huggingface / huggingface/diffusers
Getting problems saving text_encoder_ti when using train_dreambooth_lora_sd15_advanced
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描述
### Describe the bug
Hi,
I encountered an issue while training Stable Diffusion 1.5 using the script `examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py`.
The problem occurs when trying to save the text_encoder while also using textual inversion.
The following line of code seems to be causing the issue:
```python
embedding_handler.save_embeddings(f"{output_dir}/{args.output_dir}_emb.safetensors")
```
I discovered that removing {args.output_dir} from the path resolves the problem:
```
embedding_handler.save_embeddings(f"{output_dir}/ti_emb.safetensors")
```
Are there any alternative solutions to this issue? Any insights would be appreciated.
### Reproduction
Use the script train_dreambooth_lora_sd15_advanced.py
Enable textual inversion
Attempt to save the text_encoder
### Logs
_No response_
### System Info
- 🤗 Diffusers version: 0.30.0.dev0
- Platform: Linux-5.15.0-106-generic-x86_64-with-glibc2.35
- Running on a notebook?: No
- Running on Google Colab?: No
- Python version: 3.10.12
- PyTorch version (GPU?): 2.3.1+cu121 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.23.4
- Transformers version: 4.42.3
- Accelerate version: 0.31.0
- PEFT version: 0.11.1
- Bitsandbytes version: not installed
- Safetensors version: 0.4.3
- xFormers version: 0.0.27
- Accelerator: NVIDIA A40, 46068 MiB
NVIDIA A40, 46068 MiB
NVIDIA A40, 46068 MiB
NVIDIA A40, 46068 MiB VRAM
- Using GPU in script?:
- Using distributed or parallel set-up in script?:
### Who can help?
_No response_
贡献指南
调研方向
从 examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py 开始,在启用 textual inversion 和启用 text_encoder 保存的情况下重现运行。检查 issue 中显示的 embedding_handler.save_embeddings 路径,并验证 text_encoder 和 textual inversion embeddings 是否成功保存到预期的输出目录。
由索引模型根据 Issue 内容生成。
评估
- 技术栈
- python, pytorch
- 领域
- machine-learning
- Issue 类型
- 缺陷
- 难度
- 3/5
- 预计耗时
- 1-2 天
- 活跃度
- 停滞
- 描述清晰度
- 基本清楚
- 新手友好度
- 35/100