huggingface / huggingface/diffusers
scheduler文件无法识别
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Descrizione
### Describe the bug
发生异常: ValueError
Pipeline expected {'tokenizer', 'text_encoder', 'unet', 'vae', 'feature_extractor', 'safety_checker', 'scheduler'}, but only {'tokenizer', 'text_encoder', 'unet', 'vae', 'feature_extractor', 'safety_checker'} were passed.
File "D:\local_gradio\app.py", line 25, in
pipe = DiffusionPipeline.from_pretrained("LCM_Dreamshaper_v7_4k.safetensors")
ValueError: Pipeline expected {'tokenizer', 'text_encoder', 'unet', 'vae', 'feature_extractor', 'safety_checker', 'scheduler'}, but only {'tokenizer', 'text_encoder', 'unet', 'vae', 'feature_extractor', 'safety_checker'} were passed.
### Reproduction
我非常确定文件是存在的,然而总是报错无法识别
### Logs
```shell
日志五任何提示
```
### System Info
(D:\conda_envs\lcm_env) D:\>diffusers-cli env
Copy-and-paste the text below in your GitHub issue and FILL OUT the two last points.
- 🤗 Diffusers version: 0.28.0
- Platform: Windows-10-10.0.19044-SP0
- Running on a notebook?: No
- Running on Google Colab?: No
- Python version: 3.8.19
- PyTorch version (GPU?): 2.3.0+cpu (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.23.2
- Transformers version: 4.41.1
- Accelerate version: 0.30.1
- PEFT version: not installed
- Bitsandbytes version: not installed
- Safetensors version: 0.4.3
- xFormers version: not installed
- Accelerator: NVIDIA GeForce GTX 1080 Ti, 11264 MiB VRAM
- Using GPU in script?:
- Using distributed or parallel set-up in script?:
### Who can help?
莉莉娅
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Direzione di ricerca
Start with app.py line 25 and the DiffusionPipeline.from_pretrained invocation, using the listed Diffusers, Transformers, PyTorch, and Python versions to reproduce the ValueError. Compare the pipeline components expected in the error with those passed from LCM_Dreamshaper_v7_4k.safetensors; done means the pipeline loads without the missing-scheduler error.
Scritto dal modello di indicizzazione a partire dal testo della issue.
Valutazione
- Stack tecnologico
- python, pytorch
- Ambito
- machine-learning
- Tipo di issue
- Bug
- Difficoltà
- 3/5
- Tempo stimato
- 1-2 giorni
- Stato di attività
- Ferma
- Chiarezza
- Da chiarire
- Idoneità per principianti
- 25/100