
-        loadable.update(dict(self.named_buffers()))
-        seen: list[str] = []
-        loaded: set[str] = set()
-        for name, tensor in weights:
-            seen.append(name)
-            if name in loadable:
-                target = loadable[name]
-                target.data.copy_(tensor.to(dtype=target.dtype))
-                loaded.add(name)
-        self.calls.append(seen)
-        return loaded
-
-
-def _make_loader() -> DiffusersPipelineLoader:
-    loader = object.__new__(DiffusersPipelineLoader)
-    loader.load_config = SimpleNamespace(
-        download_dir="cache-dir",
-        ignore_patterns=["*.tmp"],
-    )
-    loader.od_config = None
-    loader.quant_config = None
-    loader.counter_before_loading_weights = 0.0
-    loader.counter_after_loading_weights = 0.0
-    return loader
-
-
-def test_is_gguf_quantization_accepts_dict_config():
-    loader = _make_loader()
-    loader.quant_config = {
-        "method": "gguf",
-        "gguf_model": "weights.gguf",
-    }
-
-    assert loader._is_gguf_quantization() is True
-
-
-def test_is_gguf_quantization_rejects_non_gguf_dict_config():
-    loader = _make_loader()
-    loader.quant_config = {
-        "method": "fp8",
-        "gguf_model": "weights.gguf",
-    }
-
-    assert loader._is_gguf_quantization() is False
-
-
-def test_is_gguf_quantization_requires_gguf_model_for_dict_config():
-    loader = _make_loader()
-    loader.quant_config = {"method": "gguf"}
-
-    with pytest.raises(ValueError, match="gguf_model"):
-        loader._is_gguf_quantization()
-
-
-def test_is_gguf_quantization_accepts_object_config():
-    loader = _make_loader()
-    loader.quant_config = SimpleNamespace(
-        get_name=lambda: "gguf",
-        gguf_model="weights.gguf",
-    )
-
-    assert loader._is_gguf_quantization() is True
-
-
-def test_is_gguf_quantization_uses_fallback_object_without_get_name():
-    loader = _make_loader()
-    loader.quant_config = SimpleNamespace(gguf_model="weights.gguf")
-
-    assert loader._is_gguf_quantization() is True
-
-
-def test_get_model_loadable_names_collects_parameters_and_buffers():
-    loader = _make_loader()
-    model = _DummyModel()
-
-    names = loader._get_model_loadable_names(model)
-
-    assert "transformer.weight" in names
-    assert "transformer.bias" in names
-    assert "transformer_buffer" in names
-
-
-def test_resolve_gguf_model_path_returns_local_file(tmp_path):
-    loader = _make_loader()
-    gguf_file = tmp_path / "model.gguf"
-    gguf_file.write_bytes(b"gguf")
-
-    resolved = loader._resolve_gguf_model_path(str(gguf_file), revision=None)
-
-    assert resolved == str(gguf_file)
-
-
-def test_resolve_gguf_model_path_downloads_explicit_gguf_filename(monkeypatch: pytest.MonkeyPatch):
-    loader = _make_loader()
-
-    monkeypatch.setattr(loader_module.os.path, "isfile", lambda _path: False)
-    monkeypatch.setattr(
-        loader_module,
-        "hf_hub_download",
-        lambda repo_id, filename, revision, cache_dir: f"{cache_dir}/{repo_id}/{filename}",
-    )
-
-    resolved = loader._resolve_gguf_model_path("owner/repo/model-Q4_0.gguf", revision="main")
-
-    assert resolved == "cache-dir/owner/repo/model-Q4_0.gguf"
-
-
-def test_resolve_gguf_model_path_downloads_by_quant_type(monkeypatch: pytest.MonkeyPatch):
-    loader = _make_loader()
-
-    monkeypatch.setattr(loader_module.os.path, "isfile", lambda _path: False)
-    monkeypatch.setattr(
-        loader_module,
-        "download_gguf",
-        lambda repo_id, quant_type, cache_dir, revision, ignore_patterns: f"{cache_dir}/{repo_id}/{quant_type}.gguf",
-    )
-
-    resolved = loader._resolve_gguf_model_path("owner/repo:Q4_0", revision="main")
-
-    assert resolved == "cache-dir/owner/repo/Q4_0.gguf"
-
-
-def test_get_gguf_weights_iterator_prefixes_source_names(monkeypatch: pytest.MonkeyPatch):
-    loader = _make_loader()
-    loader.quant_config = SimpleNamespace(gguf_model="weights.gguf")
-    loader.od_config = SimpleNamespace(revision="main")
-    source = DiffusersPipelineLoader.ComponentSource(
-        model_or_path="dummy",
-        subfolder="transformer",
-        revision=None,
-        prefix="transformer.",
-    )
-
-    class _Adapter:
-        def weights_iterator(self):
-            yield "weight", torch.ones((2, 2))
-            yield "bias", torch.zeros(2)
-
-    monkeypatch.setattr(loader, "_resolve_gguf_model_path", lambda gguf_model, revision: "resolved.gguf")
-    monkeypatch.setattr(loader_module, "get_gguf_adapter", lambda gguf_file, model, source, od_config: _Adapter())
-
-    weights = list(loader._get_gguf_weights_iterator(source, object()))
-
-    assert weights[0][0] == "transformer.weight"
-    assert torch.equal(weights[0][1], torch.ones((2, 2)))
-    assert weights[1][0] == "transformer.bias"
-    assert torch.equal(weights[1][1], torch.zeros(2))
-
-
-def test_load_weights_with_gguf_falls_back_only_for_missing_transformer_weights(monkeypatch: pytest.MonkeyPatch):
-    loader = _make_loader()
-    model = _DummyModel()
-
-    monkeypatch.setattr(loader, "_get_weight_sources", lambda _model: tuple(model.weights_sources))
-    monkeypatch.setattr(
-        loader,
-        "_get_gguf_weights_iterator",
-        lambda source, model: iter([("transformer.weight", torch.ones((2, 2), dtype=torch.float32))]),
-    )
-
-    def _hf_iter(source):
-        if source.subfolder == "transformer":
-            return iter([("transformer.bias", torch.zeros(2, dtype=torch.float32))])
-        return iter([("vae.weight", torch.full((2, 2), 2.0, dtype=torch.float32))])
-
-    monkeypatch.setattr(loader, "_get_weights_iterator", _hf_iter)
-    monkeypatch.setattr(
-        loader,
-        "_get_expected_parameter_names",
-        lambda _model: {"transformer.weight", "transformer.bias", "vae.weight"},
-    )
-
-    loaded = loader._load_weights_with_gguf(model)
-
-    assert loaded == {"transformer.weight", "transformer.bias", "vae.weight"}
-    assert model.calls[0] == ["transformer.weight"]
-    assert model.calls[1] == ["transformer.bias"]
-    assert model.calls[2] == ["vae.weight"]
-
-
-def test_load_weights_with_gguf_skips_transformer_fallback_when_gguf_is_complete(monkeypatch: pytest.MonkeyPatch):
-    loader = _make_loader()
-    model = _DummyModel()
-
-    monkeypatch.setattr(loader, "_get_weight_sources", lambda _model: tuple(model.weights_sources))
-    monkeypatch.setattr(
-        loader,
-        "_get_gguf_weights_iterator",
-        lambda source, model: iter(
-            [
-                ("transformer.weight", torch.ones((2, 2), dtype=torch.float32)),
-                ("transformer.bias", torch.zeros(2, dtype=torch.float32)),
-            ]
-        ),
-    )
-
-    hf_calls: list[str] = []
-
-    def _hf_iter(source):
-        hf_calls.append(source.subfolder or "")
-        if source.subfolder == "transformer":
-            return iter([("transformer.bias", torch.zeros(2, dtype=torch.float32))])
-        return iter([("vae.weight", torch.full((2, 2), 2.0, dtype=torch.float32))])
-
-    monkeypatch.setattr(loader, "_get_weights_iterator", _hf_iter)
-    monkeypatch.setattr(
-        loader,
-        "_get_expected_parameter_names",
-        lambda _model: {"transformer.weight", "transformer.bias", "vae.weight"},
-    )
-
-    loaded = loader._load_weights_with_gguf(model)
-
-    assert loaded == {"transformer.weight", "transformer.bias", "vae.weight"}
-    assert hf_calls == ["vae"]

tests/diffusion/quantization/test_fp8_config.py

@@ -154,21 +154,6 @@ def test_component_config_with_default():
     assert config.resolve("vae.encoder") is None
 
 
-def test_gguf_config():
-    from vllm_omni.quantization import build_quant_config
-    from vllm_omni.quantization.gguf_config import DiffusionGGUFConfig
-
-    config = build_quant_config(
-        {
-            "method": "gguf",
-            "gguf_model": "path/to/model.gguf",
-        }
-    )
-    assert isinstance(config, DiffusionGGUFConfig)
-    assert config.gguf_model == "path/to/model.gguf"
-    assert config.get_name() == "gguf"
-
-
 def test_integration_string():
     from vllm_omni.diffusion.data import OmniDiffusionConfig
 
@@ -236,7 +221,7 @@ def test_transformer_config_auto_detects_modelopt_fp8():
 def test_supported_methods_includes_vllm():
     from vllm_omni.quantization import SUPPORTED_QUANTIZATION_METHODS
 
-    for method in ["fp8", "gguf", "awq", "gptq", "bitsandbytes", "modelopt"]:
+    for method in ["fp8", "awq", "gptq", "bitsandbytes", "modelopt"]:
         assert method in SUPPORTED_QUANTIZATION_METHODS, f"{method} missing"
 
 

tests/diffusion/quantization/test_gguf_config.py

@@ -1,128 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Unit tests for diffusion GGUF quantization config and linear method."""
-
-from __future__ import annotations
-
-from types import SimpleNamespace
-
-import pytest
-import torch
-from vllm.model_executor.layers.linear import LinearBase, UnquantizedLinearMethod
-
-from vllm_omni.quantization import build_quant_config
-from vllm_omni.quantization.gguf_config import (
-    UNQUANTIZED_TYPES,
-    DiffusionGGUFConfig,
-    DiffusionGGUFLinearMethod,
-    dequant_gemm_gguf,
-)
-
-pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
-
-
-def test_gguf_config_creation_and_delegation():
-    config = DiffusionGGUFConfig(
-        gguf_model="weights.gguf",
-        unquantized_modules=["proj_out"],
-    )
-
-    assert config.gguf_model == "weights.gguf"
-    assert config.unquantized_modules == ["proj_out"]
-    assert config.get_name() == "gguf"
-
-
-def test_build_quant_config_builds_gguf_config():
-    config = build_quant_config(
-        "gguf",
-        gguf_model="repo/model-Q4_0.gguf",
-        unquantized_modules=["norm_out"],
-    )
-
-    assert isinstance(config, DiffusionGGUFConfig)
-    assert config.gguf_model == "repo/model-Q4_0.gguf"
-    assert config.unquantized_modules == ["norm_out"]
-
-
-def test_gguf_vllm_config_returns_diffusion_linear_method_for_linear_layers():
-    linear = object.__new__(LinearBase)
-    method = DiffusionGGUFConfig(unquantized_modules=[]).get_quant_method(linear, "transformer.img_in")
-
-    assert isinstance(method, DiffusionGGUFLinearMethod)
-
-
-def test_gguf_vllm_config_respects_unquantized_modules():
-    linear = object.__new__(LinearBase)
-    method = DiffusionGGUFConfig(unquantized_modules=["proj_out"]).get_quant_method(linear, "transformer.proj_out")
-
-    assert isinstance(method, UnquantizedLinearMethod)
-
-
-def test_gguf_vllm_config_returns_none_for_non_linear_layers():
-    method = DiffusionGGUFConfig(unquantized_modules=[]).get_quant_method(torch.nn.LayerNorm(4), "norm")
-
-    assert method is None
-
-
-def test_dequant_gemm_gguf_uses_plain_matmul_for_unquantized_types():
-    qweight_type = next(iter(UNQUANTIZED_TYPES))
-    x = torch.tensor([[1.0, 2.0]], dtype=torch.float32)
-    qweight = torch.tensor([[3.0, 4.0], [5.0, 6.0]], dtype=torch.float32)
-
-    out = dequant_gemm_gguf(x, qweight, qweight_type)
-
-    assert torch.allclose(out, x @ qweight.T)
-
-
-def test_diffusion_gguf_linear_method_applies_bias_on_unquantized_weight():
-    qweight_type = next(iter(UNQUANTIZED_TYPES))
-    method = DiffusionGGUFLinearMethod(DiffusionGGUFConfig(unquantized_modules=[]))
-    layer = SimpleNamespace(
-        qweight=torch.tensor([[1.0, 2.0], [3.0, 4.0]], dtype=torch.float32),
-        qweight_type=SimpleNamespace(weight_type=qweight_type),
-    )
-    x = torch.tensor([[[1.0, 0.5]]], dtype=torch.float32)
-    bias = torch.tensor([0.25, -0.5], dtype=torch.float32)
-
-    out = method.apply(layer, x, bias)
-
-    expected = x @ layer.qweight.T + bias
-    assert torch.allclose(out, expected)
-
-
-def test_diffusion_gguf_linear_method_concatenates_sharded_outputs():
-    qweight_type = next(iter(UNQUANTIZED_TYPES))
-    method = DiffusionGGUFLinearMethod(DiffusionGGUFConfig(unquantized_modules=[]))
-    qweight = torch.nn.Parameter(
-        torch.tensor(
-            [
-                [1.0, 0.0],
-                [0.0, 1.0],
-                [2.0, 0.0],
-                [0.0, 2.0],
-            ],
-            dtype=torch.float32,
-        ),
-        requires_grad=False,
-    )
-    qweight.shard_id = ["left", "right"]
-    qweight.shard_offset_map = {
-        "left": (0, 2, 2),
-        "right": (2, 4, 2),
-    }
-    layer = SimpleNamespace(
-        qweight=qweight,
-        qweight_type=SimpleNamespace(
-            shard_weight_type={
-                "left": qweight_type,
-                "right": qweight_type,
-            }
-        ),
-    )
-    x = torch.tensor([[1.5, 2.0]], dtype=torch.float32)
-
-    out = method.apply(layer, x)
-
-    expected_q = x @ qweight[:2].T
-    expected_k = x @ qweight[2:4].T
-    assert torch.allclose(out, torch.cat([expected_q, expected_k], dim=-1))

tests/diffusion/quantization/test_trajectory_similarity_tool.py

@@ -99,7 +99,6 @@ def test_candidate_model_can_point_to_offline_checkpoint_without_online_quantiza
         candidate_quantization_config_json=None,
         candidate_ignored_layers=None,
         ignored_layers=None,
-        candidate_gguf_model=None,
         candidate_load_format="default",
     )
 

tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py

@@ -27,15 +27,8 @@ WIDTH = 1024
 PROMPT = "A brown and white dog is running on the grass."
 MEAN_THRESHOLD = 3e-2
 P99_THRESHOLD = 3e-1
-# NOTE: re-baselined for the vLLM rebase (dev/vllm-align). HunyuanImage-3 is an
-# expert-parallel MoE model; the newer upstream vLLM (65a7b4628409, 585 commits
-# past the v0.24.0 tag that main tracks) shifted MoE kernel numerics, so the
-# structural-similarity metrics against the committed baseline drift to a
-# deterministic SSIM 0.9490 / PSNR 26.21 dB (the mean/p99 pixel-diff checks above
-# still pass — the image renders correctly). Thresholds relaxed to accept this
-# drift as the new bar. On main (older vLLM) the stricter 0.97 / 30.0 hold.
-SSIM_THRESHOLD = 0.94
-PSNR_THRESHOLD = 25.0
+SSIM_THRESHOLD = 0.97
+PSNR_THRESHOLD = 30.0
 
 _REPO_ROOT = Path(__file__).resolve().parent.parent.parent.parent
 BASELINE_PATH = _REPO_ROOT / "tests" / "assets" / "hunyuan" / "hunyuan_baseline.png"

tests/e2e/offline_inference/test_krea2_expansion.py

@@ -0,0 +1,155 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""L4 functionality tests for the Krea 2 text-to-image diffusion pipeline (``Krea2Pipeline``).
+
+Runs against the public few-step distilled checkpoint ``krea/Krea-2-Turbo`` by default (override with the
+``KREA2_MODEL`` environment variable, e.g. ``krea/Krea-2-Raw`` for the Raw checkpoint or a local diffusers
+directory). They cover a basic functional smoke plus the layerwise-CPU-offload path that the pipeline declares
+via ``SupportsComponentDiscovery`` / ``_layerwise_offload_blocks_attrs``.
+"""
+
+import os
+
+import numpy as np
+import pytest
+
+from tests.helpers.mark import hardware_test
+from tests.helpers.runtime import OmniRunnerHandler
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+from vllm_omni.lora.request import LoRARequest
+from vllm_omni.lora.utils import stable_lora_int_id
+
+MODEL = os.environ.get("KREA2_MODEL", "krea/Krea-2-Turbo")
+# vLLM-Omni-compatible PEFT repackaging of krea/Krea-2-LoRA-darkbrush (264 modules, r=alpha=32).
+LORA = os.environ.get("KREA2_LORA", "NagaSaiAbhinay/Krea-2-vllm-darkbrush-LoRA")
+PROMPT = "a fox in the snow, photorealistic"
+# darkbrush is a trigger-word LoRA: the trained monochrome ink-wash style only manifests when
+# its trigger phrase is present, so the LoRA test prompt appends it for a strong, stable signal.
+LORA_TRIGGER = "monochrome ink wash style"
+LORA_PROMPT = f"a fox in the snow, {LORA_TRIGGER}"
+
+pytestmark = [
+    pytest.mark.diffusion,
+    pytest.mark.full_model,
+]
+
+
+def _sampling() -> OmniDiffusionSamplingParams:
+    # Small resolution + few steps to keep the L4 case light. guidance_scale resolution is checkpoint-aware inside
+    # the pipeline (distilled -> no-CFG, Raw -> CFG), so this stays agnostic to which checkpoint KREA2_MODEL points at.
+    return OmniDiffusionSamplingParams(
+        height=512,
+        width=512,
+        num_inference_steps=8,
+        guidance_scale=0.0,
+        seed=42,
+    )
+
+
+@hardware_test(res={"cuda": "H100"})
+@pytest.mark.parametrize("omni_runner", [(MODEL, None)], indirect=True)
+def test_krea2_text_to_image_001(omni_runner_handler: OmniRunnerHandler) -> None:
+    """Basic functional smoke: a single prompt produces a decoded image."""
+    omni_runner_handler.send_diffusion_request({"model": MODEL, "prompt": PROMPT, "sampling_params": _sampling()})
+
+
+@hardware_test(res={"cuda": "H100"})
+@pytest.mark.parametrize(
+    "omni_runner",
+    [(MODEL, None, {"enable_layerwise_offload": True})],
+    indirect=True,
+)
+def test_krea2_layerwise_offload(omni_runner_handler: OmniRunnerHandler) -> None:
+    """Exercise layerwise CPU offload on the DiT (SupportsComponentDiscovery + _layerwise_offload_blocks_attrs)."""
+    omni_runner_handler.send_diffusion_request({"model": MODEL, "prompt": PROMPT, "sampling_params": _sampling()})
+
+
+@hardware_test(res={"cuda": "H100"})
+@pytest.mark.cache
+@pytest.mark.parametrize("omni_runner", [(MODEL, None, {"cache_backend": "cache_dit"})], indirect=True)
+def test_krea2_cache_dit(omni_runner_handler: OmniRunnerHandler) -> None:
+    """Exercise Cache-DiT on Krea 2 via the custom Krea2Pipeline enabler (``enable_cache_for_krea2``).
+
+    Validates the docs' Cache-DiT support claim for Krea 2. ``has_separate_cfg`` is checkpoint-aware
+    (False for the distilled Turbo no-CFG path, True for the Raw CFG path); the default Turbo checkpoint
+    exercises the no-CFG branch.
+    """
+    omni_runner_handler.send_diffusion_request({"model": MODEL, "prompt": PROMPT, "sampling_params": _sampling()})
+
+
+@hardware_test(res={"cuda": "H100"}, num_cards=2)
+@pytest.mark.parametrize(
+    "omni_runner",
+    # Standalone HSDP: use_hsdp=True with an explicit hsdp_shard_size shards the DiT weights
+    # across 2 ranks (replicate_size defaults to 1 -> world_size = 1 * 2 = 2). HSDP must keep
+    # tensor_parallel_size/data_parallel_size at 1 (see vllm_omni/diffusion/data.py __post_init__).
+    # Also validated locally at 8 GPUs (hsdp_shard_size=8); CI shard provisions 2.
+    [(MODEL, None, {"use_hsdp": True, "hsdp_shard_size": 2})],
+    indirect=True,
+)
+def test_krea2_hsdp(omni_runner_handler: OmniRunnerHandler) -> None:
+    """Exercise Hybrid Sharded Data Parallel (HSDP) weight sharding on the DiT across 2 GPUs.
+
+    Validates the docs' HSDP-support claim for Krea 2. Scheduled on the multi-GPU (distributed_cuda)
+    shard by ``num_cards=2``; skipped automatically on boxes with fewer than 2 CUDA devices.
+    """
+    omni_runner_handler.send_diffusion_request({"model": MODEL, "prompt": PROMPT, "sampling_params": _sampling()})
+
+
+@hardware_test(res={"cuda": "H100"}, num_cards=2)
+@pytest.mark.parametrize(
+    "omni_runner",
+    # VAE patch-parallel decode: partition the VAE decode across the TP world. Mirrors the online
+    # "tp_vae_patch" case (tensor_parallel_size=2 + vae_patch_parallel_size=2) from the wan2.2/z-image
+    # expansion suites, so vae_patch_parallel_size must equal the world size established by TP.
+    # Also validated locally at 8 GPUs (tp=8, vae_patch=8); CI shard provisions 2.
+    [(MODEL, None, {"tensor_parallel_size": 2, "vae_patch_parallel_size": 2})],
+    indirect=True,
+)
+def test_krea2_vae_patch_parallel(omni_runner_handler: OmniRunnerHandler) -> None:
+    """Exercise VAE patch-parallel (decode) across 2 GPUs.
+
+    Validates the docs' VAE-patch-parallel (decode) support claim for Krea 2. Scheduled on the
+    multi-GPU (distributed_cuda) shard by ``num_cards=2``; skipped automatically on boxes with
+    fewer than 2 CUDA devices.
+    """
+    omni_runner_handler.send_diffusion_request({"model": MODEL, "prompt": PROMPT, "sampling_params": _sampling()})
+
+
+def _generate(handler: OmniRunnerHandler, lora_request: LoRARequest | None, lora_scale: float = 1.0) -> np.ndarray:
+    sp = _sampling()
+    sp.lora_request = lora_request
+    sp.lora_scale = lora_scale
+    resp = handler.send_diffusion_request({"model": MODEL, "prompt": LORA_PROMPT, "sampling_params": sp})
+    return np.asarray(resp.images[0].convert("RGB"), dtype=np.int16)
+
+
+@hardware_test(res={"cuda": "H100"})
+@pytest.mark.parametrize("omni_runner", [(MODEL, None)], indirect=True)
+def test_krea2_lora(omni_runner_handler: OmniRunnerHandler) -> None:
+    """Validate diffusion LoRA on Krea 2: visible effect, scale sensitivity, clean deactivation.
+
+    Uses ``LORA`` (a vLLM-Omni-compatible PEFT repackaging of ``krea/Krea-2-LoRA-darkbrush``,
+    264 modules matched via the ReplicatedLinear projections) passed per-request through
+    ``OmniDiffusionSamplingParams.lora_request`` / ``lora_scale`` — the same fields the
+    diffusion LoRA manager reads to activate/deactivate the adapter.
+    """
+    lora_request = LoRARequest(lora_name="darkbrush", lora_int_id=stable_lora_int_id(LORA), lora_path=LORA)
+
+    baseline = _generate(omni_runner_handler, None)
+    img_1x = _generate(omni_runner_handler, lora_request, lora_scale=1.0)
+    img_2x = _generate(omni_runner_handler, lora_request, lora_scale=2.0)
+    restored = _generate(omni_runner_handler, None)
+
+    diff_1x = np.abs(baseline - img_1x).mean()
+    diff_2x = np.abs(baseline - img_2x).mean()
+    diff_restored = np.abs(baseline - restored).mean()
+
+    # (a) Adapter has a visible effect at both scales.
+    assert diff_1x > 0.5, f"LoRA scale=1.0 had no visible effect: diff={diff_1x}"
+    assert diff_2x > 0.5, f"LoRA scale=2.0 had no visible effect: diff={diff_2x}"
+    # (b) Scale changes the output (stronger adapter -> larger perturbation).
+    assert not np.isclose(diff_1x, diff_2x, atol=1.0), f"LoRA scale had no effect: 1x={diff_1x:.2f}, 2x={diff_2x:.2f}"
+    # (c) Passing lora_request=None cleanly deactivates: same seed -> back to baseline (modulo fp drift).
+    assert diff_restored < 5.0, f"LoRA did not deactivate cleanly: diff_restored={diff_restored:.2f}"

tests/e2e/online_serving/test_krea2_expansion.py

@@ -0,0 +1,152 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""L4 online-serving tests for the Krea 2 text-to-image diffusion pipeline.
+
+Exercises the OpenAI-compatible ``/v1/images/generations`` route (served via
+``vllm serve <model> --omni --enforce-eager``) rather than the offline engine path
+covered by ``tests/e2e/offline_inference/test_krea2_expansion.py``.
+
+Runs against the public few-step distilled checkpoint ``krea/Krea-2-Turbo`` by default
+(override with the ``KREA2_MODEL`` environment variable, e.g. ``krea/Krea-2-Raw`` for the
+Raw checkpoint or a local diffusers directory). Krea 2 shares the Qwen-Image VAE and a
+similar single-stream MMDiT, so this test mirrors the structure/fixtures/markers of
+``test_qwen_image_expansion.py``.
+
+Coverage mirrors the offline suite: a basic functional smoke plus the layerwise-CPU-offload
+path that the pipeline declares via ``SupportsComponentDiscovery`` /
+``_layerwise_offload_blocks_attrs``. Sizing is kept light (512x512, few steps) so the case
+fits single-GPU H100 CI rather than the full 2048x2048 distilled resolution.
+"""
+
+import base64
+import os
+from io import BytesIO
+
+import numpy as np
+import pytest
+import requests
+from PIL import Image
+
+from tests.helpers.mark import hardware_marks
+from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler, dummy_messages_from_mix_data
+
+pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+
+MODEL = os.environ.get("KREA2_MODEL", "krea/Krea-2-Turbo")
+# vLLM-Omni-compatible PEFT repackaging of krea/Krea-2-LoRA-darkbrush (264 modules, r=alpha=32).
+LORA = os.environ.get("KREA2_LORA", "NagaSaiAbhinay/Krea-2-vllm-darkbrush-LoRA")
+PROMPT = "a fox in the snow, photorealistic"
+# darkbrush is a trigger-word LoRA; include its trigger phrase so the LoRA case has a strong signal.
+LORA_PROMPT = "a fox in the snow, monochrome ink wash style"
+
+SINGLE_CARD_FEATURE_MARKS = hardware_marks(res={"cuda": "H100"})
+
+
+def _get_diffusion_feature_cases(model: str):
+    """Return single-card L4 online-serving cases for Krea 2.
+
+    Mirrors the offline suite: a basic functional smoke plus layerwise CPU offload.
+    """
+    return [
+        # Basic functional smoke (single-card).
+        pytest.param(
+            OmniServerParams(
+                model=model,
+                server_args=["--enforce-eager"],
+            ),
+            id="single_card_basic",
+            marks=SINGLE_CARD_FEATURE_MARKS,
+        ),
+        # Layerwise CPU offload (single-card): SupportsComponentDiscovery +
+        # _layerwise_offload_blocks_attrs.
+        pytest.param(
+            OmniServerParams(
+                model=model,
+                server_args=["--enforce-eager", "--enable-layerwise-offload"],
+            ),
+            id="single_card_layerwise_offload",
+            marks=SINGLE_CARD_FEATURE_MARKS,
+        ),
+    ]
+
+
+@pytest.mark.parametrize(
+    "omni_server",
+    _get_diffusion_feature_cases(MODEL),
+    indirect=True,
+)
+def test_krea2(omni_server: OmniServer, openai_client: OpenAIClientHandler):
+    """Serve Krea 2 and validate the OpenAI-compatible image-generation route.
+
+    guidance_scale resolution is checkpoint-aware inside the pipeline (distilled -> no-CFG,
+    Raw -> CFG), so guidance_scale=0.0 stays agnostic to which checkpoint KREA2_MODEL points at.
+    Validation is delegated to assert_diffusion_response, which checks output dimensions and
+    basic correctness.
+    """
+    messages = dummy_messages_from_mix_data(content_text=PROMPT)
+    request_config = {
+        "model": omni_server.model,
+        "messages": messages,
+        "extra_body": {
+            "height": 512,
+            "width": 512,
+            "num_inference_steps": 8,
+            "guidance_scale": 0.0,
+            "seed": 42,
+        },
+    }
+    openai_client.send_diffusion_request(request_config)
+
+
+def _basic_lora_payload(lora: dict | None = None) -> dict:
+    payload = {
+        "prompt": LORA_PROMPT,
+        "n": 1,
+        "size": "512x512",
+        "num_inference_steps": 8,
+        "guidance_scale": 0.0,
+        "seed": 42,
+    }
+    if lora is not None:
+        payload["lora"] = lora
+    return payload
+
+
+def _post_image(server: OmniServer, payload: dict) -> np.ndarray:
+    url = f"http://{server.host}:{server.port}/v1/images/generations"
+    resp = requests.post(url, json=payload, headers={"Authorization": "Bearer EMPTY"}, timeout=900)
+    resp.raise_for_status()
+    b64 = resp.json()["data"][0]["b64_json"]
+    img = Image.open(BytesIO(base64.b64decode(b64)))
+    img.load()
+    return np.asarray(img.convert("RGB"), dtype=np.int16)
+
+
+@pytest.mark.parametrize(
+    "omni_server",
+    [
+        pytest.param(
+            OmniServerParams(model=MODEL, server_args=["--enforce-eager"]),
+            id="single_card_lora",
+            marks=SINGLE_CARD_FEATURE_MARKS,
+        )
+    ],
+    indirect=True,
+)
+def test_krea2_lora(omni_server: OmniServer):
+    """Serve Krea 2 and validate per-request LoRA over ``/v1/images/generations``.
+
+    Passes ``LORA`` via the top-level ``lora`` object (``{name, path, scale}``) the diffusion
+    image route parses into a ``LoRARequest`` — no server-side LoRA flag needed. Confirms the
+    adapter has a visible effect and that omitting ``lora`` cleanly restores the base output.
+    """
+    baseline = _post_image(omni_server, _basic_lora_payload())
+    with_lora = _post_image(omni_server, _basic_lora_payload({"name": "darkbrush", "path": LORA, "scale": 1.0}))
+    restored = _post_image(omni_server, _basic_lora_payload())
+
+    diff_lora = np.abs(baseline - with_lora).mean()
+    diff_restored = np.abs(baseline - restored).mean()
+
+    assert diff_lora > 0.5, f"LoRA had no visible effect over the serving path: diff={diff_lora}"
+    assert diff_restored < 5.0, f"LoRA did not deactivate cleanly: diff_restored={diff_restored:.2f}"

tests/entrypoints/openai_api/test_qwen3_omni_realtime_websocket.py

@@ -200,22 +200,17 @@ def _assert_realtime_smoke(result: dict) -> None:
 
 def _assert_realtime_accuracy(
     result: dict,
-    threshold: float = 0.8,
     whisper_model_size: str = "large-v3",
 ) -> None:
     """Assert that whisper transcription of audio output matches model text.
 
     Args:
         result: Roundtrip result dict from ``_run_realtime_audio_roundtrip``.
-        threshold: Minimum cosine similarity between whisper text and model text.
-                   Default 0.8 is appropriate for sync mode (exact match expected).
-                   Use 0.65 for async_chunk mode where chunked upload with pacing
-                   delays introduces inherent variability in audio codec quality.
         whisper_model_size: Whisper model used to transcribe the generated audio
                    for the accuracy check. Defaults to ``large-v3``: the default
                    ``small`` model mishears short Chinese TTS clips (observed:
                    北京→韦京 and a dropped leading sentence, sim=0.443), which
-                   caused spurious sim<threshold failures under async_chunk codec
+                   caused spurious sim<0.8 failures under async_chunk codec
                    variability even though audio generation was correct. large-v3
                    transcribes these clips reliably, so a failure here now points
                    at the model, not the ASR grader.
@@ -228,7 +223,7 @@ def _assert_realtime_accuracy(
     assert whisper_text, "Whisper returned empty string for synthesized output audio"
 
     sim = cosine_similarity_text(whisper_text.lower(), final_text.lower())
-    assert sim > threshold, (
+    assert sim > 0.8, (
         f"Output audio transcript should match model text (sim={sim:.3f}): "
         f"whisper={whisper_text!r}, model_text={final_text!r}"
     )
@@ -255,12 +250,7 @@ class TestQwen3OmniRealtimeWebSocket:
         )
 
         _assert_realtime_smoke(result)
-        # Use threshold=0.65 for async_chunk: chunked upload with send_delay_ms
-        # introduces inherent variability in audio codec quality. Whisper
-        # transcription is nondeterministic and may miss 1-2 words in async
-        # mode even when model text generation is correct.
-        # Sync mode uses the default 0.8 (exact match expected).
-        _assert_realtime_accuracy(result, threshold=0.65)
+        _assert_realtime_accuracy(result)
 
     @pytest.mark.advanced_model
     @pytest.mark.omni

tests/entrypoints/test_async_omni_diffusion_config.py

@@ -359,3 +359,15 @@ def test_resolve_stage_configs_injects_additional_config_into_diffusion_stage(mo
 
     assert not hasattr(stage_configs[0].engine_args, "additional_config")
     assert stage_configs[1].engine_args.additional_config == {"torchair_graph_config": {"enabled": True}}
+
+
+def test_default_stage_config_includes_quantization_config():
+    """Ensure structured quantization_config survives default diffusion-stage creation."""
+    quantization_config = {
+        "method": "example_quant",
+        "weights": "weights.bin",
+    }
+
+    stage_cfg = AsyncOmniEngine._create_default_diffusion_stage_cfg({"quantization_config": quantization_config})[0]
+
+    assert stage_cfg["engine_args"]["quantization_config"] == quantization_config

tests/helpers/runtime.py

@@ -2826,7 +2826,7 @@ def iter_omni_server(
         # be ideal to cleanup consistently everywhere.
         original_model = model_prefix + params.model
         model = original_model
-        if run_level == "core_model":
+        if run_level == "core_model" and request.node.get_closest_marker("diffusion"):
             model = resolve_tiny_model_path(model)
         port = params.port
         stage_config_path = stage_config_path_for_run_level(params.stage_config_path, run_level)
@@ -2917,7 +2917,7 @@ def iter_omni_runner(
             extra_omni_kwargs = dict(extra) if extra is not None else {}
         stage_config_path = stage_config_path_for_run_level(stage_config_path, run_level)
         model = model_prefix + model
-        if run_level == "core_model":
+        if run_level == "core_model" and request.node.get_closest_marker("diffusion"):
             model = resolve_tiny_model_path(model)
         with OmniRunner(model, seed=42, stage_configs_path=stage_config_path, **extra_omni_kwargs) as runner:
             print("OmniRunner started successfully")

tests/model_executor/stage_input_processors/test_ming_tts_async_chunk.py

@@ -0,0 +1,102 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections import defaultdict
+from types import SimpleNamespace
+
+import pytest
+import torch
+
+from vllm_omni.model_executor.models.ming_tts.constants import LATENT_DIM, PATCH_SIZE
+from vllm_omni.model_executor.stage_input_processors.ming_tts import (
+    MING_EMIT_PATCH_COUNT_KEY,
+    llm2audio_vae_async_chunk,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def _make_transfer_manager(*, chunk_size: int = 5, initial_chunk_size: int = 2):
+    return SimpleNamespace(
+        code_prompt_token_ids=defaultdict(list),
+        put_req_chunk=defaultdict(int),
+        request_payload={},
+        connector=SimpleNamespace(
+            config={
+                "extra": {
+                    "latent_chunk_size": chunk_size,
+                    "initial_latent_chunk_size": initial_chunk_size,
+                    "latent_left_context": 0,
+                }
+            }
+        ),
+    )
+
+
+def _make_request(req_id: str = "req", *, finished: bool = False):
+    return SimpleNamespace(
+        external_req_id=req_id,
+        is_finished=lambda: finished,
+    )
+
+
+def _make_output(value: float):
+    return {
+        "ming_has_patch": torch.tensor([1], dtype=torch.bool),
+        "ming_latent_patch": torch.full((1, PATCH_SIZE, LATENT_DIM), value),
+    }
+
+
+def _append_patch(tm, req_id: str, idx: int, *, finished: bool = False):
+    return llm2audio_vae_async_chunk(
+        tm,
+        _make_output(float(idx)),
+        _make_request(req_id, finished=finished),
+        is_finished=finished,
+    )
+
+
+def test_ming_async_chunk_emits_small_initial_chunk_then_steady_chunks():
+    tm = _make_transfer_manager(chunk_size=5, initial_chunk_size=2)
+
+    assert _append_patch(tm, "req", 0) is None
+
+    first = _append_patch(tm, "req", 1)
+    assert first is not None
+    assert first.latent.shape == (2, PATCH_SIZE, LATENT_DIM)
+    assert first.kv_metadata[MING_EMIT_PATCH_COUNT_KEY] == 2
+    assert first.meta.finished.item() is False
+
+    for idx in range(2, 6):
+        assert _append_patch(tm, "req", idx) is None
+
+    second = _append_patch(tm, "req", 6)
+    assert second is not None
+    assert second.latent.shape == (5, PATCH_SIZE, LATENT_DIM)
+    assert second.kv_metadata[MING_EMIT_PATCH_COUNT_KEY] == 5
+
+
+def test_ming_async_chunk_flushes_short_final_chunk():
+    tm = _make_transfer_manager(chunk_size=5, initial_chunk_size=2)
+
+    payload = _append_patch(tm, "req", 0, finished=True)
+
+    assert payload is not None
+    assert payload.latent.shape == (1, PATCH_SIZE, LATENT_DIM)
+    assert payload.kv_metadata[MING_EMIT_PATCH_COUNT_KEY] == 1
+    assert payload.meta.finished.item() is True
+
+
+def test_ming_async_chunk_flushes_leftover_after_initial_chunk():
+    tm = _make_transfer_manager(chunk_size=5, initial_chunk_size=2)
+
+    assert _append_patch(tm, "req", 0) is None
+    assert _append_patch(tm, "req", 1) is not None
+    assert _append_patch(tm, "req", 2) is None
+
+    final = _append_patch(tm, "req", 3, finished=True)
+
+    assert final is not None
+    assert final.latent.shape == (2, PATCH_SIZE, LATENT_DIM)
+    assert final.kv_metadata[MING_EMIT_PATCH_COUNT_KEY] == 2
+    assert final.meta.finished.item() is True

tests/model_tests/diffusion/test_alignment.py

@@ -56,6 +56,7 @@ EXCLUDED_MODELS = [
     "NextStep11Pipeline",
     "FluxPipeline",
     "FluxDMD2Pipeline",
+    "Krea2Pipeline",
     "QwenImageDMD2Pipeline",
     "OmniGen2Pipeline",
     "HeliosPipeline",

tests/plugin/__init__.py

@@ -0,0 +1 @@
+# SPDX-License-Identifier: Apache-2.0

tests/plugin/test_gguf_diffusion.py

@@ -0,0 +1,154 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""End-to-end tests for GGUF quantization on diffusion models.
+
+Validates that GGUF-quantized diffusion models generate valid images and
+use less peak GPU memory than BF16 baseline.
+
+Requires vllm-omni to be installed alongside the plugin.
+
+Usage:
+    pytest tests/diffusion/test_gguf_memory.py -v
+"""
+
+from __future__ import annotations
+
+from typing import NamedTuple
+
+import numpy as np
+import pytest
+import torch
+
+from tests.helpers.env import DeviceMemoryMonitor
+from tests.helpers.mark import hardware_test
+from tests.helpers.runtime import OmniRunner
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+from vllm_omni.outputs import OmniRequestOutput
+from vllm_omni.platforms import current_omni_platform
+
+
+class DiffusionGGUFTestConfig(NamedTuple):
+    artifact_prefix: str
+    gguf_model: str
+    hf_model: str
+    min_cosine_similarity: float = 0.95
+
+
+Z_IMAGE_CONFIG = DiffusionGGUFTestConfig(
+    artifact_prefix="zimage",
+    hf_model="Tongyi-MAI/Z-Image-Turbo",
+    gguf_model="unsloth/Z-Image-Turbo-GGUF:Q4_0",
+)
+
+
+FLUX_CONFIG = DiffusionGGUFTestConfig(
+    artifact_prefix="flux2_klein",
+    hf_model="black-forest-labs/FLUX.2-klein-4B",
+    gguf_model="unsloth/FLUX.2-klein-4B-GGUF:Q8_0",
+)
+
+
+def _image_cosine_similarity(hf_image, gguf_image) -> float:
+    hf_tensor = torch.as_tensor(np.array(hf_image.convert("RGB"), copy=True), dtype=torch.float32).flatten()
+    gguf_tensor = torch.as_tensor(np.array(gguf_image.convert("RGB"), copy=True), dtype=torch.float32).flatten()
+    return torch.nn.functional.cosine_similarity(hf_tensor, gguf_tensor, dim=0).item()
+
+
+def _generate_single_stage_image(
+    model: str,
+    height: int = 256,
+    width: int = 256,
+    num_inference_steps: int = 20,
+    seed: int = 42,
+    **extra_kwargs,
+) -> tuple[list, float]:
+    """Generate an image with a single-stage diffusion model.
+
+    Returns (images, peak_memory_gib).
+    """
+    omni_kwargs = dict(extra_kwargs)
+
+    memory_monitor = DeviceMemoryMonitor(device_index=0)
+    memory_monitor.start()
+    try:
+        with OmniRunner(model, **omni_kwargs) as runner:
+            generator = torch.Generator(
+                device=current_omni_platform.device_type,
+            ).manual_seed(seed)
+            outputs = runner.omni.generate(
+                "a photo of a cat sitting on a laptop keyboard",
+                OmniDiffusionSamplingParams(
+                    height=height,
+                    width=width,
+                    num_inference_steps=num_inference_steps,
+                    guidance_scale=0.0,
+                    generator=generator,
+                ),
+            )
+    finally:
+        memory_monitor.stop()
+    peak_mem = memory_monitor.peak_used_mb / 1024
+
+    first_output = outputs[0]
+    assert first_output.final_output_type == "image"
+    if hasattr(first_output, "images") and first_output.images:
+        images = first_output.images
+    else:
+        assert hasattr(first_output, "request_output") and first_output.request_output
+        request_output = first_output.request_output
+        if isinstance(request_output, list):
+            req_out = request_output[0]
+        else:
+            req_out = request_output
+        assert isinstance(req_out, OmniRequestOutput) and hasattr(req_out, "images")
+        images = req_out.images
+    assert len(images) >= 1
+    assert images[0].width == width
+    assert images[0].height == height
+
+    return images, peak_mem
+
+
+@hardware_test(res={"cuda": "L4"})
+@pytest.mark.full_model
+@pytest.mark.diffusion
+@pytest.mark.slow
+@pytest.mark.parametrize("model", [Z_IMAGE_CONFIG, FLUX_CONFIG], ids=["Z-Image-Turbo", "FLUX.2-klein"])
+def test_single_stage_diffusion_gguf(model: DiffusionGGUFTestConfig, monkeypatch: pytest.MonkeyPatch) -> None:
+    """Z-Image-Turbo GGUF generates valid images and uses less memory than BF16."""
+    monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+
+    # BF16 baseline
+    hf_images, mem_bf16 = _generate_single_stage_image(
+        model=model.hf_model,
+    )
+
+    # GGUF
+    images, mem_gguf = _generate_single_stage_image(
+        model=model.hf_model,
+        diffusion_quantization_config={
+            "method": "gguf",
+            "gguf_model": model.gguf_model,
+        },
+    )
+
+    hf_image_path = f"test_{model.artifact_prefix}_hf.png"
+    gguf_image_path = f"test_{model.artifact_prefix}_gguf.png"
+    assert len(hf_images) >= 1
+    hf_images[0].save(hf_image_path)
+    assert len(images) >= 1
+    images[0].save(gguf_image_path)
+    print(f"Saved HF image: {hf_image_path}")
+    print(f"Saved GGUF image: {gguf_image_path}")
+
+    image_cosine_similarity = _image_cosine_similarity(hf_images[0], images[0])
+    print(f"{model.artifact_prefix} image cosine similarity: {image_cosine_similarity:.4f}")
+    assert image_cosine_similarity >= model.min_cosine_similarity, (
+        f"GGUF image cosine similarity ({image_cosine_similarity:.4f}) should be >= {model.min_cosine_similarity:.4f}"
+    )
+
+    print(f"{model.artifact_prefix} BF16 peak VRAM delta: {mem_bf16:.2f} GiB")
+    print(f"{model.artifact_prefix} GGUF peak VRAM delta: {mem_gguf:.2f} GiB")
+    reduction = (mem_bf16 - mem_gguf) / mem_bf16 * 100
+    print(f"VRAM reduction: {reduction:.1f}%")
+    assert mem_gguf < mem_bf16, f"GGUF ({mem_gguf:.2f} GiB) should use less VRAM than BF16 ({mem_bf16:.2f} GiB)"

tests/test_version.py

@@ -56,3 +56,26 @@ def test_warning_contains_version_strings():
     msg = str(record[0].message)
     assert "0.19.0rc2.dev21" in msg
     assert "0.18.0" in msg
+
+
+def test_omni_plugin_loader_invokes_vllm_general_plugins_first(monkeypatch):
+    import vllm.plugins as vllm_plugins
+
+    import vllm_omni.plugins as omni_plugins
+
+    calls = []
+    monkeypatch.setattr(omni_plugins, "omni_plugins_loaded", False)
+    monkeypatch.setattr(vllm_plugins, "load_general_plugins", lambda: calls.append("vllm"))
+    monkeypatch.setattr(
+        omni_plugins,
+        "load_omni_plugins_by_group",
+        lambda group: {"omni": lambda: calls.append(group)},
+    )
+
+    omni_plugins.load_omni_general_plugins()
+
+    assert calls == ["vllm", omni_plugins.OMNI_DEFAULT_PLUGINS_GROUP]
+
+    omni_plugins.load_omni_general_plugins()
+
+    assert calls == ["vllm", omni_plugins.OMNI_DEFAULT_PLUGINS_GROUP]

vllm_omni/config/omni_config.py

@@ -551,7 +551,7 @@ class _DiffusionConfigProjection:
             elif isinstance(self.quantization_config, str):
                 self.quantization_config = build_quant_config(self.quantization_config)
             elif isinstance(self.quantization_config, Mapping):
-                self.quantization_config = build_quant_config(dict(self.quantization_config))
+                self.quantization_config = dict(self.quantization_config)
             else:
                 raise TypeError(
                     "quantization_config must be str, dict, QuantizationConfig, or None, "

vllm_omni/deploy/ming_tts.yaml

@@ -15,6 +15,7 @@ connectors:
     name: SharedMemoryConnector
     extra:
       latent_chunk_size: 25
+      initial_latent_chunk_size: 4
       latent_left_context: 0
 
 stages:

vllm_omni/deploy/ming_tts_moe.yaml

@@ -23,6 +23,7 @@ connectors:
     name: SharedMemoryConnector
     extra:
       latent_chunk_size: 25
+      initial_latent_chunk_size: 4
       latent_left_context: 0
 
 stages:

vllm_omni/diffusion/cache/cache_dit_backend.py

@@ -975,6 +975,38 @@ def enable_cache_for_cosmos3(pipeline: Any, cache_config: Any) -> RefreshCacheCo
     return enable_cache_for_dit(pipeline, cache_config, block_adapter)
 
 
+def enable_cache_for_krea2(pipeline: Any, cache_config: Any) -> RefreshCacheContextFunc:
+    """Enable cache-dit for Krea 2.
+
+    Krea 2 is a single-stream MMDiT: each ``Krea2TransformerBlock`` takes and returns only
+    ``hidden_states`` (text is fused into the token stream), so the blocks follow
+    ``ForwardPattern.Pattern_3``.
+
+    ``has_separate_cfg`` is checkpoint-dependent, which is why this needs a custom enabler
+    rather than a static ``_cache_dit_adapter_config``: the distilled Turbo checkpoint runs
+    no-CFG (a single transformer forward per denoise step), while the Raw checkpoint runs CFG
+    as two separate forwards. cache-dit tells cond/uncond apart purely by transformer-forward
+    parity, so the flag must match the actual per-step forward count — ``True`` only for the
+    CFG (Raw) path. The pipeline exposes this via ``is_distilled`` (read from ``model_index.json``).
+
+    Args:
+        pipeline: The Krea2Pipeline instance.
+        cache_config: DiffusionCacheConfig instance with cache configuration.
+
+    Returns:
+        A refresh function that can be called to update cache context with new num_inference_steps.
+    """
+    transformer = default_get_pipeline_transformer(pipeline)
+    block_adapter = BlockAdapter(
+        transformer=transformer,
+        blocks=[transformer.transformer_blocks],
+        forward_pattern=[ForwardPattern.Pattern_3],
+        has_separate_cfg=not pipeline.is_distilled,
+        check_forward_pattern=False,
+    )
+    return enable_cache_for_dit(pipeline, cache_config, block_adapter)
+
+
 # Register custom cache-dit enablers after function definitions
 CUSTOM_DIT_ENABLERS.update(
     {
@@ -984,6 +1016,7 @@ CUSTOM_DIT_ENABLERS.update(
         "Wan22VACEPipeline": enable_cache_for_wan22,
         "Wan22S2VPipeline": enable_cache_for_wan22_s2v,
         "Cosmos3OmniDiffusersPipeline": enable_cache_for_cosmos3,
+        "Krea2Pipeline": enable_cache_for_krea2,
     }
 )
 

vllm_omni/diffusion/model_loader/diffusers_loader.py

@@ -11,7 +11,6 @@ from pathlib import Path
 from typing import cast
 
 import torch
-from huggingface_hub import hf_hub_download
 from torch import nn
 from vllm.config import ModelConfig
 from vllm.config.load import LoadConfig
@@ -36,7 +35,6 @@ from vllm_omni.diffusion.distributed.hsdp import HSDPInferenceConfig, apply_hsdp
 from vllm_omni.diffusion.model_loader.checkpoint_adapters import (
     get_checkpoint_adapter,
 )
-from vllm_omni.diffusion.model_loader.gguf_adapters import get_gguf_adapter
 from vllm_omni.diffusion.models.diffusers_adapter.pipeline_diffusers_adapter import DiffusersAdapterPipeline
 from vllm_omni.diffusion.offloader.module_collector import ModuleDiscovery
 from vllm_omni.diffusion.registry import initialize_model
@@ -381,10 +379,7 @@ class DiffusersPipelineLoader:
                     # DiffusionPipeline.from_pretrained() internally; it does
                     # not use our native customized pipeline classes.
                     cast(DiffusersAdapterPipeline, model).load_weights()
-                elif self._is_gguf_quantization():
-                    self._load_weights_with_gguf(model)
                 else:
-                    # Quantization does not happen in `load_weights` but after it
                     self.load_weights(model)
 
             # Process weights after loading for quantization (e.g., FP8 online quantization)
@@ -515,7 +510,7 @@ class DiffusersPipelineLoader:
     def _is_expected_quantized_weight(name: str) -> bool:
         """Return True if *name* is a quantization-specific parameter.
 
-        Quantization methods (GPTQ, AWQ, FP8, GGUF, Autoround, etc.) create extra
+        Quantization methods (GPTQ, AWQ, FP8, Autoround, etc.) create extra
         parameters that have no counterpart in an unquantized checkpoint.
         These are expected to be absent and should not trigger a load error.
         """
@@ -528,8 +523,6 @@ class DiffusersPipelineLoader:
             ".weight_scale",
             ".weight_scale_inv",
             ".input_scale",
-            # GGUF
-            ".qweight_type",
             # INT8  (weight_scale already covered above)
         )
         return name.endswith(_QUANTIZED_WEIGHT_SUFFIXES)
@@ -561,116 +554,6 @@ class DiffusersPipelineLoader:
         if unexpected_missing:
             raise ValueError(f"Following weights were not initialized from checkpoint: {unexpected_missing}")
 
-    def _get_gguf_model_from_config(self) -> str | None:
-        """Extract gguf_model from quant_config (handles both dict and object)."""
-        if self.quant_config is None:
-            return None
-        if isinstance(self.quant_config, dict):
-            return self.quant_config.get("gguf_model")
-        return getattr(self.quant_config, "gguf_model", None)
-
-    def _is_gguf_quantization(self) -> bool:
-        """Check whether or not this pipeline loader is pointing at a GGUF config."""
-        from vllm_omni.quantization.gguf_config import DiffusionGGUFConfig
-
-        maybe_gguf_model = self._get_gguf_model_from_config()
-        is_gguf = (
-            isinstance(self.quant_config, DiffusionGGUFConfig)
-            or (isinstance(self.quant_config, dict) and self.quant_config.get("method") == "gguf")
-            or (hasattr(self.quant_config, "get_name") and self.quant_config.get_name() == "gguf")
-        )
-        if is_gguf:
-            if not maybe_gguf_model:
-                raise ValueError("GGUF quantization requires gguf_model")
-            return True
-
-        if isinstance(self.quant_config, dict):
-            return False
-
-        return bool(maybe_gguf_model)
-
-    def _is_transformer_source(self, source: "ComponentSource") -> bool:
-        if source.subfolder == "transformer":
-            return True
-        return source.prefix.startswith("transformer.")
-
-    def _get_model_loadable_names(self, model: nn.Module) -> set[str]:
-        # Avoid model.state_dict() here because GGUF uses UninitializedParameter
-        # which raises during detach(). Collect names directly.
-        return {name for name, _ in model.named_parameters()} | {name for name, _ in model.named_buffers()}
-
-    def _resolve_gguf_model_path(self, gguf_model: str, revision: str | None) -> str:
-        if os.path.isfile(gguf_model):
-            return gguf_model
-        # repo_id/filename.gguf
-        if "/" in gguf_model and gguf_model.endswith(".gguf"):
-            repo_id, filename = gguf_model.rsplit("/", 1)
-            return hf_hub_download(
-                repo_id=repo_id,
-                filename=filename,
-                revision=revision,
-                cache_dir=self.load_config.download_dir,
-            )
-        # repo_id:quant_type
-        if "/" in gguf_model and ":" in gguf_model:
-            repo_id, quant_type = gguf_model.rsplit(":", 1)
-            return download_gguf(
-                repo_id,
-                quant_type,
-                cache_dir=self.load_config.download_dir,
-                revision=revision,
-                ignore_patterns=self.load_config.ignore_patterns,
-            )
-        raise ValueError(
-            f"Unrecognized GGUF reference: {gguf_model!r} (expected local file, "
-            "<repo_id>/<filename>.gguf, or <repo_id>:<quant_type>)"
-        )
-
-    def _get_gguf_weights_iterator(
-        self,
-        source: "ComponentSource",
-        model: nn.Module,
-    ) -> Generator[tuple[str, torch.Tensor], None, None]:
-        gguf_model = self._get_gguf_model_from_config()
-        if gguf_model is None:
-            raise ValueError("GGUF quantization requires quantization_config.gguf_model")
-        gguf_file = self._resolve_gguf_model_path(gguf_model, self.od_config.revision)
-        adapter = get_gguf_adapter(gguf_file, model, source, self.od_config)
-        weights_iter = adapter.weights_iterator()
-        return ((source.prefix + name, tensor) for (name, tensor) in weights_iter)
-
-    def _load_weights_with_gguf(self, model: nn.Module) -> set[str]:
-        sources = self._get_weight_sources(model)
-        loaded: set[str] = set()
-        loadable_names: set[str] | None = None
-
-        for source in sources:
-            if self._is_transformer_source(source):
-                loaded |= model.load_weights(self._get_gguf_weights_iterator(source, model))
-
-                # GGUF checkpoints can be transformer-only or partially quantized.
-                # Only fall back to HF if this source still has missing loadable weights.
-                loadable_names = loadable_names or self._get_model_loadable_names(model)
-                has_missing_for_source = any(
-                    name.startswith(source.prefix) and name not in loaded for name in loadable_names
-                )
-                if not has_missing_for_source:
-                    continue
-
-                hf_iter = self._get_weights_iterator(source)
-                hf_iter = (
-                    (name, tensor) for (name, tensor) in hf_iter if name in loadable_names and name not in loaded
-                )
-                loaded |= model.load_weights(hf_iter)
-            else:
-                loaded |= model.load_weights(self._get_weights_iterator(source))
-
-        weights_to_load = self._get_expected_parameter_names(model)
-        weights_not_loaded = weights_to_load - loaded
-        if weights_not_loaded:
-            raise ValueError(f"Following weights were not initialized from checkpoint: {weights_not_loaded}")
-        return loaded
-
     def _init_from_load_format(
         self,
         load_format: str,

vllm_omni/diffusion/model_loader/gguf_adapters/__init__.py

@@ -1,47 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-import torch
-
-from .base import GGUFAdapter
-from .flux2_klein import Flux2KleinGGUFAdapter
-from .qwen_image import QwenImageGGUFAdapter
-from .z_image import ZImageGGUFAdapter
-
-if TYPE_CHECKING:
-    from vllm_omni.diffusion.data import OmniDiffusionConfig
-    from vllm_omni.diffusion.model_loader.diffusers_loader import (
-        DiffusersPipelineLoader,
-    )
-
-
-def get_gguf_adapter(
-    gguf_file: str,
-    model: torch.nn.Module,
-    source: DiffusersPipelineLoader.ComponentSource,
-    od_config: OmniDiffusionConfig,
-) -> GGUFAdapter:
-    adapter_classes = (QwenImageGGUFAdapter, ZImageGGUFAdapter, Flux2KleinGGUFAdapter)
-    for adapter_cls in adapter_classes:
-        if adapter_cls.is_compatible(od_config, model, source):
-            return adapter_cls(gguf_file, model, source, od_config)
-    model_type = None
-    if od_config.tf_model_config is not None:
-        model_type = od_config.tf_model_config.get("model_type")
-    supported = ", ".join(cls.__name__ for cls in adapter_classes)
-    raise ValueError(
-        "No GGUF adapter matched diffusion model "
-        f"(model_class_name={od_config.model_class_name!r}, model_type={model_type!r}). "
-        f"Supported adapters: {supported}."
-    )
-
-
-__all__ = [
-    "GGUFAdapter",
-    "Flux2KleinGGUFAdapter",
-    "QwenImageGGUFAdapter",
-    "ZImageGGUFAdapter",
-    "get_gguf_adapter",
-]

vllm_omni/diffusion/model_loader/gguf_adapters/base.py

@@ -1,109 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from abc import ABC, abstractmethod
-from collections.abc import Generator
-from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any
-
-import numpy as np
-import torch
-
-try:
-    import gguf
-except ImportError:
-    # Optional dependency: only required for GGUF-quantized diffusion models.
-    # Keep this import lazy so a missing ``gguf`` does not break importing the
-    # diffusion model loader (and thus inspection/loading of every other
-    # diffusion model that imports through this package).
-    gguf = None
-
-if TYPE_CHECKING:
-    from vllm_omni.diffusion.data import OmniDiffusionConfig
-    from vllm_omni.diffusion.model_loader.diffusers_loader import (
-        DiffusersPipelineLoader,
-    )
-
-
-@dataclass
-class MappedTensor:
-    name: str
-    tensor: Any
-    tensor_type: Any
-    row_slice: slice | None = None
-    swap_scale_shift: bool = False
-
-
-class GGUFAdapter(ABC):
-    """Base class for model-specific GGUF adapters."""
-
-    def __init__(
-        self,
-        gguf_file: str,
-        model: torch.nn.Module,
-        source: DiffusersPipelineLoader.ComponentSource,
-        od_config: OmniDiffusionConfig,
-    ) -> None:
-        self.gguf_file = gguf_file
-        self.model = model
-        self.source = source
-        self.od_config = od_config
-
-    @staticmethod
-    def is_compatible(
-        od_config: OmniDiffusionConfig,
-        model: torch.nn.Module,
-        source: DiffusersPipelineLoader.ComponentSource,
-    ) -> bool:
-        return False
-
-    @abstractmethod
-    def weights_iterator(self) -> Generator[tuple[str, torch.Tensor], None, None]:
-        raise NotImplementedError
-
-
-# FIXME(Isotr0py): Sync implemnentation with upstream vLLM?
-def gguf_quant_weights_iterator(gguf_file: str) -> Generator[tuple[str, torch.Tensor]]:
-    """
-    Iterate over the quant weights in the model gguf files and convert
-    them to torch tensors.
-    Be careful of the order of yielding weight types and weights data,
-    we have to yield all weight types first before yielding any weights.
-    Otherwise it would cause issue when loading weights with for packed
-    layer with different quant types.
-    """
-
-    if gguf is None:
-        raise ImportError(
-            "The 'gguf' package is required to load GGUF-quantized diffusion models. "
-            "Install it with `pip install gguf`."
-        )
-    reader = gguf.GGUFReader(gguf_file)
-
-    for tensor in reader.tensors:
-        weight_type = tensor.tensor_type
-        name = tensor.name
-
-        if weight_type.name not in ("F32", "F16"):
-            weight_type_name = name.replace("weight", "qweight_type")
-            weight_type = torch.tensor(weight_type)
-            yield weight_type_name, weight_type
-
-    for tensor in reader.tensors:
-        weight = tensor.data
-        weight_type = tensor.tensor_type
-        name = tensor.name
-        if weight_type.name not in ("F32", "F16"):
-            name = name.replace("weight", "qweight")
-        if weight_type.name == "BF16" and tensor.data.dtype == np.uint8:
-            # BF16 is currently the only "quantization" type that isn't
-            # actually quantized but is read as a raw byte tensor.
-            # Reinterpret as `torch.bfloat16` tensor.
-            weight = weight.view(np.uint16)
-            if reader.byte_order == "S":
-                # GGUF endianness != system endianness
-                weight = weight.byteswap()
-            param = torch.tensor(weight).view(torch.bfloat16)
-        else:
-            param = torch.tensor(weight)
-        yield name, param

vllm_omni/diffusion/model_loader/gguf_adapters/flux2_klein.py

@@ -1,98 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from collections.abc import Iterable
-
-import torch
-from vllm.model_executor.models.utils import WeightsMapper
-
-from .base import GGUFAdapter, gguf_quant_weights_iterator
-
-FLUX2_TRANSFORMER_KEYS_RENAME_DICT = {
-    "single_blocks.": "single_transformer_blocks.",
-    # Image and text input projections
-    "img_in": "x_embedder",
-    "txt_in": "context_embedder",
-    # Timestep and guidance embeddings
-    "time_in.in_layer": "time_guidance_embed.timestep_embedder.linear_1",
-    "time_in.out_layer": "time_guidance_embed.timestep_embedder.linear_2",
-    "guidance_in.in_layer": "time_guidance_embed.guidance_embedder.linear_1",
-    "guidance_in.out_layer": "time_guidance_embed.guidance_embedder.linear_2",
-    # Modulation parameters
-    "double_stream_modulation_img.lin": "double_stream_modulation_img.linear",
-    "double_stream_modulation_txt.lin": "double_stream_modulation_txt.linear",
-    "single_stream_modulation.lin": "single_stream_modulation.linear",
-    # Final output layer
-    # "final_layer.adaLN_modulation.1": "norm_out.linear",  # Handle separately since we need to swap mod params
-    "final_layer.linear": "proj_out",
-}
-
-FLUX2_TRANSFORMER_ADA_LAYER_NORM_KEY_MAP = {
-    "final_layer.adaLN_modulation.1": "norm_out.linear",
-}
-
-FLUX2_TRANSFORMER_DOUBLE_BLOCK_KEY_MAP = {
-    "double_blocks.": "transformer_blocks.",
-    # Handle fused QKV projections separately as we need to break into Q, K, V projections
-    "img_attn.norm.query_norm": "attn.norm_q",
-    "img_attn.norm.key_norm": "attn.norm_k",
-    "img_attn.proj": "attn.to_out.0",
-    "img_mlp.0": "ff.linear_in",
-    "img_mlp.2": "ff.linear_out",
-    "txt_attn.norm.query_norm": "attn.norm_added_q",
-    "txt_attn.norm.key_norm": "attn.norm_added_k",
-    "txt_attn.proj": "attn.to_add_out",
-    "txt_mlp.0": "ff_context.linear_in",
-    "txt_mlp.2": "ff_context.linear_out",
-    # Additional for fuse qkv
-    "img_attn.qkv": "attn.to_qkv",
-    "txt_attn.qkv": "attn.add_kv_proj",
-}
-
-FLUX2_TRANSFORMER_SINGLE_BLOCK_KEY_MAP = {
-    "linear1": "attn.to_qkv_mlp_proj",
-    "linear2": "attn.to_out",
-    "norm.query_norm": "attn.norm_q",
-    "norm.key_norm": "attn.norm_k",
-}
-
-
-class Flux2KleinGGUFAdapter(GGUFAdapter):
-    """GGUF adapter for Flux2-Klein models with qkv splitting and adaLN swap."""
-
-    @staticmethod
-    def is_compatible(od_config, model: torch.nn.Module, source) -> bool:
-        model_class = od_config.model_class_name or ""
-        if model_class.startswith("Flux2"):
-            return True
-        cfg = od_config.tf_model_config
-        if cfg is not None:
-            model_type = str(cfg.get("model_type", "")).lower()
-            if model_type.startswith("flux"):
-                return True
-        return False
-
-    gguf_to_hf_mapper = WeightsMapper(
-        # double_stream_modulation
-        orig_to_new_prefix=FLUX2_TRANSFORMER_KEYS_RENAME_DICT | FLUX2_TRANSFORMER_ADA_LAYER_NORM_KEY_MAP,
-        orig_to_new_substr=FLUX2_TRANSFORMER_DOUBLE_BLOCK_KEY_MAP | FLUX2_TRANSFORMER_SINGLE_BLOCK_KEY_MAP,
-    )
-
-    def weights_iterator(self) -> Iterable[tuple[str, torch.Tensor]]:
-        def custom_weights_adapter(
-            weights: Iterable[tuple[str, torch.Tensor]],
-        ) -> Iterable[tuple[str, torch.Tensor]]:
-            for name, weight in weights:
-                # Handle the special case for adaLN modulation parameters that require swapping shift and scale
-                if name.endswith(".scale"):
-                    name = name.replace(".scale", ".weight")
-                if name == "norm_out.linear.weight":
-                    shift, scale = weight.chunk(2, dim=0)
-                    weight = torch.cat([scale, shift], dim=0)
-                    yield name, weight
-                else:
-                    yield name, weight
-
-        weights = gguf_quant_weights_iterator(self.gguf_file)
-        weights = self.gguf_to_hf_mapper.apply(weights)
-        yield from custom_weights_adapter(weights)

vllm_omni/diffusion/model_loader/gguf_adapters/qwen_image.py

@@ -1,27 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from collections.abc import Iterable
-
-import torch
-
-from .base import GGUFAdapter, gguf_quant_weights_iterator
-
-
-class QwenImageGGUFAdapter(GGUFAdapter):
-    """GGUF adapter for the Qwen-Image transformer family."""
-
-    @staticmethod
-    def is_compatible(od_config, model: torch.nn.Module, source) -> bool:
-        model_class = od_config.model_class_name or ""
-        if model_class.startswith("QwenImage"):
-            return True
-        cfg = od_config.tf_model_config
-        if cfg is not None:
-            model_type = str(cfg.get("model_type", "")).lower()
-            if model_type.startswith("qwen_image"):
-                return True
-        return False
-
-    def weights_iterator(self) -> Iterable[tuple[str, torch.Tensor]]:
-        yield from gguf_quant_weights_iterator(self.gguf_file)

vllm_omni/diffusion/model_loader/gguf_adapters/z_image.py

@@ -1,43 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from collections.abc import Iterable
-
-import torch
-from vllm.model_executor.models.utils import WeightsMapper
-
-from .base import GGUFAdapter, gguf_quant_weights_iterator
-
-Z_IMAGE_KEYS_RENAME_DICT = {
-    "final_layer.": "all_final_layer.2-1.",
-    "x_embedder.": "all_x_embedder.2-1.",
-    ".attention.qkv": ".attention.to_qkv",
-    ".attention.k_norm": ".attention.norm_k",
-    ".attention.q_norm": ".attention.norm_q",
-    ".attention.out": ".attention.to_out.0",
-    "model.diffusion_model.": "",
-}
-
-
-class ZImageGGUFAdapter(GGUFAdapter):
-    """GGUF adapter for Z-Image models with QKV/FFN shard support."""
-
-    @staticmethod
-    def is_compatible(od_config, model: torch.nn.Module, source) -> bool:
-        model_class = od_config.model_class_name or ""
-        if model_class.startswith("ZImage"):
-            return True
-        cfg = od_config.tf_model_config
-        if cfg is not None:
-            model_type = str(cfg.get("model_type", "")).lower()
-            if model_type in {"z_image", "zimage", "z-image"}:
-                return True
-        return False
-
-    gguf_to_hf_mapper = WeightsMapper(
-        orig_to_new_substr=Z_IMAGE_KEYS_RENAME_DICT,
-    )
-
-    def weights_iterator(self) -> Iterable[tuple[str, torch.Tensor]]:
-        weights = gguf_quant_weights_iterator(self.gguf_file)
-        yield from self.gguf_to_hf_mapper.apply(weights)

vllm_omni/diffusion/models/flux2/flux2_transformer.py

@@ -1034,7 +1034,7 @@ class Flux2Transformer2DModel(nn.Module):
             name = original_name
             if name not in params_dict and ".to_out.0." in name:
                 name = name.replace(".to_out.0.", ".to_out.")
-            # Some GGUF checkpoints include quantized tensors for modules that
+            # Some quantized checkpoints include tensors for modules that
             # are intentionally left unquantized in this model.
             param = params_dict.get(name)
             if param is None:

vllm_omni/diffusion/models/flux2_klein/flux2_klein_transformer.py

@@ -1044,7 +1044,7 @@ class Flux2Transformer2DModel(nn.Module):
             name = original_name
             if name not in params_dict and ".to_out.0." in name:
                 name = name.replace(".to_out.0.", ".to_out.")
-            # Some GGUF checkpoints include quantized tensors for modules that
+            # Some quantized checkpoints include tensors for modules that
             # are intentionally left unquantized in this model.
             param = params_dict.get(name)
             if param is None:

vllm_omni/diffusion/models/krea2/__init__.py

@@ -0,0 +1,15 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Krea 2 diffusion model components."""
+
+from vllm_omni.diffusion.models.krea2.krea2_transformer import Krea2Transformer2DModel
+from vllm_omni.diffusion.models.krea2.pipeline_krea2 import (
+    Krea2Pipeline,
+    get_krea2_post_process_func,
+)
+
+__all__ = [
+    "Krea2Pipeline",
+    "Krea2Transformer2DModel",
+    "get_krea2_post_process_func",
+]

vllm_omni/diffusion/models/krea2/krea2_transformer.py

@@ -0,0 +1,571 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2026 Krea AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from collections.abc import Iterable
+from typing import TYPE_CHECKING
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers.models.embeddings import apply_rotary_emb, get_1d_rotary_pos_embed
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
+from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata
+from vllm_omni.diffusion.attention.layer import Attention
+
+if TYPE_CHECKING:
+    from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+
+    from vllm_omni.diffusion.data import OmniDiffusionConfig
+
+logger = init_logger(__name__)
+
+# Devices that do not support float64; RoPE frequencies fall back to float32 there.
+_FP64_UNSUPPORTED_DEVICES = frozenset({"mps", "npu", "neuron"})
+
+
+def _rope_freqs_dtype(device: torch.device) -> torch.dtype:
+    return torch.float32 if device.type in _FP64_UNSUPPORTED_DEVICES else torch.float64
+
+
+def _join_prefix(prefix: str, name: str) -> str:
+    return f"{prefix}.{name}" if prefix else name
+
+
+def _linear(
+    in_features: int,
+    out_features: int,
+    bias: bool,
+    quant_config: "QuantizationConfig | None",
+    prefix: str,
+) -> ReplicatedLinear:
+    # ReplicatedLinear (not nn.Linear) so the diffusion LoRA manager can wrap these projections.
+    return ReplicatedLinear(
+        in_features,
+        out_features,
+        bias=bias,
+        quant_config=quant_config,
+        prefix=prefix,
+        return_bias=False,
+    )
+
+
+class Krea2RMSNorm(nn.Module):
+    """RMSNorm with a zero-centered scale: the effective multiplier is ``1 + weight``, matching the Krea 2 checkpoint
+    format. Activations are upcast so the normalization runs in float32."""
+
+    def __init__(self, dim: int, eps: float = 1e-5) -> None:
+        super().__init__()
+        self.dim = dim
+        self.eps = eps
+        self.weight = nn.Parameter(torch.zeros(dim))
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        dtype = hidden_states.dtype
+        hidden_states = F.rms_norm(hidden_states.float(), (self.dim,), weight=self.weight + 1.0, eps=self.eps)
+        return hidden_states.to(dtype)
+
+
+class Krea2Attention(nn.Module):
+    """Self-attention with grouped-query projections, q/k RMSNorm, rotary embeddings and a sigmoid output gate.
+
+    Q/K/V layout is ``[B, seq, heads, head_dim]``; ``attention_mask`` is a 2D boolean key-padding mask
+    ``(batch, key_seq_len)`` that the backend broadcasts.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int | None = None,
+        eps: float = 1e-5,
+        role: str = "self",
+        quant_config: "QuantizationConfig | None" = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        if hidden_size % num_heads != 0:
+            raise ValueError(f"hidden_size={hidden_size} must be divisible by num_heads={num_heads}")
+        self.hidden_size = hidden_size
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads
+        self.head_dim = hidden_size // num_heads
+
+        q_dim = self.head_dim * self.num_heads
+        kv_dim = self.head_dim * self.num_kv_heads
+        self.to_q = _linear(hidden_size, q_dim, False, quant_config, _join_prefix(prefix, "to_q"))
+        self.to_k = _linear(hidden_size, kv_dim, False, quant_config, _join_prefix(prefix, "to_k"))
+        self.to_v = _linear(hidden_size, kv_dim, False, quant_config, _join_prefix(prefix, "to_v"))
+        self.to_gate = _linear(hidden_size, hidden_size, False, quant_config, _join_prefix(prefix, "to_gate"))
+        self.norm_q = Krea2RMSNorm(self.head_dim, eps=eps)
+        self.norm_k = Krea2RMSNorm(self.head_dim, eps=eps)
+        self.to_out = nn.ModuleList(
+            [_linear(hidden_size, hidden_size, False, quant_config, _join_prefix(prefix, "to_out.0")), nn.Dropout(0.0)]
+        )
+
+        self.attn = Attention(
+            num_heads=self.num_heads,
+            head_size=self.head_dim,
+            softmax_scale=1.0 / (self.head_dim**0.5),
+            causal=False,
+            num_kv_heads=self.num_kv_heads,
+            role=role,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        image_rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
+    ) -> torch.Tensor:
+        query = self.to_q(hidden_states).unflatten(-1, (self.num_heads, self.head_dim))
+        key = self.to_k(hidden_states).unflatten(-1, (self.num_kv_heads, self.head_dim))
+        value = self.to_v(hidden_states).unflatten(-1, (self.num_kv_heads, self.head_dim))
+        gate = self.to_gate(hidden_states)
+
+        query = self.norm_q(query)
+        key = self.norm_k(key)
+
+        if image_rotary_emb is not None:
+            query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=1)
+            key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=1)
+        query = query.to(value.dtype)
+        key = key.to(value.dtype)
+
+        attn_metadata = AttentionMetadata(attn_mask=attention_mask) if attention_mask is not None else None
+        hidden_states = self.attn(query, key, value, attn_metadata)
+        hidden_states = hidden_states.flatten(2, 3)
+        hidden_states = hidden_states * torch.sigmoid(gate)
+        return self.to_out[0](hidden_states)
+
+
+class Krea2SwiGLU(nn.Module):
+    """SwiGLU feed-forward network."""
+
+    def __init__(
+        self, dim: int, hidden_dim: int, quant_config: "QuantizationConfig | None" = None, prefix: str = ""
+    ) -> None:
+        super().__init__()
+        self.gate = _linear(dim, hidden_dim, False, quant_config, _join_prefix(prefix, "gate"))
+        self.up = _linear(dim, hidden_dim, False, quant_config, _join_prefix(prefix, "up"))
+        self.down = _linear(hidden_dim, dim, False, quant_config, _join_prefix(prefix, "down"))
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.down(F.silu(self.gate(hidden_states)) * self.up(hidden_states))
+
+
+class Krea2TextFusionBlock(nn.Module):
+    """Pre-norm transformer block (no rotary embeddings, no time modulation) used by the text fusion stage."""
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        intermediate_size: int,
+        eps: float,
+        quant_config: "QuantizationConfig | None" = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.norm1 = Krea2RMSNorm(dim, eps=eps)
+        self.norm2 = Krea2RMSNorm(dim, eps=eps)
+        self.attn = Krea2Attention(
+            dim,
+            num_heads,
+            num_kv_heads,
+            eps=eps,
+            role="self",
+            quant_config=quant_config,
+            prefix=_join_prefix(prefix, "attn"),
+        )
+        self.ff = Krea2SwiGLU(dim, intermediate_size, quant_config=quant_config, prefix=_join_prefix(prefix, "ff"))
+
+    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor | None = None) -> torch.Tensor:
+        hidden_states = hidden_states + self.attn(self.norm1(hidden_states), attention_mask=attention_mask)
+        hidden_states = hidden_states + self.ff(self.norm2(hidden_states))
+        return hidden_states
+
+
+class Krea2TextFusion(nn.Module):
+    """Fuses the stack of tapped text-encoder hidden states into a single sequence of text features.
+
+    Two ``layerwise_blocks`` attend across the ``num_text_layers`` axis independently for every token, a linear
+    ``projector`` collapses that axis, and two ``refiner_blocks`` attend across the token sequence.
+    """
+
+    def __init__(
+        self,
+        num_text_layers: int,
+        dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        intermediate_size: int,
+        num_layerwise_blocks: int,
+        num_refiner_blocks: int,
+        eps: float,
+        quant_config: "QuantizationConfig | None" = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.layerwise_blocks = nn.ModuleList(
+            [
+                Krea2TextFusionBlock(
+                    dim,
+                    num_heads,
+                    num_kv_heads,
+                    intermediate_size,
+                    eps,
+                    quant_config=quant_config,
+                    prefix=_join_prefix(prefix, f"layerwise_blocks.{i}"),
+                )
+                for i in range(num_layerwise_blocks)
+            ]
+        )
+        self.projector = _linear(num_text_layers, 1, False, quant_config, _join_prefix(prefix, "projector"))
+        self.refiner_blocks = nn.ModuleList(
+            [
+                Krea2TextFusionBlock(
+                    dim,
+                    num_heads,
+                    num_kv_heads,
+                    intermediate_size,
+                    eps,
+                    quant_config=quant_config,
+                    prefix=_join_prefix(prefix, f"refiner_blocks.{i}"),
+                )
+                for i in range(num_refiner_blocks)
+            ]
+        )
+
+    def forward(self, encoder_hidden_states: torch.Tensor, attention_mask: torch.Tensor | None = None) -> torch.Tensor:
+        batch_size, seq_len, num_text_layers, dim = encoder_hidden_states.shape
+
+        hidden_states = encoder_hidden_states.reshape(batch_size * seq_len, num_text_layers, dim)
+        for block in self.layerwise_blocks:
+            hidden_states = block(hidden_states.contiguous())
+
+        hidden_states = hidden_states.reshape(batch_size, seq_len, num_text_layers, dim).permute(0, 1, 3, 2)
+        hidden_states = self.projector(hidden_states).squeeze(-1)
+
+        for block in self.refiner_blocks:
+            hidden_states = block(hidden_states, attention_mask=attention_mask)
+
+        return hidden_states
+
+
+class Krea2TransformerBlock(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        norm_eps: float,
+        quant_config: "QuantizationConfig | None" = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.scale_shift_table = nn.Parameter(torch.zeros(6, hidden_size))
+        self.norm1 = Krea2RMSNorm(hidden_size, eps=norm_eps)
+        self.norm2 = Krea2RMSNorm(hidden_size, eps=norm_eps)
+        self.attn = Krea2Attention(
+            hidden_size,
+            num_heads,
+            num_kv_heads,
+            eps=norm_eps,
+            role="self",
+            quant_config=quant_config,
+            prefix=_join_prefix(prefix, "attn"),
+        )
+        self.ff = Krea2SwiGLU(
+            hidden_size, intermediate_size, quant_config=quant_config, prefix=_join_prefix(prefix, "ff")
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        image_rotary_emb: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        # temb: (B, 1, 6 * hidden_size), shared across all blocks; each block only learns an additive table.
+        modulation = temb.unflatten(-1, (6, -1)) + self.scale_shift_table
+        prescale, preshift, pregate, postscale, postshift, postgate = modulation.unbind(-2)
+
+        attn_out = self.attn(
+            (1.0 + prescale) * self.norm1(hidden_states) + preshift,
+            attention_mask=attention_mask,
+            image_rotary_emb=image_rotary_emb,
+        )
+        hidden_states = hidden_states + pregate * attn_out
+        ff_out = self.ff((1.0 + postscale) * self.norm2(hidden_states) + postshift)
+        hidden_states = hidden_states + postgate * ff_out
+        return hidden_states
+
+
+class Krea2TimestepEmbedding(nn.Module):
+    """Sinusoidal flow-time embedding (cos-first, input scaled by 1000) followed by a two-layer MLP.
+
+    Keeps the sequence dimension at size 1 so the per-block modulations broadcast over tokens.
+    """
+
+    def __init__(
+        self, embed_dim: int, hidden_size: int, quant_config: "QuantizationConfig | None" = None, prefix: str = ""
+    ) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.linear_1 = _linear(embed_dim, hidden_size, True, quant_config, _join_prefix(prefix, "linear_1"))
+        self.linear_2 = _linear(hidden_size, hidden_size, True, quant_config, _join_prefix(prefix, "linear_2"))
+
+    def forward(self, timestep: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
+        half = self.embed_dim // 2
+        freqs = torch.exp(-math.log(1e4) * torch.arange(half, dtype=torch.float32, device=timestep.device) / half)
+        args = (timestep.float() * 1e3)[:, None, None] * freqs
+        emb = torch.cat([torch.cos(args), torch.sin(args)], dim=-1).to(dtype)
+        return self.linear_2(F.gelu(self.linear_1(emb), approximate="tanh"))
+
+
+class Krea2TextProjection(nn.Module):
+    """Projects the fused text features into the transformer width."""
+
+    def __init__(
+        self,
+        text_dim: int,
+        hidden_size: int,
+        eps: float,
+        quant_config: "QuantizationConfig | None" = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.norm = Krea2RMSNorm(text_dim, eps=eps)
+        self.linear_1 = _linear(text_dim, hidden_size, True, quant_config, _join_prefix(prefix, "linear_1"))
+        self.linear_2 = _linear(hidden_size, hidden_size, True, quant_config, _join_prefix(prefix, "linear_2"))
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.linear_1(self.norm(hidden_states))
+        return self.linear_2(F.gelu(hidden_states, approximate="tanh"))
+
+
+class Krea2FinalLayer(nn.Module):
+    """Final adaptive RMSNorm and output projection."""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        out_channels: int,
+        eps: float,
+        quant_config: "QuantizationConfig | None" = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.scale_shift_table = nn.Parameter(torch.zeros(2, hidden_size))
+        self.norm = Krea2RMSNorm(hidden_size, eps=eps)
+        self.linear = _linear(hidden_size, out_channels, True, quant_config, _join_prefix(prefix, "linear"))
+
+    def forward(self, hidden_states: torch.Tensor, temb: torch.Tensor) -> torch.Tensor:
+        modulation = temb + self.scale_shift_table
+        scale, shift = modulation.chunk(2, dim=1)
+        hidden_states = (1.0 + scale) * self.norm(hidden_states) + shift
+        return self.linear(hidden_states)
+
+
+class Krea2RotaryPosEmbed(nn.Module):
+    """Multi-axis (t, h, w) rotary position embedding, following the Flux/Krea 2 convention."""
+
+    def __init__(self, theta: int, axes_dim: list[int]):
+        super().__init__()
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        n_axes = ids.shape[-1]
+        cos_out = []
+        sin_out = []
+        pos = ids.float()
+        freqs_dtype = _rope_freqs_dtype(ids.device)
+        for i in range(n_axes):
+            cos, sin = get_1d_rotary_pos_embed(
+                self.axes_dim[i],
+                pos[:, i],
+                theta=self.theta,
+                repeat_interleave_real=True,
+                use_real=True,
+                freqs_dtype=freqs_dtype,
+            )
+            cos_out.append(cos)
+            sin_out.append(sin)
+        freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
+        freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
+        return freqs_cos, freqs_sin
+
+
+class Krea2Transformer2DModel(nn.Module):
+    r"""The single-stream MMDiT flow-matching backbone used by the Krea 2 pipeline (vLLM-Omni port).
+
+    Text conditioning enters as a stack of hidden states tapped from several layers of a multimodal text encoder. A
+    small text-fusion transformer collapses the layer axis and refines the token sequence; the result is concatenated
+    with the patchified image latents into a single ``[text, image]`` sequence processed by the transformer blocks.
+    The timestep conditions every block through one shared modulation vector plus per-block learned tables.
+    """
+
+    _repeated_blocks = ["Krea2TransformerBlock"]
+    _layerwise_offload_blocks_attrs = ["transformer_blocks"]
+
+    @staticmethod
+    def _is_transformer_block(name: str, module) -> bool:
+        return "transformer_blocks" in name and name.split(".")[-1].isdigit()
+
+    _hsdp_shard_conditions = [_is_transformer_block]
+
+    def __init__(
+        self,
+        in_channels: int = 64,
+        num_layers: int = 28,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 48,
+        num_key_value_heads: int = 12,
+        intermediate_size: int = 16384,
+        timestep_embed_dim: int = 256,
+        text_hidden_dim: int = 2560,
+        num_text_layers: int = 12,
+        text_num_attention_heads: int = 20,
+        text_num_key_value_heads: int = 20,
+        text_intermediate_size: int = 6912,
+        num_layerwise_text_blocks: int = 2,
+        num_refiner_text_blocks: int = 2,
+        axes_dims_rope: tuple[int, int, int] = (32, 48, 48),
+        rope_theta: float = 1000.0,
+        norm_eps: float = 1e-5,
+        od_config: "OmniDiffusionConfig | None" = None,
+        quant_config: "QuantizationConfig | None" = None,
+    ) -> None:
+        super().__init__()
+        # The pipeline reads ``self.dtype`` to cast inputs before the transformer forward.
+        self.dtype = od_config.dtype if od_config is not None else torch.get_default_dtype()
+        self.od_config = od_config
+
+        hidden_size = attention_head_dim * num_attention_heads
+        if sum(axes_dims_rope) != attention_head_dim:
+            raise ValueError(
+                f"sum(axes_dims_rope)={sum(axes_dims_rope)} must equal attention_head_dim={attention_head_dim}"
+            )
+
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+
+        self.img_in = _linear(in_channels, hidden_size, True, quant_config, "img_in")
+        self.time_embed = Krea2TimestepEmbedding(timestep_embed_dim, hidden_size, quant_config, "time_embed")
+        self.time_mod_proj = _linear(hidden_size, 6 * hidden_size, True, quant_config, "time_mod_proj")
+        self.text_fusion = Krea2TextFusion(
+            num_text_layers=num_text_layers,
+            dim=text_hidden_dim,
+            num_heads=text_num_attention_heads,
+            num_kv_heads=text_num_key_value_heads,
+            intermediate_size=text_intermediate_size,
+            num_layerwise_blocks=num_layerwise_text_blocks,
+            num_refiner_blocks=num_refiner_text_blocks,
+            eps=norm_eps,
+            quant_config=quant_config,
+            prefix="text_fusion",
+        )
+        self.txt_in = Krea2TextProjection(text_hidden_dim, hidden_size, norm_eps, quant_config, "txt_in")
+        self.rotary_emb = Krea2RotaryPosEmbed(theta=rope_theta, axes_dim=list(axes_dims_rope))
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                Krea2TransformerBlock(
+                    hidden_size=hidden_size,
+                    intermediate_size=intermediate_size,
+                    num_heads=num_attention_heads,
+                    num_kv_heads=num_key_value_heads,
+                    norm_eps=norm_eps,
+                    quant_config=quant_config,
+                    prefix=f"transformer_blocks.{i}",
+                )
+                for i in range(num_layers)
+            ]
+        )
+
+        self.final_layer = Krea2FinalLayer(hidden_size, in_channels, norm_eps, quant_config, "final_layer")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: torch.Tensor,
+        position_ids: torch.Tensor,
+        encoder_attention_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        r"""Predict the flow-matching velocity for the image tokens.
+
+        Args:
+            hidden_states: Packed (patchified) noisy image latents ``(batch, image_seq_len, in_channels)``.
+            encoder_hidden_states: Tapped text-encoder hidden states
+                ``(batch, text_seq_len, num_text_layers, text_hidden_dim)``.
+            timestep: Flow-matching time in ``[0, 1]`` of shape ``(batch,)``.
+            position_ids: ``(t, h, w)`` rotary coordinates ``(text_seq_len + image_seq_len, 3)``.
+            encoder_attention_mask: Boolean mask marking valid text tokens ``(batch, text_seq_len)`` or ``None``.
+        """
+        if position_ids.ndim != 2 or position_ids.shape[-1] != 3:
+            raise ValueError(f"`position_ids` must have shape (sequence_length, 3), got {tuple(position_ids.shape)}.")
+
+        batch_size, image_seq_len, _ = hidden_states.shape
+
+        temb = self.time_embed(timestep, dtype=hidden_states.dtype)
+        temb_mod = self.time_mod_proj(F.gelu(temb, approximate="tanh"))
+
+        # 2D key-padding masks: padded text tokens are excluded as keys; their lanes are dropped at the output slice.
+        text_attention_mask = None
+        attention_mask = None
+        if encoder_attention_mask is not None:
+            text_attention_mask = encoder_attention_mask.bool()
+            image_mask = text_attention_mask.new_ones((batch_size, image_seq_len))
+            attention_mask = torch.cat([text_attention_mask, image_mask], dim=1)
+
+        encoder_hidden_states = self.text_fusion(encoder_hidden_states, attention_mask=text_attention_mask)
+        encoder_hidden_states = self.txt_in(encoder_hidden_states)
+
+        text_seq_len = encoder_hidden_states.shape[1]
+        hidden_states = self.img_in(hidden_states)
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        image_rotary_emb = self.rotary_emb(position_ids)
+
+        for block in self.transformer_blocks:
+            hidden_states = block(hidden_states, temb_mod, image_rotary_emb, attention_mask)
+
+        hidden_states = hidden_states[:, text_seq_len:]
+        return self.final_layer(hidden_states, temb)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        # Module/parameter names mirror the diffusers ``Krea2Transformer2DModel`` exactly, so weights map 1:1.
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if name not in params_dict:
+                continue
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params

vllm_omni/diffusion/models/krea2/pipeline_krea2.py

@@ -0,0 +1,551 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2026 Krea AI and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import logging
+import os
+from collections.abc import Iterable
+from typing import ClassVar
+
+import numpy as np
+import torch
+from diffusers.image_processor import VaeImageProcessor
+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 AutoTokenizer, Qwen3VLModel
+from vllm.model_executor.models.utils import AutoWeightsLoader
+from vllm.transformers_utils.config import get_hf_file_to_dict
+
+from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.distributed.autoencoders.autoencoder_kl_qwenimage import DistributedAutoencoderKLQwenImage
+from vllm_omni.diffusion.distributed.utils import get_local_device
+from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
+from vllm_omni.diffusion.model_loader.hub_prefetch import from_pretrained_with_prefetch, prefetch_subfolders
+from vllm_omni.diffusion.models.interface import SupportsComponentDiscovery
+from vllm_omni.diffusion.models.krea2.krea2_transformer import Krea2Transformer2DModel
+from vllm_omni.diffusion.models.progress_bar import ProgressBarMixin
+from vllm_omni.diffusion.profiler.diffusion_pipeline_profiler import DiffusionPipelineProfilerMixin
+from vllm_omni.diffusion.request import OmniDiffusionRequest
+from vllm_omni.diffusion.utils.tf_utils import get_transformer_config_kwargs
+
+logger = logging.getLogger(__name__)
+
+# Text-encoder layer taps used when model_index.json does not specify ``text_encoder_select_layers``.
+DEFAULT_TEXT_ENCODER_SELECT_LAYERS = (2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35)
+
+
+def get_krea2_post_process_func(od_config: OmniDiffusionConfig):
+    model_name = od_config.model
+    # Read only the VAE config (``get_hf_file_to_dict`` resolves a local dir via
+    # ``Path(model)/file`` and a hub repo via a single-file download) instead of
+    # pulling all weights just to compute ``vae_scale_factor``.
+    vae_config = get_hf_file_to_dict("vae/config.json", model_name) or {}
+    vae_scale_factor = 2 ** len(vae_config["temperal_downsample"]) if "temperal_downsample" in vae_config else 8
+
+    patch_size = 2
+    try:
+        model_index = get_hf_file_to_dict("model_index.json", model_name) or {}
+        patch_size = int(model_index.get("patch_size", patch_size))
+    except Exception:  # noqa: BLE001
+        pass
+
+    image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor * patch_size)
+
+    def post_process_func(images: torch.Tensor):
+        return image_processor.postprocess(images)
+
+    return post_process_func
+
+
+# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: int | None = None,
+    device: str | torch.device | None = None,
+    timesteps: list[int] | None = None,
+    sigmas: list[float] | None = None,
+    **kwargs,
+) -> tuple[torch.Tensor, int]:
+    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
+
+
+class Krea2Pipeline(nn.Module, DiffusionPipelineProfilerMixin, ProgressBarMixin, SupportsComponentDiscovery):
+    """The Krea 2 text-to-image pipeline (vLLM-Omni port of ``diffusers.Krea2Pipeline``).
+
+    Components:
+    - ``scheduler``: ``FlowMatchEulerDiscreteScheduler`` (resolution-aware exponential time shift).
+    - ``vae``: the Qwen-Image VAE (f8, 16 latent channels).
+    - ``text_encoder``: a Qwen3-VL model; the pipeline consumes a stack of hidden states tapped from several
+      decoder layers rather than the last hidden state.
+    - ``tokenizer``: the tokenizer paired with the text encoder.
+    - ``transformer``: the Krea 2 single-stream MMDiT that predicts the flow-matching velocity.
+    """
+
+    _dit_modules: ClassVar[list[str]] = ["transformer"]
+    _encoder_modules: ClassVar[list[str]] = ["text_encoder"]
+    _vae_modules: ClassVar[list[str]] = ["vae"]
+
+    _PROFILER_TARGETS: ClassVar[list[str]] = ["text_encoder.forward", "diffuse", "vae.decode"]
+
+    def __init__(self, *, od_config: OmniDiffusionConfig, prefix: str = ""):
+        super().__init__()
+        self.od_config = od_config
+        self.parallel_config = od_config.parallel_config
+        # Only the transformer goes through the standard weights loader; text encoder and VAE load eagerly below.
+        self.weights_sources = [
+            DiffusersPipelineLoader.ComponentSource(
+                model_or_path=od_config.model,
+                subfolder="transformer",
+                revision=od_config.revision,
+                prefix="transformer.",
+                fall_back_to_pt=True,
+            )
+        ]
+
+        self.device = get_local_device()
+        model = od_config.model
+        local_files_only = os.path.isdir(model)
+
+        # Pipeline-level config lives in model_index.json, not the transformer config.
+        model_index = get_hf_file_to_dict("model_index.json", model) or {}
+        select_layers = model_index.get("text_encoder_select_layers") or DEFAULT_TEXT_ENCODER_SELECT_LAYERS
+        self.text_encoder_select_layers = tuple(int(i) for i in select_layers)
+        self.is_distilled = bool(model_index.get("is_distilled", False))
+        self.patch_size = int(model_index.get("patch_size", 2))
+
+        subfolders = ["scheduler", "text_encoder", "vae", "tokenizer"]
+        # See ``hub_prefetch.py`` for the transformers v5 subfolder race.
+        prefetch_subfolders(model, subfolders, local_files_only=local_files_only)
+
+        self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
+            model, subfolder="scheduler", local_files_only=local_files_only
+        )
+
+        self.text_encoder = from_pretrained_with_prefetch(
+            Qwen3VLModel.from_pretrained,
+            model,
+            subfolder="text_encoder",
+            prefetch_list=subfolders,
+            local_files_only=local_files_only,
+            torch_dtype=od_config.dtype,
+        )
+        # Drop the unused Qwen3-VL vision tower before moving to GPU so it never consumes GPU memory.
+        if hasattr(self.text_encoder, "visual"):
+            del self.text_encoder.visual
+        else:
+            logger.warning("Krea2: vision tower not found on text encoder; skipping drop")
+        self.text_encoder = self.text_encoder.to(self.device)
+
+        self.vae = from_pretrained_with_prefetch(
+            DistributedAutoencoderKLQwenImage.from_pretrained,
+            model,
+            subfolder="vae",
+            prefetch_list=subfolders,
+            local_files_only=local_files_only,
+            torch_dtype=od_config.dtype,
+        ).to(self.device)
+
+        transformer_kwargs = get_transformer_config_kwargs(od_config.tf_model_config, Krea2Transformer2DModel)
+        self.transformer = Krea2Transformer2DModel(
+            od_config=od_config, quant_config=od_config.quantization_config, **transformer_kwargs
+        )
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
+
+        self.vae_scale_factor = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * self.patch_size)
+        self.default_sample_size = 1024
+
+        # Chat template tokenized as a fixed-length block: prompt padded to a fixed length first, assistant suffix
+        # appended after the padding; the first ``prompt_template_encode_start_idx`` system tokens are later dropped.
+        self.prompt_template_encode_prefix = (
+            "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, "
+            "spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n"
+        )
+        self.prompt_template_encode_suffix = "<|im_end|>\n<|im_start|>assistant\n"
+        self.prompt_template_encode_start_idx = 34
+        self.prompt_template_encode_num_suffix_tokens = 5
+
+        self.setup_diffusion_pipeline_profiler(
+            enable_diffusion_pipeline_profiler=self.od_config.enable_diffusion_pipeline_profiler
+        )
+
+    def get_text_hidden_states(
+        self,
+        prompt: str | list[str],
+        max_sequence_length: int = 512,
+        device: torch.device | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Tokenize ``prompt`` into the fixed-length Krea 2 layout and tap the selected encoder hidden states.
+
+        Returns ``(hidden_states, attention_mask)`` of shapes ``(batch, text_seq_len, num_text_layers,
+        text_hidden_dim)`` and ``(batch, text_seq_len)`` (bool).
+        """
+        device = device or self.device
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        prefix_idx = self.prompt_template_encode_start_idx
+        text = [self.prompt_template_encode_prefix + e for e in prompt]
+        text_tokens = self.tokenizer(
+            text,
+            truncation=True,
+            padding="max_length",
+            max_length=max_sequence_length + prefix_idx - self.prompt_template_encode_num_suffix_tokens,
+            return_tensors="pt",
+        ).to(device)
+        suffix_tokens = self.tokenizer([self.prompt_template_encode_suffix] * len(text), return_tensors="pt").to(device)
+
+        input_ids = torch.cat([text_tokens.input_ids, suffix_tokens.input_ids], dim=1)
+        attention_mask = torch.cat([text_tokens.attention_mask, suffix_tokens.attention_mask], dim=1).bool()
+
+        # Padding sits in the middle (``[prefix | prompt | PAD | suffix]``), so positions must count only real tokens
+        # (padding consumes no position). Broadcast across the 3 mRoPE axes (T/H/W equal for text).
+        position_ids = (attention_mask.long().cumsum(dim=-1) - 1).clamp(min=0)
+        position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
+
+        outputs = self.text_encoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_hidden_states=True,
+        )
+        hidden_states = torch.stack([outputs.hidden_states[i] for i in self.text_encoder_select_layers], dim=2)
+
+        hidden_states = hidden_states[:, prefix_idx:]
+        attention_mask = attention_mask[:, prefix_idx:]
+        return hidden_states, attention_mask
+
+    def encode_prompt(
+        self,
+        prompt: str | list[str],
+        device: torch.device | 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 = 512,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        device = device or self.device
+
+        if prompt_embeds is None:
+            prompt_embeds, prompt_embeds_mask = self.get_text_hidden_states(prompt, max_sequence_length, device)
+
+        batch_size, seq_len, num_text_layers, dim = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, num_text_layers, dim)
+        prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_images_per_prompt)
+        prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_images_per_prompt, seq_len)
+
+        return prompt_embeds, prompt_embeds_mask
+
+    def _pack_latents(self, latents, batch_size, num_channels_latents, height, width):
+        p = self.patch_size
+        latents = latents.view(batch_size, num_channels_latents, height // p, p, width // p, p)
+        latents = latents.permute(0, 2, 4, 1, 3, 5)
+        latents = latents.reshape(batch_size, (height // p) * (width // p), num_channels_latents * p * p)
+        return latents
+
+    def _unpack_latents(self, latents, height, width):
+        batch_size, _, channels = latents.shape
+        p = self.patch_size
+
+        height = p * (int(height) // (self.vae_scale_factor * p))
+        width = p * (int(width) // (self.vae_scale_factor * p))
+
+        latents = latents.view(batch_size, height // p, width // p, channels // (p * p), p, p)
+        latents = latents.permute(0, 3, 1, 4, 2, 5)
+        latents = latents.reshape(batch_size, channels // (p * p), 1, height, width)
+        return latents
+
+    @staticmethod
+    def prepare_position_ids(text_seq_len: int, grid_height: int, grid_width: int, device: torch.device):
+        """Build the ``(text_seq_len + grid_height * grid_width, 3)`` rotary coordinates: text tokens sit at the
+        origin, image tokens carry their ``(0, h, w)`` latent-grid coordinates."""
+        text_ids = torch.zeros(text_seq_len, 3, device=device)
+        image_ids = torch.zeros(grid_height, grid_width, 3, device=device)
+        image_ids[..., 1] = torch.arange(grid_height, device=device)[:, None]
+        image_ids[..., 2] = torch.arange(grid_width, device=device)[None, :]
+        image_ids = image_ids.reshape(grid_height * grid_width, 3)
+        return torch.cat([text_ids, image_ids], dim=0)
+
+    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+
+        latent_height = height // self.vae_scale_factor
+        latent_width = width // self.vae_scale_factor
+        shape = (batch_size, num_channels_latents, latent_height, 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."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        latents = self._pack_latents(latents, batch_size, num_channels_latents, latent_height, latent_width)
+        return latents
+
+    def _extract_prompts(self, prompts):
+        """Extract prompt and negative_prompt from the request's OmniPromptType list."""
+        prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in prompts] or None
+        if all(isinstance(p, str) or p.get("negative_prompt") is None for p in prompts):
+            negative_prompt = None
+        elif prompts:
+            negative_prompt = ["" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in prompts]
+        else:
+            negative_prompt = None
+        return prompt, negative_prompt
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 0
+
+    def diffuse(
+        self,
+        latents: torch.Tensor,
+        timesteps: torch.Tensor,
+        position_ids: torch.Tensor,
+        prompt_embeds: torch.Tensor,
+        prompt_embeds_mask: torch.Tensor,
+        negative_prompt_embeds: torch.Tensor | None,
+        negative_prompt_embeds_mask: torch.Tensor | None,
+        guidance_scale: float,
+    ) -> torch.Tensor:
+        """Run the flow-matching denoising loop and return the final latents."""
+        self.scheduler.set_begin_index(0)
+        transformer_dtype = self.transformer.dtype
+        with self.progress_bar(total=len(timesteps)) as progress_bar:
+            for _, t in enumerate(timesteps):
+                timestep = (t / self.scheduler.config.num_train_timesteps).expand(latents.shape[0])
+                timestep = timestep.to(transformer_dtype)
+                latent_input = latents.to(transformer_dtype)
+
+                noise_pred = self.transformer(
+                    hidden_states=latent_input,
+                    encoder_hidden_states=prompt_embeds.to(transformer_dtype),
+                    timestep=timestep,
+                    position_ids=position_ids,
+                    encoder_attention_mask=prompt_embeds_mask,
+                )
+
+                if self.do_classifier_free_guidance:
+                    neg_noise_pred = self.transformer(
+                        hidden_states=latent_input,
+                        encoder_hidden_states=negative_prompt_embeds.to(transformer_dtype),
+                        timestep=timestep,
+                        position_ids=position_ids,
+                        encoder_attention_mask=negative_prompt_embeds_mask,
+                    )
+                    noise_pred = noise_pred + guidance_scale * (noise_pred - neg_noise_pred)
+
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+                progress_bar.update()
+
+        return latents
+
+    def forward(
+        self,
+        req: OmniDiffusionRequest,
+        prompt: str | list[str] | None = None,
+        negative_prompt: str | list[str] | None = None,
+        height: int = 1024,
+        width: int = 1024,
+        num_inference_steps: int = 28,
+        sigmas: list[float] | None = None,
+        guidance_scale: float = 4.5,
+        num_images_per_prompt: int = 1,
+        generator: torch.Generator | list[torch.Generator] | None = None,
+        latents: torch.Tensor | None = None,
+        output_type: str | None = "pil",
+        max_sequence_length: int = 512,
+    ) -> DiffusionOutput:
+        extracted_prompt, extracted_negative = self._extract_prompts(req.prompts)
+        prompt = extracted_prompt or prompt
+        if extracted_negative is not None:
+            negative_prompt = extracted_negative
+
+        sp = req.sampling_params
+        height = sp.height or height
+        width = sp.width or width
+        num_inference_steps = sp.num_inference_steps or num_inference_steps
+        sigmas = sp.sigmas or sigmas
+        max_sequence_length = sp.max_sequence_length or max_sequence_length
+        generator = sp.generator or generator
+        if sp.guidance_scale_provided:
+            guidance_scale = sp.guidance_scale
+        else:
+            # Distilled checkpoint is trained guidance-free (and the request layer coerces an explicit
+            # guidance_scale=0 to "not provided"), so default it to no CFG.
+            guidance_scale = 0.0 if self.is_distilled else guidance_scale
+        num_images_per_prompt = sp.num_outputs_per_prompt if sp.num_outputs_per_prompt > 0 else num_images_per_prompt
+
+        multiple = self.vae_scale_factor * self.patch_size
+        if height % multiple != 0 or width % multiple != 0:
+            rounded_height = ((height + multiple - 1) // multiple) * multiple
+            rounded_width = ((width + multiple - 1) // multiple) * multiple
+            logger.warning(
+                "`height` and `width` must be multiples of %d; rounding up from %dx%d to %dx%d.",
+                multiple,
+                height,
+                width,
+                rounded_height,
+                rounded_width,
+            )
+            height, width = rounded_height, rounded_width
+
+        device = self.device
+        self._guidance_scale = guidance_scale
+
+        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 = 1
+
+        prompt_embeds, prompt_embeds_mask = self.encode_prompt(
+            prompt=prompt,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+        )
+        negative_prompt_embeds = None
+        negative_prompt_embeds_mask = None
+        if self.do_classifier_free_guidance:
+            if negative_prompt is None:
+                negative_prompt = ""
+            if isinstance(negative_prompt, str):
+                negative_prompt = [negative_prompt] * batch_size
+            negative_prompt_embeds, negative_prompt_embeds_mask = self.encode_prompt(
+                prompt=negative_prompt,
+                device=device,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+            )
+
+        num_channels_latents = self.transformer.in_channels // (self.patch_size**2)
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+        grid_height = height // (self.vae_scale_factor * self.patch_size)
+        grid_width = width // (self.vae_scale_factor * self.patch_size)
+        position_ids = self.prepare_position_ids(prompt_embeds.shape[1], grid_height, grid_width, device)
+
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
+        image_seq_len = latents.shape[1]
+        if self.is_distilled:
+            mu = 1.15
+        else:
+            mu = calculate_shift(
+                image_seq_len,
+                self.scheduler.config.get("base_image_seq_len", 256),
+                self.scheduler.config.get("max_image_seq_len", 6400),
+                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, device, sigmas=sigmas, mu=mu
+        )
+
+        latents = self.diffuse(
+            latents=latents,
+            timesteps=timesteps,
+            position_ids=position_ids,
+            prompt_embeds=prompt_embeds,
+            prompt_embeds_mask=prompt_embeds_mask,
+            negative_prompt_embeds=negative_prompt_embeds,
+            negative_prompt_embeds_mask=negative_prompt_embeds_mask,
+            guidance_scale=guidance_scale,
+        )
+
+        if output_type == "latent":
+            image = latents
+        else:
+            latents = self._unpack_latents(latents, height, width)
+            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,
+            stage_durations=self.stage_durations if hasattr(self, "stage_durations") else None,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        loaded_weights = loader.load_weights(weights)
+        # VAE and text encoder load eagerly in __init__; record their params so strict load checks pass.
+        loaded_weights |= {f"vae.{name}" for name, _ in self.vae.named_parameters()}
+        if self.text_encoder is not None:
+            loaded_weights |= {f"text_encoder.{name}" for name, _ in self.text_encoder.named_parameters()}
+        return loaded_weights

vllm_omni/diffusion/registry.py

@@ -306,6 +306,11 @@ _DIFFUSION_MODELS = {
         "pipeline_sdxl",
         "StableDiffusionXLPipeline",
     ),
+    "Krea2Pipeline": (
+        "krea2",
+        "pipeline_krea2",
+        "Krea2Pipeline",
+    ),
 }
 
 
@@ -533,6 +538,7 @@ _DIFFUSION_POST_PROCESS_FUNCS = {
     "Cosmos3OmniDiffusersPipeline": "get_cosmos3_post_process_func",
     "HiDreamImagePipeline": "get_hidream_image_post_process_func",
     "StableDiffusionXLPipeline": "get_sdxl_image_post_process_func",
+    "Krea2Pipeline": "get_krea2_post_process_func",
 }
 
 _DIFFUSION_ACTION_POST_PROCESS_FUNCS = {

vllm_omni/engine/async_omni_engine.py

@@ -1043,6 +1043,7 @@ class AsyncOmniEngine:
             "enable_multithread_weight_load": kwargs.get("enable_multithread_weight_load", True),
             "num_weight_load_threads": kwargs.get("num_weight_load_threads", 4),
             "quantization": kwargs.get("quantization", None),
+            "quantization_config": kwargs.get("quantization_config", None),
             "diffusion_kv_cache_dtype": kwargs.get("diffusion_kv_cache_dtype", None),
             "diffusion_kv_cache_skip_steps": kwargs.get("diffusion_kv_cache_skip_steps", None),
             "diffusion_kv_cache_skip_layers": kwargs.get("diffusion_kv_cache_skip_layers", None),
@@ -1233,7 +1234,7 @@ class AsyncOmniEngine:
                             kwargs.get("diffusion_attention_config"),
                             attention_backend=kwargs.get("diffusion_attention_backend"),
                         )
-                quantization_config = kwargs.get("diffusion_quantization_config")
+                quantization_config = kwargs.get("diffusion_quantization_config") or kwargs.get("quantization_config")
                 if quantization_config is not None:
                     if (
                         not hasattr(cfg.engine_args, "quantization_config")

vllm_omni/entrypoints/cli/serve.py

@@ -478,7 +478,7 @@ class OmniServeCommand(CLISubcommand):
             default=None,
             help=(
                 "JSON string for diffusion quantization_config. "
-                'Example: \'{"method":"gguf","gguf_model":"/path/to/model.gguf"}\'.'
+                'Example: \'{"method":"fp8","activation_scheme":"dynamic"}\'.'
             ),
         )
         omni_config_group.add_argument(

vllm_omni/model_executor/models/ming_tts/config_ming_tts.py

@@ -22,6 +22,7 @@ from .constants import (
     DEFAULT_SIGMA,
     DEFAULT_TEMPERATURE,
     HISTORY_PATCH_SIZE,
+    INITIAL_LATENT_CHUNK_SIZE,
     KEY_CFG,
     KEY_CHUNK_ID,
     KEY_DECODE_STEP,
@@ -239,6 +240,7 @@ class MingTTSConfig:
     max_decode_steps: int = MAX_DECODE_STEPS
 
     latent_chunk_size: int = LATENT_CHUNK_SIZE
+    initial_latent_chunk_size: int = INITIAL_LATENT_CHUNK_SIZE
     latent_left_context: int = LATENT_LEFT_CONTEXT
 
     text_eos_token_id: int = TEXT_EOS_TOKEN_ID

vllm_omni/model_executor/models/ming_tts/constants.py

@@ -58,6 +58,7 @@ DEFAULT_TEMPERATURE = 0.0
 
 # Connector / Stage-2 streaming defaults (runtime tuning)
 LATENT_CHUNK_SIZE = 25
+INITIAL_LATENT_CHUNK_SIZE = 4
 LATENT_LEFT_CONTEXT = 0
 MAX_DECODE_STEPS = 200
 

vllm_omni/model_executor/models/ming_tts/validation.py

@@ -172,6 +172,9 @@ def validate_ming_tts_config(cfg: Any) -> None:
 
     if cfg.latent_chunk_size <= 0:
         raise ValueError(f"latent_chunk_size must be > 0, got {cfg.latent_chunk_size}.")
+    initial_latent_chunk_size = getattr(cfg, "initial_latent_chunk_size", 0)
+    if initial_latent_chunk_size < 0:
+        raise ValueError(f"initial_latent_chunk_size must be >= 0, got {initial_latent_chunk_size}.")
     if cfg.latent_left_context < 0:
         raise ValueError(f"latent_left_context must be >= 0, got {cfg.latent_left_context}.")
     if cfg.max_decode_steps <= 0:

vllm_omni/model_executor/stage_input_processors/ming_tts.py

@@ -12,6 +12,7 @@ from vllm.logger import init_logger
 from vllm_omni.data_entry_keys import CodesStruct, MetaStruct, OmniPayloadStruct
 from vllm_omni.inputs.data import OmniTokensPrompt
 from vllm_omni.model_executor.models.ming_tts.config_ming_tts import (
+    INITIAL_LATENT_CHUNK_SIZE,
     KEY_CHUNK_ID,
     KEY_REQUEST_ID,
     LATENT_CHUNK_SIZE,
@@ -116,7 +117,7 @@ def _decode_stop_reason(value: Any) -> str | None:
     return MING_STOP_REASON_BY_CODE.get(int(value))
 
 
-def _get_async_chunk_config(transfer_manager: Any) -> tuple[int, int]:
+def _get_async_chunk_config(transfer_manager: Any) -> tuple[int, int, int]:
     connector = getattr(transfer_manager, "connector", None)
     raw_cfg = getattr(connector, "config", {}) or {}
     cfg = raw_cfg.get("extra", raw_cfg) if isinstance(raw_cfg, Mapping) else {}
@@ -131,9 +132,19 @@ def _get_async_chunk_config(transfer_manager: Any) -> tuple[int, int]:
             LATENT_LEFT_CONTEXT,
         )
     chunk_size = int(cfg.get("latent_chunk_size", LATENT_CHUNK_SIZE))
+    initial_chunk_size = int(cfg.get("initial_latent_chunk_size", INITIAL_LATENT_CHUNK_SIZE))
     left_context = int(cfg.get("latent_left_context", LATENT_LEFT_CONTEXT))
     if chunk_size <= 0:
         raise ValueError(f"Invalid Ming latent_chunk_size={chunk_size}")
+    if initial_chunk_size < 0:
+        raise ValueError(f"Invalid Ming initial_latent_chunk_size={initial_chunk_size}")
+    if initial_chunk_size > chunk_size:
+        logger.warning(
+            "Ming initial_latent_chunk_size=%d > latent_chunk_size=%d, clamping to latent_chunk_size.",
+            initial_chunk_size,
+            chunk_size,
+        )
+        initial_chunk_size = chunk_size
     # Stage-2 VAE caches past_key_values and stream_state by request_id.
     # Replaying left-context latents would double-feed cached decoder state.
     if left_context != 0:
@@ -142,7 +153,7 @@ def _get_async_chunk_config(transfer_manager: Any) -> tuple[int, int]:
             "Ming boundary continuity is handled by per-request decoder state cache, not "
             f"latent replay. Got latent_left_context={left_context}."
         )
-    return chunk_size, left_context
+    return chunk_size, initial_chunk_size, left_context
 
 
 def _build_chunk_observability(
@@ -196,7 +207,7 @@ def llm2audio_vae_async_chunk(
     if patch is not None:
         transfer_manager.code_prompt_token_ids[request_id].append(patch)
 
-    chunk_size, _ = _get_async_chunk_config(transfer_manager)
+    chunk_size, initial_chunk_size, _ = _get_async_chunk_config(transfer_manager)
 
     patches = transfer_manager.code_prompt_token_ids[request_id]
     seen_patch_len = int(state.get("seen_patch_len", 0))
@@ -225,11 +236,17 @@ def llm2audio_vae_async_chunk(
             )
         return None
 
-    chunk_length = length % chunk_size
-    if chunk_length != 0 and not finished:
-        return None
-
-    emit_count = chunk_length if chunk_length != 0 else chunk_size
+    use_first_chunk = 0 < initial_chunk_size < chunk_size and seen_patch_len == 0
+    if finished:
+        emit_count = length
+    elif use_first_chunk:
+        if length < initial_chunk_size:
+            return None
+        emit_count = initial_chunk_size
+    else:
+        if length < chunk_size:
+            return None
+        emit_count = chunk_size
     emit_patches = list(new_patches[:emit_count])
     state["seen_patch_len"] = seen_patch_len + len(emit_patches)
     latent_patches = torch.stack(emit_patches, dim=0)

vllm_omni/plugins/__init__.py

@@ -68,6 +68,10 @@ def load_omni_general_plugins() -> None:
         return
     omni_plugins_loaded = True
 
+    from vllm.plugins import load_general_plugins
+
+    load_general_plugins()
+
     plugins = load_omni_plugins_by_group(group=OMNI_DEFAULT_PLUGINS_GROUP)
     # General plugins: we only need to execute the loaded functions.
     for func in plugins.values():

vllm_omni/quantization/__init__.py

@@ -12,10 +12,10 @@ Adds per-component quantization for multi-stage models.
 """
 
 from .component_config import ComponentQuantizationConfig
-from .factory import SUPPORTED_QUANTIZATION_METHODS, build_quant_config
+from .factory import SUPPORTED_QUANTIZATION_METHODS, build_quant_config, register_quantization_override
 from .inc_config import OmniINCConfig
 
-# Heavy configs (GGUF, MXFP8) are NOT imported here to avoid pulling in
+# Heavy configs are NOT imported here to avoid pulling in
 # optional dependencies (pynvml, torch_npu) at module load time.
 # Import them directly when needed:
 #   from vllm_omni.quantization.mxfp8_config import DiffusionMXFP8Config
@@ -25,4 +25,5 @@ __all__ = [
     "ComponentQuantizationConfig",
     "OmniINCConfig",
     "SUPPORTED_QUANTIZATION_METHODS",
+    "register_quantization_override",
 ]

vllm_omni/quantization/factory.py

@@ -3,8 +3,8 @@
 """Factory for building quantization configs.
 
 build_quant_config() delegates to vLLM's quantization registry.
-The only extension point is _OVERRIDES for methods that need a
-different QuantizationConfig subclass in the OMNI context (e.g. GGUF).
+Out-of-tree integrations can register Omni-specific builders with
+register_quantization_override().
 """
 
 from __future__ import annotations
@@ -81,13 +81,6 @@ from .component_config import ComponentQuantizationConfig  # noqa: E402
 logger = init_logger(__name__)
 
 
-def _build_gguf(**kw: Any) -> QuantizationConfig:
-    """Lazy import to avoid pulling in CUDA/pynvml at module load time."""
-    from .gguf_config import DiffusionGGUFConfig
-
-    return DiffusionGGUFConfig(**kw)
-
-
 def _build_int8(**kw: Any) -> QuantizationConfig:
     """Lazy import for Int8 diffusion config (supports CUDA + NPU)."""
     from .int8_config import DiffusionInt8Config
@@ -140,7 +133,6 @@ def _build_inc(**kw: Any) -> QuantizationConfig:
 
 
 _OVERRIDES: dict[str, Callable[..., QuantizationConfig]] = {
-    "gguf": _build_gguf,
     "int8": _build_int8,
     "mxfp8": _build_mxfp8,
     "mxfp4": _build_mxfp4,
@@ -150,7 +142,24 @@ _OVERRIDES: dict[str, Callable[..., QuantizationConfig]] = {
     "auto_round": _build_inc,
 }
 
-SUPPORTED_QUANTIZATION_METHODS: list[str] = list(dict.fromkeys(QUANTIZATION_METHODS + list(_OVERRIDES.keys())))
+
+def _compute_supported_quantization_methods() -> list[str]:
+    return list(dict.fromkeys(QUANTIZATION_METHODS + list(_OVERRIDES.keys())))
+
+
+SUPPORTED_QUANTIZATION_METHODS: list[str] = _compute_supported_quantization_methods()
+
+
+def _refresh_registered_methods() -> None:
+    SUPPORTED_QUANTIZATION_METHODS[:] = _compute_supported_quantization_methods()
+    global _CACHED_ALIAS_MAP
+    _CACHED_ALIAS_MAP = None
+
+
+def register_quantization_override(method: str, builder: Callable[..., QuantizationConfig]) -> None:
+    """Register an Omni-specific quantization config builder."""
+    _OVERRIDES[_normalize_method_name(method)] = builder
+    _refresh_registered_methods()
 
 
 def _build_reverse_alias_map() -> dict[str, str]:

vllm_omni/quantization/gguf_config.py

@@ -1,323 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""GGUF quantization config for diffusion transformers.
-
-Uses dequant+GEMM instead of the fused kernel path (which expects 2D inputs).
-
-The GGUF quantization module was migrated to an external plugin in upstream
-vLLM (commit 6635279d8).  This file inlines the base classes that
-DiffusionGGUFConfig / DiffusionGGUFLinearMethod depend on, reproduced from
-the last upstream version before the migration.
-"""
-
-from __future__ import annotations
-
-from collections.abc import Mapping
-from types import MappingProxyType
-from typing import TYPE_CHECKING, Any
-
-import gguf
-import torch
-from gguf import GGMLQuantizationType as WeightType
-from torch.nn.parameter import Parameter, UninitializedParameter
-from vllm import _custom_ops as ops
-from vllm.logger import init_logger
-from vllm.model_executor.layers.linear import (
-    LinearBase,
-    LinearMethodBase,
-    UnquantizedLinearMethod,
-)
-from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig,
-    QuantizeMethodBase,
-)
-from vllm.model_executor.utils import set_weight_attrs
-
-if TYPE_CHECKING:
-    from vllm.model_executor.layers.quantization import QuantizationMethods
-
-logger = init_logger(__name__)
-
-# ---------------------------------------------------------------------------
-# GGUF base types (inlined from upstream vllm before migration to plugin)
-# ---------------------------------------------------------------------------
-
-UNQUANTIZED_TYPES: set[WeightType] = {WeightType.F32, WeightType.F16, WeightType.BF16}
-
-STANDARD_QUANT_TYPES: set[WeightType] = {
-    WeightType.Q4_0,
-    WeightType.Q4_1,
-    WeightType.Q5_0,
-    WeightType.Q5_1,
-    WeightType.Q8_0,
-    WeightType.Q8_1,
-}
-KQUANT_TYPES: set[WeightType] = {
-    WeightType.Q2_K,
-    WeightType.Q3_K,
-    WeightType.Q4_K,
-    WeightType.Q5_K,
-    WeightType.Q6_K,
-}
-IMATRIX_QUANT_TYPES: set[WeightType] = {
-    WeightType.IQ1_M,
-    WeightType.IQ1_S,
-    WeightType.IQ2_XXS,
-    WeightType.IQ2_XS,
-    WeightType.IQ2_S,
-    WeightType.IQ3_XXS,
-    WeightType.IQ3_S,
-    WeightType.IQ4_XS,
-    WeightType.IQ4_NL,
-}
-DEQUANT_TYPES: set[WeightType] = STANDARD_QUANT_TYPES | KQUANT_TYPES | IMATRIX_QUANT_TYPES
-
-
-def is_layer_skipped_gguf(
-    prefix: str,
-    unquantized_modules: list[str],
-    fused_mapping: Mapping[str, list[str]] = MappingProxyType({}),
-) -> bool:
-    # Fused layers like gate_up_proj or qkv_proj will not be fused
-    # in the safetensors checkpoint. So, we convert the name
-    # from the fused version to unfused + check to make sure that
-    # each shard of the fused layer has the same scheme.
-    proj_name = prefix.split(".")[-1]
-    if proj_name in fused_mapping:
-        shard_prefixes = [prefix.replace(proj_name, shard_proj_name) for shard_proj_name in fused_mapping[proj_name]]
-
-        is_skipped = None
-        for shard_prefix in shard_prefixes:
-            is_shard_skipped = any(shard_prefix in module_name for module_name in unquantized_modules)
-
-            if is_skipped is None:
-                is_skipped = is_shard_skipped
-            elif is_shard_skipped != is_skipped:
-                raise ValueError(
-                    f"Detected some but not all shards of {prefix} "
-                    "are quantized. All shards of fused layers "
-                    "to have the same precision."
-                )
-    else:
-        is_skipped = any(module_name in prefix for module_name in unquantized_modules)
-
-    assert is_skipped is not None
-    return is_skipped
-
-
-class _GGUFUninitializedParameter(UninitializedParameter):
-    """UninitializedParameter subclass that stores GGUF weight data in a list.
-
-    The actual tensor is materialized later via _create_padded_weight_param.
-    """
-
-    cls_to_become = Parameter
-    data_container: list[torch.Tensor]
-
-
-class GGUFConfig(QuantizationConfig):
-    """Config class for GGUF (inlined from upstream vllm)."""
-
-    def __init__(self, unquantized_modules: list[str] | None = None) -> None:
-        super().__init__()
-        self.unquantized_modules = unquantized_modules or []
-
-    def __repr__(self) -> str:
-        return "GGUFConfig()"
-
-    def get_name(self) -> QuantizationMethods:
-        return "gguf"
-
-    def get_supported_act_dtypes(self) -> list[torch.dtype]:
-        return [torch.half, torch.bfloat16, torch.float32]
-
-    @classmethod
-    def get_min_capability(cls) -> int:
-        return 60
-
-    @classmethod
-    def get_config_filenames(cls) -> list[str]:
-        return []  # no extra configs.
-
-    @classmethod
-    def from_config(cls, config: dict[str, Any]) -> GGUFConfig:
-        return cls()
-
-    @classmethod
-    def override_quantization_method(
-        cls, hf_quant_cfg: dict[str, Any], user_quant: str | None, hf_config=None
-    ) -> QuantizationMethods | None:
-        if user_quant == "gguf":
-            return "gguf"
-        return None
-
-    def get_quant_method(self, layer: torch.nn.Module, prefix: str) -> QuantizeMethodBase | None:
-        if isinstance(layer, LinearBase):
-            if is_layer_skipped_gguf(prefix, self.unquantized_modules, self.packed_modules_mapping):
-                return UnquantizedLinearMethod()
-            return GGUFLinearMethod(self)
-        return None
-
-
-class GGUFLinearMethod(LinearMethodBase):
-    """Linear method for GGUF (inlined from upstream vllm)."""
-
-    def __init__(self, quant_config: GGUFConfig):
-        self.quant_config = quant_config
-
-    def create_weights(
-        self,
-        layer: torch.nn.Module,
-        input_size_per_partition: int,
-        output_partition_sizes: list[int],
-        input_size: int,
-        output_size: int,
-        params_dtype: torch.dtype,
-        **extra_weight_attrs,
-    ):
-        self.params_dtype = params_dtype
-        output_size_per_partition = sum(output_partition_sizes)
-
-        tensor_shape = (output_size_per_partition, input_size_per_partition)
-        qweight = _GGUFUninitializedParameter(requires_grad=False)
-        set_weight_attrs(
-            qweight,
-            {
-                "input_dim": 1,
-                "output_dim": 0,
-                "tensor_shape": tensor_shape,
-                "is_gguf_weight": True,
-                "data_container": [],
-                "shard_id": [],
-                "shard_id_map": {},
-            },
-        )
-        set_weight_attrs(qweight, extra_weight_attrs)
-        layer.register_parameter("qweight", qweight)
-
-        qweight_type = Parameter(
-            torch.empty(len(output_partition_sizes), dtype=torch.uint8),
-            requires_grad=False,
-        )
-        set_weight_attrs(
-            qweight_type,
-            {
-                "is_gguf_weight_type": True,
-                "weight_type": 0,
-                "shard_weight_type": {},
-                "ignore_warning": True,
-            },
-        )
-        set_weight_attrs(qweight_type, extra_weight_attrs)
-        layer.register_parameter("qweight_type", qweight_type)
-
-    def process_weights_after_loading(self, layer: torch.nn.Module):
-        qweight_type = layer.qweight_type.weight_type
-        if not (qweight_type in UNQUANTIZED_TYPES or qweight_type in DEQUANT_TYPES):
-            qweight_type = WeightType(qweight_type)
-            raise ValueError(f"Unsupported GGUF quantization type {qweight_type} in layer {layer}.")
-        self._create_padded_weight_param(layer)
-
-    def _create_padded_weight_param(self, layer: torch.nn.Module):
-        """Create padded weight parameter for GGUF MergedLinear layer."""
-        qweight = layer.qweight
-        shard_id_map = qweight.shard_id_map
-        shard_id = qweight.shard_id
-        if len(data_container := qweight.data_container) > 1:
-            dtype = {data.dtype for data in data_container}
-            assert len(dtype) == 1, ValueError(f"Data container has mixed dtypes: {dtype}")
-            dtype = next(iter(dtype))
-            padded_side = max(x.size(1) for x in data_container)
-            concat_side = sum(x.size(0) for x in data_container)
-            padded_data = torch.zeros((concat_side, padded_side), dtype=dtype, device=qweight.device)
-            shard_offset_map = dict[str, tuple[int, int, int]]()
-            for idx in shard_id:
-                id_in_container = shard_id_map[idx]
-                start = sum(x.size(0) for x in data_container[:id_in_container])
-                end = start + data_container[id_in_container].size(0)
-                size = data_container[id_in_container].size(1)
-                padded_data[start:end, :size] = data_container[id_in_container]
-                shard_offset_map[idx] = (start, end, size)
-            qweight.data_container.clear()
-            padded_param = Parameter(padded_data, requires_grad=False)
-            set_weight_attrs(padded_param, vars(qweight))
-            set_weight_attrs(padded_param, {"shard_offset_map": shard_offset_map})
-            layer.register_parameter("qweight", padded_param)
-
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        bias: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        # Base apply: overridden by DiffusionGGUFLinearMethod.
-        raise NotImplementedError("Use DiffusionGGUFLinearMethod for diffusion GGUF")
-
-
-# ---------------------------------------------------------------------------
-# Diffusion-specific GGUF (dequant+GEMM, replaces fused kernel path)
-# ---------------------------------------------------------------------------
-
-
-def dequant_gemm_gguf(x: torch.Tensor, qweight: torch.Tensor, qweight_type: int) -> torch.Tensor:
-    if qweight_type in UNQUANTIZED_TYPES:
-        return x @ qweight.T
-    block_size, type_size = gguf.GGML_QUANT_SIZES[qweight_type]
-    shape = (qweight.shape[0], qweight.shape[1] // type_size * block_size)
-    weight = ops.ggml_dequantize(qweight, qweight_type, *shape, x.dtype)
-    return x @ weight.T
-
-
-class DiffusionGGUFLinearMethod(GGUFLinearMethod):
-    """GGUF linear method using dequant+GEMM for N-D diffusion tensors."""
-
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        bias: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        shard_id = getattr(layer.qweight, "shard_id", None)
-
-        if shard_id:
-            shard_id = ["q", "k", "v"] if "q" in shard_id else shard_id
-            qweight = layer.qweight
-            result = []
-            for idx in shard_id:
-                start, end, offset = layer.qweight.shard_offset_map[idx]
-                qweight_type = layer.qweight_type.shard_weight_type[idx]
-                result.append(
-                    dequant_gemm_gguf(
-                        x,
-                        qweight[start:end, :offset].contiguous(),
-                        qweight_type,
-                    )
-                )
-            out = torch.cat(result, axis=-1)
-        else:
-            qweight = layer.qweight
-            qweight_type = layer.qweight_type.weight_type
-            out = dequant_gemm_gguf(x, qweight, qweight_type)
-
-        if bias is not None:
-            out.add_(bias)
-        return out
-
-
-class DiffusionGGUFConfig(GGUFConfig):
-    """GGUF config that carries gguf_model path and uses dequant+GEMM."""
-
-    def __init__(
-        self,
-        gguf_model: str | None = None,
-        unquantized_modules: list[str] | None = None,
-    ) -> None:
-        super().__init__(unquantized_modules=unquantized_modules or [])
-        self.gguf_model = gguf_model
-
-    def get_quant_method(self, layer: torch.nn.Module, prefix: str) -> QuantizeMethodBase | None:
-        if isinstance(layer, LinearBase):
-            if is_layer_skipped_gguf(prefix, self.unquantized_modules, self.packed_modules_mapping):
-                return UnquantizedLinearMethod()
-            return DiffusionGGUFLinearMethod(self)
-        return None

vllm_omni/quantization/tools/compare_diffusion_trajectory_similarity.py

@@ -280,7 +280,6 @@ def _build_quantization_config(
     quantization: str | None,
     quantization_config_json: str | None,
     ignored_layers: str | None,
-    gguf_model: str | None,
 ) -> dict[str, Any] | None:
     explicit = _load_json_arg(quantization_config_json)
     if explicit is not None:
@@ -292,8 +291,6 @@ def _build_quantization_config(
     ignored = _split_csv(ignored_layers)
     if ignored:
         config["ignored_layers"] = ignored
-    if gguf_model is not None:
-        config["gguf_model"] = gguf_model
     return config
 
 
@@ -305,7 +302,6 @@ def _build_variant_config(args: argparse.Namespace, variant: str) -> VariantConf
         quantization=quantization,
         quantization_config_json=getattr(args, f"{variant}_quantization_config_json"),
         ignored_layers=ignored_layers,
-        gguf_model=getattr(args, f"{variant}_gguf_model"),
     )
     return VariantConfig(
         label=variant,
@@ -554,8 +550,6 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--reference-ignored-layers", help="Comma-separated reference ignored layer patterns.")
     parser.add_argument("--candidate-ignored-layers", help="Comma-separated candidate ignored layer patterns.")
     parser.add_argument("--ignored-layers", help="Alias for --candidate-ignored-layers.")
-    parser.add_argument("--reference-gguf-model", help="Reference GGUF file or HF reference when using gguf.")
-    parser.add_argument("--candidate-gguf-model", help="Candidate GGUF file or HF reference when using gguf.")
     parser.add_argument("--reference-load-format", default="default", help="Reference diffusion load format.")
     parser.add_argument("--candidate-load-format", default="default", help="Candidate diffusion load format.")
     parser.add_argument("--prompt", default="a cup of coffee on the table")