Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix the test script in examples/text_to_image/README.md #6209

Merged
merged 2 commits into from
Dec 18, 2023
Merged

Fix the test script in examples/text_to_image/README.md #6209

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
1160bae
Update examples/text_to_image/README.md
krahets Dec 18, 2023
49e808b
Update examples/text_to_image/README.md
krahets Dec 18, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 4 additions & 3 deletions examples/text_to_image/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -101,8 +101,8 @@ accelerate launch --mixed_precision="fp16" train_text_to_image.py \

Once the training is finished the model will be saved in the `output_dir` specified in the command. In this example it's `sd-pokemon-model`. To load the fine-tuned model for inference just pass that path to `StableDiffusionPipeline`


```python
import torch
from diffusers import StableDiffusionPipeline

model_path = "path_to_saved_model"
Expand All @@ -114,12 +114,13 @@ image.save("yoda-pokemon.png")
```

Checkpoints only save the unet, so to run inference from a checkpoint, just load the unet

```python
import torch
from diffusers import StableDiffusionPipeline, UNet2DConditionModel

model_path = "path_to_saved_model"

unet = UNet2DConditionModel.from_pretrained(model_path + "/checkpoint-<N>/unet")
unet = UNet2DConditionModel.from_pretrained(model_path + "/checkpoint-<N>/unet", torch_dtype=torch.float16)

pipe = StableDiffusionPipeline.from_pretrained("<initial model>", unet=unet, torch_dtype=torch.float16)
pipe.to("cuda")
Expand Down
Loading