From 75ea54a1512ac443d517ab35cb9bf45f8d6f326e Mon Sep 17 00:00:00 2001
From: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Date: Wed, 1 Nov 2023 15:36:22 -0700
Subject: [PATCH] [docs] Kandinsky guide (#4555)
* kandinsky 2.1 first draft
* add kandinsky 2.2
* fix identical section headers
* try hfoptions syntax
* add img2img
* add inpaint
* add interpolate
* fix tag
* more cleanups
* typo
* update hfoptions id
* align hfoptions tags
---
docs/source/en/_toctree.yml | 6 +-
docs/source/en/api/pipelines/kandinsky.md | 436 +----------
docs/source/en/api/pipelines/kandinsky_v22.md | 323 +-------
docs/source/en/using-diffusers/kandinsky.md | 692 ++++++++++++++++++
4 files changed, 740 insertions(+), 717 deletions(-)
create mode 100644 docs/source/en/using-diffusers/kandinsky.md
diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
index 0a983a3a9e47..488219e7a88e 100644
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -40,6 +40,8 @@
title: Push files to the Hub
title: Loading & Hub
- sections:
+ - local: using-diffusers/pipeline_overview
+ title: Overview
- local: using-diffusers/unconditional_image_generation
title: Unconditional image generation
- local: using-diffusers/conditional_image_generation
@@ -70,6 +72,8 @@
title: Overview
- local: using-diffusers/sdxl
title: Stable Diffusion XL
+ - local: using-diffusers/kandinsky
+ title: Kandinsky
- local: using-diffusers/controlnet
title: ControlNet
- local: using-diffusers/shap-e
@@ -241,7 +245,7 @@
- local: api/pipelines/pix2pix
title: InstructPix2Pix
- local: api/pipelines/kandinsky
- title: Kandinsky
+ title: Kandinsky 2.1
- local: api/pipelines/kandinsky_v22
title: Kandinsky 2.2
- local: api/pipelines/latent_consistency_models
diff --git a/docs/source/en/api/pipelines/kandinsky.md b/docs/source/en/api/pipelines/kandinsky.md
index 086821a3bc0a..30bc29a5e12e 100644
--- a/docs/source/en/api/pipelines/kandinsky.md
+++ b/docs/source/en/api/pipelines/kandinsky.md
@@ -7,462 +7,60 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License.
-->
-# Kandinsky
+# Kandinsky 2.1
-## Overview
+Kandinsky 2.1 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).
-Kandinsky inherits best practices from [DALL-E 2](https://huggingface.co/papers/2204.06125) and [Latent Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/latent_diffusion), while introducing some new ideas.
+The description from it's GitHub page is:
-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.
+*Kandinsky 2.1 inherits best practicies from Dall-E 2 and Latent diffusion, while introducing some new ideas. As text and image encoder it uses CLIP model and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases 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). The original codebase can be found [here](https://github.com/ai-forever/Kandinsky-2)
-
-
-## Usage example
-
-In the following, we will walk you through some examples of how to use 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.
-
-```py
-prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
-```
-
-First, let's instantiate the prior pipeline and the text-to-image pipeline. Both
-pipelines are diffusion models.
-
-
-```py
-from diffusers import DiffusionPipeline
-import torch
-
-pipe_prior = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16)
-pipe_prior.to("cuda")
-
-t2i_pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
-t2i_pipe.to("cuda")
-```
-
-
-
-By default, the text-to-image pipeline use [`DDIMScheduler`], you can change the scheduler to [`DDPMScheduler`]
-
-```py
-scheduler = DDPMScheduler.from_pretrained("kandinsky-community/kandinsky-2-1", subfolder="ddpm_scheduler")
-t2i_pipe = DiffusionPipeline.from_pretrained(
- "kandinsky-community/kandinsky-2-1", scheduler=scheduler, torch_dtype=torch.float16
-)
-t2i_pipe.to("cuda")
-```
-
-
-
-Now we pass the prompt through the prior to generate image embeddings. The prior
-returns both the image embeddings corresponding to the prompt and negative/unconditional image
-embeddings corresponding to an empty string.
-
-```py
-image_embeds, negative_image_embeds = pipe_prior(prompt, guidance_scale=1.0).to_tuple()
-```
-
-
-
-The text-to-image pipeline expects both `image_embeds`, `negative_image_embeds` and the original
-`prompt` as the text-to-image pipeline uses another text encoder to better guide the second diffusion
-process of `t2i_pipe`.
-
-By default, the prior returns unconditioned negative image embeddings corresponding to the negative prompt of `""`.
-For better results, you can also pass a `negative_prompt` to the prior. This will increase the effective batch size
-of the prior by a factor of 2.
-
-```py
-prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
-negative_prompt = "low quality, bad quality"
-
-image_embeds, negative_image_embeds = pipe_prior(prompt, negative_prompt, guidance_scale=1.0).to_tuple()
-```
-
-
-
-
-Next, we can pass the embeddings as well as the prompt to the text-to-image pipeline. Remember that
-in case you are using a customized negative prompt, that you should pass this one also to the text-to-image pipelines
-with `negative_prompt=negative_prompt`:
-
-```py
-image = t2i_pipe(
- prompt, image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=768, width=768
-).images[0]
-image.save("cheeseburger_monster.png")
-```
-
-One cheeseburger monster coming up! Enjoy!
-
-
-
-
-
-We also provide an end-to-end Kandinsky pipeline [`KandinskyCombinedPipeline`], which combines both the prior pipeline and text-to-image pipeline, and lets you perform inference in a single step. You can create the combined pipeline with the [`~AutoPipelineForText2Image.from_pretrained`] method
-
-```python
-from diffusers import AutoPipelineForText2Image
-import torch
-
-pipe = AutoPipelineForText2Image.from_pretrained(
- "kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16
-)
-pipe.enable_model_cpu_offload()
-```
-
-Under the hood, it will automatically load both [`KandinskyPriorPipeline`] and [`KandinskyPipeline`]. To generate images, you no longer need to call both pipelines and pass the outputs from one to another. You only need to call the combined pipeline once. You can set different `guidance_scale` and `num_inference_steps` for the prior pipeline with the `prior_guidance_scale` and `prior_num_inference_steps` arguments.
-
-```python
-prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
-negative_prompt = "low quality, bad quality"
-
-image = pipe(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale =1.0, guidance_scacle = 4.0, height=768, width=768).images[0]
-```
-
-
-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"
-```
-
-
-```python
-prompt = "A car exploding into colorful dust"
-```
-
-
-```python
-prompt = "editorial photography of an organic, almost liquid smoke style armchair"
-```
-
-
-```python
-prompt = "birds eye view of a quilted paper style alien planet landscape, vibrant colours, Cinematic lighting"
-```
-
-
-
-
-### 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))
-```
-
-
-
-```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"
-
-image_embeds, negative_image_embeds = pipe_prior(prompt, negative_prompt).to_tuple()
-
-out = pipe(
- prompt,
- image=original_image,
- image_embeds=image_embeds,
- negative_image_embeds=negative_image_embeds,
- height=768,
- width=768,
- strength=0.3,
-)
-
-out.images[0].save("fantasy_land.png")
-```
-
-
-
-
-
-
-You can also use the [`KandinskyImg2ImgCombinedPipeline`] for end-to-end image-to-image generation with Kandinsky 2.1
-
-```python
-from diffusers import AutoPipelineForImage2Image
-import torch
-import requests
-from io import BytesIO
-from PIL import Image
-import os
-
-pipe = AutoPipelineForImage2Image.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
-pipe.enable_model_cpu_offload()
-
-prompt = "A fantasy landscape, Cinematic lighting"
-negative_prompt = "low quality, bad quality"
-
-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.thumbnail((768, 768))
-
-image = pipe(prompt=prompt, image=original_image, strength=0.3).images[0]
-```
-
-
-### 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.
-
-```py
-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"
-prior_output = pipe_prior(prompt)
-
-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.zeros((768, 768), dtype=np.float32)
-# Let's mask out an area above the cat's head
-mask[:250, 250:-250] = 1
-
-out = pipe(
- prompt,
- image=init_image,
- mask_image=mask,
- **prior_output,
- height=768,
- width=768,
- num_inference_steps=150,
-)
-
-image = out.images[0]
-image.save("cat_with_hat.png")
-```
-
-
-
-
-To use the [`KandinskyInpaintCombinedPipeline`] to perform end-to-end image inpainting generation, you can run below code instead
-
-```python
-from diffusers import AutoPipelineForInpainting
-
-pipe = AutoPipelineForInpainting.from_pretrained("kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16)
-pipe.enable_model_cpu_offload()
-image = pipe(prompt=prompt, image=original_image, mask_image=mask).images[0]
-```
-
-
-🚨🚨🚨 __Breaking change for Kandinsky Mask Inpainting__ 🚨🚨🚨
-
-We introduced a breaking change for Kandinsky inpainting pipeline in the following pull request: https://github.com/huggingface/diffusers/pull/4207. Previously we accepted a mask format where black pixels represent the masked-out area. This is inconsistent with all other pipelines in diffusers. We have changed the mask format in Knaindsky and now using white pixels instead.
-Please upgrade your inpainting code to follow the above. If you are using Kandinsky Inpaint in production. You now need to change the mask to:
-
-```python
-# For PIL input
-import PIL.ImageOps
-mask = PIL.ImageOps.invert(mask)
-
-# For PyTorch and Numpy input
-mask = 1 - mask
-```
-
-### 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
-
-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]
-
-# We can leave the prompt empty
-prompt = ""
-prior_out = pipe_prior.interpolate(images_texts, weights)
-
-pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
-pipe.to("cuda")
-
-image = pipe(prompt, **prior_out, height=768, width=768).images[0]
-
-image.save("starry_cat.png")
-```
-
-
-## Optimization
-
-Running Kandinsky in inference requires running both a first prior pipeline: [`KandinskyPriorPipeline`]
-and a second image decoding pipeline which is one of [`KandinskyPipeline`], [`KandinskyImg2ImgPipeline`], or [`KandinskyInpaintPipeline`].
-
-The bulk of the computation time will always be the second image decoding pipeline, so when looking
-into optimizing the model, one should look into the second image decoding pipeline.
-
-When running with PyTorch < 2.0, we strongly recommend making use of [`xformers`](https://github.com/facebookresearch/xformers)
-to speed-up the optimization. This can be done by simply running:
-
-```py
-from diffusers import DiffusionPipeline
-import torch
-
-t2i_pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
-t2i_pipe.enable_xformers_memory_efficient_attention()
-```
-
-When running on PyTorch >= 2.0, PyTorch's SDPA attention will automatically be used. For more information on
-PyTorch's SDPA, feel free to have a look at [this blog post](https://pytorch.org/blog/accelerated-diffusers-pt-20/).
-
-To have explicit control , you can also manually set the pipeline to use PyTorch's 2.0 efficient attention:
-
-```py
-from diffusers.models.attention_processor import AttnAddedKVProcessor2_0
-
-t2i_pipe.unet.set_attn_processor(AttnAddedKVProcessor2_0())
-```
-
-The slowest and most memory intense attention processor is the default `AttnAddedKVProcessor` processor.
-We do **not** recommend using it except for testing purposes or cases where very high determistic behaviour is desired.
-You can set it with:
-
-```py
-from diffusers.models.attention_processor import AttnAddedKVProcessor
-
-t2i_pipe.unet.set_attn_processor(AttnAddedKVProcessor())
-```
-
-With PyTorch >= 2.0, you can also use Kandinsky with `torch.compile` which depending
-on your hardware can significantly speed-up your inference time once the model is compiled.
-To use Kandinsksy with `torch.compile`, you can do:
-
-```py
-t2i_pipe.unet.to(memory_format=torch.channels_last)
-t2i_pipe.unet = torch.compile(t2i_pipe.unet, mode="reduce-overhead", fullgraph=True)
-```
-
-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).
+The original codebase can be found at [ai-forever/Kandinsky-2](https://github.com/ai-forever/Kandinsky-2).
-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
+Check out the [Kandinsky Community](https://huggingface.co/kandinsky-community) organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.
-## Available Pipelines:
-
-| Pipeline | Tasks |
-|---|---|
-| [pipeline_kandinsky.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py) | *Text-to-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_kandinsky_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py) | *Image-Guided Image Generation* |
-
-
-### KandinskyPriorPipeline
+## KandinskyPriorPipeline
[[autodoc]] KandinskyPriorPipeline
- all
- __call__
- interpolate
-### KandinskyPipeline
+## KandinskyPipeline
[[autodoc]] KandinskyPipeline
- all
- __call__
-### KandinskyImg2ImgPipeline
+## KandinskyCombinedPipeline
-[[autodoc]] KandinskyImg2ImgPipeline
+[[autodoc]] KandinskyCombinedPipeline
- all
- __call__
-### KandinskyInpaintPipeline
+## KandinskyImg2ImgPipeline
-[[autodoc]] KandinskyInpaintPipeline
+[[autodoc]] KandinskyImg2ImgPipeline
- all
- __call__
-### KandinskyCombinedPipeline
+## KandinskyImg2ImgCombinedPipeline
-[[autodoc]] KandinskyCombinedPipeline
+[[autodoc]] KandinskyImg2ImgCombinedPipeline
- all
- __call__
-### KandinskyImg2ImgCombinedPipeline
+## KandinskyInpaintPipeline
-[[autodoc]] KandinskyImg2ImgCombinedPipeline
+[[autodoc]] KandinskyInpaintPipeline
- all
- __call__
-### KandinskyInpaintCombinedPipeline
+## KandinskyInpaintCombinedPipeline
[[autodoc]] KandinskyInpaintCombinedPipeline
- all
diff --git a/docs/source/en/api/pipelines/kandinsky_v22.md b/docs/source/en/api/pipelines/kandinsky_v22.md
index 44c10fd07789..350b96c3a9be 100644
--- a/docs/source/en/api/pipelines/kandinsky_v22.md
+++ b/docs/source/en/api/pipelines/kandinsky_v22.md
@@ -9,348 +9,77 @@ specific language governing permissions and limitations under the License.
# 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`.
+Kandinsky 2.1 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).
-Same as with Kandinsky 2.1, the easiest way to perform text-to-image generation is to use the combined Kandinsky pipeline. This process is exactly the same as Kandinsky 2.1. All you need to do is to replace the Kandinsky 2.1 checkpoint with 2.2.
+The description from it's GitHub page is:
-```python
-from diffusers import AutoPipelineForText2Image
-import torch
+*Kandinsky 2.2 brings substantial improvements upon its predecessor, Kandinsky 2.1, by introducing a new, more powerful image encoder - CLIP-ViT-G and the ControlNet support. The switch to CLIP-ViT-G as the image encoder significantly increases the model's capability to generate more aesthetic pictures and better understand text, thus enhancing the model's overall performance. The addition of the ControlNet mechanism allows the model to effectively control the process of generating images. This leads to more accurate and visually appealing outputs and opens new possibilities for text-guided image manipulation.*
-pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
-pipe.enable_model_cpu_offload()
-
-prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
-negative_prompt = "low quality, bad quality"
-
-image = pipe(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale =1.0, height=768, width=768).images[0]
-```
-
-Now, let's look at an example where we take separate steps to run the prior pipeline and text-to-image pipeline. This way, we can understand what's happening under the hood and how Kandinsky 2.2 differs from Kandinsky 2.1.
-
-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")
-```
-
-
-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.
-
-### 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))
-```
-
-
-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:
-
-
-### 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.
-
-
-
-## Optimization
-
-Running Kandinsky in inference requires running both a first prior pipeline: [`KandinskyPriorPipeline`]
-and a second image decoding pipeline which is one of [`KandinskyPipeline`], [`KandinskyImg2ImgPipeline`], or [`KandinskyInpaintPipeline`].
-
-The bulk of the computation time will always be the second image decoding pipeline, so when looking
-into optimizing the model, one should look into the second image decoding pipeline.
-
-When running with PyTorch < 2.0, we strongly recommend making use of [`xformers`](https://github.com/facebookresearch/xformers)
-to speed-up the optimization. This can be done by simply running:
-
-```py
-from diffusers import DiffusionPipeline
-import torch
-
-t2i_pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
-t2i_pipe.enable_xformers_memory_efficient_attention()
-```
-
-When running on PyTorch >= 2.0, PyTorch's SDPA attention will automatically be used. For more information on
-PyTorch's SDPA, feel free to have a look at [this blog post](https://pytorch.org/blog/accelerated-diffusers-pt-20/).
-
-To have explicit control , you can also manually set the pipeline to use PyTorch's 2.0 efficient attention:
-
-```py
-from diffusers.models.attention_processor import AttnAddedKVProcessor2_0
-
-t2i_pipe.unet.set_attn_processor(AttnAddedKVProcessor2_0())
-```
-
-The slowest and most memory intense attention processor is the default `AttnAddedKVProcessor` processor.
-We do **not** recommend using it except for testing purposes or cases where very high determistic behaviour is desired.
-You can set it with:
-
-```py
-from diffusers.models.attention_processor import AttnAddedKVProcessor
-
-t2i_pipe.unet.set_attn_processor(AttnAddedKVProcessor())
-```
-
-With PyTorch >= 2.0, you can also use Kandinsky with `torch.compile` which depending
-on your hardware can significantly speed-up your inference time once the model is compiled.
-To use Kandinsksy with `torch.compile`, you can do:
-
-```py
-t2i_pipe.unet.to(memory_format=torch.channels_last)
-t2i_pipe.unet = torch.compile(t2i_pipe.unet, mode="reduce-overhead", fullgraph=True)
-```
-
-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).
+The original codebase can be found at [ai-forever/Kandinsky-2](https://github.com/ai-forever/Kandinsky-2).
-To generate images directly from a single pipeline, you can use [`KandinskyV22CombinedPipeline`], [`KandinskyV22Img2ImgCombinedPipeline`], [`KandinskyV22InpaintCombinedPipeline`].
-These combined pipelines wrap the [`KandinskyV22PriorPipeline`] and [`KandinskyV22Pipeline`], [`KandinskyV22Img2ImgPipeline`], [`KandinskyV22InpaintPipeline`] respectively into a single
-pipeline for a simpler user experience
+Check out the [Kandinsky Community](https://huggingface.co/kandinsky-community) organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.
-## 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_kandinsky2_2_combined.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py) | *End-to-end Text-to-Image, image-to-image, Inpainting 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_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_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* |
+## KandinskyV22PriorPipeline
-
-### KandinskyV22Pipeline
-
-[[autodoc]] KandinskyV22Pipeline
+[[autodoc]] KandinskyV22PriorPipeline
- all
- __call__
+ - interpolate
-### KandinskyV22ControlnetPipeline
+## KandinskyV22Pipeline
-[[autodoc]] KandinskyV22ControlnetPipeline
+[[autodoc]] KandinskyV22Pipeline
- all
- __call__
-### KandinskyV22ControlnetImg2ImgPipeline
+## KandinskyV22CombinedPipeline
-[[autodoc]] KandinskyV22ControlnetImg2ImgPipeline
+[[autodoc]] KandinskyV22CombinedPipeline
- all
- __call__
-### KandinskyV22Img2ImgPipeline
+## KandinskyV22ControlnetPipeline
-[[autodoc]] KandinskyV22Img2ImgPipeline
+[[autodoc]] KandinskyV22ControlnetPipeline
- all
- __call__
-### KandinskyV22InpaintPipeline
+## KandinskyV22PriorEmb2EmbPipeline
-[[autodoc]] KandinskyV22InpaintPipeline
+[[autodoc]] KandinskyV22PriorEmb2EmbPipeline
- all
- __call__
+ - interpolate
-### KandinskyV22PriorPipeline
+## KandinskyV22Img2ImgPipeline
-[[autodoc]] KandinskyV22PriorPipeline
+[[autodoc]] KandinskyV22Img2ImgPipeline
- all
- __call__
- - interpolate
-### KandinskyV22PriorEmb2EmbPipeline
+## KandinskyV22Img2ImgCombinedPipeline
-[[autodoc]] KandinskyV22PriorEmb2EmbPipeline
+[[autodoc]] KandinskyV22Img2ImgCombinedPipeline
- all
- __call__
- - interpolate
-### KandinskyV22CombinedPipeline
+## KandinskyV22ControlnetImg2ImgPipeline
-[[autodoc]] KandinskyV22CombinedPipeline
+[[autodoc]] KandinskyV22ControlnetImg2ImgPipeline
- all
- __call__
-### KandinskyV22Img2ImgCombinedPipeline
+## KandinskyV22InpaintPipeline
-[[autodoc]] KandinskyV22Img2ImgCombinedPipeline
+[[autodoc]] KandinskyV22InpaintPipeline
- all
- __call__
-### KandinskyV22InpaintCombinedPipeline
+## KandinskyV22InpaintCombinedPipeline
[[autodoc]] KandinskyV22InpaintCombinedPipeline
- all
diff --git a/docs/source/en/using-diffusers/kandinsky.md b/docs/source/en/using-diffusers/kandinsky.md
new file mode 100644
index 000000000000..4ca544270766
--- /dev/null
+++ b/docs/source/en/using-diffusers/kandinsky.md
@@ -0,0 +1,692 @@
+# Kandinsky
+
+[[open-in-colab]]
+
+The Kandinsky models are a series of multilingual text-to-image generation models. The Kandinsky 2.0 model uses two multilingual text encoders and concatenates those results for the UNet.
+
+[Kandinsky 2.1](../api/pipelines/kandinsky) changes the architecture to include an image prior model ([`CLIP`](https://huggingface.co/docs/transformers/model_doc/clip)) to generate a mapping between text and image embeddings. The mapping provides better text-image alignment and it is used with the text embeddings during training, leading to higher quality results. Finally, Kandinsky 2.1 uses a [Modulating Quantized Vectors (MoVQ)](https://huggingface.co/papers/2209.09002) decoder - which adds a spatial conditional normalization layer to increase photorealism - to decode the latents into images.
+
+[Kandinsky 2.2](../api/pipelines/kandinsky_v22) improves on the previous model by replacing the image encoder of the image prior model with a larger CLIP-ViT-G model to improve quality. The image prior model was also retrained on images with different resolutions and aspect ratios to generate higher-resolution images and different image sizes.
+
+This guide will show you how to use the Kandinsky models for text-to-image, image-to-image, inpainting, interpolation, and more.
+
+Before you begin, make sure you have the following libraries installed:
+
+```py
+# uncomment to install the necessary libraries in Colab
+#!pip install transformers accelerate safetensors
+```
+
+
+
+Kandinsky 2.1 and 2.2 usage is very similar! The only difference is Kandinsky 2.2 doesn't accept `prompt` as an input when decoding the latents. Instead, Kandinsky 2.2 only accepts `image_embeds` during decoding.
+
+
+
+## Text-to-image
+
+To use the Kandinsky models for any task, you always start by setting up the prior pipeline to encode the prompt and generate the image embeddings. The prior pipeline also generates `negative_image_embeds` that correspond to the negative prompt `""`. For better results, you can pass an actual `negative_prompt` to the prior pipeline, but this'll increase the effective batch size of the prior pipeline by 2x.
+
+
+
+
+```py
+from diffusers import KandinskyPriorPipeline, KandinskyPipeline
+import torch
+
+prior_pipeline = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16).to("cuda")
+pipeline = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16).to("cuda")
+
+prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
+negative_prompt = "low quality, bad quality" # optional to include a negative prompt, but results are usually better
+image_embeds, negative_image_embeds = prior_pipeline(prompt, negative_prompt, guidance_scale=1.0).to_tuple()
+```
+
+Now pass all the prompts and embeddings to the [`KandinskyPipeline`] to generate an image:
+
+```py
+image = pipeline(prompt, image_embeds=image_embeds, negative_prompt=negative_prompt, negative_image_embeds=negative_image_embeds, height=768, width=768).images[0]
+```
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
a cat
+
+
+
+
Van Gogh's Starry Night painting
+
+
+
+
+
+
+
+
+
+
+
+