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Сombine images using usual diffusion models.

images_mixing 최신버젼 다운로드

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# CLIP Guided Images Mixing With Stable Diffusion
Now you can use this images mixing in <a href="https://github.com/huggingface/diffusers/tree/main/examples/community">official diffusers repo</a>. 
 
This approach allows you to combine two images using standard diffusion models `without any prior models`.
Modified and extended existing <a href="https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion_img2img.py">clip guided stable diffusion algorithm</a> to work with images. 

 <img src="./images/main.png" width="1200" height="400" title="preview"/>

```WARNING: It's hard to get a good result of image mixing the first time.```

## Code examples description
<a target="_blank" href="https://colab.research.google.com/github/TheDenk/images_mixing/blob/main/jupyters/colab_example_images_mixing.ipynb">
 <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
 
All examples you can find in ./jupyters folder:

| File Name | Description |
|---|---|
| <a href="https://github.com/TheDenk/images_mixing/blob/main/jupyters/example-no-CoCa.ipynb">example-no-CoCa.ipynb</a> | Short minimal example for images mixing. The weakness of this approach is that you should write prompts for each image. |
| <a href="https://github.com/TheDenk/images_mixing/blob/main/jupyters/example-stable-diffusion-2-base.ipynb">example-stable-diffusion-2-base.ipynb</a> | Example with stable-diffusion-2-base. For prompt generation CoCa is used.|
| <a href="https://github.com/TheDenk/images_mixing/blob/main/jupyters/example-load-by-parts.ipynb">example-load-by-parts.ipynb</a> | Example where each diffusers module is loading separately. |
| <a href="https://github.com/TheDenk/images_mixing/blob/main/jupyters/example-find-best-mix-result.ipynb">example-find-best-mix-result.ipynb</a> | Step by step explained how to get the parameters for mixing. (By complete enumeration of each parameter. xD) |
| <a href="https://github.com/TheDenk/images_mixing/blob/main/jupyters/example-as-augmentation.ipynb">example-as-augmentation.ipynb.ipynb</a> | Using image mixing for image augmentation. Summer to winter example. |

## Short Method Description
Algorithm based on idea of <a href="https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion_img2img.py">clip guided stable diffusion img2img</a>. But with some modifications:
 - Now two images and (optionaly) two prompts (description of each image) are expected.
 - Using interpolated (content-style) CLIP image embedding. (CLIP text embedding in original)
 - Using interpolated (content-style) text embedding for guidance. (text embedding in original)
 - (Optionaly) Using CoCa model for generation image description
### Using different coefficients you can select type of mixing: from style to content or from content to style. Parameters description see below.
`Style to prompt` and `Prompt to style` give different result. Example.

 <img src="./images/difference.png" width="600" height="300" title="content_style_approach"/>

## Getting Started
```
git clone https://github.com/TheDenk/images_mixing.git
cd images_mixing

pip -r install requirements.txt
```
## Short Example
```python
import torch
from PIL import Image
from diffusers import DiffusionPipeline
from transformers import CLIPFeatureExtractor, CLIPModel

# Loading additional models
feature_extractor = CLIPFeatureExtractor.from_pretrained(
    "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
)
clip_model = CLIPModel.from_pretrained(
    "laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16
)

# Pipline creating
mixing_pipeline = DiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    custom_pipeline="./images_mixing.py",
    clip_model=clip_model,
    feature_extractor=feature_extractor,
    torch_dtype=torch.float16,
)
mixing_pipeline.enable_attention_slicing()
mixing_pipeline = mixing_pipeline.to("cuda")

# Pipline running
generator = torch.Generator(device="cuda").manual_seed(117)
content_image = Image.open('./images/boromir.jpg').convert("RGB")
style_image = Image.open('./images/gigachad.jpg').convert("RGB")

pipe_images = mixing_pipeline(
 content_prompt='boromir',
 style_prompt='gigachad',
 num_inference_steps=50,
 content_image=content_image,
 style_image=style_image,
 noise_strength=0.6,
 slerp_latent_style_strength=0.8,
 slerp_prompt_style_strength=0.2,
 slerp_clip_image_style_strength=0.2,
 guidance_scale=9.0,
 batch_size=1,
 clip_guidance_scale=100,
 generator=generator,
).images
pipe_images[0]
```

 <img src="./images/boromir_gigachad_with_prompts.png" width="900" height="300" title="boromir_gigachad"/>

## Using as augmentation
With <a href="https://github.com/facebookresearch/segment-anything.git">Segment anything</a> you can effectively augmenting a dataset of images (<a href="https://github.com/TheDenk/images_mixing/blob/main/jupyters/example-as-augmentation.ipynb">Jupyter notebook example</a>). 

 <img src="./images/add_winter_on_railway.png" width="480" height="600" title="boromir_gigachad"/>

## Short Parameters Description
#### Each `slerp_` parameter has an impact on both images - style and content (more style - less content and and vice versa)
$content strength = 1.0 - stylestrength$

| Parameter Name | Description |
|---|---|
| **slerp_latent_style_strength** | parameter has an impact on start noised latent space. Calculate as spherical distance between latent spaces of style image and content image. |
| **slerp_prompt_style_strength** | parameter has an impact on each diffusion iteration as usual prompt and for clip-guided algorithm. Calculate with CLIP text model as spherical distance between clip text embeddings of style prompt and content prompt. |
| **slerp_clip_image_style_strength** | parameter has an impact on each diffusion iteration for clip-guided algorithm. Calculate with CLIP image model as spherical distance between clip image embeddings of style image and content image. |
| **noise_strength** | just noise coefficient. Less value - more original information from start latent space. Recommended minimum value - 0.5, maximum - 0.7. |

### From style to content recommended start parameters:
```
noise_strength=0.5
slerp_latent_style_strength=0.8
slerp_prompt_style_strength=0.2
slerp_clip_image_style_strength=0.2
```

### From content to style recommended start parameters:
```
noise_strength=0.5
slerp_latent_style_strength=0.2
slerp_prompt_style_strength=0.8
slerp_clip_image_style_strength=0.8
```

## Contacts
Issues should be raised directly in the repository. For professional support and recommendations please <a>welcomedenk@gmail.com</a>.

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