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1 change: 1 addition & 0 deletions docs/source/en/api/pipelines/text_to_video_zero.md
Original file line number Diff line number Diff line change
Expand Up @@ -40,6 +40,7 @@ To generate a video from prompt, run the following Python code:
```python
import torch
from diffusers import TextToVideoZeroPipeline
import imageio

model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
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2 changes: 1 addition & 1 deletion docs/source/en/optimization/coreml.md
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Expand Up @@ -95,7 +95,7 @@ print(f"Model downloaded at {model_path}")
Once you have downloaded a snapshot of the model, you can test it using Apple's Python script.

```shell
python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" -i models/coreml-stable-diffusion-v1-4_original_packages -o </path/to/output/image> --compute-unit CPU_AND_GPU --seed 93
python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" -i ./models/coreml-stable-diffusion-v1-4_original_packages/original/packages -o </path/to/output/image> --compute-unit CPU_AND_GPU --seed 93
```

Pass the path of the downloaded checkpoint with `-i` flag to the script. `--compute-unit` indicates the hardware you want to allow for inference. It must be one of the following options: `ALL`, `CPU_AND_GPU`, `CPU_ONLY`, `CPU_AND_NE`. You may also provide an optional output path, and a seed for reproducibility.
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