From 61f8f32cb3261651a5c20ef331bfb6fabdd694de Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Wed, 15 Oct 2025 11:09:36 -0400 Subject: [PATCH] update changelog and readme Signed-off-by: Vladimir Mandic --- CHANGELOG.md | 48 +++++++++++++++++++++++++----------------------- README.md | 10 +++++----- wiki | 2 +- 3 files changed, 31 insertions(+), 29 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 2a0af3ba9..0b41b3816 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,21 +1,22 @@ # 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 diff --git a/README.md b/README.md index f57feb916..b81f1a8aa 100644 --- a/README.md +++ b/README.md @@ -31,12 +31,12 @@ All individual features are not listed here, instead check [ChangeLog](CHANGELOG ▹ **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 screenshot-modernui-mobile -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/)
@@ -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 diff --git a/wiki b/wiki index bf77a802d..ae55ef9b4 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit bf77a802db032e2f5a48c379088f35cdadd76a8b +Subproject commit ae55ef9b423918e096d516ee1646c0b59fbc9528