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* Add combined pipeline

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* up

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* fix final

* Add enable model cpu offload kandinsky

* finish

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* Fix

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* make style

* fix kandinsky mask

* fix inpainting test

* add callbacks

* add tests

* fix tests

* Apply suggestions from code review

Co-authored-by: YiYi Xu <[email protected]>

* docs

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---------

Co-authored-by: YiYi Xu <[email protected]>
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patrickvonplaten and yiyixuxu authored Jul 26, 2023
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2 changes: 2 additions & 0 deletions docs/source/en/_toctree.yml
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Expand Up @@ -206,6 +206,8 @@
title: InstructPix2Pix
- local: api/pipelines/kandinsky
title: Kandinsky
- local: api/pipelines/kandinsky_v22
title: Kandinsky 2.2
- local: api/pipelines/latent_diffusion
title: Latent Diffusion
- local: api/pipelines/panorama
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278 changes: 27 additions & 251 deletions docs/source/en/api/pipelines/kandinsky.mdx
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Expand Up @@ -212,9 +212,9 @@ 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)
mask = np.zeros((768, 768), dtype=np.float32)
# Let's mask out an area above the cat's head
mask[:250, 250:-250] = 0
mask[:250, 250:-250] = 1

out = pipe(
prompt,
Expand Down Expand Up @@ -276,208 +276,6 @@ image.save("starry_cat.png")
```
![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/starry_cat.png)


### Text-to-Image Generation with ControlNet Conditioning

In the following, we give a simple example of how to use [`KandinskyV22ControlnetPipeline`] to add control to the text-to-image generation with a depth image.

First, let's take an image and extract its depth map.

```python
from diffusers.utils import load_image

img = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png"
).resize((768, 768))
```
![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png)

We can use the `depth-estimation` pipeline from transformers to process the image and retrieve its depth map.

```python
import torch
import numpy as np

from transformers import pipeline
from diffusers.utils import load_image


def make_hint(image, depth_estimator):
image = depth_estimator(image)["depth"]
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
detected_map = torch.from_numpy(image).float() / 255.0
hint = detected_map.permute(2, 0, 1)
return hint


depth_estimator = pipeline("depth-estimation")
hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
```
Now, we load the prior pipeline and the text-to-image controlnet pipeline

```python
from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline

pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
)
pipe_prior = pipe_prior.to("cuda")

pipe = KandinskyV22ControlnetPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
)
pipe = pipe.to("cuda")
```

We pass the prompt and negative prompt through the prior to generate image embeddings

```python
prompt = "A robot, 4k photo"

negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

generator = torch.Generator(device="cuda").manual_seed(43)
image_emb, zero_image_emb = pipe_prior(
prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
).to_tuple()
```

Now we can pass the image embeddings and the depth image we extracted to the controlnet pipeline. With Kandinsky 2.2, only prior pipelines accept `prompt` input. You do not need to pass the prompt to the controlnet pipeline.

```python
images = pipe(
image_embeds=image_emb,
negative_image_embeds=zero_image_emb,
hint=hint,
num_inference_steps=50,
generator=generator,
height=768,
width=768,
).images

images[0].save("robot_cat.png")
```

The output image looks as follow:
![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/robot_cat_text2img.png)

### Image-to-Image Generation with ControlNet Conditioning

Kandinsky 2.2 also includes a [`KandinskyV22ControlnetImg2ImgPipeline`] that will allow you to add control to the image generation process with both the image and its depth map. This pipeline works really well with [`KandinskyV22PriorEmb2EmbPipeline`], which generates image embeddings based on both a text prompt and an image.

For our robot cat example, we will pass the prompt and cat image together to the prior pipeline to generate an image embedding. We will then use that image embedding and the depth map of the cat to further control the image generation process.

We can use the same cat image and its depth map from the last example.

```python
import torch
import numpy as np

from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
from diffusers.utils import load_image
from transformers import pipeline

img = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinskyv22/cat.png"
).resize((768, 768))


def make_hint(image, depth_estimator):
image = depth_estimator(image)["depth"]
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
detected_map = torch.from_numpy(image).float() / 255.0
hint = detected_map.permute(2, 0, 1)
return hint


depth_estimator = pipeline("depth-estimation")
hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
)
pipe_prior = pipe_prior.to("cuda")

pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
)
pipe = pipe.to("cuda")

prompt = "A robot, 4k photo"
negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

generator = torch.Generator(device="cuda").manual_seed(43)

# run prior pipeline

img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

# run controlnet img2img pipeline
images = pipe(
image=img,
strength=0.5,
image_embeds=img_emb.image_embeds,
negative_image_embeds=negative_emb.image_embeds,
hint=hint,
num_inference_steps=50,
generator=generator,
height=768,
width=768,
).images

images[0].save("robot_cat.png")
```

Here is the output. Compared with the output from our text-to-image controlnet example, it kept a lot more cat facial details from the original image and worked into the robot style we asked for.

![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/robot_cat.png)

## Kandinsky 2.2

The Kandinsky 2.2 release includes robust new text-to-image models that support text-to-image generation, image-to-image generation, image interpolation, and text-guided image inpainting. The general workflow to perform these tasks using Kandinsky 2.2 is the same as in Kandinsky 2.1. First, you will need to use a prior pipeline to generate image embeddings based on your text prompt, and then use one of the image decoding pipelines to generate the output image. The only difference is that in Kandinsky 2.2, all of the decoding pipelines no longer accept the `prompt` input, and the image generation process is conditioned with only `image_embeds` and `negative_image_embeds`.

Let's look at an example of how to perform text-to-image generation using Kandinsky 2.2.

First, let's create the prior pipeline and text-to-image pipeline with Kandinsky 2.2 checkpoints.

```python
from diffusers import DiffusionPipeline
import torch

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

t2i_pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
t2i_pipe.to("cuda")
```

You can then use `pipe_prior` to generate image embeddings.

```python
prompt = "portrait of a women, blue eyes, cinematic"
negative_prompt = "low quality, bad quality"

image_embeds, negative_image_embeds = pipe_prior(prompt, guidance_scale=1.0).to_tuple()
```

Now you can pass these embeddings to the text-to-image pipeline. When using Kandinsky 2.2 you don't need to pass the `prompt` (but you do with the previous version, Kandinsky 2.1).

```
image = t2i_pipe(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=768, width=768).images[
0
]
image.save("portrait.png")
```
![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/%20blue%20eyes.png)

We used the text-to-image pipeline as an example, but the same process applies to all decoding pipelines in Kandinsky 2.2. For more information, please refer to our API section for each pipeline.


## Optimization

Running Kandinsky in inference requires running both a first prior pipeline: [`KandinskyPriorPipeline`]
Expand Down Expand Up @@ -530,85 +328,63 @@ t2i_pipe.unet = torch.compile(t2i_pipe.unet, mode="reduce-overhead", fullgraph=T
After compilation you should see a very fast inference time. For more information,
feel free to have a look at [Our PyTorch 2.0 benchmark](https://huggingface.co/docs/diffusers/main/en/optimization/torch2.0).

<Tip>

To generate images directly from a single pipeline, you can use [`KandinskyCombinedPipeline`], [`KandinskyImg2ImgCombinedPipeline`], [`KandinskyInpaintCombinedPipeline`].
These combined pipelines wrap the [`KandinskyPriorPipeline`] and [`KandinskyPipeline`], [`KandinskyImg2ImgPipeline`], [`KandinskyInpaintPipeline`] respectively into a single
pipeline for a simpler user experience

</Tip>

## Available Pipelines:

| Pipeline | Tasks |
|---|---|
| [pipeline_kandinsky2_2.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py) | *Text-to-Image Generation* |
| [pipeline_kandinsky.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py) | *Text-to-Image Generation* |
| [pipeline_kandinsky2_2_inpaint.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpaint.py) | *Image-Guided Image Generation* |
| [pipeline_kandinsky_combined.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky_combined.py) | *End-to-end Text-to-Image, image-to-image, Inpainting 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_kandinsky2_2_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.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* |
| [pipeline_kandinsky2_2_controlnet.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py) | *Image-Guided Image Generation* |
| [pipeline_kandinsky2_2_controlnet_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py) | *Image-Guided Image Generation* |


### KandinskyV22Pipeline

[[autodoc]] KandinskyV22Pipeline
- all
- __call__

### KandinskyV22ControlnetPipeline

[[autodoc]] KandinskyV22ControlnetPipeline
- all
- __call__

### KandinskyV22ControlnetImg2ImgPipeline
### KandinskyPriorPipeline

[[autodoc]] KandinskyV22ControlnetImg2ImgPipeline
[[autodoc]] KandinskyPriorPipeline
- all
- __call__
- interpolate

### KandinskyPipeline

### KandinskyV22Img2ImgPipeline

[[autodoc]] KandinskyV22Img2ImgPipeline
[[autodoc]] KandinskyPipeline
- all
- __call__

### KandinskyV22InpaintPipeline
### KandinskyImg2ImgPipeline

[[autodoc]] KandinskyV22InpaintPipeline
[[autodoc]] KandinskyImg2ImgPipeline
- all
- __call__

### KandinskyV22PriorPipeline

[[autodoc]] ## KandinskyV22PriorPipeline
- all
- __call__
- interpolate

### KandinskyV22PriorEmb2EmbPipeline
### KandinskyInpaintPipeline

[[autodoc]] KandinskyV22PriorEmb2EmbPipeline
[[autodoc]] KandinskyInpaintPipeline
- all
- __call__
- interpolate

### KandinskyPriorPipeline
### KandinskyCombinedPipeline

[[autodoc]] KandinskyPriorPipeline
[[autodoc]] KandinskyCombinedPipeline
- all
- __call__
- interpolate

### KandinskyPipeline

[[autodoc]] KandinskyPipeline
- all
- __call__

### KandinskyImg2ImgPipeline
### KandinskyImg2ImgCombinedPipeline

[[autodoc]] KandinskyImg2ImgPipeline
[[autodoc]] KandinskyImg2ImgCombinedPipeline
- all
- __call__

### KandinskyInpaintPipeline
### KandinskyInpaintCombinedPipeline

[[autodoc]] KandinskyInpaintPipeline
[[autodoc]] KandinskyInpaintCombinedPipeline
- all
- __call__
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