AI imagined images. Pythonic generation of stable diffusion images.
"just works" on Linux and macOS(M1) (and maybe windows?).
# on macOS, make sure rust is installed first
>> 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
🤖🧠 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
Automated Replacement (txt2mask) by clipseg
>> imagine --init-image pearl_earring.jpg --mask-prompt face --mask-mode keep --init-image-strength .4 "a female doctor" "an elegant woman"
➡️
>> imagine --init-image fruit-bowl.jpg --mask-prompt fruit --mask-mode replace --init-image-strength .1 "a bowl of pears" "a bowl of gold" "a bowl of popcorn" "a bowl of spaghetti"➡️
Face Enhancement by CodeFormer
>> imagine "a couple smiling" --steps 40 --seed 1 --fix-faces
➡️
Upscaling by RealESRGAN
>> imagine "colorful smoke" --steps 40 --upscale
➡️
>> imagine "gold coins" "a lush forest" "piles of old books" leaves --tile
>> imagine "portrait of a smiling lady. oil painting" --init-image girl_with_a_pearl_earring.jpg
➡️
>> aimg describe assets/mask_examples/bowl001.jpg
a bowl full of gold bars sitting on a table- 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)
- No more distorted faces!
- 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
- Edit images by describing the part you want edited (see example above)
- Have AI generate captions for images
aimg describe <filename-or-url>
For full command line instructions run aimg --help
from imaginairy import imagine, imagine_image_files, ImaginePrompt, WeightedPrompt, LazyLoadingImage
url = "https://upload.wikimedia.org/wikipedia/commons/thumb/6/6c/Thomas_Cole_-_Architect%E2%80%99s_Dream_-_Google_Art_Project.jpg/540px-Thomas_Cole_-_Architect%E2%80%99s_Dream_-_Google_Art_Project.jpg"
prompts = [
ImaginePrompt("a scenic landscape", seed=1),
ImaginePrompt("a bowl of fruit"),
ImaginePrompt([
WeightedPrompt("cat", weight=1),
WeightedPrompt("dog", weight=1),
]),
ImaginePrompt(
"a spacious building",
init_image=LazyLoadingImage(url=url)
),
ImaginePrompt(
"a bowl of strawberries",
init_image=LazyLoadingImage(filepath="mypath/to/bowl_of_fruit.jpg"),
mask_prompt="fruit|stems",
mask_mode="replace",
mask_expansion=3
)
]
for result in imagine(prompts):
# do something
result.save("my_image.jpg")
# or
imagine_image_files(prompts, outdir="./my-art")- ~10 gb space for models to download
- A decent computer with either a CUDA supported graphics card or M1 processor.
- Python installed. Preferably Python 3.10.
- For macOS rust must be installed
to compile the
tokenizerlibrary. be installed via:curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
See example Dockerfile (works on machine where you can pass the gpu into the container)
docker build . -t imaginairy
# you really want to map the cache or you end up wasting a lot of time and space redownloading the model weights
docker run -it --gpus all -v $HOME/.cache/huggingface:/root/.cache/huggingface -v $HOME/.cache/torch:/root/.cache/torch -v `pwd`/outputs:/outputs imaginairy /bin/bash1.5.3
- fix: missing config file for describe feature
1.5.1
- img2img now supported with PLMS (instead of just DDIM)
- added image captioning feature
aimg describe dog.jpg=>a brown dog sitting on grass - added new commandline tool
aimgfor additional image manipulation functionality
1.4.0
- support multiple additive targets for masking with
|symbol. Example: "fruit|stem|fruit stem"
1.3.0
- added prompt based image editing. Example: "fruit => gold coins"
- test coverage improved
1.2.0
- allow urls as init-images
** previous **
- img2img actually does # of steps you specify
- performance optimizations
- numerous other changes
- CLIP - https://openai.com/blog/clip/
- LDM - Latent Diffusion
- Stable Diffusion
- a web interface. this is a python library
- training
- performance optimizations
- ✅ 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/
- https://github.com/neonsecret/stable-diffusion CompVis/stable-diffusion#177
- https://github.com/huggingface/diffusers/pull/532/files
- ✅ deploy to pypi
- find similar images https://knn5.laion.ai/?back=https%3A%2F%2Fknn5.laion.ai%2F&index=laion5B&useMclip=false
- Development Environment
- ✅ add tests
- set up ci (test/lint/format)
- add docs
- remove yaml config
- delete more unused code
- Interface improvements
- ✅ init-image at command line
- prompt expansion
- Image Generation Features
- ✅ add k-diffusion sampling methods
- why is k-diffusion so slow compared to plms? 2 it/s vs 8 it/s
- negative prompting
- some syntax to allow it in a text string
- upscaling
- ✅ realesrgan
- ldm
- https://github.com/lowfuel/progrock-stable
- stable super-res?
- todo: try with 1-0-0-0 mask at full image resolution (rencoding entire image+predicted image at every step)
- todo: use a gaussian pyramid and only include the "high-detail" level of the pyramid into the next step
- ✅ face enhancers
- ✅ gfpgan - https://github.com/TencentARC/GFPGAN
- ✅ codeformer - https://github.com/sczhou/CodeFormer
- ✅ image describe feature -
- outpainting
- ✅ inpainting
- https://github.com/andreas128/RePaint
- 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
- https://github.com/pesser/stable-diffusion/commit/bbb52981460707963e2a62160890d7ecbce00e79
- CPU support
- ✅ img2img for plms
- img2img for kdiff functions
- images as actual prompts instead of just init images
- requires model fine-tuning since SD1.4 expects 77x768 text encoding input
- https://twitter.com/Buntworthy/status/1566744186153484288
- https://github.com/justinpinkney/stable-diffusion
- https://github.com/LambdaLabsML/lambda-diffusers
- https://www.reddit.com/r/MachineLearning/comments/x6k5bm/n_stable_diffusion_image_variations_released/
- cross-attention control:
- guided generation
- ✅ 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
- https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion_textual_inversion_library_navigator.ipynb
- fix saturation at high CFG https://www.reddit.com/r/StableDiffusion/comments/xalo78/fixing_excessive_contrastsaturation_resulting/
- https://www.reddit.com/r/StableDiffusion/comments/xbrrgt/a_rundown_of_twenty_new_methodsoptions_added_to/