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update changelog and readme

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
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Vladimir Mandic
2025-10-15 11:09:36 -04:00
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CHANGELOG.md
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# Change Log for SD.Next
## Update for 2025-10-14
## Update for 2025-10-15
### Highlights for 2025-10-14
### Highlights for 2025-10-15
It's been a month since the last release and number of changes is yet again massive with over 250 commits!
It's been a month since the last release and number of changes is yet again massive with over 300 commits!
Highlight are:
- **Torch**: ROCm on Windows for AMD GPUs
if you have a compatible GPU, performance gains are significant!
- **Models**:
new WAN variants, a lot of new stuff with Qwen Image Edit including multi-image edits, expanded Nunchaku support and new SOTA upscaler with SeedVR2
a lot of new stuff with **Qwen-Image-Edit** including multi-image edits and distilled variants,
new **Flux**, **WAN**, **HiDream** variants, expanded **Nunchaku** support and new SOTA upscaler with **SeedVR2**
plus improved video support in general, including new methods of video encoding
- **Quantization**:
new **SVD**-style quantization using SDNQ ofers almost zero loss even with **4bit** quantization
new **SVD**-style quantization using SDNQ offers almost zero-loss even with **4bit** quantization
and now you can also test your favorite quantization on-the-fly and then save/load model for future use
### Details for 2025-10-14
### Details for 2025-10-15
- **Models**
- [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)
@@ -28,7 +29,7 @@ Highlight are:
SRPO is trained by Tencent with specific technique: directly aligning the full diffusion trajectory with fine-grained human preference
- [Nunchaku SDXL](https://huggingface.co/nunchaku-tech/nunchaku-sdxl) and [Nunchaku SDXL Turbo](https://huggingface.co/nunchaku-tech/nunchaku-sdxl-turbo)
impact of nunchaku engine on unet-based model such as sdxl is much less than on a dit-based models, but its still significantly faster than baseline
note that nunchaku optimized and prequantized unet is replacement for base unet, so its only applicable to base models, not any of finetunes
note that nunchaku optimized and pre-quantized unet is replacement for base unet, so its only applicable to base models, not any of fine-tunes
*how to use*: enable nunchaku in settings -> quantization and then load either sdxl-base or sdxl-base-turbo reference models
- [HiDream E1.1](https://huggingface.co/HiDream-ai/HiDream-E1-1)
updated version of E1 image editing model
@@ -39,13 +40,13 @@ Highlight are:
note: seedvr is highly sensitive to its cfg scale, set in *settings -> postprocessing*
lower values will result in smoother output while higher values add details
- [X-Omni SFT](https://x-omni-team.github.io/)
*experimental*: X-omni is a transformer-only discrete autoregressive image generative model trained with reinforcement learning
*experimental*: X-omni is a transformer-only discrete auto-regressive image generative model trained with reinforcement learning
- **Features**
- **Model save**: ability to save currently loaded model as a new standalone model
why? SD.Next always prefers to start with full model and quantize on-demand during load
however, when you find your exact preferred quantization settings that work well for you,
saving such model as a new model allows for faster loads and reduced disk space usage
so its best of both worlds: you can experiment and test different quantizations and once you find the one that works for you, save it as a new model
so its best of both worlds: you can experiment and test different quantization methods and once you find the one that works for you, save it as a new model
saved models appear in network tab as normal models and can be loaded as such
available in *models* tab
- [Qwen Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) multi-image editing
@@ -56,15 +57,15 @@ Highlight are:
- [Cache-DiT](https://github.com/vipshop/cache-dit)
cache-dit is a unified, flexible and training-free cache acceleration framework
compatible with many dit-based models such as FLUX.1, Qwen, HunyuanImage, Wan2.2, Chroma, etc.
enable in *settings -> pipeline modifers -> cache-dit*
enable in *settings -> pipeline modifiers -> cache-dit*
- [Nunchaku Flux.1 PulID](https://nunchaku.tech/docs/nunchaku/python_api/nunchaku.pipeline.pipeline_flux_pulid.html)
automatically enabled if loaded model is FLUX.1 with Nunchaku engine enabled and when PulID script is enabled
- **Compute**
- **ROCm** for Windows
support for both official torch preview release of `torch-rocm` for windows and **TheRock** unoffical `torch-rocm` builds for windows
support for both official torch preview release of `torch-rocm` for windows and **TheRock** unofficial `torch-rocm` builds for windows
note that rocm for windows is still in preview and has limited gpu support, please check rocm docs for details
- **DirectML** warn as *end-of-life*
`torch-directml` received no updates in over 1 year and its currently superceded by `rocm` or `zluda`
`torch-directml` received no updates in over 1 year and its currently superseded by `rocm` or `zluda`
- command line params `--use-zluda` and `--use-rocm` will attempt desired operation or fail if not possible
previously sdnext was performing a fallback to `torch-cpu` which is not desired
- **installer** if `--use-cuda` or `--use-rocm` are specified and `torch-cpu` is installed, installer will attempt to reinstall correct torch package
@@ -95,11 +96,11 @@ Highlight are:
- **SDNQ**
- add `SVDQuant` quantization method support
- make sdnq scales compatible with balanced offload
- add int8 matmul support for RDNA2 GPUs via triton
- improve int8 mamtul performance on Intel GPUs
- add int8 `matmul` support for RDNA2 GPUs via triton
- improve int8 `matmul` performance on Intel GPUs
- **Other**
- server will note when restart is recommended due to package updates
- **interrrupt** will now show last known preview image
- **interrupt** will now show last known preview image
*keep incomplete* setting is now *save interrupted*
- **logging** enable `debug`, `docs` and `api-docs` by default
- **logging** add detailed ram/vram utilization info to log
@@ -143,7 +144,8 @@ Highlight are:
allows for using many different guidance methods:
*CFG, CFGZero, PAG, APG, SLG, SEG, TCFG, FDG*
- **Wiki**
- updates to *AMD-ROCm, ZLUDA, LoRA, DirectML, SDNQ* pages
- updates to *AMD-ROCm, ZLUDA, LoRA, DirectML, SDNQ, Quantization, Prompting, LoRA* pages
- new *Stability-Matrix* page
- **Fixes**
- **Microsoft Florence 2** both base and large variants
*note* this will trigger download of the new variant of the model, feel free to delete older variant in `huggingface` folder
@@ -168,7 +170,7 @@ Highlight are:
**StandardUI** is still available and can be selected in settings, but ModernUI is now the default for new installs
*What's else*? **Chroma** is in its final form, there are several new **Qwen-Image** variants and **Nunchaku** hit version 1.0!
Also, there are quite a few offloading improvements and many quality-of-life changes to UI and overal workflows
Also, there are quite a few offloading improvements and many quality-of-life changes to UI and overall workflows
And check out new **history** tab in the right panel, it now shows visualization of entire processing timeline!
![Screenshot](https://github.com/user-attachments/assets/d6119a63-6ee5-4597-95f6-29ed0701d3b5)
@@ -182,13 +184,13 @@ And check out new **history** tab in the right panel, it now shows visualization
- **Qwen-Image** [InstantX ControlNet Union](https://huggingface.co/InstantX/Qwen-Image-ControlNet-Union) support
*note* qwen-image is already a very large model and controlnet adds 3.5GB on top of that so quantization and offloading are highly recommended!
- [Qwen-Lightning-Edit](https://huggingface.co/vladmandic/Qwen-Lightning-Edit) and [Qwen-Image-Distill](https://huggingface.co/SahilCarterr/Qwen-Image-Distill-Full) variants
- **Nuchaku** variants of [Qwen-Image-Lightning](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image), [Qwen-Image-Edit](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image-edit), [Nunchaku-Qwen-Image-Edit-Lightning](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image-edit)
- **Nunchaku** variants of [Qwen-Image-Lightning](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image), [Qwen-Image-Edit](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image-edit), [Nunchaku-Qwen-Image-Edit-Lightning](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image-edit)
- **Nunchaku** variant of [Flux.1-Krea-Dev](https://huggingface.co/nunchaku-tech/nunchaku-flux.1-krea-dev)
if you have a compatible nVidia GPU, Nunchaku is the fastest quantization & inference engine
- [HunyuanDiT ControlNet](https://huggingface.co/Tencent-Hunyuan/HYDiT-ControlNet-v1.2) Canny, Depth, Pose
- [KBlueLeaf/HDM-xut-340M-anime](https://huggingface.co/KBlueLeaf/HDM-xut-340M-anime)
highly experimental: HDM *Home-made-Diffusion-Model* is a project to investigate specialized training recipe/scheme
for pretraining T2I model at home based on super-light architecture
for pre-training T2I model at home based on super-light architecture
*requires*: generator=cpu, dtype=float16, offload=none, both positive and negative prompts are required and must be long & detailed
- [Apple FastVLM](https://huggingface.co/apple/FastVLM-0.5B) in 0.5B, 1.5B and 7B variants
available in captioning tab
@@ -207,13 +209,13 @@ And check out new **history** tab in the right panel, it now shows visualization
- additional artwork for reference models in networks, thanks @liutyi
- improve ui hints display
- restyled all toolbuttons to be modernui native
- reodered system settings
- reordered system settings
- dynamic direction of dropdowns
- improve process tab layout
- improve detection of active tab
- configurable horizontal vs vertical panel layout
in settings -> user interface -> panel min width
*example*: if panel width is less than specified value, layout switches to verical
*example*: if panel width is less than specified value, layout switches to vertical
- configurable grid images size
in *settings -> user interface -> grid image size*
- gallery now includes reference model images
@@ -224,10 +226,10 @@ And check out new **history** tab in the right panel, it now shows visualization
- improve offloading of models with multiple dits
- improve offloading of models with impliciy vae processing
- improve offloading of models with controlnet
- more aggressive offloading of controlnets with lowvram flag
- more aggressive offloading of controlnet with lowvram flag
- **group**
- new offloading method, using *type=leaf* works on a similar level as sequential offloading
and can present siginificant savings on low-vram gpus, but comes at the higher performace cost
and can present significant savings on low-vram gpus, but comes at the higher performance cost
- **Quantization**
- option to specify models types not to quantize: *settings -> quantization*
allows for having quantization enabled, but skipping specific model types that do not need it
README.md
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▹ **Standard | Modern**
- Multiple [diffusion models](https://vladmandic.github.io/sdnext-docs/Model-Support/)!
- Built-in Control for Text, Image, Batch and Video processing!
- Multiplatform!
- Multi-platform!
▹ **Windows | Linux | MacOS | nVidia CUDA | AMD ROCm | Intel Arc / IPEX XPU | DirectML | OpenVINO | ONNX+Olive | ZLUDA**
- Platform specific autodetection and tuning performed on install
- Platform specific auto-detection and tuning performed on install
- Optimized processing with latest `torch` developments with built-in support for model compile and quantize
Compile backends: *Triton | StableFast | DeepCache | OneDiff | TeaCache | etc.*
Quantization methods: *SDNQ | BitsAndBytes | Optimum-Quanto | TorchAO*
Quantization methods: *SDNQ | BitsAndBytes | Optimum-Quanto | TorchAO / LayerWise*
- **Interrogate/Captioning** with 150+ **OpenCLiP** models and 20+ built-in **VLMs**
- Built-in queue management
- Built in installer with automatic updates and dependency management
@@ -54,7 +54,7 @@ All individual features are not listed here, instead check [ChangeLog](CHANGELOG
<img src="https://github.com/user-attachments/assets/ced9fe0c-d2c2-46d1-94a7-8f9f2307ce38" alt="screenshot-modernui-mobile" width="35%">
</div>
For screenshots and informations on other available themes, see [Themes](https://vladmandic.github.io/sdnext-docs/Themes/)
For screenshots and information on other available themes, see [Themes](https://vladmandic.github.io/sdnext-docs/Themes/)
<br>
@@ -75,7 +75,7 @@ SD.Next supports broad range of models: [supported models](https://vladmandic.gi
- *ONNX/Olive*
- *AMD* GPUs on Windows using **ZLUDA** libraries
Plus Docker container receipes for: [CUDA, ROCm, Intel IPEX and OpenVINO](https://vladmandic.github.io/sdnext-docs/Docker/)
Plus Docker container recipes for: [CUDA, ROCm, Intel IPEX and OpenVINO](https://vladmandic.github.io/sdnext-docs/Docker/)
## Getting started
wiki
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Submodule wiki updated: bf77a802db...ae55ef9b42