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# Kandinsky

## Overview

Kandinsky 2.1 inherits best practices from [DALL-E 2](https://arxiv.org/abs/2204.06125) and [Latent Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/latent_diffusion), while introducing some new ideas.

It uses [CLIP](https://huggingface.co/docs/transformers/model_doc/clip) for encoding images and text, and a diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach enhances the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.

The Kandinsky model is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey) and [Denis Dimitrov](https://github.com/denndimitrov) and the original codebase can be found [here](https://github.com/ai-forever/Kandinsky-2)

## Available Pipelines:

| Pipeline | Tasks | Colab
|---|---|:---:|
| [pipeline_kandinsky.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py) | *Text-to-Image Generation* | - |
| [pipeline_kandinsky_inpaint.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py) | *Image-Guided Image Generation* | - |
| [pipeline_kandinsky_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py) | *Image-Guided Image Generation* | - |

## Usage example

In the following, we will walk you through some cool examples of using the Kandinsky pipelines to create some visually aesthetic artwork.

### Text-to-Image Generation

For text-to-image generation, we need to use both [`KandinskyPriorPipeline`] and [`KandinskyPipeline`]. The first step is to encode text prompts with CLIP and then diffuse the CLIP text embeddings to CLIP image embeddings, as first proposed in [DALL-E 2](https://cdn.openai.com/papers/dall-e-2.pdf). Let's throw a fun prompt at Kandinsky to see what it comes up with :)

```python
prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
negative_prompt = "low quality, bad quality"
```

We will pass both the `prompt` and `negative_prompt` to our prior diffusion pipeline. In contrast to other diffusion pipelines, such as Stable Diffusion, the `prompt` and `negative_prompt` shall be passed separately so that we can retrieve a CLIP image embedding for each prompt input. You can use `guidance_scale`, and `num_inference_steps` arguments to guide this process, just like how you would normally do with all other pipelines in diffusers.

```python
from diffusers import KandinskyPriorPipeline
import torch

# create prior
pipe_prior = KandinskyPriorPipeline.from_pretrained(
    "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
)
pipe_prior.to("cuda")

generator = torch.Generator(device="cuda").manual_seed(12)
image_emb = pipe_prior(
    prompt, guidance_scale=1.0, num_inference_steps=25, generator=generator, negative_prompt=negative_prompt
).images

zero_image_emb = pipe_prior(
    negative_prompt, guidance_scale=1.0, num_inference_steps=25, generator=generator, negative_prompt=negative_prompt
).images
```

Once we create the image embedding, we can use [`KandinskyPipeline`] to generate images.

```python
from PIL import Image
from diffusers import KandinskyPipeline


def image_grid(imgs, rows, cols):
    assert len(imgs) == rows * cols

    w, h = imgs[0].size
    grid = Image.new("RGB", size=(cols * w, rows * h))
    grid_w, grid_h = grid.size

    for i, img in enumerate(imgs):
        grid.paste(img, box=(i % cols * w, i // cols * h))
    return grid


# create diffuser pipeline
pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
pipe.to("cuda")

images = pipe(
    prompt,
    image_embeds=image_emb,
    negative_image_embeds=zero_image_emb,
    num_images_per_prompt=2,
    height=768,
    width=768,
    num_inference_steps=100,
    guidance_scale=4.0,
    generator=generator,
).images
```

One cheeseburger monster coming up! Enjoy! 

![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/cheeseburger.png)

The Kandinsky model works extremely well with creative prompts. Here is some of the amazing art that can be created using the exact same process but with different prompts.

```python
prompt = "bird eye view shot of a full body woman with cyan light orange magenta makeup, digital art, long braided hair her face separated by makeup in the style of yin Yang surrealism, symmetrical face, real image, contrasting tone, pastel gradient background"
```
![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/hair.png)

```python
prompt = "A car exploding into colorful dust"
```
![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/dusts.png)

```python
prompt = "editorial photography of an organic, almost liquid smoke style armchair"
```
![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/smokechair.png)

```python
prompt = "birds eye view of a quilted paper style alien planet landscape, vibrant colours, Cinematic lighting"
```
![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/alienplanet.png)


### Text Guided Image-to-Image Generation

The same Kandinsky model weights can be used for text-guided image-to-image translation. In this case, just make sure to load the weights using the [`KandinskyImg2ImgPipeline`] pipeline.

**Note**: You can also directly move the weights of the text-to-image pipelines to the image-to-image pipelines
without loading them twice by making use of the [`~DiffusionPipeline.components`] function as explained [here](#converting-between-different-pipelines).

Let's download an image.

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

# download image
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
response = requests.get(url)
original_image = Image.open(BytesIO(response.content)).convert("RGB")
original_image = original_image.resize((768, 512))
```

![img](https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg)

```python
import torch
from diffusers import KandinskyImg2ImgPipeline, KandinskyPriorPipeline

# create prior
pipe_prior = KandinskyPriorPipeline.from_pretrained(
    "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
)
pipe_prior.to("cuda")

# create img2img pipeline
pipe = KandinskyImg2ImgPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
pipe.to("cuda")

prompt = "A fantasy landscape, Cinematic lighting"
negative_prompt = "low quality, bad quality"

generator = torch.Generator(device="cuda").manual_seed(30)
image_emb = pipe_prior(
    prompt, guidance_scale=4.0, num_inference_steps=25, generator=generator, negative_prompt=negative_prompt
).images

zero_image_emb = pipe_prior(
    negative_prompt, guidance_scale=4.0, num_inference_steps=25, generator=generator, negative_prompt=negative_prompt
).images

out = pipe(
    prompt,
    image=original_image,
    image_embeds=image_emb,
    negative_image_embeds=zero_image_emb,
    height=768,
    width=768,
    num_inference_steps=500,
    strength=0.3,
)

out.images[0].save("fantasy_land.png")
```

![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/img2img_fantasyland.png)


### Text Guided Inpainting Generation

You can use [`KandinskyInpaintPipeline`] to edit images. In this example, we will add a hat to the portrait of a cat.

```python
from diffusers import KandinskyInpaintPipeline, KandinskyPriorPipeline
from diffusers.utils import load_image
import torch
import numpy as np

pipe_prior = KandinskyPriorPipeline.from_pretrained(
    "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
)
pipe_prior.to("cuda")

prompt = "a hat"
image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

pipe = KandinskyInpaintPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16)
pipe.to("cuda")

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

mask = np.ones((768, 768), dtype=np.float32)
# Let's mask out an area above the cat's head
mask[:250, 250:-250] = 0

out = pipe(
    prompt,
    image=init_image,
    mask_image=mask,
    image_embeds=image_emb,
    negative_image_embeds=zero_image_emb,
    height=768,
    width=768,
    num_inference_steps=150,
)

image = out.images[0]
image.save("cat_with_hat.png")
```
![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/inpaint_cat_hat.png)

### Interpolate 

The [`KandinskyPriorPipeline`] also comes with a cool utility function that will allow you to interpolate the latent space of different images and texts super easily. Here is an example of how you can create an Impressionist-style portrait for your pet based on "The Starry Night". 

Note that you can interpolate between texts and images - in the below example, we passed a text prompt "a cat" and two images to the `interplate` function, along with a `weights` variable containing the corresponding weights for each condition we interplate. 

```python
from diffusers import KandinskyPriorPipeline, KandinskyPipeline
from diffusers.utils import load_image
import PIL

import torch
from torchvision import transforms

pipe_prior = KandinskyPriorPipeline.from_pretrained(
    "kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16
)
pipe_prior.to("cuda")

img1 = load_image(
    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"
)

img2 = load_image(
    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/starry_night.jpeg"
)

# add all the conditions we want to interpolate, can be either text or image
images_texts = ["a cat", img1, img2]
# specify the weights for each condition in images_texts
weights = [0.3, 0.3, 0.4]
image_emb, zero_image_emb = pipe_prior.interpolate(images_texts, weights)

pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
pipe.to("cuda")

image = pipe(
    "", image_embeds=image_emb, negative_image_embeds=zero_image_emb, height=768, width=768, num_inference_steps=150
).images[0]

image.save("starry_cat.png")
```
![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/starry_cat.png)


## KandinskyPriorPipeline

[[autodoc]] KandinskyPriorPipeline
	- all
	- __call__
	- interpolate
	
## KandinskyPipeline

[[autodoc]] KandinskyPipeline
	- all
	- __call__

## KandinskyInpaintPipeline

[[autodoc]] KandinskyInpaintPipeline
	- all
	- __call__
	
## KandinskyImg2ImgPipeline

[[autodoc]] KandinskyImg2ImgPipeline
	- all
	- __call__

