mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 17:24:32 +02:00
Vladimir Mandic
+12
-9
@@ -1,20 +1,20 @@
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# Change Log for SD.Next
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## Update for 2025-09-03
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## Update for 2025-09-05
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- **Models**
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- **Models**
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- **Chroma** final versions: [Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD), [Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base) and [Chroma1-Flash](https://huggingface.co/lodestones/Chroma1-Flash)
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- **Qwen-Image** [InstantX ControlNet Union](https://huggingface.co/InstantX/Qwen-Image-ControlNet-Union) support
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*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!
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- [Nunchaku-Qwen-Image-Lightning](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image)
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if you have a compatible nVidia GPU, Nunchaku is the fastest quantization engine, currently available for Flux.1, SANA and Qwen-Image models
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*note*: release version of `nunchaku==0.3.2` does NOT include support, so you need to build [nunchaku](https://nunchaku.tech/docs/nunchaku/installation/installation.html) from source
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- [Nunchaku-Qwen-Image-Lightning](https://huggingface.co/nunchaku-tech/nunchaku-qwen-image) and [Nunchaku-Flux.1-Krea-Dev](https://huggingface.co/nunchaku-tech/nunchaku-flux.1-krea-dev)
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if you have a compatible nVidia GPU, Nunchaku is the fastest quantization engine,
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- [HunyuanDiT ControlNet](https://huggingface.co/Tencent-Hunyuan/HYDiT-ControlNet-v1.2) Canny, Depth, Pose
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- [KBlueLeaf/HDM-xut-340M-anime](https://huggingface.co/KBlueLeaf/HDM-xut-340M-anime)
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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
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highly experimental: HDM *Home-made-Diffusion-Model* is a project to investigate specialized training recipe/scheme
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for pretraining T2I model at home based on super-light architecture
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requires: generator=cpu, dtype=float16, offload=none
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- updated [SD.Next Model Samples Gallery](https://vladmandic.github.io/sd-samples/compare.html)
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- **UI**
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- **UI**
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- default to **ModernUI**
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standard ui is still available via *settings -> user interface -> theme type*
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- mobile-friendly!
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@@ -41,8 +41,11 @@
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- improve offloading of models with impliciy vae processing
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- improve offloading of models with controlnet
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- more aggressive offloading of controlnets with lowvram flag
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- **SDNQ**
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- add quantized matmul support for all quantization types and group sizes
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- **Quantization**
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- **sdnq**: add quantized matmul support for all quantization types and group sizes
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- **nunchaku**: update to `nunchaku==1.0.0`
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*note*: nunchaku updated the repo which will trigger re-download of nunchaku models when first used
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nunchaku is currently available for: *Flux.1 Dev/Schnell/Kontext/Krea/Depth/Fill*, *Qwen-Image/Qwen-Lightning*, *SANA-1.6B*
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- **Other**
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- refactor reuse-seed and add functionality to all tabs
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- refactor modernui js codebase
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@@ -4,7 +4,7 @@ from installer import log, pip
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from modules import devices
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ver = '0.3.2'
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ver = '1.0.0'
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ok = False
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@@ -46,7 +46,7 @@ def install_nunchaku():
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log.error(f'Nunchaku: backend={devices.backend} unsupported')
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return False
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torch_ver = torch.__version__[:3]
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if torch_ver not in ['2.5', '2.6', '2.7', '2.8']:
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if torch_ver not in ['2.5', '2.6', '2.7', '2.8', '2.9']:
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log.error(f'Nunchaku: torch={torch.__version__} unsupported')
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suffix = 'x86_64' if arch == 'linux' else 'win_amd64'
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url = os.environ.get('NUNCHAKU_COMMAND', None)
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@@ -55,7 +55,6 @@ def install_nunchaku():
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url = f'https://huggingface.co/nunchaku-tech/nunchaku/resolve/main/nunchaku-{ver}'
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url += f'+torch{torch_ver}-cp{python_ver}-cp{python_ver}-{arch}{suffix}.whl'
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cmd = f'install --upgrade {url}'
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# pip install https://huggingface.co/mit-han-lab/nunchaku/resolve/main/nunchaku-0.2.0+torch2.6-cp311-cp311-linux_x86_64.whl
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log.debug(f'Nunchaku: install="{url}"')
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pip(cmd, ignore=False, uv=False)
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importlib.reload(pkg_resources)
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@@ -6,18 +6,20 @@ def load_flux_nunchaku(repo_id):
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nunchaku_precision = nunchaku.utils.get_precision()
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nunchaku_repo = None
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transformer = None
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if 'flux.1-kontext' in repo_id.lower():
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nunchaku_repo = f"mit-han-lab/nunchaku-flux.1-kontext-dev/svdq-{nunchaku_precision}_r32-flux.1-kontext-dev.safetensors"
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elif 'flux.1-dev' in repo_id.lower():
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nunchaku_repo = f"mit-han-lab/nunchaku-flux.1-dev/svdq-{nunchaku_precision}_r32-flux.1-dev.safetensors"
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if 'flux.1-dev' in repo_id.lower():
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nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-dev/svdq-{nunchaku_precision}_r32-flux.1-dev.safetensors"
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elif 'flux.1-schnell' in repo_id.lower():
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nunchaku_repo = f"mit-han-lab/nunchaku-flux.1-schnell/svdq-{nunchaku_precision}_r32-flux.1-schnell.safetensors"
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nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-schnell/svdq-{nunchaku_precision}_r32-flux.1-schnell.safetensors"
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elif 'flux.1-kontext' in repo_id.lower():
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nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-kontext-dev/svdq-{nunchaku_precision}_r32-flux.1-kontext-dev.safetensors"
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elif 'flux.1-krea' in repo_id.lower():
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nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-krea-dev/svdq-{nunchaku_precision}_r32-flux.1-krea-dev.safetensors"
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elif 'flux.1-fill' in repo_id.lower():
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nunchaku_repo = f"mit-han-lab/svdq-fp4-flux.1-fill-dev/svdq-{nunchaku_precision}_r32-flux.1-schnell.safetensors"
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nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-fill-dev/svdq-{nunchaku_precision}-flux.1-fill-dev.safetensors"
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elif 'flux.1-depth' in repo_id.lower():
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nunchaku_repo = f"mit-han-lab/svdq-int4-flux.1-depth-dev/svdq-{nunchaku_precision}_r32-flux.1-schnell.safetensors"
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nunchaku_repo = f"nunchaku-tech/nunchaku-flux.1-depth-dev/svdq-{nunchaku_precision}-flux.1-depth-dev.safetensors"
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elif 'shuttle' in repo_id.lower():
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nunchaku_repo = f"mit-han-lab/nunchaku-shuttle-jaguar/svdq-{nunchaku_precision}_r32-shuttle-jaguar.safetensors"
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nunchaku_repo = f"nunchaku-tech/nunchaku-shuttle-jaguar/svdq-{nunchaku_precision}-shuttle-jaguar.safetensors"
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else:
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shared.log.error(f'Load module: quant=Nunchaku module=transformer repo="{repo_id}" unsupported')
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if nunchaku_repo is not None:
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@@ -6,10 +6,11 @@ from modules import shared, sd_models, sd_hijack_te, devices, model_quant
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def load_quants(kwargs, repo_id, cache_dir):
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kwargs_copy = kwargs.copy()
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if 'Sana_1600M' in repo_id and model_quant.check_nunchaku('Model'): # only sana-1600m
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if 'Sana_1600M_1024px' in repo_id and model_quant.check_nunchaku('Model'): # only available model
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import nunchaku
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nunchaku_precision = nunchaku.utils.get_precision()
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nunchaku_repo = f"mit-han-lab/svdq-{nunchaku_precision}-sana-1600m"
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nunchaku_repo = "nunchaku-tech/nunchaku-sana/svdq-int4_r32-sana1.6b.safetensors"
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# https://huggingface.co/nunchaku-tech/nunchaku-sana/blob/main/svdq-int4_r32-sana1.6b.safetensors
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shared.log.debug(f'Load module: quant=Nunchaku module=transformer repo="{nunchaku_repo}" precision={nunchaku_precision} attention={shared.opts.nunchaku_attention}')
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kwargs['transformer'] = nunchaku.NunchakuSanaTransformer2DModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype)
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elif model_quant.check_quant('Model'):
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