lllyasviel / lllyasviel/FramePack

3060ti上运行很慢

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#733 1 comment 0 reactions 0 assignees View on GitHub
Dominant language
Python
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Description

Currently enabled native sdp backends: ['flash', 'math', 'mem_efficient', 'cudnn']
Xformers is not installed!
Flash Attn is not installed!
Sage Attn is not installed!
Namespace(share=False, server='127.0.0.1', port=None, inbrowser=True)
Free VRAM 6.9609375 GB
High-VRAM Mode: False
Downloading shards: 100%|██████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 1996.81it/s]
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 4/4 [00:01<00:00, 2.03it/s]
Fetching 3 files: 100%|████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 3000.22it/s]
Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████| 3/3 [00:01<00:00, 1.82it/s]
transformer.high_quality_fp32_output_for_inference = True
* Running on local URL: http://127.0.0.1:7860

To create a public link, set `share=True` in `launch()`.
Unloaded DynamicSwap_LlamaModel as complete.
Unloaded CLIPTextModel as complete.
Unloaded SiglipVisionModel as complete.
Unloaded AutoencoderKLHunyuanVideo as complete.
Unloaded DynamicSwap_HunyuanVideoTransformer3DModelPacked as complete.
Loaded CLIPTextModel to cuda:0 as complete.
Unloaded CLIPTextModel as complete.
Loaded AutoencoderKLHunyuanVideo to cuda:0 as complete.
Unloaded AutoencoderKLHunyuanVideo as complete.
Loaded SiglipVisionModel to cuda:0 as complete.
latent_padding_size = 27, is_last_section = False
Unloaded SiglipVisionModel as complete.
Moving DynamicSwap_HunyuanVideoTransformer3DModelPacked to cuda:0 with preserved memory: 6 GB
52%|██████████████████████████████████████████▋ | 13/25 [08:24<04:00, 20.02s/it]

Contributor guide

No contributing guide indexed for this repository

Research direction

No source files or tests are identified in the report. Start by reproducing the slow run on an RTX 3060 Ti using the provided runtime log, then inspect the attention backends and model-loading behavior; done means identifying and documenting the bottleneck or a reproducible cause.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
machine-learning, performance
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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