# ImaginAIry 🤖ðŸ§
AI imagined images. Pythonic generation of stable diffusion images.
"just works" on Linux and OSX(M1).
## Examples
```bash
>> pip install imaginairy
>> imagine "a scenic landscape" "a photo of a dog" "photo of a fruit bowl" "portrait photo of a freckled woman"
```
Console Output
```bash
🤖🧠received 4 prompt(s) and will repeat them 1 times to create 4 images.
Loading model onto mps backend...
Generating 🖼 : "a scenic landscape" 512x512px seed:557988237 prompt-strength:7.5 steps:40 sampler-type:PLMS
PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:29<00:00, 1.36it/s]
🖼 saved to: ./outputs/000001_557988237_PLMS40_PS7.5_a_scenic_landscape.jpg
Generating 🖼 : "a photo of a dog" 512x512px seed:277230171 prompt-strength:7.5 steps:40 sampler-type:PLMS
PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:28<00:00, 1.41it/s]
🖼 saved to: ./outputs/000002_277230171_PLMS40_PS7.5_a_photo_of_a_dog.jpg
Generating 🖼 : "photo of a fruit bowl" 512x512px seed:639753980 prompt-strength:7.5 steps:40 sampler-type:PLMS
PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:28<00:00, 1.40it/s]
🖼 saved to: ./outputs/000003_639753980_PLMS40_PS7.5_photo_of_a_fruit_bowl.jpg
Generating 🖼 : "portrait photo of a freckled woman" 512x512px seed:500686645 prompt-strength:7.5 steps:40 sampler-type:PLMS
PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:29<00:00, 1.37it/s]
🖼 saved to: ./outputs/000004_500686645_PLMS40_PS7.5_portrait_photo_of_a_freckled_woman.jpg
```
### Tiled Images
```bash
>> imagine "gold coins" "a lush forest" "piles of old books" leaves --tile
```
### Image-to-Image
```bash
>> imagine "portrait of a smiling lady. oil painting" --init-image girl_with_a_pearl_earring.jpg
```
=>
## Features
- It makes images from text descriptions! 🎉
- Generate images either in code or from command line.
- It just works. Proper requirements are installed. model weights are automatically downloaded. No huggingface account needed.
(if you have the right hardware... and aren't on windows)
- Noisy logs are gone (which was surprisingly hard to accomplish)
- WeightedPrompts let you smash together separate prompts (cat-dog)
- Tile Mode creates tileable images
- Prompt metadata saved into image file metadata
## How To
```python
from imaginairy import imagine_images, imagine_image_files, ImaginePrompt, WeightedPrompt
prompts = [
ImaginePrompt("a scenic landscape", seed=1),
ImaginePrompt("a bowl of fruit"),
ImaginePrompt([
WeightedPrompt("cat", weight=1),
WeightedPrompt("dog", weight=1),
])
]
for result in imagine_images(prompts):
# do something
result.save("my_image.jpg")
# or
imagine_image_files(prompts, outdir="./my-art")
```
## Requirements
- ~10 gb space for models to download
- A decent computer with either a CUDA supported graphics card or M1 processor.
## Improvements from CompVis
- img2img actually does # of steps you specify
- performance optimizations
## Models Used
- CLIP - https://openai.com/blog/clip/
- LDM - Latent Diffusion
- Stable Diffusion
- https://github.com/CompVis/stable-diffusion
- https://huggingface.co/CompVis/stable-diffusion-v1-4
- https://laion.ai/blog/laion-5b/
## Not Supported
- a web interface. this is a python library
## Todo
- performance optimizations
- https://github.com/neonsecret/stable-diffusion https://github.com/CompVis/stable-diffusion/pull/177
- ✅ https://github.com/huggingface/diffusers/blob/main/docs/source/optimization/fp16.mdx
- ✅ https://github.com/CompVis/stable-diffusion/compare/main...Doggettx:stable-diffusion:autocast-improvements#
- ✅ https://www.reddit.com/r/StableDiffusion/comments/xalaws/test_update_for_less_memory_usage_and_higher/
- deploy to pypi
- add tests
- set up ci (test/lint/format)
- add docs
- notify https://github.com/CompVis/stable-diffusion/issues/25
- remove yaml config
- delete more unused code
- Interface improvements
- ✅ init-image at command line
- prompt expansion
- Image Generation Features
- upscaling
- https://github.com/lowfuel/progrock-stable
- face improvements
- gfpgan - https://github.com/TencentARC/GFPGAN
- codeformer - https://github.com/sczhou/CodeFormer
- image describe feature - https://replicate.com/methexis-inc/img2prompt
- outpainting
- inpainting
- https://github.com/andreas128/RePaint
- add more sampling methods?
- img2img but keeps img stable
- https://www.reddit.com/r/StableDiffusion/comments/xboy90/a_better_way_of_doing_img2img_by_finding_the/
- https://gist.github.com/trygvebw/c71334dd127d537a15e9d59790f7f5e1
- img2img for plms?
- images as actual prompts instead of just init images
- cross-attention control:
- https://github.com/bloc97/CrossAttentionControl/blob/main/CrossAttention_Release_NoImages.ipynb
- guided generation https://colab.research.google.com/drive/1dlgggNa5Mz8sEAGU0wFCHhGLFooW_pf1#scrollTo=UDeXQKbPTdZI
- tiling
- output show-work videos
- image variations https://github.com/lstein/stable-diffusion/blob/main/VARIATIONS.md
- textual inversion
- https://www.reddit.com/r/StableDiffusion/comments/xbwb5y/how_to_run_textual_inversion_locally_train_your/
- https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb#scrollTo=50JuJUM8EG1h
- zooming videos? a la disco diffusion
- fix saturation at high CFG https://www.reddit.com/r/StableDiffusion/comments/xalo78/fixing_excessive_contrastsaturation_resulting/