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# Stable unCLIP

Stable unCLIP checkpoints are finetuned from [stable diffusion 2.1](./stable_diffusion_2) checkpoints to condition on CLIP image embeddings.
Stable unCLIP also still conditions on text embeddings. Given the two separate conditionings, stable unCLIP can be used
for text guided image variation. When combined with an unCLIP prior, it can also be used for full text to image generation.

To know more about the unCLIP process, check out the following paper:

[Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125) by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen.

## Tips

Stable unCLIP takes a `noise_level` as input during inference. `noise_level` determines how much noise is added 
to the image embeddings. A higher `noise_level` increases variation in the final un-noised images. By default, 
we do not add any additional noise to the image embeddings i.e. `noise_level = 0`.

### Available checkpoints:

* Image variation
	* [stabilityai/stable-diffusion-2-1-unclip](https://hf.co/stabilityai/stable-diffusion-2-1-unclip)
	* [stabilityai/stable-diffusion-2-1-unclip-small](https://hf.co/stabilityai/stable-diffusion-2-1-unclip-small)
* Text-to-image 
	* Coming soon!

### Text-to-Image Generation

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

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")

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

Optionally, you can also pass a prompt to `pipe` such as:

```python 
prompt = "A fantasy landscape, trending on artstation"

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

### StableUnCLIPPipeline

[[autodoc]] StableUnCLIPPipeline
	- all
	- __call__
	- enable_attention_slicing
	- disable_attention_slicing
	- enable_vae_slicing
	- disable_vae_slicing
	- enable_xformers_memory_efficient_attention
	- disable_xformers_memory_efficient_attention


### StableUnCLIPImg2ImgPipeline

[[autodoc]] StableUnCLIPImg2ImgPipeline
	- all
	- __call__
	- enable_attention_slicing
	- disable_attention_slicing
	- enable_vae_slicing
	- disable_vae_slicing
	- enable_xformers_memory_efficient_attention
	- disable_xformers_memory_efficient_attention