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improve stable unclip doc. #2823

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Mar 28, 2023
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improve stable unclip doc. #2823

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58 changes: 48 additions & 10 deletions docs/source/en/api/pipelines/stable_unclip.mdx
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Original file line number Diff line number Diff line change
Expand Up @@ -42,25 +42,20 @@ Coming soon!
### Text guided Image-to-Image Variation

```python
import requests
import torch
from PIL import Image
from io import BytesIO

from diffusers import StableUnCLIPImg2ImgPipeline
from diffusers.utils import load_image
import torch

pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
)
pipe = pipe.to("cuda")

url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png"

response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = load_image(url)

images = pipe(init_image).images
images[0].save("fantasy_landscape.png")
images[0].save("variation_image.png")
```

Optionally, you can also pass a prompt to `pipe` such as:
Expand All @@ -69,7 +64,50 @@ Optionally, you can also pass a prompt to `pipe` such as:
prompt = "A fantasy landscape, trending on artstation"

images = pipe(init_image, prompt=prompt).images
images[0].save("fantasy_landscape.png")
images[0].save("variation_image_two.png")
```

### Memory optimization

If you are short on GPU memory, you can enable smart CPU offloading so that models that are not needed
immediately for a computation can be offloaded to CPU:

```python
from diffusers import StableUnCLIPImg2ImgPipeline
from diffusers.utils import load_image
import torch

pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
)
# Offload to CPU.
pipe.enable_model_cpu_offload()

url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png"
init_image = load_image(url)

images = pipe(init_image).images
images[0]
```

Further memory optimizations are possible by enabling VAE slicing on the pipeline:

```python
from diffusers import StableUnCLIPImg2ImgPipeline
from diffusers.utils import load_image
import torch

pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
)
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()

url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png"
init_image = load_image(url)

images = pipe(init_image).images
images[0]
```

### StableUnCLIPPipeline
Expand Down