mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 09:14:35 +02:00
fix wan and add hf mirror setting
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
+7
-3
@@ -1,8 +1,8 @@
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# Change Log for SD.Next
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## Update for 2025-10-15
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## Update for 2025-10-16
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### Highlights for 2025-10-15
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### Highlights for 2025-10-16
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It's been a month since the last release and number of changes is yet again massive with over 300 commits!
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Highlight are:
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@@ -15,8 +15,9 @@ Highlight are:
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- **Quantization**:
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new **SVD**-style quantization using SDNQ offers almost zero-loss even with **4bit** quantization
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and now you can also test your favorite quantization on-the-fly and then save/load model for future use
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- Other: support for **Huggingface** mirrors, changes to installer to prevent unwanted `torch-cpu` operations, improved previews, etc.
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### Details for 2025-10-15
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### Details for 2025-10-16
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- **Models**
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- [WAN 2.2 14B VACE](https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B)
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@@ -62,6 +63,9 @@ Highlight are:
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enable in *settings -> pipeline modifiers -> cache-dit*
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- [Nunchaku Flux.1 PulID](https://nunchaku.tech/docs/nunchaku/python_api/nunchaku.pipeline.pipeline_flux_pulid.html)
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automatically enabled if loaded model is FLUX.1 with Nunchaku engine enabled and when PulID script is enabled
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- **Huggingface mirror** in *settings -> huggingface*
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if you're working from location with limited access to huggingface, you can now specify a mirror site
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for example enter, `https://hf-mirror.com`
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- **Compute**
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- **ROCm** for Windows
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support for both official torch preview release of `torch-rocm` for windows and **TheRock** unofficial `torch-rocm` builds for windows
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@@ -27,6 +27,8 @@ def hf_login(token=None):
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if token is None or len(token) <= 4:
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log.debug('HF login: no token provided')
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return False
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if len(shared.opts.huggingface_mirror.strip()) > 0 and os.environ.get('HF_ENDPOINT', None) is None:
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os.environ['HF_ENDPOINT'] = shared.opts.huggingface_mirror.strip()
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if os.environ.get('HUGGING_FACE_HUB_TOKEN', None) is not None:
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os.environ.pop('HUGGING_FACE_HUB_TOKEN', None)
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os.unsetenv('HUGGING_FACE_HUB_TOKEN')
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@@ -688,6 +688,7 @@ options_templates.update(options_section(('huggingface', "Huggingface"), {
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"diffuser_cache_config": OptionInfo(True, "Use cached model config when available"),
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"huggingface_token": OptionInfo('', 'HuggingFace token', gr.Textbox, {"lines": 2}),
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"hf_transfer_mode": OptionInfo("rust", "HuggingFace download method", gr.Radio, {"choices": ['requests', 'rust', 'xet']}),
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"huggingface_mirror": OptionInfo('', 'HuggingFace mirror', gr.Textbox),
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"diffusers_model_load_variant": OptionInfo("default", "Preferred Model variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
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"diffusers_vae_load_variant": OptionInfo("default", "Preferred VAE variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
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@@ -153,76 +153,76 @@ models = {
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url='https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers',
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repo='Wan-AI/Wan2.2-TI2V-5B-Diffusers',
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repo_cls=diffusers.WanPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanTransformer3DModel),
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Model(name='WAN 2.2 5B I2V',
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url='https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers',
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repo='Wan-AI/Wan2.2-TI2V-5B-Diffusers',
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repo_cls=diffusers.WanImageToVideoPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanTransformer3DModel),
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Model(name='WAN 2.2 A14B T2V',
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url='https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers',
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repo='Wan-AI/Wan2.2-T2V-A14B-Diffusers',
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repo_cls=diffusers.WanPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanTransformer3DModel,
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dit_folder=("transformer", "transformer_2")),
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Model(name='WAN 2.2 A14B I2V',
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url='https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers',
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repo='Wan-AI/Wan2.2-I2V-A14B-Diffusers',
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repo_cls=diffusers.WanImageToVideoPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanTransformer3DModel,
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dit_folder=("transformer", "transformer_2")),
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Model(name='WAN 2.2 14B VACE',
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url='https://huggingface.co/linoyts/Wan2.2-VACE-Fun-14B-diffusers',
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repo='linoyts/Wan2.2-VACE-Fun-14B-diffusers',
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repo_cls=diffusers.WanVACEPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanVACETransformer3DModel,
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dit_folder=("transformer", "transformer_2")),
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Model(name='WAN 2.1 1.3B T2V',
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url='https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers',
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repo='Wan-AI/Wan2.1-T2V-1.3B-Diffusers',
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repo_cls=diffusers.WanPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanTransformer3DModel),
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Model(name='WAN 2.1 14B T2V',
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url='https://huggingface.co/Wan-AI/Wan2.1-T2V-14B-Diffusers',
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repo='Wan-AI/Wan2.1-T2V-14B-Diffusers',
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repo_cls=diffusers.WanPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanTransformer3DModel),
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Model(name='WAN 2.1 14B I2V 480p',
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url='https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P-Diffusers',
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repo='Wan-AI/Wan2.1-I2V-14B-480P-Diffusers',
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repo_cls=diffusers.WanImageToVideoPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanTransformer3DModel),
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Model(name='WAN 2.1 14B I2V 720p',
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url='https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers',
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repo='Wan-AI/Wan2.1-I2V-14B-720P-Diffusers',
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repo_cls=diffusers.WanImageToVideoPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanTransformer3DModel),
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Model(name='WAN 2.1 14B FLF2V 720p',
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url='https://huggingface.co/Wan-AI/Wan2.1-FLF2V-14B-720P',
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repo='Wan-AI/Wan2.1-FLF2V-14B-720P-diffusers',
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repo_cls=diffusers.WanImageToVideoPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanTransformer3DModel),
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Model(name='WAN 2.1 VACE 1.3B',
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url='https://huggingface.co/Wan-AI/Wan2.1-VACE-1.3B-diffusers',
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repo='Wan-AI/Wan2.1-VACE-1.3B-diffusers',
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repo_cls=diffusers.WanVACEPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanVACETransformer3DModel),
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Model(name='WAN 2.1 VACE 14B',
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url='https://huggingface.co/Wan-AI/Wan2.1-VACE-14B-diffusers',
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repo='Wan-AI/Wan2.1-VACE-14B-diffusers',
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repo_cls=diffusers.WanVACEPipeline,
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te_cls=transformers.T5EncoderModel,
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te_cls=transformers.UMT5EncoderModel,
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dit_cls=diffusers.WanVACETransformer3DModel),
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],
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'SkyReels V2': [
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@@ -31,6 +31,10 @@ def load_model(selected: models_def.Model):
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selected.te = 'Disty0/t5-xxl'
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selected.te_folder = ''
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selected.te_revision = None
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if selected.te_cls.__name__ == 'UMT5EncoderModel' and shared.opts.te_shared_t5:
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selected.te = 'Wan-AI/Wan2.2-TI2V-5B-Diffusers'
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selected.te_folder = 'text_encoder'
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selected.te_revision = None
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if selected.te_cls.__name__ == 'LlamaModel' and shared.opts.te_shared_t5:
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selected.te = 'hunyuanvideo-community/HunyuanVideo'
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selected.te_folder = 'text_encoder'
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@@ -6,6 +6,7 @@ import numpy as np
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import torch
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import einops
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from modules import shared, errors ,timer, rife
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from modules.video_models.video_utils import check_av
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def get_video_filename(frames:int, codec:str):
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@@ -27,11 +28,9 @@ def images_to_tensor(images):
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def atomic_save_video(filename, tensor:torch.Tensor, fps:float=24, codec:str='libx264', pix_fmt:str='yuv420p', options:str='', metadata:dict={}, pbar=None):
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try:
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import av
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av.logging.set_level(av.logging.ERROR) # pylint: disable=c-extension-no-member
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except Exception as e:
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shared.log.error(f'Video: {e}')
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av = check_av()
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if av is None or av is False:
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shared.log.error('Video: ffmpeg/av not available')
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return
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savejob = shared.state.begin('Save video')
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@@ -2,6 +2,7 @@ import os
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import sys
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import time
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from PIL import Image
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from installer import install
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from modules import shared, sd_models, timer, errors, devices
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@@ -18,8 +19,10 @@ def get_url(url):
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def check_av():
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install('av')
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try:
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import av
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av.logging.set_level(av.logging.ERROR) # pylint: disable=c-extension-no-member
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except Exception as e:
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shared.log.error(f'av package: {e}')
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return False
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