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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 
	* [stabilityai/stable-diffusion-2-1-unclip-small](https://hf.co/stabilityai/stable-diffusion-2-1-unclip-small)

### Text-to-Image Generation
Stable unCLIP can be leveraged for text-to-image generation by pipelining it with the prior model of KakaoBrain's open source DALL-E 2 replication [Karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha)

```python
import torch
from diffusers import UnCLIPScheduler, DDPMScheduler, StableUnCLIPPipeline
from diffusers.models import PriorTransformer
from transformers import CLIPTokenizer, CLIPTextModelWithProjection

prior_model_id = "kakaobrain/karlo-v1-alpha"
data_type = torch.float16
prior = PriorTransformer.from_pretrained(prior_model_id, subfolder="prior", torch_dtype=data_type)

prior_text_model_id = "openai/clip-vit-large-patch14"
prior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id)
prior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type)
prior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder="prior_scheduler")
prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)

stable_unclip_model_id = "stabilityai/stable-diffusion-2-1-unclip-small"

pipe = StableUnCLIPPipeline.from_pretrained(
    stable_unclip_model_id,
    torch_dtype=data_type,
    variant="fp16",
    prior_tokenizer=prior_tokenizer,
    prior_text_encoder=prior_text_model,
    prior=prior,
    prior_scheduler=prior_scheduler,
)

pipe = pipe.to("cuda")
wave_prompt = "dramatic wave, the Oceans roar, Strong wave spiral across the oceans as the waves unfurl into roaring crests; perfect wave form; perfect wave shape; dramatic wave shape; wave shape unbelievable; wave; wave shape spectacular"

images = pipe(prompt=wave_prompt).images
images[0].save("waves.png")
```
<Tip warning={true}>

For text-to-image we use `stabilityai/stable-diffusion-2-1-unclip-small` as it was trained on CLIP ViT-L/14 embedding, the same as the Karlo model prior. [stabilityai/stable-diffusion-2-1-unclip](https://hf.co/stabilityai/stable-diffusion-2-1-unclip) was trained on OpenCLIP ViT-H, so we don't recommend its use. 

</Tip>

### Text guided Image-to-Image Variation

```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 = pipe.to("cuda")

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].save("variation_image.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("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

[[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