### Install Kandinsky 2.0 Library

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/Kandinsky_2_0_text2img.ipynb

Installs the Kandinsky 2.0 library from its GitHub repository. This is a prerequisite for using the model.

```python
!pip install "git+https://github.com/ai-forever/Kandinsky-2.0.git"
```

--------------------------------

### Install Diffusers Library

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Installs the diffusers library from the cloned local path. This command should be run after cloning the repository.

```bash
!pip install /content/diffusers
```

--------------------------------

### Install Kandinsky 2.1 and CLIP

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky_2_1_text2img.ipynb

Installs the necessary libraries for Kandinsky 2.1 and CLIP. Run these commands in a notebook environment.

```python
!pip install 'git+https://github.com/ai-forever/Kandinsky-2.git'
!pip install git+https://github.com/openai/CLIP.git
```

--------------------------------

### Install Diffusers and Dependencies

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2.ipynb

Installs the diffusers library from GitHub and other required packages like transformers and accelerate.

```python
!pip install git+https://github.com/ai-forever/diffusers.git
!pip install transformers
!pip install accelerate
```

--------------------------------

### Install Additional Dependencies

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Installs essential Python packages required for the project, including transformers, accelerate, and fastparquet.

```bash
!pip install transformers
!pip install accelerate
!pip install fastparquet
```

--------------------------------

### Clone Repository and Install Dependencies

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_prior.ipynb

Clones the Kandinsky-2 repository and installs necessary packages, including CLIP from a specific Git URL.

```bash
!git clone https://github.com/ai-forever/Kandinsky-2
!pip install './Kandinsky-2'
!pip install git+https://github.com/openai/CLIP.git
```

--------------------------------

### Install Dependencies for Kandinsky 2.2 and ControlNet

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

Installs required Python packages including diffusers, ControlNet, einops, timm, transformers, basicsr, and accelerate. Ensure you have a compatible Python environment.

```python
!pip install git+https://github.com/huggingface/diffusers.git
!git clone https://github.com/lllyasviel/ControlNet
!pip install einops
!pip install timm
!pip install transformers
!pip install basicsr
!pip install accelerate
```

--------------------------------

### Check PyTorch Version

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

Verifies the installed PyTorch version. Ensure you have a compatible version for Kandinsky and ControlNet.

```python
import torch
torch.__version__
```

--------------------------------

### Load Dataset with Pandas

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Loads the downloaded parquet file into a pandas DataFrame for further processing. Ensure pandas, PIL, io, and os are installed.

```python
import pandas as pd
from PIL import Image
import io
import os
df_dataset = pd.read_parquet('/content/train-00000-of-00001-566cc9b19d7203f8.parquet', engine='fastparquet')
```

--------------------------------

### Image-to-Image Translation with Kandinsky 2.0

Source: https://github.com/ai-forever/kandinsky-2/blob/main/README.md

Translate an input image to a new image based on a text prompt and a strength parameter. This example uses 'cuda' and allows customization of generation parameters.

```python
from kandinsky2 import get_kandinsky2
from PIL import Image

model = get_kandinsky2('cuda', task_type='img2img')
init_image = Image.open('image.jpg')
images = model.generate_img2img('кошка', init_image, strength=0.8, num_steps=50, denoised_type='dynamic_threshold', dynamic_threshold_v=99.5, sampler='ddim_sampler', ddim_eta=0.05, guidance_scale=10)
```

--------------------------------

### Execute Decoder LoRA Training

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Executes the constructed command to start the training process for the Kandinsky 2.2 decoder LoRA adapter. This will download model components and begin the training loop.

```bash
!{command}
```

--------------------------------

### Inpainting with Kandinsky 2.0

Source: https://github.com/ai-forever/kandinsky-2/blob/main/README.md

Perform inpainting on an image using a text prompt, an initial image, and a mask. This example uses 'cuda' and specifies generation parameters. The mask is defined using NumPy.

```python
from kandinsky2 import get_kandinsky2
from PIL import Image
import numpy as np

model = get_kandinsky2('cuda', task_type='inpainting')
init_image = Image.open('image.jpg')
mask = np.ones((512, 512), dtype=np.float32)
mask[100:] =  0
images = model.generate_inpainting('Девушка в красном платье', init_image, mask, num_steps=50, denoised_type='dynamic_threshold', dynamic_threshold_v=99.5, sampler='ddim_sampler', ddim_eta=0.05, guidance_scale=10)
```

--------------------------------

### Clone Diffusers Repository

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Clones the diffusers repository from GitHub. This is a prerequisite for installing the library locally.

```bash
!git clone https://github.com/ai-forever/diffusers
```

--------------------------------

### Text-to-Image Generation with Kandinsky 2.0

Source: https://github.com/ai-forever/kandinsky-2/blob/main/README.md

Generate images from text prompts using the Kandinsky 2.0 model. This example uses 'cuda' for processing and specifies parameters like batch size, image dimensions, and sampling steps.

```python
from kandinsky2 import get_kandinsky2

model = get_kandinsky2('cuda', task_type='text2img')
images = model.generate_text2img('A teddy bear на красной площади', batch_size=4, h=512, w=512, num_steps=75, denoised_type='dynamic_threshold', dynamic_threshold_v=99.5, sampler='ddim_sampler', ddim_eta=0.05, guidance_scale=10)
```

--------------------------------

### Generate Image Embeddings and ControlNet Hint

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

This snippet generates image embeddings for the prior and the final decoder, and creates a depth hint from the input image. It also defines negative prompts to guide the generation away from undesirable features.

```python
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'
torch.manual_seed(42)
clip_img_emb = prior.interpolate(images_and_prompts=[img], weights=[1], num_images_per_prompt=1, ).image_embeds
img_emb = prior(prompt='A capybara, 4k photo', image=clip_img_emb, strength=0.85, num_inference_steps=25, num_images_per_prompt=1,)
negative_emb = prior(prompt=negative_prior_prompt, image=clip_img_emb, strength=1, num_inference_steps=25, num_images_per_prompt=1)
hint = make_hint(img, detector).unsqueeze(0).half().to('cuda').repeat(1, 1, 1, 1)
```

--------------------------------

### Generate Image Embeddings with Prior

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Generates image embeddings using the LoRA-enabled prior model. A negative prompt is defined to guide the generation away from undesirable artifacts.

```python
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'
torch.manual_seed(42)
img_emb = prior(prompt='A robot pokemon, 4k photo', num_inference_steps=25, num_images_per_prompt=1,)
```

--------------------------------

### Prepare Sample Dataset and Directory

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_prior.ipynb

Creates a sample CSV file for training data and an 'images' directory with a placeholder image.

```python
#create data, load your data
pd.DataFrame([['images/test.jpg', 'test']], columns=['image_name', 'caption']).to_csv('test.csv', index=False)
os.mkdir('images')
Image.new('RGB', (768, 768)).save('images/test.jpg')
```

--------------------------------

### Execute Prior Model Training

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_prior.ipynb

Runs the training script for the prior model using the specified configuration file.

```bash
!python Kandinsky-2/train_prior.py --config train_config.yaml
```

--------------------------------

### Load Training Configuration

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_2_1_unclip.ipynb

Loads the UnCLIP 2.1 training configuration from a YAML file.

```python
config = OmegaConf.load("Kandinsky-2/train_configs/config_unclip_2_1.yaml")
```

--------------------------------

### Load Training Configuration

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_prior.ipynb

Loads the prior training configuration from a YAML file using OmegaConf.

```python
config = OmegaConf.load("Kandinsky-2/train_configs/config_prior.yaml")
```

--------------------------------

### Import Libraries and Set Device

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2.ipynb

Imports essential libraries for image generation and sets the computation device to CUDA.

```python
import sys
from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
import torch
import PIL
import torch
from diffusers.utils import load_image
from torchvision import transforms
from transformers import CLIPVisionModelWithProjection
from diffusers.models import UNet2DConditionModel
import numpy as np

DEVICE = torch.device('cuda:7')
```

--------------------------------

### Execute Kandinsky 2.1 UnCLIP Training Script

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_2_1_unclip.ipynb

Runs the main training script for Kandinsky 2.1 UnCLIP using the prepared configuration file.

```bash
!python Kandinsky-2/train_2_1_unclip.py --config train_config.yaml
```

--------------------------------

### Image Hint Generation Function

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

Prepares an input image for ControlNet by resizing and detecting features using a specified detector (e.g., Midas for depth). The output is a normalized tensor suitable for model input.

```python
def make_hint(img, detector):
    input_image = np.array(img)
    img = resize_image(HWC3(input_image), input_image.shape[1])
    H, W, C = img.shape
    detected_map, _ = detector(img)
    detected_map = HWC3(detected_map)
    detected_map = torch.from_numpy(detected_map.copy()).float() / 255.0
    hint = detected_map.permute(2, 0, 1)
    return hint
```

--------------------------------

### Update Training Configuration Parameters

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_prior.ipynb

Modifies the loaded configuration to point to the downloaded model weights, the prepared dataset, and sets the number of epochs and save path. It also ensures the save directory exists.

```python
config['params_path'] = 'kandinsky2/2_1/prior_fp16.ckpt'
config['clip_mean_std_path'] = 'kandinsky2/2_1/ViT-L-14_stats.th'
config['data']['train']['df_path'] = 'test.csv'
config['num_epochs'] = 1001
config['save_path'] = 'saves'
if not os.path.exists('saves'):
    os.mkdir('saves')
```

--------------------------------

### Execute Model Weight Download

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_prior.ipynb

Calls the function to download the necessary model weights for Kandinsky 2.1.

```python
get_kandinsky2_1()
```

--------------------------------

### Load Kandinsky Prior Pipeline

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2.ipynb

Initializes the KandinskyV22PriorPipeline using the pre-loaded image encoder. The pipeline is configured with float16 precision and moved to the specified device.

```python
prior = KandinskyV22PriorPipeline.from_pretrained(
    'kandinsky-community/kandinsky-2-2-prior',
    image_encoder=image_encoder, 
    torch_dtype=torch.float16
).to(DEVICE)
```

--------------------------------

### Download Dataset

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Downloads the Pokemon BLIP captions dataset from Hugging Face using wget.

```python
%%capture
!wget https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions/resolve/main/data/train-00000-of-00001-566cc9b19d7203f8.parquet
```

--------------------------------

### Download Kandinsky 2.1 Model Weights

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_2_1_unclip.ipynb

Downloads the necessary model weights and configuration files for Kandinsky 2.1 text-to-image or inpainting tasks using Hugging Face Hub.

```python
def get_kandinsky2_1(
    task_type="text2img",
    cache_dir="./kandinsky2",
    use_auth_token=None,
):
    cache_dir = os.path.join(cache_dir, "2_1")
    if task_type == "text2img":
        model_name = "decoder_fp16.ckpt"
        config_file_url = hf_hub_url(repo_id="sberbank-ai/Kandinsky_2.1", filename=model_name)
    elif task_type == "inpainting":
        model_name = "inpainting_fp16.ckpt"
        config_file_url = hf_hub_url(repo_id="sberbank-ai/Kandinsky_2.1", filename=model_name)
    cached_download(
        config_file_url,
        cache_dir=cache_dir,
        force_filename=model_name,
        use_auth_token=use_auth_token,
    )

    cache_dir_text_en = os.path.join(cache_dir, "text_encoder")
    for name in [
        "config.json",
        "pytorch_model.bin",
        "sentencepiece.bpe.model",
        "special_tokens_map.json",
        "tokenizer.json",
        "tokenizer_config.json",
    ]:
        config_file_url = hf_hub_url(repo_id="sberbank-ai/Kandinsky_2.1", filename=f"text_encoder/{name}")
        cached_download(
            config_file_url,
            cache_dir=cache_dir_text_en,
            force_filename=name,
            use_auth_token=use_auth_token,
        )

    config_file_url = hf_hub_url(repo_id="sberbank-ai/Kandinsky_2.1", filename="movq_final.ckpt")
    cached_download(
        config_file_url,
        cache_dir=cache_dir,
        force_filename="movq_final.ckpt",
        use_auth_token=use_auth_token,
    )

```

--------------------------------

### Create Image Directory

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Creates a directory named 'images' in the /content directory to store processed images. Requires the 'os' module.

```python
dir_name = '/content/images'
os.mkdir(dir_name)
```

--------------------------------

### Image Fusion with Kandinsky 2.1

Source: https://github.com/ai-forever/kandinsky-2/blob/main/README.md

Fuse multiple images and text prompts with specified weights. Requires Pillow for image handling. Adjust parameters like num_steps, guidance_scale, and sampler for desired output.

```python
from kandinsky2 import get_kandinsky2
from PIL import Image
model = get_kandinsky2('cuda', task_type='text2img', model_version='2.1', use_flash_attention=False)
images_texts = ['red cat', Image.open('img1.jpg'), Image.open('img2.jpg'), 'a man']
weights = [0.25, 0.25, 0.25, 0.25]
images = model.mix_images(
    images_texts, 
    weights, 
    num_steps=150,
    batch_size=1, 
    guidance_scale=5,
    h=768, w=768,
    sampler='p_sampler', 
    prior_cf_scale=4,
    prior_steps="5"
)
```

--------------------------------

### Load Input Image

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

Loads an image from a URL and resizes it to the specified dimensions. This image will be used as the input for the image-to-image generation.

```python
img = load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinsky/cat.png"
        ).resize((512, 512))
```

--------------------------------

### Generate Text-to-Image

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/Kandinsky_2_0_text2img.ipynb

Generates images from a text prompt using the initialized model. Customize parameters like batch size, image dimensions, number of steps, denoising settings, sampler, and guidance scale.

```python
images = model.generate_text2img('кошка в космосе', batch_size=4, h=512, w=512, num_steps=75, denoised_type='dynamic_threshold', dynamic_threshold_v=99.5, sampler='ddim_sampler', ddim_eta=0.01, guidance_scale=10)
```

--------------------------------

### Import Necessary Libraries

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Imports essential libraries for using the Kandinsky pipelines, PyTorch, PIL, and Hugging Face utilities.

```python
import sys
from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
import torch
import PIL
import torch
from diffusers.utils import load_image
from torchvision import transforms
from transformers import CLIPVisionModelWithProjection
from diffusers.models import UNet2DConditionModel
import numpy as np
```

--------------------------------

### Initialize Kandinsky 2.0 Model

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/Kandinsky_2_0_text2img.ipynb

Initializes the Kandinsky 2.0 text-to-image model. Specify the device (e.g., 'cuda'), model version, and task type.

```python
model = get_kandinsky2('cuda', model_version="2.0", task_type='text2img')
```

--------------------------------

### Generate Another Image from Text Prompt

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky_2_1_mixing.ipynb

Generate a second image from a different text prompt, using similar generation parameters as the first image.

```python
image_tiger = model.generate_text2img(
    "tiger",
    num_steps=100,
    batch_size=1,
    guidance_scale=4,
    h=768,
    w=768,
    sampler='p_sampler', 
    prior_cf_scale=4,
    prior_steps="5"
)[0]
```

--------------------------------

### Process and Save Images and Captions

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Iterates through the dataset, opens image bytes, saves them as JPG files, and creates a new DataFrame with image paths and captions. This DataFrame is then saved as 'captions.csv'. Requires pandas, PIL, io, and os.

```python
df = []
for i in range(len(df_dataset)):
    image = Image.open(io.BytesIO(df_dataset['image.bytes'].iloc[i]))
    img_path = os.path.join(dir_name, f'{i}.jpg')
    image.save(img_path)
    df.append([img_path, df_dataset['text'].iloc[i]])
df = pd.DataFrame(df)
df.columns = ['paths', 'caption']
df.to_csv('captions.csv', index=False)
```

--------------------------------

### Download Kandinsky 2.1 Prior Model Weights

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_prior.ipynb

Defines a function to download the prior model checkpoint and associated statistics from Hugging Face Hub, caching them locally.

```python
def get_kandinsky2_1(
    cache_dir="./kandinsky2",
    use_auth_token=None,
):
    cache_dir = os.path.join(cache_dir, "2_1")
    
    prior_name = "prior_fp16.ckpt"
    config_file_url = hf_hub_url(repo_id="sberbank-ai/Kandinsky_2.1", filename=prior_name)
    cached_download(
        config_file_url,
        cache_dir=cache_dir,
        force_filename=prior_name,
        use_auth_token=use_auth_token,
    )

    config_file_url = hf_hub_url(repo_id="sberbank-ai/Kandinsky_2.1", filename="ViT-L-14_stats.th")
    cached_download(
        config_file_url,
        cache_dir=cache_dir,
        force_filename="ViT-L-14_stats.th",
        use_auth_token=use_auth_token,
    )
```

--------------------------------

### Generate Image-to-Image with Kandinsky 2.0

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/Kandinsky_2_0_img2img.ipynb

Generates a new image based on an initial image ('image.jpg') and a text prompt ('кошка'). Configures generation parameters such as strength, steps, dynamic thresholding, sampler, and guidance scale.

```python
init_image = Image.open('image.jpg')
images = model.generate_img2img('кошка',  init_image, strength=0.8, num_steps=50, denoised_type='dynamic_threshold', dynamic_threshold_v=99.5, sampler='ddim_sampler', ddim_eta=0.05, guidance_scale=10)
```

--------------------------------

### Generate Image Embeddings and Image

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2.ipynb

Sets a manual seed for reproducibility, defines negative prompts, generates image embeddings using the prior pipeline, and then generates the final image using the decoder pipeline with specified dimensions and steps.

```python
torch.manual_seed(42)

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'
img_emb = prior(
    prompt='A robot, 4k photo',
    num_inference_steps=25, 
    num_images_per_prompt=1
)

negative_emb = prior(
    prompt=negative_prior_prompt,
    num_inference_steps=25,
    num_images_per_prompt=1
)


images = decoder(image_embeds=img_emb.image_embeds, negative_image_embeds=negative_emb.image_embeds, num_inference_steps=50, height=512, width=512)
```

--------------------------------

### Load Kandinsky Decoder Pipeline

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2.ipynb

Initializes the KandinskyV22Pipeline (decoder) using the pre-loaded UNet model. The pipeline is configured with float16 precision and moved to the specified device.

```python
decoder = KandinskyV22Pipeline.from_pretrained(
    'kandinsky-community/kandinsky-2-2-decoder',
    unet=unet, 
    torch_dtype=torch.float16
).to(DEVICE)
```

--------------------------------

### Load Image Encoder

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2.ipynb

Loads the CLIP Vision Model with Projection for the image encoder, essential for the prior pipeline. It's loaded in half precision and moved to the specified device.

```python
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
    'kandinsky-community/kandinsky-2-2-prior',
    subfolder='image_encoder'
).half().to(DEVICE)
```

--------------------------------

### Text-to-Image Generation with Kandinsky 2.2

Source: https://github.com/ai-forever/kandinsky-2/blob/main/README.md

Generates an image from a text prompt using the Kandinsky 2.2 model. Ensure 'cuda' is available for GPU acceleration.

```python
from kandinsky2 import get_kandinsky2
model = get_kandinsky2('cuda', task_type='text2img', model_version='2.2')
images = model.generate_text2img(
    "red cat, 4k photo", 
    decoder_steps=50,
    batch_size=1, 
    h=1024,
    w=768,
)
```

--------------------------------

### Define Prior LoRA Training Command

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Constructs the command-line arguments for training the Kandinsky 2.2 prior LoRA adapter. Similar to the decoder training, this specifies dataset path, batch size, mixed precision, training steps, learning rate, output directory, and LoRA rank.

```python
command = (f'python3 /content/diffusers/examples/kandinsky2_2_train/tune_prior_lora.py ' 
           f'--train_images_paths_csv=/content/captions.csv ' 
           f'--train_batch_size=1 ' 
           f'--gradient_accumulation_steps=1 ' 
           f'--gradient_checkpointing ' 
           f'--mixed_precision="fp16"  ' 
           f'--max_train_steps=500 ' 
           f'--lr=1e-05 ' 
           f'--max_grad_norm=1 ' 
           f'--lr_scheduler="constant" ' 
           f'--lr_warmup_steps=0 ' 
           f'--output_dir=/content/decoder_prior_saves ' 
           f'--rank=4 ' 
           f'--checkpointing_steps=500')
```

--------------------------------

### Initialize Kandinsky 2.0 Image-to-Image Model

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/Kandinsky_2_0_img2img.ipynb

Initializes the Kandinsky 2.0 model for image-to-image generation on a CUDA-enabled device. Specifies the model version and task type.

```python
model = get_kandinsky2('cuda', model_version="2.0", task_type='img2img')
```

--------------------------------

### Import Necessary Libraries

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_prior.ipynb

Imports essential Python libraries for file operations, model configuration, image handling, and data manipulation.

```python
import os
from huggingface_hub import hf_hub_url, cached_download
from copy import deepcopy
from omegaconf.dictconfig import DictConfig
from omegaconf import OmegaConf
from PIL import Image
import numpy as np
import pandas as pd
```

--------------------------------

### Display DataFrame Head

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Displays the first few rows of the loaded DataFrame to inspect its structure and content.

```python
df_dataset
```

--------------------------------

### Text-to-Image Generation with Kandinsky 2.1

Source: https://github.com/ai-forever/kandinsky-2/blob/main/README.md

Generates an image from a text prompt using the Kandinsky 2.1 model. Requires PyTorch and specific model version. Flash attention is disabled.

```python
from kandinsky2 import get_kandinsky2
model = get_kandinsky2('cuda', task_type='text2img', model_version='2.1', use_flash_attention=False)
images = model.generate_text2img(
    "red cat, 4k photo", 
    num_steps=100,
    batch_size=1, 
    guidance_scale=4,
    h=768, w=768,
    sampler='p_sampler', 
    prior_cf_scale=4,
    prior_steps="5"
)
```

--------------------------------

### Inpainting with Kandinsky 2.1

Source: https://github.com/ai-forever/kandinsky-2/blob/main/README.md

Perform inpainting on an image using a text prompt and a mask. Requires Pillow and NumPy. The mask defines the area to be inpainted. Customize generation parameters as needed.

```python
from kandinsky2 import get_kandinsky2
from PIL import Image
import numpy as np

model = get_kandinsky2('cuda', task_type='inpainting', model_version='2.1', use_flash_attention=False)
init_image = Image.open('img.jpg')
mask = np.ones((768, 768), dtype=np.float32)
mask[:,:550] =  0
images = model.generate_inpainting(
    'man 4k photo', 
    init_image, 
    mask, 
    num_steps=150,
    batch_size=1, 
    guidance_scale=5,
    h=768, w=768,
    sampler='p_sampler', 
    prior_cf_scale=4,
    prior_steps="5"
)
```

--------------------------------

### Import Necessary Libraries

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

Imports all required libraries and modules for Kandinsky 2.2 and ControlNet functionality, including image processing and diffusion models.

```python
import sys
sys.path.append('/content/ControlNet/')
from annotator.util import resize_image, HWC3
from annotator.midas import MidasDetector
from diffusers import KandinskyV22PriorEmb2EmbPipeline
from diffusers import KandinskyV22ControlnetImg2ImgPipeline
import torch
import PIL
import torch
from diffusers.utils import load_image
from torchvision import transforms
from transformers import CLIPVisionModelWithProjection
from diffusers.models import UNet2DConditionModel
import numpy as np
```

--------------------------------

### Display Generated Image

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky_2_1_mixing.ipynb

Display the generated image. This is typically done in an environment that supports image rendering, like a Jupyter notebook.

```python
image_yoda
```

--------------------------------

### Load UNet Model

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2.ipynb

Loads the UNet2DConditionModel for the decoder pipeline. The model is loaded in half precision and moved to the specified device.

```python
unet = UNet2DConditionModel.from_pretrained(
    'kandinsky-community/kandinsky-2-2-decoder', 
    subfolder='unet'
).half().to(DEVICE)
```

--------------------------------

### Generate Image Embeddings with Prior

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Generates image embeddings using the prior model. Specify the prompt, number of inference steps, and images per prompt.

```python
negative_emb = prior(prompt=negative_prior_prompt, num_inference_steps=25, num_images_per_prompt=1)
```

--------------------------------

### Display Second Generated Image

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky_2_1_mixing.ipynb

Display the second generated image.

```python
image_tiger
```

--------------------------------

### Define Decoder LoRA Training Command

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Constructs the command-line arguments for training the Kandinsky 2.2 decoder LoRA adapter. This includes dataset path, image resolution, batch size, mixed precision, training steps, learning rate, output directory, and LoRA rank.

```python
command = (f'python3 /content/diffusers/examples/kandinsky2_2_train/tune_decoder_lora.py ' 
           f'--train_images_paths_csv=/content/captions.csv ' 
           f'--image_resolution=768 ' 
           f'--train_batch_size=1 ' 
           f'--gradient_accumulation_steps=1 ' 
           f'--gradient_checkpointing ' 
           f'--mixed_precision="fp16"  ' 
           f'--max_train_steps=500 ' 
           f'--lr=1e-05 ' 
           f'--max_grad_norm=1 ' 
           f'--lr_scheduler="constant" ' 
           f'--lr_warmup_steps=0 ' 
           f'--output_dir=/content/decoder_lora_saves ' 
           f'--rank=4 ' 
           f'--checkpointing_steps=500')
```

--------------------------------

### Initialize Midas Detector

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

Initializes the Midas depth estimation detector. This is used to generate depth maps from input images.

```python
detector = MidasDetector()
```

--------------------------------

### Load Kandinsky Prior Pipeline

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

Loads the Kandinsky 2.2 Prior embedding-to-embedding pipeline, using the pre-loaded image encoder and setting the device to CUDA with float16 precision.

```python
prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained('kandinsky-community/kandinsky-2-2-prior', image_encoder=image_encoder, torch_dtype=torch.float16)
prior = prior.to("cuda")
```

--------------------------------

### Generate Image from Text Prompt

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky_2_1_text2img.ipynb

Generates an image based on a text prompt using the loaded Kandinsky 2.1 model. Adjust parameters like `num_steps`, `guidance_scale`, image dimensions, and sampler for desired results.

```python
images = model.generate_text2img(
    "red cat, 4k photo",
    num_steps=100,
    batch_size=1,
    guidance_scale=4,
    h=768,
    w=768,
    sampler='p_sampler', 
    prior_cf_scale=4,
    prior_steps="5"
)
```

--------------------------------

### Load Kandinsky 2.2 ControlNet Pipeline

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

Loads the Kandinsky 2.2 ControlNet depth pipeline from pre-trained weights. Ensure you have the necessary dependencies and models downloaded.

```python
decoder = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained('kandinsky-community/kandinsky-2-2-controlnet-depth', unet=unet, torch_dtype=torch.float16)
decoder = decoder.to("cuda")
```

--------------------------------

### Decode Image with ControlNet

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

Generates the final image using the decoder, incorporating the input image, text prompts, negative embeddings, and the ControlNet hint. Adjust `num_inference_steps` for quality vs. speed.

```python
images = decoder(image=img, strength=0.5, image_embeds=img_emb.image_embeds, negative_image_embeds=negative_emb.image_embeds, hint=hint, num_inference_steps=50, height=512, width=512)
```

--------------------------------

### Load Kandinsky Models and Move to CUDA

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Loads the pre-trained Kandinsky V2.2 prior and decoder models, including the image encoder and UNet, and moves them to the CUDA-enabled GPU with float16 precision.

```python
image_encoder = CLIPVisionModelWithProjection.from_pretrained('kandinsky-community/kandinsky-2-2-prior', subfolder='image_encoder').to(torch.float16).to('cuda')
unet = UNet2DConditionModel.from_pretrained('kandinsky-community/kandinsky-2-2-decoder', subfolder='unet').to(torch.float16).to('cuda')
prior = KandinskyV22PriorPipeline.from_pretrained('kandinsky-community/kandinsky-2-2-prior', image_encoder=image_encoder, torch_dtype=torch.float16)
prior = prior.to("cuda")
decoder = KandinskyV22Pipeline.from_pretrained('kandinsky-community/kandinsky-2-2-decoder', unet=unet, torch_dtype=torch.float16)
decoder = decoder.to("cuda")
```

--------------------------------

### Load Kandinsky 2.1 Model

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky_2_1_text2img.ipynb

Loads the Kandinsky 2.1 model for text-to-image generation. Specify the device, task type, cache directory, model version, and flash attention usage.

```python
model = get_kandinsky2(
    'cuda', 
    task_type='text2img',
    cache_dir='/tmp/kandinsky2',
    model_version='2.1',
    use_flash_attention=False
)
```

--------------------------------

### Display Generated Image

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2.ipynb

Accesses and displays the first generated image from the 'images' object.

```python
images.images[0]
```

--------------------------------

### Save Updated Training Configuration

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/train_prior.ipynb

Saves the modified training configuration to a new YAML file named 'train_config.yaml'.

```python
with open("train_config.yaml", "w") as f:
    OmegaConf.save(config, f)
```

--------------------------------

### Display Mixed Image

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky_2_1_mixing.ipynb

Display the resulting image after mixing the two input images.

```python
image_mixed
```

--------------------------------

### Import Kandinsky 2.0 Function

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/Kandinsky_2_0_text2img.ipynb

Imports the `get_kandinsky2` function, which is used to load the model.

```python
from kandinsky2 import get_kandinsky2
```

--------------------------------

### Load Kandinsky 2.0 Inpainting Model

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/Kandinsky_2_0_inpainting.ipynb

Loads the Kandinsky 2.0 model for inpainting tasks, specifying the device (e.g., 'cuda') and model version. Ensure CUDA is available if 'cuda' is specified.

```python
model = get_kandinsky2('cuda', model_version="2.0", task_type='inpainting')
```

--------------------------------

### Apply LoRA to Kandinsky Prior

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Initializes LoRA attention processors for the prior model and loads the LoRA weights. This snippet configures LoRA for the prior's attention layers.

```python
from diffusers.models.attention_processor import LoRAAttnProcessor, LoRAAttnAddedKVProcessor
lora_attn_procs = {}
for name in prior.prior.attn_processors.keys():
    lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=2048).to('cuda')
prior.prior.set_attn_processor(lora_attn_procs)
prior.prior.load_state_dict(torch.load('/content/decoder_prior_saves/checkpoint-500/pytorch_model.bin'), strict=False)
None
```

--------------------------------

### Import necessary libraries

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/Kandinsky_2_0_inpainting.ipynb

Imports the required modules from the Kandinsky library and standard Python libraries like PIL and NumPy for image manipulation.

```python
from kandinsky2 import get_kandinsky2
from PIL import Image
import numpy as np
```

--------------------------------

### Mix Two Generated Images

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky_2_1_mixing.ipynb

Mix two previously generated images with specified weights. This function allows blending the content of multiple images.

```python
image_mixed = model.mix_images(
    [image_yoda, image_tiger], [0.5, 0.5], 
    num_steps=100, 
    batch_size=1, 
    guidance_scale=4, 
    h=768, 
    w=768, 
    sampler='p_sampler', 
    prior_cf_scale=4, 
    prior_steps="5",
)[0]
```

--------------------------------

### Load Kandinsky Image Encoder

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

Loads the CLIP image encoder for Kandinsky 2.2, specifying the model path and moving it to the CUDA device with float16 precision.

```python
image_encoder = CLIPVisionModelWithProjection.from_pretrained('kandinsky-community/kandinsky-2-2-prior', subfolder='image_encoder').to(torch.float16).to('cuda')
```

--------------------------------

### Access Generated Image

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/Kandinsky_2_0_text2img.ipynb

Accesses the first generated image from the list of results. The output is a PIL Image object.

```python
images[0]
```

--------------------------------

### Apply LoRA to Kandinsky Decoder

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Initializes LoRA attention processors for the decoder's UNet and loads the LoRA weights. This snippet configures LoRA for cross-attention layers within the decoder's UNet.

```python
from diffusers.models.attention_processor import LoRAAttnProcessor, LoRAAttnAddedKVProcessor
lora_attn_procs = {}
d = torch.load('/content/decoder_lora_saves/checkpoint-500/pytorch_model.bin')
for name in decoder.unet.attn_processors.keys():
    cross_attention_dim = None if name.endswith("attn1.processor") else decoder.unet.config.cross_attention_dim
    if name.startswith("mid_block"):
        hidden_size = decoder.unet.config.block_out_channels[-1]
    elif name.startswith("up_blocks"):
        block_id = int(name[len("up_blocks.")])
        hidden_size = list(reversed(decoder.unet.config.block_out_channels))[block_id]
    elif name.startswith("down_blocks"):
        block_id = int(name[len("down_blocks.")])
        hidden_size = decoder.unet.config.block_out_channels[block_id]
    lora_attn_procs[name] = LoRAAttnAddedKVProcessor(
            hidden_size=hidden_size,
            cross_attention_dim=cross_attention_dim,
            rank=4,
    ).to('cuda')

decoder.unet.set_attn_processor(lora_attn_procs)
decoder.unet.load_state_dict(d, strict=False)
None
```

--------------------------------

### Load Kandinsky ControlNet UNet

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/kandinsky2_2_controlnet.ipynb

Loads the UNet model specifically for Kandinsky 2.2 ControlNet (depth variant), ensuring it's on the CUDA device with float16 precision.

```python
unet = UNet2DConditionModel.from_pretrained('kandinsky-community/kandinsky-2-2-controlnet-depth', subfolder='unet').to(torch.float16).to('cuda')
```

--------------------------------

### Decode Embeddings to Image

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/lora_decoder.ipynb

Decodes image embeddings into an image using the decoder model. Parameters include image embeddings, negative image embeddings, inference steps, height, and width.

```python
images = decoder(image_embeds=img_emb.image_embeds, negative_image_embeds=negative_emb.image_embeds, num_inference_steps=50, height=512, width=512)
```

--------------------------------

### Generate Inpainted Image

Source: https://github.com/ai-forever/kandinsky-2/blob/main/notebooks/Kandinsky_2_0_inpainting.ipynb

Generates an inpainted image using the loaded Kandinsky 2.0 model. Requires an initial image, a mask defining the area to be inpainted, and various generation parameters.

```python
init_image = Image.open('image.jpg')
mask = np.ones((512, 512), dtype=np.float32)
mask[-300:] =  0
images = model.generate_inpainting('красная кошка', init_image, mask, num_steps=50, denoised_type='dynamic_threshold', dynamic_threshold_v=99.5, sampler='ddim_sampler', ddim_eta=0.05, guidance_scale=10)
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.