docs: update CONTRIBUTING.md

Co-authored-by: leejet <leejet714@gmail.com>

CONTRIBUTING.md

@@ -14,6 +14,8 @@ If you want to update a third-party dependency, please open an issue first inste
 
 Keep each PR focused on one clear change. Large or overly complex PRs are harder to review and may not be merged.
 
+Do not include test code or test scripts in commits or PRs. Keep them local and report verification results in the PR description.
+
 Follow Conventional Commit-style subjects seen in history: `feat:`, `fix:`, `refactor:`, `ci:`, `docs:`, `chore:`. Keep subjects imperative and scoped.
 
 PRs should include:
@@ -35,12 +37,11 @@ Naming conventions:
 - In `PascalCase` names, preserve common abbreviations in uppercase, for example `SD`, `API`, `HTTP`, `JSON`, `RGB`, `VAE`, `TAE`, `LoRA`, and `WebP`.
 - Use `snake_case` for functions, methods, variables, and file names unless an existing API requires a different style.
 - Use a trailing underscore for private data member names, for example `hidden_size_` or `tokenizer_`.
-- Use `.h` for C and C++ header files. Do not introduce new `.hpp` headers.
+- Use `.hpp` for model headers under `src/model/`, including new model headers. Do not rename these headers to `.h`. Use `.h` for other C and C++ header files.
 - Use macro-based header include guards instead of `#pragma once`.
 - Format header include guards as `__SD_{PATH}__`, where `{PATH}` is the header path in uppercase snake case without the file extension. For example, `src/sample.h` should use `__SD_SAMPLE_H__`.
 - Do not introduce anonymous namespaces in new or modified code; prefer `static` file-local functions/variables or an explicit named namespace when scoping is needed.
 - In `class`/`struct` definitions, place data members before member functions unless an existing type already clearly follows a different pattern.
-- Keep `test_*.cpp` / `test_*.py` naming for tests.
 
 Some older code in the project may not fully follow the current conventions. Please do not submit PRs that only rewrite existing code to match style rules.
 

README.md

@@ -15,6 +15,7 @@ API and command-line option may change frequently.***
 
 ## 🔥Important News
 
+* **2026/09/20** 🚀 stable-diffusion.cpp adds **Day-0 support for Qwen-Image-2.1**
 * **2026/08/20** 🚀 stable-diffusion.cpp now supports **LTX-2.5**
 * **2026/08/04** 🚀 stable-diffusion.cpp adds **Day-1 support for MiniMax-H3**
 * **2026/06/25** 🚀 stable-diffusion.cpp now supports **Krea2**
@@ -47,6 +48,7 @@ API and command-line option may change frequently.***
     - [Chroma](./docs/chroma.md)
     - [Chroma1-Radiance](./docs/chroma_radiance.md)
     - [Qwen Image](./docs/qwen_image.md)
+    - [Qwen Image 2.1](./docs/qwen_image_2.1.md)
     - [PiD](./docs/pid.md)
     - [LongCat Image](./docs/longcat_image.md)
     - [Z-Image](./docs/z_image.md)
@@ -61,12 +63,14 @@ API and command-line option may change frequently.***
     - [SeFi-Image](./docs/sefi_image.md)
     - [HiDream-O1-Image](./docs/hidream_o1_image.md)
     - [Ideogram4](./docs/ideogram4.md)
+    - [LLaDA-Image](./docs/llada_image.md)
   - [Image Edit Models](./docs/edit.md)
     - [FLUX.1-Kontext-dev](./docs/kontext.md)
     - [Qwen Image Edit series](./docs/qwen_image_edit.md)
     - [LongCat Image Edit](./docs/longcat_image.md)
     - [Boogu Image Edit](./docs/boogu_image.md)
     - [Mage-Flow-Edit](./docs/mage_flow.md#image-editing)
+    - [LLaDA-Image Edit](./docs/llada_image.md#image-editing)
   - Video Models
     - [Wan2.1/Wan2.2](./docs/wan.md)
     - [MiniMax-H3](./docs/minimax_h3.md)

cmake/ggml.cmake

@@ -23,5 +23,5 @@ set_property(TARGET ${SD_LIB} PROPERTY SD_GGML_PRIVATE_INCLUDE_DIR "${sd_ggml_pr
 
 if(SD_USE_UPSTREAM_GGML)
     target_compile_definitions(${SD_LIB} PUBLIC SD_USE_UPSTREAM_GGML)
-    message(WARNING "Using upstream GGML: FP8 and INT8 tensorwise/convrot are disabled. Some operators may be unsupported and performance may be lower than with patched GGML.")
+    message(WARNING "Using upstream GGML: INT8 tensorwise/convrot is disabled and FP8 weights are converted to F16 at load time. Some operators may be unsupported and performance may be lower than with patched GGML.")
 endif()

docs/build.md

@@ -32,10 +32,12 @@ Backend options such as `-DSD_CUDA=ON` apply to the selected tree as usual.
 
 `SD_USE_UPSTREAM_GGML` defaults to `OFF`, which enables the patched GGML
 extensions. Set it to `ON` when using upstream GGML; it selects the compatibility
-mode and does not download or replace the GGML source tree. Upstream mode
-disables FP8 and INT8 tensorwise/convrot and rejects their model files with an
-explicit error. FP8 weight type requests, tensor type rules and conversion
-outputs are also rejected; no automatic conversion is performed.
+mode and does not download or replace the GGML source tree. Upstream mode keeps
+the original FP8 safetensors handling: FP8 tensors are converted to F16 at load
+time (one byte per element in the file, two in RAM and VRAM). INT8
+tensorwise/convrot is disabled and its model files are rejected with an explicit
+error. FP8 GGUF files, FP8 weight type requests and tensor type rules are also
+rejected; no automatic conversion is performed.
 
 Upstream GGML may lack some operators and performance optimizations provided by
 the patched version. A warning is emitted during CMake configuration and when
@@ -88,6 +90,10 @@ cmake --build . --config Release
 
 ## Build with CUDA
 
+Native SageAttention is included when using CUDA with patched GGML
+(`SD_USE_UPSTREAM_GGML=OFF`).
+See [SageAttention](sage_attention.md) for GPU requirements and `--sage-attn` usage.
+
 This provides GPU acceleration using NVIDIA GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). Recommended to have at least 4 GB of VRAM.
 
 ```shell

docs/edit.md

@@ -17,6 +17,7 @@ Depending on the architecture, different models handle reference images differen
 | [**Boogu Image Edit**](./boogu_image.md) | `z_image_omni` |
 | **Krea2 (Community Edit LoRAs)** | `krea2_ostris_edit` |
 | [**Mage-Flow-Edit**](./mage_flow.md#image-editing) | `mage_flow` |
+| [**LLaDA-Image**](./llada_image.md#image-editing) | `llada_image` |
 | **Anima (Community Edit LoRAs)** | `cosmos_reference` |
 
 Stable-diffusion.spp also supports basic Unet-based editing models like instruct-pix2pix or CosXL-Edit. This document is not about those.

docs/ideogram4.md

@@ -18,6 +18,9 @@ at one byte per element in RAM and VRAM. Backends that cannot multiply FP8
 weights directly cast only the active layer to a temporary BF16 tensor during
 execution; the loader does not expand the entire checkpoint to BF16.
 
+With `SD_USE_UPSTREAM_GGML=ON`, FP8 tensors are converted to F16 at load time
+instead (two bytes per element in RAM and VRAM).
+
 Use `ideogram4_fp8.safetensors` and `ideogram4_uncond_fp8.safetensors` directly
 with `--diffusion-model` and `--uncond-diffusion-model`, respectively.
 

docs/llada_image.md

@@ -0,0 +1,156 @@
+# How to Use
+
+LLaDA-Image is a 6B text-to-image and instruction-guided editing model. The denoiser is a
+Lumina2/Z-Image-style NextDiT conditioned by a LLaDA2-MoE diffusion-LLM text encoder, and it
+reuses the Flux.2 VAE. Two checkpoints are published: a 50-step base model and
+LLaDA-Image-Turbo, a 4-step distilled model.
+
+## Download weights
+
+Four components are required: a transformer, a text encoder, a VAE, and a connectors file
+holding the QueryFormer, the text projection and, for editing, the SigVQ image encoder.
+
+The two published checkpoints are **not** interchangeable. LLaDA-Image-Turbo and LLaDA-Image
+ship different transformers, text encoders, QueryFormers and text projections; only the VAE,
+the SigVQ encoder and the tokenizer are shared. Mixing the two produces degraded output rather
+than a clean error, so keep each checkpoint's files together.
+
+Both need an external LLaDA2 `tokenizer.json`, which is not embedded in sd.cpp and is the same
+file for either checkpoint. Take `tokenizer/tokenizer.json` from either repository and pass it
+with `--tokenizer`. See [JSON tokenizers](tokenizers.md) for CLI and C API usage.
+
+### LLaDA-Image-Turbo (4 steps)
+
+Converted transformer, text encoder and pre-merged connectors are at
+https://huggingface.co/fszontagh/LLaDA-Image-Turbo-GGUF:
+
+- `llada-image-turbo-f16.gguf`
+- `llada-image-turbo-text_encoder-q8_0.gguf`
+- `llada-image-turbo-connectors.safetensors` for text to image, or
+  `llada-image-turbo-connectors-edit.safetensors`, which also carries the SigVQ encoder that
+  editing needs.
+
+Other quantizations of the transformer and the text encoder are in the same repository.
+
+The VAE comes from the original repository,
+https://huggingface.co/inclusionAI/LLaDA-Image-Turbo: `vae/diffusion_pytorch_model.safetensors`,
+referred to below as `llada_vae.safetensors`.
+
+### LLaDA-Image (50 steps)
+
+Converted transformer, text encoder and pre-merged connectors are at
+https://huggingface.co/fszontagh/LLaDA-Image-GGUF:
+
+- `llada-image-f16.gguf`
+- `llada-image-text_encoder-q8_0.gguf`
+- `llada-image-connectors.safetensors` for text to image, or
+  `llada-image-connectors-edit.safetensors`, which also carries the SigVQ encoder that editing
+  needs.
+
+Other quantizations of the transformer and the text encoder are in the same repository.
+
+The VAE comes from the original repository,
+https://huggingface.co/inclusionAI/LLaDA-Image, and is the same file as the Turbo one.
+
+### Converting the weights yourself
+
+The transformer has to go in through `--diffusion-model` so that its tensor names keep the
+prefix the loader expects, while the text encoder goes in through `-m`:
+
+```bash
+./bin/sd-cli -M convert --diffusion-model transformer/diffusion_pytorch_model.safetensors.index.json \
+  -o llada-image-f16.gguf --type f16
+./bin/sd-cli -M convert -m text_encoder/model.safetensors.index.json \
+  -o llada-image-text_encoder-q8_0.gguf --type q8_0
+```
+
+### Building the connector file yourself
+
+`--embeddings-connectors` takes one file, so the QueryFormer, the text projection and
+(for editing) the SigVQ encoder have to be combined into a single Safetensors file, each
+tensor name prefixed with its component name. Leaving `sigvq` out skips loading the 2.6 GB
+encoder:
+
+```python
+from safetensors.torch import load_file, save_file
+
+merged = {}
+for prefix, path in [
+    ("queryformer", "queryformer/diffusion_pytorch_model.safetensors"),
+    ("text_projection", "text_projection/diffusion_pytorch_model.safetensors"),
+    ("sigvq", "sigvq/diffusion_pytorch_model.safetensors"),
+]:
+    for name, tensor in load_file(path).items():
+        merged[f"{prefix}.{name}"] = tensor
+save_file(merged, "llada_connectors.safetensors")
+```
+
+## Examples
+
+### Text to image
+
+```bash
+./bin/sd-cli \
+  --diffusion-model /path/to/llada-image-turbo-f16.gguf \
+  --llm /path/to/llada-image-turbo-text_encoder-q8_0.gguf \
+  --tokenizer /path/to/tokenizer.json \
+  --vae /path/to/llada_vae.safetensors \
+  --embeddings-connectors /path/to/llada-image-turbo-connectors.safetensors \
+  --prompt "a lovely cat holding a sign says 'llada.cpp'" \
+  --width 1024 \
+  --height 1024 \
+  --steps 4 \
+  --cfg-scale 1.0 \
+  --seed 42 \
+  --output output.png
+```
+
+<img width="256" alt="LLaDA-Image example" src="../assets/llada_image/example.png" />
+
+### Image editing
+
+```bash
+./bin/sd-cli \
+  --diffusion-model /path/to/llada-image-turbo-f16.gguf \
+  --llm /path/to/llada-image-turbo-text_encoder-q8_0.gguf \
+  --tokenizer /path/to/tokenizer.json \
+  --vae /path/to/llada_vae.safetensors \
+  --embeddings-connectors /path/to/llada-image-turbo-connectors-edit.safetensors \
+  --ref-image /path/to/input.png \
+  --prompt "change the sign text to 'sd.cpp'" \
+  --width 1024 \
+  --height 1024 \
+  --steps 4 \
+  --cfg-scale 1.0 \
+  --diffusion-fa \
+  --output output.png
+```
+
+<img width="256" alt="LLaDA-Image edit example" src="../assets/llada_image/edit_example.png" />
+
+See [edit.md](./edit.md) for the shared reference-image options. LLaDA-Image uses the
+`llada_image` preset by default, resizing the reference image to the output width and height
+before VAE encoding. SigVQ uses bilinear resizing to half the output resolution and inputs
+normalized to `[-1, 1]`. CFG keeps the source latent in both branches and uses SigVQ features
+only in the positive branch. Editing requires connectors that include the SigVQ weights.
+
+## Notes
+
+- Use 4 steps and `--cfg-scale 1.0` for LLaDA-Image-Turbo; the guidance is distilled away, so
+  a higher CFG degrades output and doubles the text encoder cost. The 50-step base model uses
+  `--steps 50 --cfg-scale 5`.
+- Width and height are rounded up to a multiple of 16. For editing the reference pipeline
+  requires them to be divisible by 32.
+- Edit the 50-step base model at 1024x1024. At 512x512 it returns the reference image almost
+  unchanged instead of applying the instruction; LLaDA-Image-Turbo edits correctly at both.
+- Editing runs the reference and the target in one sequence, so it needs roughly twice the
+  tokens of text to image at the same size. On 12 GB, editing at 1024x1024 needs
+  `--diffusion-fa`; without it the diffusion graph does not fit.
+- The weights total about 16 GB, but segmented execution streams them, so a much smaller
+  budget works. At 512x512, `--max-vram 6` costs almost nothing over unconstrained execution,
+  and `--max-vram 3` still produces byte-identical output at roughly 2.5x the time.
+- `--scheduler` defaults to `llada_image`, which reproduces the reference Kumaraswamy sigma
+  grid. `--extra-sample-args uniform=1` selects the uniform grid instead.
+- Prompt templating is handled automatically; pass a plain description.
+- VQ-conditioned generation (`generation_mode="vq"`, where the text encoder decodes image
+  tokens before diffusion) is not implemented.

docs/qwen_image_2.1.md

@@ -0,0 +1,41 @@
+# How to Use
+
+Qwen Image 2.1 supports text-to-image generation and image editing, using Qwen3-VL-8B as the text encoder and its own VAE.
+
+## Download weights
+
+- Download Qwen Image 2.1
+    - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-2.1/tree/main/diffusion_models
+    - gguf: https://huggingface.co/leejet/Qwen-Image-2.1-GGUF/tree/main
+- Download vae
+    - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-2.1/tree/main/vae
+- Download Qwen3-VL-8B-Instruct
+    - safetensors (BF16 or INT8 convrot): https://huggingface.co/Comfy-Org/Qwen-Image-2.1/tree/main/text_encoders
+    - gguf: https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct-GGUF/tree/main
+    - For image editing with a GGUF text encoder, also download `mmproj-Qwen3VL-8B-Instruct-F16.gguf` from the same repository and pass it with `--llm_vision`.
+
+Use `qwen_image_2.1_vae_bf16.safetensors` with this model. The earlier Qwen Image and Wan 2.2 VAE weights are not interchangeable with the Qwen Image 2.1 VAE weights.
+
+## Examples
+
+Run the following commands from the build directory. Use image dimensions divisible by 32. The resolution-dependent flow schedule is selected automatically.
+
+### Text to image
+
+```powershell
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\qwen_image_2.1_int8_convrot.safetensors --vae ..\models\vae\qwen_image_2.1_vae_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf -p "a lovely cat holding a sign says 'qwen2.1.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu -o qwen_image_2.1.png
+```
+
+<img alt="Qwen Image 2.1 example" src="../assets/qwen/qwen_image_2.1.png" />
+
+To use GGUF diffusion weights, set `--diffusion-model` to the path of a file such as `qwen_image_2.1-Q4_K.gguf`.
+
+### Image editing
+
+Pass the reference image with `-r` and describe the edit in `-p`. Vision weights are required; the example below loads them separately with `--llm_vision`.
+
+```powershell
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\qwen_image_2.1_int8_convrot.safetensors --vae ..\models\vae\qwen_image_2.1_vae_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\Qwen3VL-8B-Instruct-mmproj-BF16.gguf -r ..\assets\qwen\qwen_image_2.1.png -p "change 'qwen2.1.cpp' to 'sd.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu -o qwen_image_2.1_edit.png
+```
+
+For multiple reference images, repeat `-r` in the desired order, for example `-r first.png -r second.png`.

docs/sage_attention.md

@@ -0,0 +1,68 @@
+# SageAttention
+
+`--sage-attn` enables native CUDA SageAttention in the diffusion model, including
+the high-noise diffusion model when present. Python, PyTorch, and Triton are not
+required at build time or runtime.
+
+The CUDA backend automatically selects a kernel supported by both the GPU and
+the compiled CUDA toolkit:
+
+| GPU / toolkit | Implementation |
+| --- | --- |
+| SM89 or newer, CUDA 12.8 or newer (except SM90) | SageAttention2++: per-thread INT8 Q/K, FP8 PV, FP16 instruction accumulation with an FP32 buffer |
+| SM89 or newer, CUDA 12.4 or newer; SM90 also uses this path with newer toolkits | SageAttention2: per-thread INT8 Q/K, FP8 PV, two-level FP32 accumulation |
+| SM80 or newer, CUDA 12.0 or newer | INT8 Q/K, FP16 PV compatibility path |
+
+The FP8 paths smooth K, quantize V per channel, and pad and permute V for FP8
+Tensor Cores. The 2++ path uses the upstream V scale limit of 2.25 to avoid
+overflow in its FP16 instruction accumulator. The public output remains FP32.
+These are the upstream **INT8** SageAttention2/2++ variants; the paper's INT4
+variant and Hopper-specific WGMMA kernel are not implemented here.
+
+## Build
+
+Use the bundled patched GGML, CUDA Toolkit 12.0 or newer, and an NVIDIA GPU with
+compute capability 8.0 or newer. Compile kernels for the GPU being used.
+
+```sh
+cmake -S . -B build -DSD_CUDA=ON -DSD_USE_UPSTREAM_GGML=OFF
+cmake --build build --config Release
+```
+
+No separate SageAttention build option is needed. Upstream GGML builds do not
+support it. A system GGML must include the matching patched API and CUDA
+backend. Enabling `--sage-attn` with an unavailable build or diffusion device
+reports an error. Building with CUDA 12.4 selects SageAttention2 on an RTX 4090;
+rebuild with CUDA 12.8 or newer to use SageAttention2++.
+
+## Use
+
+Replace `--diffusion-fa` with `--sage-attn` in an existing command. For example,
+from the build directory:
+
+```powershell
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，
+形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --video-frames 33 --sage-attn
+```
+
+SageAttention currently handles unmasked attention with head dimensions from
+1 through 128, including grouped-query attention, different query/key lengths,
+and multiple batches. Dimensions below 64 are zero-padded to 64; dimensions
+between 65 and 127 are zero-padded to 128. The original softmax scale is preserved,
+and the output is cropped back to the original dimension. Other attention
+operations fall back to FlashAttention when supported, then ordinary attention.
+SageAttention takes precedence in diffusion
+when combined with `--fa` or `--diffusion-fa`; `--fa` continues to control other
+modules. Existing attention scaling overrides remain effective.
+
+Attention quantization changes numerical results. Compare image quality and
+end-to-end generation time using the same seed, dimensions, and sampling
+settings. Compare sampling steps after the first step for warmed-up inference
+speed, and report model loading and first-step initialization separately.
+Quantization, smoothing, and format conversion costs are included in generation
+time, so short sequences may not benefit.
+
+Library callers set `sd_ctx_params_t.sage_attn = true` before `new_sd_ctx()`,
+like `diffusion_flash_attn`. Context creation fails if the requested feature is
+unavailable. Initialize the parameter structure with `sd_ctx_params_init()`.
+Rebuild library callers against the updated public header.

examples/common/common.cpp

@@ -618,6 +618,10 @@ ArgOptions SDContextParams::get_options() {
          "--diffusion-fa",
          "use flash attention in the diffusion model only",
          true, &diffusion_flash_attn},
+        {"",
+         "--sage-attn",
+         "use native CUDA SageAttention in the diffusion model, with flash/default attention fallback",
+         true, &sage_attn},
         {"",
          "--diffusion-conv-direct",
          "use ggml_conv2d_direct in the diffusion model",
@@ -938,6 +942,7 @@ std::string SDContextParams::to_string() const {
         << "  vae_on_cpu: " << (vae_on_cpu ? "true" : "false") << ",\n"
         << "  flash_attn: " << (flash_attn ? "true" : "false") << ",\n"
         << "  diffusion_flash_attn: " << (diffusion_flash_attn ? "true" : "false") << ",\n"
+        << "  sage_attn: " << (sage_attn ? "true" : "false") << ",\n"
         << "  linear_scale: " << linear_scale << ",\n"
         << "  attn_scale: " << attn_scale << ",\n"
         << "  diffusion_conv_direct: " << (diffusion_conv_direct ? "true" : "false") << ",\n"
@@ -995,6 +1000,7 @@ sd_ctx_params_t SDContextParams::to_sd_ctx_params_t(bool taesd_preview) {
     sd_ctx_params.enable_mmap                     = enable_mmap;
     sd_ctx_params.flash_attn                      = flash_attn;
     sd_ctx_params.diffusion_flash_attn            = diffusion_flash_attn;
+    sd_ctx_params.sage_attn                       = sage_attn;
     sd_ctx_params.linear_scale                    = linear_scale;
     sd_ctx_params.attn_scale                      = attn_scale;
     sd_ctx_params.tae_preview_only                = taesd_preview;
@@ -1109,7 +1115,7 @@ ArgOptions SDGenerationParams::get_options() {
          &hires_upscaler},
         {"",
          "--extra-sample-args",
-         "extra sampler/scheduler/guidance args, key=value list. CFG supports guidance_schedule; APG supports apg_eta, apg_momentum, apg_norm_threshold, apg_norm_threshold_smoothing; SLG supports slg_uncond; lcm supports noise_clip_std, noise_scale_start, noise_scale_end; flux supports base_shift, max_shift; ltx2 supports max_shift, base_shift, stretch, terminal; euler_ge supports gamma; beta scheduler supports alpha, beta; logit_normal supports mu, std, logsnr_min, logsnr_max, resolution_aware; lms supports lms_max_order, lms_shift, lms_divisions; noise-injecting samplers support noise_sampler with value iid (default except for dpm++2m_sde_bt) or brownian_tree; brownian_tree_rng supports cpu (default), cuda, std_default or sampler_rng",
+         "extra sampler/scheduler/guidance args, key=value list. CFG supports guidance_schedule; APG supports apg_eta, apg_momentum, apg_norm_threshold, apg_norm_threshold_smoothing; SLG supports slg_uncond; lcm supports noise_clip_std, noise_scale_start, noise_scale_end; flux supports base_shift, max_shift; ltx2 supports max_shift, base_shift, stretch, terminal; euler_ge supports gamma; beta scheduler supports alpha, beta; logit_normal supports mu, std, logsnr_min, logsnr_max, resolution_aware; llada_image supports uniform; lms supports lms_max_order, lms_shift, lms_divisions; noise-injecting samplers support noise_sampler with value iid (default except for dpm++2m_sde_bt) or brownian_tree; brownian_tree_rng supports cpu (default), cuda, std_default or sampler_rng",
          (int)',',
          &extra_sample_args},
         {"",

examples/common/common.h

@@ -170,6 +170,7 @@ struct SDContextParams {
     bool vae_on_cpu            = false;
     bool flash_attn            = false;
     bool diffusion_flash_attn  = false;
+    bool sage_attn             = false;
     bool diffusion_conv_direct = false;
     bool vae_conv_direct       = false;
 

ggml

@@ -1 +1 @@
-Subproject commit c6632cd905401abc58b6f5cdd52d228aa7ca1b88
+Subproject commit f583f393cd5dfdc129360bbf75cb3d49ccc837a8

include/stable-diffusion.h

@@ -79,6 +79,7 @@ enum scheduler_t {
     FLUX2_SCHEDULER,
     FLUX_SCHEDULER,
     BETA_SCHEDULER,
+    LLADA_IMAGE_SCHEDULER,
     SCHEDULER_COUNT
 };
 
@@ -245,6 +246,7 @@ typedef struct {
     float linear_scale;              // Override linear input scaling; 0 keeps the model default
     float attn_scale;                // Override flash-attention K/V scaling; 0 keeps the model default
     const char* tokenizer;           // tokenizer.json path or main=FILE,clip-l=FILE,clip-g=FILE assignments; required for PiD and Lens
+    bool sage_attn;
 } sd_ctx_params_t;
 
 typedef struct {

src/conditioning/conditioner.hpp

@@ -14,6 +14,7 @@
 #include "core/util.h"
 #include "model/diffusion/model.hpp"
 #include "model/te/clip.hpp"
+#include "model/te/llada_image_te.hpp"
 #include "model/te/llm.hpp"
 #include "model/te/t5.hpp"
 #include "model_loader.h"
@@ -1978,7 +1979,8 @@ struct LLMEmbedder : public Conditioner {
             arch = LLM::LLMArch::GPT_OSS_20B;
         } else if (sd_version_is_pid(version)) {
             arch = LLM::LLMArch::GEMMA2_2B;
-        } else if (sd_version_is_lingbot_video(version) ||
+        } else if (version == VERSION_QWEN_IMAGE_2_1 ||
+                   sd_version_is_lingbot_video(version) ||
                    sd_version_is_ideogram4(version) ||
                    sd_version_is_boogu_image(version) ||
                    sd_version_is_sefi_image(version) ||
@@ -2547,6 +2549,67 @@ struct LLMEmbedder : public Conditioner {
             prompt += conditioner_params.text;
             prompt_attn_range = {0, 0};
             prompt += "<|im_end|>\n<|im_start|>assistant\n";
+        } else if (version == VERSION_QWEN_IMAGE_2_1) {
+            if (!llm->enable_vision && conditioner_params.ref_images != nullptr && !conditioner_params.ref_images->empty()) {
+                LOG_ERROR("Qwen Image 2.1 editing requires Qwen3-VL vision weights; provide --llm_vision or a combined encoder");
+                return {};
+            }
+            prompt = "<|im_start|>system\nComprehend and analyze the provided prompt.<|im_end|>\n";
+            std::vector<int> system_tokens;
+            if (!tokenizer->encode(prompt, system_tokens, nullptr)) {
+                return {};
+            }
+            prompt_template_encode_start_idx = static_cast<int>(system_tokens.size());
+            out_layers                       = {static_cast<int>(llm->config.num_layers)};
+            prompt += "<|im_start|>user\n";
+            if (llm->enable_vision && conditioner_params.ref_images != nullptr) {
+                for (size_t i = 0; i < conditioner_params.ref_images->size(); ++i) {
+                    const auto& image = (*conditioner_params.ref_images)[i];
+                    int64_t width     = image.shape()[0];
+                    int64_t height    = image.shape()[1];
+                    int64_t pixels    = width * height;
+                    if (width % 32 != 0 || height % 32 != 0) {
+                        LOG_ERROR("Qwen Image 2.1 reference dimensions must be multiples of 32");
+                        return {};
+                    }
+                    auto rgb = sd::Tensor<float>({width, height, 3, 1});
+                    for (int64_t p = 0; p < pixels; ++p) {
+                        float alpha = image.shape()[2] == 4 ? image[p + 3 * pixels] : 1.f;
+                        for (int c = 0; c < 3; ++c) {
+                            rgb[p + c * pixels] = 2.f * (image[p + c * pixels] * alpha + 1.f - alpha) - 1.f;
+                        }
+                    }
+                    auto outputs = llm->encode_image_outputs(n_threads, rgb, false);
+                    if (outputs.empty()) {
+                        return {};
+                    }
+                    prompt += (i == 0 ? "" : " ") + std::string("<image") + std::to_string(i + 1) + "><|vision_start|>";
+                    std::vector<int> prefix_tokens;
+                    if (!tokenizer->encode(prompt, prefix_tokens, nullptr)) {
+                        return {};
+                    }
+                    int index = static_cast<int>(prefix_tokens.size());
+                    int count = static_cast<int>(outputs[0].shape()[1]);
+                    image_embeds.emplace_back(index, std::move(outputs[0]));
+                    if (deepstack_image_embeds.empty()) {
+                        deepstack_image_embeds.resize(outputs.size() - 1);
+                    }
+                    for (size_t layer = 1; layer < outputs.size(); ++layer) {
+                        deepstack_image_embeds[layer - 1].emplace_back(index, std::move(outputs[layer]));
+                    }
+                    image_grids.push_back({index, count,
+                                           static_cast<int>(height) / llm->config.vision.patch_size,
+                                           static_cast<int>(width) / llm->config.vision.patch_size});
+                    for (int j = 0; j < count; ++j) {
+                        prompt += "<|image_pad|>";
+                    }
+                    prompt += "<|vision_end|>";
+                }
+            }
+            prompt_attn_range.first = static_cast<int>(prompt.size());
+            prompt += conditioner_params.text.empty() ? " " : conditioner_params.text;
+            prompt_attn_range.second = static_cast<int>(prompt.size());
+            prompt += "<|im_end|>\n<|im_start|>assistant\n";
         } else if (sd_version_is_qwen_image(version) || sd_version_is_mage_flow(version)) {
             if (llm->enable_vision && conditioner_params.ref_images != nullptr && !conditioner_params.ref_images->empty()) {
                 LOG_INFO("%s", sd_version_is_mage_flow(version) ? "MageFlowEditPipeline" : "QwenImageEditPlusPipeline");
@@ -3074,6 +3137,21 @@ struct LLMEmbedder : public Conditioner {
         SDCondition result;
         result.c_crossattn        = std::move(hidden_states);
         result.extra_c_crossattns = std::move(extra_hidden_states_vec);
+        if (version == VERSION_QWEN_IMAGE_2_1) {
+            auto slots = sd::Tensor<int32_t>::zeros({result.c_crossattn.shape()[1]});
+            for (size_t i = 0; i < image_embeds.size(); ++i) {
+                int64_t begin = image_embeds[i].first - prompt_template_encode_start_idx;
+                int64_t end   = begin + image_embeds[i].second.shape()[1];
+                if (begin < 0 || end > slots.numel()) {
+                    LOG_ERROR("Qwen Image 2.1 image slots exceed the encoded prompt");
+                    return {};
+                }
+                for (int64_t j = begin; j < end; ++j) {
+                    slots[j] = static_cast<int32_t>(i + 1);
+                }
+            }
+            result.c_token_types = std::move(slots);
+        }
         if (sd_version_is_minimax_h3(version)) {
             std::vector<int32_t> tags(static_cast<size_t>(result.c_crossattn.shape()[1]), 1);
             for (const auto& [index, image_embed] : image_embeds) {
@@ -3159,6 +3237,214 @@ struct LTXAVTextProjectionRunner : public GGMLRunner {
     }
 };
 
+// LLaDA-Image's text path is a three-stage pipeline rather than a single encoder pass:
+// the token embeddings feed a QueryFormer whose 256 queries are appended to the backbone
+// input, and the backbone's final hidden states are projected to the denoiser's caption dim.
+// Ref: LLaDAImagePipeline._encode_text.
+struct LLaDAImageEmbedder : public Conditioner {
+    std::shared_ptr<Tokenizer> tokenizer;
+    std::shared_ptr<LLM::LLMRunner> llm;
+    std::shared_ptr<LLaDAImageTE::QueryFormerRunner> query_former;
+    std::shared_ptr<LLaDAImageTE::TextProjectionRunner> text_projection;
+    std::shared_ptr<LLaDAImageTE::SigVQRunner> sigvq;
+
+    std::string llm_prefix;
+    std::string query_former_prefix;
+    std::string text_projection_prefix;
+    std::string sigvq_prefix;
+
+    LLaDAImageEmbedder(ggml_backend_t backend,
+                       const String2TensorStorage& tensor_storage_map      = {},
+                       const std::string& llm_prefix                       = "text_encoders.llm",
+                       const std::string& query_former_prefix              = "queryformer",
+                       const std::string& text_projection_prefix           = "text_projection",
+                       const std::string& sigvq_prefix                     = "sigvq",
+                       std::shared_ptr<RunnerWeightManager> weight_manager = nullptr,
+                       const TokenizerConfig& tokenizers                   = {})
+        : llm_prefix(llm_prefix),
+          query_former_prefix(query_former_prefix),
+          text_projection_prefix(text_projection_prefix),
+          sigvq_prefix(sigvq_prefix) {
+        if (!tokenizers.has(TokenizerConfig::MAIN)) {
+            throw std::runtime_error("LLaDA-Image requires an external LLaDA2 tokenizer.json; pass --tokenizer FILE or set sd_ctx_params_t::tokenizer");
+        }
+        llm = std::make_shared<LLM::LLMRunner>(LLM::LLMArch::LLADA2_MOE,
+                                               backend,
+                                               tensor_storage_map,
+                                               llm_prefix,
+                                               false,
+                                               weight_manager);
+        // <|endoftext|> doubles as the pad token in LLaDA2's tokenizer.json.
+        tokenizer       = tokenizers.create(TokenizerConfig::MAIN, llm->config.vocab_size, 156892);
+        query_former    = std::make_shared<LLaDAImageTE::QueryFormerRunner>(backend,
+                                                                         tensor_storage_map,
+                                                                         query_former_prefix,
+                                                                         weight_manager);
+        text_projection = std::make_shared<LLaDAImageTE::TextProjectionRunner>(backend,
+                                                                               tensor_storage_map,
+                                                                               text_projection_prefix,
+                                                                               weight_manager);
+
+        // SigVQ is only present when the user supplies the editing weights.
+        for (const auto& [name, _] : tensor_storage_map) {
+            if (starts_with(name, sigvq_prefix + ".")) {
+                sigvq = std::make_shared<LLaDAImageTE::SigVQRunner>(backend,
+                                                                    tensor_storage_map,
+                                                                    sigvq_prefix,
+                                                                    weight_manager);
+                break;
+            }
+        }
+    }
+
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        llm->get_param_tensors(tensors, llm_prefix);
+        query_former->get_param_tensors(tensors, query_former_prefix);
+        text_projection->get_param_tensors(tensors, text_projection_prefix);
+        if (sigvq != nullptr) {
+            sigvq->get_param_tensors(tensors, sigvq_prefix);
+        }
+    }
+
+    void get_param_tensor_ops(std::map<ggml_tensor*, enum ggml_op>& tensor_ops) override {
+        llm->get_param_tensor_ops(tensor_ops);
+    }
+
+    void set_flash_attention_enabled(bool enabled) override {
+        llm->set_flash_attention_enabled(enabled);
+        query_former->set_flash_attention_enabled(enabled);
+        text_projection->set_flash_attention_enabled(enabled);
+        if (sigvq != nullptr) {
+            sigvq->set_flash_attention_enabled(enabled);
+        }
+    }
+
+    void set_max_graph_vram_bytes(size_t max_vram_bytes) override {
+        llm->set_max_graph_vram_bytes(max_vram_bytes);
+        query_former->set_max_graph_vram_bytes(max_vram_bytes);
+        text_projection->set_max_graph_vram_bytes(max_vram_bytes);
+        if (sigvq != nullptr) {
+            sigvq->set_max_graph_vram_bytes(max_vram_bytes);
+        }
+    }
+
+    void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        llm->set_runtime_backends(backends);
+    }
+
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        llm->set_graph_cut_layer_split_enabled(enabled);
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        llm->set_graph_cut_layer_split_backend_vram_limits(limits);
+    }
+
+    void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        llm->get_param_tensors(tensors, llm_prefix);
+    }
+
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        llm->set_weight_adapter(adapter);
+        query_former->set_weight_adapter(adapter);
+        text_projection->set_weight_adapter(adapter);
+        if (sigvq != nullptr) {
+            sigvq->set_weight_adapter(adapter);
+        }
+    }
+
+    void runner_end() override {
+        llm->runner_end();
+        query_former->runner_end();
+        text_projection->runner_end();
+        if (sigvq != nullptr) {
+            sigvq->runner_end();
+        }
+    }
+
+    SDCondition get_learned_condition(int n_threads,
+                                      const ConditionerParams& conditioner_params) override {
+        const int64_t num_queries = 256;
+        const bool has_ref_images = conditioner_params.ref_images != nullptr && !conditioner_params.ref_images->empty();
+        if (has_ref_images && sigvq == nullptr) {
+            LOG_ERROR("LLaDA-Image editing requires connectors with SigVQ weights");
+            return {};
+        }
+
+        std::string text = conditioner_params.text;
+        while (!text.empty() && std::isspace(static_cast<unsigned char>(text.front()))) {
+            text.erase(text.begin());
+        }
+        while (!text.empty() && std::isspace(static_cast<unsigned char>(text.back()))) {
+            text.pop_back();
+        }
+        std::string prompt = text.empty()
+                                 ? "<role>HUMAN</role> Generate an image.\n<role>ASSISTANT</role>\n<IMAGE1>"
+                                 : "<role>HUMAN</role> Generate an image: " + text + "\n<role>ASSISTANT</role>\n<IMAGE1>";
+
+        std::vector<int> tokens;
+        if (!tokenizer->encode(prompt, tokens, nullptr)) {
+            return {};
+        }
+        int64_t n_text = static_cast<int64_t>(tokens.size());
+        GGML_ASSERT(n_text > 0);
+
+        sd::Tensor<int32_t> text_ids({n_text}, std::vector<int32_t>(tokens.begin(), tokens.end()));
+        auto inputs_embeds = llm->compute_input_embeds(n_threads, text_ids);
+        auto query_embeds  = query_former->compute(n_threads, inputs_embeds);
+
+        // splice_image_embeds() replaces tokens in place, so the query slots have to exist in
+        // input_ids; their ids are irrelevant because the embeddings are overwritten.
+        std::vector<int32_t> padded(tokens.begin(), tokens.end());
+        padded.resize(static_cast<size_t>(n_text + num_queries), tokenizer->PAD_TOKEN_ID);
+        int64_t n_total = static_cast<int64_t>(padded.size());
+        sd::Tensor<int32_t> input_ids({n_total}, padded);
+
+        // Bidirectional everywhere except that the text tokens must not see the appended
+        // queries, matching backbone_attention_mask[:, :, :text_length, text_length:] = min.
+        const float mask_min = std::numeric_limits<float>::lowest() / 4.0f;
+        sd::Tensor<float> attention_mask({n_total, n_total});
+        for (int64_t i1 = 0; i1 < n_total; ++i1) {
+            for (int64_t i0 = 0; i0 < n_total; ++i0) {
+                float value                       = (i1 < n_text && i0 >= n_text) ? mask_min : 0.0f;
+                attention_mask[i0 + n_total * i1] = value;
+            }
+        }
+
+        LLM::ImageEmbeds image_embeds;
+        image_embeds.emplace_back(static_cast<int>(n_text), query_embeds);
+
+        std::set<int> out_layers = {static_cast<int>(llm->config.num_layers) + 1};
+        auto hidden_states       = llm->compute(n_threads,
+                                                input_ids,
+                                                attention_mask,
+                                                image_embeds,
+                                                out_layers);
+
+        SDCondition result;
+        result.c_crossattn = text_projection->compute(n_threads, hidden_states);
+
+        // Editing: SigVQ sees the reference at half the output resolution, as in
+        // LLaDAImagePipeline._encode_source_image.
+        if (has_ref_images) {
+            const auto& ref = conditioner_params.ref_images->front();
+            auto resized    = sd::ops::interpolate(ref,
+                                                   {conditioner_params.width / 2,
+                                                    conditioner_params.height / 2,
+                                                    ref.shape()[2],
+                                                    ref.shape()[3]},
+                                                   sd::ops::InterpolateMode::Bilinear);
+            resized         = resized * 2.f - 1.f;
+            auto semantic   = sigvq->compute(n_threads, resized);
+            if (semantic.empty()) {
+                return {};
+            }
+            result.extra_c_crossattns.push_back(std::move(semantic));
+        }
+        return result;
+    }
+};
+
 struct LTXAVEmbedder : public Conditioner {
     static constexpr int64_t kHiddenSize = 3840;
     static constexpr int64_t kNumStates  = 49;

src/core/ggml_extend.cpp

@@ -622,7 +622,8 @@ ggml_tensor* ggml_ext_attention_ext(ggml_context* ctx,
                                     ggml_tensor* mask,
                                     bool skip_reshape,
                                     bool flash_attn,
-                                    float kv_scale) {  // avoid overflow
+                                    float kv_scale,
+                                    bool sage_attn) {  // avoid overflow
     int64_t L_q;
     int64_t L_k;
     int64_t C;
@@ -713,7 +714,37 @@ ggml_tensor* ggml_ext_attention_ext(ggml_context* ctx,
         return out;
     };
 
-    if (flash_attn) {
+#ifndef SD_USE_UPSTREAM_GGML
+    if (sage_attn && mask == nullptr && d_head > 0 && d_head <= 128) {
+        auto q_in                 = ggml_reshape_4d(ctx, ggml_ext_cont(ctx, q->type == GGML_TYPE_F32 ? q : ggml_cast(ctx, q, GGML_TYPE_F32)), d_head, L_q, n_head, N);
+        auto k_in                 = ggml_reshape_4d(ctx, ggml_ext_cont(ctx, k->type == GGML_TYPE_F32 ? k : ggml_cast(ctx, k, GGML_TYPE_F32)), d_head, L_k, n_kv_head, N);
+        auto v_in                 = ggml_ext_cont(ctx, ggml_permute(ctx, v, 0, 2, 1, 3));
+        const int64_t padded_head = d_head <= 64 ? 64 : 128;
+        if ((padded_head != d_head || kv_scale != 1.0f) && v_in->type != GGML_TYPE_F32) {
+            v_in = ggml_cast(ctx, v_in, GGML_TYPE_F32);
+        }
+        if (padded_head != d_head) {
+            // Keep the original head's softmax scale when padding for the CUDA kernel.
+            q_in = ggml_pad(ctx, q_in, padded_head - d_head, 0, 0, 0);
+            k_in = ggml_pad(ctx, k_in, padded_head - d_head, 0, 0, 0);
+            v_in = ggml_pad(ctx, v_in, padded_head - d_head, 0, 0, 0);
+        }
+        if (kv_scale != 1.0f) {
+            k_in = ggml_ext_scale(ctx, k_in, kv_scale);
+            v_in = ggml_ext_scale(ctx, v_in, kv_scale);
+        }
+        v_in     = ggml_cast(ctx, v_in, GGML_TYPE_F16);
+        auto out = ggml_sage_attn(ctx, q_in, k_in, v_in, scale / kv_scale, GGML_SAGE_ATTN_AUTO);
+        if (ggml_backend_supports_op(backend, out)) {
+            kqv = kv_scale != 1.0f ? ggml_ext_scale(ctx, out, 1.0f / kv_scale) : out;
+            if (padded_head != d_head) {
+                kqv = ggml_ext_slice(ctx, kqv, 0, 0, d_head);
+            }
+        }
+    }
+#endif
+
+    if (kqv == nullptr && (flash_attn || sage_attn)) {
         // LOG_VERBOSE("attention_ext L_q:%d L_k:%d n_head:%d C:%d d_head:%d N:%d", L_q, L_k, n_head, C, d_head, N);
         bool can_use_flash_attn = true;
         if (mask != nullptr) {

src/core/ggml_extend.h

@@ -220,7 +220,8 @@ ggml_tensor* ggml_ext_attention_ext(ggml_context* ctx,
                                     ggml_tensor* mask = nullptr,
                                     bool skip_reshape = false,
                                     bool flash_attn   = false,
-                                    float kv_scale    = 1.0f);
+                                    float kv_scale    = 1.0f,
+                                    bool sage_attn    = false);
 
 ggml_tensor* ggml_ext_layer_norm(ggml_context* ctx,
                                  ggml_tensor* x,

src/core/ggml_runner.cpp

@@ -25,7 +25,7 @@ ggml_tensor* ggml_ext_attention_ext(GGMLRunnerContext* ctx,
     if (ctx->attn_scale > 0.f) {
         kv_scale = ctx->attn_scale;
     }
-    return ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, mask, skip_reshape, flash_attn, kv_scale);
+    return ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, mask, skip_reshape, flash_attn, kv_scale, ctx->sage_attn_enabled);
 }
 
 void GGMLRunner::alloc_params_ctx() {
@@ -520,6 +520,7 @@ GGMLRunnerContext GGMLRunner::get_context() {
     runner_ctx.ggml_ctx              = compute_ctx;
     runner_ctx.backend               = runtime_backend;
     runner_ctx.flash_attn_enabled    = flash_attn_enabled;
+    runner_ctx.sage_attn_enabled     = sage_attn_enabled;
     runner_ctx.linear_scale          = linear_scale;
     runner_ctx.attn_scale            = attn_scale;
     runner_ctx.conv2d_direct_enabled = conv2d_direct_enabled;

src/core/ggml_runner.h

@@ -68,6 +68,7 @@ struct GGMLRunnerContext {
     ggml_backend_t backend                                           = nullptr;
     ggml_context* ggml_ctx                                           = nullptr;
     bool flash_attn_enabled                                          = false;
+    bool sage_attn_enabled                                           = false;
     float linear_scale                                               = 0.f;
     float attn_scale                                                 = 0.f;
     bool conv2d_direct_enabled                                       = false;
@@ -176,6 +177,7 @@ protected:
     const std::string final_result_name = "ggml_runner_final_result_tensor";
 
     bool flash_attn_enabled    = false;
+    bool sage_attn_enabled     = false;
     float linear_scale         = 0.f;
     float attn_scale           = 0.f;
     bool conv2d_direct_enabled = false;
@@ -337,6 +339,14 @@ public:
         flash_attn_enabled = enabled;
     }
 
+    void set_sage_attention_enabled(bool enabled) {
+        if (sage_attn_enabled != enabled) {
+            free_cache_ctx_and_buffer();
+            graph_cut_plan_cache_.graph_cut_plans.clear();
+            sage_attn_enabled = enabled;
+        }
+    }
+
     void set_scale_overrides(float linear_scale, float attn_scale) {
         this->linear_scale = linear_scale;
         this->attn_scale   = attn_scale;

src/core/util.cpp

@@ -62,17 +62,34 @@ void replace_all_chars(std::string& str, char target, char replacement) {
     }
 }
 
+static std::string sd_vformat(const char* fmt, va_list ap) {
+    char small[128];
+    va_list ap2;
+    va_copy(ap2, ap);
+    int size = vsnprintf(small, sizeof small, fmt, ap);
+    if (size < 0) {
+        va_end(ap2);
+        return {};
+    }
+    size_t needed = (size_t)size;
+    if (needed < sizeof small) {
+        va_end(ap2);
+        return std::string(small, needed);
+    }
+    std::string out(needed, '\0');
+    int size2 = vsnprintf(out.data(), needed + 1, fmt, ap2);
+    va_end(ap2);
+    if (size2 < 0)
+        out.clear();
+    return out;
+}
+
 std::string sd_format(const char* fmt, ...) {
     va_list ap;
-    va_list ap2;
     va_start(ap, fmt);
-    va_copy(ap2, ap);
-    int size = vsnprintf(nullptr, 0, fmt, ap);
-    std::vector<char> buf(size + 1);
-    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
-    va_end(ap2);
+    std::string result = sd_vformat(fmt, ap);
     va_end(ap);
-    return std::string(buf.data(), size);
+    return result;
 }
 
 int round_up_to(int value, int base) {
@@ -624,47 +641,45 @@ std::string trim(const std::string& s) {
 static sd_log_cb_t sd_log_cb = nullptr;
 void* sd_log_cb_data         = nullptr;
 
-#define LOG_BUFFER_SIZE 4096
+static void sd_log_dispatch(sd_log_level_t level, const std::string& origin, const std::string& text) {
+    if (sd_log_cb == nullptr)
+        return;
+    std::string message = origin + " - " + text;
+    if (message.back() != '\n') {
+        message += '\n';
+    }
+    sd_log_cb(level, message.c_str(), sd_log_cb_data);
+}
 
 void log_printf(sd_log_level_t level, const char* file, int line, const char* format, ...) {
     va_list args;
     va_start(args, format);
-
-    static char log_buffer[LOG_BUFFER_SIZE + 1];
-    int written = snprintf(log_buffer, LOG_BUFFER_SIZE, "%s:%-4d - ", sd_basename(file).c_str(), line);
-
-    if (written >= 0 && written < LOG_BUFFER_SIZE) {
-        vsnprintf(log_buffer + written, LOG_BUFFER_SIZE - written, format, args);
-    }
-    size_t len = strlen(log_buffer);
-    if (log_buffer[len - 1] != '\n') {
-        strncat(log_buffer, "\n", LOG_BUFFER_SIZE - len);
-    }
-
-    if (sd_log_cb) {
-        sd_log_cb(level, log_buffer, sd_log_cb_data);
-    }
-
+    std::string message = sd_vformat(format, args);
     va_end(args);
+    std::string origin = sd_format("%s:%-4d", sd_basename(file).c_str(), line);
+    sd_log_dispatch(level, origin, message);
 }
 
 void sd_ggml_log_callback(ggml_log_level level, const char* text, void*) {
+    sd_log_level_t sd_level = SD_LOG_VERBOSE;
     switch (level) {
         case GGML_LOG_LEVEL_DEBUG:
-            LOG_VERBOSE(text);
+            sd_level = SD_LOG_VERBOSE;
             break;
         case GGML_LOG_LEVEL_INFO:
-            LOG_INFO(text);
+            sd_level = SD_LOG_INFO;
             break;
         case GGML_LOG_LEVEL_WARN:
-            LOG_WARN(text);
+            sd_level = SD_LOG_WARN;
             break;
         case GGML_LOG_LEVEL_ERROR:
-            LOG_ERROR(text);
+            sd_level = SD_LOG_ERROR;
             break;
         default:
-            LOG_VERBOSE(text);
+            sd_level = SD_LOG_VERBOSE;
+            break;
     }
+    sd_log_dispatch(sd_level, "ggml", text);
 }
 
 void sd_set_log_callback(sd_log_cb_t cb, void* data) {

src/model.h

@@ -39,6 +39,7 @@ enum SDVersion {
     VERSION_LINGBOT_VIDEO,
     VERSION_QWEN_IMAGE,
     VERSION_QWEN_IMAGE_LAYERED,
+    VERSION_QWEN_IMAGE_2_1,
     VERSION_HUNYUAN_VIDEO,
     VERSION_ANIMA,
     VERSION_FLUX2,
@@ -59,6 +60,7 @@ enum SDVersion {
     VERSION_KREA2,
     VERSION_MAGE_FLOW,
     VERSION_SENSENOVA_U1_5,
+    VERSION_LLADA_IMAGE,
     VERSION_ESRGAN,
     VERSION_COUNT,
 };
@@ -145,7 +147,7 @@ static inline bool sd_version_is_lingbot_video(SDVersion version) {
 }
 
 static inline bool sd_version_is_qwen_image(SDVersion version) {
-    if (version == VERSION_QWEN_IMAGE || version == VERSION_QWEN_IMAGE_LAYERED) {
+    if (version == VERSION_QWEN_IMAGE || version == VERSION_QWEN_IMAGE_LAYERED || version == VERSION_QWEN_IMAGE_2_1) {
         return true;
     }
     return false;
@@ -172,6 +174,13 @@ static inline bool sd_version_is_z_image(SDVersion version) {
     return false;
 }
 
+static inline bool sd_version_is_llada_image(SDVersion version) {
+    if (version == VERSION_LLADA_IMAGE) {
+        return true;
+    }
+    return false;
+}
+
 static inline bool sd_version_is_boogu_image(SDVersion version) {
     if (version == VERSION_BOOGU_IMAGE) {
         return true;
@@ -251,7 +260,7 @@ static inline bool sd_version_uses_flux_vae(SDVersion version) {
 }
 
 static inline bool sd_version_uses_flux2_vae(SDVersion version) {
-    if (sd_version_is_flux2(version) || sd_version_is_ernie_image(version) || sd_version_is_lens(version) || sd_version_is_ideogram4(version) || sd_version_is_sefi_image(version)) {
+    if (sd_version_is_flux2(version) || sd_version_is_ernie_image(version) || sd_version_is_lens(version) || sd_version_is_ideogram4(version) || sd_version_is_sefi_image(version) || sd_version_is_llada_image(version)) {
         return true;
     }
     return false;
@@ -292,6 +301,7 @@ static inline bool sd_version_is_dit(SDVersion version) {
         version == VERSION_HIDREAM_O1 ||
         sd_version_is_anima(version) ||
         sd_version_is_z_image(version) ||
+        sd_version_is_llada_image(version) ||
         sd_version_is_boogu_image(version) ||
         sd_version_is_ernie_image(version) ||
         sd_version_is_lens(version) ||

src/model/common/ggml_block.hpp

@@ -839,21 +839,30 @@ class RMSNorm : public UnaryBlock {
 protected:
     int64_t hidden_size;
     float eps;
+    bool elementwise_affine;
     std::string prefix;
 
     void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
-        this->prefix         = prefix;
+        this->prefix = prefix;
+        if (!elementwise_affine) {
+            return;
+        }
         enum ggml_type wtype = GGML_TYPE_F32;
         params["weight"]     = ggml_new_tensor_1d(ctx, wtype, hidden_size);
     }
 
 public:
     RMSNorm(int64_t hidden_size,
-            float eps = 1e-06f)
+            float eps               = 1e-06f,
+            bool elementwise_affine = true)
         : hidden_size(hidden_size),
-          eps(eps) {}
+          eps(eps),
+          elementwise_affine(elementwise_affine) {}
 
     ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+        if (!elementwise_affine) {
+            return ggml_rms_norm(ctx->ggml_ctx, x, eps);
+        }
         ggml_tensor* w = params["weight"];
         if (ctx->weight_adapter) {
             w = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, ctx->backend, w, prefix + "weight");

src/model/common/rope.hpp

@@ -929,6 +929,145 @@ namespace Rope {
         return ids;
     }
 
+    // LLaDA-Image shares Lumina2/z_image's axes layout, but assigns position (0,0,0) to the
+    // padding slots of the caption stream instead of continuing the caption ramp through them.
+    __STATIC_INLINE__ std::vector<std::vector<float>> gen_llada_image_ids(int h,
+                                                                          int w,
+                                                                          int patch_size,
+                                                                          int bs,
+                                                                          int context_len,
+                                                                          int seq_multi_of) {
+        int context_pad_len    = bound_mod(context_len, seq_multi_of);
+        int padded_context_len = context_len + context_pad_len;
+        auto txt_ids           = std::vector<std::vector<float>>(bs * padded_context_len, std::vector<float>(3, 0.0f));
+        for (int i = 0; i < bs * padded_context_len; i++) {
+            int pos = i % padded_context_len;
+            if (pos < context_len) {
+                txt_ids[i][0] = pos + 1.f;
+            }
+        }
+
+        int axes_dim_num = 3;
+        int index        = padded_context_len + 1;
+        auto img_ids     = gen_flux_img_ids(h, w, patch_size, bs, axes_dim_num, index);
+
+        int img_pad_len = bound_mod(static_cast<int>(img_ids.size() / bs), seq_multi_of);
+        if (img_pad_len > 0) {
+            std::vector<std::vector<float>> img_pad_ids(bs * img_pad_len, std::vector<float>(3, 0.f));
+            img_ids = concat_ids(img_ids, img_pad_ids, bs);
+        }
+
+        return concat_ids(txt_ids, img_ids, bs);
+    }
+
+    // LLaDA-Image editing packs two caption copies (clean and noisy), the source and target
+    // latents anchored at their own caption's end position, and the SigVQ stream after both.
+    // Padding slots keep position (0,0,0), as in the text-only layout.
+    __STATIC_INLINE__ std::vector<std::vector<float>> gen_llada_image_edit_ids(int h,
+                                                                               int w,
+                                                                               int patch_size,
+                                                                               int context_len,
+                                                                               int sigvq_len,
+                                                                               int seq_multi_of) {
+        const int context_pad    = bound_mod(context_len, seq_multi_of);
+        const int padded_context = context_len + context_pad;
+        const int h_len          = (h + (patch_size / 2)) / patch_size;
+        const int w_len          = (w + (patch_size / 2)) / patch_size;
+        const int image_len      = h_len * w_len;
+        const int image_pad      = bound_mod(image_len, seq_multi_of);
+        const int padded_image   = image_len + image_pad;
+        const int sigvq_pad      = bound_mod(sigvq_len, seq_multi_of);
+
+        std::vector<std::vector<float>> cap_ids;
+        std::vector<int> cap_end_positions;
+        int cursor = 1;
+        for (int copy = 0; copy < 2; ++copy) {
+            for (int i = 0; i < padded_context; ++i) {
+                std::vector<float> id(3, 0.f);
+                if (i < context_len) {
+                    id[0] = static_cast<float>(cursor + i);
+                }
+                cap_ids.push_back(id);
+            }
+            cursor += context_len;
+            cap_end_positions.push_back(cursor);
+            cursor += 2;
+        }
+
+        std::vector<std::vector<float>> img_ids;
+        for (int copy = 0; copy < 2; ++copy) {
+            auto ids = gen_flux_img_ids(h, w, patch_size, 1, 3, cap_end_positions[copy]);
+            img_ids.insert(img_ids.end(), ids.begin(), ids.end());
+            img_ids.insert(img_ids.end(), image_pad, std::vector<float>(3, 0.f));
+        }
+
+        const int sigvq_start = static_cast<int>(cap_ids.size() + img_ids.size()) + 1;
+        std::vector<std::vector<float>> sigvq_ids;
+        for (int i = 0; i < sigvq_len + sigvq_pad; ++i) {
+            std::vector<float> id(3, 0.f);
+            if (i < sigvq_len) {
+                id[0] = static_cast<float>(sigvq_start + i);
+            }
+            sigvq_ids.push_back(id);
+        }
+
+        std::vector<std::vector<float>> ids;
+        ids.reserve(cap_ids.size() + img_ids.size() + sigvq_ids.size());
+        ids.insert(ids.end(), cap_ids.begin(), cap_ids.end());
+        ids.insert(ids.end(), img_ids.begin(), img_ids.end());
+        ids.insert(ids.end(), sigvq_ids.begin(), sigvq_ids.end());
+        SD_UNUSED(padded_image);
+        return ids;
+    }
+
+    __STATIC_INLINE__ std::vector<float> gen_llada_image_edit_pe(int h,
+                                                                 int w,
+                                                                 int patch_size,
+                                                                 int context_len,
+                                                                 int sigvq_len,
+                                                                 int seq_multi_of,
+                                                                 int theta,
+                                                                 const std::vector<int>& axes_dim) {
+        auto ids = gen_llada_image_edit_ids(h, w, patch_size, context_len, sigvq_len, seq_multi_of);
+        return embed_nd(ids, 1, static_cast<float>(theta), axes_dim, {});
+    }
+
+    __STATIC_INLINE__ std::vector<float> gen_llada_image_pe(int h,
+                                                            int w,
+                                                            int patch_size,
+                                                            int bs,
+                                                            int context_len,
+                                                            int seq_multi_of,
+                                                            int theta,
+                                                            bool circular_h,
+                                                            bool circular_w,
+                                                            const std::vector<int>& axes_dim) {
+        std::vector<std::vector<float>> ids = gen_llada_image_ids(h, w, patch_size, bs, context_len, seq_multi_of);
+        std::vector<std::vector<int>> wrap_dims;
+        if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
+            int pad_h = (patch_size - (h % patch_size)) % patch_size;
+            int pad_w = (patch_size - (w % patch_size)) % patch_size;
+            int h_len = (h + pad_h) / patch_size;
+            int w_len = (w + pad_w) / patch_size;
+            if (h_len > 0 && w_len > 0) {
+                size_t pos_len = ids.size() / bs;
+                wrap_dims.assign(axes_dim.size(), std::vector<int>(pos_len, 0));
+                size_t cursor     = context_len + bound_mod(context_len, seq_multi_of);
+                size_t img_tokens = static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
+                for (size_t token_i = 0; token_i < img_tokens; ++token_i) {
+                    if (circular_h) {
+                        wrap_dims[1][cursor + token_i] = h_len;
+                    }
+                    if (circular_w) {
+                        wrap_dims[2][cursor + token_i] = w_len;
+                    }
+                }
+            }
+        }
+
+        return embed_nd(ids, bs, static_cast<float>(theta), axes_dim, wrap_dims);
+    }
+
     // Generate z_image positional embeddings
     __STATIC_INLINE__ std::vector<float> gen_z_image_pe(int h,
                                                         int w,

src/model/diffusion/llada_image.hpp

@@ -0,0 +1,527 @@
+#ifndef __SD_MODEL_DIFFUSION_LLADA_IMAGE_HPP__
+#define __SD_MODEL_DIFFUSION_LLADA_IMAGE_HPP__
+
+#include <algorithm>
+#include <cinttypes>
+
+#include "core/ggml_extend.h"
+#include "core/ggml_runner.h"
+#include "core/util.h"
+#include "model/common/ggml_block.hpp"
+#include "model/diffusion/model.hpp"
+#include "model/diffusion/z_image.hpp"
+#include "model_loader.h"
+
+// Ref: https://github.com/inclusionAI/LLaDA-Image/blob/main/src/models/transformer_llada_image.py
+//
+// The denoiser is Lumina2/z_image's NextDiT with identical hyperparameters, so the blocks are
+// reused from ZImage. Two things differ: every norm here is non-parametric (the checkpoint
+// carries no norm weights at all), and latents arrive already patchified from the Flux2 VAE,
+// so patch_size is 1 over 128 channels.
+
+namespace LLaDAImage {
+    constexpr int LLADA_IMAGE_GRAPH_SIZE = 20480;
+
+    struct LLaDAImageConfig {
+        int patch_size             = 1;
+        int64_t hidden_size        = 3840;
+        int64_t in_channels        = 128;
+        int64_t out_channels       = 128;
+        int64_t num_layers         = 30;
+        int64_t num_refiner_layers = 2;
+        int64_t head_dim           = 128;
+        int64_t num_heads          = 30;
+        int64_t num_kv_heads       = 30;
+        int64_t multiple_of        = 256;
+        float ffn_dim_multiplier   = 8.0f / 3.0f;
+        float norm_eps             = 1e-5f;
+        bool qk_norm               = true;
+        int64_t cap_feat_dim       = 2560;
+        int64_t semantic_feat_dim  = 4096;
+        int theta                  = 256;
+        std::vector<int> axes_dim  = {32, 48, 48};
+        int64_t axes_dim_sum       = 128;
+
+        static int64_t count_blocks(const String2TensorStorage& tensor_storage_map,
+                                    const std::string& prefix,
+                                    const std::string& block_prefix) {
+            int64_t count = 0;
+            for (const auto& [name, _] : tensor_storage_map) {
+                if (!starts_with(name, prefix)) {
+                    continue;
+                }
+                size_t pos = name.find(block_prefix);
+                if (pos == std::string::npos) {
+                    continue;
+                }
+                auto items = split_string(name.substr(pos), '.');
+                if (items.size() > 1) {
+                    count = std::max<int64_t>(count, atoi(items[1].c_str()) + 1);
+                }
+            }
+            return count;
+        }
+
+        static LLaDAImageConfig detect_from_weights(const String2TensorStorage& tensor_storage_map, const std::string& prefix) {
+            LLaDAImageConfig config;
+            int64_t detected_q_dim  = 0;
+            int64_t detected_kv_dim = 0;
+
+            for (const auto& [name, tensor_storage] : tensor_storage_map) {
+                if (!starts_with(name, prefix)) {
+                    continue;
+                }
+                if (ends_with(name, "x_embedder.weight") && tensor_storage.n_dims == 2) {
+                    int64_t patch_area = config.patch_size * config.patch_size;
+                    config.in_channels = tensor_storage.ne[0] / patch_area;
+                    config.hidden_size = tensor_storage.ne[1];
+                } else if (ends_with(name, "cap_embedder.1.weight") && tensor_storage.n_dims == 2) {
+                    config.cap_feat_dim = tensor_storage.ne[0];
+                    config.hidden_size  = tensor_storage.ne[1];
+                } else if (ends_with(name, "sigvq_embedder.1.weight") && tensor_storage.n_dims == 2) {
+                    config.semantic_feat_dim = tensor_storage.ne[0];
+                } else if (ends_with(name, "layers.0.attention.to_q.weight") && tensor_storage.n_dims == 2) {
+                    detected_q_dim = tensor_storage.ne[1];
+                } else if (ends_with(name, "layers.0.attention.to_k.weight") && tensor_storage.n_dims == 2) {
+                    detected_kv_dim = tensor_storage.ne[1];
+                } else if (ends_with(name, "final_layer.linear.weight") && tensor_storage.n_dims == 2) {
+                    int64_t patch_area  = config.patch_size * config.patch_size;
+                    config.out_channels = tensor_storage.ne[1] / patch_area;
+                }
+            }
+
+            int64_t detected_layers  = count_blocks(tensor_storage_map, prefix, "layers.");
+            int64_t detected_refiner = std::max(count_blocks(tensor_storage_map, prefix, "noise_refiner."),
+                                                count_blocks(tensor_storage_map, prefix, "context_refiner."));
+            if (detected_layers > 0) {
+                config.num_layers = detected_layers;
+            }
+            if (detected_refiner > 0) {
+                config.num_refiner_layers = detected_refiner;
+            }
+            if (detected_q_dim > 0) {
+                config.num_heads = detected_q_dim / config.head_dim;
+            }
+            if (detected_kv_dim > 0) {
+                config.num_kv_heads = detected_kv_dim / config.head_dim;
+            } else if (detected_q_dim > 0) {
+                config.num_kv_heads = config.num_heads;
+            }
+
+            LOG_VERBOSE("llada_image: num_layers = %" PRId64 ", num_refiner_layers = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %" PRId64 ", num_kv_heads = %" PRId64 ", in_channels = %" PRId64 ", out_channels = %" PRId64 ", cap_feat_dim = %" PRId64 ", semantic_feat_dim = %" PRId64,
+                        config.num_layers,
+                        config.num_refiner_layers,
+                        config.hidden_size,
+                        config.num_heads,
+                        config.num_kv_heads,
+                        config.in_channels,
+                        config.out_channels,
+                        config.cap_feat_dim,
+                        config.semantic_feat_dim);
+            return config;
+        }
+    };
+
+    class LLaDAImageModel : public GGMLBlock {
+    protected:
+        LLaDAImageConfig config;
+
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+            params["cap_pad_token"]   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, config.hidden_size);
+            params["x_pad_token"]     = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, config.hidden_size);
+            params["sigvq_pad_token"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, config.hidden_size);
+        }
+
+        std::shared_ptr<ZImage::JointTransformerBlock> make_block(bool modulation) {
+            return std::make_shared<ZImage::JointTransformerBlock>(0,
+                                                                   config.hidden_size,
+                                                                   config.head_dim,
+                                                                   config.num_heads,
+                                                                   config.num_kv_heads,
+                                                                   config.multiple_of,
+                                                                   config.ffn_dim_multiplier,
+                                                                   config.norm_eps,
+                                                                   config.qk_norm,
+                                                                   modulation,
+                                                                   false,
+                                                                   true);
+        }
+
+    public:
+        LLaDAImageModel() = default;
+        LLaDAImageModel(LLaDAImageConfig config)
+            : config(config) {
+            blocks["x_embedder"] = std::make_shared<Linear>(config.patch_size * config.patch_size * config.in_channels, config.hidden_size);
+            blocks["t_embedder"] = std::make_shared<TimestepEmbedder>(MIN(config.hidden_size, 1024), 256, ZImage::ADALN_EMBED_DIM);
+
+            blocks["cap_embedder.0"] = std::make_shared<RMSNorm>(config.cap_feat_dim, config.norm_eps, false);
+            blocks["cap_embedder.1"] = std::make_shared<Linear>(config.cap_feat_dim, config.hidden_size);
+
+            blocks["semantic_embedder.0"] = std::make_shared<RMSNorm>(config.semantic_feat_dim, config.norm_eps, false);
+            blocks["semantic_embedder.1"] = std::make_shared<Linear>(config.semantic_feat_dim, config.hidden_size);
+            blocks["sigvq_embedder.0"]    = std::make_shared<RMSNorm>(config.semantic_feat_dim, config.norm_eps, false);
+            blocks["sigvq_embedder.1"]    = std::make_shared<Linear>(config.semantic_feat_dim, config.hidden_size);
+
+            for (int i = 0; i < config.num_refiner_layers; i++) {
+                blocks["noise_refiner." + std::to_string(i)]   = make_block(true);
+                blocks["context_refiner." + std::to_string(i)] = make_block(false);
+                blocks["sigvq_refiner." + std::to_string(i)]   = make_block(false);
+            }
+            for (int i = 0; i < config.num_layers; i++) {
+                blocks["layers." + std::to_string(i)] = make_block(true);
+            }
+
+            blocks["final_layer"] = std::make_shared<ZImage::FinalLayer>(config.hidden_size, config.patch_size, config.out_channels);
+        }
+
+        ggml_tensor* forward_core(GGMLRunnerContext* ctx,
+                                  ggml_tensor* x,
+                                  ggml_tensor* timestep,
+                                  ggml_tensor* context,
+                                  ggml_tensor* pe) {
+            auto x_embedder     = std::dynamic_pointer_cast<Linear>(blocks["x_embedder"]);
+            auto t_embedder     = std::dynamic_pointer_cast<TimestepEmbedder>(blocks["t_embedder"]);
+            auto cap_embedder_0 = std::dynamic_pointer_cast<RMSNorm>(blocks["cap_embedder.0"]);
+            auto cap_embedder_1 = std::dynamic_pointer_cast<Linear>(blocks["cap_embedder.1"]);
+            auto final_layer    = std::dynamic_pointer_cast<ZImage::FinalLayer>(blocks["final_layer"]);
+
+            auto txt_pad_token = params["cap_pad_token"];
+            auto img_pad_token = params["x_pad_token"];
+
+            int64_t N           = x->ne[2];
+            int64_t n_img_token = x->ne[1];
+            int64_t n_txt_token = context->ne[1];
+
+            // sdcpp's flow denoiser already hands over sigma * 1000, which is the range the
+            // reference reaches via its own t_scale, so no further scaling here.
+            auto t_emb = t_embedder->forward(ctx, timestep);
+
+            auto txt = cap_embedder_1->forward(ctx, cap_embedder_0->forward(ctx, context));  // [N, n_txt_token, hidden_size]
+            auto img = x_embedder->forward(ctx, x);                                          // [N, n_img_token, hidden_size]
+            sd::ggml_graph_cut::mark_graph_cut(txt, "llada_image.prelude", "txt");
+            sd::ggml_graph_cut::mark_graph_cut(img, "llada_image.prelude", "img");
+            sd::ggml_graph_cut::mark_graph_cut(t_emb, "llada_image.prelude", "t_emb");
+
+            int64_t n_txt_pad_token = Rope::bound_mod(static_cast<int>(n_txt_token), ZImage::SEQ_MULTI_OF);
+            if (n_txt_pad_token > 0) {
+                auto txt_pad_tokens = ggml_repeat_4d(ctx->ggml_ctx, txt_pad_token, txt_pad_token->ne[0], n_txt_pad_token, N, 1);
+                txt                 = ggml_concat(ctx->ggml_ctx, txt, txt_pad_tokens, 1);
+            }
+
+            int64_t n_img_pad_token = Rope::bound_mod(static_cast<int>(n_img_token), ZImage::SEQ_MULTI_OF);
+            if (n_img_pad_token > 0) {
+                auto img_pad_tokens = ggml_repeat_4d(ctx->ggml_ctx, img_pad_token, img_pad_token->ne[0], n_img_pad_token, N, 1);
+                img                 = ggml_concat(ctx->ggml_ctx, img, img_pad_tokens, 1);
+            }
+
+            GGML_ASSERT(txt->ne[1] + img->ne[1] == pe->ne[3]);
+
+            auto txt_pe = ggml_ext_slice(ctx->ggml_ctx, pe, 3, 0, txt->ne[1]);
+            auto img_pe = ggml_ext_slice(ctx->ggml_ctx, pe, 3, txt->ne[1], pe->ne[3]);
+
+            for (int i = 0; i < config.num_refiner_layers; i++) {
+                auto block = std::dynamic_pointer_cast<ZImage::JointTransformerBlock>(blocks["context_refiner." + std::to_string(i)]);
+
+                txt = block->forward(ctx, txt, txt_pe, nullptr, nullptr);
+                sd::ggml_graph_cut::mark_graph_cut(txt, "llada_image.context_refiner." + std::to_string(i), "txt");
+            }
+
+            for (int i = 0; i < config.num_refiner_layers; i++) {
+                auto block = std::dynamic_pointer_cast<ZImage::JointTransformerBlock>(blocks["noise_refiner." + std::to_string(i)]);
+
+                img = block->forward(ctx, img, img_pe, nullptr, t_emb);
+                sd::ggml_graph_cut::mark_graph_cut(img, "llada_image.noise_refiner." + std::to_string(i), "img");
+            }
+
+            auto txt_img = ggml_concat(ctx->ggml_ctx, txt, img, 1);
+            sd::ggml_graph_cut::mark_graph_cut(txt_img, "llada_image.prelude", "txt_img");
+
+            for (int i = 0; i < config.num_layers; i++) {
+                auto block = std::dynamic_pointer_cast<ZImage::JointTransformerBlock>(blocks["layers." + std::to_string(i)]);
+
+                txt_img = block->forward(ctx, txt_img, pe, nullptr, t_emb);
+                sd::ggml_graph_cut::mark_graph_cut(txt_img, "llada_image.layers." + std::to_string(i), "txt_img");
+            }
+
+            txt_img = final_layer->forward(ctx, txt_img, t_emb);
+
+            return ggml_ext_slice(ctx->ggml_ctx, txt_img, 1, n_txt_token + n_txt_pad_token, n_txt_token + n_txt_pad_token + n_img_token);
+        }
+
+        ggml_tensor* pad_stream(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* pad_token) {
+            int64_t n_pad = Rope::bound_mod(static_cast<int>(x->ne[1]), ZImage::SEQ_MULTI_OF);
+            if (n_pad == 0) {
+                return x;
+            }
+            auto pads = ggml_repeat_4d(ctx->ggml_ctx, pad_token, pad_token->ne[0], n_pad, x->ne[2], 1);
+            return ggml_concat(ctx->ggml_ctx, x, pads, 1);
+        }
+
+        // Editing runs one joint sequence carrying two timesteps: the caption and source latent
+        // are clean (t = 0) while the second caption copy and the target latent are noisy. adaLN
+        // is a linear map of the timestep embedding, so feeding a per-token embedding selects the
+        // right modulation exactly, without duplicating the modulation projections.
+        ggml_tensor* forward_editing(GGMLRunnerContext* ctx,
+                                     ggml_tensor* x,
+                                     ggml_tensor* timestep,
+                                     ggml_tensor* context,
+                                     ggml_tensor* semantic,
+                                     ggml_tensor* source_latent,
+                                     ggml_tensor* pe) {
+            ggml_context* gctx = ctx->ggml_ctx;
+
+            auto x_embedder     = std::dynamic_pointer_cast<Linear>(blocks["x_embedder"]);
+            auto t_embedder     = std::dynamic_pointer_cast<TimestepEmbedder>(blocks["t_embedder"]);
+            auto cap_embedder_0 = std::dynamic_pointer_cast<RMSNorm>(blocks["cap_embedder.0"]);
+            auto cap_embedder_1 = std::dynamic_pointer_cast<Linear>(blocks["cap_embedder.1"]);
+            auto sigvq_embed_0  = std::dynamic_pointer_cast<RMSNorm>(blocks["sigvq_embedder.0"]);
+            auto sigvq_embed_1  = std::dynamic_pointer_cast<Linear>(blocks["sigvq_embedder.1"]);
+            auto final_layer    = std::dynamic_pointer_cast<ZImage::FinalLayer>(blocks["final_layer"]);
+
+            auto t_noisy = t_embedder->forward(ctx, timestep);
+            auto t_clean = t_embedder->forward(ctx, ggml_scale(gctx, timestep, 0.f));
+
+            auto per_token = [&](ggml_tensor* emb, int64_t n) {
+                return ggml_repeat_4d(gctx, emb, emb->ne[0], n, 1, 1);
+            };
+
+            auto cap        = cap_embedder_1->forward(ctx, cap_embedder_0->forward(ctx, context));
+            cap             = pad_stream(ctx, cap, params["cap_pad_token"]);
+            int64_t cap_len = cap->ne[1];
+            cap             = ggml_concat(gctx, cap, cap, 1);
+
+            auto src            = pad_stream(ctx, x_embedder->forward(ctx, source_latent), params["x_pad_token"]);
+            auto tgt_embed      = x_embedder->forward(ctx, x);
+            int64_t n_img_token = tgt_embed->ne[1];
+            auto tgt            = pad_stream(ctx, tgt_embed, params["x_pad_token"]);
+            int64_t img_len     = tgt->ne[1];
+            auto img            = ggml_concat(gctx, src, tgt, 1);
+
+            ggml_tensor* sig = nullptr;
+            int64_t sig_len  = 0;
+            if (semantic != nullptr) {
+                sig     = sigvq_embed_1->forward(ctx, sigvq_embed_0->forward(ctx, semantic));
+                sig     = pad_stream(ctx, sig, params["sigvq_pad_token"]);
+                sig_len = sig->ne[1];
+            }
+
+            GGML_ASSERT(cap_len * 2 + img_len * 2 + sig_len == pe->ne[3]);
+
+            auto cap_pe = ggml_ext_slice(gctx, pe, 3, 0, cap_len * 2);
+            auto img_pe = ggml_ext_slice(gctx, pe, 3, cap_len * 2, cap_len * 2 + img_len * 2);
+
+            auto img_adaln = ggml_concat(gctx, per_token(t_clean, img_len), per_token(t_noisy, img_len), 1);
+
+            for (int i = 0; i < config.num_refiner_layers; i++) {
+                auto block = std::dynamic_pointer_cast<ZImage::JointTransformerBlock>(blocks["context_refiner." + std::to_string(i)]);
+                cap        = block->forward(ctx, cap, cap_pe, nullptr, nullptr);
+            }
+            for (int i = 0; i < config.num_refiner_layers; i++) {
+                auto block = std::dynamic_pointer_cast<ZImage::JointTransformerBlock>(blocks["noise_refiner." + std::to_string(i)]);
+                img        = block->forward(ctx, img, img_pe, nullptr, img_adaln);
+            }
+            if (sig != nullptr) {
+                auto sig_pe = ggml_ext_slice(gctx, pe, 3, cap_len * 2 + img_len * 2, pe->ne[3]);
+                for (int i = 0; i < config.num_refiner_layers; i++) {
+                    auto block = std::dynamic_pointer_cast<ZImage::JointTransformerBlock>(blocks["sigvq_refiner." + std::to_string(i)]);
+                    sig        = block->forward(ctx, sig, sig_pe, nullptr, nullptr);
+                }
+            }
+
+            auto seq = ggml_concat(gctx, cap, img, 1);
+
+            auto cap_adaln = ggml_concat(gctx, per_token(t_clean, cap_len), per_token(t_noisy, cap_len), 1);
+            auto seq_adaln = ggml_concat(gctx, cap_adaln, img_adaln, 1);
+            if (sig != nullptr) {
+                seq       = ggml_concat(gctx, seq, sig, 1);
+                seq_adaln = ggml_concat(gctx, seq_adaln, per_token(t_clean, sig_len), 1);
+            }
+
+            for (int i = 0; i < config.num_layers; i++) {
+                auto block = std::dynamic_pointer_cast<ZImage::JointTransformerBlock>(blocks["layers." + std::to_string(i)]);
+                seq        = block->forward(ctx, seq, pe, nullptr, seq_adaln);
+                sd::ggml_graph_cut::mark_graph_cut(seq, "llada_image.layers." + std::to_string(i), "seq");
+            }
+
+            seq = final_layer->forward(ctx, seq, seq_adaln);
+
+            // Only the target latent is denoised; the source half of the image stream is context.
+            // The stream is padded to SEQ_MULTI_OF, so drop the pad tokens: they are not part of
+            // the latent grid that unpatchify reconstructs.
+            int64_t target_start = cap_len * 2 + img_len;
+            return ggml_ext_slice(gctx, seq, 1, target_start, target_start + n_img_token);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timestep,
+                             ggml_tensor* context,
+                             ggml_tensor* pe) {
+            // x: [N, C, H, W]
+            // timestep: [N,]
+            // context: [N, L, cap_feat_dim]
+            // pe: [L, d_head/2, 2, 2]
+            // return: [N, C, H, W]
+            int64_t W = x->ne[0];
+            int64_t H = x->ne[1];
+
+            int patch_size = config.patch_size;
+
+            auto img = DiT::pad_and_patchify(ctx, x, patch_size, patch_size, false);
+
+            auto out = forward_core(ctx, img, timestep, context, pe);
+
+            out = DiT::unpatchify_and_crop(ctx->ggml_ctx, out, H, W, patch_size, patch_size, false);
+
+            // The reference pipeline negates the model output before the scheduler step.
+            return ggml_ext_scale(ctx->ggml_ctx, out, -1.f);
+        }
+    };
+
+    struct LLaDAImageRunner : public DiffusionModelRunner {
+    public:
+        LLaDAImageConfig config;
+        LLaDAImageModel llada_image;
+        std::vector<float> pe_vec;
+
+        LLaDAImageRunner(ggml_backend_t backend,
+                         const String2TensorStorage& tensor_storage_map      = {},
+                         const std::string prefix                            = "",
+                         std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
+              config(LLaDAImageConfig::detect_from_weights(tensor_storage_map, prefix)) {
+            llada_image = LLaDAImageModel(config);
+            llada_image.init(params_ctx, tensor_storage_map, prefix);
+        }
+
+        std::string get_desc() override {
+            return "llada_image";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix) override {
+            llada_image.get_param_tensors(tensors, prefix);
+        }
+
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor) {
+            ggml_cgraph* gf        = new_graph_custom(LLADA_IMAGE_GRAPH_SIZE);
+            ggml_tensor* x         = make_input(x_tensor);
+            ggml_tensor* timesteps = make_input(timesteps_tensor);
+            GGML_ASSERT(x->ne[3] == 1);
+            GGML_ASSERT(!context_tensor.empty());
+            ggml_tensor* context = make_input(context_tensor);
+
+            pe_vec      = Rope::gen_llada_image_pe(static_cast<int>(x->ne[1]),
+                                                   static_cast<int>(x->ne[0]),
+                                                   config.patch_size,
+                                                   static_cast<int>(x->ne[3]),
+                                                   static_cast<int>(context->ne[1]),
+                                                   ZImage::SEQ_MULTI_OF,
+                                                   config.theta,
+                                                   circular_y_enabled,
+                                                   circular_x_enabled,
+                                                   config.axes_dim);
+            int pos_len = static_cast<int>(pe_vec.size() / config.axes_dim_sum / 2);
+            auto pe     = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.axes_dim_sum / 2, pos_len);
+            set_backend_tensor_data(pe, pe_vec.data());
+            auto runner_ctx = get_context();
+
+            ggml_tensor* out = llada_image.forward(&runner_ctx, x, timesteps, context, pe);
+
+            ggml_build_forward_expand(gf, out);
+
+            return gf;
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context) {
+            // x: [N, in_channels, h, w]
+            // timesteps: [N, ]
+            // context: [N, max_position, cap_feat_dim]
+            auto get_graph = [&]() -> ggml_cgraph* {
+                return build_graph(x, timesteps, context);
+            };
+
+            return restore_trailing_singleton_dims(GGMLRunner::compute(get_graph, n_threads, false), x.dim());
+        }
+
+        ggml_cgraph* build_edit_graph(const sd::Tensor<float>& x_tensor,
+                                      const sd::Tensor<float>& timesteps_tensor,
+                                      const sd::Tensor<float>& context_tensor,
+                                      const sd::Tensor<float>& semantic_tensor,
+                                      const sd::Tensor<float>& source_tensor) {
+            ggml_cgraph* gf        = new_graph_custom(LLADA_IMAGE_GRAPH_SIZE);
+            ggml_tensor* x         = make_input(x_tensor);
+            ggml_tensor* timesteps = make_input(timesteps_tensor);
+            ggml_tensor* context   = make_input(context_tensor);
+            ggml_tensor* semantic  = make_optional_input(semantic_tensor);
+            ggml_tensor* source    = make_input(source_tensor);
+            GGML_ASSERT(x->ne[3] == 1);
+
+            pe_vec      = Rope::gen_llada_image_edit_pe(static_cast<int>(x->ne[1]),
+                                                        static_cast<int>(x->ne[0]),
+                                                        config.patch_size,
+                                                        static_cast<int>(context->ne[1]),
+                                                   semantic != nullptr ? static_cast<int>(semantic->ne[1]) : 0,
+                                                        ZImage::SEQ_MULTI_OF,
+                                                        config.theta,
+                                                        config.axes_dim);
+            int pos_len = static_cast<int>(pe_vec.size() / config.axes_dim_sum / 2);
+            auto pe     = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.axes_dim_sum / 2, pos_len);
+            set_backend_tensor_data(pe, pe_vec.data());
+            auto runner_ctx = get_context();
+
+            int64_t W   = x->ne[0];
+            int64_t H   = x->ne[1];
+            auto target = DiT::pad_and_patchify(&runner_ctx, x, config.patch_size, config.patch_size, false);
+            auto src    = DiT::pad_and_patchify(&runner_ctx, source, config.patch_size, config.patch_size, false);
+
+            auto out = llada_image.forward_editing(&runner_ctx, target, timesteps, context, semantic, src, pe);
+            out      = DiT::unpatchify_and_crop(runner_ctx.ggml_ctx, out, H, W, config.patch_size, config.patch_size, false);
+            out      = ggml_ext_scale(runner_ctx.ggml_ctx, out, -1.f);
+
+            ggml_build_forward_expand(gf, out);
+            return gf;
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const DiffusionParams& diffusion_params) override {
+            GGML_ASSERT(diffusion_params.x != nullptr);
+            GGML_ASSERT(diffusion_params.timesteps != nullptr);
+
+            const auto* extra   = std::get_if<LLaDAImageDiffusionExtra>(&diffusion_params.extra);
+            bool has_semantic   = extra != nullptr && extra->semantic != nullptr && !extra->semantic->empty();
+            bool has_ref_latent = diffusion_params.ref_latents != nullptr && !diffusion_params.ref_latents->empty();
+            if (has_semantic && !has_ref_latent) {
+                LOG_WARN("llada_image: SigVQ features without a reference latent are not supported; falling back to text to image");
+            }
+            if (has_ref_latent) {
+                const auto& source = diffusion_params.ref_latents->front();
+                if (source.shape() != diffusion_params.x->shape()) {
+                    LOG_ERROR("llada_image: reference latent must match the target shape; use resize_vae_to_target=1");
+                    return {};
+                }
+                auto get_graph = [&]() -> ggml_cgraph* {
+                    return build_edit_graph(*diffusion_params.x,
+                                            *diffusion_params.timesteps,
+                                            tensor_or_empty(diffusion_params.context),
+                                            tensor_or_empty(extra != nullptr ? extra->semantic : nullptr),
+                                            source);
+                };
+                return restore_trailing_singleton_dims(GGMLRunner::compute(get_graph, n_threads, false),
+                                                       diffusion_params.x->dim());
+            }
+
+            return compute(n_threads,
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           tensor_or_empty(diffusion_params.context));
+        }
+    };
+
+}  // namespace LLaDAImage
+
+#endif  // __SD_MODEL_DIFFUSION_LLADA_IMAGE_HPP__

src/model/diffusion/model.hpp

@@ -39,6 +39,9 @@ const std::unordered_map<std::string, RefImageParams> REF_IMAGE_PRESETS = {
     {"z_image_omni", {true, true, Rope::RefIndexMode::FIXED, false, true, -1, RefImageResizeMode::AREA, -1, -1}},
     {"krea2_ostris_edit", {true, true, Rope::RefIndexMode::INCREASE, true, true, -1, RefImageResizeMode::AREA, -1, -1}},
     {"krea2_edit", {true, true, Rope::RefIndexMode::INCREASE, false, true, -1, RefImageResizeMode::LONGEST_SIDE, 768, 768}},
+    // pass_to_vlm routes the reference image to the conditioner, which is where LLaDA-Image's
+    // SigVQ encoder lives; it does its own half-resolution resize.
+    {"llada_image", {true, true, Rope::RefIndexMode::FIXED, true, true, -1, RefImageResizeMode::NONE, -1, -1, true}},
     {"cosmos_reference", {false, true, Rope::RefIndexMode::INCREASE, false, false, -1, RefImageResizeMode::NONE, -1, -1}},
 };
 
@@ -66,6 +69,10 @@ struct AnimaDiffusionExtra {
     const sd::Tensor<float>* t5_weights = nullptr;
 };
 
+struct QwenImage21DiffusionExtra {
+    const sd::Tensor<int32_t>* image_slots = nullptr;
+};
+
 struct WanDiffusionExtra {
     const sd::Tensor<float>* vace_context = nullptr;
     float vace_strength                   = 1.f;
@@ -127,18 +134,25 @@ struct HunyuanVideoDiffusionExtra {
     const sd::Tensor<float>* timestep_r = nullptr;
 };
 
+struct LLaDAImageDiffusionExtra {
+    // SigVQ semantic features of the reference image; present only in editing mode.
+    const sd::Tensor<float>* semantic = nullptr;
+};
+
 using DiffusionExtraParams = std::variant<std::monostate,
                                           UNetDiffusionExtra,
                                           SkipLayerDiffusionExtra,
                                           FluxDiffusionExtra,
                                           AnimaDiffusionExtra,
+                                          QwenImage21DiffusionExtra,
                                           WanDiffusionExtra,
                                           HiDreamO1DiffusionExtra,
                                           LTXAVDiffusionExtra,
                                           MiniMaxH3DiffusionExtra,
                                           MiniT2IDiffusionExtra,
                                           SenseNovaU1DiffusionExtra,
-                                          HunyuanVideoDiffusionExtra>;
+                                          HunyuanVideoDiffusionExtra,
+                                          LLaDAImageDiffusionExtra>;
 
 struct DiffusionParams {
     const sd::Tensor<float>* x                        = nullptr;

src/model/diffusion/qwen_image_2_1.hpp

@@ -0,0 +1,384 @@
+#ifndef __SD_MODEL_DIFFUSION_QWEN_IMAGE_2_1_H__
+#define __SD_MODEL_DIFFUSION_QWEN_IMAGE_2_1_H__
+
+#include "model/diffusion/qwen_image.hpp"
+
+namespace Qwen {
+
+    struct QwenImage21Config {
+        int64_t in_channels       = 64;
+        int64_t out_channels      = 64;
+        int64_t hidden_size       = 4096;
+        int64_t context_dim       = 4096;
+        int64_t head_dim          = 128;
+        int64_t intermediate_size = 12288;
+        int num_layers            = 32;
+        bool fused_mlp            = false;
+        std::vector<int> axes_dim = {16, 56, 56};
+
+        static QwenImage21Config detect_from_weights(const String2TensorStorage& weights, const std::string& prefix) {
+            QwenImage21Config config;
+            auto find = [&](const std::string& suffix) -> const TensorStorage* {
+                auto it = weights.find(prefix + "." + suffix);
+                return it == weights.end() ? nullptr : &it->second;
+            };
+            if (auto w = find("img_in.weight")) {
+                config.in_channels = w->ne[0];
+                config.hidden_size = w->ne[1];
+            }
+            if (auto w = find("proj_out.weight")) {
+                config.out_channels = w->ne[1];
+            }
+            if (auto w = find("txt_in.in_layer.weight")) {
+                config.context_dim = w->ne[0];
+            }
+            if (auto w = find("transformer_blocks.0.attn.norm_q.weight")) {
+                config.head_dim = w->ne[0];
+            }
+            if (auto w = find("transformer_blocks.0.img_mlp.gate_up.weight")) {
+                config.intermediate_size = w->ne[1] / 2;
+                config.fused_mlp         = true;
+            } else if (auto w = find("transformer_blocks.0.img_mlp.proj.weight")) {
+                config.intermediate_size = w->ne[1];
+            }
+            int layers                     = 0;
+            const std::string block_prefix = prefix + ".transformer_blocks.";
+            for (const auto& [name, _] : weights) {
+                if (starts_with(name, block_prefix)) {
+                    layers = std::max(layers, atoi(name.substr(block_prefix.size()).c_str()) + 1);
+                }
+            }
+            if (layers > 0) {
+                config.num_layers = layers;
+                LOG_VERBOSE("qwen_image_2_1: layers = %d, hidden_size = %" PRId64 ", context_dim = %" PRId64,
+                            layers, config.hidden_size, config.context_dim);
+            }
+            return config;
+        }
+    };
+
+    struct QwenImage21Segment {
+        int64_t start;
+        int64_t end;
+        int64_t context_start;
+        int image_index;
+    };
+
+    struct QwenImage21Layout {
+        std::vector<QwenImage21Segment> segments;
+        std::vector<std::vector<float>> positions;
+        int64_t prefix_length = 0;
+
+        static QwenImage21Layout build(int64_t text_length,
+                                       const sd::Tensor<int32_t>& image_slots,
+                                       const std::vector<std::pair<int64_t, int64_t>>& image_shapes) {
+            if (image_shapes.empty() || (!image_slots.empty() && image_slots.numel() != text_length)) {
+                throw std::runtime_error("Qwen Image 2.1: invalid image token layout");
+            }
+            QwenImage21Layout layout;
+            int64_t position  = 0;
+            int next_image    = 0;
+            auto append_image = [&](int index, int64_t context_start) {
+                auto [height, width] = image_shapes[index];
+                int64_t start        = static_cast<int64_t>(layout.positions.size());
+                layout.segments.push_back({start, start + height * width, context_start, index});
+                for (int64_t h = 0; h < height; ++h) {
+                    for (int64_t w = 0; w < width; ++w) {
+                        layout.positions.push_back({static_cast<float>(position),
+                                                    static_cast<float>(h - (height - height / 2)),
+                                                    static_cast<float>(w - (width - width / 2))});
+                    }
+                }
+                position += std::max(height, width);
+            };
+            for (int64_t i = 0; i < text_length;) {
+                int tag       = image_slots.empty() ? 0 : image_slots[i];
+                int64_t begin = i++;
+                while (i < text_length && (image_slots.empty() ? 0 : image_slots[i]) == tag) {
+                    ++i;
+                }
+                if (tag != 0) {
+                    if (tag != next_image + 1 || next_image + 1 >= static_cast<int>(image_shapes.size()) ||
+                        (i - begin) * 4 != image_shapes[next_image].first * image_shapes[next_image].second) {
+                        throw std::runtime_error("Qwen Image 2.1: vision slots and reference latents must have matching sizes");
+                    }
+                    append_image(next_image++, begin);
+                } else {
+                    int64_t start = static_cast<int64_t>(layout.positions.size());
+                    layout.segments.push_back({start, start + i - begin, begin, -1});
+                    for (int64_t j = begin; j < i; ++j, ++position) {
+                        float p = static_cast<float>(position);
+                        layout.positions.push_back({p, p, p});
+                    }
+                }
+            }
+            if (next_image + 1 != static_cast<int>(image_shapes.size())) {
+                throw std::runtime_error("Qwen Image 2.1: missing reference image slots");
+            }
+            layout.prefix_length = static_cast<int64_t>(layout.positions.size());
+            append_image(next_image, text_length);
+            return layout;
+        }
+    };
+
+    class QwenImage21ZeroCenterRMSNorm : public RMSNorm {
+    public:
+        using RMSNorm::RMSNorm;
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+            auto weight = params["weight"];
+            if (ctx->weight_adapter) {
+                weight = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, ctx->backend, weight, prefix + "weight");
+            }
+            weight = ggml_scale_bias(ctx->ggml_ctx, weight, 1.f, 1.f);
+            return ggml_mul(ctx->ggml_ctx, ggml_rms_norm(ctx->ggml_ctx, x, eps), weight);
+        }
+    };
+
+    class QwenImage21TextProjection : public GGMLBlock {
+    public:
+        QwenImage21TextProjection(const QwenImage21Config& config) {
+            blocks["text_norm"] = std::make_shared<QwenImage21ZeroCenterRMSNorm>(config.context_dim, 1e-6f);
+            blocks["in_layer"]  = std::make_shared<Linear>(config.context_dim, config.hidden_size, false);
+            blocks["out_layer"] = std::make_shared<Linear>(config.hidden_size, config.hidden_size, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            x = std::dynamic_pointer_cast<QwenImage21ZeroCenterRMSNorm>(blocks["text_norm"])->forward(ctx, x);
+            x = std::dynamic_pointer_cast<Linear>(blocks["in_layer"])->forward(ctx, x);
+            x = ggml_ext_gelu(ctx->ggml_ctx, x);
+            return std::dynamic_pointer_cast<Linear>(blocks["out_layer"])->forward(ctx, x);
+        }
+    };
+
+    class QwenImage21Attention : public QwenImageAttention {
+    public:
+        QwenImage21Attention(const QwenImage21Config& config)
+            : QwenImageAttention(config.hidden_size, config.head_dim, config.hidden_size / config.head_dim, 0, 0, false, false) {
+            for (const auto* name : {"add_q_proj", "add_k_proj", "add_v_proj", "norm_added_q", "norm_added_k", "to_add_out"}) {
+                blocks.erase(name);
+            }
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* pe, const std::vector<QwenImage21Segment>& segments, const std::vector<ggml_tensor*>& masks) {
+            int64_t heads = x->ne[0] / dim_head;
+            auto project  = [&](const char* name) {
+                auto h = std::dynamic_pointer_cast<Linear>(blocks[name])->forward(ctx, x);
+                return ggml_reshape_4d(ctx->ggml_ctx, h, dim_head, heads, x->ne[1], x->ne[2]);
+            };
+            auto q              = project("to_q");
+            auto k              = project("to_k");
+            auto v              = project("to_v");
+            q                   = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_q"])->forward(ctx, q);
+            k                   = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_k"])->forward(ctx, k);
+            q                   = Rope::apply_rope(ctx->ggml_ctx, q, pe);
+            k                   = Rope::apply_rope(ctx->ggml_ctx, k, pe);
+            ggml_tensor* result = nullptr;
+            for (size_t i = 0; i < segments.size(); ++i) {
+                const auto& segment = segments[i];
+                auto sq             = ggml_ext_slice(ctx->ggml_ctx, q, 1, segment.start, segment.end);
+                auto sk             = ggml_ext_slice(ctx->ggml_ctx, k, 1, 0, segment.end);
+                auto sv             = ggml_ext_slice(ctx->ggml_ctx, v, 2, 0, segment.end);
+                auto out            = ggml_ext_attention_ext(ctx, sq, sk, sv, heads, masks[i], true, ctx->flash_attn_enabled);
+                result              = result == nullptr ? out : ggml_concat(ctx->ggml_ctx, result, out, 1);
+            }
+            auto to_out = std::dynamic_pointer_cast<Linear>(blocks["to_out.0"]);
+            if (sd_backend_is(ctx->backend, "Vulkan") || sd_backend_is(ctx->backend, "ROCm")) {
+                to_out->set_force_prec_f32(true);
+            }
+            return to_out->forward(ctx, result);
+        }
+    };
+
+    class QwenImage21TransformerBlock : public GGMLBlock {
+    public:
+        QwenImage21TransformerBlock(const QwenImage21Config& config) {
+            blocks["img_norm1"] = std::make_shared<LayerNorm>(config.hidden_size, 1e-6f, false);
+            blocks["img_norm2"] = std::make_shared<LayerNorm>(config.hidden_size, 1e-6f, false);
+            blocks["attn"]      = std::make_shared<QwenImage21Attention>(config);
+            if (config.fused_mlp) {
+                blocks["img_mlp.gate_up"] = std::make_shared<Linear>(config.hidden_size, 2 * config.intermediate_size, false);
+            } else {
+                blocks["img_mlp.proj"]       = std::make_shared<Linear>(config.hidden_size, config.intermediate_size, false);
+                blocks["img_mlp.gate_layer"] = std::make_shared<Linear>(config.hidden_size, config.intermediate_size, false);
+            }
+            blocks["img_mlp.out"] = std::make_shared<Linear>(config.intermediate_size, config.hidden_size, false);
+        }
+
+        static ggml_tensor* modulate(ggml_context* ctx, ggml_tensor* x, ggml_tensor* params, int64_t prefix_length, bool gate = false) {
+            auto rows  = ggml_ext_chunk(ctx, params, 2, 1);
+            auto apply = [&](ggml_tensor* part, ggml_tensor* row) {
+                row = gate ? ggml_tanh(ctx, row) : ggml_scale_bias(ctx, row, 1.f, 1.f);
+                return ggml_mul(ctx, part, row);
+            };
+            auto target = apply(ggml_ext_slice(ctx, x, 1, prefix_length, x->ne[1]), rows[0]);
+            if (prefix_length == 0) {
+                return target;
+            }
+            auto prefix = apply(ggml_ext_slice(ctx, x, 1, 0, prefix_length), rows[1]);
+            return ggml_concat(ctx, prefix, target, 1);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, const std::vector<ggml_tensor*>& modulation, ggml_tensor* pe, const QwenImage21Layout& layout, const std::vector<ggml_tensor*>& masks) {
+            auto h = std::dynamic_pointer_cast<LayerNorm>(blocks["img_norm1"])->forward(ctx, x);
+            h      = modulate(ctx->ggml_ctx, h, modulation[0], layout.prefix_length);
+            h      = std::dynamic_pointer_cast<QwenImage21Attention>(blocks["attn"])->forward(ctx, h, pe, layout.segments, masks);
+            x      = ggml_add(ctx->ggml_ctx, x, modulate(ctx->ggml_ctx, h, modulation[1], layout.prefix_length, true));
+            h      = std::dynamic_pointer_cast<LayerNorm>(blocks["img_norm2"])->forward(ctx, x);
+            h      = modulate(ctx->ggml_ctx, h, modulation[2], layout.prefix_length);
+            ggml_tensor* gate;
+            auto fused = blocks.find("img_mlp.gate_up");
+            if (fused != blocks.end()) {
+                auto gate_up = std::dynamic_pointer_cast<Linear>(fused->second)->forward(ctx, h);
+                auto parts   = ggml_ext_chunk(ctx->ggml_ctx, gate_up, 2, 0);
+                gate         = parts[0];
+                h            = parts[1];
+            } else {
+                gate = std::dynamic_pointer_cast<Linear>(blocks["img_mlp.gate_layer"])->forward(ctx, h);
+                h    = std::dynamic_pointer_cast<Linear>(blocks["img_mlp.proj"])->forward(ctx, h);
+            }
+            h = ggml_mul(ctx->ggml_ctx, h, ggml_silu(ctx->ggml_ctx, gate));
+            h = std::dynamic_pointer_cast<Linear>(blocks["img_mlp.out"])->forward(ctx, h);
+            return ggml_add(ctx->ggml_ctx, x, modulate(ctx->ggml_ctx, h, modulation[3], layout.prefix_length, true));
+        }
+    };
+
+    class QwenImage21Model : public GGMLBlock {
+        QwenImage21Config config;
+
+    public:
+        QwenImage21Model(const QwenImage21Config& config)
+            : config(config) {
+            blocks["time_text_embed.timestep_embedder"] = std::make_shared<TimestepEmbedding>(256, config.hidden_size, 0, 0, false);
+            blocks["txt_in"]                            = std::make_shared<QwenImage21TextProjection>(config);
+            blocks["img_in"]                            = std::make_shared<Linear>(config.in_channels, config.hidden_size, false);
+            blocks["modulation.1"]                      = std::make_shared<Linear>(config.hidden_size, 4 * config.hidden_size, false);
+            blocks["norm_out.linear"]                   = std::make_shared<Linear>(config.hidden_size, config.hidden_size, false);
+            blocks["norm_out.norm"]                     = std::make_shared<LayerNorm>(config.hidden_size, 1e-6f, false);
+            blocks["proj_out"]                          = std::make_shared<Linear>(config.hidden_size, config.out_channels, false);
+            for (int i = 0; i < config.num_layers; ++i) {
+                blocks["transformer_blocks." + std::to_string(i)] = std::make_shared<QwenImage21TransformerBlock>(config);
+            }
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* timestep, ggml_tensor* context, const std::vector<ggml_tensor*>& refs, ggml_tensor* pe, const QwenImage21Layout& layout, const std::vector<ggml_tensor*>& masks) {
+            auto time = ggml_concat(ctx->ggml_ctx, timestep, ggml_ext_zeros_like(ctx->ggml_ctx, timestep), 0);
+            // Runtime flow timesteps already use the [0, 1000] scale.
+            time               = ggml_ext_timestep_embedding(ctx->ggml_ctx, time, 256, 10000, 1.f);
+            time               = std::dynamic_pointer_cast<TimestepEmbedding>(blocks["time_text_embed.timestep_embedder"])->forward(ctx, time);
+            time               = ggml_silu(ctx->ggml_ctx, time);
+            auto modulation    = std::dynamic_pointer_cast<Linear>(blocks["modulation.1"])->forward(ctx, time);
+            auto mod           = ggml_ext_chunk(ctx->ggml_ctx, modulation, 4, 0);
+            auto text          = std::dynamic_pointer_cast<QwenImage21TextProjection>(blocks["txt_in"])->forward(ctx, context);
+            auto img_in        = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
+            ggml_tensor* joint = nullptr;
+            for (const auto& segment : layout.segments) {
+                ggml_tensor* h;
+                if (segment.image_index < 0) {
+                    h = ggml_ext_slice(ctx->ggml_ctx, text, 1, segment.context_start,
+                                       segment.context_start + segment.end - segment.start);
+                } else {
+                    auto image = segment.image_index == static_cast<int>(refs.size()) ? x : refs[segment.image_index];
+                    h          = img_in->forward(ctx, DiT::patchify(ctx->ggml_ctx, image, 1, 1));
+                }
+                joint = joint == nullptr ? h : ggml_concat(ctx->ggml_ctx, joint, h, 1);
+            }
+            sd::ggml_graph_cut::mark_graph_cut(joint, "qwen_image_2_1.prelude", "joint");
+            for (int i = 0; i < config.num_layers; ++i) {
+                auto block = std::dynamic_pointer_cast<QwenImage21TransformerBlock>(blocks["transformer_blocks." + std::to_string(i)]);
+                joint      = block->forward(ctx, joint, mod, pe, layout, masks);
+                sd::ggml_graph_cut::mark_graph_cut(joint, "qwen_image_2_1.transformer_blocks." + std::to_string(i), "joint");
+            }
+            joint      = ggml_ext_slice(ctx->ggml_ctx, joint, 1, layout.prefix_length, joint->ne[1]);
+            auto scale = std::dynamic_pointer_cast<Linear>(blocks["norm_out.linear"])->forward(ctx, ggml_ext_chunk(ctx->ggml_ctx, time, 2, 1)[0]);
+            joint      = std::dynamic_pointer_cast<LayerNorm>(blocks["norm_out.norm"])->forward(ctx, joint);
+            joint      = ggml_mul(ctx->ggml_ctx, joint, ggml_scale_bias(ctx->ggml_ctx, scale, 1.f, 1.f));
+            joint      = std::dynamic_pointer_cast<Linear>(blocks["proj_out"])->forward(ctx, joint);
+            return DiT::unpatchify_and_crop(ctx->ggml_ctx, joint, x->ne[1], x->ne[0], 1, 1);
+        }
+    };
+
+    struct QwenImage21Runner : public DiffusionModelRunner {
+        QwenImage21Config config;
+        QwenImage21Model model;
+        std::vector<float> pe_data;
+        std::vector<sd::Tensor<float>> mask_data;
+
+        QwenImage21Runner(ggml_backend_t backend, const String2TensorStorage& weights, const std::string& prefix, std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
+              config(QwenImage21Config::detect_from_weights(weights, prefix)),
+              model(config) {
+            model.init(params_ctx, weights, prefix);
+        }
+
+        std::string get_desc() override { return "qwen_image_2_1"; }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix) override {
+            model.get_param_tensors(tensors, prefix);
+        }
+
+        sd::Tensor<float> compute(int n_threads, const DiffusionParams& inputs) override {
+            const auto& x       = tensor_or_empty(inputs.x);
+            const auto& context = tensor_or_empty(inputs.context);
+            if (x.empty() || context.empty() || context.dim() < 2 || context.shape()[0] != config.context_dim ||
+                tensor_or_empty(inputs.timesteps).numel() != 1 ||
+                x.dim() != 4 || x.shape()[3] != 1 || x.shape()[2] != config.in_channels) {
+                LOG_ERROR("Qwen Image 2.1 requires an image latent and text conditioning with batch size 1");
+                return {};
+            }
+            static const std::vector<sd::Tensor<float>> empty_refs;
+            const auto& refs = inputs.ref_latents && inputs.ref_image_params.pass_to_dit ? *inputs.ref_latents : empty_refs;
+            std::vector<std::pair<int64_t, int64_t>> shapes;
+            for (const auto& ref : refs) {
+                if (ref.dim() != 4 || ref.shape()[2] != config.in_channels || ref.shape()[3] != 1) {
+                    LOG_ERROR("Qwen Image 2.1: invalid reference latent shape");
+                    return {};
+                }
+                shapes.emplace_back(ref.shape()[1], ref.shape()[0]);
+            }
+            shapes.emplace_back(x.shape()[1], x.shape()[0]);
+            const auto* extra = std::get_if<QwenImage21DiffusionExtra>(&inputs.extra);
+            QwenImage21Layout layout;
+            try {
+                layout = QwenImage21Layout::build(context.shape()[1], tensor_or_empty(extra ? extra->image_slots : nullptr), shapes);
+            } catch (const std::exception& error) {
+                LOG_ERROR("%s", error.what());
+                return {};
+            }
+            pe_data = Rope::embed_nd(layout.positions, 1, 10000.f, config.axes_dim);
+            mask_data.clear();
+            for (const auto& segment : layout.segments) {
+                sd::Tensor<float> mask;
+                if (segment.image_index < 0) {
+                    mask = sd::Tensor<float>::zeros({segment.end, segment.end - segment.start});
+                    for (int64_t q = segment.start; q < segment.end; ++q) {
+                        for (int64_t k = q + 1; k < segment.end; ++k) {
+                            mask[k + segment.end * (q - segment.start)] = -INFINITY;
+                        }
+                    }
+                }
+                mask_data.push_back(std::move(mask));
+            }
+            auto build = [&]() {
+                auto graph = new_graph_custom(QWEN_IMAGE_GRAPH_SIZE * 2);
+                auto pe    = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.head_dim / 2, layout.positions.size());
+                set_backend_tensor_data(pe, pe_data.data());
+                std::vector<ggml_tensor*> masks, ref_inputs;
+                for (const auto& mask : mask_data) {
+                    masks.push_back(mask.empty() ? nullptr : make_input(mask));
+                }
+                for (const auto& ref : refs) {
+                    ref_inputs.push_back(make_input(ref));
+                }
+                auto ctx = get_context();
+                auto out = model.forward(&ctx, make_input(x), make_input(*inputs.timesteps), make_input(context),
+                                         ref_inputs, pe, layout, masks);
+                ggml_build_forward_expand(graph, out);
+                return graph;
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute(build, n_threads, false), x.dim());
+        }
+    };
+}
+
+#endif  // __SD_MODEL_DIFFUSION_QWEN_IMAGE_2_1_H__

src/model/diffusion/z_image.hpp

@@ -131,16 +131,30 @@ namespace ZImage {
         int64_t num_heads;
         int64_t num_kv_heads;
         bool qk_norm;
+        bool split_qkv;
 
     public:
-        JointAttention(int64_t hidden_size, int64_t head_dim, int64_t num_heads, int64_t num_kv_heads, bool qk_norm)
-            : head_dim(head_dim), num_heads(num_heads), num_kv_heads(num_kv_heads), qk_norm(qk_norm) {
-            blocks["qkv"] = std::make_shared<Linear>(hidden_size, (num_heads + num_kv_heads * 2) * head_dim, false);
-            float scale   = 1.f;
-            blocks["out"] = std::make_shared<Linear>(num_heads * head_dim, hidden_size, false, false, false, scale);
+        JointAttention(int64_t hidden_size,
+                       int64_t head_dim,
+                       int64_t num_heads,
+                       int64_t num_kv_heads,
+                       bool qk_norm,
+                       bool norm_elementwise_affine = true,
+                       bool split_qkv               = false)
+            : head_dim(head_dim), num_heads(num_heads), num_kv_heads(num_kv_heads), qk_norm(qk_norm), split_qkv(split_qkv) {
+            float scale = 1.f;
+            if (split_qkv) {
+                blocks["to_q"]     = std::make_shared<Linear>(hidden_size, num_heads * head_dim, false);
+                blocks["to_k"]     = std::make_shared<Linear>(hidden_size, num_kv_heads * head_dim, false);
+                blocks["to_v"]     = std::make_shared<Linear>(hidden_size, num_kv_heads * head_dim, false);
+                blocks["to_out.0"] = std::make_shared<Linear>(num_heads * head_dim, hidden_size, false, false, false, scale);
+            } else {
+                blocks["qkv"] = std::make_shared<Linear>(hidden_size, (num_heads + num_kv_heads * 2) * head_dim, false);
+                blocks["out"] = std::make_shared<Linear>(num_heads * head_dim, hidden_size, false, false, false, scale);
+            }
             if (qk_norm) {
-                blocks["q_norm"] = std::make_shared<RMSNorm>(head_dim);
-                blocks["k_norm"] = std::make_shared<RMSNorm>(head_dim);
+                blocks["q_norm"] = std::make_shared<RMSNorm>(head_dim, 1e-06f, norm_elementwise_affine);
+                blocks["k_norm"] = std::make_shared<RMSNorm>(head_dim, 1e-06f, norm_elementwise_affine);
             }
         }
 
@@ -151,8 +165,35 @@ namespace ZImage {
             // x: [N, n_token, hidden_size]
             int64_t n_token = x->ne[1];
             int64_t N       = x->ne[2];
-            auto qkv_proj   = std::dynamic_pointer_cast<Linear>(blocks["qkv"]);
-            auto out_proj   = std::dynamic_pointer_cast<Linear>(blocks["out"]);
+            auto out_proj   = std::dynamic_pointer_cast<Linear>(blocks[split_qkv ? "to_out.0" : "out"]);
+
+            if (split_qkv) {
+                auto q_proj = std::dynamic_pointer_cast<Linear>(blocks["to_q"]);
+                auto k_proj = std::dynamic_pointer_cast<Linear>(blocks["to_k"]);
+                auto v_proj = std::dynamic_pointer_cast<Linear>(blocks["to_v"]);
+
+                if (sd_backend_is(ctx->backend, "ROCm")) {
+                    out_proj->set_scale(1.f / 16.f);
+                    out_proj->set_force_prec_f32(true);
+                    q_proj->set_force_prec_f32(true);
+                    k_proj->set_force_prec_f32(true);
+                    v_proj->set_force_prec_f32(true);
+                }
+
+                auto q = ggml_reshape_4d(ctx->ggml_ctx, q_proj->forward(ctx, x), head_dim, num_heads, n_token, N);
+                auto k = ggml_reshape_4d(ctx->ggml_ctx, k_proj->forward(ctx, x), head_dim, num_kv_heads, n_token, N);
+                auto v = ggml_reshape_4d(ctx->ggml_ctx, v_proj->forward(ctx, x), head_dim, num_kv_heads, n_token, N);
+
+                if (qk_norm) {
+                    q = std::dynamic_pointer_cast<RMSNorm>(blocks["q_norm"])->forward(ctx, q);
+                    k = std::dynamic_pointer_cast<RMSNorm>(blocks["k_norm"])->forward(ctx, k);
+                }
+
+                auto out = Rope::attention(ctx, q, k, v, pe, mask, 1.f / 128.f);
+                return out_proj->forward(ctx, out);
+            }
+
+            auto qkv_proj = std::dynamic_pointer_cast<Linear>(blocks["qkv"]);
 
             if (sd_backend_is(ctx->backend, "ROCm")) {
                 out_proj->set_scale(1.f / 16.f);
@@ -252,9 +293,12 @@ namespace ZImage {
                                             ggml_tensor* x,
                                             ggml_tensor* scale) {
         // x: [N, L, C]
-        // scale: [N, C]
-        scale = ggml_reshape_3d(ctx, scale, scale->ne[0], 1, scale->ne[1]);  // [N, 1, C]
-        x     = ggml_add(ctx, x, ggml_mul(ctx, x, scale));
+        // scale: [N, C], or [N, L, C] when the caller modulates per token (LLaDA-Image editing
+        // feeds a per-token timestep embedding so each segment carries its own modulation).
+        if (scale->ne[1] != x->ne[1]) {
+            scale = ggml_reshape_3d(ctx, scale, scale->ne[0], 1, scale->ne[1]);  // [N, 1, C]
+        }
+        x = ggml_add(ctx, x, ggml_mul(ctx, x, scale));
         return x;
     }
 
@@ -272,14 +316,16 @@ namespace ZImage {
                               float ffn_dim_multiplier,
                               float norm_eps,
                               bool qk_norm,
-                              bool modulation = true)
+                              bool modulation              = true,
+                              bool norm_elementwise_affine = true,
+                              bool split_qkv               = false)
             : modulation(modulation) {
-            blocks["attention"]       = std::make_shared<JointAttention>(hidden_size, head_dim, num_heads, num_kv_heads, qk_norm);
+            blocks["attention"]       = std::make_shared<JointAttention>(hidden_size, head_dim, num_heads, num_kv_heads, qk_norm, norm_elementwise_affine, split_qkv);
             blocks["feed_forward"]    = std::make_shared<FeedForward>(hidden_size, hidden_size, multiple_of, ffn_dim_multiplier);
-            blocks["attention_norm1"] = std::make_shared<RMSNorm>(hidden_size, norm_eps);
-            blocks["ffn_norm1"]       = std::make_shared<RMSNorm>(hidden_size, norm_eps);
-            blocks["attention_norm2"] = std::make_shared<RMSNorm>(hidden_size, norm_eps);
-            blocks["ffn_norm2"]       = std::make_shared<RMSNorm>(hidden_size, norm_eps);
+            blocks["attention_norm1"] = std::make_shared<RMSNorm>(hidden_size, norm_eps, norm_elementwise_affine);
+            blocks["ffn_norm1"]       = std::make_shared<RMSNorm>(hidden_size, norm_eps, norm_elementwise_affine);
+            blocks["attention_norm2"] = std::make_shared<RMSNorm>(hidden_size, norm_eps, norm_elementwise_affine);
+            blocks["ffn_norm2"]       = std::make_shared<RMSNorm>(hidden_size, norm_eps, norm_elementwise_affine);
             if (modulation) {
                 blocks["adaLN_modulation.0"] = std::make_shared<Linear>(MIN(hidden_size, ADALN_EMBED_DIM), 4 * hidden_size);
             }

src/model/te/llada_image_te.hpp

@@ -0,0 +1,604 @@
+#ifndef __SD_MODEL_TE_LLADA_IMAGE_TE_HPP__
+#define __SD_MODEL_TE_LLADA_IMAGE_TE_HPP__
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+
+#include "core/ggml_extend.h"
+#include "core/ggml_runner.h"
+#include "model/common/ggml_block.hpp"
+#include "model_loader.h"
+
+// The conditioning components LLaDA-Image puts around its LLaDA2-MoE backbone.
+// Ref: LLaDAImageQueryFormerModel / LLaDAImageTextProjectionModel in
+// https://github.com/inclusionAI/LLaDA-Image/blob/main/src/models/transformer_llada_image.py
+//
+// QueryFormer turns the LLaDA token embeddings into 256 learned queries that the pipeline
+// appends to the backbone input; TextProjection maps the backbone hidden states to the
+// denoiser's caption dimension. Neither uses RoPE, and every norm is parameter-free.
+// Both MLPs use the tanh GELU approximation, so ggml_gelu (not ggml_gelu_erf).
+//
+// SigVQ is the editing-only image encoder: a 40-layer ViT whose output is quantized against a
+// 16384-entry codebook, with the resulting ids embedded and projected into the semantic features
+// the denoiser consumes. Its MLP uses the exact erf GELU, unlike the two above.
+
+namespace LLaDAImageTE {
+    constexpr int LLADA_IMAGE_TE_GRAPH_SIZE = 16384;
+
+    struct QueryFormerConfig {
+        int64_t num_queries       = 256;
+        int64_t hidden_size       = 2048;
+        int64_t num_layers        = 1;
+        int64_t num_heads         = 16;
+        int64_t intermediate_size = 8192;
+        float norm_eps            = 1e-6f;
+    };
+
+    struct TextProjectionConfig {
+        int64_t hidden_size       = 2048;
+        int64_t intermediate_size = 8960;
+        int64_t num_layers        = 6;
+        int64_t num_heads         = 32;
+        int64_t projection_dim    = 2560;
+        float norm_eps            = 1e-6f;
+    };
+
+    // Cross-attention with a single fused in_proj over q (from the queries) and k/v (from the
+    // token embeddings). The checkpoint stores in_proj as one [3*hidden, hidden] parameter.
+    struct QueryAttention : public GGMLBlock {
+    protected:
+        int64_t hidden_size;
+        int64_t num_heads;
+
+        void init_params(ggml_context* ctx,
+                         const String2TensorStorage& tensor_storage_map = {},
+                         std::string prefix                             = "") override {
+            GGMLBlock::init_params(ctx, tensor_storage_map, prefix);
+            enum ggml_type wtype     = get_type(prefix + "in_proj_weight", tensor_storage_map, GGML_TYPE_F32);
+            params["in_proj_weight"] = ggml_new_tensor_2d(ctx, wtype, hidden_size, hidden_size * 3);
+            params["in_proj_bias"]   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size * 3);
+        }
+
+    public:
+        QueryAttention(int64_t hidden_size, int64_t num_heads)
+            : hidden_size(hidden_size), num_heads(num_heads) {
+            blocks["out_proj"] = std::make_shared<Linear>(hidden_size, hidden_size, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* query,
+                             ggml_tensor* context,
+                             ggml_tensor* mask = nullptr) {
+            // query: [N, num_queries, hidden_size], context: [N, n_token, hidden_size]
+            ggml_context* gctx = ctx->ggml_ctx;
+            auto out_proj      = std::dynamic_pointer_cast<Linear>(blocks["out_proj"]);
+
+            auto w = params["in_proj_weight"];
+            auto b = params["in_proj_bias"];
+
+            auto slice_w = [&](int64_t index) {
+                return ggml_ext_slice(gctx, w, 1, index * hidden_size, (index + 1) * hidden_size);
+            };
+            auto slice_b = [&](int64_t index) {
+                return ggml_ext_slice(gctx, b, 0, index * hidden_size, (index + 1) * hidden_size);
+            };
+
+            auto q = ggml_ext_linear(gctx, query, slice_w(0), slice_b(0));
+            auto k = ggml_ext_linear(gctx, context, slice_w(1), slice_b(1));
+            auto v = ggml_ext_linear(gctx, context, slice_w(2), slice_b(2));
+
+            auto x = ggml_ext_attention_ext(ctx, q, k, v, num_heads, mask);  // [N, num_queries, hidden_size]
+            return out_proj->forward(ctx, x);
+        }
+    };
+
+    struct QueryFormerBlock : public GGMLBlock {
+    protected:
+        QueryFormerConfig config;
+
+    public:
+        QueryFormerBlock(const QueryFormerConfig& config)
+            : config(config) {
+            blocks["norm_q"]     = std::make_shared<LayerNorm>(config.hidden_size, config.norm_eps, false);
+            blocks["norm_k"]     = std::make_shared<LayerNorm>(config.hidden_size, config.norm_eps, false);
+            blocks["cross_attn"] = std::make_shared<QueryAttention>(config.hidden_size, config.num_heads);
+            blocks["norm1"]      = std::make_shared<LayerNorm>(config.hidden_size, config.norm_eps, false);
+            blocks["mlp.fc1"]    = std::make_shared<Linear>(config.hidden_size, config.intermediate_size, true);
+            blocks["mlp.fc2"]    = std::make_shared<Linear>(config.intermediate_size, config.hidden_size, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* query,
+                             ggml_tensor* context,
+                             ggml_tensor* mask = nullptr) {
+            auto norm_q     = std::dynamic_pointer_cast<LayerNorm>(blocks["norm_q"]);
+            auto norm_k     = std::dynamic_pointer_cast<LayerNorm>(blocks["norm_k"]);
+            auto cross_attn = std::dynamic_pointer_cast<QueryAttention>(blocks["cross_attn"]);
+            auto norm1      = std::dynamic_pointer_cast<LayerNorm>(blocks["norm1"]);
+            auto fc1        = std::dynamic_pointer_cast<Linear>(blocks["mlp.fc1"]);
+            auto fc2        = std::dynamic_pointer_cast<Linear>(blocks["mlp.fc2"]);
+
+            // The reference overwrites query_embeds with its normalized value before the
+            // residual add, so both residuals here are on normalized activations.
+            query      = norm_q->forward(ctx, query);
+            auto ctx_n = norm_k->forward(ctx, context);
+            query      = ggml_add(ctx->ggml_ctx, query, cross_attn->forward(ctx, query, ctx_n, mask));
+            query      = norm1->forward(ctx, query);
+
+            auto h = fc1->forward(ctx, query);
+            h      = ggml_gelu(ctx->ggml_ctx, h);
+            h      = fc2->forward(ctx, h);
+            return ggml_add(ctx->ggml_ctx, query, h);
+        }
+    };
+
+    struct QueryFormerModel : public GGMLBlock {
+    protected:
+        QueryFormerConfig config;
+
+        void init_params(ggml_context* ctx,
+                         const String2TensorStorage& tensor_storage_map = {},
+                         const std::string prefix                       = "") override {
+            params["meta_queries"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, config.hidden_size, config.num_queries);
+        }
+
+    public:
+        QueryFormerModel() = default;
+        QueryFormerModel(const QueryFormerConfig& config)
+            : config(config) {
+            for (int i = 0; i < config.num_layers; i++) {
+                blocks["query_blocks." + std::to_string(i)] = std::make_shared<QueryFormerBlock>(config);
+            }
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* inputs_embeds,
+                             ggml_tensor* mask = nullptr) {
+            // inputs_embeds: [N, n_token, hidden_size] -> [N, num_queries, hidden_size]
+            auto query = params["meta_queries"];
+            query      = ggml_reshape_3d(ctx->ggml_ctx, query, config.hidden_size, config.num_queries, 1);
+
+            for (int i = 0; i < config.num_layers; i++) {
+                auto block = std::dynamic_pointer_cast<QueryFormerBlock>(blocks["query_blocks." + std::to_string(i)]);
+                query      = block->forward(ctx, query, inputs_embeds, mask);
+            }
+            return query;
+        }
+    };
+
+    struct TextProjectionAttention : public GGMLBlock {
+    protected:
+        int64_t num_heads;
+        int64_t head_dim;
+
+    public:
+        TextProjectionAttention(const TextProjectionConfig& config)
+            : num_heads(config.num_heads), head_dim(config.hidden_size / config.num_heads) {
+            blocks["q_proj"]   = std::make_shared<Linear>(config.hidden_size, config.hidden_size, true);
+            blocks["k_proj"]   = std::make_shared<Linear>(config.hidden_size, config.hidden_size, true);
+            blocks["v_proj"]   = std::make_shared<Linear>(config.hidden_size, config.hidden_size, true);
+            blocks["out_proj"] = std::make_shared<Linear>(config.hidden_size, config.hidden_size, true);
+            blocks["q_norm"]   = std::make_shared<RMSNorm>(head_dim, config.norm_eps, false);
+            blocks["k_norm"]   = std::make_shared<RMSNorm>(head_dim, config.norm_eps, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            // x: [N, n_token, hidden_size]
+            ggml_context* gctx = ctx->ggml_ctx;
+            int64_t n_token    = x->ne[1];
+            int64_t N          = x->ne[2];
+
+            auto q_proj   = std::dynamic_pointer_cast<Linear>(blocks["q_proj"]);
+            auto k_proj   = std::dynamic_pointer_cast<Linear>(blocks["k_proj"]);
+            auto v_proj   = std::dynamic_pointer_cast<Linear>(blocks["v_proj"]);
+            auto out_proj = std::dynamic_pointer_cast<Linear>(blocks["out_proj"]);
+            auto q_norm   = std::dynamic_pointer_cast<RMSNorm>(blocks["q_norm"]);
+            auto k_norm   = std::dynamic_pointer_cast<RMSNorm>(blocks["k_norm"]);
+
+            auto q = q_proj->forward(ctx, x);
+            auto k = k_proj->forward(ctx, x);
+            auto v = v_proj->forward(ctx, x);
+
+            q = ggml_reshape_4d(gctx, q, head_dim, num_heads, n_token, N);
+            k = ggml_reshape_4d(gctx, k, head_dim, num_heads, n_token, N);
+            q = q_norm->forward(ctx, q);
+            k = k_norm->forward(ctx, k);
+            q = ggml_reshape_3d(gctx, q, head_dim * num_heads, n_token, N);
+            k = ggml_reshape_3d(gctx, k, head_dim * num_heads, n_token, N);
+
+            auto out = ggml_ext_attention_ext(ctx, q, k, v, num_heads);
+            return out_proj->forward(ctx, out);
+        }
+    };
+
+    struct TextProjectionBlock : public GGMLBlock {
+    public:
+        TextProjectionBlock(const TextProjectionConfig& config) {
+            blocks["self_attn"]   = std::make_shared<TextProjectionAttention>(config);
+            blocks["layer_norm1"] = std::make_shared<RMSNorm>(config.hidden_size, config.norm_eps, false);
+            blocks["layer_norm2"] = std::make_shared<RMSNorm>(config.hidden_size, config.norm_eps, false);
+            blocks["mlp.fc1"]     = std::make_shared<Linear>(config.hidden_size, config.intermediate_size, true);
+            blocks["mlp.fc2"]     = std::make_shared<Linear>(config.intermediate_size, config.hidden_size, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto self_attn   = std::dynamic_pointer_cast<TextProjectionAttention>(blocks["self_attn"]);
+            auto layer_norm1 = std::dynamic_pointer_cast<RMSNorm>(blocks["layer_norm1"]);
+            auto layer_norm2 = std::dynamic_pointer_cast<RMSNorm>(blocks["layer_norm2"]);
+            auto fc1         = std::dynamic_pointer_cast<Linear>(blocks["mlp.fc1"]);
+            auto fc2         = std::dynamic_pointer_cast<Linear>(blocks["mlp.fc2"]);
+
+            x = ggml_add(ctx->ggml_ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x)));
+
+            auto h = fc1->forward(ctx, layer_norm2->forward(ctx, x));
+            h      = ggml_gelu(ctx->ggml_ctx, h);
+            h      = fc2->forward(ctx, h);
+            return ggml_add(ctx->ggml_ctx, x, h);
+        }
+    };
+
+    struct TextProjectionModel : public GGMLBlock {
+    protected:
+        TextProjectionConfig config;
+
+    public:
+        TextProjectionModel() = default;
+        TextProjectionModel(const TextProjectionConfig& config)
+            : config(config) {
+            for (int i = 0; i < config.num_layers; i++) {
+                blocks["layers." + std::to_string(i)] = std::make_shared<TextProjectionBlock>(config);
+            }
+            blocks["projector"] = std::make_shared<Linear>(config.hidden_size, config.projection_dim, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            // x: [N, n_token, hidden_size] -> [N, n_token, projection_dim]
+            for (int i = 0; i < config.num_layers; i++) {
+                auto block = std::dynamic_pointer_cast<TextProjectionBlock>(blocks["layers." + std::to_string(i)]);
+                x          = block->forward(ctx, x);
+            }
+            auto projector = std::dynamic_pointer_cast<Linear>(blocks["projector"]);
+            return projector->forward(ctx, x);
+        }
+    };
+
+    struct SigVQConfig {
+        int64_t image_size         = 2048;
+        int patch_size             = 16;
+        int64_t in_channels        = 3;
+        int64_t hidden_size        = 1536;
+        int64_t intermediate_size  = 6144;
+        int64_t num_layers         = 40;
+        int64_t num_heads          = 16;
+        int64_t codebook_size      = 16384;
+        int64_t codebook_embed_dim = 2048;
+        int64_t semantic_embed_dim = 4096;
+        float norm_eps             = 1e-6f;
+    };
+
+    struct SigVQAttention : public GGMLBlock {
+    protected:
+        int64_t num_heads;
+        int64_t head_dim;
+
+    public:
+        SigVQAttention(const SigVQConfig& config)
+            : num_heads(config.num_heads), head_dim(config.hidden_size / config.num_heads) {
+            blocks["qkv"]  = std::make_shared<Linear>(config.hidden_size, config.hidden_size * 3, true);
+            blocks["proj"] = std::make_shared<Linear>(config.hidden_size, config.hidden_size, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            // x: [N, n_token, hidden_size]
+            ggml_context* gctx = ctx->ggml_ctx;
+            auto qkv_proj      = std::dynamic_pointer_cast<Linear>(blocks["qkv"]);
+            auto out_proj      = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
+
+            int64_t hidden_size = num_heads * head_dim;
+            auto qkv            = qkv_proj->forward(ctx, x);
+            auto q              = ggml_ext_slice(gctx, qkv, 0, 0, hidden_size);
+            auto k              = ggml_ext_slice(gctx, qkv, 0, hidden_size, hidden_size * 2);
+            auto v              = ggml_ext_slice(gctx, qkv, 0, hidden_size * 2, hidden_size * 3);
+
+            auto out = ggml_ext_attention_ext(ctx, q, k, v, num_heads);
+            return out_proj->forward(ctx, out);
+        }
+    };
+
+    struct SigVQBlock : public GGMLBlock {
+    public:
+        SigVQBlock(const SigVQConfig& config) {
+            blocks["norm1"]   = std::make_shared<LayerNorm>(config.hidden_size, config.norm_eps);
+            blocks["norm2"]   = std::make_shared<LayerNorm>(config.hidden_size, config.norm_eps);
+            blocks["attn"]    = std::make_shared<SigVQAttention>(config);
+            blocks["mlp.fc1"] = std::make_shared<Linear>(config.hidden_size, config.intermediate_size, true);
+            blocks["mlp.fc2"] = std::make_shared<Linear>(config.intermediate_size, config.hidden_size, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["norm1"]);
+            auto norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["norm2"]);
+            auto attn  = std::dynamic_pointer_cast<SigVQAttention>(blocks["attn"]);
+            auto fc1   = std::dynamic_pointer_cast<Linear>(blocks["mlp.fc1"]);
+            auto fc2   = std::dynamic_pointer_cast<Linear>(blocks["mlp.fc2"]);
+
+            x      = ggml_add(ctx->ggml_ctx, x, attn->forward(ctx, norm1->forward(ctx, x)));
+            auto h = fc1->forward(ctx, norm2->forward(ctx, x));
+            h      = ggml_gelu_erf(ctx->ggml_ctx, h);
+            h      = fc2->forward(ctx, h);
+            return ggml_add(ctx->ggml_ctx, x, h);
+        }
+    };
+
+    struct SigVQModel : public GGMLBlock {
+    protected:
+        SigVQConfig config;
+
+    public:
+        SigVQModel() = default;
+        SigVQModel(const SigVQConfig& config)
+            : config(config) {
+            blocks["visual.patch_embed.proj"] = std::make_shared<Conv2d>(config.in_channels,
+                                                                         config.hidden_size,
+                                                                         std::make_pair(config.patch_size, config.patch_size),
+                                                                         std::make_pair(config.patch_size, config.patch_size));
+            for (int i = 0; i < config.num_layers; i++) {
+                blocks["visual.blocks." + std::to_string(i)] = std::make_shared<SigVQBlock>(config);
+            }
+            blocks["vqmodel.quant_conv"]         = std::make_shared<Conv2d>(config.hidden_size,
+                                                                    config.codebook_embed_dim,
+                                                                    std::make_pair(1, 1));
+            blocks["prior_projector.net.0.proj"] = std::make_shared<Linear>(config.semantic_embed_dim, config.semantic_embed_dim, true);
+            blocks["prior_projector.net.2"]      = std::make_shared<Linear>(config.semantic_embed_dim, config.semantic_embed_dim, true);
+        }
+
+        void init_params(ggml_context* ctx,
+                         const String2TensorStorage& tensor_storage_map = {},
+                         const std::string prefix                       = "") override {
+            params["visual.embeddings.position_embedding.weight"] =
+                ggml_new_tensor_2d(ctx, GGML_TYPE_F32, config.hidden_size, (config.image_size / config.patch_size) * (config.image_size / config.patch_size));
+            params["vqmodel.quantize.embedding.weight"] =
+                ggml_new_tensor_2d(ctx, GGML_TYPE_F32, config.codebook_embed_dim, config.codebook_size);
+            params["prior_token_embedding.weight"] =
+                ggml_new_tensor_2d(ctx, GGML_TYPE_F32, config.semantic_embed_dim, config.codebook_size);
+        }
+
+        // Bilinear-resamples the square position-embedding grid onto the image's patch grid.
+        // The reference uses grid_sample(align_corners=False, padding_mode="border"); the source
+        // coordinate for output index j is therefore (j + 0.5) * side / out - 0.5, clamped.
+        ggml_tensor* resample_pos_embed(GGMLRunnerContext* ctx,
+                                        ggml_tensor* pos_idx,
+                                        ggml_tensor* pos_weight) {
+            auto pos_embed = params["visual.embeddings.position_embedding.weight"];
+            auto gathered  = ggml_get_rows(ctx->ggml_ctx, pos_embed, pos_idx);
+            return ggml_mul(ctx->ggml_ctx, gathered, pos_weight);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* pixel_values,
+                             const std::vector<ggml_tensor*>& pos_idx,
+                             const std::vector<ggml_tensor*>& pos_weight) {
+            // pixel_values: [N, in_channels, H, W] -> [N, grid_h * grid_w, semantic_embed_dim]
+            ggml_context* gctx = ctx->ggml_ctx;
+
+            auto patch_embed = std::dynamic_pointer_cast<Conv2d>(blocks["visual.patch_embed.proj"]);
+            auto quant_conv  = std::dynamic_pointer_cast<Conv2d>(blocks["vqmodel.quant_conv"]);
+            auto proj_0      = std::dynamic_pointer_cast<Linear>(blocks["prior_projector.net.0.proj"]);
+            auto proj_2      = std::dynamic_pointer_cast<Linear>(blocks["prior_projector.net.2"]);
+
+            auto x          = patch_embed->forward(ctx, pixel_values);  // [N, hidden_size, grid_h, grid_w]
+            int64_t grid_w  = x->ne[0];
+            int64_t grid_h  = x->ne[1];
+            int64_t n_token = grid_h * grid_w;
+            int64_t N       = x->ne[3];
+
+            x = ggml_reshape_3d(gctx, x, n_token, config.hidden_size, N);
+            x = ggml_cont(gctx, ggml_permute(gctx, x, 1, 0, 2, 3));  // [N, n_token, hidden_size]
+
+            ggml_tensor* pos = nullptr;
+            for (size_t i = 0; i < pos_idx.size(); i++) {
+                auto corner = resample_pos_embed(ctx, pos_idx[i], pos_weight[i]);
+                pos         = pos == nullptr ? corner : ggml_add(gctx, pos, corner);
+            }
+            x = ggml_add(gctx, x, ggml_reshape_3d(gctx, pos, config.hidden_size, n_token, N));
+
+            for (int i = 0; i < config.num_layers; i++) {
+                auto block = std::dynamic_pointer_cast<SigVQBlock>(blocks["visual.blocks." + std::to_string(i)]);
+                x          = block->forward(ctx, x);
+            }
+
+            // quant_conv is 1x1, so run it as a per-token projection rather than reshaping to 2-D.
+            x = ggml_cont(gctx, ggml_permute(gctx, x, 1, 0, 2, 3));  // [N, hidden_size, n_token]
+            x = ggml_reshape_4d(gctx, x, n_token, 1, config.hidden_size, N);
+            x = quant_conv->forward(ctx, x);  // [N, codebook_embed_dim, 1, n_token]
+            x = ggml_reshape_3d(gctx, x, n_token, config.codebook_embed_dim, N);
+            x = ggml_cont(gctx, ggml_permute(gctx, x, 1, 0, 2, 3));  // [N, n_token, codebook_embed_dim]
+
+            // Both sides are L2-normalized, so the nearest codebook entry by euclidean distance
+            // is the one with the largest dot product.
+            auto codebook  = ggml_l2_norm(gctx, params["vqmodel.quantize.embedding.weight"], 1e-12f);
+            auto normed    = ggml_l2_norm(gctx, x, 1e-12f);
+            auto logits    = ggml_mul_mat(gctx, codebook, normed);  // [N, n_token, codebook_size]
+            auto token_ids = ggml_argmax(gctx, ggml_reshape_2d(gctx, logits, config.codebook_size, n_token * N));
+
+            auto semantic = ggml_get_rows(gctx, params["prior_token_embedding.weight"], token_ids);
+            semantic      = ggml_reshape_3d(gctx, semantic, config.semantic_embed_dim, n_token, N);
+
+            auto h = proj_0->forward(ctx, semantic);
+            h      = ggml_silu(gctx, h);
+            return proj_2->forward(ctx, h);
+        }
+    };
+
+    struct QueryFormerRunner : public GGMLRunner {
+    public:
+        QueryFormerConfig config;
+        QueryFormerModel query_former;
+
+        QueryFormerRunner(ggml_backend_t backend,
+                          const String2TensorStorage& tensor_storage_map      = {},
+                          const std::string prefix                            = "",
+                          std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : GGMLRunner(backend, weight_manager) {
+            query_former = QueryFormerModel(config);
+            query_former.init(params_ctx, tensor_storage_map, prefix);
+        }
+
+        std::string get_desc() override {
+            return "llada_image_queryformer";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix) {
+            query_former.get_param_tensors(tensors, prefix);
+        }
+
+        sd::Tensor<float> compute(int n_threads, const sd::Tensor<float>& inputs_embeds) {
+            auto get_graph = [&]() -> ggml_cgraph* {
+                ggml_cgraph* gf  = new_graph_custom(LLADA_IMAGE_TE_GRAPH_SIZE);
+                ggml_tensor* x   = make_input(inputs_embeds);
+                auto runner_ctx  = get_context();
+                ggml_tensor* out = query_former.forward(&runner_ctx, x);
+                ggml_build_forward_expand(gf, out);
+                return gf;
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute(get_graph, n_threads, true),
+                                                   inputs_embeds.dim());
+        }
+    };
+
+    struct TextProjectionRunner : public GGMLRunner {
+    public:
+        TextProjectionConfig config;
+        TextProjectionModel text_projection;
+
+        TextProjectionRunner(ggml_backend_t backend,
+                             const String2TensorStorage& tensor_storage_map      = {},
+                             const std::string prefix                            = "",
+                             std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : GGMLRunner(backend, weight_manager) {
+            text_projection = TextProjectionModel(config);
+            text_projection.init(params_ctx, tensor_storage_map, prefix);
+        }
+
+        std::string get_desc() override {
+            return "llada_image_text_projection";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix) {
+            text_projection.get_param_tensors(tensors, prefix);
+        }
+
+        sd::Tensor<float> compute(int n_threads, const sd::Tensor<float>& hidden_states) {
+            auto get_graph = [&]() -> ggml_cgraph* {
+                ggml_cgraph* gf  = new_graph_custom(LLADA_IMAGE_TE_GRAPH_SIZE);
+                ggml_tensor* x   = make_input(hidden_states);
+                auto runner_ctx  = get_context();
+                ggml_tensor* out = text_projection.forward(&runner_ctx, x);
+                ggml_build_forward_expand(gf, out);
+                return gf;
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute(get_graph, n_threads, true),
+                                                   hidden_states.dim());
+        }
+    };
+
+    struct SigVQRunner : public GGMLRunner {
+    public:
+        SigVQConfig config;
+        SigVQModel sigvq;
+        std::array<std::vector<int32_t>, 4> pos_idx_data;
+        std::array<std::vector<float>, 4> pos_weight_data;
+
+        SigVQRunner(ggml_backend_t backend,
+                    const String2TensorStorage& tensor_storage_map      = {},
+                    const std::string prefix                            = "",
+                    std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : GGMLRunner(backend, weight_manager) {
+            sigvq = SigVQModel(config);
+            sigvq.init(params_ctx, tensor_storage_map, prefix);
+        }
+
+        std::string get_desc() override {
+            return "llada_image_sigvq";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix) {
+            sigvq.get_param_tensors(tensors, prefix);
+        }
+
+        // Precomputes the four bilinear taps that resample the square position-embedding grid
+        // onto a grid_h x grid_w patch grid, matching grid_sample(align_corners=False,
+        // padding_mode="border").
+        void build_pos_embed_taps(int64_t grid_h, int64_t grid_w) {
+            const int64_t side = config.image_size / config.patch_size;
+            for (auto& v : pos_idx_data) {
+                v.clear();
+            }
+            for (auto& v : pos_weight_data) {
+                v.clear();
+            }
+
+            auto clamp_index = [side](int64_t v) {
+                return static_cast<int32_t>(std::min<int64_t>(std::max<int64_t>(v, 0), side - 1));
+            };
+
+            for (int64_t i = 0; i < grid_h; ++i) {
+                double src_h    = (static_cast<double>(i) + 0.5) * side / static_cast<double>(grid_h) - 0.5;
+                int64_t h_floor = static_cast<int64_t>(std::floor(src_h));
+                double dh       = src_h - static_cast<double>(h_floor);
+                for (int64_t j = 0; j < grid_w; ++j) {
+                    double src_w    = (static_cast<double>(j) + 0.5) * side / static_cast<double>(grid_w) - 0.5;
+                    int64_t w_floor = static_cast<int64_t>(std::floor(src_w));
+                    double dw       = src_w - static_cast<double>(w_floor);
+
+                    int32_t h0 = clamp_index(h_floor);
+                    int32_t h1 = clamp_index(h_floor + 1);
+                    int32_t w0 = clamp_index(w_floor);
+                    int32_t w1 = clamp_index(w_floor + 1);
+
+                    pos_idx_data[0].push_back(h0 * static_cast<int32_t>(side) + w0);
+                    pos_idx_data[1].push_back(h0 * static_cast<int32_t>(side) + w1);
+                    pos_idx_data[2].push_back(h1 * static_cast<int32_t>(side) + w0);
+                    pos_idx_data[3].push_back(h1 * static_cast<int32_t>(side) + w1);
+
+                    pos_weight_data[0].push_back(static_cast<float>((1.0 - dh) * (1.0 - dw)));
+                    pos_weight_data[1].push_back(static_cast<float>((1.0 - dh) * dw));
+                    pos_weight_data[2].push_back(static_cast<float>(dh * (1.0 - dw)));
+                    pos_weight_data[3].push_back(static_cast<float>(dh * dw));
+                }
+            }
+        }
+
+        sd::Tensor<float> compute(int n_threads, const sd::Tensor<float>& pixel_values) {
+            auto get_graph = [&]() -> ggml_cgraph* {
+                ggml_cgraph* gf = new_graph_custom(LLADA_IMAGE_TE_GRAPH_SIZE);
+                ggml_tensor* x  = make_input(pixel_values);
+
+                int64_t grid_h = x->ne[1] / config.patch_size;
+                int64_t grid_w = x->ne[0] / config.patch_size;
+                build_pos_embed_taps(grid_h, grid_w);
+
+                std::vector<ggml_tensor*> pos_idx;
+                std::vector<ggml_tensor*> pos_weight;
+                for (int i = 0; i < 4; i++) {
+                    auto idx = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_I32, static_cast<int64_t>(pos_idx_data[i].size()));
+                    set_backend_tensor_data(idx, pos_idx_data[i].data());
+                    auto w = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_F32, 1, static_cast<int64_t>(pos_weight_data[i].size()));
+                    set_backend_tensor_data(w, pos_weight_data[i].data());
+                    pos_idx.push_back(idx);
+                    pos_weight.push_back(w);
+                }
+
+                auto runner_ctx  = get_context();
+                ggml_tensor* out = sigvq.forward(&runner_ctx, x, pos_idx, pos_weight);
+                ggml_build_forward_expand(gf, out);
+                return gf;
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute(get_graph, n_threads, true), 3);
+        }
+    };
+
+}  // namespace LLaDAImageTE
+
+#endif  // __SD_MODEL_TE_LLADA_IMAGE_TE_HPP__

src/model/te/llm.hpp

@@ -49,6 +49,7 @@ namespace LLM {
         GEMMA2_2B,
         GEMMA4_12B,
         GPT_OSS_20B,
+        LLADA2_MOE,
         ARCH_COUNT,
     };
 
@@ -62,6 +63,7 @@ namespace LLM {
         "gemma2_2b",
         "gemma4_12b",
         "gpt_oss_20b",
+        "llada2_moe",
     };
 
     enum class MLPActivation {
@@ -125,6 +127,17 @@ namespace LLM {
         std::vector<int> sliding_attention;
         int64_t num_experts         = 0;
         int64_t num_experts_per_tok = 0;
+        bool qkv_fused              = false;
+        bool bidirectional          = false;
+        float partial_rotary        = 1.f;
+
+        // DeepSeek-V3-style grouped-sigmoid MoE routing (LLaDA2)
+        int64_t moe_intermediate_size = 0;
+        int64_t num_shared_experts    = 0;
+        int64_t first_k_dense_replace = 0;
+        int64_t n_group               = 0;
+        int64_t topk_group            = 0;
+        float routed_scaling_factor   = 1.f;
         LLMVisionConfig vision;
         bool have_vision_weight = false;
         bool llama_cpp_style    = false;
@@ -212,6 +225,31 @@ namespace LLM {
                 config.intermediate_size       = 9216;
                 config.num_layers              = 26;
                 config.vocab_size              = 256000;
+            } else if (arch == LLMArch::LLADA2_MOE) {
+                config.head_dim                = 128;
+                config.num_heads               = 16;
+                config.num_kv_heads            = 4;
+                config.qkv_bias                = false;
+                config.attention_out_bias      = false;
+                config.qk_norm                 = true;
+                config.rms_norm_eps            = 1e-6f;
+                config.hidden_size             = 2048;
+                config.intermediate_size       = 5120;
+                config.num_layers              = 20;
+                config.vocab_size              = 173568;
+                config.max_position_embeddings = 16384;
+                config.rope_thetas             = {600000.f};
+                config.qkv_fused               = true;
+                config.bidirectional           = true;
+                config.partial_rotary          = 0.5f;
+                config.num_experts             = 256;
+                config.num_experts_per_tok     = 8;
+                config.moe_intermediate_size   = 512;
+                config.num_shared_experts      = 1;
+                config.first_k_dense_replace   = 1;
+                config.n_group                 = 8;
+                config.topk_group              = 4;
+                config.routed_scaling_factor   = 2.5f;
             } else if (arch == LLMArch::GPT_OSS_20B) {
                 config.head_dim                = 64;
                 config.num_heads               = 64;
@@ -419,6 +457,195 @@ namespace LLM {
         }
     };
 
+    // LLaDA2's MoE differs from GPT-OSS's in three ways that all change the result:
+    // routing scores are sigmoid (not softmax over the selected logits), expert selection is
+    // group-limited and uses a bias term that the returned weights do NOT include, and the
+    // experts carry no biases. Ref: LLaDA2MoeGate / LLaDA2MoeSparseMoeBlock in
+    // modeling_llada2uni_moe.py.
+    struct LLaDA2MoEMLP : public GGMLBlock {
+    protected:
+        int64_t hidden_size;
+        int64_t moe_intermediate_size;
+        int64_t num_experts;
+        int64_t num_experts_per_tok;
+        int64_t n_group;
+        int64_t topk_group;
+        float routed_scaling_factor;
+
+        void init_params(ggml_context* ctx,
+                         const String2TensorStorage& tensor_storage_map = {},
+                         std::string prefix                             = "") override {
+            GGMLBlock::init_params(ctx, tensor_storage_map, prefix);
+
+            auto supported_type = [](ggml_type wtype, int64_t in_features) {
+                if (in_features % ggml_blck_size(wtype) != 0) {
+                    return GGML_TYPE_F32;
+                }
+                return wtype;
+            };
+
+            // The reference runs the router in fp32; keep the weight in fp32 so the sigmoid
+            // scores and the group sums match.
+            params["gate.weight"]      = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hidden_size, num_experts);
+            params["gate.expert_bias"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, num_experts);
+
+            ggml_type gate_type = supported_type(get_type(prefix + "experts.gate_proj.weight", tensor_storage_map, GGML_TYPE_F32), hidden_size);
+            ggml_type up_type   = supported_type(get_type(prefix + "experts.up_proj.weight", tensor_storage_map, GGML_TYPE_F32), hidden_size);
+            ggml_type down_type = supported_type(get_type(prefix + "experts.down_proj.weight", tensor_storage_map, GGML_TYPE_F32), moe_intermediate_size);
+
+            // HF ships the stacked experts as 3-D nn.Parameters, while the ComfyUI GGUF repack
+            // flattens the expert axis into ne[1]. Declare whichever the file holds - the two are
+            // bit-identical, and forward() reshapes to 3-D for ggml_mul_mat_id either way.
+            auto declare_experts = [&](const std::string& name, ggml_type type, int64_t in_dim, int64_t out_dim) {
+                auto storage = tensor_storage_map.find(prefix + name);
+                if (storage != tensor_storage_map.end() && storage->second.n_dims == 2) {
+                    GGML_ASSERT(storage->second.nelements() == in_dim * out_dim * num_experts);
+                    params[name] = ggml_new_tensor_2d(ctx, type, in_dim, out_dim * num_experts);
+                } else {
+                    params[name] = ggml_new_tensor_3d(ctx, type, in_dim, out_dim, num_experts);
+                }
+            };
+
+            declare_experts("experts.gate_proj.weight", gate_type, hidden_size, moe_intermediate_size);
+            declare_experts("experts.up_proj.weight", up_type, hidden_size, moe_intermediate_size);
+            declare_experts("experts.down_proj.weight", down_type, moe_intermediate_size, hidden_size);
+        }
+
+    public:
+        LLaDA2MoEMLP(const LLMConfig& config)
+            : hidden_size(config.hidden_size),
+              moe_intermediate_size(config.moe_intermediate_size),
+              num_experts(config.num_experts),
+              num_experts_per_tok(config.num_experts_per_tok),
+              n_group(config.n_group),
+              topk_group(config.topk_group),
+              routed_scaling_factor(config.routed_scaling_factor) {
+            if (config.num_shared_experts > 0) {
+                blocks["shared_experts"] = std::make_shared<MLP>(config.hidden_size,
+                                                                 config.moe_intermediate_size * config.num_shared_experts,
+                                                                 false,
+                                                                 config.mlp_activation);
+            }
+        }
+
+        // Reproduces group_limited_topk(): keep the topk_group groups with the highest
+        // "sum of the two best scores in the group", then take the global top-k among them.
+        ggml_tensor* group_limited_mask(GGMLRunnerContext* ctx,
+                                        ggml_tensor* routing_scores,
+                                        int64_t n_token_total) {
+            ggml_context* gctx      = ctx->ggml_ctx;
+            const int64_t per_group = num_experts / n_group;
+
+            // [experts_per_group, n_group * tokens] so top-2 runs per (group, token) row.
+            auto grouped     = ggml_reshape_2d(gctx, routing_scores, per_group, n_group * n_token_total);
+            auto best2_idx   = ggml_argsort_top_k(gctx, grouped, 2);  // [2, n_group * tokens]
+            auto grouped_val = ggml_reshape_3d(gctx, grouped, 1, per_group, n_group * n_token_total);
+            auto best2       = ggml_get_rows(gctx, grouped_val, best2_idx);  // [1, 2, n_group * tokens]
+            best2            = ggml_reshape_2d(gctx, best2, 2, n_group * n_token_total);
+            auto group_score = ggml_reshape_2d(gctx, ggml_sum_rows(gctx, best2), n_group, n_token_total);  // [n_group, tokens]
+
+            // Threshold = the topk_group-th largest group score, taken from the sorted top-k.
+            auto top_groups = ggml_argsort_top_k(gctx, group_score, (int)topk_group);  // [topk_group, tokens]
+            auto group_val  = ggml_reshape_3d(gctx, group_score, 1, n_group, n_token_total);
+            auto top_scores = ggml_get_rows(gctx, group_val, top_groups);  // [1, topk_group, tokens]
+            top_scores      = ggml_reshape_2d(gctx, top_scores, topk_group, n_token_total);
+            auto threshold  = ggml_view_2d(gctx,
+                                           top_scores,
+                                           1,
+                                           n_token_total,
+                                           top_scores->nb[1],
+                                           (topk_group - 1) * top_scores->nb[0]);  // [1, tokens]
+            threshold       = ggml_cont(gctx, threshold);
+
+            // keep = 1 - step(threshold - score). step(0) == 0, so the group sitting exactly on
+            // the threshold is kept without needing an epsilon.
+            auto diff = ggml_sub(gctx, ggml_repeat(gctx, threshold, group_score), group_score);
+            auto keep = ggml_scale_bias(gctx, ggml_step(gctx, diff), -1.f, 1.f);  // [n_group, tokens]
+
+            // 0 for kept groups, a large negative for dropped ones, broadcast over the group.
+            auto additive = ggml_scale_bias(gctx, keep, 1e30f, -1e30f);
+            additive      = ggml_reshape_3d(gctx, additive, 1, n_group, n_token_total);
+            auto expanded = ggml_repeat_4d(gctx, additive, per_group, n_group, n_token_total, 1);
+            return ggml_reshape_2d(gctx, expanded, num_experts, n_token_total);
+        }
+
+        ggml_tensor* expert_linear(GGMLRunnerContext* ctx,
+                                   const std::string& weight_name,
+                                   ggml_tensor* x,
+                                   ggml_tensor* selected_experts) {
+            ggml_tensor* w = params[weight_name];
+            if (w->ne[2] != num_experts) {
+                // Flattened layout: split the expert axis back out. ne[0] is untouched, so this
+                // stays valid for quantized types.
+                w = ggml_reshape_3d(ctx->ggml_ctx, w, w->ne[0], w->ne[1] / num_experts, num_experts);
+            }
+            return ggml_mul_mat_id(ctx->ggml_ctx, w, x, selected_experts);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            // x: [N, n_token, hidden_size]
+            GGML_ASSERT(num_experts > 0 && num_experts_per_tok > 0);
+            GGML_ASSERT(n_group > 0 && topk_group > 0 && num_experts % n_group == 0);
+
+            ggml_context* gctx          = ctx->ggml_ctx;
+            const int64_t n_token       = x->ne[1];
+            const int64_t N             = x->ne[2];
+            const int64_t n_token_total = n_token * N;
+
+            auto identity = x;
+
+            auto logits = ggml_mul_mat(gctx, params["gate.weight"], x);
+            logits      = ggml_reshape_2d(gctx, logits, num_experts, n_token_total);
+            auto scores = ggml_sigmoid(gctx, logits);  // [num_experts, tokens]
+
+            // The bias steers selection only; the combine weights come from the unbiased scores.
+            auto routing = ggml_add(gctx, scores, params["gate.expert_bias"]);
+            routing      = ggml_add(gctx, routing, group_limited_mask(ctx, routing, n_token_total));
+
+            auto selected_experts = ggml_argsort_top_k(gctx, routing, (int)num_experts_per_tok);  // [top_k, tokens]
+            auto score_rows       = ggml_reshape_3d(gctx, scores, 1, num_experts, n_token_total);
+            auto weights          = ggml_get_rows(gctx, score_rows, selected_experts);  // [1, top_k, tokens]
+            weights               = ggml_reshape_2d(gctx, weights, num_experts_per_tok, n_token_total);
+
+            if (num_experts_per_tok > 1) {
+                auto denom = ggml_scale_bias(gctx, ggml_sum_rows(gctx, weights), 1.f, 1e-20f);  // [1, tokens]
+                weights    = ggml_div(gctx, weights, ggml_repeat(gctx, denom, weights));
+            }
+            weights = ggml_scale(gctx, weights, routed_scaling_factor);
+            weights = ggml_reshape_3d(gctx, weights, 1, num_experts_per_tok, n_token_total);
+
+            auto xf        = ggml_reshape_3d(gctx, x, hidden_size, 1, n_token_total);
+            auto gate      = expert_linear(ctx, "experts.gate_proj.weight", xf, selected_experts);
+            auto up        = expert_linear(ctx, "experts.up_proj.weight", xf, selected_experts);
+            auto activated = ggml_swiglu_split(gctx, gate, up);
+            auto experts   = expert_linear(ctx, "experts.down_proj.weight", activated, selected_experts);
+            experts        = ggml_mul(gctx, experts, weights);
+
+            ggml_tensor* out = nullptr;
+            for (int64_t i = 0; i < num_experts_per_tok; ++i) {
+                auto expert_out = ggml_view_2d(gctx,
+                                               experts,
+                                               hidden_size,
+                                               n_token_total,
+                                               experts->nb[2],
+                                               i * experts->nb[1]);
+                out             = out == nullptr ? expert_out : ggml_add(gctx, out, expert_out);
+            }
+            if (num_experts_per_tok == 1) {
+                out = ggml_cont(gctx, out);
+            }
+            out = ggml_reshape_3d(gctx, out, hidden_size, n_token, N);
+
+            auto shared_it = blocks.find("shared_experts");
+            if (shared_it != blocks.end()) {
+                auto shared_experts = std::dynamic_pointer_cast<MLP>(shared_it->second);
+                out                 = ggml_add(gctx, out, shared_experts->forward(ctx, identity));
+            }
+
+            return out;
+        }
+    };
+
     struct GPTOSSMLP : public GGMLBlock {
     protected:
         int64_t hidden_size;
@@ -605,21 +832,31 @@ namespace LLM {
             }
             txt_token_end = image_embeds[i].first;
 
-            auto txt_embed = ggml_ext_slice(ctx->ggml_ctx, raw_x, 1, txt_token_start, txt_token_end);
-            if (input_embed == nullptr) {
-                input_embed = txt_embed;
-            } else {
-                input_embed = ggml_concat(ctx->ggml_ctx, input_embed, txt_embed, 1);
+            // An embed can sit flush against the previous one or at the very start/end of the
+            // sequence, leaving no text tokens to splice around it.
+            if (txt_token_end > txt_token_start) {
+                auto txt_embed = ggml_ext_slice(ctx->ggml_ctx, raw_x, 1, txt_token_start, txt_token_end);
+                if (input_embed == nullptr) {
+                    input_embed = txt_embed;
+                } else {
+                    input_embed = ggml_concat(ctx->ggml_ctx, input_embed, txt_embed, 1);
+                }
             }
 
-            input_embed = ggml_concat(ctx->ggml_ctx, input_embed, image_embeds[i].second, 1);
+            if (input_embed == nullptr) {
+                input_embed = image_embeds[i].second;
+            } else {
+                input_embed = ggml_concat(ctx->ggml_ctx, input_embed, image_embeds[i].second, 1);
+            }
         }
 
         txt_token_start = image_embeds[image_embeds.size() - 1].first + image_embeds[image_embeds.size() - 1].second->ne[1];
         txt_token_end   = raw_x->ne[1];
 
-        auto final_txt_embed = ggml_ext_slice(ctx->ggml_ctx, raw_x, 1, txt_token_start, txt_token_end);
-        input_embed          = ggml_concat(ctx->ggml_ctx, input_embed, final_txt_embed, 1);
+        if (txt_token_end > txt_token_start) {
+            auto final_txt_embed = ggml_ext_slice(ctx->ggml_ctx, raw_x, 1, txt_token_start, txt_token_end);
+            input_embed          = ggml_concat(ctx->ggml_ctx, input_embed, final_txt_embed, 1);
+        }
         GGML_ASSERT(raw_x->ne[1] == input_embed->ne[1]);
         return input_embed;
     }
@@ -1122,6 +1359,7 @@ namespace LLM {
         bool k_eq_v;
         bool v_norm;
         bool unscaled_attention;
+        bool qkv_fused;
         float rms_norm_eps;
         int rope_pairs;
 
@@ -1147,12 +1385,20 @@ namespace LLM {
               k_eq_v(global_layer && config.global_k_eq_v),
               v_norm(config.v_norm),
               unscaled_attention(config.unscaled_attention),
+              qkv_fused(config.qkv_fused),
               rms_norm_eps(config.rms_norm_eps),
               rope_pairs(0) {
-            blocks["q_proj"] = std::make_shared<Linear>(config.hidden_size, num_heads * head_dim, config.qkv_bias);
-            blocks["k_proj"] = std::make_shared<Linear>(config.hidden_size, num_kv_heads * head_dim, config.qkv_bias);
-            if (!k_eq_v) {
-                blocks["v_proj"] = std::make_shared<Linear>(config.hidden_size, num_kv_heads * head_dim, config.qkv_bias);
+            if (qkv_fused) {
+                // The checkpoint ships q, k and v as one tensor and the loader cannot split a
+                // source tensor, so keep it fused and slice it in forward().
+                GGML_ASSERT(!k_eq_v);
+                blocks["query_key_value"] = std::make_shared<Linear>(config.hidden_size, (num_heads + num_kv_heads * 2) * head_dim, config.qkv_bias);
+            } else {
+                blocks["q_proj"] = std::make_shared<Linear>(config.hidden_size, num_heads * head_dim, config.qkv_bias);
+                blocks["k_proj"] = std::make_shared<Linear>(config.hidden_size, num_kv_heads * head_dim, config.qkv_bias);
+                if (!k_eq_v) {
+                    blocks["v_proj"] = std::make_shared<Linear>(config.hidden_size, num_kv_heads * head_dim, config.qkv_bias);
+                }
             }
             blocks["o_proj"] = std::make_shared<Linear>(num_heads * head_dim, config.hidden_size, config.attention_out_bias);
             if (config.qk_norm) {
@@ -1161,7 +1407,7 @@ namespace LLM {
             }
             // Proportional RoPE rotates only the leading `rope_pairs` dimension pairs of the head;
             // the rest are left unrotated through freq_factors (see rope_freq_factors()).
-            float partial = global_layer ? config.global_partial_rotary : 1.f;
+            float partial = global_layer && config.global_partial_rotary != 1.f ? config.global_partial_rotary : config.partial_rotary;
             rope_pairs    = static_cast<int>(partial * head_dim / 2.f);
         }
 
@@ -1186,14 +1432,28 @@ namespace LLM {
             // x: [N, n_token, hidden_size]
             int64_t n_token = x->ne[1];
             int64_t N       = x->ne[2];
-            auto q_proj     = std::dynamic_pointer_cast<Linear>(blocks["q_proj"]);
-            auto k_proj     = std::dynamic_pointer_cast<Linear>(blocks["k_proj"]);
-            auto v_proj     = k_eq_v ? nullptr : std::dynamic_pointer_cast<Linear>(blocks["v_proj"]);
             auto out_proj   = std::dynamic_pointer_cast<Linear>(blocks["o_proj"]);
 
-            auto q = q_proj->forward(ctx, x);               // [N, n_token, num_heads*head_dim]
-            auto k = k_proj->forward(ctx, x);               // [N, n_token, num_kv_heads*head_dim]
-            auto v = k_eq_v ? k : v_proj->forward(ctx, x);  // [N, n_token, num_kv_heads*head_dim]
+            ggml_tensor* q = nullptr;
+            ggml_tensor* k = nullptr;
+            ggml_tensor* v = nullptr;
+            if (qkv_fused) {
+                auto qkv_proj = std::dynamic_pointer_cast<Linear>(blocks["query_key_value"]);
+                auto qkv      = qkv_proj->forward(ctx, x);  // [N, n_token, (num_heads + num_kv_heads*2)*head_dim]
+                int64_t q_len = num_heads * head_dim;
+                int64_t k_len = num_kv_heads * head_dim;
+                q             = ggml_ext_slice(ctx->ggml_ctx, qkv, 0, 0, q_len);
+                k             = ggml_ext_slice(ctx->ggml_ctx, qkv, 0, q_len, q_len + k_len);
+                v             = ggml_ext_slice(ctx->ggml_ctx, qkv, 0, q_len + k_len, q_len + k_len * 2);
+            } else {
+                auto q_proj = std::dynamic_pointer_cast<Linear>(blocks["q_proj"]);
+                auto k_proj = std::dynamic_pointer_cast<Linear>(blocks["k_proj"]);
+                auto v_proj = k_eq_v ? nullptr : std::dynamic_pointer_cast<Linear>(blocks["v_proj"]);
+
+                q = q_proj->forward(ctx, x);               // [N, n_token, num_heads*head_dim]
+                k = k_proj->forward(ctx, x);               // [N, n_token, num_kv_heads*head_dim]
+                v = k_eq_v ? k : v_proj->forward(ctx, x);  // [N, n_token, num_kv_heads*head_dim]
+            }
 
             q = ggml_reshape_4d(ctx->ggml_ctx, q, head_dim, num_heads, n_token, N);     // [N, n_token, num_heads, head_dim]
             k = ggml_reshape_4d(ctx->ggml_ctx, k, head_dim, num_kv_heads, n_token, N);  // [N, n_token, num_kv_heads, head_dim]
@@ -1336,6 +1596,38 @@ namespace LLM {
                                   1.f,
                                   32.f,
                                   1.f);
+            } else if (arch == LLMArch::LLADA2_MOE) {
+                // LLaDA2 slices the head (query[..., :rotary_dim]) instead of zero-padding
+                // inv_freq like gemma does, so rotate_half pairs i with i + rotary_dim/2 and the
+                // frequencies use rotary_dim as the exponent denominator. Passing n_dims =
+                // rotary_dim reproduces both; freq_factors would give the wrong pairing.
+                int rotary_dim = rope_pairs * 2;
+                q              = ggml_rope_ext(ctx->ggml_ctx,
+                                               q,
+                                               input_pos,
+                                               nullptr,
+                                               rotary_dim,
+                                               GGML_ROPE_TYPE_NEOX,
+                                               static_cast<int>(max_position_embeddings),
+                                               rope_thetas[0],
+                                               1.f,
+                                               0.f,
+                                               1.f,
+                                               32.f,
+                                               1.f);
+                k              = ggml_rope_ext(ctx->ggml_ctx,
+                                               k,
+                                               input_pos,
+                                               nullptr,
+                                               rotary_dim,
+                                               GGML_ROPE_TYPE_NEOX,
+                                               static_cast<int>(max_position_embeddings),
+                                               rope_thetas[0],
+                                               1.f,
+                                               0.f,
+                                               1.f,
+                                               32.f,
+                                               1.f);
             } else if (arch == LLMArch::QWEN3_VL) {
                 int sections[4] = {24, 20, 20, 0};
                 q               = ggml_rope_multi(ctx->ggml_ctx, q, input_pos, nullptr, head_dim, sections, GGML_ROPE_TYPE_IMROPE, 262144, 5000000.f, 1.f, 0.f, 1.f, 32.f, 1.f);
@@ -1432,6 +1724,8 @@ namespace LLM {
             blocks["self_attn"] = std::make_shared<Attention>(config, sliding_attention == 0);
             if (config.arch == LLMArch::GPT_OSS_20B) {
                 blocks["mlp"] = std::make_shared<GPTOSSMLP>(config);
+            } else if (config.arch == LLMArch::LLADA2_MOE && layer_index >= config.first_k_dense_replace) {
+                blocks["mlp"] = std::make_shared<LLaDA2MoEMLP>(config);
             } else {
                 blocks["mlp"] = std::make_shared<MLP>(config.hidden_size,
                                                       config.intermediate_size,
@@ -1485,6 +1779,10 @@ namespace LLM {
             if (arch == LLMArch::GPT_OSS_20B) {
                 auto mlp = std::dynamic_pointer_cast<GPTOSSMLP>(blocks["mlp"]);
                 x        = mlp->forward(ctx, x);
+            } else if (auto moe_mlp = std::dynamic_pointer_cast<LLaDA2MoEMLP>(blocks["mlp"])) {
+                // LLaDA2 is dense for the first first_k_dense_replace layers and MoE afterwards,
+                // so the block type varies per layer rather than per arch.
+                x = moe_mlp->forward(ctx, x);
             } else {
                 auto mlp = std::dynamic_pointer_cast<MLP>(blocks["mlp"]);
                 x        = mlp->forward(ctx, x);
@@ -1650,6 +1948,11 @@ namespace LLM {
             return x;
         }
 
+        ggml_tensor* embed(GGMLRunnerContext* ctx, ggml_tensor* input_ids) {
+            auto model = std::dynamic_pointer_cast<TextModel>(blocks["model"]);
+            return model->embed(ctx, input_ids);
+        }
+
         std::shared_ptr<VisionModel> vision_model() {
             GGML_ASSERT(enable_vision);
             return std::dynamic_pointer_cast<VisionModel>(blocks["visual"]);
@@ -1990,7 +2293,8 @@ namespace LLM {
                 config.arch == LLMArch::GEMMA3_12B ||
                 config.arch == LLMArch::GEMMA4_12B ||
                 config.arch == LLMArch::GEMMA2_2B ||
-                config.arch == LLMArch::GPT_OSS_20B) {
+                config.arch == LLMArch::GPT_OSS_20B ||
+                config.arch == LLMArch::LLADA2_MOE) {
                 input_pos_vec.resize(n_tokens);
                 for (int i = 0; i < n_tokens; ++i) {
                     input_pos_vec[i] = i;
@@ -2042,8 +2346,9 @@ namespace LLM {
                 attention_mask_vec.resize(n_tokens * n_tokens);
                 for (int i0 = 0; i0 < n_tokens; i0++) {
                     for (int i1 = 0; i1 < n_tokens; i1++) {
+                        // Diffusion LLMs attend in both directions; only causal LMs get the triangle.
                         float value = 0.f;
-                        if (i0 > i1) {
+                        if (!config.bidirectional && i0 > i1) {
                             value = -INFINITY;
                         }
                         attention_mask_vec[i1 * n_tokens + i0] = value;
@@ -2115,6 +2420,22 @@ namespace LLM {
                                                    input_ids.dim() + 1);
         }
 
+        // LLaDA-Image's QueryFormer consumes the raw token embeddings before the backbone runs,
+        // so it needs the embedding lookup on its own.
+        sd::Tensor<float> compute_input_embeds(const int n_threads,
+                                               const sd::Tensor<int32_t>& input_ids) {
+            auto get_graph = [&]() -> ggml_cgraph* {
+                ggml_cgraph* gf  = new_graph_custom(LLM_GRAPH_SIZE);
+                ggml_tensor* ids = make_input(input_ids);
+                auto runner_ctx  = get_context();
+                ggml_tensor* out = model.embed(&runner_ctx, ids);
+                ggml_build_forward_expand(gf, out);
+                return gf;
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute(get_graph, n_threads, true),
+                                                   input_ids.dim() + 1);
+        }
+
         int64_t get_num_image_tokens(int64_t t, int64_t h, int64_t w) {
             int64_t grid_t     = 1;
             int64_t grid_h     = h / config.vision.patch_size;
@@ -2370,11 +2691,13 @@ namespace LLM {
                 pad_id = 199999;
             } else if (arch == LLMArch::GEMMA2_2B) {
                 pad_id = 0;
+            } else if (arch == LLMArch::LLADA2_MOE) {
+                pad_id = 156892;
             }
             tokenizer = tokenizers.create(TokenizerConfig::MAIN, model.config.vocab_size, pad_id);
             if (!tokenizer) {
-                if (arch == LLMArch::GPT_OSS_20B || arch == LLMArch::GEMMA2_2B) {
-                    throw std::runtime_error("GPT-OSS and Gemma 2 require an external tokenizer.json in the main tokenizer slot");
+                if (arch == LLMArch::GPT_OSS_20B || arch == LLMArch::GEMMA2_2B || arch == LLMArch::LLADA2_MOE) {
+                    throw std::runtime_error("GPT-OSS, Gemma 2 and LLaDA2 require an external tokenizer.json in the main tokenizer slot");
                 }
                 if (arch == LLMArch::MISTRAL_SMALL_3_2 || arch == LLMArch::MINISTRAL_3_3B) {
                     tokenizer = std::make_shared<MistralTokenizer>();

src/model/vae/vae.hpp

@@ -162,7 +162,7 @@ public:
         int scale_factor = 8;
         if (version == VERSION_LTXAV) {
             scale_factor = 32;
-        } else if (version == VERSION_WAN2_2_TI2V || sd_version_is_hunyuan_video(version) || sd_version_is_mage_flow(version) || sd_version_is_minimax_h3(version)) {
+        } else if (version == VERSION_WAN2_2_TI2V || version == VERSION_QWEN_IMAGE_2_1 || sd_version_is_hunyuan_video(version) || sd_version_is_mage_flow(version) || sd_version_is_minimax_h3(version)) {
             scale_factor = 16;
         } else if (sd_version_uses_flux2_vae(version)) {
             scale_factor = 16;

src/model/vae/wan_vae.hpp

@@ -26,6 +26,13 @@ namespace WAN {
         bool bias;
 
         void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+            auto weight = tensor_storage_map.find(prefix + "weight");
+            if (weight != tensor_storage_map.end() && weight->second.ne[2] == 1 &&
+                weight->second.ne[3] == in_channels * out_channels) {
+                // Image VAE exports may retain Conv3d weights with a singleton temporal kernel.
+                std::get<0>(kernel_size) = 1;
+                std::get<0>(padding)     = 0;
+            }
             params["weight"] = ggml_new_tensor_4d(ctx,
                                                   GGML_TYPE_F16,
                                                   std::get<2>(kernel_size),
@@ -140,7 +147,7 @@ namespace WAN {
         std::string mode;
 
     public:
-        Resample(int64_t dim, const std::string& mode, bool wan2_2 = false)
+        Resample(int64_t dim, const std::string& mode, bool wan2_2 = false, bool is_2D = false)
             : dim(dim), mode(mode) {
             if (mode == "upsample2d") {
                 if (wan2_2) {
@@ -154,12 +161,20 @@ namespace WAN {
                 } else {
                     blocks["resample.1"] = std::shared_ptr<GGMLBlock>(new Conv2d(dim, dim / 2, {3, 3}, {1, 1}, {1, 1}));
                 }
-                blocks["time_conv"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(dim, dim * 2, {3, 1, 1}, {1, 1, 1}, {1, 0, 0}));
+                if (is_2D) {
+                    blocks["time_conv"] = std::make_shared<Conv2dBut3d>(dim, dim * 2, std::pair<int, int>{1, 1});
+                } else {
+                    blocks["time_conv"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(dim, dim * 2, {3, 1, 1}, {1, 1, 1}, {1, 0, 0}));
+                }
             } else if (mode == "downsample2d") {
                 blocks["resample.1"] = std::shared_ptr<GGMLBlock>(new Conv2d(dim, dim, {3, 3}, {2, 2}));
             } else if (mode == "downsample3d") {
                 blocks["resample.1"] = std::shared_ptr<GGMLBlock>(new Conv2d(dim, dim, {3, 3}, {2, 2}));
-                blocks["time_conv"]  = std::shared_ptr<GGMLBlock>(new CausalConv3d(dim, dim, {3, 1, 1}, {2, 1, 1}, {0, 0, 0}));
+                if (is_2D) {
+                    blocks["time_conv"] = std::make_shared<Conv2dBut3d>(dim, dim, std::pair<int, int>{1, 1});
+                } else {
+                    blocks["time_conv"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(dim, dim, {3, 1, 1}, {2, 1, 1}, {0, 0, 0}));
+                }
             } else if (mode == "none") {
                 // nn.Identity()
             } else {
@@ -469,7 +484,7 @@ namespace WAN {
             }
             if (down_flag) {
                 std::string mode                           = temperal_downsample ? "downsample3d" : "downsample2d";
-                blocks["downsamples." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new Resample(out_dim, mode, true));
+                blocks["downsamples." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new Resample(out_dim, mode, true, is_2D));
                 i++;
             }
         }
@@ -532,7 +547,7 @@ namespace WAN {
             }
             if (up_flag) {
                 std::string mode                         = temperal_upsample ? "upsample3d" : "upsample2d";
-                blocks["upsamples." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new Resample(out_dim, mode, true));
+                blocks["upsamples." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new Resample(out_dim, mode, true, is_2D));
                 i++;
             }
         }
@@ -1054,9 +1069,24 @@ namespace WAN {
                 input_channels = 4;
             }
 
+            if (version == VERSION_QWEN_IMAGE_2_1) {
+                wan2_2         = true;
+                dec_dim        = 144;
+                z_dim          = 64;
+                input_channels = 4;
+                dim_mult       = {1, 2, 4, 8, 8};
+            }
+
             if (is_2D) {
-                temperal_upsample   = {false, false, false};
-                temperal_downsample = {false, false, false};
+                temperal_upsample.assign(dim_mult.size() - 1, false);
+                temperal_downsample.assign(dim_mult.size() - 1, false);
+            }
+            if (version == VERSION_QWEN_IMAGE_2_1) {
+                // Temporal shortcut factors still affect single-frame channel grouping.
+                temperal_upsample   = {true, true, true, false};
+                temperal_downsample = {false, true, true, true};
+                _conv_num           = 2 * (2 + static_cast<int>(dim_mult.size()) * (num_res_blocks + 1)) + 3 + (is_2D ? 0 : 2);
+                _enc_conv_num       = 2 * (2 + static_cast<int>(dim_mult.size()) * num_res_blocks) + 3 + (is_2D ? 0 : 2);
             }
 
             if (!decode_only) {
@@ -1271,18 +1301,9 @@ namespace WAN {
                      SDVersion version                                   = VERSION_WAN2,
                      std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
             : VAE(version, backend, prefix, weight_manager), decode_only(decode_only) {
-            bool is_2D = false;
-            for (const auto& [name, tensor_storage] : tensor_storage_map) {
-                if (ends_with(name, "decoder.conv1.weight")) {
-                    if (tensor_storage.ne[2] > 3) {
-                        is_2D = true;
-                    }
-                    break;
-                }
-            }
-            if (is_2D) {
-                LOG_VERBOSE("USING 2D VAE");
-            }
+            const auto conv_in = tensor_storage_map.find((prefix.empty() ? "" : prefix + ".") + "decoder.conv1.weight");
+            const bool is_2D   = conv_in != tensor_storage_map.end() && conv_in->second.ne[2] > 3;
+            LOG_VERBOSE("Wan VAE convolution type: %s", is_2D ? "2D" : "3D");
             ae = WanVAE(decode_only, version, is_2D);
             ae.init(params_ctx, tensor_storage_map, prefix);
         }
@@ -1342,6 +1363,28 @@ namespace WAN {
                 std_tensor.reshape_(stats_shape);
                 return {std::move(mean_tensor), std::move(std_tensor)};
             }
+            if (version == VERSION_QWEN_IMAGE_2_1 && latents.shape()[channel_dim] == 64) {
+                stats_shape[static_cast<size_t>(channel_dim)] = 64;
+                auto mean_tensor                              = sd::Tensor<float>::from_vector({0.5126f, 0.7721f, -0.0631f, 1.3506f, -0.7855f, -2.1025f, -0.3458f, 1.3722f,
+                                                                                                1.8873f, -1.7177f, -0.6510f, 0.2732f, 0.7562f, -0.6163f, -1.0277f, 3.8363f,
+                                                                                                2.0210f, 0.0472f, 0.9320f, 2.0087f, 2.4954f, -0.1391f, -1.4249f, 1.8464f,
+                                                                                                -0.5236f, 1.2826f, 3.7046f, -1.3035f, 2.7286f, -1.4518f, -1.9036f, -1.9955f,
+                                                                                                -0.0342f, -1.0265f, -0.7636f, 3.0555f, 0.0746f, -3.0751f, -0.1076f, 1.7376f,
+                                                                                                -1.0914f, -1.9435f, -0.2784f, -1.3680f, 0.4809f, -0.4433f, 0.3764f, 0.5729f,
+                                                                                                -2.0595f, 1.0960f, -1.3260f, -2.0211f, -5.0179f, 0.5275f, 4.0162f, 1.8505f,
+                                                                                                0.3026f, 1.9373f, 1.4937f, 0.2632f, 0.5547f, -1.7121f, -0.1562f, 0.0304f});
+                auto std_tensor                               = sd::Tensor<float>::from_vector({3.2001f, 3.2936f, 3.4321f, 3.0091f, 3.1061f, 4.0379f, 4.0705f, 3.7910f,
+                                                                                                3.0785f, 3.6500f, 3.9308f, 3.0904f, 2.8778f, 3.7675f, 3.7320f, 5.0756f,
+                                                                                                3.2864f, 4.0397f, 3.1317f, 4.0443f, 2.9249f, 3.9454f, 3.0988f, 4.2489f,
+                                                                                                3.4896f, 3.8513f, 3.9323f, 3.4719f, 3.7498f, 4.2830f, 3.5694f, 4.2467f,
+                                                                                                3.9037f, 3.2947f, 5.0770f, 3.5075f, 3.2700f, 3.4767f, 2.8063f, 5.1125f,
+                                                                                                3.5327f, 4.7833f, 3.1286f, 4.1819f, 3.8527f, 3.8312f, 3.5605f, 4.3875f,
+                                                                                                3.9624f, 4.0168f, 3.5643f, 4.0550f, 5.5614f, 4.2963f, 4.4080f, 3.4959f,
+                                                                                                3.8747f, 3.7608f, 3.5735f, 3.1490f, 3.7662f, 3.6746f, 3.4563f, 3.8161f});
+                mean_tensor.reshape_(stats_shape);
+                std_tensor.reshape_(stats_shape);
+                return {std::move(mean_tensor), std::move(std_tensor)};
+            }
             GGML_ABORT("unexpected latent channel dimension %lld for version %d",
                        (long long)latents.shape()[channel_dim],
                        version);

src/model_io/safetensors_io.cpp

@@ -86,7 +86,12 @@ static ggml_type safetensors_dtype_to_ggml_type(const std::string& dtype) {
         ttype = GGML_TYPE_F32;
     } else if (dtype == "F64") {
         ttype = GGML_TYPE_F32;
-#ifndef SD_USE_UPSTREAM_GGML
+#ifdef SD_USE_UPSTREAM_GGML
+    } else if (dtype == "F8_E4M3") {
+        ttype = GGML_TYPE_F16;
+    } else if (dtype == "F8_E5M2") {
+        ttype = GGML_TYPE_F16;
+#else
     } else if (dtype == "F8_E4M3") {
         ttype = GGML_TYPE_F8_E4M3;
     } else if (dtype == "F8_E5M2") {
@@ -287,12 +292,6 @@ bool read_safetensors_file(const std::string& file_path,
             continue;
         }
 
-#ifdef SD_USE_UPSTREAM_GGML
-        if (dtype == "F8_E4M3" || dtype == "F8_E5M2") {
-            set_error(error, "FP8 is not supported by this ggml build (tensor '" + name + "')");
-            return false;
-        }
-#endif
         ggml_type type = safetensors_dtype_to_ggml_type(dtype);
         if (type == GGML_TYPE_COUNT) {
             set_error(error, "unsupported dtype '" + dtype + "' (tensor '" + name + "')");
@@ -376,10 +375,20 @@ bool read_safetensors_file(const std::string& file_path,
         bool tensor_size_ok;
         if (dtype == "F8_E4M3") {
             tensor_storage.is_f8_e4m3 = true;
-            tensor_size_ok            = (tensor_storage.nbytes() == tensor_data_size);
+#ifdef SD_USE_UPSTREAM_GGML
+            // f8 -> f16
+            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size * 2);
+#else
+            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size);
+#endif
         } else if (dtype == "F8_E5M2") {
             tensor_storage.is_f8_e5m2 = true;
-            tensor_size_ok            = (tensor_storage.nbytes() == tensor_data_size);
+#ifdef SD_USE_UPSTREAM_GGML
+            // f8 -> f16
+            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size * 2);
+#else
+            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size);
+#endif
         } else if (dtype == "F64") {
             tensor_storage.is_f64 = true;
             // f64 -> f32

src/model_io/tensor_storage.h

@@ -58,6 +58,10 @@ struct TensorStorage {
     int64_t nbytes_to_read() const {
         if (is_f64 || is_i64) {
             return nbytes() * 2;
+#ifdef SD_USE_UPSTREAM_GGML
+        } else if (is_f8_e4m3 || is_f8_e5m2) {
+            return nbytes() / 2;
+#endif
         } else {
             return nbytes();
         }

src/model_loader.cpp

@@ -35,6 +35,66 @@
 
 /*================================================= Preprocess ==================================================*/
 
+#ifdef SD_USE_UPSTREAM_GGML
+uint16_t f8_e4m3_to_f16(uint8_t f8) {
+    const uint32_t exponent_bias = 7;
+    if (f8 == 0xff) {
+        return ggml_fp32_to_fp16(-NAN);
+    } else if (f8 == 0x7f) {
+        return ggml_fp32_to_fp16(NAN);
+    }
+
+    uint32_t sign     = f8 & 0x80;
+    uint32_t exponent = (f8 & 0x78) >> 3;
+    uint32_t mantissa = f8 & 0x07;
+    uint32_t result   = sign << 24;
+    if (exponent == 0) {
+        if (mantissa > 0) {
+            exponent = 0x7f - exponent_bias;
+
+            // yes, 2 times
+            if ((mantissa & 0x04) == 0) {
+                mantissa &= 0x03;
+                mantissa <<= 1;
+                exponent -= 1;
+            }
+            if ((mantissa & 0x04) == 0) {
+                mantissa &= 0x03;
+                mantissa <<= 1;
+                exponent -= 1;
+            }
+
+            result |= (mantissa & 0x03) << 21;
+            result |= exponent << 23;
+        }
+    } else {
+        result |= mantissa << 20;
+        exponent += 0x7f - exponent_bias;
+        result |= exponent << 23;
+    }
+
+    return ggml_fp32_to_fp16(*reinterpret_cast<const float*>(&result));
+}
+
+uint16_t f8_e5m2_to_f16(uint8_t fp8) {
+    return static_cast<uint16_t>(fp8) << 8;
+}
+
+void f8_e4m3_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
+    // support inplace op
+    for (int64_t i = n - 1; i >= 0; i--) {
+        dst[i] = f8_e4m3_to_f16(src[i]);
+    }
+}
+
+void f8_e5m2_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
+    // support inplace op
+    for (int64_t i = n - 1; i >= 0; i--) {
+        dst[i] = f8_e5m2_to_f16(src[i]);
+    }
+}
+#endif
+
 void f64_to_f32_vec(double* src, float* dst, int64_t n) {
     // support inplace op
     for (int64_t i = 0; i < n; i++) {
@@ -423,6 +483,9 @@ SDVersion ModelLoader::get_sd_version() const {
         if (tensor_storage.name.find("language_model.model.layers.0.self_attn.q_proj_mot_gen.weight") != std::string::npos) {
             return VERSION_SENSENOVA_U1_5;
         }
+        if (tensor_storage.name == "model.diffusion_model.txt_in.text_norm.weight") {
+            return VERSION_QWEN_IMAGE_2_1;
+        }
         if (tensor_storage.name.find("model.diffusion_model.transformer_blocks.0.img_mod.1.weight") != std::string::npos) {
             auto img_in = tensor_storage_map.find("model.diffusion_model.img_in.weight");
             if (img_in != tensor_storage_map.end() && img_in->second.ne[0] == 128) {
@@ -451,6 +514,9 @@ SDVersion ModelLoader::get_sd_version() const {
         if (tensor_storage.name.find("model.diffusion_model.double_blocks.0.img_mlp.gate_proj.weight") != std::string::npos) {
             return VERSION_OVIS_IMAGE;
         }
+        if (tensor_storage.name.find("model.diffusion_model.sigvq_embedder.1.weight") != std::string::npos) {
+            return VERSION_LLADA_IMAGE;
+        }
         if (tensor_storage.name.find("model.diffusion_model.cap_embedder.0.weight") != std::string::npos) {
             return VERSION_Z_IMAGE;
         }
@@ -858,6 +924,10 @@ std::vector<MmapTensorStore> ModelLoader::mmap_tensors(std::map<std::string, ggm
 
             if (tensor_storage.is_f64 ||
                 tensor_storage.is_i64 ||
+#ifdef SD_USE_UPSTREAM_GGML
+                tensor_storage.is_f8_e4m3 ||
+                tensor_storage.is_f8_e5m2 ||
+#endif
                 tensor_storage.type != dst_tensor->type) {
                 continue;
             }
@@ -1147,6 +1217,12 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
                         f64_to_f32_vec((double*)read_buf, (float*)target_buf, tensor_storage.nelements());
                     } else if (tensor_storage.is_i64) {
                         i64_to_i32_vec((int64_t*)read_buf, (int32_t*)target_buf, tensor_storage.nelements());
+#ifdef SD_USE_UPSTREAM_GGML
+                    } else if (tensor_storage.is_f8_e4m3) {
+                        f8_e4m3_to_f16_vec((uint8_t*)read_buf, (uint16_t*)target_buf, tensor_storage.nelements());
+                    } else if (tensor_storage.is_f8_e5m2) {
+                        f8_e5m2_to_f16_vec((uint8_t*)read_buf, (uint16_t*)target_buf, tensor_storage.nelements());
+#endif
                     }
                     if (tensor_storage.type != dst_tensor->type) {
                         if (convert_buf == nullptr) {
@@ -1463,6 +1539,9 @@ bool ModelLoader::tensor_should_be_converted(const TensorStorage& tensor_storage
             // Pass, do not convert. For Unet
         } else if (contains(name, "embedding")) {
             // Pass, do not convert embedding
+        } else if (ends_with(name, "_pad_token")) {
+            // Pass, do not convert. LLaDA-Image stores its pad tokens far outside the f16
+            // range, so any format with an f16 scale or payload turns them into inf.
         } else {
             return true;
         }

src/model_loader_files.cpp

@@ -147,7 +147,7 @@ bool ModelLoader::add_file_impl(const std::string& path, const std::string& pref
         }
         if (tensor.index_in_zip < 0) {
             const auto& stamp = physical_files[tensor.file_index];
-            if (tensor.offset > stamp.size || elements / block_size * type_size > stamp.size - tensor.offset) {
+            if (tensor.offset > stamp.size || static_cast<uint64_t>(tensor.nbytes_to_read()) > stamp.size - tensor.offset) {  // kcpp int8 fp8
                 LOG_ERROR("tensor '%s' extends beyond its model file", tensor.name.c_str());
                 return false;
             }

src/name_conversion.cpp

@@ -103,6 +103,8 @@ std::string convert_open_clip_to_hf_clip_name(std::string name) {
     return name;
 }
 
+std::string convert_llada2_moe_te_name(std::string name);
+
 std::string convert_cond_stage_model_name(std::string name, std::string prefix) {
     static const std::vector<std::pair<std::string, std::string>> clip_name_map{
         {"transformer.text_projection.weight", "transformer.text_model.text_projection"},
@@ -177,6 +179,7 @@ std::string convert_cond_stage_model_name(std::string name, std::string prefix)
             replace_with_name_map(name, llm_vision_name_map);
         } else {
             replace_with_name_map(name, llm_name_map);
+            name = convert_llada2_moe_te_name(name);
         }
     } else {
         name = convert_open_clip_to_hf_clip_name(name);
@@ -749,6 +752,52 @@ std::string convert_hunyuan_video_to_original_flux(std::string name) {
     return name;
 }
 
+// LLaDA-Image's LLaDA2-MoE text encoder. Both published layouts use these names; the ComfyUI
+// GGUF repack differs only by appending ".weight" to the bare 3-D expert parameters.
+// Called with the "text_encoders." prefix already stripped, so the name still carries "llm.".
+std::string convert_llada2_moe_te_name(std::string name) {
+    static const std::vector<std::pair<std::string, std::string>> name_map = {
+        {"model.language_model.word_embeddings.", "model.embed_tokens."},
+        {"model.language_model.norm.", "model.norm."},
+        {"model.language_model.lm_head.", "lm_head."},
+        {"model.language_model.layers.", "model.layers."},
+        {"attention.query_key_value.", "self_attn.query_key_value."},
+        {"attention.dense.", "self_attn.o_proj."},
+        {"attention.query_layernorm.", "self_attn.q_norm."},
+        {"attention.key_layernorm.", "self_attn.k_norm."},
+    };
+    replace_with_name_map(name, name_map);
+
+    // The HF checkpoint stores the stacked experts as bare nn.Parameters with no ".weight".
+    static const std::vector<std::string> bare_expert_params = {
+        "mlp.experts.gate_proj",
+        "mlp.experts.up_proj",
+        "mlp.experts.down_proj",
+    };
+    for (const auto& suffix : bare_expert_params) {
+        if (ends_with(name, suffix)) {
+            name += ".weight";
+            break;
+        }
+    }
+
+    return name;
+}
+
+// The attention projections keep their diffusers names (JointAttention's split_qkv mode), so
+// only the patch-size-keyed dicts need flattening. Latents arrive already patchified from the
+// Flux2 VAE, so the only patch key is 1-1.
+std::string convert_diffusers_dit_to_original_llada_image(std::string name) {
+    static const std::vector<std::pair<std::string, std::string>> prefix_map = {
+        {"all_x_embedder.1-1.", "x_embedder."},
+        {"all_final_layer.1-1.", "final_layer."},
+    };
+
+    replace_with_prefix_map(name, prefix_map);
+
+    return name;
+}
+
 std::string convert_diffusers_dit_to_original_lumina2(std::string name) {
     int num_layers         = 30;
     int num_refiner_layers = 2;
@@ -896,6 +945,8 @@ std::string convert_diffusion_model_name(std::string name, std::string prefix, S
         name = convert_hunyuan_video_to_original_flux(name);
     } else if (sd_version_is_z_image(version)) {
         name = convert_diffusers_dit_to_original_lumina2(name);
+    } else if (sd_version_is_llada_image(version)) {
+        name = convert_diffusers_dit_to_original_llada_image(name);
     } else if (sd_version_is_anima(version)) {
         name = convert_other_dit_to_original_anima(name);
     } else if (sd_version_is_krea2(version)) {
@@ -999,7 +1050,30 @@ std::string convert_diffusers_vae_to_original_sd1(std::string name) {
     return result;
 }
 
-std::string convert_diffusers_to_original_wan_vae(std::string name) {
+std::string convert_diffusers_to_original_wan_vae(std::string name, bool qwen_image_2_1 = false) {
+    if (qwen_image_2_1) {
+        for (int i = 0; i < 5; ++i) {
+            const auto index = std::to_string(i);
+            for (const auto& side : {std::string("encoder"), std::string("decoder")}) {
+                const bool encoder           = side == "encoder";
+                const std::string old_prefix = side + (encoder ? ".down_blocks." : ".up_blocks.") + index + ".";
+                const std::string new_prefix = side + (encoder ? ".downsamples." : ".upsamples.") + index + ".";
+                if (!starts_with(name, old_prefix)) {
+                    continue;
+                }
+                name.replace(0, old_prefix.size(), new_prefix);
+                const std::string layers = encoder ? "downsamples." : "upsamples.";
+                for (int j = 0; j < (encoder ? 2 : 3); ++j) {
+                    const auto old_resnet = new_prefix + "resnets." + std::to_string(j) + ".";
+                    const auto new_resnet = new_prefix + layers + std::to_string(j) + ".";
+                    replace_with_prefix_map(name, std::vector<std::pair<std::string, std::string>>{{old_resnet + "conv_shortcut.", new_resnet + "shortcut."},
+                                                                                                   {old_resnet, new_resnet + "residual."}});
+                }
+                replace_with_prefix_map(name, std::vector<std::pair<std::string, std::string>>{{new_prefix + (encoder ? "downsampler." : "upsampler."),
+                                                                                                new_prefix + layers + (encoder ? "2." : "3.")}});
+            }
+        }
+    }
     static const std::vector<std::pair<std::string, std::string>> prefix_map = {
         {"quant_conv.", "conv1."},
         {"post_quant_conv.", "conv2."},
@@ -1055,7 +1129,11 @@ std::string convert_diffusers_to_original_wan_vae(std::string name) {
     };
 
     replace_with_name_map(name, shared_name_map);
-    replace_with_prefix_map(name, prefix_map);
+    if (qwen_image_2_1) {
+        replace_with_prefix_map(name, std::vector<std::pair<std::string, std::string>>{{"quant_conv.", "conv1."}, {"post_quant_conv.", "conv2."}});
+    } else {
+        replace_with_prefix_map(name, prefix_map);
+    }
 
     // Only apply the ResNet-specific renaming if the tensor belongs to a ResNet block.
     // This prevents generic ".conv1." or ".conv2." matching on top-level encoder/decoder convolutions.
@@ -1071,7 +1149,7 @@ std::string convert_first_stage_model_name(std::string name, std::string prefix,
         return name;
     }
     if (sd_version_uses_wan_vae(version)) {
-        return convert_diffusers_to_original_wan_vae(name);
+        return convert_diffusers_to_original_wan_vae(name, version == VERSION_QWEN_IMAGE_2_1);
     }
     static std::unordered_map<std::string, std::string> vae_name_map = {
         {"decoder.post_quant_conv.", "post_quant_conv."},
@@ -1489,7 +1567,7 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
 
     replace_with_prefix_map(name, prefix_map);
 
-    if (sd_version_is_boogu_image(version) || sd_version_is_krea2(version) || sd_version_is_mage_flow(version) || sd_version_is_minimax_h3(version)) {
+    if (version == VERSION_QWEN_IMAGE_2_1 || sd_version_is_boogu_image(version) || sd_version_is_krea2(version) || sd_version_is_mage_flow(version) || sd_version_is_minimax_h3(version)) {
         const std::string hf_vision_prefix = "text_encoders.llm.model.visual.";
         if (starts_with(name, hf_vision_prefix)) {
             name = "text_encoders.llm.visual." + name.substr(hf_vision_prefix.size());

src/pipeline/diffusion_engine.cpp

@@ -80,6 +80,7 @@ const char* model_version_to_str[] = {
     "LingBot Video",
     "Qwen Image",
     "Qwen Image Layered",
+    "Qwen Image 2.1",
     "Hunyuan Video",
     "Anima",
     "Flux.2",
@@ -100,6 +101,7 @@ const char* model_version_to_str[] = {
     "Krea2",
     "Mage Flow",
     "SenseNova U1.5",
+    "LLaDA-Image",
     "ESRGAN",
 };
 
@@ -856,11 +858,53 @@ bool StableDiffusionGGML::init_model_loader(ModelLoader& model_loader, ModelConf
     return true;
 }
 
+bool StableDiffusionGGML::set_sage_attention_enabled(bool enabled) {
+    if (!diffusion_model) {
+        return false;
+    }
+    if (enabled) {
+#ifndef SD_USE_UPSTREAM_GGML
+        auto* ctx = ggml_init({4 * ggml_tensor_overhead(), nullptr, true});
+        if (ctx == nullptr) {
+            return false;
+        }
+        auto* q        = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 128, 128, 1, 1);
+        auto* k        = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 128, 128, 1, 1);
+        auto* v        = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 128, 128, 1, 1);
+        auto* op       = ggml_sage_attn(ctx, q, k, v, 1.f / sqrtf(128.f), GGML_SAGE_ATTN_AUTO);
+        bool supported = true;
+        for (auto backend : backend_manager.runtime_backends(SDBackendModule::DIFFUSION)) {
+            if (!ggml_backend_supports_op(backend, op)) {
+                LOG_ERROR("SageAttention is unavailable on %s; it requires patched GGML, CUDA Toolkit 12.0 or newer, and SM80 or newer kernels",
+                          ggml_backend_name(backend));
+                supported = false;
+            }
+        }
+        ggml_free(ctx);
+        if (!supported) {
+            return false;
+        }
+#else
+        LOG_ERROR("SageAttention requires -DSD_USE_UPSTREAM_GGML=OFF and a CUDA backend");
+        return false;
+#endif
+    }
+    diffusion_model->set_sage_attention_enabled(enabled);
+    if (high_noise_diffusion_model) {
+        high_noise_diffusion_model->set_sage_attention_enabled(enabled);
+    }
+    if (enabled) {
+        LOG_INFO("Using SageAttention in the diffusion model; CUDA selects the supported kernel, unsupported layers use flash/default attention");
+    }
+    return true;
+}
+
 bool StableDiffusionGGML::init(const sd_ctx_params_t* sd_ctx_params) {
 #ifdef SD_USE_UPSTREAM_GGML
     LOG_WARN(
-        "Using upstream GGML: FP8 and INT8 tensorwise/convrot are disabled. "
-        "Some operators may be unsupported and performance may be lower than with patched GGML.");
+        "Using upstream GGML: INT8 tensorwise/convrot is disabled and FP8 weights are "
+        "converted to F16 at load time. Some operators may be unsupported and performance "
+        "may be lower than with patched GGML.");
 #endif
     if (!validate_tensor_types(sd_ctx_params->wtype, sd_ctx_params->tensor_type_rules)) {
         return false;
@@ -1133,6 +1177,9 @@ bool StableDiffusionGGML::validate_and_load_runners() {
             high_noise_diffusion_model->set_flash_attention_enabled(true);
         }
     }
+    if (sd_ctx_params->sage_attn && !set_sage_attention_enabled(true)) {
+        return false;
+    }
     LOG_VERBOSE("validating model metadata");
 
     std::set<std::string> ignore_tensors;
@@ -1296,6 +1343,7 @@ bool StableDiffusionGGML::build_denoiser() {
                    sd_version_is_anima(version) ||
                    sd_version_is_ernie_image(version) ||
                    sd_version_is_z_image(version) ||
+                   sd_version_is_llada_image(version) ||
                    sd_version_is_boogu_image(version) ||
                    sd_version_is_pid(version) ||
                    sd_version_is_ideogram4(version)) {
@@ -1316,6 +1364,8 @@ bool StableDiffusionGGML::build_denoiser() {
                 default_flow_shift = 3.16f;
             } else if (sd_version_is_mage_flow(version)) {
                 default_flow_shift = 6.f;
+            } else if (sd_version_is_llada_image(version)) {
+                default_flow_shift = 1.0f;  // unused: LLADA_IMAGE_SCHEDULER builds a fixed grid
             } else {
                 default_flow_shift = 3.f;
             }
@@ -2375,6 +2425,8 @@ sd::Tensor<float> StableDiffusionGGML::sample(const std::shared_ptr<DiffusionMod
             } else if (sd_version_is_flux(version) || sd_version_is_flux2(version) || sd_version_is_longcat(version) || sd_version_is_sefi_image(version)) {
                 diffusion_params.extra = FluxDiffusionExtra{&guidance_tensor,
                                                             local_skip_layers};
+            } else if (version == VERSION_QWEN_IMAGE_2_1) {
+                diffusion_params.extra = QwenImage21DiffusionExtra{&condition.c_token_types};
             } else if (sd_version_is_anima(version)) {
                 diffusion_params.extra = AnimaDiffusionExtra{condition.c_t5_ids.empty() ? nullptr : &condition.c_t5_ids,
                                                              condition.c_t5_weights.empty() ? nullptr : &condition.c_t5_weights};
@@ -2395,6 +2447,9 @@ sd::Tensor<float> StableDiffusionGGML::sample(const std::shared_ptr<DiffusionMod
                     condition.c_token_types.empty() ? nullptr : &condition.c_token_types,
                     condition.c_vinput_mask.empty() ? nullptr : &condition.c_vinput_mask,
                     condition.c_image_embeds.empty() ? nullptr : &condition.c_image_embeds};
+            } else if (sd_version_is_llada_image(version)) {
+                diffusion_params.extra = LLaDAImageDiffusionExtra{
+                    condition.extra_c_crossattns.empty() ? nullptr : &condition.extra_c_crossattns[0]};
             } else if (sd_version_is_minimax_h3(version)) {
                 diffusion_params.extra = MiniMaxH3DiffusionExtra{
                     condition.c_token_types.empty() ? nullptr : &condition.c_token_types,
@@ -2575,7 +2630,7 @@ int StableDiffusionGGML::get_diffusion_model_down_factor() {
     if (sd_version_is_dit(version)) {
         if (sd_version_is_sensenova_u1(version)) {
             down_factor = 32;
-        } else if (sd_version_is_wan(version) || sd_version_is_lingbot_video(version) || sd_version_is_minimax_h3(version)) {
+        } else if (version == VERSION_QWEN_IMAGE_2_1 || sd_version_is_wan(version) || sd_version_is_lingbot_video(version) || sd_version_is_minimax_h3(version)) {
             down_factor = 2;
         } else {
             down_factor = 1;
@@ -2591,6 +2646,8 @@ int StableDiffusionGGML::get_latent_channel() {
             latent_channel = 128;
         } else if (sd_version_is_minimax_h3(version)) {
             latent_channel = 24;
+        } else if (version == VERSION_QWEN_IMAGE_2_1) {
+            latent_channel = 64;
         } else if (version == VERSION_WAN2_2_TI2V) {
             latent_channel = 48;
         } else if (sd_version_is_hunyuan_video(version)) {
@@ -2619,7 +2676,7 @@ int StableDiffusionGGML::get_latent_channel() {
 }
 
 int StableDiffusionGGML::get_image_channels() const {
-    return version == VERSION_QWEN_IMAGE_LAYERED ? 4 : 3;
+    return version == VERSION_QWEN_IMAGE_LAYERED || version == VERSION_QWEN_IMAGE_2_1 ? 4 : 3;
 }
 
 int StableDiffusionGGML::get_image_seq_len(int h, int w) {
@@ -2772,6 +2829,8 @@ std::string StableDiffusionGGML::get_default_ref_image_preset(SDVersion version)
         return "mage_flow";
     } else if (sd_version_is_z_image(version) || sd_version_is_boogu_image(version)) {
         return "z_image_omni";
+    } else if (sd_version_is_llada_image(version)) {
+        return "llada_image";
     } else if (sd_version_is_krea2(version)) {
         // have to make a choice between "krea2_edit" mode (for lbouaraba/krea2edit)
         // and "krea2_ostris_edit" (for krea2 ostris edit)

src/pipeline/diffusion_engine.h

@@ -37,7 +37,7 @@ extern const char* model_version_to_str[];
 static inline bool sd_version_supports_ref_latent_img_cfg(SDVersion version) {
     return version == VERSION_FLUX ||
            sd_version_is_flux2(version) ||
-           sd_version_is_qwen_image(version) ||
+           (sd_version_is_qwen_image(version) && version != VERSION_QWEN_IMAGE_2_1) ||
            sd_version_is_mage_flow(version) ||
            sd_version_is_longcat(version) ||
            sd_version_is_z_image(version) ||
@@ -312,6 +312,7 @@ public:
     bool init_model_loader(ModelLoader& model_loader, ModelConfig& configuration);
 
     bool init(const sd_ctx_params_t* sd_ctx_params);
+    bool set_sage_attention_enabled(bool enabled);
 
     bool uses_tae() const;
 

src/pipeline/image.cpp

@@ -285,7 +285,8 @@ namespace sd::pipeline {
                     vae_width  = request->width;
                     vae_height = request->height;
                 } else {
-                    int target_pixels  = ref_image_params.vae_input_max_pixels > 0 ? ref_image_params.vae_input_max_pixels : 1024 * 1024;
+                    int default_pixels = sd->version == VERSION_QWEN_IMAGE_2_1 ? request->width * request->height : 1024 * 1024;
+                    int target_pixels  = ref_image_params.vae_input_max_pixels > 0 ? ref_image_params.vae_input_max_pixels : default_pixels;
                     int vae_image_size = std::min(target_pixels, request->width * request->height);
                     vae_width          = sqrt(vae_image_size * ref_images[i].shape()[0] / ref_images[i].shape()[1]);
                     vae_height         = vae_width * ref_images[i].shape()[1] / ref_images[i].shape()[0];
@@ -309,6 +310,9 @@ namespace sd::pipeline {
                             resized_ref_img.shape()[0]);
 
                 ref_latent = sd->encode_first_stage(resized_ref_img);
+                if (sd->version == VERSION_QWEN_IMAGE_2_1) {
+                    ref_images[i] = std::move(resized_ref_img);
+                }
             } else {
                 ref_latent = sd->encode_first_stage(ref_images[i]);
             }
@@ -471,8 +475,12 @@ namespace sd::pipeline {
                 }
                 condition_params.text            = request->negative_prompt;
                 condition_params.zero_out_masked = zero_out_masked;
-                uncond                           = sd->cond_stage_model->get_learned_condition(sd->n_threads,
-                                                                                               condition_params);
+                if (sd_version_is_llada_image(sd->version)) {
+                    // LLaDA-Image CFG keeps the source latent but drops its SigVQ features.
+                    condition_params.ref_images = nullptr;
+                }
+                uncond = sd->cond_stage_model->get_learned_condition(sd->n_threads,
+                                                                     condition_params);
                 if (uncond.empty()) {
                     LOG_ERROR("failed to encode negative prompt");
                     return std::nullopt;

src/pipeline/model_builders.cpp

@@ -20,6 +20,7 @@
 #include "model/diffusion/krea2.hpp"
 #include "model/diffusion/lens.hpp"
 #include "model/diffusion/lingbot_video.hpp"
+#include "model/diffusion/llada_image.hpp"
 #include "model/diffusion/ltxv.hpp"
 #include "model/diffusion/mage_flow.hpp"
 #include "model/diffusion/minimax_h3.hpp"
@@ -28,6 +29,7 @@
 #include "model/diffusion/model.hpp"
 #include "model/diffusion/pid.hpp"
 #include "model/diffusion/qwen_image.hpp"
+#include "model/diffusion/qwen_image_2_1.hpp"
 #include "model/diffusion/sensenova_u1.h"
 #include "model/diffusion/unet.hpp"
 #include "model/diffusion/wan.hpp"
@@ -285,12 +287,19 @@ namespace sd::model_builders {
                                                                enable_vision,
                                                                weight_manager,
                                                                tokenizers);
-            result.diffusion   = std::make_shared<Qwen::QwenImageRunner>(ctx.backends.runtime_backend(SDBackendModule::DIFFUSION),
-                                                                       tensor_storage_map,
-                                                                       "model.diffusion_model",
-                                                                       version,
-                                                                       weight_manager,
-                                                                       sd_ctx_params->model_args);
+            if (version == VERSION_QWEN_IMAGE_2_1) {
+                result.diffusion = std::make_shared<Qwen::QwenImage21Runner>(ctx.backends.runtime_backend(SDBackendModule::DIFFUSION),
+                                                                             tensor_storage_map,
+                                                                             "model.diffusion_model",
+                                                                             weight_manager);
+            } else {
+                result.diffusion = std::make_shared<Qwen::QwenImageRunner>(ctx.backends.runtime_backend(SDBackendModule::DIFFUSION),
+                                                                           tensor_storage_map,
+                                                                           "model.diffusion_model",
+                                                                           version,
+                                                                           weight_manager,
+                                                                           sd_ctx_params->model_args);
+            }
         } else if (sd_version_is_mage_flow(version)) {
             result.conditioner = std::make_shared<LLMEmbedder>(ctx.backends.runtime_backend(SDBackendModule::TE),
                                                                tensor_storage_map,
@@ -362,6 +371,19 @@ namespace sd::model_builders {
                                                                       "model.diffusion_model",
                                                                       version,
                                                                       weight_manager);
+        } else if (sd_version_is_llada_image(version)) {
+            result.conditioner = std::make_shared<LLaDAImageEmbedder>(ctx.backends.runtime_backend(SDBackendModule::TE),
+                                                                      tensor_storage_map,
+                                                                      "text_encoders.llm",
+                                                                      "queryformer",
+                                                                      "text_projection",
+                                                                      "sigvq",
+                                                                      weight_manager,
+                                                                      tokenizers);
+            result.diffusion   = std::make_shared<LLaDAImage::LLaDAImageRunner>(ctx.backends.runtime_backend(SDBackendModule::DIFFUSION),
+                                                                              tensor_storage_map,
+                                                                              "model.diffusion_model",
+                                                                              weight_manager);
         } else if (sd_version_is_boogu_image(version)) {
             result.conditioner = std::make_shared<LLMEmbedder>(ctx.backends.runtime_backend(SDBackendModule::TE),
                                                                tensor_storage_map,

src/pipeline/request.cpp

@@ -65,7 +65,7 @@ namespace sd::pipeline {
             return LCM_SCHEDULER;
         } else if (sample_method == DDIM_TRAILING_SAMPLE_METHOD) {
             return SIMPLE_SCHEDULER;
-        } else if (sd != nullptr && sd_version_is_flux(sd->version)) {
+        } else if (sd != nullptr && (sd_version_is_flux(sd->version) || sd->version == VERSION_QWEN_IMAGE_2_1)) {
             return FLUX_SCHEDULER;
         } else if (sd != nullptr && sd_version_is_flux2(sd->version)) {
             return FLUX2_SCHEDULER;
@@ -73,6 +73,8 @@ namespace sd::pipeline {
             return LTX2_SCHEDULER;
         } else if (sd != nullptr && sd_version_is_ideogram4(sd->version)) {
             return LOGIT_NORMAL_SCHEDULER;
+        } else if (sd != nullptr && sd_version_is_llada_image(sd->version)) {
+            return LLADA_IMAGE_SCHEDULER;
         }
         return DISCRETE_SCHEDULER;
     }

src/runtime/denoiser.hpp

@@ -786,6 +786,54 @@ struct FluxScheduler : SigmaScheduler {
 };
 
 // https://github.com/black-forest-labs/flux2/blob/main/src/flux2/sampling.py#L244
+// LLaDA-Image does not use a shift-based flow schedule. The reference pipeline builds a
+// Kumaraswamy-shaped grid over t = linspace(0.001, 1, n + 1)[:-1]:
+//     schedule = (1 - (1 - t^1.17)^0.8)^1.1
+//     sigma    = 1 - schedule
+// Its scheduler config can also set use_uniform_sigmas, which replaces the whole curve with a
+// plain linspace(1, 0, n + 1)[:-1] pre-shift grid.
+struct LLaDAImageScheduler : SigmaScheduler {
+    bool uniform_sigmas = false;
+
+    explicit LLaDAImageScheduler(const char* extra_sample_args = nullptr) {
+        parse_extra_sample_args(extra_sample_args);
+    }
+
+    void parse_extra_sample_args(const char* extra_sample_args) {
+        for (const auto& [key, value] : parse_key_value_args(extra_sample_args, "llada_image scheduler arg")) {
+            if (key == "uniform") {
+                if (!parse_strict_bool(value, uniform_sigmas)) {
+                    LOG_WARN("ignoring invalid llada_image scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            }
+        }
+    }
+
+    std::vector<float> get_sigmas(uint32_t n, float /*sigma_min*/, float /*sigma_max*/, t_to_sigma_t /*t_to_sigma*/) override {
+        std::vector<float> sigmas;
+        sigmas.reserve(n + 1);
+
+        if (n == 0) {
+            sigmas.push_back(1.0f);
+            return sigmas;
+        }
+
+        for (uint32_t i = 0; i < n; ++i) {
+            float progress = static_cast<float>(i) / static_cast<float>(n);
+            if (uniform_sigmas) {
+                sigmas.push_back(1.0f - progress);
+            } else {
+                float t        = 0.001f + progress * (1.0f - 0.001f);
+                float schedule = powf(1.0f - powf(1.0f - powf(t, 1.17f), 0.8f), 1.1f);
+                sigmas.push_back(1.0f - schedule);
+            }
+        }
+
+        sigmas.push_back(0.0f);
+        return sigmas;
+    }
+};
+
 struct Flux2Scheduler : SigmaScheduler {
     int image_seq_len = 0;
 
@@ -1123,6 +1171,11 @@ struct Denoiser {
                 scheduler = std::make_shared<Flux2Scheduler>(image_seq_len);
                 break;
             }
+            case LLADA_IMAGE_SCHEDULER: {
+                LOG_INFO("get_sigmas with LLaDA-Image scheduler");
+                scheduler = std::make_shared<LLaDAImageScheduler>(extra_sample_args);
+                break;
+            }
             case FLUX_SCHEDULER: {
                 LOG_INFO("get_sigmas with Flux scheduler");
                 scheduler = std::make_shared<FluxScheduler>(image_seq_len, extra_sample_args);

src/stable-diffusion.cpp

@@ -137,6 +137,7 @@ const char* scheduler_to_str[] = {
     "flux2",
     "flux",
     "beta",
+    "llada_image",
 };
 
 static_assert(SCHEDULER_COUNT == sizeof(scheduler_to_str) / sizeof(scheduler_to_str[0]),
@@ -336,6 +337,7 @@ void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
     sd_ctx_params->eager_load                = false;
     sd_ctx_params->enable_mmap               = false;
     sd_ctx_params->diffusion_flash_attn      = false;
+    sd_ctx_params->sage_attn                 = false;
     sd_ctx_params->linear_scale              = 0.f;
     sd_ctx_params->attn_scale                = 0.f;
     sd_ctx_params->vae_format                = SD_VAE_FORMAT_AUTO;
@@ -391,6 +393,7 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              "auto_fit: %s\n"
              "flash_attn: %s\n"
              "diffusion_flash_attn: %s\n"
+             "sage_attn: %s\n"
              "linear_scale: %g\n"
              "attn_scale: %g\n"
              "vae_format: %s\n",
@@ -430,6 +433,7 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              BOOL_STR(sd_ctx_params->auto_fit),
              BOOL_STR(sd_ctx_params->flash_attn),
              BOOL_STR(sd_ctx_params->diffusion_flash_attn),
+             BOOL_STR(sd_ctx_params->sage_attn),
              sd_ctx_params->linear_scale,
              sd_ctx_params->attn_scale,
              sd_vae_format_name(sd_ctx_params->vae_format));