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# ImaginAIry 🤖🧠
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AI imagined images.
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"just works" on Linux and OSX(M1).
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## Examples
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```bash
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>> pip install imaginairy
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>> imagine "a scenic landscape" "a photo of a dog" "photo of a fruit bowl" "portrait photo of a freckled woman"
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🤖🧠 received 4 prompt(s) and will repeat them 1 times to create 4 images.
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Loading model onto mps backend...
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Generating 🖼 : "a scenic landscape" 512x512px seed:557988237 prompt-strength:7.5 steps:40 sampler-type:PLMS
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PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:29<00:00, 1.36it/s]
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🖼 saved to: ./outputs/000001_557988237_PLMS40_PS7.5_a_scenic_landscape.jpg
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Generating 🖼 : "a photo of a dog" 512x512px seed:277230171 prompt-strength:7.5 steps:40 sampler-type:PLMS
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PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:28<00:00, 1.41it/s]
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🖼 saved to: ./outputs/000002_277230171_PLMS40_PS7.5_a_photo_of_a_dog.jpg
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Generating 🖼 : "photo of a fruit bowl" 512x512px seed:639753980 prompt-strength:7.5 steps:40 sampler-type:PLMS
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PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:28<00:00, 1.40it/s]
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🖼 saved to: ./outputs/000003_639753980_PLMS40_PS7.5_photo_of_a_fruit_bowl.jpg
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Generating 🖼 : "portrait photo of a freckled woman" 512x512px seed:500686645 prompt-strength:7.5 steps:40 sampler-type:PLMS
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PLMS Sampler: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:29<00:00, 1.37it/s]
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🖼 saved to: ./outputs/000004_500686645_PLMS40_PS7.5_portrait_photo_of_a_freckled_woman.jpg
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```
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<img src="assets/000019_786355545_PLMS50_PS7.5_a_scenic_landscape.jpg" width="256" height="256">
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<img src="assets/000032_337692011_PLMS40_PS7.5_a_photo_of_a_dog.jpg" width="256" height="256">
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<img src="assets/000056_293284644_PLMS40_PS7.5_photo_of_a_bowl_of_fruit.jpg" width="256" height="256">
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<img src="assets/000078_260972468_PLMS40_PS7.5_portrait_photo_of_a_freckled_woman.jpg" width="256" height="256">
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## Features
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- It makes images from text descriptions!
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- Generate images either in code or from command line.
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- It just works (if you have the right hardware... and aren't on windows)
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- Noisy logs are gone (which was surprisingly hard to accomplish)
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- WeightedPrompts let you smash together separate prompts (cat-dog)
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- Tile Mode creates tileable images
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## How To
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```python
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from imaginairy import imagine_images, imagine_image_files, ImaginePrompt, WeightedPrompt
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prompts = [
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ImaginePrompt("a scenic landscape", seed=1),
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ImaginePrompt("a bowl of fruit"),
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ImaginePrompt([
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WeightedPrompt("cat", weight=1),
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WeightedPrompt("dog", weight=1),
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])
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]
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for result in imagine_images(prompts):
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# do something
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result.save("my_image.jpg")
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# or
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imagine_image_files(prompts, outdir="./my-art")
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```
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# Requirements
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- Computer with CUDA supported graphics card. ~10 gb video ram
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OR
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- Apple M1 computer
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# Improvements from CompVis
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- img2img actually does # of steps you specify
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- performance optimizations
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-
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# Models Used
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- CLIP - https://openai.com/blog/clip/
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- LDM - Latent Diffusion
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- Stable Diffusion
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- https://github.com/CompVis/stable-diffusion
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- https://huggingface.co/CompVis/stable-diffusion-v1-4
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- https://laion.ai/blog/laion-5b/
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# Todo
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- performance optimizations
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- https://github.com/huggingface/diffusers/blob/main/docs/source/optimization/fp16.mdx
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- https://github.com/neonsecret/stable-diffusion
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- ✅ https://github.com/CompVis/stable-diffusion/compare/main...Doggettx:stable-diffusion:autocast-improvements#
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- ✅ https://www.reddit.com/r/StableDiffusion/comments/xalaws/test_update_for_less_memory_usage_and_higher/
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- deploy to pypi
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- add tests
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- set up ci (test/lint/format)
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- add docs
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- notify https://github.com/CompVis/stable-diffusion/issues/25
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- remove yaml config
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- delete more unused code
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- Interface improvements
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- init-image at command line
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- prompt expansion?
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- webserver interface (low priority, this is a library)
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- Image Generation Features
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- upscaling
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- https://github.com/lowfuel/progrock-stable
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- face improvements
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- codeformer
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- image describe feature - https://replicate.com/methexis-inc/img2prompt
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- outpainting
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- inpainting
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- https://github.com/andreas128/RePaint
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- add more sampling methods?
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- img2img but keeps img stable
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- https://www.reddit.com/r/StableDiffusion/comments/xboy90/a_better_way_of_doing_img2img_by_finding_the/
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- https://gist.github.com/trygvebw/c71334dd127d537a15e9d59790f7f5e1
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- img2img for plms?
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- images as actual prompts instead of just init images
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- cross-attention control:
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- https://github.com/bloc97/CrossAttentionControl/blob/main/CrossAttention_Release_NoImages.ipynb
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- guided generation https://colab.research.google.com/drive/1dlgggNa5Mz8sEAGU0wFCHhGLFooW_pf1#scrollTo=UDeXQKbPTdZI
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- tiling
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- output show-work videos
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- image variations https://github.com/lstein/stable-diffusion/blob/main/VARIATIONS.md
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- textual inversion
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- https://www.reddit.com/r/StableDiffusion/comments/xbwb5y/how_to_run_textual_inversion_locally_train_your/
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- https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb#scrollTo=50JuJUM8EG1h
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- zooming videos? a la disco diffusion
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- fix saturation at high CFG https://www.reddit.com/r/StableDiffusion/comments/xalo78/fixing_excessive_contrastsaturation_resulting/
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