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Updated lora inference instructions (#6913)

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* Updated lora inference instructions

* Update examples/dreambooth/README.md

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update README.md

* Update README.md

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
This commit is contained in:
Alex Umnov
2024-02-13 05:05:20 +01:00
committed by GitHub
parent 4b89aeffe1
commit e7696e20f9
1 changed files with 4 additions and 8 deletions
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examples/dreambooth/README.md
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@@ -376,18 +376,14 @@ After training, LoRA weights can be loaded very easily into the original pipelin
load the original pipeline:
```python
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch
pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("base-model-name").to("cuda")
```
Next, we can load the adapter layers into the UNet with the [`load_attn_procs` function](https://huggingface.co/docs/diffusers/api/loaders#diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs).
Next, we can load the adapter layers into the pipeline with the [`load_lora_weights` function](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters#lora).
```python
pipe.unet.load_attn_procs("patrickvonplaten/lora_dreambooth_dog_example")
pipe.load_lora_weights("path-to-the-lora-checkpoint")
```
Finally, we can run the model in inference.