feat: add Krea2OstrisEdit support (#1775)

.github/workflows/build.yml

@@ -15,7 +15,7 @@ on:
       [
         ".github/workflows/**",
         ".dockerignore",
-        "Dockerfile*",
+        "docker/Dockerfile*",
         "**/CMakeLists.txt",
         "**/Makefile",
         "**/*.h",
@@ -32,7 +32,7 @@ on:
       [
         ".github/workflows/**",
         ".dockerignore",
-        "Dockerfile*",
+        "docker/Dockerfile*",
         "**/CMakeLists.txt",
         "**/Makefile",
         "**/*.h",
@@ -264,7 +264,7 @@ jobs:
           context: .
           platforms: ${{ matrix.platform }}
           push: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-          file: Dockerfile.${{ matrix.variant }}
+          file: docker/Dockerfile.${{ matrix.variant }}
           tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ env.BRANCH_NAME }}-${{ matrix.variant }}${{ matrix.tag-suffix }}
           labels: ${{ steps.meta.outputs.labels }}
           annotations: ${{ steps.meta.outputs.annotations }}

.github/workflows/close-inactive-issues.yml

@@ -0,0 +1,48 @@
+name: Close inactive issues
+
+on:
+  schedule:
+    # Run daily. GitHub cron schedules use UTC.
+    - cron: "30 1 * * *"
+  workflow_dispatch:
+    inputs:
+      debug_only:
+        description: "Dry run: log intended actions without changing issues"
+        required: false
+        default: false
+        type: boolean
+
+permissions:
+  issues: write
+
+concurrency:
+  group: ${{ github.workflow }}
+  cancel-in-progress: false
+
+jobs:
+  close-inactive-issues:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Comment and close inactive issues
+        uses: actions/stale@v10
+        with:
+          days-before-issue-stale: 365
+          days-before-issue-close: 0
+
+          days-before-pr-stale: -1
+          days-before-pr-close: -1
+
+          stale-issue-label: issue:inactive
+          close-issue-label: issue:auto-closed
+          close-issue-reason: completed
+          stale-issue-message: ""
+          close-issue-message: >
+            This issue has had no activity for one year. The latest version of
+            the code may already have fixed the problem.
+
+            If the issue still exists in the latest version, you can reopen
+            this issue at any time with updated reproduction details.
+
+          remove-issue-stale-when-updated: true
+          operations-per-run: 1000
+          debug-only: ${{ github.event_name == 'workflow_dispatch' && inputs.debug_only || false }}

.gitignore

@@ -13,3 +13,7 @@ output*.png
 models*
 *.log
 preview.png
+.claude/
+CLAUDE.local.md
+.agents/
+.codex/

AGENTS.md

@@ -0,0 +1,183 @@
+# Instructions for stable-diffusion.cpp
+
+This document is for AI coding agents working in this repository. It should
+describe agent-specific workflow, repository routing, editing boundaries, and
+project-specific pitfalls.
+
+For general contribution rules, including PR scope, commit conventions, code
+style, dependency updates, security hygiene, and AI-assisted contribution policy,
+see `CONTRIBUTING.md`.
+
+---
+
+## Agent Operating Rules
+
+Before analyzing or modifying the repository:
+
+1. Read this file.
+2. Use `rg` / `rg --files` or directory listing commands to confirm the current
+   tree before relying on a path.
+3. Start from `src/` and relevant `docs/` for runtime behavior.
+4. Read the relevant code before editing.
+5. Prefer the smallest change that fits the existing architecture.
+6. Report focused verification and mention any tests not run.
+
+Agents must not:
+
+* Run `git push`, create PRs, or submit issue/PR comments on the user's behalf.
+* Create commits unless the user explicitly requests that specific commit.
+* Modify `ggml/`, `thirdparty/`, or `examples/server/frontend/` unless
+  explicitly requested and necessary.
+* Read large local model files or tokenizer vocabulary files.
+* Rewrite unrelated code for style-only reasons.
+* Add secrets, model weights, generated binaries, local absolute paths, or
+  machine-specific output.
+
+When a change is large, architectural, or likely to affect public behavior,
+pause and present a short plan before editing.
+
+---
+
+## Repository Map and Editing Boundaries
+
+This is a routing map for agents, not a full architecture document. The layout
+can change, so verify paths before using them. Do not inspect excluded
+large-data directories while checking the tree.
+
+### Primary Project Code
+
+Core implementation lives under `src/`.
+
+Current source layout includes:
+
+* `src/core/` - shared tensor, ggml integration, backend, graph, RNG, and utility
+  code.
+* `src/model/` - model families and model components.
+* `src/model_io/` - model file loading, GGUF, safetensors, pickle, and related
+  serialization helpers.
+* `src/runtime/` - sampling, denoising, guidance, caching, preprocessing, and
+  runtime execution helpers.
+* `src/tokenizers/` - tokenizer implementations.
+* `src/conditioning/` - conditioning and prompt-related implementation.
+* `src/extensions/` - optional feature extensions.
+* top-level `src/*.cpp` and `src/*.h` files - public implementation entry
+  points, model loading, conversion, versioning, and shared managers.
+
+`src/tokenizers/vocab/` contains large tokenizer vocabulary data. Do not read or
+parse files in this directory; reference the path only when necessary.
+
+### Public API
+
+`include/` contains the C API exposed by the project. Currently the primary
+public header is `include/stable-diffusion.h`.
+
+Treat public headers as stable API. Avoid breaking compatibility unless the user
+explicitly requests it. If public behavior changes, update relevant examples or
+documentation.
+
+### Examples
+
+`examples/` contains programs demonstrating library usage.
+
+* `examples/cli/` - command line program for running models, testing features,
+  and debugging.
+* `examples/common/` - shared example support code.
+* `examples/server/` - server application built on top of the library.
+* `examples/server/frontend/` - git submodule containing independent frontend
+  code. Avoid modifying it unless explicitly requested.
+
+### Documentation and Tooling
+
+* `docs/` - documentation for supported models, build options, behavior, and
+  workflows.
+* `scripts/` - development, model processing, build automation, formatting, and
+  tooling scripts.
+* `cmake/` - CMake support modules.
+* `docker/` - Docker-related project files.
+* `assets/` - documentation assets; not runtime code.
+
+### External, Local, and Generated State
+
+* `ggml/` - git submodule for the ggml dependency.
+* `thirdparty/` - vendored third-party dependencies.
+* `models/` - local model storage. Ignore this directory and do not read model
+  files.
+* `test/` - local testing scripts. Use only when relevant to the task.
+* `build/`, `build_*`, and similar directories - generated build outputs.
+  Inspect them only when debugging a build result.
+
+---
+
+## Agent Workflow for Code Changes
+
+1. Identify the relevant modules under `src/`.
+2. Check whether the change touches the public API in `include/`.
+3. Consult relevant `docs/` and examples before changing user-facing behavior.
+4. Follow existing local patterns before adding new abstractions.
+5. Keep edits scoped to the requested behavior.
+6. Run the narrowest useful build, test, or inspection command available.
+
+Follow `CONTRIBUTING.md` for formatting, naming, PR expectations, dependency
+update policy, and security rules.
+
+---
+
+## Code Comments
+
+Keep comments rare and useful.
+
+Do not add comments that only describe what the code does. Add comments only
+when the code cannot fully express the logic, the logic is unusually complex, or
+there are historical reasons, invariants, constraints, compatibility concerns,
+or known pitfalls that future maintainers need to understand.
+
+Do not add task-specific comments that will be meaningless after review.
+
+Examples from the current codebase:
+
+```cpp
+// GOOD: explains a safety constraint that is not obvious from the assignment.
+// From src/model_io/pickle_io.cpp.
+// Non-tensor checkpoint metadata can use REDUCE for arbitrary
+// Python objects. Do not execute it; keep stack shape only.
+stack.push_back(make_none_value());
+
+// BAD: describes only what the next line does.
+// Set the token count to zero.
+token_count = 0;
+```
+
+---
+
+## Text File Encoding
+
+When reading or editing repository text files:
+
+* Prefer UTF-8 with LF for Markdown, frontend source, JSON, and other text-first
+  project files unless the file already clearly uses a different encoding.
+* Do not assume terminal output encoding matches file encoding on Windows.
+* A file that looks garbled in PowerShell output may still be valid UTF-8.
+* When inspecting UTF-8 files in PowerShell, prefer explicit UTF-8 reads such as:
+  * `[Console]::OutputEncoding = [System.Text.Encoding]::UTF8`
+  * `Get-Content -Encoding utf8 <path>`
+* Avoid rewriting a file purely because console output looked garbled; verify
+  the actual file encoding first.
+
+---
+
+## Tensor and Layout Notes
+
+Additional tensor/layout rules for this codebase:
+
+* `sd::Tensor` shape order is not PyTorch/NumPy-style. `shape()[0]` is the
+  lowest and most contiguous dimension, and higher indices are higher
+  dimensions.
+* Broadcasting for `sd::Tensor` must align dimensions from low to high dimension
+  indices. If one tensor has fewer dimensions, append implicit `1`s at the
+  higher-dimension end.
+* `ggml_n_dims` / `ggml_n_dims(tensor)` can drop trailing singleton high
+  dimensions. Do not assume a logical trailing dimension of `1` will still be
+  counted in ggml metadata.
+* Internal tensor-returning interfaces use an empty `sd::Tensor` to represent
+  null, absent, or failure states. Do not add `std::optional<sd::Tensor<...>>`
+  for internal APIs unless a distinct semantic state is truly required.

CLAUDE.md

@@ -0,0 +1,11 @@
+@AGENTS.md
+
+## Claude Code
+
+Follow `AGENTS.md` as the shared repository instructions.
+
+Do not duplicate contribution, style, PR, dependency, or security policy here;
+use `CONTRIBUTING.md` as the canonical source for those rules.
+
+Keep Claude-specific project notes in this file only when they do not apply to
+other coding agents.

CONTRIBUTING.md

@@ -27,7 +27,7 @@ PRs should include:
 
 Format code according to the repository style before submitting changes.
 
-Formatting follows `.clang-format` (Chromium base, 4-space indent, no tabs). Run `format-code.sh` before opening a PR. Keep C++ standard at C++17-compatible patterns used in this repo.
+Formatting follows `.clang-format` (Chromium base, 4-space indent, no tabs). Run `scripts/format-code.sh` or `scripts/format-code.ps1` before opening a PR. Keep C++ standard at C++17-compatible patterns used in this repo.
 
 Naming conventions:
 

README.md

@@ -180,19 +180,3 @@ Thank you to all the people who have already contributed to stable-diffusion.cpp
 ## Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=leejet/stable-diffusion.cpp&type=Date)](https://star-history.com/#leejet/stable-diffusion.cpp&Date)
-
-## References
-
-- [ggml](https://github.com/ggml-org/ggml)
-- [diffusers](https://github.com/huggingface/diffusers)
-- [stable-diffusion](https://github.com/CompVis/stable-diffusion)
-- [sd3-ref](https://github.com/Stability-AI/sd3-ref)
-- [stable-diffusion-stability-ai](https://github.com/Stability-AI/stablediffusion)
-- [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-- [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
-- [k-diffusion](https://github.com/crowsonkb/k-diffusion)
-- [latent-consistency-model](https://github.com/luosiallen/latent-consistency-model)
-- [generative-models](https://github.com/Stability-AI/generative-models/)
-- [PhotoMaker](https://github.com/TencentARC/PhotoMaker)
-- [Wan2.1](https://github.com/Wan-Video/Wan2.1)
-- [Wan2.2](https://github.com/Wan-Video/Wan2.2)

docs/anima.md

@@ -15,7 +15,7 @@
 ## Examples
 
 ```sh
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\anima-preview.safetensors --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_06b_base.safetensors  -p "a lovely cat holding a sign says 'anima.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\anima-preview.safetensors --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_3_06b_base.safetensors  -p "a lovely cat holding a sign says 'anima.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu --diffusion-fa
 ```
 
 <img alt="anima image example" src="../assets/anima/example.png" />

docs/boogu_image.md

@@ -17,7 +17,7 @@ Boogu Image uses a Boogu diffusion transformer, the FLUX VAE, and Qwen3-VL as th
 ### Boogu Image Base
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\..\ComfyUI\models\diffusion_models\boogu_image_base_bf16.safetensors --llm ..\..\llm\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\ae.sft -p "a lovely cat" --diffusion-fa -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\boogu_image_base_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\models\vae\ae.sft -p "a lovely cat" --diffusion-fa -v --offload-to-cpu
 ```
 
 <img width="256" alt="Boogu Image Base example" src="../assets/boogu/example.png" />
@@ -25,7 +25,7 @@ Boogu Image uses a Boogu diffusion transformer, the FLUX VAE, and Qwen3-VL as th
 ### Boogu Image Edit
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\..\ComfyUI\models\diffusion_models\boogu_image_edit_bf16.safetensors --llm ..\..\llm\Qwen3VL-8B-Instruct-Q4_K_M.gguf --llm_vision ..\..\llm\mmproj-Qwen3VL-8B-Instruct-F16.gguf --vae ..\..\ComfyUI\models\vae\ae.sft --diffusion-fa -v --offload-to-cpu -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'boogu.cpp'"
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\boogu_image_edit_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\mmproj-Qwen3VL-8B-Instruct-F16.gguf --vae ..\models\vae\ae.sft --diffusion-fa -v --offload-to-cpu -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'boogu.cpp'"
 ```
 
 <img width="256" alt="Boogu Image Edit example" src="../assets/boogu/edit_example.png" />

docs/chroma.md

@@ -15,7 +15,7 @@ You can run Chroma using stable-diffusion.cpp with a GPU that has 6GB or even 4G
 You can download the preconverted gguf weights from [silveroxides/Chroma-GGUF](https://huggingface.co/silveroxides/Chroma-GGUF), this way you don't have to do the conversion yourself.
 
 ```
-.\bin\Release\sd-cli.exe -M convert -m ..\..\ComfyUI\models\unet\chroma-unlocked-v40.safetensors -o ..\models\chroma-unlocked-v40-q8_0.gguf -v --type q8_0
+.\bin\Release\sd-cli.exe -M convert -m ..\models\unet\chroma-unlocked-v40.safetensors -o ..\models\chroma-unlocked-v40-q8_0.gguf -v --type q8_0
 ```
 
 ## Run
@@ -24,10 +24,9 @@ You can download the preconverted gguf weights from [silveroxides/Chroma-GGUF](h
 For example:
 
 ```
- .\bin\Release\sd-cli.exe --diffusion-model  ..\models\chroma-unlocked-v40-q8_0.gguf --vae ..\models\ae.sft --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'chroma.cpp'" --cfg-scale 4.0 --sampling-method euler -v --chroma-disable-dit-mask --clip-on-cpu
+ .\bin\Release\sd-cli.exe --diffusion-model  ..\models\chroma-unlocked-v40-q8_0.gguf --vae ..\models\ae.sft --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'chroma.cpp'" --cfg-scale 4.0 --sampling-method euler -v --model-args chroma_use_dit_mask=false --clip-on-cpu
 ```
 
 ![](../assets/flux/chroma_v40.png)
 
 
-

docs/chroma_radiance.md

@@ -12,7 +12,7 @@
 ## Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Chroma1-Radiance-v0.4-Q8_0.gguf --t5xxl ..\..\ComfyUI\models\clip\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'chroma  radiance cpp'" --cfg-scale 4.0 --sampling-method euler -v
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\Chroma1-Radiance-v0.4-Q8_0.gguf --t5xxl ..\models\clip\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'chroma  radiance cpp'" --cfg-scale 4.0 --sampling-method euler -v
 ```
 
 <img alt="Chroma1-Radiance" src="../assets/flux/chroma1-radiance.png" />

docs/docker.md

@@ -19,7 +19,7 @@ docker run --rm --init -v /path/to/models:/models -v /path/to/output/:/output -p
 ## Building using Docker
 
 ```shell
-docker build -t sd .
+docker build -f docker/Dockerfile -t sd .
 ```
 
 ## Building variants using Docker
@@ -27,7 +27,7 @@ docker build -t sd .
 Vulkan:
 
 ```shell
-docker build -f Dockerfile.vulkan -t sd .
+docker build -f docker/Dockerfile.vulkan -t sd .
 ```
 
 ## Run locally built image's CLI

docs/ernie_image.md

@@ -21,7 +21,7 @@ You can run ERNIE-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — o
 ### ERNIE-Image-Turbo
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\ernie-image-turbo.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 8 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\ernie-image-turbo.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 8 -v --offload-to-cpu --diffusion-fa
 ```
 
 <img width="256" alt="ERNIE-Image Turbo example" src="../assets/ernie_image/turbo_example.png" />
@@ -29,7 +29,7 @@ You can run ERNIE-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — o
 ### ERNIE-Image
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\ernie-image-UD-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\ernie-image-UD-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa
 ```
 
 <img width="256" alt="ERNIE-Image example" src="../assets/ernie_image/example.png" />

docs/flux.md

@@ -17,7 +17,7 @@ You can download the preconverted gguf weights from [FLUX.1-dev-gguf](https://hu
 
 For example:
 ```
-.\bin\Release\sd-cli.exe -M convert -m ..\..\ComfyUI\models\unet\flux1-dev.sft -o ..\models\flux1-dev-q8_0.gguf -v --type q8_0
+.\bin\Release\sd-cli.exe -M convert -m ..\models\unet\flux1-dev.sft -o ..\models\flux1-dev-q8_0.gguf -v --type q8_0
 ```
 
 ## Run

docs/flux2.md

@@ -16,7 +16,7 @@
 ### Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux2-dev-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -r .\kontext_input.png -p "change 'flux.cpp' to 'flux2-dev.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux2-dev-Q4_K_S.gguf --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -r .\kontext_input.png -p "change 'flux.cpp' to 'flux2-dev.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu
 ```
 
 <img alt="flux2 example" src="../assets/flux2/example.png" />
@@ -42,19 +42,19 @@
 ### Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu --diffusion-fa
 ```
 
 <img alt="flux2-klein-4b" src="../assets/flux2/flux2-klein-4b.png" />
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu --steps 4
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_4b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu --steps 4
 ```
 
 <img alt="flux2-klein-4b-edit" src="../assets/flux2/flux2-klein-4b-edit.png" />
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-base-4b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-base-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu --diffusion-fa
 ```
 
 <img alt="flux2-klein-base-4b" src="../assets/flux2/flux2-klein-base-4b.png" />
@@ -78,19 +78,19 @@
 ### Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu --diffusion-fa
 ```
 
 <img alt="flux2-klein-9b" src="../assets/flux2/flux2-klein-9b.png" />
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_8b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu --steps 4
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_8b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu --steps 4
 ```
 
 <img alt="flux2-klein-9b-edit" src="../assets/flux2/flux2-klein-9b-edit.png" />
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-base-9b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-base-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu --diffusion-fa
 ```
 
 <img alt="flux2-klein-base-9b" src="../assets/flux2/flux2-klein-base-9b.png" />

docs/hidream_o1_image.md

@@ -12,7 +12,7 @@
 ### HiDream-O1-Image-Dev
 
 ```
-.\bin\Release\sd-cli.exe -m  ..\..\ComfyUI\models\diffusion_models\hidream_o1_image_dev_bf16.safetensors -p "a lovely cat holding a sign says 
+.\bin\Release\sd-cli.exe -m  ..\models\diffusion_models\hidream_o1_image_dev_bf16.safetensors -p "a lovely cat holding a sign says 
 'hidream o1 cpp'" --cfg-scale 1.0  -v -H 1024 -W 1024
 ```
 

docs/ideogram4.md

@@ -34,7 +34,7 @@ If you want lower VRAM usage, you can change the quantization from q8_0 to a low
 ## Examples
 
 ```sh
-.\bin\Release\sd-cli.exe --diffusion-model ideogram4-Q8_0.gguf --uncond-diffusion-model ideogram4_uncond-Q8_0.gguf --llm ..\..\llm\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors -p '{"high_level_description":"A square 1024 x 1024 luxury fashion magazine cover featuring exactly one short chubby fluffy cat as the main model. The cat sits on a soft ivory studio floor, facing the viewer with a stylish calm expression, wearing tiny black sunglasses, a red silk scarf, and a small gold collar charm. In front of the cat on the floor is a wide horizontal luxury nameplate that clearly reads ideogram4.cpp. The whole design feels premium, fashionable, clean, and editorial.","style_description":{"aesthetics":"luxury fashion magazine cover, high-end pet couture campaign, minimalist editorial design, elegant studio photography, soft paper texture, refined typography, fashionable and polished","lighting":"Soft diffused studio lighting, gentle spotlight on the cat, subtle floor shadow, warm ivory highlights, clean separation between subject and background","photo":"high-resolution fashion editorial photography look, front-facing cat portrait, crisp fur details, glossy sunglasses, clear readable nameplate text, shallow depth of field","medium":"mixed media fashion photography and premium editorial graphic design","color_palette":["#F4EFE7","#111111","#D8B56D","#B73A3A","#FFFFFF","#8A7A6A"]},"compositional_deconstruction":{"canvas":"Square 1024 x 1024 canvas with a normal upright orientation. Do not rotate the poster or any text. Use a clean fashion magazine cover layout.","background":"Warm ivory studio backdrop with subtle paper grain, a soft spotlight gradient, faint floor shadow, and a few minimal gold editorial lines. The background is spacious, premium, and uncluttered.","layout":"Top center has a small elegant headline. Center area features one cat as the main fashion model. Lower foreground has a wide horizontal luxury nameplate placed on the floor in front of the cat. Bottom center has a small footer. All text is horizontal, upright, and readable left to right.","elements":[{"type":"text","desc":"Top center headline reading LOOK WHAT I FOUND in a refined high-fashion serif font. The headline is horizontal, centered, elegant, and secondary to the nameplate text."},{"type":"obj","desc":"Exactly one short chubby fluffy cat sitting in the center like a luxury fashion model. The cat has a large round head, compact body, short legs, soft detailed fur, expressive eyes, and a calm confident pose. The cat is cute and rounded, not tall, not stretched, not duplicated."},{"type":"obj","desc":"Tiny glossy black sunglasses worn naturally by the cat, slightly oversized but still showing the cat face clearly. The sunglasses add a chic fashion-editorial attitude."},{"type":"obj","desc":"A red silk scarf tied neatly around the cat neck, with soft folds and a couture feeling. The scarf must not cover the cat face or the nameplate."},{"type":"obj","desc":"A small gold collar charm or fashion accessory under the scarf, subtle and premium, adding a luxury campaign detail."},{"type":"obj","desc":"In the lower foreground, place a wide horizontal luxury nameplate on the floor in front of the cat. The nameplate is low, flat, landscape-oriented, much wider than tall, like a fashion show seat card or premium display plaque. It is centered, front-facing, level, and fully visible. It must not become vertical, tall, standing, rotated, or side-facing."},{"type":"text","desc":"Print the exact text ideogram4.cpp only on the wide horizontal nameplate. Use clean bold black lettering, perfectly spelled, lowercase, with the number 4 and .cpp extension. The text must fit completely inside the nameplate, stay horizontal, and be readable from left to right."},{"type":"obj","desc":"Add sparse premium editorial accents around the edges: thin gold lines, small code brackets, tiny cursor marks, subtle dots, and minimal geometric details. No extra cats, no stickers, no animal faces, no busy decorations."},{"type":"text","desc":"Bottom center footer reading tiny paws, big compile energy in a small refined monospace or editorial font. The footer is horizontal, centered, understated, and much smaller than the nameplate text."}]}}'  --diffusion-fa -v --offload-to-cpu -H 1024 -W 1024
+.\bin\Release\sd-cli.exe --diffusion-model ideogram4-Q8_0.gguf --uncond-diffusion-model ideogram4_uncond-Q8_0.gguf --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors -p '{"high_level_description":"A square 1024 x 1024 luxury fashion magazine cover featuring exactly one short chubby fluffy cat as the main model. The cat sits on a soft ivory studio floor, facing the viewer with a stylish calm expression, wearing tiny black sunglasses, a red silk scarf, and a small gold collar charm. In front of the cat on the floor is a wide horizontal luxury nameplate that clearly reads ideogram4.cpp. The whole design feels premium, fashionable, clean, and editorial.","style_description":{"aesthetics":"luxury fashion magazine cover, high-end pet couture campaign, minimalist editorial design, elegant studio photography, soft paper texture, refined typography, fashionable and polished","lighting":"Soft diffused studio lighting, gentle spotlight on the cat, subtle floor shadow, warm ivory highlights, clean separation between subject and background","photo":"high-resolution fashion editorial photography look, front-facing cat portrait, crisp fur details, glossy sunglasses, clear readable nameplate text, shallow depth of field","medium":"mixed media fashion photography and premium editorial graphic design","color_palette":["#F4EFE7","#111111","#D8B56D","#B73A3A","#FFFFFF","#8A7A6A"]},"compositional_deconstruction":{"canvas":"Square 1024 x 1024 canvas with a normal upright orientation. Do not rotate the poster or any text. Use a clean fashion magazine cover layout.","background":"Warm ivory studio backdrop with subtle paper grain, a soft spotlight gradient, faint floor shadow, and a few minimal gold editorial lines. The background is spacious, premium, and uncluttered.","layout":"Top center has a small elegant headline. Center area features one cat as the main fashion model. Lower foreground has a wide horizontal luxury nameplate placed on the floor in front of the cat. Bottom center has a small footer. All text is horizontal, upright, and readable left to right.","elements":[{"type":"text","desc":"Top center headline reading LOOK WHAT I FOUND in a refined high-fashion serif font. The headline is horizontal, centered, elegant, and secondary to the nameplate text."},{"type":"obj","desc":"Exactly one short chubby fluffy cat sitting in the center like a luxury fashion model. The cat has a large round head, compact body, short legs, soft detailed fur, expressive eyes, and a calm confident pose. The cat is cute and rounded, not tall, not stretched, not duplicated."},{"type":"obj","desc":"Tiny glossy black sunglasses worn naturally by the cat, slightly oversized but still showing the cat face clearly. The sunglasses add a chic fashion-editorial attitude."},{"type":"obj","desc":"A red silk scarf tied neatly around the cat neck, with soft folds and a couture feeling. The scarf must not cover the cat face or the nameplate."},{"type":"obj","desc":"A small gold collar charm or fashion accessory under the scarf, subtle and premium, adding a luxury campaign detail."},{"type":"obj","desc":"In the lower foreground, place a wide horizontal luxury nameplate on the floor in front of the cat. The nameplate is low, flat, landscape-oriented, much wider than tall, like a fashion show seat card or premium display plaque. It is centered, front-facing, level, and fully visible. It must not become vertical, tall, standing, rotated, or side-facing."},{"type":"text","desc":"Print the exact text ideogram4.cpp only on the wide horizontal nameplate. Use clean bold black lettering, perfectly spelled, lowercase, with the number 4 and .cpp extension. The text must fit completely inside the nameplate, stay horizontal, and be readable from left to right."},{"type":"obj","desc":"Add sparse premium editorial accents around the edges: thin gold lines, small code brackets, tiny cursor marks, subtle dots, and minimal geometric details. No extra cats, no stickers, no animal faces, no busy decorations."},{"type":"text","desc":"Bottom center footer reading tiny paws, big compile energy in a small refined monospace or editorial font. The footer is horizontal, centered, understated, and much smaller than the nameplate text."}]}}'  --diffusion-fa -v --offload-to-cpu -H 1024 -W 1024
 ```
 
 <img alt="ideogram4 image example" src="../assets/ideogram4/example.png" />

docs/kontext.md

@@ -16,7 +16,7 @@ You can run Kontext using stable-diffusion.cpp with a GPU that has 6GB or even 4
 You can download the preconverted gguf weights from [FLUX.1-Kontext-dev-GGUF](https://huggingface.co/QuantStack/FLUX.1-Kontext-dev-GGUF), this way you don't have to do the conversion yourself.
 
 ```
-.\bin\Release\sd-cli.exe -M convert -m ..\..\ComfyUI\models\unet\flux1-kontext-dev.safetensors -o ..\models\flux1-kontext-dev-q8_0.gguf -v --type q8_0
+.\bin\Release\sd-cli.exe -M convert -m ..\models\unet\flux1-kontext-dev.safetensors -o ..\models\flux1-kontext-dev-q8_0.gguf -v --type q8_0
 ```
 
 ## Run

docs/krea2.md

@@ -21,7 +21,7 @@ Krea2 uses a Krea2 diffusion transformer, the Wan2.1 VAE, and Qwen3-VL 4B as the
 ### Krea2
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\..\ComfyUI\models\diffusion_models\Krea-2-Raw-Q8_0.gguf --llm ..\..\ComfyUI\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors -p "a lovely cat holding a sign says 'krea2.cpp'"  --diffusion-fa -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Krea-2-Raw-Q8_0.gguf --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p "a lovely cat holding a sign says 'krea2.cpp'"  --diffusion-fa -v --offload-to-cpu
 ```
 
 <img width="256" alt="Krea2 Raw example" src="../assets/krea2/example.png" />

docs/lens.md

@@ -18,7 +18,7 @@ Lens uses a Lens diffusion transformer, the FLUX.2 VAE, and GPT-OSS-20B as the L
 ### Lens
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\..\ComfyUI\models\diffusion_models\lens_bf16.safetensors --llm "..\..\llm\gpt-oss-20b-UD-Q8_K_XL.gguf" --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors --cfg-scale 5.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" --diffusion-fa -v
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\lens_bf16.safetensors --llm "..\models\text_encoders\gpt-oss-20b-UD-Q8_K_XL.gguf" --vae ..\models\vae\flux2_ae.safetensors --cfg-scale 5.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" --diffusion-fa -v
 ```
 
 <img width="256" alt="Lens example" src="../assets/lens/example.png" />
@@ -26,7 +26,7 @@ Lens uses a Lens diffusion transformer, the FLUX.2 VAE, and GPT-OSS-20B as the L
 ### Lens Turbo
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\..\ComfyUI\models\diffusion_models\lens_turbo_bf16.safetensors --llm "..\..\llm\gpt-oss-20b-UD-Q8_K_XL.gguf" --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors --cfg-scale 1.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" --diffusion-fa -v --steps 4
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\lens_turbo_bf16.safetensors --llm "..\models\text_encoders\gpt-oss-20b-UD-Q8_K_XL.gguf" --vae ..\models\vae\flux2_ae.safetensors --cfg-scale 1.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" --diffusion-fa -v --steps 4
 ```
 
 <img width="256" alt="Lens Turbo example" src="../assets/lens/turbo_example.png" />

docs/longcat_image.md

@@ -24,7 +24,7 @@ LongCat uses quoted text for character-level text rendering. Put target text ins
 ### LongCat Image
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\LongCat-Image-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\ae.sft --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf -p "a lovely cat holding a sign says 'longcat.cpp'" --cfg-scale 5.0 --sampling-method euler --flow-shift 3 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\LongCat-Image-Q4_K_M.gguf --vae ..\models\vae\ae.sft --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf -p "a lovely cat holding a sign says 'longcat.cpp'" --cfg-scale 5.0 --sampling-method euler --flow-shift 3 -v --offload-to-cpu --diffusion-fa
 ```
 
 <img alt="longcat example" src="../assets/longcat/example.png" />

docs/ltx2.md

@@ -21,7 +21,7 @@
 ### LTX-2.3 dev T2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\..\ComfyUI\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\..\ComfyUI\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "worst quality, low quality, blurry, distorted, artifacts" -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 --fps 24 -o t2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "worst quality, low quality, blurry, distorted, artifacts" -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 --fps 24 -o t2v.webm
 ```
 
 <video
@@ -33,7 +33,7 @@
 ### LTX-2.3 dev I2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\..\ComfyUI\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\..\ComfyUI\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o i2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o i2v.webm
 ```
 
 <video
@@ -45,7 +45,7 @@
 ### LTX-2.3 dev FLF2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\..\ComfyUI\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\..\ComfyUI\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png -o flf2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png -o flf2v.webm
 ```
 
 <video
@@ -61,7 +61,7 @@ LTX spatial latent upscale runs a model-backed x2 latent upsampler between the l
 Put `ltx-2.3-spatial-upscaler-x2-1.1.safetensors` under the directory passed to `--hires-upscalers-dir`, then use the model name without path or extension in `--hires-upscaler`.
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\..\ComfyUI\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\..\ComfyUI\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors --hires-upscalers-dir ..\..\ComfyUI\models\latent_upscale_models --hires-upscaler ltx-2.3-spatial-upscaler-x2-1.1 --hires --hires-steps 4 -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 640 -H 360 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o hires_i2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors --hires-upscalers-dir ..\models\latent_upscale_models --hires-upscaler ltx-2.3-spatial-upscaler-x2-1.1 --hires --hires-steps 4 -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 640 -H 360 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o hires_i2v.webm
 ```
 
 By default, the hires refine pass uses the main sampler and scheduler, then trims the second-pass sigma schedule by `--hires-denoising-strength` (`0.7` by default). To reproduce a ComfyUI-style explicit refine schedule, pass custom hires sigmas:

docs/ovis_image.md

@@ -13,7 +13,7 @@
 ## Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ovis_image-Q4_0.gguf --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\ovis_2.5.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa
+.\bin\Release\sd-cli.exe --diffusion-model  ovis_image-Q4_0.gguf --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\ovis_2.5.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa
 ```
 
 <img alt="ovis image example" src="../assets/ovis_image/example.png" />

docs/performance.md

@@ -53,6 +53,26 @@ Per-module assignments can target only the largest modules:
 
 See [backend selection](./backend.md) for full syntax.
 
+## Run models that don't fit in VRAM (CPU streaming).
+
+`--offload-to-cpu` alone keeps every parameter in system RAM and stages it to the runtime backend on first use, then leaves it resident there. If the diffusion model is larger than the runtime backend's free memory (e.g. Flux dev at bf16 on an 8 GiB GPU), that residency stops fitting during the sampling loop and generation fails. Two additional flags make it fit by trading a small amount of speed for room:
+
+- `--max-vram <GiB>` sets a VRAM budget the graph-cut segmenter respects. It cuts each forward pass into segments sized to fit the budget, running them in sequence and freeing intermediate activations between them. Negative values auto-detect free VRAM and spare the given amount (`--max-vram -1` uses most of the free VRAM and keeps ~1 GiB headroom), a positive value caps the budget, `0` disables segmentation.
+- `--stream-layers` streams the diffusion model's transformer blocks one at a time. Each block's parameters are copied from the CPU to the runtime backend just before it runs and evicted when the residency budget is reached. Prefetching hides most of the copy latency behind compute. This flag only takes effect when the diffusion params backend is CPU, so it must be combined with `--offload-to-cpu` (or an explicit `--params-backend diffusion=cpu`); a warning is logged and the flag is ignored otherwise.
+
+The three flags stack. The recommended shape for "biggest model my card can host":
+
+```shell
+sd-cli --diffusion-model flux1-dev.safetensors ... \
+       --offload-to-cpu --max-vram -1 --stream-layers
+```
+
+- `--offload-to-cpu`: params in RAM, staged as needed.
+- `--max-vram -1`: use most of the free VRAM as the compute budget, spare 1 GiB headroom, let the graph-cut segmenter split each forward pass to fit.
+- `--stream-layers`: on top of the segmenter, stream individual transformer blocks so their weights don't all need to be resident at once.
+
+Ordered from fastest to smallest-VRAM: no flags → `--offload-to-cpu` → `--offload-to-cpu --max-vram <N>` → `--offload-to-cpu --max-vram <N> --stream-layers`. Each step down costs a few percent of throughput to buy more room; combined they can run models roughly 3-4x larger than the raw VRAM would allow.
+
 ## Use quantization to reduce memory usage.
 
 [quantization](./quantization_and_gguf.md)

docs/pid.md

@@ -22,7 +22,7 @@ The official PiD model card should be checked before use. At the time of the ini
 ## Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\..\ComfyUI\models\diffusion_models\pid_flux1_512_to_2048_4step_bf16.safetensors --llm "..\..\ComfyUI\models\text_encoders\gemma_2_2b_it_elm_bf16.safetensors" --vae ..\..\ComfyUI\models\vae\ae.sft --vae-format flux --cfg-scale 1.0  -p "a lovely cat" -r ..\assets\ernie_image\turbo_example.png --diffusion-fa -v --steps 4 -H 2048 -W 2048 --rng cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\pid_flux1_512_to_2048_4step_bf16.safetensors --llm "..\models\text_encoders\gemma_2_2b_it_elm_bf16.safetensors" --vae ..\models\vae\ae.sft --vae-format flux --cfg-scale 1.0  -p "a lovely cat" -r ..\assets\ernie_image\turbo_example.png --diffusion-fa -v --steps 4 -H 2048 -W 2048 --rng cpu
 ```
 
 Before:

docs/pulid.md

@@ -52,7 +52,7 @@ to a `.pulidembd` binary file (about 131 KB). Run it once per source
 person; the same file is reused for any number of generations.
 
 A reference Python script is provided alongside this docs file at
-[`script/pulid_extract_id.py`](../script/pulid_extract_id.py). It
+[`scripts/pulid_extract_id.py`](../scripts/pulid_extract_id.py). It
 requires:
 - A working CUDA / CPU PyTorch stack
 - `insightface`, `facexlib`, `eva-clip`, `torchvision`, `opencv-python`,

docs/qwen_image.md

@@ -14,7 +14,7 @@
 ## Examples
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\qwen-image-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf  -p '一个穿着"QWEN"标志的T恤的中国美女正拿着黑色的马克笔面相镜头微笑。她身后的玻璃板上手写体写着 “一、Qwen-Image的技术路线： 探索视觉生成基础模型的极限，开创理解与生成一体化的未来。二、Qwen-Image的模型特色：1、复杂文字渲染。支持中英渲染、自动布局； 2、精准图像编辑。支持文字编辑、物体增减、风格变换。三、Qwen-Image的未来愿景：赋能专业内容创作、助力生成式AI发展。”' --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu -H 1024 -W 1024 --diffusion-fa --flow-shift 3
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\qwen-image-Q8_0.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf  -p '一个穿着"QWEN"标志的T恤的中国美女正拿着黑色的马克笔面相镜头微笑。她身后的玻璃板上手写体写着 “一、Qwen-Image的技术路线： 探索视觉生成基础模型的极限，开创理解与生成一体化的未来。二、Qwen-Image的模型特色：1、复杂文字渲染。支持中英渲染、自动布局； 2、精准图像编辑。支持文字编辑、物体增减、风格变换。三、Qwen-Image的未来愿景：赋能专业内容创作、助力生成式AI发展。”' --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu -H 1024 -W 1024 --diffusion-fa --flow-shift 3
 ```
 
 <img alt="qwen example" src="../assets/qwen/example.png" />

docs/qwen_image_edit.md

@@ -23,7 +23,7 @@
 ### Qwen Image Edit
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen_Image_Edit-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --seed 1118877715456453
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\Qwen_Image_Edit-Q8_0.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --seed 1118877715456453
 ```
 
 <img alt="qwen_image_edit" src="../assets/qwen/qwen_image_edit.png" />
@@ -32,17 +32,17 @@
 ### Qwen Image Edit 2509
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --llm_vision ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct.mmproj-Q8_0.gguf --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'Qwen Image Edit 2509'"
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --llm_vision ..\models\text_encoders\Qwen2.5-VL-7B-Instruct.mmproj-Q8_0.gguf --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'Qwen Image Edit 2509'"
 ```
 
 <img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2509.png" />
 
 ### Qwen Image Edit 2511
 
-To use the new Qwen Image Edit 2511 mode, the  `--qwen-image-zero-cond-t` flag must be enabled; otherwise, image editing quality will degrade significantly.
+To use the new Qwen Image Edit 2511 mode, `--model-args qwen_image_zero_cond_t=true` must be enabled; otherwise, image editing quality will degrade significantly.
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\qwen-image-edit-2511-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'"  --qwen-image-zero-cond-t
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\qwen-image-edit-2511-Q4_K_M.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --model-args qwen_image_zero_cond_t=true
 ```
 
-<img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2511.png" />
+<img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2511.png" />

docs/sefi_image.md

@@ -13,8 +13,8 @@ The SeFi-Image family ships in three scales (1B / 2B / 5B) and three families (B
 Convert the transformer and text encoder to sd.cpp safetensors:
 
 ```bash
-python3 script/convert_sefi.py     <hf_repo_dir>                          <out_dir>/sefi_<scale>_<family>.safetensors
-python3 script/convert_qwen3_vl.py  <hf_repo_dir>/Qwen3-VL-XB-Instruct    <out_dir>/qwen3_vl_<X>b.safetensors
+python3 scripts/convert_sefi.py     <hf_repo_dir>                          <out_dir>/sefi_<scale>_<family>.safetensors
+python3 scripts/convert_qwen3_vl.py  <hf_repo_dir>/Qwen3-VL-XB-Instruct    <out_dir>/qwen3_vl_<X>b.safetensors
 ```
 
 ## Variant defaults

docs/wan.md

@@ -55,7 +55,7 @@
 ### Wan2.1 T2V 1.3B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1_t2v_1.3B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1_t2v_1.3B_fp16.safetensors --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -63,7 +63,7 @@
 ### Wan2.1 T2V 14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-t2v-14b-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-t2v-14b-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 6.0 --sampling-method euler -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 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.1_14B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -73,7 +73,7 @@
 ### Wan2.1 I2V 14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-i2v-14b-480p-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\..\ComfyUI\models\clip_vision\clip_vision_h.safetensors -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-i2v-14b-480p-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\models\clip_vision\clip_vision_h.safetensors -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.1_14B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -81,7 +81,7 @@
 ### Wan2.2 T2V A14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\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 --flow-shift 3.0
+.\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 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -89,7 +89,7 @@
 ### Wan2.2 I2V A14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\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 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-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 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -97,7 +97,7 @@
 ### Wan2.2 T2V A14B T2I
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\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 --flow-shift 3.0
+.\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 --flow-shift 3.0
 ```
 
 <img width="832" height="480" alt="Wan2 2_14B_t2i" src="../assets/wan/Wan2.2_14B_t2i.png" />
@@ -105,7 +105,7 @@
 ### Wan2.2 T2V 14B with Lora
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat<lora:wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise:1><lora:|high_noise|wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise:1>" --cfg-scale 3.5 --sampling-method euler --steps 4 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 4 -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 --lora-model-dir ..\..\ComfyUI\models\loras --video-frames 33 --flow-shift 3.0
+.\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<lora:wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise:1><lora:|high_noise|wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise:1>" --cfg-scale 3.5 --sampling-method euler --steps 4 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 4 -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 --lora-model-dir ..\models\loras --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_t2v_lora.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -117,7 +117,7 @@
 #### T2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan2.2_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\models\vae\wan2.2_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_5B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -125,7 +125,7 @@
 #### I2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan2.2_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\models\vae\wan2.2_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_5B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -133,7 +133,7 @@
 ### Wan2.1 FLF2V 14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-flf2v-14b-720p-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\..\ComfyUI\models\clip_vision\clip_vision_h.safetensors -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-flf2v-14b-720p-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\models\clip_vision\clip_vision_h.safetensors -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
 ```
 
 
@@ -142,7 +142,7 @@
 ### Wan2.2 FLF2V 14B
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --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 -p "glass flower blossom" -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 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --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 -p "glass flower blossom" -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 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
 ```
 
 <video src=../assets/wan/Wan2.2_14B_flf2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -152,7 +152,7 @@
 #### T2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 --video-frames 1 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-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 6.0 --sampling-method euler -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 --video-frames 1 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_vace_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -161,7 +161,7 @@
 #### R2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-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 6.0 --sampling-method euler -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 -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_vace_r2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -172,7 +172,7 @@
 ```
 mkdir post+depth
 ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\frame_%04d.jpg
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_1.3B_vace_v2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -182,7 +182,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### T2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-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 6.0 --sampling-method euler -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 --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_14B_vace_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -191,7 +191,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### R2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -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 -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-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 6.0 --sampling-method euler -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 -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_14B_vace_r2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -201,7 +201,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### V2V
 
 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -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 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
 ```
 
 <video src=../assets/wan/Wan2.1_14B_vace_v2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>

docs/z_image.md

@@ -21,7 +21,7 @@ You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or ev
 ### Z-Image-Turbo
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  z_image_turbo-Q3_K.gguf --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\Qwen3-4B-Instruct-2507-Q4_K_M.gguf -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 1.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512 --steps 8
+.\bin\Release\sd-cli.exe --diffusion-model  z_image_turbo-Q3_K.gguf --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\Qwen3-4B-Instruct-2507-Q4_K_M.gguf -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 1.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512 --steps 8
 ```
 
 <img width="256" alt="z-image example" src="../assets/z_image/q3_K.png" />
@@ -29,7 +29,7 @@ You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or ev
 ### Z-Image-Base
 
 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\z_image_bf16.safetensors --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\z_image_bf16.safetensors --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512
 ```
 
 <img width="256" alt="z-image example" src="../assets/z_image/base_bf16.png" />

examples/cli/main.cpp

@@ -653,7 +653,8 @@ int main(int argc, const char* argv[]) {
                                                cli_params.output_path.c_str(),
                                                ctx_params.wtype,
                                                ctx_params.tensor_type_rules.c_str(),
-                                               cli_params.convert_name);
+                                               cli_params.convert_name,
+                                               ctx_params.n_threads);
         if (!success) {
             LOG_ERROR("convert '%s'/'%s' to '%s' failed",
                       ctx_params.model_path.c_str(),

examples/common/common.cpp

@@ -443,6 +443,12 @@ ArgOptions SDContextParams::get_options() {
          "weight type per tensor pattern (example: \"^vae\\.=f16,model\\.=q8_0\")",
          (int)',',
          &tensor_type_rules},
+        {"",
+         "--model-args",
+         "extra model args, key=value list. Supports chroma_use_dit_mask, chroma_use_t5_mask, "
+         "chroma_t5_mask_pad, qwen_image_zero_cond_t",
+         (int)',',
+         &model_args},
         {"",
          "--photo-maker",
          "path to PHOTOMAKER model",
@@ -493,10 +499,6 @@ ArgOptions SDContextParams::get_options() {
          "number of threads to use during computation (default: -1). "
          "If threads <= 0, then threads will be set to the number of CPU physical cores",
          &n_threads},
-        {"",
-         "--chroma-t5-mask-pad",
-         "t5 mask pad size of chroma",
-         &chroma_t5_mask_pad},
     };
 
     options.bool_options = {
@@ -554,30 +556,6 @@ ArgOptions SDContextParams::get_options() {
          "--vae-conv-direct",
          "use ggml_conv2d_direct in the vae model",
          true, &vae_conv_direct},
-        {"",
-         "--circular",
-         "enable circular padding for convolutions",
-         true, &circular},
-        {"",
-         "--circularx",
-         "enable circular RoPE wrapping on x-axis (width) only",
-         true, &circular_x},
-        {"",
-         "--circulary",
-         "enable circular RoPE wrapping on y-axis (height) only",
-         true, &circular_y},
-        {"",
-         "--chroma-disable-dit-mask",
-         "disable dit mask for chroma",
-         false, &chroma_use_dit_mask},
-        {"",
-         "--qwen-image-zero-cond-t",
-         "enable zero_cond_t for qwen image",
-         true, &qwen_image_zero_cond_t},
-        {"",
-         "--chroma-enable-t5-mask",
-         "enable t5 mask for chroma",
-         true, &chroma_use_t5_mask},
     };
 
     auto on_type_arg = [&](int argc, const char** argv, int index) {
@@ -844,6 +822,7 @@ std::string SDContextParams::to_string() const {
         << "  backend: \"" << backend << "\",\n"
         << "  params_backend: \"" << params_backend << "\",\n"
         << "  split_mode: \"" << split_mode << "\",\n"
+        << "  model_args: \"" << model_args << "\",\n"
         << "  auto_fit: " << (auto_fit ? "true" : "false") << ",\n"
         << "  enable_mmap: " << (enable_mmap ? "true" : "false") << ",\n"
         << "  control_net_cpu: " << (control_net_cpu ? "true" : "false") << ",\n"
@@ -853,13 +832,6 @@ std::string SDContextParams::to_string() const {
         << "  diffusion_flash_attn: " << (diffusion_flash_attn ? "true" : "false") << ",\n"
         << "  diffusion_conv_direct: " << (diffusion_conv_direct ? "true" : "false") << ",\n"
         << "  vae_conv_direct: " << (vae_conv_direct ? "true" : "false") << ",\n"
-        << "  circular: " << (circular ? "true" : "false") << ",\n"
-        << "  circular_x: " << (circular_x ? "true" : "false") << ",\n"
-        << "  circular_y: " << (circular_y ? "true" : "false") << ",\n"
-        << "  chroma_use_dit_mask: " << (chroma_use_dit_mask ? "true" : "false") << ",\n"
-        << "  qwen_image_zero_cond_t: " << (qwen_image_zero_cond_t ? "true" : "false") << ",\n"
-        << "  chroma_use_t5_mask: " << (chroma_use_t5_mask ? "true" : "false") << ",\n"
-        << "  chroma_t5_mask_pad: " << chroma_t5_mask_pad << ",\n"
         << "  prediction: " << sd_prediction_name(prediction) << ",\n"
         << "  lora_apply_mode: " << sd_lora_apply_mode_name(lora_apply_mode) << ",\n"
         << "  force_sdxl_vae_conv_scale: " << (force_sdxl_vae_conv_scale ? "true" : "false") << "\n"
@@ -912,13 +884,7 @@ sd_ctx_params_t SDContextParams::to_sd_ctx_params_t(bool taesd_preview) {
     sd_ctx_params.tae_preview_only                = taesd_preview;
     sd_ctx_params.diffusion_conv_direct           = diffusion_conv_direct;
     sd_ctx_params.vae_conv_direct                 = vae_conv_direct;
-    sd_ctx_params.circular_x                      = circular || circular_x;
-    sd_ctx_params.circular_y                      = circular || circular_y;
     sd_ctx_params.force_sdxl_vae_conv_scale       = force_sdxl_vae_conv_scale;
-    sd_ctx_params.chroma_use_dit_mask             = chroma_use_dit_mask;
-    sd_ctx_params.chroma_use_t5_mask              = chroma_use_t5_mask;
-    sd_ctx_params.chroma_t5_mask_pad              = chroma_t5_mask_pad;
-    sd_ctx_params.qwen_image_zero_cond_t          = qwen_image_zero_cond_t;
     sd_ctx_params.vae_format                      = str_to_vae_format(vae_format);
     sd_ctx_params.max_vram                        = max_vram.c_str();
     sd_ctx_params.stream_layers                   = stream_layers;
@@ -928,6 +894,7 @@ sd_ctx_params_t SDContextParams::to_sd_ctx_params_t(bool taesd_preview) {
     sd_ctx_params.split_mode                      = split_mode.c_str();
     sd_ctx_params.auto_fit                        = auto_fit;
     sd_ctx_params.rpc_servers                     = rpc_servers.c_str();
+    sd_ctx_params.model_args                      = model_args.empty() ? nullptr : model_args.c_str();
     return sd_ctx_params;
 }
 
@@ -1106,7 +1073,7 @@ ArgOptions SDGenerationParams::get_options() {
          &sample_params.guidance.slg.layer_end},
         {"",
          "--eta",
-         "noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde, dpm++2s_a and dpm++2m_sde)",
+         "noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde, dpm++2s_a, dpm++2m_sde and dpm++2m_sde_bt)",
          &sample_params.eta},
         {"",
          "--flow-shift",
@@ -1138,7 +1105,7 @@ ArgOptions SDGenerationParams::get_options() {
          &high_noise_sample_params.guidance.slg.layer_end},
         {"",
          "--high-noise-eta",
-         "(high noise) noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde, dpm++2s_a and dpm++2m_sde)",
+         "(high noise) noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde, dpm++2s_a, dpm++2m_sde and dpm++2m_sde_bt)",
          &high_noise_sample_params.eta},
         {"",
          "--strength",
@@ -1189,6 +1156,18 @@ ArgOptions SDGenerationParams::get_options() {
          "disable auto resize of ref images",
          false,
          &auto_resize_ref_image},
+        {"",
+         "--circular",
+         "enable circular padding on both axes for tileable output",
+         true, &circular},
+        {"",
+         "--circularx",
+         "enable circular padding on x-axis (width) only",
+         true, &circular_x},
+        {"",
+         "--circulary",
+         "enable circular padding on y-axis (height) only",
+         true, &circular_y},
         {"",
          "--disable-image-metadata",
          "do not embed generation metadata on image files",
@@ -1509,12 +1488,12 @@ ArgOptions SDGenerationParams::get_options() {
          on_seed_arg},
         {"",
          "--sampling-method",
-         "sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, dpm++2m_sde, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp]"
+         "sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, dpm++2m_sde, dpm++2m_sde_bt, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp]"
          "(default: euler for Flux/SD3/Wan, euler_a otherwise)",
          on_sample_method_arg},
         {"",
          "--high-noise-sampling-method",
-         "(high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, dpm++2m_sde, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp]"
+         "(high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, dpm++2m_sde, dpm++2m_sde_bt, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp]"
          " default: euler for Flux/SD3/Wan, euler_a otherwise",
          on_high_noise_sample_method_arg},
         {"",
@@ -2475,6 +2454,8 @@ sd_img_gen_params_t SDGenerationParams::to_sd_img_gen_params_t() {
     params.hires.upscale_tile_size   = hires_upscale_tile_size;
     params.hires.custom_sigmas       = hires_custom_sigmas.empty() ? nullptr : hires_custom_sigmas.data();
     params.hires.custom_sigmas_count = static_cast<int>(hires_custom_sigmas.size());
+    params.circular_x                = circular || circular_x;
+    params.circular_y                = circular || circular_y;
     return params;
 }
 
@@ -2540,6 +2521,8 @@ sd_vid_gen_params_t SDGenerationParams::to_sd_vid_gen_params_t() {
     params.hires.upscale_tile_size   = hires_upscale_tile_size;
     params.hires.custom_sigmas       = hires_custom_sigmas.empty() ? nullptr : hires_custom_sigmas.data();
     params.hires.custom_sigmas_count = static_cast<int>(hires_custom_sigmas.size());
+    params.circular_x                = circular || circular_x;
+    params.circular_y                = circular || circular_y;
     return params;
 }
 

examples/common/common.h

@@ -152,6 +152,7 @@ struct SDContextParams {
     std::string backend;
     std::string params_backend;
     std::string split_mode;
+    std::string model_args;
     bool auto_fit = false;
     std::string rpc_servers;
     std::string effective_backend;
@@ -165,16 +166,6 @@ struct SDContextParams {
     bool diffusion_conv_direct = false;
     bool vae_conv_direct       = false;
 
-    bool circular   = false;
-    bool circular_x = false;
-    bool circular_y = false;
-
-    bool chroma_use_dit_mask = true;
-    bool chroma_use_t5_mask  = false;
-    int chroma_t5_mask_pad   = 1;
-
-    bool qwen_image_zero_cond_t = false;
-
     prediction_t prediction           = PREDICTION_COUNT;
     lora_apply_mode_t lora_apply_mode = LORA_APPLY_AUTO;
 
@@ -246,6 +237,10 @@ struct SDGenerationParams {
     int upscale_repeats   = 1;
     int upscale_tile_size = 128;
 
+    bool circular   = false;
+    bool circular_x = false;
+    bool circular_y = false;
+
     bool hires_enabled         = false;
     std::string hires_upscaler = "Latent";
     std::string hires_upscaler_model_path;

examples/server/routes_sdapi.cpp

@@ -65,6 +65,8 @@ static enum sample_method_t get_sdapi_sample_method(std::string name) {
         {"k_dpmpp_2m", DPMPP2M_SAMPLE_METHOD},
         {"dpm++ 2m sde", DPMPP2M_SDE_SAMPLE_METHOD},
         {"k_dpmpp_2m_sde", DPMPP2M_SDE_SAMPLE_METHOD},
+        {"dpm++ 2m sde gpu", DPMPP2M_SDE_BT_SAMPLE_METHOD},
+        {"k_dpmpp_2m_sde_gpu", DPMPP2M_SDE_BT_SAMPLE_METHOD},
         {"res multistep", RES_MULTISTEP_SAMPLE_METHOD},
         {"k_res_multistep", RES_MULTISTEP_SAMPLE_METHOD},
         {"res 2s", RES_2S_SAMPLE_METHOD},
@@ -279,7 +281,7 @@ static nlohmann::json prepare_info_field(const SDContextParams& ctx_params,
         jsoninfo["clip_skip"] = gen_params.clip_skip;
     }
     if (gen_params.sample_params.scheduler != scheduler_t::SCHEDULER_COUNT) {
-        jsoninfo["extra_generation_params"] = nlohmann::json::object();
+        jsoninfo["extra_generation_params"]                  = nlohmann::json::object();
         jsoninfo["extra_generation_params"]["Schedule type"] = sd_scheduler_name(gen_params.sample_params.scheduler);
     }
     if (img2img) {
@@ -361,18 +363,18 @@ void register_sdapi_endpoints(httplib::Server& svr, ServerRuntime& rt) {
                     continue;
                 }
 
-                bool embed_meta    = request.gen_params.embed_image_metadata;
+                bool embed_meta = request.gen_params.embed_image_metadata;
 
                 std::string params = get_image_params(*runtime->ctx_params,
                                                       request.gen_params,
                                                       request.gen_params.seed + i / images_per_batch);
 
-                auto image_bytes   = encode_image_to_vector(EncodedImageFormat::PNG,
-                                                            results[i].data,
-                                                            results[i].width,
-                                                            results[i].height,
-                                                            results[i].channel,
-                                                            embed_meta ? params : "");
+                auto image_bytes = encode_image_to_vector(EncodedImageFormat::PNG,
+                                                          results[i].data,
+                                                          results[i].width,
+                                                          results[i].height,
+                                                          results[i].channel,
+                                                          embed_meta ? params : "");
 
                 if (image_bytes.empty()) {
                     LOG_ERROR("write image to mem failed");

include/stable-diffusion.h

@@ -55,6 +55,7 @@ enum sample_method_t {
     EULER_A_CFG_PP_SAMPLE_METHOD,
     EULER_GE_SAMPLE_METHOD,
     DPMPP2M_SDE_SAMPLE_METHOD,
+    DPMPP2M_SDE_BT_SAMPLE_METHOD,
     SAMPLE_METHOD_COUNT
 };
 
@@ -215,13 +216,7 @@ typedef struct {
     bool tae_preview_only;
     bool diffusion_conv_direct;
     bool vae_conv_direct;
-    bool circular_x;
-    bool circular_y;
     bool force_sdxl_vae_conv_scale;
-    bool chroma_use_dit_mask;
-    bool chroma_use_t5_mask;
-    int chroma_t5_mask_pad;
-    bool qwen_image_zero_cond_t;
     enum sd_vae_format_t vae_format;
     const char* max_vram;  // GiB budget or backend assignment spec for graph-cut segmented param offload (0 = disabled, -1 = auto)
     bool stream_layers;  // Enable residency+prefetch streaming on top of --max-vram (no effect without --max-vram)
@@ -231,6 +226,7 @@ typedef struct {
     const char* split_mode;  // weight distribution for multi-device modules: layer (default) or row, or per-module assignments e.g. "diffusion=row"
     bool auto_fit;
     const char* rpc_servers;
+    const char* model_args;
 } sd_ctx_params_t;
 
 typedef struct {
@@ -384,6 +380,8 @@ typedef struct {
     sd_cache_params_t cache;
     sd_hires_params_t hires;
     int qwen_image_layers;
+    bool circular_x;
+    bool circular_y;
 } sd_img_gen_params_t;
 
 typedef struct {
@@ -409,6 +407,8 @@ typedef struct {
     sd_tiling_params_t vae_tiling_params;
     sd_cache_params_t cache;
     sd_hires_params_t hires;
+    bool circular_x;
+    bool circular_y;
 } sd_vid_gen_params_t;
 
 typedef struct sd_ctx_t sd_ctx_t;
@@ -428,6 +428,11 @@ SD_API const char* sd_get_system_info();
 SD_API bool sd_ctx_supports_image_generation(const sd_ctx_t* sd_ctx);
 SD_API bool sd_ctx_supports_video_generation(const sd_ctx_t* sd_ctx);
 
+// ControlNet hot-swap APIs are not safe to call while generation is in flight.
+SD_API bool sd_ctx_load_control_net(sd_ctx_t* sd_ctx, const char* path);
+SD_API bool sd_ctx_unload_control_net(sd_ctx_t* sd_ctx);
+SD_API bool sd_ctx_has_control_net(const sd_ctx_t* sd_ctx);
+
 SD_API const char* sd_type_name(enum sd_type_t type);
 SD_API enum sd_type_t str_to_sd_type(const char* str);
 SD_API const char* sd_rng_type_name(enum rng_type_t rng_type);
@@ -520,7 +525,8 @@ SD_API bool convert_with_components(const char* model_path,
                                     const char* output_path,
                                     enum sd_type_t output_type,
                                     const char* tensor_type_rules,
-                                    bool convert_name);
+                                    bool convert_name,
+                                    int n_threads);
 
 SD_API bool preprocess_canny(sd_image_t image,
                              float high_threshold,

scripts/convert_qwen3_vl.py

@@ -9,7 +9,7 @@ the text side; the vision side is converted by sd.cpp's own
 Operates on raw safetensors bytes so any dtype (BF16/F16/F32) is preserved.
 
 Usage:
-    python3 script/convert_qwen3_vl.py <hf_qwen3_vl_dir_or_safetensors> <output.safetensors>
+    python3 scripts/convert_qwen3_vl.py <hf_qwen3_vl_dir_or_safetensors> <output.safetensors>
 """
 
 import argparse

scripts/convert_sefi.py

@@ -5,7 +5,7 @@ Operates on raw safetensors bytes so any dtype (BF16, F32, ...) is preserved exa
 No numpy or torch dependency required.
 
 Usage:
-    python3 script/convert_sefi.py <sefi_diffusers_dir> <output.safetensors>
+    python3 scripts/convert_sefi.py <sefi_diffusers_dir> <output.safetensors>
 """
 
 import argparse

scripts/format-code.ps1

@@ -31,24 +31,30 @@ $patterns = @(
     "examples/common/*.cpp"
 )
 
-$root = (Get-Location).Path
+Push-Location (Join-Path $PSScriptRoot "..")
 
-foreach ($pattern in $patterns) {
-    $files = Get-ChildItem -Path $pattern -File -ErrorAction SilentlyContinue | Sort-Object FullName
+try {
+    $root = (Get-Location).Path
 
-    foreach ($file in $files) {
-        $relativePath = $file.FullName.Substring($root.Length).TrimStart('\', '/') -replace '\\', '/'
+    foreach ($pattern in $patterns) {
+        $files = Get-ChildItem -Path $pattern -File -ErrorAction SilentlyContinue | Sort-Object FullName
 
-        if ($relativePath -like "vocab*") {
-            continue
+        foreach ($file in $files) {
+            $relativePath = $file.FullName.Substring($root.Length).TrimStart('\', '/') -replace '\\', '/'
+
+            if ($relativePath -like "vocab*") {
+                continue
+            }
+
+            Write-Host "formatting '$relativePath'"
+
+            # if ($relativePath -ne "stable-diffusion.h") {
+            #     clang-tidy -fix -p build_linux/ "$relativePath"
+            # }
+
+            & clang-format -style=file -i $relativePath
         }
-
-        Write-Host "formatting '$relativePath'"
-
-        # if ($relativePath -ne "stable-diffusion.h") {
-        #     clang-tidy -fix -p build_linux/ "$relativePath"
-        # }
-
-        & clang-format -style=file -i $relativePath
     }
+} finally {
+    Pop-Location
 }

scripts/format-code.sh

@@ -1,3 +1,10 @@
+#!/usr/bin/env bash
+
+set -e
+
+repo_root="$(cd "$(dirname "$0")/.." && pwd)"
+cd "$repo_root"
+
 for f in src/*.cpp src/*.h src/*.hpp \
          src/conditioning/*.cpp src/conditioning/*.h src/conditioning/*.hpp \
          src/core/*.cpp src/core/*.h src/core/*.hpp \

scripts/pulid_extract_id.py

@@ -13,7 +13,7 @@ packaging quirks):
   - numpy, Pillow
 
 Usage:
-  python script/pulid_extract_id.py \\
+  python scripts/pulid_extract_id.py \\
     --portrait /path/to/source-photo.jpg \\
     --pulid-weights /path/to/pulid_flux_v0.9.1.safetensors \\
     --out /path/to/source.pulidembd

src/conditioning/conditioner.hpp

@@ -6,6 +6,7 @@
 #include <optional>
 
 #include "core/tensor_ggml.hpp"
+#include "core/util.h"
 #include "model/te/clip.hpp"
 #include "model/te/llm.hpp"
 #include "model/te/t5.hpp"
@@ -117,6 +118,8 @@ public:
     virtual void set_max_graph_vram_bytes(size_t max_vram_bytes) {}
     virtual void set_stream_layers_enabled(bool enabled) {}
     virtual void set_runtime_backends(const std::vector<ggml_backend_t>& backends) {}
+    virtual void set_graph_cut_layer_split_enabled(bool enabled) {}
+    virtual void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) {}
     virtual void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) {}
     virtual void set_flash_attention_enabled(bool enabled) = 0;
     virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {}
@@ -180,6 +183,27 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         }
     }
 
+    void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        text_model->set_runtime_backends(backends);
+        if (sd_version_is_sdxl(version)) {
+            text_model2->set_runtime_backends(backends);
+        }
+    }
+
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        text_model->set_graph_cut_layer_split_enabled(enabled);
+        if (sd_version_is_sdxl(version)) {
+            text_model2->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        text_model->set_graph_cut_layer_split_backend_vram_limits(limits);
+        if (sd_version_is_sdxl(version)) {
+            text_model2->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+    }
+
     void set_flash_attention_enabled(bool enabled) override {
         text_model->set_flash_attention_enabled(enabled);
         if (sd_version_is_sdxl(version)) {
@@ -638,11 +662,41 @@ struct SD3CLIPEmbedder : public Conditioner {
     }
 
     void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        if (clip_l) {
+            clip_l->set_runtime_backends(backends);
+        }
+        if (clip_g) {
+            clip_g->set_runtime_backends(backends);
+        }
         if (t5) {
             t5->set_runtime_backends(backends);
         }
     }
 
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        if (clip_l) {
+            clip_l->set_graph_cut_layer_split_enabled(enabled);
+        }
+        if (clip_g) {
+            clip_g->set_graph_cut_layer_split_enabled(enabled);
+        }
+        if (t5) {
+            t5->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        if (clip_l) {
+            clip_l->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+        if (clip_g) {
+            clip_g->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+        if (t5) {
+            t5->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+    }
+
     void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         if (t5) {
             t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
@@ -1009,11 +1063,32 @@ struct FluxCLIPEmbedder : public Conditioner {
     }
 
     void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        if (clip_l) {
+            clip_l->set_runtime_backends(backends);
+        }
         if (t5) {
             t5->set_runtime_backends(backends);
         }
     }
 
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        if (clip_l) {
+            clip_l->set_graph_cut_layer_split_enabled(enabled);
+        }
+        if (t5) {
+            t5->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        if (clip_l) {
+            clip_l->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+        if (t5) {
+            t5->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+    }
+
     void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         if (t5) {
             t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
@@ -1217,8 +1292,27 @@ struct T5CLIPEmbedder : public Conditioner {
                    bool use_mask                                       = false,
                    int mask_pad                                        = 0,
                    bool is_umt5                                        = false,
-                   std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+                   std::shared_ptr<RunnerWeightManager> weight_manager = nullptr,
+                   const char* model_args                              = nullptr)
         : use_mask(use_mask), mask_pad(mask_pad), t5_tokenizer(is_umt5) {
+        for (const auto& [key, value] : parse_key_value_args(model_args, "model arg")) {
+            if (key == "chroma_use_t5_mask") {
+                bool parsed = false;
+                if (parse_strict_bool(value, parsed)) {
+                    this->use_mask = parsed;
+                } else {
+                    LOG_WARN("ignoring invalid Chroma T5 model arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            } else if (key == "chroma_t5_mask_pad") {
+                int parsed = 0;
+                if (parse_strict_int(value, parsed)) {
+                    this->mask_pad = parsed;
+                } else {
+                    LOG_WARN("ignoring invalid Chroma T5 model arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            }
+        }
+
         bool use_t5 = false;
         for (auto pair : tensor_storage_map) {
             if (pair.first.find("text_encoders.t5xxl") != std::string::npos) {
@@ -1258,6 +1352,18 @@ struct T5CLIPEmbedder : public Conditioner {
         }
     }
 
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        if (t5) {
+            t5->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        if (t5) {
+            t5->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+    }
+
     void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         if (t5) {
             t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
@@ -1462,6 +1568,18 @@ struct MiniT2IConditioner : public Conditioner {
         }
     }
 
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        if (t5) {
+            t5->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        if (t5) {
+            t5->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+    }
+
     void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         if (t5) {
             t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
@@ -1556,6 +1674,14 @@ struct AnimaConditioner : public Conditioner {
         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, "text_encoders.llm");
     }
@@ -1709,6 +1835,18 @@ struct LLMEmbedder : public Conditioner {
         llm->set_runtime_backends(backends);
     }
 
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        if (llm) {
+            llm->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        if (llm) {
+            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, "text_encoders.llm");
     }
@@ -2003,6 +2141,41 @@ struct LLMEmbedder : public Conditioner {
             out_layers                       = {2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35};
 
             prompt = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n";
+            if (llm->enable_vision && conditioner_params.ref_images != nullptr && !conditioner_params.ref_images->empty()) {
+                std::string img_prompt        = "";
+                const std::string placeholder = "<|image_pad|>";
+
+                for (int i = 0; i < conditioner_params.ref_images->size(); i++) {
+                    const auto& image = (*conditioner_params.ref_images)[i];
+                    double factor     = llm->config.vision.patch_size * llm->config.vision.spatial_merge_size;
+                    int height        = static_cast<int>(image.shape()[1]);
+                    int width         = static_cast<int>(image.shape()[0]);
+                    double beta       = std::sqrt((384.0 * 384.0) / (static_cast<double>(height) * static_cast<double>(width)));
+                    int h_bar         = std::max(static_cast<int>(factor),
+                                                 static_cast<int>(std::round(height * beta / factor)) * static_cast<int>(factor));
+                    int w_bar         = std::max(static_cast<int>(factor),
+                                                 static_cast<int>(std::round(width * beta / factor)) * static_cast<int>(factor));
+
+                    LOG_DEBUG("resize conditioner ref image %d from %dx%d to %dx%d", i, height, width, h_bar, w_bar);
+
+                    auto resized_image = clip_preprocess(image, w_bar, h_bar);
+                    auto image_embed   = llm->encode_image(n_threads, resized_image, false, true, true);
+                    GGML_ASSERT(!image_embed.empty());
+
+                    std::string image_prefix = prompt + img_prompt + "Picture " + std::to_string(i + 1) + ": <|vision_start|>";
+                    int image_embed_idx      = static_cast<int>(tokenizer->encode(image_prefix, nullptr).size());
+                    image_embeds.emplace_back(image_embed_idx, image_embed);
+
+                    img_prompt += "Picture " + std::to_string(i + 1) + ": <|vision_start|>";
+                    int64_t num_image_tokens = image_embed.shape()[1];
+                    img_prompt.reserve(img_prompt.size() + static_cast<size_t>(num_image_tokens) * placeholder.size() + 32);
+                    for (int j = 0; j < num_image_tokens; j++) {
+                        img_prompt += placeholder;
+                    }
+                    img_prompt += "<|vision_end|>";
+                }
+                prompt += img_prompt;
+            }
 
             prompt_attn_range.first = static_cast<int>(prompt.size());
             prompt += conditioner_params.text;
@@ -2386,6 +2559,14 @@ struct LTXAVEmbedder : public Conditioner {
         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, "text_encoders.llm");
     }

src/convert.cpp

@@ -1,14 +1,33 @@
+#include <algorithm>
+#include <condition_variable>
+#include <cstdint>
 #include <cstring>
+#include <exception>
+#include <fstream>
+#include <memory>
 #include <mutex>
 #include <regex>
+#include <string>
+#include <thread>
 #include <vector>
 
+#include "core/util.h"
 #include "model_io/gguf_io.h"
 #include "model_io/safetensors_io.h"
+#include "model_io/streaming_writer.h"
 #include "model_loader.h"
-#include "util.h"
 
-#include "ggml_extend_backend.h"
+struct TensorExportInfo {
+    TensorStorage storage;
+    ggml_type type;
+};
+
+struct TensorExportJob {
+    TensorExportInfo info;
+    std::vector<uint8_t> data;
+    std::string error;
+    bool success = false;
+};
 
 static ggml_type get_export_tensor_type(ModelLoader& model_loader,
                                         const TensorStorage& tensor_storage,
@@ -33,47 +52,262 @@ static ggml_type get_export_tensor_type(ModelLoader& model_loader,
     return tensor_type;
 }
 
-static bool load_tensors_for_export(ModelLoader& model_loader,
-                                    ggml_context* ggml_ctx,
-                                    ggml_type type,
-                                    const TensorTypeRules& tensor_type_rules,
-                                    std::vector<TensorWriteInfo>& tensors) {
-    std::mutex tensor_mutex;
-    auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
-        const std::string& name = tensor_storage.name;
-        ggml_type tensor_type   = get_export_tensor_type(model_loader, tensor_storage, type, tensor_type_rules);
+static bool collect_tensors_for_export(ModelLoader& model_loader,
+                                       ggml_type type,
+                                       const TensorTypeRules& tensor_type_rules,
+                                       std::vector<TensorExportInfo>& tensors) {
+    tensors.clear();
+    tensors.reserve(model_loader.get_tensor_storage_map().size());
+    for (const auto& kv : model_loader.get_tensor_storage_map()) {
+        const TensorStorage& tensor_storage = kv.second;
+        TensorExportInfo info;
+        info.storage = tensor_storage;
+        info.type    = get_export_tensor_type(model_loader, tensor_storage, type, tensor_type_rules);
+        tensors.push_back(std::move(info));
+    }
+    LOG_INFO("collected %zu tensors for export", tensors.size());
+    return true;
+}
 
-        std::lock_guard<std::mutex> lock(tensor_mutex);
-        ggml_tensor* tensor = ggml_new_tensor(ggml_ctx, tensor_type, tensor_storage.n_dims, tensor_storage.ne);
-        if (tensor == nullptr) {
-            LOG_ERROR("ggml_new_tensor failed");
+static size_t export_tensor_nbytes(const TensorExportInfo& info) {
+    TensorStorage output_storage = info.storage;
+    output_storage.type          = info.type;
+    return static_cast<size_t>(output_storage.nbytes());
+}
+
+static TensorWritePlan tensor_write_plan_from_export_info(const TensorExportInfo& info) {
+    TensorWritePlan plan;
+    plan.name   = info.storage.name;
+    plan.type   = info.type;
+    plan.n_dims = info.storage.n_dims;
+    for (int i = 0; i < SD_MAX_DIMS; i++) {
+        plan.ne[i] = info.storage.ne[i];
+    }
+    return plan;
+}
+
+static std::vector<TensorWritePlan> tensor_write_plans_from_export_infos(const std::vector<TensorExportInfo>& tensors) {
+    std::vector<TensorWritePlan> plans;
+    plans.reserve(tensors.size());
+    for (const TensorExportInfo& info : tensors) {
+        plans.push_back(tensor_write_plan_from_export_info(info));
+    }
+    return plans;
+}
+
+static bool preallocate_output_file(const std::string& output_path, uint64_t file_size, std::string* error) {
+    if (file_size == 0) {
+        return true;
+    }
+
+    std::fstream file(output_path, std::ios::binary | std::ios::in | std::ios::out);
+    if (!file.is_open()) {
+        if (error != nullptr) {
+            *error = "failed to open output file '" + output_path + "' for preallocation";
+        }
+        return false;
+    }
+
+    // This portable fallback sets the final file size. A platform-specific
+    // posix_fallocate/ftruncate path can replace it later.
+    file.seekp(static_cast<std::streamoff>(file_size - 1), std::ios::beg);
+    file.put('\0');
+    file.flush();
+    if (!file) {
+        if (error != nullptr) {
+            *error = "failed to preallocate output file '" + output_path + "'";
+        }
+        return false;
+    }
+    return true;
+}
+
+static bool load_tensor_for_export(ModelLoader& model_loader, TensorExportJob& job) {
+    size_t mem_size = 1 * 1024 * 1024;
+    mem_size += ggml_tensor_overhead();
+    TensorStorage output_storage = job.info.storage;
+    output_storage.type          = job.info.type;
+    mem_size += static_cast<size_t>(output_storage.nbytes());
+
+    ggml_context* ggml_ctx = ggml_init({mem_size, nullptr, false});
+    if (ggml_ctx == nullptr) {
+        job.error = "ggml_init failed for tensor '" + job.info.storage.name + "'";
+        return false;
+    }
+
+    ggml_tensor* tensor = ggml_new_tensor(ggml_ctx, job.info.type, job.info.storage.n_dims, job.info.storage.ne);
+    if (tensor == nullptr) {
+        ggml_free(ggml_ctx);
+        job.error = "ggml_new_tensor failed for tensor '" + job.info.storage.name + "'";
+        return false;
+    }
+    ggml_set_name(tensor, job.info.storage.name.c_str());
+
+    const size_t tensor_nbytes = ggml_nbytes(tensor);
+    if (tensor_nbytes > 0 && !model_loader.load_tensor(job.info.storage, tensor)) {
+        ggml_free(ggml_ctx);
+        job.error = "failed to load tensor '" + job.info.storage.name + "'";
+        return false;
+    }
+
+    job.data.resize(tensor_nbytes);
+    if (tensor_nbytes > 0) {
+        memcpy(job.data.data(), tensor->data, tensor_nbytes);
+    }
+    ggml_free(ggml_ctx);
+    return true;
+}
+
+static bool stream_tensor_data(ModelLoader& model_loader,
+                               const std::string& output_path,
+                               const std::vector<TensorExportInfo>& tensors,
+                               const StreamingModelWriter& writer,
+                               int n_threads,
+                               std::string* error) {
+    n_threads = n_threads > 0 ? n_threads : sd_get_num_physical_cores();
+    n_threads = std::max(1, n_threads);
+    LOG_INFO("streaming convert with %d threads", n_threads);
+
+    int64_t start_time       = ggml_time_ms();
+    uint64_t bytes_written   = 0;
+    size_t tensors_written   = 0;
+    size_t next_tensor_index = 0;
+    bool failed              = false;
+    std::string failure;
+
+    const size_t memory_budget = 1024ull * 1024ull * 1024ull;
+    size_t reserved_bytes      = 0;
+
+    std::mutex work_mutex;
+    std::mutex progress_mutex;
+    std::condition_variable memory_cv;
+    std::vector<std::thread> workers;
+    workers.reserve(n_threads);
+
+    auto reserve_memory = [&](size_t bytes) -> bool {
+        std::unique_lock<std::mutex> lock(work_mutex);
+        memory_cv.wait(lock, [&]() {
+            return failed || reserved_bytes == 0 || reserved_bytes + bytes <= memory_budget;
+        });
+        if (failed) {
             return false;
         }
-        ggml_set_name(tensor, name.c_str());
-
-        if (!tensor->data) {
-            GGML_ASSERT(ggml_nelements(tensor) == 0);
-            // Avoid crashing writers by setting a dummy pointer for zero-sized tensors.
-            LOG_DEBUG("setting dummy pointer for zero-sized tensor %s", name.c_str());
-            tensor->data = ggml_get_mem_buffer(ggml_ctx);
-        }
-
-        TensorWriteInfo write_info;
-        write_info.tensor = tensor;
-        write_info.n_dims = tensor_storage.n_dims;
-        for (int i = 0; i < tensor_storage.n_dims; ++i) {
-            write_info.ne[i] = tensor_storage.ne[i];
-        }
-
-        *dst_tensor = tensor;
-        tensors.push_back(std::move(write_info));
-
+        reserved_bytes += bytes;
         return true;
     };
 
-    bool success = model_loader.load_tensors(on_new_tensor_cb);
-    LOG_INFO("load tensors done");
-    return success;
+    auto release_memory = [&](size_t bytes) {
+        {
+            std::lock_guard<std::mutex> lock(work_mutex);
+            reserved_bytes -= std::min(reserved_bytes, bytes);
+        }
+        memory_cv.notify_all();
+    };
+
+    auto fail = [&](const std::string& message) {
+        {
+            std::lock_guard<std::mutex> lock(work_mutex);
+            if (!failed) {
+                failed  = true;
+                failure = message;
+            }
+        }
+        memory_cv.notify_all();
+    };
+
+    for (int worker = 0; worker < n_threads; worker++) {
+        workers.emplace_back([&]() {
+            std::fstream output_file(output_path, std::ios::binary | std::ios::in | std::ios::out);
+            if (!output_file.is_open()) {
+                fail("failed to open output file '" + output_path + "' for tensor writing");
+                return;
+            }
+
+            while (true) {
+                size_t tensor_index = 0;
+                {
+                    std::lock_guard<std::mutex> lock(work_mutex);
+                    if (failed || next_tensor_index >= tensors.size()) {
+                        return;
+                    }
+                    tensor_index = next_tensor_index++;
+                }
+
+                const size_t tensor_bytes = export_tensor_nbytes(tensors[tensor_index]);
+                if (!reserve_memory(tensor_bytes)) {
+                    return;
+                }
+
+                TensorExportJob job;
+                job.info = tensors[tensor_index];
+                try {
+                    job.success = load_tensor_for_export(model_loader, job);
+                } catch (const std::exception& e) {
+                    job.error   = e.what();
+                    job.success = false;
+                }
+
+                if (!job.success) {
+                    release_memory(tensor_bytes);
+                    fail(job.error.empty() ? "streaming conversion failed" : job.error);
+                    return;
+                }
+
+                std::string write_error;
+                if (!writer.write_tensor(output_file,
+                                         tensor_index,
+                                         job.data.empty() ? nullptr : job.data.data(),
+                                         job.data.size(),
+                                         &write_error)) {
+                    release_memory(tensor_bytes);
+                    fail(write_error.empty() ? "streaming conversion write failed" : write_error);
+                    return;
+                }
+
+                {
+                    std::lock_guard<std::mutex> lock(progress_mutex);
+                    bytes_written += job.data.size();
+                    tensors_written++;
+                    float elapsed_seconds = (ggml_time_ms() - start_time) / 1000.0f;
+                    pretty_bytes_progress(static_cast<int>(tensors_written),
+                                          static_cast<int>(tensors.size()),
+                                          bytes_written,
+                                          elapsed_seconds);
+                }
+                release_memory(tensor_bytes);
+            }
+        });
+    }
+
+    for (auto& worker : workers) {
+        worker.join();
+    }
+    printf("\n");
+    if (failed) {
+        if (error != nullptr) {
+            *error = failure;
+        }
+        return false;
+    }
+    LOG_INFO("streaming conversion completed, taking %.2fs", (ggml_time_ms() - start_time) / 1000.f);
+    return true;
+}
+
+static bool write_model_file_streaming(ModelLoader& model_loader,
+                                       const std::string& output_path,
+                                       const std::vector<TensorExportInfo>& tensors,
+                                       StreamingModelWriter& writer,
+                                       int n_threads,
+                                       std::string* error) {
+    std::vector<TensorWritePlan> plans = tensor_write_plans_from_export_infos(tensors);
+    if (!writer.write_metadata(output_path, plans, error)) {
+        return false;
+    }
+    if (!preallocate_output_file(output_path, writer.file_size(), error)) {
+        return false;
+    }
+    model_loader.process_model_files(false, false);
+    return stream_tensor_data(model_loader, output_path, tensors, writer, n_threads, error);
 }
 
 static bool init_convert_path(ModelLoader& model_loader, const char* path, const char* prefix, bool& loaded_any) {
@@ -91,42 +325,29 @@ static bool init_convert_path(ModelLoader& model_loader, const char* path, const
 static bool export_loaded_model(ModelLoader& model_loader,
                                 const char* output_path,
                                 sd_type_t output_type,
-                                const char* tensor_type_rules) {
+                                const char* tensor_type_rules,
+                                int n_threads) {
     ggml_type type             = sd_type_to_ggml_type(output_type);
     bool output_is_safetensors = ends_with(output_path, ".safetensors");
     TensorTypeRules type_rules = parse_tensor_type_rules(tensor_type_rules);
 
-    auto backend    = sd_backend_cpu_init();
-    size_t mem_size = 1 * 1024 * 1024;  // for padding
-    mem_size += model_loader.get_tensor_storage_map().size() * ggml_tensor_overhead();
-    mem_size += model_loader.get_params_mem_size(backend, type);
-    LOG_INFO("model tensors mem size: %.2fMB", mem_size / 1024.f / 1024.f);
-    ggml_context* ggml_ctx = ggml_init({mem_size, nullptr, false});
-
-    if (ggml_ctx == nullptr) {
-        LOG_ERROR("ggml_init failed for converter");
-        ggml_backend_free(backend);
-        return false;
-    }
-
-    std::vector<TensorWriteInfo> tensors;
-    bool success = load_tensors_for_export(model_loader, ggml_ctx, type, type_rules, tensors);
-    ggml_backend_free(backend);
-
+    std::vector<TensorExportInfo> tensors;
+    bool success = collect_tensors_for_export(model_loader, type, type_rules, tensors);
     std::string error;
     if (success) {
+        std::unique_ptr<StreamingModelWriter> writer;
         if (output_is_safetensors) {
-            success = write_safetensors_file(output_path, tensors, &error);
+            writer = std::make_unique<SafetensorsStreamingWriter>();
         } else {
-            success = write_gguf_file(output_path, tensors, &error);
+            writer = std::make_unique<GGUFStreamingWriter>();
         }
+        success = write_model_file_streaming(model_loader, output_path, tensors, *writer, n_threads, &error);
     }
 
     if (!success && !error.empty()) {
         LOG_ERROR("%s", error.c_str());
     }
 
-    ggml_free(ggml_ctx);
     return success;
 }
 
@@ -139,7 +360,8 @@ bool convert_with_components(const char* model_path,
                              const char* output_path,
                              sd_type_t output_type,
                              const char* tensor_type_rules,
-                             bool convert_name) {
+                             bool convert_name,
+                             int n_threads) {
     ModelLoader model_loader;
     bool loaded_any = false;
 
@@ -161,7 +383,7 @@ bool convert_with_components(const char* model_path,
         model_loader.convert_tensors_name();
     }
 
-    return export_loaded_model(model_loader, output_path, output_type, tensor_type_rules);
+    return export_loaded_model(model_loader, output_path, output_type, tensor_type_rules, n_threads);
 }
 
 bool convert(const char* input_path,
@@ -179,5 +401,6 @@ bool convert(const char* input_path,
                                    output_path,
                                    output_type,
                                    tensor_type_rules,
-                                   convert_name);
+                                   convert_name,
+                                   0);
 }

src/core/ggml_extend.hpp

@@ -21,10 +21,12 @@
 #include <sstream>
 #include <string>
 #include <unordered_map>
+#include <unordered_set>
 #include <vector>
 
 #include "core/ggml_extend_backend.h"
 #include "core/ggml_graph_cut.h"
+#include "core/layer_split_partition.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 #include "ggml.h"
@@ -1745,6 +1747,8 @@ protected:
     size_t max_graph_vram_bytes           = 0;
     bool stream_layers_enabled            = false;
     size_t observed_max_effective_budget_ = 0;
+    bool graph_cut_layer_split_enabled    = false;
+    std::vector<size_t> graph_cut_layer_split_backend_vram_limits_;
 
     std::vector<ggml_backend_t> extra_runtime_backends;  // borrowed (SDBackendManager-owned)
     ggml_backend_sched_t sched             = nullptr;    // owned, multi-device only
@@ -1776,6 +1780,9 @@ protected:
 
     sd::ggml_graph_cut::PlanCache graph_cut_plan_cache_;
     std::unordered_set<const ggml_tensor*> params_tensor_set_;
+    std::unordered_map<const ggml_tensor*, ggml_backend_t> graph_cut_layer_split_assignments_;
+    std::unordered_map<const ggml_tensor*, ggml_backend_t> graph_cut_layer_split_node_assignments_;
+    bool graph_cut_layer_split_primary_notice_logged_ = false;
 
     template <typename T>
     static sd::Tensor<T> take_or_empty(std::optional<sd::Tensor<T>> tensor) {
@@ -1874,6 +1881,20 @@ protected:
         params_tensor_set_dirty_ = false;
     }
 
+    ggml_tensor* canonical_param_tensor(ggml_tensor* tensor) {
+        if (tensor == nullptr) {
+            return nullptr;
+        }
+        if (params_tensor_set_.find(tensor) != params_tensor_set_.end()) {
+            return tensor;
+        }
+        if (tensor->view_src != nullptr &&
+            params_tensor_set_.find(tensor->view_src) != params_tensor_set_.end()) {
+            return tensor->view_src;
+        }
+        return nullptr;
+    }
+
     std::vector<ggml_tensor*> collect_used_param_tensors(ggml_cgraph* gf) {
         std::vector<ggml_tensor*> used_params;
         rebuild_params_tensor_set();
@@ -1886,12 +1907,8 @@ protected:
         seen_params.reserve(static_cast<size_t>(n_leafs));
         for (int i = 0; i < n_leafs; ++i) {
             ggml_tensor* leaf       = sd::ggml_graph_cut::leaf_tensor(gf, i);
-            ggml_tensor* param_leaf = leaf;
-            if (param_leaf != nullptr && params_tensor_set_.find(param_leaf) == params_tensor_set_.end()) {
-                param_leaf = param_leaf->view_src;
-            }
+            ggml_tensor* param_leaf = canonical_param_tensor(leaf);
             if (param_leaf != nullptr &&
-                params_tensor_set_.find(param_leaf) != params_tensor_set_.end() &&
                 seen_params.insert(param_leaf).second) {
                 used_params.push_back(param_leaf);
             }
@@ -2101,11 +2118,17 @@ protected:
         ggml_backend_t current = runtime_backend;
         const int n_nodes      = ggml_graph_n_nodes(gf);
         for (int i = 0; i < n_nodes; i++) {
-            ggml_tensor* node = ggml_graph_node(gf, i);
+            ggml_tensor* node    = ggml_graph_node(gf, i);
+            auto node_assignment = graph_cut_layer_split_node_assignments_.find(node);
+            if (node_assignment != graph_cut_layer_split_node_assignments_.end()) {
+                current = node_assignment->second;
+            }
             for (int s = 0; s < GGML_MAX_SRC; s++) {
                 ggml_backend_t weight_backend = backend_for_weight(node->src[s]);
                 if (weight_backend != nullptr) {
-                    current = weight_backend;
+                    if (node_assignment == graph_cut_layer_split_node_assignments_.end()) {
+                        current = weight_backend;
+                    }
                 }
             }
             if (node->op == GGML_OP_NONE || node->op == GGML_OP_VIEW || node->op == GGML_OP_RESHAPE ||
@@ -2435,6 +2458,123 @@ protected:
         return true;
     }
 
+    bool resolve_graph_cut_layer_split_plan(ggml_cgraph* gf,
+                                            GraphCutPlan* plan_out) {
+        GGML_ASSERT(plan_out != nullptr);
+        GGML_ASSERT(gf != nullptr);
+        *plan_out = sd::ggml_graph_cut::resolve_plan(runtime_backend,
+                                                     gf,
+                                                     &graph_cut_plan_cache_,
+                                                     0,
+                                                     params_tensor_set_,
+                                                     get_desc().c_str());
+        return true;
+    }
+
+    bool assign_graph_cut_layer_split_backends(ggml_cgraph* gf) {
+        graph_cut_layer_split_node_assignments_.clear();
+        if (!graph_cut_layer_split_enabled) {
+            return true;
+        }
+        if (!is_multi_device()) {
+            LOG_ERROR("%s graph-cut layer split requires multiple runtime backends", get_desc().c_str());
+            return false;
+        }
+
+        GraphCutPlan plan;
+        if (!resolve_graph_cut_layer_split_plan(gf, &plan)) {
+            return false;
+        }
+        if (!plan.valid || !plan.has_cuts || plan.segments.size() <= 1) {
+            auto manager = weight_manager.lock();
+            if (manager == nullptr) {
+                LOG_ERROR("%s weight manager is not set for graph-cut layer split", get_desc().c_str());
+                return false;
+            }
+            std::vector<ggml_tensor*> graph_params = collect_used_param_tensors(gf);
+            if (!graph_params.empty() &&
+                !manager->assign_compute_backend(graph_params, runtime_backend)) {
+                LOG_ERROR("%s graph-cut layer split failed to assign unmarked graph params to %s",
+                          get_desc().c_str(),
+                          sd::layer_split_backend_device_display_name(runtime_backend).c_str());
+                return false;
+            }
+            for (ggml_tensor* param : graph_params) {
+                if (param != nullptr) {
+                    graph_cut_layer_split_assignments_[param] = runtime_backend;
+                }
+            }
+            const int n_nodes = ggml_graph_n_nodes(gf);
+            for (int i = 0; i < n_nodes; i++) {
+                ggml_tensor* node = ggml_graph_node(gf, i);
+                if (node != nullptr) {
+                    graph_cut_layer_split_node_assignments_[node] = runtime_backend;
+                }
+            }
+            if (!graph_cut_layer_split_primary_notice_logged_) {
+                LOG_WARN("%s graph-cut layer split: graph has no mark_graph_cut segments; using primary backend %s for %zu graph params",
+                         get_desc().c_str(),
+                         sd::layer_split_backend_device_display_name(runtime_backend).c_str(),
+                         graph_params.size());
+                graph_cut_layer_split_primary_notice_logged_ = true;
+            } else {
+                LOG_DEBUG("%s graph-cut layer split: graph has no mark_graph_cut segments; using primary backend %s for %zu graph params",
+                          get_desc().c_str(),
+                          sd::layer_split_backend_device_display_name(runtime_backend).c_str(),
+                          graph_params.size());
+            }
+            return true;
+        }
+
+        std::vector<ggml_backend_t> split_backends;
+        split_backends.reserve(extra_runtime_backends.size() + 1);
+        split_backends.push_back(runtime_backend);
+        for (ggml_backend_t backend : extra_runtime_backends) {
+            if (backend != nullptr) {
+                split_backends.push_back(backend);
+            }
+        }
+
+        auto manager = weight_manager.lock();
+        if (manager == nullptr) {
+            LOG_ERROR("%s weight manager is not set for graph-cut layer split", get_desc().c_str());
+            return false;
+        }
+
+        sd::GraphCutLayerSplitAssignment assignment;
+        auto canonicalize_param = [this](ggml_tensor* tensor) {
+            return canonical_param_tensor(tensor);
+        };
+        if (!sd::partition_graph_cut_layer_split(get_desc().c_str(),
+                                                 gf,
+                                                 plan,
+                                                 split_backends,
+                                                 graph_cut_layer_split_backend_vram_limits_,
+                                                 max_graph_vram_bytes,
+                                                 graph_cut_layer_split_assignments_,
+                                                 canonicalize_param,
+                                                 &assignment)) {
+            return false;
+        }
+
+        for (size_t i = 0; i < split_backends.size(); i++) {
+            if (assignment.tensors_by_backend[i].empty()) {
+                continue;
+            }
+            if (!manager->assign_compute_backend(assignment.tensors_by_backend[i], split_backends[i])) {
+                LOG_ERROR("%s graph-cut layer split failed to assign params to %s",
+                          get_desc().c_str(),
+                          sd::layer_split_backend_device_display_name(split_backends[i]).c_str());
+                return false;
+            }
+        }
+
+        graph_cut_layer_split_node_assignments_ = std::move(assignment.node_assignments);
+        sd::log_graph_cut_layer_split_assignment(get_desc().c_str(), split_backends, assignment);
+
+        return true;
+    }
+
     struct PersistentExternalBinding {
         ggml_backend_buffer_t buffer = nullptr;
         void* data                   = nullptr;
@@ -2972,6 +3112,11 @@ public:
         GGML_ASSERT(gf != nullptr);
         rebuild_params_tensor_set();
 
+        if (!assign_graph_cut_layer_split_backends(gf)) {
+            free_compute_ctx();
+            return std::nullopt;
+        }
+
         if (can_attempt_graph_cut_segmented_compute()) {
             GraphCutPlan plan;
             if (!resolve_graph_cut_plan(gf, &plan)) {
@@ -3025,6 +3170,22 @@ public:
         stream_layers_enabled = enabled;
     }
 
+    void set_graph_cut_layer_split_enabled(bool enabled) {
+        graph_cut_layer_split_enabled = enabled;
+        if (!enabled) {
+            graph_cut_layer_split_assignments_.clear();
+            graph_cut_layer_split_node_assignments_.clear();
+            graph_cut_layer_split_primary_notice_logged_ = false;
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) {
+        graph_cut_layer_split_backend_vram_limits_ = limits;
+        graph_cut_layer_split_assignments_.clear();
+        graph_cut_layer_split_node_assignments_.clear();
+        graph_cut_layer_split_primary_notice_logged_ = false;
+    }
+
     void set_runtime_backends(const std::vector<ggml_backend_t>& backends) {
         extra_runtime_backends.clear();
         for (ggml_backend_t backend : backends) {
@@ -3036,6 +3197,9 @@ public:
                 extra_runtime_backends.push_back(backend);
             }
         }
+        graph_cut_layer_split_assignments_.clear();
+        graph_cut_layer_split_node_assignments_.clear();
+        graph_cut_layer_split_primary_notice_logged_ = false;
         if (is_multi_device() && stream_layers_enabled) {
             LOG_WARN("%s: --stream-layers is not supported with multiple runtime backends; ignoring",
                      get_desc().c_str());

src/core/layer_split_partition.cpp

@@ -1,9 +1,11 @@
 #include "core/layer_split_partition.h"
 
 #include <algorithm>
-#include <cstdint>
 #include <cstdlib>
 #include <cstring>
+#include <limits>
+#include <unordered_set>
+#include <utility>
 
 #include "core/util.h"
 
@@ -62,160 +64,194 @@ namespace sd {
         return name != nullptr ? name : "unknown";
     }
 
-    static bool layer_split_backend_supports_tensor(ggml_backend_t backend, const ggml_tensor* tensor) {
-        return backend != nullptr && tensor != nullptr && ggml_backend_supports_op(backend, tensor);
+    static size_t graph_cut_layer_split_backend_vram_limit(const std::vector<size_t>& backend_vram_limits,
+                                                           size_t backend_index,
+                                                           size_t primary_backend_vram_limit) {
+        if (backend_index < backend_vram_limits.size()) {
+            return backend_vram_limits[backend_index];
+        }
+        return backend_index == 0 ? primary_backend_vram_limit : 0;
     }
 
-    static size_t layer_split_supported_target(const std::string& desc,
-                                               const std::string& tensor_name,
-                                               const ggml_tensor* tensor,
-                                               const std::vector<ggml_backend_t>& backends,
-                                               size_t preferred) {
-        if (tensor == nullptr || backends.empty()) {
-            return preferred;
-        }
-        size_t preferred_safe = std::min(preferred, backends.size() - 1);
-        if (layer_split_backend_supports_tensor(backends[preferred_safe], tensor)) {
-            return preferred_safe;
-        }
-        for (size_t i = 0; i < backends.size(); i++) {
-            if (layer_split_backend_supports_tensor(backends[i], tensor)) {
-                LOG_WARN("%s layer split: moving tensor '%s' from %s to %s because the preferred backend cannot run op=%s type=%s nbytes=%.2f MB",
-                         desc.c_str(),
-                         tensor_name.c_str(),
-                         layer_split_backend_device_display_name(backends[preferred_safe]).c_str(),
-                         layer_split_backend_device_display_name(backends[i]).c_str(),
-                         ggml_op_name(tensor->op),
-                         ggml_type_name(tensor->type),
-                         ggml_nbytes(tensor) / (1024.0 * 1024.0));
-                return i;
-            }
-        }
-        LOG_WARN("%s layer split: tensor '%s' is not supported by any split backend: op=%s type=%s nbytes=%.2f MB",
-                 desc.c_str(),
-                 tensor_name.c_str(),
-                 ggml_op_name(tensor->op),
-                 ggml_type_name(tensor->type),
-                 ggml_nbytes(tensor) / (1024.0 * 1024.0));
-        return preferred_safe;
-    }
-
-    std::vector<std::map<std::string, ggml_tensor*>> partition_layer_split_tensors(
-        const std::string& desc,
-        const std::map<std::string, ggml_tensor*>& tensors,
-        const std::map<std::string, ggml_tensor*>& split_tensors,
-        const std::vector<ggml_backend_t>& backends) {
-        std::vector<std::map<std::string, ggml_tensor*>> partitions(backends.size());
-        if (backends.empty()) {
-            LOG_WARN("%s: no backend available for a layer split", desc.c_str());
-            return partitions;
-        }
-
-        std::map<int, int64_t> block_bytes;
-        std::map<std::string, size_t> non_block_targets;
-        std::vector<int64_t> other_bytes_by_backend(backends.size(), 0);
-        int64_t total_block_bytes = 0;
-        int64_t total_other_bytes = 0;
-        int n_blocks              = 0;
-        for (const auto& kv : tensors) {
-            int64_t bytes = (int64_t)ggml_nbytes(kv.second);
-            int idx       = split_tensors.count(kv.first) != 0 ? layer_split_tensor_block_index(kv.first) : -1;
-            if (idx >= 0) {
-                block_bytes[idx] += bytes;
-                total_block_bytes += bytes;
-                n_blocks = std::max(n_blocks, idx + 1);
-            } else {
-                size_t target               = layer_split_supported_target(desc, kv.first, kv.second, backends, 0);
-                non_block_targets[kv.first] = target;
-                other_bytes_by_backend[target] += bytes;
-                total_other_bytes += bytes;
-            }
-        }
-        if (n_blocks == 0) {
-            LOG_WARN("%s: no transformer blocks found for a layer split; keeping tensors on compatible backends starting from %s",
-                     desc.c_str(),
-                     layer_split_backend_device_display_name(backends[0]).c_str());
-            for (const auto& kv : tensors) {
-                size_t target  = 0;
-                auto target_it = non_block_targets.find(kv.first);
-                if (target_it != non_block_targets.end()) {
-                    target = target_it->second;
-                }
-                partitions[target][kv.first] = kv.second;
-            }
-            return partitions;
-        }
-
-        // Reserve compute headroom and subtract each device's actual non-block
-        // bytes from its block budget.
+    static std::vector<int64_t> graph_cut_layer_split_backend_capacities(const std::vector<ggml_backend_t>& backends,
+                                                                         const std::vector<size_t>& backend_vram_limits,
+                                                                         size_t primary_backend_vram_limit) {
+        std::vector<int64_t> capacities(backends.size(), std::numeric_limits<int64_t>::max() / 4);
         constexpr int64_t compute_headroom_bytes = 2ll * 1024 * 1024 * 1024;
-        std::vector<double> device_weights(backends.size(), 1.0);
-        double weight_sum = 0.0;
         for (size_t i = 0; i < backends.size(); i++) {
             ggml_backend_dev_t dev = ggml_backend_get_device(backends[i]);
             size_t free_bytes = 0, total_bytes = 0;
             if (dev != nullptr) {
                 ggml_backend_dev_memory(dev, &free_bytes, &total_bytes);
             }
-            // Keep a small share even for tight devices instead of dropping them.
-            int64_t usable_bytes = std::max<int64_t>((int64_t)free_bytes - compute_headroom_bytes,
-                                                     (int64_t)free_bytes / 8);
-            device_weights[i]    = usable_bytes > 0 ? (double)usable_bytes : 1.0;
-            weight_sum += device_weights[i];
+            if (free_bytes > 0) {
+                capacities[i] = std::max<int64_t>((int64_t)free_bytes - compute_headroom_bytes, 0);
+            }
+            size_t limit_bytes = graph_cut_layer_split_backend_vram_limit(backend_vram_limits,
+                                                                          i,
+                                                                          primary_backend_vram_limit);
+            if (limit_bytes > 0) {
+                capacities[i] = std::min<int64_t>(capacities[i], (int64_t)limit_bytes);
+            }
         }
+        return capacities;
+    }
 
-        std::vector<int64_t> block_budgets(backends.size(), 0);
-        const int64_t total_bytes = total_block_bytes + total_other_bytes;
-        for (size_t i = 0; i < backends.size(); i++) {
-            int64_t budget   = (int64_t)((double)total_bytes * device_weights[i] / weight_sum);
-            budget           = std::max<int64_t>(budget - other_bytes_by_backend[i], 0);
-            block_budgets[i] = budget;
-        }
+    bool partition_graph_cut_layer_split(const char* desc,
+                                         ggml_cgraph* gf,
+                                         const sd::ggml_graph_cut::Plan& plan,
+                                         const std::vector<ggml_backend_t>& split_backends,
+                                         const std::vector<size_t>& backend_vram_limits,
+                                         size_t primary_backend_vram_limit,
+                                         std::unordered_map<const ggml_tensor*, ggml_backend_t>& param_assignments,
+                                         const std::function<ggml_tensor*(ggml_tensor*)>& canonical_param_tensor,
+                                         GraphCutLayerSplitAssignment* assignment_out) {
+        GGML_ASSERT(gf != nullptr);
+        GGML_ASSERT(assignment_out != nullptr);
+        GGML_ASSERT(canonical_param_tensor != nullptr);
+        GGML_ASSERT(!split_backends.empty());
+
+        GraphCutLayerSplitAssignment assignment;
+        assignment.segment_count = plan.segments.size();
+        assignment.tensors_by_backend.resize(split_backends.size());
+        assignment.bytes_by_backend.resize(split_backends.size(), 0);
+        assignment.first_segment_by_backend.resize(split_backends.size(), plan.segments.size());
+        assignment.last_segment_by_backend.resize(split_backends.size(), 0);
 
-        std::vector<int> boundaries(backends.size(), n_blocks);
-        size_t current = 0;
-        int64_t used   = 0;
-        for (int b = 0; b < n_blocks; b++) {
-            int64_t bytes = block_bytes.count(b) != 0 ? block_bytes[b] : 0;
-            if (current + 1 < backends.size() && used > 0 && used + bytes > block_budgets[current]) {
-                boundaries[current] = b;
-                current++;
-                used = 0;
+        std::vector<std::vector<ggml_tensor*>> segment_params(plan.segments.size());
+        std::vector<int64_t> segment_param_bytes(plan.segments.size(), 0);
+        std::unordered_set<ggml_tensor*> seen_params;
+        for (size_t seg_idx = 0; seg_idx < plan.segments.size(); seg_idx++) {
+            std::vector<ggml_tensor*> params = sd::ggml_graph_cut::param_tensors(gf, plan.segments[seg_idx]);
+            for (ggml_tensor* raw_param : params) {
+                ggml_tensor* param = canonical_param_tensor(raw_param);
+                if (param == nullptr || !seen_params.insert(param).second) {
+                    continue;
+                }
+                segment_params[seg_idx].push_back(param);
+                segment_param_bytes[seg_idx] += (int64_t)ggml_nbytes(param);
             }
-            used += bytes;
         }
 
-        for (const auto& kv : tensors) {
-            size_t target = 0;
-            int idx       = split_tensors.count(kv.first) != 0 ? layer_split_tensor_block_index(kv.first) : -1;
-            if (idx >= 0) {
-                while (target < boundaries.size() && idx >= boundaries[target]) {
-                    target++;
+        int64_t total_param_bytes = 0;
+        for (int64_t bytes : segment_param_bytes) {
+            total_param_bytes += bytes;
+        }
+        if (total_param_bytes <= 0) {
+            LOG_ERROR("%s graph-cut layer split found no graph params to assign", desc);
+            return false;
+        }
+
+        std::vector<int64_t> backend_capacities = graph_cut_layer_split_backend_capacities(split_backends,
+                                                                                           backend_vram_limits,
+                                                                                           primary_backend_vram_limit);
+
+        std::vector<ggml_backend_t> backend_by_segment(plan.segments.size(), split_backends[0]);
+        size_t current_backend = 0;
+        int64_t current_used   = 0;
+        for (size_t seg_idx = 0; seg_idx < plan.segments.size(); seg_idx++) {
+            int64_t bytes = segment_param_bytes[seg_idx];
+            while (current_backend + 1 < split_backends.size() &&
+                   bytes > 0 &&
+                   current_used + bytes > backend_capacities[current_backend]) {
+                current_backend++;
+                current_used = 0;
+            }
+            if (bytes > 0 && current_used + bytes > backend_capacities[current_backend]) {
+                LOG_ERROR("%s graph-cut layer split: segment %zu needs %.1f MB on %s, but only %.1f MB is available under current VRAM limits",
+                          desc,
+                          seg_idx,
+                          (current_used + bytes) / (1024.0 * 1024.0),
+                          layer_split_backend_device_display_name(split_backends[current_backend]).c_str(),
+                          backend_capacities[current_backend] / (1024.0 * 1024.0));
+                return false;
+            }
+            current_used += bytes;
+            backend_by_segment[seg_idx] = split_backends[current_backend];
+
+            for (ggml_tensor* param : segment_params[seg_idx]) {
+                ggml_backend_t target_backend = split_backends[current_backend];
+                auto assigned_it              = param_assignments.find(param);
+                if (assigned_it == param_assignments.end()) {
+                    param_assignments[param]            = target_backend;
+                    assignment.has_new_param_assignment = true;
+                } else {
+                    target_backend = assigned_it->second;
                 }
-                target = std::min(target, backends.size() - 1);
-                target = layer_split_supported_target(desc, kv.first, kv.second, backends, target);
+
+                auto backend_it = std::find(split_backends.begin(), split_backends.end(), target_backend);
+                if (backend_it == split_backends.end()) {
+                    LOG_ERROR("%s graph-cut layer split tensor '%s' is assigned to an unavailable backend",
+                              desc,
+                              ggml_get_name(param));
+                    return false;
+                }
+                size_t backend_idx                               = (size_t)std::distance(split_backends.begin(), backend_it);
+                assignment.first_segment_by_backend[backend_idx] = std::min(assignment.first_segment_by_backend[backend_idx], seg_idx);
+                assignment.last_segment_by_backend[backend_idx]  = std::max(assignment.last_segment_by_backend[backend_idx], seg_idx + 1);
+                assignment.tensors_by_backend[backend_idx].push_back(param);
+                assignment.bytes_by_backend[backend_idx] += (int64_t)ggml_nbytes(param);
+            }
+        }
+
+        const int n_nodes = ggml_graph_n_nodes(gf);
+        for (size_t seg_idx = 0; seg_idx < plan.segments.size(); seg_idx++) {
+            ggml_backend_t backend = backend_by_segment[seg_idx];
+            const auto& segment    = plan.segments[seg_idx];
+            for (int node_index : segment.internal_node_indices) {
+                if (node_index < 0 || node_index >= n_nodes) {
+                    continue;
+                }
+                ggml_tensor* node = ggml_graph_node(gf, node_index);
+                if (node != nullptr) {
+                    assignment.node_assignments[node] = backend;
+                }
+            }
+            for (int node_index : segment.output_node_indices) {
+                if (node_index < 0 || node_index >= n_nodes) {
+                    continue;
+                }
+                ggml_tensor* node = ggml_graph_node(gf, node_index);
+                if (node != nullptr) {
+                    assignment.node_assignments[node] = backend;
+                }
+            }
+        }
+
+        *assignment_out = std::move(assignment);
+        return true;
+    }
+
+    void log_graph_cut_layer_split_assignment(const char* desc,
+                                              const std::vector<ggml_backend_t>& split_backends,
+                                              const GraphCutLayerSplitAssignment& assignment) {
+        for (size_t i = 0; i < split_backends.size(); i++) {
+            if (i >= assignment.tensors_by_backend.size() ||
+                assignment.tensors_by_backend[i].empty()) {
+                continue;
+            }
+            size_t first_segment = assignment.first_segment_by_backend[i] == assignment.segment_count
+                                       ? 0
+                                       : assignment.first_segment_by_backend[i];
+            size_t last_segment  = assignment.last_segment_by_backend[i];
+            if (assignment.has_new_param_assignment) {
+                LOG_INFO("%s graph-cut layer split: %s <- segments [%zu, %zu), %zu tensors, %.1f MB",
+                         desc,
+                         layer_split_backend_device_display_name(split_backends[i]).c_str(),
+                         first_segment,
+                         last_segment,
+                         assignment.tensors_by_backend[i].size(),
+                         assignment.bytes_by_backend[i] / (1024.0 * 1024.0));
             } else {
-                auto target_it = non_block_targets.find(kv.first);
-                if (target_it != non_block_targets.end()) {
-                    target = target_it->second;
-                }
+                LOG_DEBUG("%s graph-cut layer split: %s <- segments [%zu, %zu), %zu tensors, %.1f MB",
+                          desc,
+                          layer_split_backend_device_display_name(split_backends[i]).c_str(),
+                          first_segment,
+                          last_segment,
+                          assignment.tensors_by_backend[i].size(),
+                          assignment.bytes_by_backend[i] / (1024.0 * 1024.0));
             }
-            partitions[target][kv.first] = kv.second;
         }
-
-        int range_start = 0;
-        for (size_t i = 0; i < backends.size(); i++) {
-            int range_end                = boundaries[i];
-            const char* non_block_suffix = other_bytes_by_backend[i] > 0 ? " + non-block tensors" : "";
-            LOG_INFO("%s layer split: %s <- blocks [%d, %d)%s",
-                     desc.c_str(),
-                     layer_split_backend_device_display_name(backends[i]).c_str(),
-                     range_start,
-                     range_end,
-                     non_block_suffix);
-            range_start = range_end;
-        }
-        return partitions;
     }
 
 }  // namespace sd

src/core/layer_split_partition.h

@@ -1,23 +1,43 @@
 #ifndef __SD_CORE_LAYER_SPLIT_PARTITION_H__
 #define __SD_CORE_LAYER_SPLIT_PARTITION_H__
 
-#include <map>
+#include <cstdint>
+#include <functional>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 #include "ggml-backend.h"
 #include "ggml.h"
 
+#include "core/ggml_graph_cut.h"
+
 namespace sd {
 
+    struct GraphCutLayerSplitAssignment {
+        std::vector<std::vector<ggml_tensor*>> tensors_by_backend;
+        std::vector<int64_t> bytes_by_backend;
+        std::vector<size_t> first_segment_by_backend;
+        std::vector<size_t> last_segment_by_backend;
+        std::unordered_map<const ggml_tensor*, ggml_backend_t> node_assignments;
+        size_t segment_count          = 0;
+        bool has_new_param_assignment = false;
+    };
+
     std::string layer_split_backend_device_display_name(ggml_backend_t backend);
     int layer_split_tensor_block_index(const std::string& name);
-
-    std::vector<std::map<std::string, ggml_tensor*>> partition_layer_split_tensors(
-        const std::string& desc,
-        const std::map<std::string, ggml_tensor*>& tensors,
-        const std::map<std::string, ggml_tensor*>& split_tensors,
-        const std::vector<ggml_backend_t>& backends);
+    bool partition_graph_cut_layer_split(const char* desc,
+                                         ggml_cgraph* gf,
+                                         const sd::ggml_graph_cut::Plan& plan,
+                                         const std::vector<ggml_backend_t>& split_backends,
+                                         const std::vector<size_t>& backend_vram_limits,
+                                         size_t primary_backend_vram_limit,
+                                         std::unordered_map<const ggml_tensor*, ggml_backend_t>& param_assignments,
+                                         const std::function<ggml_tensor*(ggml_tensor*)>& canonical_param_tensor,
+                                         GraphCutLayerSplitAssignment* assignment_out);
+    void log_graph_cut_layer_split_assignment(const char* desc,
+                                              const std::vector<ggml_backend_t>& split_backends,
+                                              const GraphCutLayerSplitAssignment& assignment);
 
 }  // namespace sd
 

src/model/common/block.hpp

@@ -294,7 +294,7 @@ public:
 
         auto net_0 = std::dynamic_pointer_cast<UnaryBlock>(blocks["net.0"]);
         auto net_2 = std::dynamic_pointer_cast<Linear>(blocks["net.2"]);
-        if (sd_backend_is(ctx->backend, "Vulkan")) {
+        if (sd_backend_is(ctx->backend, "Vulkan") || sd_backend_is(ctx->backend, "ROCm")) {
             net_2->set_force_prec_f32(true);
         }
 

src/model/diffusion/boogu.hpp

@@ -199,7 +199,7 @@ namespace Boogu {
             auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["linear_2"]);
             auto linear_3 = std::dynamic_pointer_cast<Linear>(blocks["linear_3"]);
 
-            if (sd_backend_is(ctx->backend, "Vulkan")) {
+            if (sd_backend_is(ctx->backend, "Vulkan") || sd_backend_is(ctx->backend, "ROCm")) {
                 linear_2->set_force_prec_f32(true);
             }
 
@@ -259,7 +259,7 @@ namespace Boogu {
             auto norm_k   = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_k"]);
             auto to_out_0 = std::dynamic_pointer_cast<Linear>(blocks["to_out.0"]);
 
-            if (sd_backend_is(ctx->backend, "Vulkan")) {
+            if (sd_backend_is(ctx->backend, "Vulkan") || sd_backend_is(ctx->backend, "ROCm")) {
                 to_out_0->set_force_prec_f32(true);
             }
 
@@ -383,7 +383,7 @@ namespace Boogu {
             auto instruct_out  = std::dynamic_pointer_cast<Linear>(blocks["processor.instruct_out"]);
             auto img_out       = std::dynamic_pointer_cast<Linear>(blocks["processor.img_out"]);
 
-            if (sd_backend_is(ctx->backend, "Vulkan")) {
+            if (sd_backend_is(ctx->backend, "Vulkan") || sd_backend_is(ctx->backend, "ROCm")) {
                 to_out_0->set_force_prec_f32(true);
             }
 

src/model/diffusion/control.hpp

@@ -5,7 +5,8 @@
 #include "model_loader.h"
 #include "model_manager.h"
 
-#define CONTROL_NET_GRAPH_SIZE 1536
+// Match main UNet's MAX_GRAPH_SIZE so SDXL ControlNet (transformer_depth={1,2,10}) fits.
+#define CONTROL_NET_GRAPH_SIZE MAX_GRAPH_SIZE
 
 /*
     =================================== ControlNet ===================================

src/model/diffusion/flux.hpp

@@ -4,6 +4,7 @@
 #include <memory>
 #include <vector>
 
+#include "core/util.h"
 #include "model/adapter/pulid.hpp"
 #include "model/common/rope.hpp"
 #include "model/diffusion/dit.hpp"
@@ -1400,18 +1401,28 @@ namespace Flux {
         std::vector<float> dct_vec;
         sd::Tensor<float> guidance_tensor;
         SDVersion version;
-        bool use_mask = false;
+        bool use_mask = true;
 
         FluxRunner(ggml_backend_t backend,
                    const String2TensorStorage& tensor_storage_map      = {},
                    const std::string prefix                            = "",
                    SDVersion version                                   = VERSION_FLUX,
-                   bool use_mask                                       = false,
-                   std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+                   std::shared_ptr<RunnerWeightManager> weight_manager = nullptr,
+                   const char* model_args                              = nullptr)
             : DiffusionModelRunner(backend, prefix, weight_manager),
               config(FluxConfig::detect_from_weights(tensor_storage_map, prefix, version)),
-              version(version),
-              use_mask(use_mask) {
+              version(version) {
+            for (const auto& [key, value] : parse_key_value_args(model_args, "model arg")) {
+                if (key == "chroma_use_dit_mask") {
+                    bool parsed = true;
+                    if (parse_strict_bool(value, parsed)) {
+                        use_mask = parsed;
+                    } else {
+                        LOG_WARN("ignoring invalid Chroma DiT model arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                }
+            }
+
             if (config.is_chroma) {
                 LOG_INFO("Using pruned modulation (Chroma)");
             }
@@ -1718,7 +1729,6 @@ namespace Flux {
                                                                             tensor_storage_map,
                                                                             "model.diffusion_model",
                                                                             VERSION_FLUX2,
-                                                                            false,
                                                                             model_manager);
 
             if (!model_manager->register_runner_params("Flux test",

src/model/diffusion/krea2.hpp

@@ -267,7 +267,7 @@ namespace Krea2 {
             auto knorm = std::dynamic_pointer_cast<KreaRMSNorm>(blocks["qknorm.knorm"]);
             auto wo    = std::dynamic_pointer_cast<Linear>(blocks["wo"]);
 
-            if (sd_backend_is(ctx->backend, "Vulkan")) {
+            if (sd_backend_is(ctx->backend, "Vulkan") || sd_backend_is(ctx->backend, "ROCm")) {
                 wo->set_force_prec_f32(true);
             }
 
@@ -421,29 +421,88 @@ namespace Krea2 {
         ggml_tensor* forward(GGMLRunnerContext* ctx,
                              ggml_tensor* x,
                              ggml_tensor* vec,
-                             ggml_tensor* pe) {
+                             ggml_tensor* pe,
+                             ggml_tensor* vec_refs = nullptr,
+                             int64_t ref_start     = -1) {
             auto mod      = std::dynamic_pointer_cast<KreaDoubleSharedModulation>(blocks["mod"]);
             auto prenorm  = std::dynamic_pointer_cast<KreaRMSNorm>(blocks["prenorm"]);
             auto postnorm = std::dynamic_pointer_cast<KreaRMSNorm>(blocks["postnorm"]);
             auto attn     = std::dynamic_pointer_cast<KreaAttention>(blocks["attn"]);
             auto mlp      = std::dynamic_pointer_cast<KreaSwiGLU>(blocks["mlp"]);
 
-            auto mods       = mod->forward(ctx, vec);
-            auto attn_input = Flux::modulate(ctx->ggml_ctx,
-                                             prenorm->forward(ctx, x),
-                                             mods[1],
-                                             mods[0],
-                                             true);
-            auto attn_out   = attn->forward(ctx, attn_input, pe);
-            x               = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, attn_out, mods[2]));
+            if (ref_start >= 0 && vec_refs) {
+                // same as normal, but since vec is different for refs and the rest, needs a lot of views and concats
+                auto mods_main = mod->forward(ctx, vec);
+                auto mods_refs = mod->forward(ctx, vec_refs);
 
-            auto mlp_input = Flux::modulate(ctx->ggml_ctx,
-                                            postnorm->forward(ctx, x),
-                                            mods[4],
-                                            mods[3],
-                                            true);
-            auto mlp_out   = mlp->forward(ctx, mlp_input);
-            x              = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, mlp_out, mods[5]));
+                int64_t D  = x->ne[0];
+                int64_t N  = x->ne[1];
+                int64_t B  = x->ne[2];
+                size_t nb1 = x->nb[1];
+                size_t nb2 = x->nb[2];
+
+                int64_t len_main = ref_start;
+                int64_t len_refs = N - ref_start;
+
+                auto pre_x = prenorm->forward(ctx, x);
+
+                auto pre_x_main = ggml_view_3d(ctx->ggml_ctx, pre_x, D, len_main, B, nb1, nb2, 0);
+                auto pre_x_refs = ggml_view_3d(ctx->ggml_ctx, pre_x, D, len_refs, B, nb1, nb2, len_main * nb1);
+
+                auto attn_in_main = Flux::modulate(ctx->ggml_ctx, pre_x_main, mods_main[1], mods_main[0], true);
+                auto attn_in_refs = Flux::modulate(ctx->ggml_ctx, pre_x_refs, mods_refs[1], mods_refs[0], true);
+
+                auto attn_input = ggml_concat(ctx->ggml_ctx, attn_in_main, attn_in_refs, 1);
+
+                auto attn_out = attn->forward(ctx, attn_input, pe);
+
+                auto attn_out_main = ggml_view_3d(ctx->ggml_ctx, attn_out, D, len_main, B, attn_out->nb[1], attn_out->nb[2], 0);
+                auto attn_out_refs = ggml_view_3d(ctx->ggml_ctx, attn_out, D, len_refs, B, attn_out->nb[1], attn_out->nb[2], len_main * attn_out->nb[1]);
+
+                auto res_main = ggml_mul(ctx->ggml_ctx, attn_out_main, mods_main[2]);
+                auto res_refs = ggml_mul(ctx->ggml_ctx, attn_out_refs, mods_refs[2]);
+
+                auto attn_res = ggml_concat(ctx->ggml_ctx, res_main, res_refs, 1);
+
+                x = ggml_add(ctx->ggml_ctx, x, attn_res);
+
+                auto post_x = postnorm->forward(ctx, x);
+
+                auto post_x_main = ggml_view_3d(ctx->ggml_ctx, post_x, D, len_main, B, post_x->nb[1], post_x->nb[2], 0);
+                auto post_x_refs = ggml_view_3d(ctx->ggml_ctx, post_x, D, len_refs, B, post_x->nb[1], post_x->nb[2], len_main * post_x->nb[1]);
+
+                auto mlp_in_main = Flux::modulate(ctx->ggml_ctx, post_x_main, mods_main[4], mods_main[3], true);
+                auto mlp_in_refs = Flux::modulate(ctx->ggml_ctx, post_x_refs, mods_refs[4], mods_refs[3], true);
+
+                auto mlp_input = ggml_concat(ctx->ggml_ctx, mlp_in_main, mlp_in_refs, 1);
+                auto mlp_out   = mlp->forward(ctx, mlp_input);
+
+                auto mlp_out_main = ggml_view_3d(ctx->ggml_ctx, mlp_out, D, len_main, B, mlp_out->nb[1], mlp_out->nb[2], 0);
+                auto mlp_out_refs = ggml_view_3d(ctx->ggml_ctx, mlp_out, D, len_refs, B, mlp_out->nb[1], mlp_out->nb[2], len_main * mlp_out->nb[1]);
+
+                auto mlp_res_main = ggml_mul(ctx->ggml_ctx, mlp_out_main, mods_main[5]);
+                auto mlp_res_refs = ggml_mul(ctx->ggml_ctx, mlp_out_refs, mods_refs[5]);
+
+                auto mlp_res = ggml_concat(ctx->ggml_ctx, mlp_res_main, mlp_res_refs, 1);
+                x            = ggml_add(ctx->ggml_ctx, x, mlp_res);
+            } else {
+                auto mods       = mod->forward(ctx, vec);
+                auto attn_input = Flux::modulate(ctx->ggml_ctx,
+                                                 prenorm->forward(ctx, x),
+                                                 mods[1],
+                                                 mods[0],
+                                                 true);
+                auto attn_out   = attn->forward(ctx, attn_input, pe);
+                x               = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, attn_out, mods[2]));
+
+                auto mlp_input = Flux::modulate(ctx->ggml_ctx,
+                                                postnorm->forward(ctx, x),
+                                                mods[4],
+                                                mods[3],
+                                                true);
+                auto mlp_out   = mlp->forward(ctx, mlp_input);
+                x              = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, mlp_out, mods[5]));
+            }
             return x;
         }
 
@@ -555,7 +614,8 @@ namespace Krea2 {
                              ggml_tensor* x,
                              ggml_tensor* timestep,
                              ggml_tensor* context,
-                             ggml_tensor* pe) {
+                             ggml_tensor* pe,
+                             std::vector<ggml_tensor*> ref_latents = {}) {
             int64_t W = x->ne[0];
             int64_t H = x->ne[1];
             int64_t N = x->ne[3];
@@ -570,6 +630,13 @@ namespace Krea2 {
 
             auto img        = DiT::pad_and_patchify(ctx, x, config.patch_size, config.patch_size, true);
             int64_t img_len = img->ne[1];
+            if (ref_latents.size() > 0) {
+                for (ggml_tensor* ref : ref_latents) {
+                    ref = DiT::pad_and_patchify(ctx, ref, config.patch_size, config.patch_size, true);
+                    img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
+                }
+            }
+            int64_t ref_len = img->ne[1] - img_len;
             img             = first->forward(ctx, img);
 
             auto t    = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep, static_cast<int>(config.timestep_dim), 10000, 1000.f);
@@ -577,19 +644,30 @@ namespace Krea2 {
             t         = ggml_reshape_3d(ctx->ggml_ctx, t, t->ne[0], 1, t->ne[1]);
             auto tvec = tproj->forward(ctx, t);
 
+            ggml_tensor* tvec_0 = nullptr;
+            if (ref_latents.size() > 0) {
+                // "index_timestep_zero" mode: use timestep = 0 for ref latents
+                auto timestep_0 = ggml_scale(ctx->ggml_ctx, timestep, 0.0f);
+                auto t_0        = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep_0, static_cast<int>(config.timestep_dim), 10000, 1000.f);
+                t_0             = tmlp->forward(ctx, t_0);
+                t_0             = ggml_reshape_3d(ctx->ggml_ctx, t_0, t_0->ne[0], 1, t_0->ne[1]);
+                tvec_0          = tproj->forward(ctx, t_0);
+            }
+
             auto txt        = txtfusion->forward(ctx, context);
             txt             = txtmlp->forward(ctx, txt);
             int64_t txt_len = txt->ne[1];
 
             auto hidden_states = ggml_concat(ctx->ggml_ctx, txt, img, 1);
+            int64_t ref_start  = hidden_states->ne[1] - ref_len;
             for (int i = 0; i < config.layers; ++i) {
                 auto block    = std::dynamic_pointer_cast<KreaSingleStreamBlock>(blocks["blocks." + std::to_string(i)]);
-                hidden_states = block->forward(ctx, hidden_states, tvec, pe);
+                hidden_states = block->forward(ctx, hidden_states, tvec, pe, tvec_0, ref_start);
                 sd::ggml_graph_cut::mark_graph_cut(hidden_states, "krea2.blocks." + std::to_string(i), "hidden_states");
             }
 
-            hidden_states = last->forward(ctx, hidden_states, t);
             hidden_states = ggml_ext_slice(ctx->ggml_ctx, hidden_states, 1, txt_len, txt_len + img_len);
+            hidden_states = last->forward(ctx, hidden_states, t);
             hidden_states = DiT::unpatchify_and_crop(ctx->ggml_ctx, hidden_states, H, W, config.patch_size, config.patch_size, true);
             return hidden_states;
         }
@@ -601,10 +679,16 @@ namespace Krea2 {
                                                       int bs,
                                                       int context_len,
                                                       float theta,
-                                                      const std::vector<int>& axes_dim) {
+                                                      const std::vector<int>& axes_dim,
+                                                      const std::vector<ggml_tensor*>& ref_latents,
+                                                      Rope::RefIndexMode ref_index_mode) {
         auto txt_ids = Rope::gen_flux_txt_ids(bs, context_len, 3, {});
         auto img_ids = Rope::gen_flux_img_ids(h, w, patch_size, bs, 3, 0, 0, 0, false);
         auto ids     = Rope::concat_ids(txt_ids, img_ids, bs);
+        if (ref_latents.size() > 0) {
+            auto refs_ids = Rope::gen_refs_ids(patch_size, bs, 3, 1, ref_latents, ref_index_mode, 1.0f, false, 0);
+            ids           = Rope::concat_ids(ids, refs_ids, bs);
+        }
         return Rope::embed_nd(ids, bs, theta, axes_dim);
     }
 
@@ -633,7 +717,9 @@ namespace Krea2 {
 
         ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
                                  const sd::Tensor<float>& timesteps_tensor,
-                                 const sd::Tensor<float>& context_tensor) {
+                                 const sd::Tensor<float>& context_tensor,
+                                 const std::vector<sd::Tensor<float>>& ref_latents_tensor = {},
+                                 Rope::RefIndexMode ref_index_mode                        = Rope::RefIndexMode::FIXED) {
             ggml_cgraph* gf        = new_graph_custom(KREA2_GRAPH_SIZE);
             ggml_tensor* x         = make_input(x_tensor);
             ggml_tensor* timesteps = make_input(timesteps_tensor);
@@ -641,19 +727,27 @@ namespace Krea2 {
             GGML_ASSERT(!context_tensor.empty());
             ggml_tensor* context = make_input(context_tensor);
 
+            std::vector<ggml_tensor*> ref_latents;
+            ref_latents.reserve(ref_latents_tensor.size());
+            for (const auto& ref_latent_tensor : ref_latents_tensor) {
+                ref_latents.push_back(make_input(ref_latent_tensor));
+            }
+
             pe_vec      = gen_krea2_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]),
                                        config.theta,
-                                       config.axes_dim);
+                                       config.axes_dim,
+                                       ref_latents,
+                                       ref_index_mode);
             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 = model.forward(&runner_ctx, x, timesteps, context, pe);
+            ggml_tensor* out = model.forward(&runner_ctx, x, timesteps, context, pe, ref_latents);
             ggml_build_forward_expand(gf, out);
             return gf;
         }
@@ -661,9 +755,11 @@ namespace Krea2 {
         sd::Tensor<float> compute(int n_threads,
                                   const sd::Tensor<float>& x,
                                   const sd::Tensor<float>& timesteps,
-                                  const sd::Tensor<float>& context) {
+                                  const sd::Tensor<float>& context,
+                                  const std::vector<sd::Tensor<float>>& ref_latents = {},
+                                  Rope::RefIndexMode ref_index_mode                 = Rope::RefIndexMode::FIXED) {
             auto get_graph = [&]() -> ggml_cgraph* {
-                return build_graph(x, timesteps, context);
+                return build_graph(x, timesteps, context, ref_latents, ref_index_mode);
             };
             return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
         }
@@ -672,10 +768,13 @@ namespace Krea2 {
                                   const DiffusionParams& diffusion_params) override {
             GGML_ASSERT(diffusion_params.x != nullptr);
             GGML_ASSERT(diffusion_params.timesteps != nullptr);
+            static const std::vector<sd::Tensor<float>> empty_ref_latents;
             return compute(n_threads,
                            *diffusion_params.x,
                            *diffusion_params.timesteps,
-                           tensor_or_empty(diffusion_params.context));
+                           tensor_or_empty(diffusion_params.context),
+                           diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
+                           diffusion_params.ref_index_mode);
         }
     };
 }  // namespace Krea2

src/model/diffusion/qwen_image.hpp

@@ -3,6 +3,7 @@
 
 #include <memory>
 
+#include "core/util.h"
 #include "model/common/block.hpp"
 #include "model/diffusion/dit.hpp"
 #include "model/diffusion/flux.hpp"
@@ -182,7 +183,7 @@ namespace Qwen {
             auto to_v     = std::dynamic_pointer_cast<Linear>(blocks["to_v"]);
             auto to_out_0 = std::dynamic_pointer_cast<Linear>(blocks["to_out.0"]);
 
-            if (sd_backend_is(ctx->backend, "Vulkan")) {
+            if (sd_backend_is(ctx->backend, "Vulkan") || sd_backend_is(ctx->backend, "ROCm")) {
                 to_out_0->set_force_prec_f32(true);
             }
 
@@ -566,12 +567,21 @@ namespace Qwen {
                         const String2TensorStorage& tensor_storage_map      = {},
                         const std::string prefix                            = "",
                         SDVersion version                                   = VERSION_QWEN_IMAGE,
-                        bool zero_cond_t                                    = false,
-                        std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+                        std::shared_ptr<RunnerWeightManager> weight_manager = nullptr,
+                        const char* model_args                              = nullptr)
             : DiffusionModelRunner(backend, prefix, weight_manager),
               config(QwenImageConfig::detect_from_weights(tensor_storage_map, prefix)),
               version(version) {
-            config.zero_cond_t = config.zero_cond_t || zero_cond_t;
+            for (const auto& [key, value] : parse_key_value_args(model_args, "model arg")) {
+                if (key == "qwen_image_zero_cond_t") {
+                    bool parsed = false;
+                    if (parse_strict_bool(value, parsed)) {
+                        config.zero_cond_t = config.zero_cond_t || parsed;
+                    } else {
+                        LOG_WARN("ignoring invalid Qwen Image model arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                }
+            }
             if (version == VERSION_QWEN_IMAGE_LAYERED) {
                 config.use_additional_t_cond = true;
             }
@@ -775,7 +785,6 @@ namespace Qwen {
                                                                                             tensor_storage_map,
                                                                                             "model.diffusion_model",
                                                                                             VERSION_QWEN_IMAGE,
-                                                                                            false,
                                                                                             model_manager);
 
             if (!model_manager->register_runner_params("Qwen image test",

src/model/diffusion/z_image.hpp

@@ -150,6 +150,8 @@ namespace ZImage {
 
             if (sd_backend_is(ctx->backend, "ROCm")) {
                 out_proj->set_scale(1.f / 16.f);
+                out_proj->set_force_prec_f32(true);
+                qkv_proj->set_force_prec_f32(true);
             }
 
             auto qkv = qkv_proj->forward(ctx, x);                                                                            // [N, n_token, (num_heads + num_kv_heads*2)*head_dim]
@@ -227,7 +229,7 @@ namespace ZImage {
             auto w2 = std::dynamic_pointer_cast<Linear>(blocks["w2"]);
             auto w3 = std::dynamic_pointer_cast<Linear>(blocks["w3"]);
 
-            if (sd_backend_is(ctx->backend, "Vulkan")) {
+            if (sd_backend_is(ctx->backend, "Vulkan") || sd_backend_is(ctx->backend, "ROCm")) {
                 w2->set_force_prec_f32(true);
             }
 

src/model/vae/vae.hpp

@@ -133,7 +133,11 @@ public:
             int64_t H              = input.shape()[1] / scale_factor;
             float tile_overlap;
             int tile_size_x, tile_size_y;
-            get_tile_sizes(tile_size_x, tile_size_y, tile_overlap, tiling_params, W, H, 1.30539f);
+            // Image VAE encode is more sensitive to tile boundary context than decode.
+            // Keep the smaller legacy factor for video VAEs, but default image encode
+            // tiles to 64 latent pixels so a 512px SD image is encoded as one tile.
+            const float encode_tile_factor = (sd_version_is_wan(version) || sd_version_is_ltxav(version)) ? 1.30539f : 2.0f;
+            get_tile_sizes(tile_size_x, tile_size_y, tile_overlap, tiling_params, W, H, encode_tile_factor);
             LOG_DEBUG("VAE Tile size: %dx%d", tile_size_x, tile_size_y);
             output = tiled_compute(input,
                                    n_threads,

src/model_io/gguf_io.cpp

@@ -1,7 +1,10 @@
 #include "gguf_io.h"
 
+#include <algorithm>
 #include <cstdint>
+#include <cstdio>
 #include <fstream>
+#include <ostream>
 #include <string>
 #include <vector>
 
@@ -121,3 +124,115 @@ bool write_gguf_file(const std::string& file_path,
     gguf_free(gguf_ctx);
     return success;
 }
+
+GGUFStreamingWriter::~GGUFStreamingWriter() {
+    close();
+}
+
+bool GGUFStreamingWriter::write_metadata(const std::string& file_path,
+                                         const std::vector<TensorWritePlan>& tensors,
+                                         std::string* error) {
+    close();
+    tensors_   = tensors;
+    file_size_ = 0;
+
+    size_t meta_mem = 1 * 1024 * 1024 + tensors.size() * ggml_tensor_overhead();
+    meta_ctx_       = ggml_init({meta_mem, nullptr, true});
+    if (meta_ctx_ == nullptr) {
+        set_error(error, "ggml_init failed for GGUF metadata");
+        return false;
+    }
+
+    gguf_ctx_ = gguf_init_empty();
+    if (gguf_ctx_ == nullptr) {
+        set_error(error, "gguf_init_empty failed");
+        close();
+        return false;
+    }
+
+    for (const TensorWritePlan& plan : tensors) {
+        ggml_tensor* tensor = ggml_new_tensor(meta_ctx_, plan.type, plan.n_dims, plan.ne);
+        if (tensor == nullptr) {
+            set_error(error, "ggml_new_tensor failed for tensor '" + plan.name + "'");
+            close();
+            return false;
+        }
+        ggml_set_name(tensor, plan.name.c_str());
+        gguf_add_tensor(gguf_ctx_, tensor);
+    }
+
+    LOG_INFO("trying to save tensors to %s", file_path.c_str());
+    FILE* file = fopen(file_path.c_str(), "wb+");
+    if (file == nullptr) {
+        set_error(error, "failed to open output file '" + file_path + "'");
+        close();
+        return false;
+    }
+
+    // ggml exposes GGUF metadata writing through FILE* only. Keep FILE usage
+    // isolated here; tensor data is written through std::fstream by the shared
+    // streaming pipeline.
+    if (!gguf_write_to_file_ptr(gguf_ctx_, file, true)) {
+        fclose(file);
+        set_error(error, "failed to write GGUF metadata to '" + file_path + "'");
+        close();
+        return false;
+    }
+    fclose(file);
+
+    const uint64_t data_start = gguf_get_meta_size(gguf_ctx_);
+    tensor_offsets_.resize(tensors.size());
+    file_size_ = data_start;
+    for (size_t i = 0; i < tensors.size(); i++) {
+        tensor_offsets_[i] = data_start + gguf_get_tensor_offset(gguf_ctx_, static_cast<int64_t>(i));
+        file_size_         = std::max(file_size_, tensor_offsets_[i] + tensors[i].nbytes());
+    }
+    return true;
+}
+
+bool GGUFStreamingWriter::write_tensor(std::ostream& output,
+                                       size_t tensor_index,
+                                       const uint8_t* data,
+                                       size_t size,
+                                       std::string* error) const {
+    if (tensor_index >= tensors_.size() || tensor_index >= tensor_offsets_.size()) {
+        set_error(error, "invalid GGUF tensor index");
+        return false;
+    }
+    const TensorWritePlan& plan = tensors_[tensor_index];
+    if (size != plan.nbytes()) {
+        set_error(error, "size mismatch while writing tensor '" + plan.name + "'");
+        return false;
+    }
+    output.seekp(static_cast<std::streamoff>(tensor_offsets_[tensor_index]), std::ios::beg);
+    if (!output) {
+        set_error(error, "failed to seek output for tensor '" + plan.name + "'");
+        return false;
+    }
+    if (size > 0) {
+        output.write(reinterpret_cast<const char*>(data), static_cast<std::streamsize>(size));
+    }
+    if (!output) {
+        set_error(error, "failed to write tensor '" + plan.name + "'");
+        return false;
+    }
+    return true;
+}
+
+uint64_t GGUFStreamingWriter::file_size() const {
+    return file_size_;
+}
+
+void GGUFStreamingWriter::close() {
+    tensor_offsets_.clear();
+    tensors_.clear();
+    file_size_ = 0;
+    if (gguf_ctx_ != nullptr) {
+        gguf_free(gguf_ctx_);
+        gguf_ctx_ = nullptr;
+    }
+    if (meta_ctx_ != nullptr) {
+        ggml_free(meta_ctx_);
+        meta_ctx_ = nullptr;
+    }
+}

src/model_io/gguf_io.h

@@ -4,8 +4,12 @@
 #include <string>
 #include <vector>
 
+#include "streaming_writer.h"
 #include "tensor_storage.h"
 
+struct ggml_context;
+struct gguf_context;
+
 bool is_gguf_file(const std::string& file_path);
 bool read_gguf_file(const std::string& file_path,
                     std::vector<TensorStorage>& tensor_storages,
@@ -14,4 +18,28 @@ bool write_gguf_file(const std::string& file_path,
                      const std::vector<TensorWriteInfo>& tensors,
                      std::string* error = nullptr);
 
+class GGUFStreamingWriter : public StreamingModelWriter {
+public:
+    GGUFStreamingWriter() = default;
+    ~GGUFStreamingWriter();
+
+    bool write_metadata(const std::string& file_path,
+                        const std::vector<TensorWritePlan>& tensors,
+                        std::string* error = nullptr) override;
+    bool write_tensor(std::ostream& output,
+                      size_t tensor_index,
+                      const uint8_t* data,
+                      size_t size,
+                      std::string* error = nullptr) const override;
+    uint64_t file_size() const override;
+    void close();
+
+private:
+    std::vector<TensorWritePlan> tensors_;
+    std::vector<uint64_t> tensor_offsets_;
+    uint64_t file_size_     = 0;
+    ggml_context* meta_ctx_ = nullptr;
+    gguf_context* gguf_ctx_ = nullptr;
+};
+
 #endif  // __SD_MODEL_IO_GGUF_IO_H__

src/model_io/safetensors_io.cpp

@@ -1,15 +1,21 @@
 #include "safetensors_io.h"
 
+#include <algorithm>
 #include <cstdint>
 #include <exception>
+#include <filesystem>
 #include <fstream>
+#include <ostream>
 #include <string>
+#include <unordered_set>
 #include <vector>
 
 #include "binary_io.h"
 #include "core/util.h"
 #include "json.hpp"
 
+namespace fs = std::filesystem;
+
 static constexpr size_t ST_HEADER_SIZE_LEN = 8;
 
 static void set_error(std::string* error, const std::string& message) {
@@ -18,6 +24,14 @@ static void set_error(std::string* error, const std::string& message) {
     }
 }
 
+static std::string resolve_index_shard_path(const std::string& index_path, const std::string& shard_path) {
+    fs::path shard_fs_path(shard_path);
+    if (shard_fs_path.is_absolute()) {
+        return shard_fs_path.lexically_normal().string();
+    }
+    return (fs::path(index_path).parent_path() / shard_fs_path).lexically_normal().string();
+}
+
 bool is_safetensors_file(const std::string& file_path) {
     std::ifstream file(file_path, std::ios::binary);
     if (!file.is_open()) {
@@ -41,7 +55,7 @@ bool is_safetensors_file(const std::string& file_path) {
     }
 
     size_t header_size_ = model_io::read_u64(header_size_buf);
-    if (header_size_ >= file_size_ || header_size_ <= 2) {
+    if (header_size_ > file_size_ - ST_HEADER_SIZE_LEN || header_size_ <= 2) {
         return false;
     }
 
@@ -112,10 +126,11 @@ bool read_safetensors_file(const std::string& file_path,
     }
 
     size_t header_size_ = model_io::read_u64(header_size_buf);
-    if (header_size_ >= file_size_) {
+    if (header_size_ > file_size_ - ST_HEADER_SIZE_LEN) {
         set_error(error, "invalid safetensor file '" + file_path + "'");
         return false;
     }
+    const size_t data_start = ST_HEADER_SIZE_LEN + header_size_;
 
     // read header
     std::vector<char> header_buf;
@@ -154,6 +169,10 @@ bool read_safetensors_file(const std::string& file_path,
 
         size_t begin = tensor_info["data_offsets"][0].get<size_t>();
         size_t end   = tensor_info["data_offsets"][1].get<size_t>();
+        if (begin > end || end > file_size_ - data_start) {
+            set_error(error, "data offsets out of bounds for tensor '" + name + "'");
+            return false;
+        }
 
         ggml_type type = safetensors_dtype_to_ggml_type(dtype);
         if (type == GGML_TYPE_COUNT) {
@@ -185,7 +204,7 @@ bool read_safetensors_file(const std::string& file_path,
             n_dims = 1;
         }
 
-        TensorStorage tensor_storage(name, type, ne, n_dims, 0, ST_HEADER_SIZE_LEN + header_size_ + begin);
+        TensorStorage tensor_storage(name, type, ne, n_dims, 0, data_start + begin);
         tensor_storage.reverse_ne();
 
         size_t tensor_data_size = end - begin;
@@ -223,6 +242,52 @@ bool read_safetensors_file(const std::string& file_path,
     return true;
 }
 
+bool read_safetensors_index_file(const std::string& file_path,
+                                 std::vector<std::string>& shard_paths,
+                                 std::string* error) {
+    shard_paths.clear();
+
+    std::ifstream file(file_path);
+    if (!file.is_open()) {
+        set_error(error, "failed to open '" + file_path + "'");
+        return false;
+    }
+
+    nlohmann::json index;
+    try {
+        index = nlohmann::json::parse(file);
+    } catch (const std::exception&) {
+        set_error(error, "parsing safetensors index failed: '" + file_path + "'");
+        return false;
+    }
+
+    if (!index.is_object() || !index.contains("weight_map") || !index["weight_map"].is_object()) {
+        set_error(error, "invalid safetensors index '" + file_path + "'");
+        return false;
+    }
+
+    std::unordered_set<std::string> seen_shard_paths;
+    for (const auto& item : index["weight_map"].items()) {
+        if (!item.value().is_string()) {
+            set_error(error, "invalid shard path for tensor '" + item.key() + "'");
+            return false;
+        }
+
+        std::string shard_path = resolve_index_shard_path(file_path,
+                                                          item.value().get<std::string>());
+        if (seen_shard_paths.insert(shard_path).second) {
+            shard_paths.push_back(std::move(shard_path));
+        }
+    }
+
+    if (shard_paths.empty()) {
+        set_error(error, "safetensors index has no tensors: '" + file_path + "'");
+        return false;
+    }
+
+    return true;
+}
+
 static bool ggml_type_to_safetensors_dtype(ggml_type type, std::string* dtype) {
     switch (type) {
         case GGML_TYPE_F16:
@@ -314,3 +379,102 @@ bool write_safetensors_file(const std::string& file_path,
 
     return true;
 }
+
+bool SafetensorsStreamingWriter::write_metadata(const std::string& file_path,
+                                                const std::vector<TensorWritePlan>& tensors,
+                                                std::string* error) {
+    file_path_ = file_path;
+    tensors_   = tensors;
+    tensor_offsets_.clear();
+    data_start_ = 0;
+    file_size_  = 0;
+
+    nlohmann::ordered_json header = nlohmann::ordered_json::object();
+    uint64_t data_offset          = 0;
+    tensor_offsets_.resize(tensors.size());
+    for (size_t i = 0; i < tensors.size(); i++) {
+        const TensorWritePlan& plan = tensors[i];
+        std::string dtype;
+        if (!ggml_type_to_safetensors_dtype(plan.type, &dtype)) {
+            set_error(error,
+                      "unsupported safetensors dtype '" + std::string(ggml_type_name(plan.type)) +
+                          "' for tensor '" + plan.name + "'");
+            return false;
+        }
+
+        nlohmann::ordered_json json_tensor_info = nlohmann::ordered_json::object();
+        json_tensor_info["dtype"]               = dtype;
+
+        nlohmann::ordered_json shape = nlohmann::ordered_json::array();
+        for (int j = 0; j < plan.n_dims; ++j) {
+            shape.push_back(plan.ne[plan.n_dims - 1 - j]);
+        }
+        json_tensor_info["shape"] = shape;
+
+        nlohmann::ordered_json data_offsets = nlohmann::ordered_json::array();
+        data_offsets.push_back(data_offset);
+        data_offsets.push_back(data_offset + plan.nbytes());
+        json_tensor_info["data_offsets"] = data_offsets;
+
+        header[plan.name]  = json_tensor_info;
+        tensor_offsets_[i] = data_offset;
+        data_offset += plan.nbytes();
+    }
+
+    const std::string header_str = header.dump();
+    data_start_                  = ST_HEADER_SIZE_LEN + header_str.size();
+
+    LOG_INFO("trying to save tensors to %s", file_path.c_str());
+    std::ofstream file(file_path, std::ios::binary | std::ios::trunc);
+    if (!file.is_open()) {
+        set_error(error, "failed to open '" + file_path + "' for writing");
+        return false;
+    }
+
+    uint8_t header_size[ST_HEADER_SIZE_LEN];
+    for (int i = 0; i < static_cast<int>(ST_HEADER_SIZE_LEN); ++i) {
+        header_size[i] = static_cast<uint8_t>((header_str.size() >> (8 * i)) & 0xFF);
+    }
+    file.write(reinterpret_cast<const char*>(header_size), sizeof(header_size));
+    file.write(header_str.data(), static_cast<std::streamsize>(header_str.size()));
+    if (!file) {
+        set_error(error, "failed to write safetensors header to '" + file_path + "'");
+        return false;
+    }
+
+    file_size_ = data_start_ + data_offset;
+    return true;
+}
+
+bool SafetensorsStreamingWriter::write_tensor(std::ostream& output,
+                                              size_t tensor_index,
+                                              const uint8_t* data,
+                                              size_t size,
+                                              std::string* error) const {
+    if (tensor_index >= tensors_.size() || tensor_index >= tensor_offsets_.size()) {
+        set_error(error, "invalid safetensors tensor index");
+        return false;
+    }
+    const TensorWritePlan& plan = tensors_[tensor_index];
+    if (size != plan.nbytes()) {
+        set_error(error, "size mismatch while writing tensor '" + plan.name + "'");
+        return false;
+    }
+    output.seekp(static_cast<std::streamoff>(data_start_ + tensor_offsets_[tensor_index]), std::ios::beg);
+    if (!output) {
+        set_error(error, "failed to seek output for tensor '" + plan.name + "'");
+        return false;
+    }
+    if (size > 0) {
+        output.write(reinterpret_cast<const char*>(data), static_cast<std::streamsize>(size));
+    }
+    if (!output) {
+        set_error(error, "failed to write tensor '" + plan.name + "' to '" + file_path_ + "'");
+        return false;
+    }
+    return true;
+}
+
+uint64_t SafetensorsStreamingWriter::file_size() const {
+    return file_size_;
+}

src/model_io/safetensors_io.h

@@ -4,14 +4,40 @@
 #include <string>
 #include <vector>
 
+#include "streaming_writer.h"
 #include "tensor_storage.h"
 
 bool is_safetensors_file(const std::string& file_path);
 bool read_safetensors_file(const std::string& file_path,
                            std::vector<TensorStorage>& tensor_storages,
                            std::string* error = nullptr);
+bool read_safetensors_index_file(const std::string& file_path,
+                                 std::vector<std::string>& shard_paths,
+                                 std::string* error = nullptr);
 bool write_safetensors_file(const std::string& file_path,
                             const std::vector<TensorWriteInfo>& tensors,
                             std::string* error = nullptr);
 
+class SafetensorsStreamingWriter : public StreamingModelWriter {
+public:
+    SafetensorsStreamingWriter() = default;
+
+    bool write_metadata(const std::string& file_path,
+                        const std::vector<TensorWritePlan>& tensors,
+                        std::string* error = nullptr) override;
+    bool write_tensor(std::ostream& output,
+                      size_t tensor_index,
+                      const uint8_t* data,
+                      size_t size,
+                      std::string* error = nullptr) const override;
+    uint64_t file_size() const override;
+
+private:
+    std::string file_path_;
+    std::vector<TensorWritePlan> tensors_;
+    std::vector<uint64_t> tensor_offsets_;
+    uint64_t data_start_ = 0;
+    uint64_t file_size_  = 0;
+};
+
 #endif  // __SD_MODEL_IO_SAFETENSORS_IO_H__

src/model_io/streaming_writer.h

@@ -0,0 +1,26 @@
+#ifndef __SD_MODEL_IO_STREAMING_WRITER_H__
+#define __SD_MODEL_IO_STREAMING_WRITER_H__
+
+#include <cstdint>
+#include <iosfwd>
+#include <string>
+#include <vector>
+
+#include "tensor_storage.h"
+
+class StreamingModelWriter {
+public:
+    virtual ~StreamingModelWriter() = default;
+
+    virtual bool write_metadata(const std::string& file_path,
+                                const std::vector<TensorWritePlan>& tensors,
+                                std::string* error = nullptr)     = 0;
+    virtual bool write_tensor(std::ostream& output,
+                              size_t tensor_index,
+                              const uint8_t* data,
+                              size_t size,
+                              std::string* error = nullptr) const = 0;
+    virtual uint64_t file_size() const                            = 0;
+};
+
+#endif  // __SD_MODEL_IO_STREAMING_WRITER_H__

src/model_io/tensor_storage.h

@@ -127,6 +127,25 @@ struct TensorWriteInfo {
     ggml_tensor* tensor     = nullptr;
 };
 
+struct TensorWritePlan {
+    std::string name;
+    ggml_type type          = GGML_TYPE_F32;
+    int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
+    int n_dims              = 0;
+
+    int64_t nelements() const {
+        int64_t n = 1;
+        for (int i = 0; i < SD_MAX_DIMS; i++) {
+            n *= ne[i];
+        }
+        return n;
+    }
+
+    uint64_t nbytes() const {
+        return nelements() * ggml_type_size(type) / ggml_blck_size(type);
+    }
+};
+
 typedef std::function<bool(const TensorStorage&, ggml_tensor**)> on_new_tensor_cb_t;
 
 #endif  // __SD_TENSOR_STORAGE_H__

src/model_loader.cpp

@@ -235,6 +235,9 @@ bool ModelLoader::init_from_file(const std::string& file_path, const std::string
     } else if (is_gguf_file(file_path)) {
         LOG_INFO("load %s using gguf format", file_path.c_str());
         return init_from_gguf_file(file_path, prefix);
+    } else if (ends_with(file_path, ".json")) {
+        LOG_INFO("load %s using safetensors index format", file_path.c_str());
+        return init_from_safetensors_index_file(file_path, prefix);
     } else if (is_safetensors_file(file_path)) {
         LOG_INFO("load %s using safetensors format", file_path.c_str());
         return init_from_safetensors_file(file_path, prefix);
@@ -339,6 +342,25 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
     return true;
 }
 
+bool ModelLoader::init_from_safetensors_index_file(const std::string& file_path, const std::string& prefix) {
+    LOG_DEBUG("init from safetensors index '%s', prefix = '%s'", file_path.c_str(), prefix.c_str());
+
+    std::vector<std::string> shard_paths;
+    std::string error;
+    if (!read_safetensors_index_file(file_path, shard_paths, &error)) {
+        LOG_ERROR("%s", error.c_str());
+        return false;
+    }
+
+    for (const std::string& shard_path : shard_paths) {
+        if (!init_from_file(shard_path, prefix)) {
+            return false;
+        }
+    }
+
+    return true;
+}
+
 /*================================================= TorchLegacyModelLoader ==================================================*/
 
 bool ModelLoader::init_from_torch_legacy_file(const std::string& file_path, const std::string& prefix) {
@@ -943,7 +965,8 @@ std::vector<MmapTensorStore> ModelLoader::mmap_tensors(std::map<std::string, ggm
 
 bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
                                bool enable_mmap,
-                               const std::set<std::string>* target_tensor_names) {
+                               const std::set<std::string>* target_tensor_names,
+                               bool log_progress) {
     process_model_files(enable_mmap, false);
 
     std::atomic<int64_t> read_time_ms(0);
@@ -1212,7 +1235,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
             }
             size_t curr_num       = total_tensors_processed + current_idx;
             float elapsed_seconds = (ggml_time_ms() - t_start) / 1000.0f;
-            if (total_tensors_to_process > 0) {
+            if (log_progress && total_tensors_to_process > 0) {
                 pretty_bytes_progress(static_cast<int>(curr_num),
                                       static_cast<int>(total_tensors_to_process),
                                       bytes_processed.load(),
@@ -1230,27 +1253,81 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
             break;
         }
         total_tensors_processed += tensors_to_process.size();
-        if (total_tensors_to_process > 0) {
+        if (log_progress && total_tensors_to_process > 0) {
             pretty_bytes_progress(static_cast<int>(total_tensors_processed),
                                   static_cast<int>(total_tensors_to_process),
                                   bytes_processed.load(),
                                   (ggml_time_ms() - t_start) / 1000.0f);
         }
-        if (total_tensors_processed < total_tensors_to_process && total_tensors_to_process > 0) {
+        if (log_progress && total_tensors_processed < total_tensors_to_process && total_tensors_to_process > 0) {
             printf("\n");
         }
     }
 
     int64_t end_time = ggml_time_ms();
-    LOG_INFO("loading tensors completed, taking %.2fs (read: %.2fs, memcpy: %.2fs, convert: %.2fs, copy_to_backend: %.2fs)",
-             (end_time - start_time) / 1000.f,
-             (read_time_ms.load() / (float)last_n_threads) / 1000.f,
-             (memcpy_time_ms.load() / (float)last_n_threads) / 1000.f,
-             (convert_time_ms.load() / (float)last_n_threads) / 1000.f,
-             (copy_to_backend_time_ms.load() / (float)last_n_threads) / 1000.f);
+    if (log_progress) {
+        LOG_INFO("loading tensors completed, taking %.2fs (read: %.2fs, memcpy: %.2fs, convert: %.2fs, copy_to_backend: %.2fs)",
+                 (end_time - start_time) / 1000.f,
+                 (read_time_ms.load() / (float)last_n_threads) / 1000.f,
+                 (memcpy_time_ms.load() / (float)last_n_threads) / 1000.f,
+                 (convert_time_ms.load() / (float)last_n_threads) / 1000.f,
+                 (copy_to_backend_time_ms.load() / (float)last_n_threads) / 1000.f);
+    }
     return success;
 }
 
+bool ModelLoader::load_tensor(const TensorStorage& tensor_storage, ggml_tensor* dst_tensor) {
+    if (dst_tensor == nullptr || dst_tensor->data == nullptr) {
+        LOG_ERROR("load tensor failed: null destination for '%s'", tensor_storage.name.c_str());
+        return false;
+    }
+
+    bool loaded = false;
+    std::set<std::string> target_tensor_names{tensor_storage.name};
+    auto on_new_tensor_cb = [&](const TensorStorage& current_tensor_storage, ggml_tensor** out_tensor) -> bool {
+        *out_tensor = nullptr;
+        if (current_tensor_storage.name != tensor_storage.name) {
+            return true;
+        }
+
+        if (current_tensor_storage.file_index != tensor_storage.file_index ||
+            current_tensor_storage.offset != tensor_storage.offset ||
+            current_tensor_storage.index_in_zip != tensor_storage.index_in_zip) {
+            LOG_ERROR("load tensor failed: storage mismatch for '%s'", tensor_storage.name.c_str());
+            return false;
+        }
+
+        if (current_tensor_storage.n_dims != tensor_storage.n_dims ||
+            current_tensor_storage.nelements() != tensor_storage.nelements()) {
+            LOG_ERROR("load tensor failed: metadata changed for '%s'", tensor_storage.name.c_str());
+            return false;
+        }
+
+        for (int i = 0; i < current_tensor_storage.n_dims; i++) {
+            if (current_tensor_storage.ne[i] != dst_tensor->ne[i]) {
+                LOG_ERROR("load tensor failed: shape mismatch for '%s'", tensor_storage.name.c_str());
+                return false;
+            }
+        }
+
+        *out_tensor = dst_tensor;
+        loaded      = true;
+        return true;
+    };
+
+    if (!load_tensors(on_new_tensor_cb, false, &target_tensor_names, false)) {
+        LOG_ERROR("load tensor failed: '%s'", tensor_storage.name.c_str());
+        return false;
+    }
+
+    if (!loaded) {
+        LOG_ERROR("load tensor failed: tensor '%s' not found", tensor_storage.name.c_str());
+        return false;
+    }
+
+    return true;
+}
+
 bool ModelLoader::load_float_tensor(const std::string& name,
                                     std::vector<float>& data,
                                     int n_threads,

src/model_loader.h

@@ -43,6 +43,7 @@ protected:
 
     bool init_from_gguf_file(const std::string& file_path, const std::string& prefix = "");
     bool init_from_safetensors_file(const std::string& file_path, const std::string& prefix = "");
+    bool init_from_safetensors_index_file(const std::string& file_path, const std::string& prefix = "");
     bool init_from_torch_zip_file(const std::string& file_path, const std::string& prefix = "");
     bool init_from_torch_legacy_file(const std::string& file_path, const std::string& prefix = "");
     bool init_from_diffusers_file(const std::string& file_path, const std::string& prefix = "");
@@ -70,7 +71,8 @@ public:
                                               bool writable                        = true);
     bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
                       bool use_mmap                                    = false,
-                      const std::set<std::string>* target_tensor_names = nullptr);
+                      const std::set<std::string>* target_tensor_names = nullptr,
+                      bool log_progress                                = true);
     bool load_tensors(std::map<std::string, ggml_tensor*>& tensors,
                       std::set<std::string> ignore_tensors = {},
                       bool use_mmap                        = false);
@@ -78,6 +80,7 @@ public:
                            std::vector<float>& data,
                            int n_threads = 0,
                            bool use_mmap = false);
+    bool load_tensor(const TensorStorage& tensor_storage, ggml_tensor* dst_tensor);
 
     std::vector<std::string> get_tensor_names() const {
         std::vector<std::string> names;

src/model_manager.cpp

@@ -134,7 +134,8 @@ bool ModelManager::register_param_tensors(const std::string& desc,
                                           ggml_backend_t compute_backend,
                                           ggml_backend_t params_backend,
                                           size_t* registered_tensor_size,
-                                          bool allow_split_buffer) {
+                                          bool allow_split_buffer,
+                                          bool params_follow_compute_backend) {
     if (desc.empty()) {
         LOG_ERROR("model manager tensor desc is empty");
         return false;
@@ -158,14 +159,15 @@ bool ModelManager::register_param_tensors(const std::string& desc,
         }
         ggml_set_name(tensor, name.c_str());
 
-        auto state                = std::make_unique<TensorState>();
-        state->name               = name;
-        state->tensor             = tensor;
-        state->desc               = desc;
-        state->residency_mode     = residency_mode;
-        state->compute_backend    = compute_backend;
-        state->params_backend     = params_backend;
-        state->allow_split_buffer = allow_split_buffer;
+        auto state                           = std::make_unique<TensorState>();
+        state->name                          = name;
+        state->tensor                        = tensor;
+        state->desc                          = desc;
+        state->residency_mode                = residency_mode;
+        state->compute_backend               = compute_backend;
+        state->params_backend                = params_backend;
+        state->allow_split_buffer            = allow_split_buffer;
+        state->params_follow_compute_backend = params_follow_compute_backend;
         new_states.push_back(std::move(state));
     }
 
@@ -177,6 +179,102 @@ bool ModelManager::register_param_tensors(const std::string& desc,
     return true;
 }
 
+bool ModelManager::unregister_param_tensors(const std::string& desc, size_t* registered_tensor_size) {
+    if (desc.empty()) {
+        return true;
+    }
+
+    std::unordered_set<TensorState*> target_states;
+    size_t released_size = 0;
+    for (auto& state : tensor_states_) {
+        if (state == nullptr || state->desc != desc) {
+            continue;
+        }
+        if (state->active_prepare_count > 0) {
+            LOG_ERROR("model manager cannot unregister active %s tensor '%s'",
+                      desc.c_str(),
+                      state->name.c_str());
+            return false;
+        }
+        target_states.insert(state.get());
+        if (state->tensor != nullptr) {
+            released_size += ggml_nbytes(state->tensor);
+        }
+    }
+
+    if (target_states.empty()) {
+        return true;
+    }
+
+    release_compute_staging_blocks(false);
+
+    std::vector<ParamsStorageBlock*> storage_blocks_to_release;
+    std::unordered_set<TensorState*> affected_storage_states;
+    for (const auto& block : params_storage_blocks_) {
+        if (block == nullptr) {
+            continue;
+        }
+        bool has_target_state = false;
+        for (TensorState* state : block->states) {
+            if (state != nullptr && target_states.count(state) > 0) {
+                has_target_state = true;
+                break;
+            }
+        }
+        if (!has_target_state) {
+            continue;
+        }
+        storage_blocks_to_release.push_back(block.get());
+        for (TensorState* state : block->states) {
+            if (state != nullptr) {
+                affected_storage_states.insert(state);
+            }
+        }
+    }
+
+    for (TensorState* state : affected_storage_states) {
+        if (state == nullptr) {
+            continue;
+        }
+        if (state->active_prepare_count > 0 || state->staged_to_compute_backend) {
+            LOG_ERROR("model manager cannot unregister %s while tensor '%s' is active",
+                      desc.c_str(),
+                      state->name.c_str());
+            return false;
+        }
+    }
+
+    for (ParamsStorageBlock* block : storage_blocks_to_release) {
+        if (block != nullptr) {
+            free_params_storage_block(*block);
+            erase_params_storage_block(block);
+        }
+    }
+
+    for (auto it = tensor_states_by_name_.begin(); it != tensor_states_by_name_.end();) {
+        if (target_states.count(it->second) > 0) {
+            it = tensor_states_by_name_.erase(it);
+        } else {
+            ++it;
+        }
+    }
+    tensor_states_.erase(std::remove_if(tensor_states_.begin(),
+                                        tensor_states_.end(),
+                                        [&](const std::unique_ptr<TensorState>& s) {
+                                            return s == nullptr || target_states.count(s.get()) > 0;
+                                        }),
+                         tensor_states_.end());
+
+    if (registered_tensor_size != nullptr) {
+        if (released_size > *registered_tensor_size) {
+            *registered_tensor_size = 0;
+        } else {
+            *registered_tensor_size -= released_size;
+        }
+    }
+    return true;
+}
+
 bool ModelManager::load_all_params_eagerly() {
     std::vector<TensorState*> all_states;
     all_states.reserve(tensor_states_.size());
@@ -919,6 +1017,54 @@ bool ModelManager::resolve_required_tensor_states(const std::vector<ggml_tensor*
     return true;
 }
 
+bool ModelManager::assign_compute_backend(const std::vector<ggml_tensor*>& tensors,
+                                          ggml_backend_t compute_backend) {
+    if (tensors.empty()) {
+        return true;
+    }
+    if (compute_backend == nullptr) {
+        LOG_ERROR("model manager cannot assign tensors to a null compute backend");
+        return false;
+    }
+
+    std::vector<TensorState*> required_states;
+    if (!resolve_required_tensor_states(tensors, required_states)) {
+        return false;
+    }
+
+    for (TensorState* state : required_states) {
+        if (state == nullptr || state->tensor == nullptr) {
+            continue;
+        }
+
+        const bool params_follow_compute = state->params_follow_compute_backend ||
+                                           state->residency_mode == ResidencyMode::Disk;
+        const bool compute_changes = state->compute_backend != compute_backend;
+        const bool params_changes  = params_follow_compute && state->params_backend != compute_backend;
+        if (!compute_changes && !params_changes) {
+            continue;
+        }
+
+        if (state->active_prepare_count > 0 || state->staged_to_compute_backend) {
+            LOG_ERROR("model manager cannot move active tensor '%s' to another compute backend",
+                      state->name.c_str());
+            return false;
+        }
+        if (params_changes && state->loaded_to_params_backend) {
+            LOG_ERROR("model manager cannot move loaded tensor '%s' to another params backend",
+                      state->name.c_str());
+            return false;
+        }
+
+        state->compute_backend = compute_backend;
+        if (params_follow_compute) {
+            state->params_backend = compute_backend;
+        }
+    }
+
+    return true;
+}
+
 bool ModelManager::prepare_params(const std::vector<ggml_tensor*>& tensors) {
     if (tensors.empty()) {
         return true;

src/model_manager.h

@@ -33,11 +33,12 @@ private:
         ggml_tensor* tensor = nullptr;
         std::string desc;
 
-        ResidencyMode residency_mode   = ResidencyMode::ParamBackend;
-        ggml_backend_t compute_backend = nullptr;
-        ggml_backend_t params_backend  = nullptr;
-        bool allow_split_buffer        = false;
-        bool metadata_validated        = false;
+        ResidencyMode residency_mode       = ResidencyMode::ParamBackend;
+        ggml_backend_t compute_backend     = nullptr;
+        ggml_backend_t params_backend      = nullptr;
+        bool allow_split_buffer            = false;
+        bool params_follow_compute_backend = false;
+        bool metadata_validated            = false;
 
         int active_prepare_count = 0;
 
@@ -129,8 +130,12 @@ public:
                                 ResidencyMode residency_mode,
                                 ggml_backend_t compute_backend,
                                 ggml_backend_t params_backend,
-                                size_t* registered_tensor_size = nullptr,
-                                bool allow_split_buffer        = false);
+                                size_t* registered_tensor_size     = nullptr,
+                                bool allow_split_buffer            = false,
+                                bool params_follow_compute_backend = false);
+
+    bool unregister_param_tensors(const std::string& desc,
+                                  size_t* registered_tensor_size = nullptr);
 
     template <typename Runner>
     bool register_runner_params(const std::string& desc,
@@ -170,6 +175,8 @@ public:
     bool validate_registered_tensors();
     bool load_all_params_eagerly();
 
+    bool assign_compute_backend(const std::vector<ggml_tensor*>& tensors,
+                                ggml_backend_t compute_backend) override;
     bool prepare_params(const std::vector<ggml_tensor*>& tensors) override;
     void release_compute_backend_params(const std::vector<ggml_tensor*>& tensors) override;
     void release_params_backend_params(const std::vector<ggml_tensor*>& tensors) override;

src/name_conversion.cpp

@@ -736,6 +736,62 @@ std::string convert_diffusers_dit_to_original_krea2(std::string name) {
     return name;
 }
 
+// Convert a diffusers-format ControlNet tensor name to the original (LDM/lllyasviel) layout
+// declared by ControlNetBlock. Reuses the UNet down/mid conversion for the shared encoder
+// (down_blocks, mid_block, time_embedding, add_embedding, conv_in) and adds the ControlNet-only
+// mappings: input_hint_block, zero_convs, middle_block_out.
+std::string convert_diffusers_controlnet_to_original_sdxl(std::string name) {
+    name = convert_diffusers_unet_to_original_sdxl(std::move(name));
+
+    static const std::vector<std::pair<std::string, std::string>> prefix_map = {
+        {"controlnet_cond_embedding.conv_in.", "input_hint_block.0."},
+        {"controlnet_cond_embedding.blocks.0.", "input_hint_block.2."},
+        {"controlnet_cond_embedding.blocks.1.", "input_hint_block.4."},
+        {"controlnet_cond_embedding.blocks.2.", "input_hint_block.6."},
+        {"controlnet_cond_embedding.blocks.3.", "input_hint_block.8."},
+        {"controlnet_cond_embedding.blocks.4.", "input_hint_block.10."},
+        {"controlnet_cond_embedding.blocks.5.", "input_hint_block.12."},
+        {"controlnet_cond_embedding.conv_out.", "input_hint_block.14."},
+        {"controlnet_mid_block.", "middle_block_out.0."},
+    };
+    for (const auto& p : prefix_map) {
+        if (starts_with(name, p.first)) {
+            return p.second + name.substr(p.first.size());
+        }
+    }
+
+    static const std::string controlnet_down_prefix = "controlnet_down_blocks.";
+    if (starts_with(name, controlnet_down_prefix)) {
+        size_t rest_start = controlnet_down_prefix.size();
+        size_t dot        = name.find('.', rest_start);
+        if (dot != std::string::npos) {
+            std::string idx = name.substr(rest_start, dot - rest_start);
+            return "zero_convs." + idx + ".0" + name.substr(dot);
+        }
+    }
+
+    return name;
+}
+
+static bool is_diffusers_controlnet_name(const std::string& name) {
+    static const std::vector<std::string> heads = {
+        "controlnet_cond_embedding.",
+        "controlnet_down_blocks.",
+        "controlnet_mid_block.",
+        "down_blocks.",
+        "mid_block.",
+        "time_embedding.",
+        "add_embedding.",
+        "conv_in.",
+    };
+    for (const auto& h : heads) {
+        if (starts_with(name, h)) {
+            return true;
+        }
+    }
+    return false;
+}
+
 std::string convert_diffusion_model_name(std::string name, std::string prefix, SDVersion version) {
     if (sd_version_is_sd1(version) || sd_version_is_sd2(version)) {
         name = convert_diffusers_unet_to_original_sd1(name);
@@ -1338,6 +1394,9 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
                 name = name.substr(pos + 1);
             }
         }
+        if (sd_version_is_sdxl(version) && is_diffusers_controlnet_name(name)) {
+            name = convert_diffusers_controlnet_to_original_sdxl(name);
+        }
     }
 
     if (is_lora) {

src/runtime/denoiser.hpp

@@ -4,7 +4,10 @@
 #include <algorithm>
 #include <cctype>
 #include <cmath>
+#include <cstring>
 #include <functional>
+#include <limits>
+#include <map>
 #include <string>
 #include <utility>
 
@@ -1013,6 +1016,7 @@ struct Denoiser {
                                             const sd::Tensor<float>& latent)         = 0;
     virtual sd::Tensor<float> inverse_noise_scaling(float sigma,
                                                     const sd::Tensor<float>& latent) = 0;
+    virtual float noise_level_to_sigma(float noise_level)                            = 0;
 
     virtual std::vector<float> get_sigmas(uint32_t n, int image_seq_len, scheduler_t scheduler_type, SDVersion version, const char* extra_sample_args = nullptr) {
         auto bound_t_to_sigma = std::bind(&Denoiser::t_to_sigma, this, std::placeholders::_1);
@@ -1160,6 +1164,10 @@ struct CompVisDenoiser : public Denoiser {
         SD_UNUSED(sigma);
         return latent;
     }
+
+    float noise_level_to_sigma(float noise_level) {
+        return noise_level / (1.0f - noise_level);
+    }
 };
 
 struct CompVisVDenoiser : public CompVisDenoiser {
@@ -1247,6 +1255,10 @@ struct DiscreteFlowDenoiser : public Denoiser {
     sd::Tensor<float> inverse_noise_scaling(float sigma, const sd::Tensor<float>& latent) override {
         return latent * (1.0f / (1.0f - sigma));
     }
+
+    float noise_level_to_sigma(float noise_level) {
+        return noise_level;
+    }
 };
 
 struct FluxFlowDenoiser : public DiscreteFlowDenoiser {
@@ -1384,6 +1396,11 @@ struct MiniT2IFlowDenoiser : public Denoiser {
         return latent;
     }
 
+    float noise_level_to_sigma(float noise_level) {
+        SD_UNUSED(noise_level);
+        return 1.0f;
+    }
+
     std::vector<float> get_sigmas(uint32_t n, int image_seq_len, scheduler_t scheduler_type, SDVersion version, const char* extra_sample_args = nullptr) override {
         SD_UNUSED(image_seq_len);
         SD_UNUSED(scheduler_type);
@@ -1860,6 +1877,158 @@ static sd::Tensor<float> sample_dpmpp_2m_sde(denoise_cb_t model,
     return x;
 }
 
+// Seeded Brownian tree providing deterministic, step-count-stable Gaussian
+// increments for stochastic samplers. Constructed once per generation; each
+// call returns unit-variance noise for interval [sigma_a, sigma_b].
+// Reference: torchsde BrownianTree; k-diffusion BatchedBrownianTree.
+class BrownianTreeNoiseSampler {
+public:
+    BrownianTreeNoiseSampler(const sd::Tensor<float>& x_template,
+                             double sigma_min,
+                             double sigma_max,
+                             uint64_t seed)
+        : t_min_(sigma_min),
+          t_max_(sigma_max),
+          shape_(x_template.shape()),
+          root_seed_(mix64(seed, 0x9E3779B97F4A7C15ULL)) {
+        auto rng = std::make_shared<STDDefaultRNG>();
+        rng->manual_seed(mix64(seed, 0xBF58476D1CE4E5B9ULL));
+        w_at_tmax_ = sd::Tensor<float>::randn(shape_, rng) * std::sqrt(static_cast<float>(t_max_ - t_min_));
+    }
+
+    sd::Tensor<float> operator()(double sigma_a, double sigma_b) {
+        double a   = clamp(std::min(sigma_a, sigma_b));
+        double b   = clamp(std::max(sigma_a, sigma_b));
+        auto dW    = w(b) - w(a);
+        float span = static_cast<float>(std::max(std::abs(sigma_b - sigma_a), 1e-12));
+        return dW * (1.0f / std::sqrt(span));
+    }
+
+private:
+    static constexpr int kMaxDepth = 24;
+
+    static uint64_t mix64(uint64_t v, uint64_t salt) {
+        uint64_t z = v + salt;
+        z          = (z ^ (z >> 30)) * 0xBF58476D1CE4E5B9ULL;
+        z          = (z ^ (z >> 27)) * 0x94D049BB133111EBULL;
+        return z ^ (z >> 31);
+    }
+
+    double clamp(double t) const {
+        return std::min(std::max(t, t_min_), t_max_);
+    }
+
+    sd::Tensor<float> w(double t) {
+        auto it = cache_.find(t);
+        if (it != cache_.end()) {
+            return it->second;
+        }
+        sd::Tensor<float> zero = sd::Tensor<float>::zeros(shape_);
+        sd::Tensor<float> out  = bridge(t_min_, t_max_, zero, w_at_tmax_, t, root_seed_, kMaxDepth);
+        cache_.emplace(t, out);
+        return out;
+    }
+
+    sd::Tensor<float> bridge(double a,
+                             double c,
+                             const sd::Tensor<float>& w_a,
+                             const sd::Tensor<float>& w_c,
+                             double t,
+                             uint64_t node_seed,
+                             int depth) {
+        if (depth <= 0 || c - a < 1e-9) {
+            float alpha = (c > a) ? static_cast<float>((t - a) / (c - a)) : 0.5f;
+            return (1.0f - alpha) * w_a + alpha * w_c;
+        }
+        double m       = 0.5 * (a + c);
+        double std_dev = std::sqrt((c - m) * (m - a) / (c - a));
+        auto rng       = std::make_shared<STDDefaultRNG>();
+        rng->manual_seed(node_seed);
+        auto z   = sd::Tensor<float>::randn(shape_, rng);
+        auto w_m = 0.5f * (w_a + w_c) + static_cast<float>(std_dev) * z;
+        if (t == m) {
+            return w_m;
+        }
+        if (t < m) {
+            return bridge(a, m, w_a, w_m, t, mix64(node_seed, 1), depth - 1);
+        }
+        return bridge(m, c, w_m, w_c, t, mix64(node_seed, 2), depth - 1);
+    }
+
+    double t_min_;
+    double t_max_;
+    std::vector<int64_t> shape_;
+    uint64_t root_seed_;
+    sd::Tensor<float> w_at_tmax_;
+    std::map<double, sd::Tensor<float>> cache_;
+};
+
+// DPM-Solver++(2M) SDE, midpoint variant, with step-count-stable Brownian-tree
+// noise. Same trajectory shape at any step count for a given seed. Aliased in
+// k-diffusion / ComfyUI as sample_dpmpp_2m_sde_gpu.
+// Ref: Lu et al. arXiv:2211.01095; torchsde BrownianTree.
+static sd::Tensor<float> sample_dpmpp_2m_sde_bt(denoise_cb_t model,
+                                                sd::Tensor<float> x,
+                                                const std::vector<float>& sigmas,
+                                                std::shared_ptr<RNG> rng,
+                                                float eta) {
+    double sigma_max = 0.0;
+    double sigma_min = std::numeric_limits<double>::infinity();
+    for (float s : sigmas) {
+        if (s > 0.0f) {
+            sigma_max = std::max(sigma_max, static_cast<double>(s));
+            sigma_min = std::min(sigma_min, static_cast<double>(s));
+        }
+    }
+    if (sigma_max <= sigma_min) {
+        return x;
+    }
+    uint64_t tree_seed = 0;
+    {
+        auto draw = rng->randn(2);
+        std::memcpy(&tree_seed, draw.data(), sizeof(tree_seed));
+    }
+    BrownianTreeNoiseSampler noise_sampler(x, sigma_min, sigma_max, tree_seed);
+
+    sd::Tensor<float> old_denoised;
+    bool have_old_denoised = false;
+    float h_last           = 0.f;
+
+    int steps = static_cast<int>(sigmas.size()) - 1;
+    for (int i = 0; i < steps; i++) {
+        auto denoised_opt = model(x, sigmas[i], i + 1);
+        if (denoised_opt.pred.empty()) {
+            return {};
+        }
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
+
+        if (sigmas[i + 1] == 0.f) {
+            x = denoised;
+        } else {
+            float t     = -std::log(sigmas[i]);
+            float s     = -std::log(sigmas[i + 1]);
+            float h     = s - t;
+            float eta_h = eta * h;
+            float a     = sigmas[i + 1] / sigmas[i] * std::exp(-eta_h);
+            float b     = -std::expm1(-h - eta_h);
+
+            x = a * x + b * denoised;
+
+            if (have_old_denoised) {
+                float r = h_last / h;
+                x += (0.5f * b / r) * (denoised - old_denoised);
+            }
+            if (eta > 0.f) {
+                x += noise_sampler(sigmas[i], sigmas[i + 1]) * (sigmas[i + 1] * std::sqrt(-std::expm1(-2.f * eta_h)));
+            }
+            h_last = h;
+        }
+        old_denoised      = denoised;
+        have_old_denoised = true;
+    }
+    return x;
+}
+
 using SamplerExtraArgs = KeyValueArgs;
 
 static sd::Tensor<float> sample_lcm(denoise_cb_t model,
@@ -2538,6 +2707,8 @@ static sd::Tensor<float> sample_k_diffusion(sample_method_t method,
             return sample_er_sde(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
         case DPMPP2M_SDE_SAMPLE_METHOD:
             return sample_dpmpp_2m_sde(model, std::move(x), sigmas, rng, eta);
+        case DPMPP2M_SDE_BT_SAMPLE_METHOD:
+            return sample_dpmpp_2m_sde_bt(model, std::move(x), sigmas, rng, eta);
         case DDIM_TRAILING_SAMPLE_METHOD:
             // DDIM is equivalent to Euler Ancestral with the Simple scheduler
             return sample_euler_ancestral(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);

src/runtime/imatrix.cpp

@@ -287,6 +287,10 @@ bool IMatrixCollector::load_imatrix(const char* fname) {
         if (e.values.empty()) {
             e.values.resize(nval, 0);
             e.counts.resize(nval, 0);
+        } else if (e.values.size() != (size_t)nval) {
+            LOG_ERROR("inconsistent size for a repeated entry (%d vs %d)\n", (int)e.values.size(), nval);
+            stats_ = {};
+            return false;
         }
 
         std::vector<float> tmp(nval);

src/stable-diffusion.cpp

@@ -57,11 +57,11 @@
 #include "name_conversion.h"
 #include "runtime/latent-preview.h"
 
+#include <atomic>
+
 const char* sd_vae_format_name(enum sd_vae_format_t format);
 static SDVersion sd_vae_format_to_version(enum sd_vae_format_t format, SDVersion fallback);
 
-#include <atomic>
-
 const char* model_version_to_str[] = {
     "SD 1.x",
     "SD 1.x Inpaint",
@@ -222,9 +222,13 @@ public:
     std::string split_mode_spec;
     bool auto_fit_enabled = false;
 
+    bool diffusion_conv_direct = false;
+
     bool is_using_v_parameterization     = false;
     bool is_using_edm_v_parameterization = false;
 
+    size_t control_net_params_mem_size = 0;
+
     std::shared_ptr<ModelManager> model_manager;
 
     std::shared_ptr<Denoiser> denoiser = std::make_shared<CompVisDenoiser>();
@@ -268,6 +272,15 @@ public:
         return max_vram_assignment.bytes_for_backend(backend_for(module));
     }
 
+    std::vector<size_t> layer_split_vram_limits_for_backends(const std::vector<ggml_backend_t>& backends) {
+        std::vector<size_t> limits;
+        limits.reserve(backends.size());
+        for (ggml_backend_t backend : backends) {
+            limits.push_back(max_vram_assignment.bytes_for_backend(backend));
+        }
+        return limits;
+    }
+
     bool ensure_backend_pair(SDBackendModule module) {
         if (backend_for(module) == nullptr) {
             return false;
@@ -427,8 +440,9 @@ public:
                                                      params_mem_size);
     }
 
-    // Register each layer-split partition with its compute backend; the
-    // ModelManager handles allocation, staging, and LoRA by backend.
+    // Register graph-cut layer-split tensors on the primary backend first.
+    // The first real graph assigns each param tensor to a runtime backend
+    // before weights are loaded or staged.
     template <typename T>
     bool register_layer_split_runner_params(const std::string& desc,
                                             const std::shared_ptr<T>& model,
@@ -459,51 +473,98 @@ public:
                                                          params_mem_size);
         }
 
-        std::map<std::string, ggml_tensor*> split_tensors;
-        if constexpr (std::is_base_of_v<Conditioner, T>) {
-            model->get_layer_split_param_tensors(split_tensors);
-        } else {
-            split_tensors = group_tensors;
-        }
-
-        auto partitions = sd::partition_layer_split_tensors(desc, group_tensors, split_tensors, module_backends);
-        bool is_split   = false;
-        for (size_t i = 1; i < partitions.size(); i++) {
-            if (!partitions[i].empty()) {
-                is_split = true;
-                break;
-            }
-        }
-        if (!is_split) {
-            return model_manager->register_param_tensors(desc,
-                                                         std::move(group_tensors),
-                                                         residency_mode,
-                                                         module_backends[0],
-                                                         params_backend_for(module),
-                                                         params_mem_size);
-        }
-
         model->set_runtime_backends(module_backends);
+        model->set_graph_cut_layer_split_backend_vram_limits(layer_split_vram_limits_for_backends(module_backends));
+        model->set_graph_cut_layer_split_enabled(true);
         const bool params_follow_runtime = backend_manager.params_backend_follows_runtime(module) ||
                                            backend_manager.params_backend_is_disk(module);
-        for (size_t i = 0; i < module_backends.size(); i++) {
-            if (partitions[i].empty()) {
-                continue;
-            }
-            ggml_backend_t partition_params_backend =
-                params_follow_runtime ? module_backends[i] : params_backend_for(module);
-            if (partition_params_backend == nullptr) {
-                return false;
-            }
-            if (!model_manager->register_param_tensors(desc,
-                                                       std::move(partitions[i]),
-                                                       residency_mode,
-                                                       module_backends[i],
-                                                       partition_params_backend,
-                                                       params_mem_size)) {
+        ggml_backend_t initial_params_backend = params_follow_runtime ? module_backends[0] : params_backend_for(module);
+        if (initial_params_backend == nullptr) {
+            return false;
+        }
+
+        LOG_INFO("%s graph-cut layer split: deferring %zu tensors across %zu runtime backends until first graph",
+                 desc.c_str(),
+                 group_tensors.size(),
+                 module_backends.size());
+
+        return model_manager->register_param_tensors(desc,
+                                                     std::move(group_tensors),
+                                                     residency_mode,
+                                                     module_backends[0],
+                                                     initial_params_backend,
+                                                     params_mem_size,
+                                                     false,
+                                                     params_follow_runtime);
+    }
+
+    bool unload_control_net() {
+        if (control_net == nullptr) {
+            return true;
+        }
+        if (model_manager != nullptr) {
+            if (!model_manager->unregister_param_tensors("ControlNet", &control_net_params_mem_size)) {
                 return false;
             }
         }
+        control_net.reset();
+        control_net_params_mem_size = 0;
+        return true;
+    }
+
+    bool load_control_net_from_file(const std::string& path) {
+        if (path.empty()) {
+            LOG_ERROR("sd_ctx_load_control_net: empty path");
+            return false;
+        }
+        if (model_manager == nullptr) {
+            LOG_ERROR("sd_ctx_load_control_net: model_manager not initialized");
+            return false;
+        }
+
+        if (!unload_control_net()) {
+            return false;
+        }
+
+        ModelLoader& shared_loader = model_manager->loader();
+        if (!shared_loader.init_from_file(path)) {
+            LOG_ERROR("sd_ctx_load_control_net: failed to load '%s'", path.c_str());
+            return false;
+        }
+        shared_loader.convert_tensors_name();
+
+        if (!ensure_backend_pair(SDBackendModule::CONTROL_NET)) {
+            LOG_ERROR("sd_ctx_load_control_net: control_net backend unavailable");
+            return false;
+        }
+
+        control_net = std::make_shared<ControlNet>(backend_for(SDBackendModule::CONTROL_NET),
+                                                   params_backend_for(SDBackendModule::CONTROL_NET),
+                                                   shared_loader.get_tensor_storage_map(),
+                                                   version,
+                                                   "",
+                                                   model_manager);
+        if (diffusion_conv_direct) {
+            LOG_INFO("Using Conv2d direct in the control net");
+            control_net->set_conv2d_direct_enabled(true);
+        }
+        if (!register_runner_params("ControlNet",
+                                    control_net,
+                                    SDBackendModule::CONTROL_NET,
+                                    &control_net_params_mem_size)) {
+            LOG_ERROR("sd_ctx_load_control_net: register_runner_params failed");
+            control_net.reset();
+            control_net_params_mem_size = 0;
+            return false;
+        }
+        if (!model_manager->validate_registered_tensors()) {
+            LOG_ERROR("sd_ctx_load_control_net: registered tensors validation failed");
+            unload_control_net();
+            return false;
+        }
+        LOG_INFO("sd_ctx_load_control_net: loaded '%s' (%.2f MB)",
+                 path.c_str(),
+                 control_net_params_mem_size / 1024.0 / 1024.0);
         return true;
     }
 
@@ -529,6 +590,16 @@ public:
         return false;
     }
 
+    bool graph_cut_layer_split_active() {
+        for (SDBackendModule module : {SDBackendModule::DIFFUSION, SDBackendModule::TE}) {
+            if (backend_manager.split_mode(module) == SDSplitMode::LAYER &&
+                backend_manager.runtime_backends(module).size() > 1) {
+                return true;
+            }
+        }
+        return false;
+    }
+
     std::shared_ptr<RNG> get_rng(rng_type_t rng_type) {
         if (rng_type == STD_DEFAULT_RNG) {
             return std::make_shared<STDDefaultRNG>();
@@ -785,6 +856,10 @@ public:
             LOG_WARN("--stream-layers has no effect unless diffusion params backend is cpu; ignoring");
             stream_layers = false;
         }
+        if (eager_load && graph_cut_layer_split_active()) {
+            LOG_WARN("--eager-load is not supported with graph-cut layer split; weights will be prepared lazily");
+            eager_load = false;
+        }
 
         std::map<ggml_type, uint32_t> wtype_stat                 = model_loader.get_wtype_stat();
         std::map<ggml_type, uint32_t> conditioner_wtype_stat     = model_loader.get_conditioner_wtype_stat();
@@ -851,10 +926,12 @@ public:
         model_loader.process_model_files(enable_mmap, needs_writable_mmap);
         load_alphas_cumprod(model_loader);
 
+        diffusion_conv_direct = sd_ctx_params->diffusion_conv_direct;
+
         size_t text_encoder_params_mem_size = 0;
         size_t unet_params_mem_size         = 0;
         size_t vae_params_mem_size          = 0;
-        size_t control_net_params_mem_size  = 0;
+        control_net_params_mem_size         = 0;
         size_t extension_params_mem_size    = 0;
 
         bool tae_preview_only = sd_ctx_params->tae_preview_only;
@@ -863,10 +940,6 @@ public:
             use_tae          = true;
         }
 
-        if (sd_ctx_params->circular_x || sd_ctx_params->circular_y) {
-            LOG_INFO("Using circular padding for convolutions");
-        }
-
         {
             if (!ensure_backend_pair(SDBackendModule::TE) ||
                 !ensure_backend_pair(SDBackendModule::DIFFUSION)) {
@@ -908,7 +981,7 @@ public:
                                                                  tensor_storage_map,
                                                                  version,
                                                                  "",
-                                                                 false,
+                                                                 true,
                                                                  model_manager);
                 diffusion_model  = std::make_shared<Krea2::Krea2Runner>(backend_for(SDBackendModule::DIFFUSION),
                                                                        tensor_storage_map,
@@ -925,10 +998,11 @@ public:
                 if (is_chroma) {
                     cond_stage_model = std::make_shared<T5CLIPEmbedder>(backend_for(SDBackendModule::TE),
                                                                         tensor_storage_map,
-                                                                        sd_ctx_params->chroma_use_t5_mask,
-                                                                        sd_ctx_params->chroma_t5_mask_pad,
                                                                         false,
-                                                                        model_manager);
+                                                                        1,
+                                                                        false,
+                                                                        model_manager,
+                                                                        sd_ctx_params->model_args);
                 } else if (version == VERSION_OVIS_IMAGE) {
                     cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
                                                                      tensor_storage_map,
@@ -945,8 +1019,8 @@ public:
                                                                      tensor_storage_map,
                                                                      "model.diffusion_model",
                                                                      version,
-                                                                     sd_ctx_params->chroma_use_dit_mask,
-                                                                     model_manager);
+                                                                     model_manager,
+                                                                     sd_ctx_params->model_args);
             } else if (sd_version_is_flux2(version) || sd_version_is_sefi_image(version)) {
                 bool is_chroma   = false;
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
@@ -959,8 +1033,8 @@ public:
                                                                      tensor_storage_map,
                                                                      "model.diffusion_model",
                                                                      version,
-                                                                     sd_ctx_params->chroma_use_dit_mask,
-                                                                     model_manager);
+                                                                     model_manager,
+                                                                     sd_ctx_params->model_args);
             } else if (sd_version_is_ltxav(version)) {
                 cond_stage_model = std::make_shared<LTXAVEmbedder>(backend_for(SDBackendModule::TE),
                                                                    tensor_storage_map,
@@ -1018,8 +1092,8 @@ public:
                                                                           tensor_storage_map,
                                                                           "model.diffusion_model",
                                                                           version,
-                                                                          sd_ctx_params->qwen_image_zero_cond_t,
-                                                                          model_manager);
+                                                                          model_manager,
+                                                                          sd_ctx_params->model_args);
             } else if (sd_version_is_longcat(version)) {
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
@@ -1031,8 +1105,8 @@ public:
                                                                      tensor_storage_map,
                                                                      "model.diffusion_model",
                                                                      version,
-                                                                     sd_ctx_params->chroma_use_dit_mask,
-                                                                     model_manager);
+                                                                     model_manager,
+                                                                     sd_ctx_params->model_args);
             } else if (version == VERSION_HIDREAM_O1) {
                 cond_stage_model = std::make_shared<HiDreamO1::HiDreamO1Conditioner>(backend_for(SDBackendModule::TE),
                                                                                      tensor_storage_map,
@@ -1368,16 +1442,6 @@ public:
                     high_noise_diffusion_model->set_flash_attention_enabled(true);
                 }
             }
-
-            diffusion_model->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
-            if (high_noise_diffusion_model) {
-                high_noise_diffusion_model->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
-            }
-            if (control_net) {
-                control_net->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
-            }
-            circular_x = sd_ctx_params->circular_x;
-            circular_y = sd_ctx_params->circular_y;
         }
 
         LOG_DEBUG("validating model metadata");
@@ -2819,6 +2883,7 @@ const char* sample_method_to_str[] = {
     "euler_a_cfg_pp",
     "euler_ge",
     "dpm++2m_sde",
+    "dpm++2m_sde_bt",
 };
 
 const char* sd_sample_method_name(enum sample_method_t sample_method) {
@@ -3060,17 +3125,13 @@ 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->circular_x           = false;
-    sd_ctx_params->circular_y           = false;
-    sd_ctx_params->chroma_use_dit_mask  = true;
-    sd_ctx_params->chroma_use_t5_mask   = false;
-    sd_ctx_params->chroma_t5_mask_pad   = 1;
     sd_ctx_params->vae_format           = SD_VAE_FORMAT_AUTO;
     sd_ctx_params->backend              = nullptr;
     sd_ctx_params->params_backend       = nullptr;
     sd_ctx_params->split_mode           = nullptr;
     sd_ctx_params->auto_fit             = false;
     sd_ctx_params->rpc_servers          = nullptr;
+    sd_ctx_params->model_args           = nullptr;
     sd_ctx_params->pulid_weights_path   = nullptr;
 }
 
@@ -3110,14 +3171,10 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              "backend: %s\n"
              "params_backend: %s\n"
              "split_mode: %s\n"
+             "model_args: %s\n"
              "auto_fit: %s\n"
              "flash_attn: %s\n"
              "diffusion_flash_attn: %s\n"
-             "circular_x: %s\n"
-             "circular_y: %s\n"
-             "chroma_use_dit_mask: %s\n"
-             "chroma_use_t5_mask: %s\n"
-             "chroma_t5_mask_pad: %d\n"
              "vae_format: %s\n",
              SAFE_STR(sd_ctx_params->model_path),
              SAFE_STR(sd_ctx_params->clip_l_path),
@@ -3148,14 +3205,10 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              SAFE_STR(sd_ctx_params->backend),
              SAFE_STR(sd_ctx_params->params_backend),
              SAFE_STR(sd_ctx_params->split_mode),
+             SAFE_STR(sd_ctx_params->model_args),
              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->circular_x),
-             BOOL_STR(sd_ctx_params->circular_y),
-             BOOL_STR(sd_ctx_params->chroma_use_dit_mask),
-             BOOL_STR(sd_ctx_params->chroma_use_t5_mask),
-             sd_ctx_params->chroma_t5_mask_pad,
              sd_vae_format_name(sd_ctx_params->vae_format));
 
     return buf;
@@ -3233,6 +3286,8 @@ void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params) {
     sd_img_gen_params->batch_count       = 1;
     sd_img_gen_params->control_strength  = 0.9f;
     sd_img_gen_params->qwen_image_layers = 3;
+    sd_img_gen_params->circular_x        = false;
+    sd_img_gen_params->circular_y        = false;
     sd_img_gen_params->pm_params         = {nullptr, 0, nullptr, 20.f};
     sd_img_gen_params->pulid_params      = {nullptr, 1.0f};
     sd_img_gen_params->vae_tiling_params = {false, false, 0, 0, 0.5f, 0.0f, 0.0f, nullptr};
@@ -3266,6 +3321,8 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
              "control_strength: %.2f\n"
              "photo maker: {style_strength = %.2f, id_images_count = %d, id_embed_path = %s}\n"
              "VAE tiling: %s (temporal=%s, extra_tiling_args=%s)\n"
+             "circular_x: %s\n"
+             "circular_y: %s\n"
              "hires: {enabled=%s, upscaler=%s, model_path=%s, scale=%.2f, target=%dx%d, steps=%d, denoising_strength=%.2f}\n",
              SAFE_STR(sd_img_gen_params->prompt),
              SAFE_STR(sd_img_gen_params->negative_prompt),
@@ -3287,6 +3344,8 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
              BOOL_STR(sd_img_gen_params->vae_tiling_params.enabled),
              BOOL_STR(sd_img_gen_params->vae_tiling_params.temporal_tiling),
              SAFE_STR(sd_img_gen_params->vae_tiling_params.extra_tiling_args),
+             BOOL_STR(sd_img_gen_params->circular_x),
+             BOOL_STR(sd_img_gen_params->circular_y),
              BOOL_STR(sd_img_gen_params->hires.enabled),
              sd_hires_upscaler_name(sd_img_gen_params->hires.upscaler),
              SAFE_STR(sd_img_gen_params->hires.model_path),
@@ -3335,6 +3394,8 @@ void sd_vid_gen_params_init(sd_vid_gen_params_t* sd_vid_gen_params) {
     sd_vid_gen_params->hires.upscale_tile_size               = 128;
     sd_vid_gen_params->hires.custom_sigmas                   = nullptr;
     sd_vid_gen_params->hires.custom_sigmas_count             = 0;
+    sd_vid_gen_params->circular_x                            = false;
+    sd_vid_gen_params->circular_y                            = false;
     sd_cache_params_init(&sd_vid_gen_params->cache);
 }
 
@@ -3444,6 +3505,27 @@ SD_API bool sd_ctx_supports_video_generation(const sd_ctx_t* sd_ctx) {
     return sd_version_supports_video_generation(sd_ctx->sd->version);
 }
 
+SD_API bool sd_ctx_load_control_net(sd_ctx_t* sd_ctx, const char* path) {
+    if (sd_ctx == nullptr || sd_ctx->sd == nullptr || path == nullptr) {
+        return false;
+    }
+    return sd_ctx->sd->load_control_net_from_file(path);
+}
+
+SD_API bool sd_ctx_unload_control_net(sd_ctx_t* sd_ctx) {
+    if (sd_ctx == nullptr || sd_ctx->sd == nullptr) {
+        return false;
+    }
+    return sd_ctx->sd->unload_control_net();
+}
+
+SD_API bool sd_ctx_has_control_net(const sd_ctx_t* sd_ctx) {
+    if (sd_ctx == nullptr || sd_ctx->sd == nullptr) {
+        return false;
+    }
+    return sd_ctx->sd->control_net != nullptr;
+}
+
 enum sample_method_t sd_get_default_sample_method(const sd_ctx_t* sd_ctx) {
     if (sd_ctx != nullptr && sd_ctx->sd != nullptr) {
         if (sd_version_is_pid(sd_ctx->sd->version)) {
@@ -3521,6 +3603,7 @@ static float resolve_eta(sd_ctx_t* sd_ctx,
             case ER_SDE_SAMPLE_METHOD:
             case EULER_A_CFG_PP_SAMPLE_METHOD:
             case DPMPP2M_SDE_SAMPLE_METHOD:
+            case DPMPP2M_SDE_BT_SAMPLE_METHOD:
                 return 1.0f;
             default:;
         }
@@ -4209,6 +4292,25 @@ struct CircularAxesState {
     bool circular_y = false;
 };
 
+static void apply_circular_axes_to_diffusion(sd_ctx_t* sd_ctx, bool circular_x, bool circular_y) {
+    sd_ctx->sd->circular_x = circular_x;
+    sd_ctx->sd->circular_y = circular_y;
+    if (sd_ctx->sd->diffusion_model) {
+        sd_ctx->sd->diffusion_model->set_circular_axes(circular_x, circular_y);
+    }
+    if (sd_ctx->sd->high_noise_diffusion_model) {
+        sd_ctx->sd->high_noise_diffusion_model->set_circular_axes(circular_x, circular_y);
+    }
+    if (sd_ctx->sd->control_net) {
+        sd_ctx->sd->control_net->set_circular_axes(circular_x, circular_y);
+    }
+    if (circular_x || circular_y) {
+        LOG_INFO("Using circular padding for convolutions (x=%s, y=%s)",
+                 circular_x ? "true" : "false",
+                 circular_y ? "true" : "false");
+    }
+}
+
 static CircularAxesState configure_image_vae_axes(sd_ctx_t* sd_ctx,
                                                   const sd_img_gen_params_t* sd_img_gen_params,
                                                   const GenerationRequest& request) {
@@ -4319,13 +4421,56 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
         LOG_INFO("IMG2IMG");
 
         if (request->strength < 1.f) {
-            size_t t_enc = static_cast<size_t>(plan->sample_steps * request->strength);
-            if (t_enc == static_cast<size_t>(plan->sample_steps)) {
-                t_enc--;
+            bool strength_as_noise_level = false;
+            bool force_first_sigma       = false;
+            for (const auto& [key, value] : parse_key_value_args(sd_img_gen_params->sample_params.extra_sample_args, "img2img arg")) {
+                if (key == "strength_as_noise_level") {
+                    if (!parse_strict_bool(value, strength_as_noise_level)) {
+                        LOG_WARN("ignoring invalid img2img sample arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                } else if (key == "force_first_sigma") {
+                    if (!parse_strict_bool(value, force_first_sigma)) {
+                        LOG_WARN("ignoring invalid img2img sample arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                }
+            }
+
+            size_t t_enc;
+            float target_sigma = -1;
+            if (!strength_as_noise_level) {
+                t_enc = static_cast<size_t>(plan->sample_steps * request->strength);
+                if (t_enc == static_cast<size_t>(plan->sample_steps)) {
+                    t_enc--;
+                }
+            } else {
+                LOG_DEBUG("Interpreting denoise strength as relative noise level");
+                // assume x_noised = K * (x * (1-noise_level) + noise * noise_level) = K * lerp(x, noise, noise_level)
+                // K = 1, noise_level = sigma for flow models
+                // K = 1+sigma, noise_level=sigma/(1+sigma) for diffusion models
+                float target_noise_level = request->strength;
+                target_sigma             = sd_ctx->sd->denoiser->noise_level_to_sigma(target_noise_level);
+                size_t start_index       = 0;
+                for (size_t i = 0; i < plan->sigmas.size(); ++i) {
+                    if (plan->sigmas[i] <= target_sigma) {
+                        start_index = i;
+                        break;
+                    }
+                }
+
+                if (start_index >= plan->sigmas.size() - 1) {
+                    start_index = plan->sigmas.size() - 2;  // Leave at least 1 step
+                }
+                t_enc = plan->sample_steps - start_index - 1;
             }
             LOG_INFO("target t_enc is %zu steps", t_enc);
             std::vector<float> sigma_sched;
             sigma_sched.assign(plan->sigmas.begin() + plan->sample_steps - t_enc - 1, plan->sigmas.end());
+
+            if (target_sigma > 0 && force_first_sigma && strength_as_noise_level) {
+                LOG_DEBUG("force_first_sigma to %.4f (from %.4f)", target_sigma, sigma_sched[0]);
+                sigma_sched[0] = target_sigma;
+            }
+
             plan->sigmas       = std::move(sigma_sched);
             plan->sample_steps = static_cast<int>(plan->sigmas.size() - 1);
         }
@@ -4922,6 +5067,7 @@ SD_API bool generate_image(sd_ctx_t* sd_ctx,
     sd_ctx->sd->sampler_rng->manual_seed(request.seed);
     sd_ctx->sd->set_flow_shift(sd_img_gen_params->sample_params.flow_shift);
     sd_ctx->sd->apply_loras(sd_img_gen_params->loras, sd_img_gen_params->lora_count);
+    apply_circular_axes_to_diffusion(sd_ctx, sd_img_gen_params->circular_x, sd_img_gen_params->circular_y);
 
     ImageVaeAxesGuard axes_guard(sd_ctx, sd_img_gen_params, request);
 
@@ -5760,6 +5906,7 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
     }
     int64_t t0                    = ggml_time_ms();
     sd_ctx->sd->vae_tiling_params = sd_vid_gen_params->vae_tiling_params;
+    apply_circular_axes_to_diffusion(sd_ctx, sd_vid_gen_params->circular_x, sd_vid_gen_params->circular_y);
     GenerationRequest request(sd_ctx, sd_vid_gen_params);
     bool latent_upscale_enabled     = request.hires.enabled;
     GenerationRequest hires_request = request;

src/weight_manager.h

@@ -3,10 +3,14 @@
 
 #include <vector>
 
+#include "ggml-backend.h"
+
 struct ggml_tensor;
 
 struct RunnerWeightManager {
     virtual ~RunnerWeightManager()                                                        = default;
+    virtual bool assign_compute_backend(const std::vector<ggml_tensor*>& tensors,
+                                        ggml_backend_t compute_backend)                   = 0;
     virtual bool prepare_params(const std::vector<ggml_tensor*>& tensors)                 = 0;
     virtual void release_compute_backend_params(const std::vector<ggml_tensor*>& tensors) = 0;
     virtual void release_params_backend_params(const std::vector<ggml_tensor*>& tensors)  = 0;