RIVAL
[NeurIPS 2023 Spotlight] Real-World Image Variation by Aligning Diffusion Inversion Chain (by dvlab-research)
sd_lite
set-up Stable Diffusion with minimal dependencies and a single multi-function pipe (by thekitchenscientist)
RIVAL | sd_lite | |
---|---|---|
1 | 15 | |
129 | 18 | |
7.8% | - | |
6.9 | 4.5 | |
5 months ago | about 1 year ago | |
Python | Python | |
Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
RIVAL
Posts with mentions or reviews of RIVAL.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-10.
sd_lite
Posts with mentions or reviews of sd_lite.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-10.
- List of Stable Diffusion research softwares that I don't think gotten widespread adoption.
- Comparing 5 recent SD distillation methods SSD/LCM/Turbo to find the best option for low-VRAM users (images and statistical analysis included). SD-Turbo scores significantly higher on aesthetics, the boost to SD-21 is remarkable
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Latent Jitter: a simple method for generating variations on a prompt to composite into a final image. Stacks well with prompt delay and The Stable Artist to give you 4+ options from a single seed/prompt.
The full details of how to do this are available on Github: latent jitter ยท thekitchenscientist/sd_lite but I will explain the idea briefly here. I have read this could be done with perlin or simplex noise but the code was too complex for my taste. This gets the job done with only minor modifications to the standard pipe.
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"SEGA: Instructing Diffusion using Semantic Dimensions": Paper + GitHub repo + web app + Colab notebook for generating images that are variations of a base image generation by specifying secondary text prompt(s). In this example, the secondary text prompt was "smiling". See comment for details.
I did successfully swap the effiel tower for the burj Khalifa but that required additional steps https://github.com/thekitchenscientist/sd_lite/wiki/latent-jitter
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Are there any sure-fire 100% SFW models for Stable Diffusion? Project for kids
I use it in my pipe as a general image beautifier. https://github.com/thekitchenscientist/sd_lite/wiki/safe-latent-diffusion
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Messing with the denoising loop can allow you to reach new places in latent space. Over 8+ different research papers/Auto1111 extension ideas in a single pipe. Load once and do lots of different things (SD 2.1 or 1.5)
The pipe is available at sd_lite/pipeline_stable_diffusion_multi.py (github.com) it combines:
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Comparison of new UniPC sampler method added to Automatic1111
This community has published many XY plots of CFG versus steps. https://github.com/thekitchenscientist/sd_lite/wiki/recommended The consistent theme is low CFG, lower steps; high CFG, more steps. UniPC can reach convergence in as few as 8 steps, so I increased by 1/3 to account for more complex prompts needing longer
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Create Panorama images of ANY size using less then 6GB VRAM, also x6-10 speed-up and added support for batch mode! A modification of MultiDiffusion. Potato computers of the world rejoice. SD2.0 768 model gives fastest creation of larger sizes but the VAE image slicing means no VRAM spike.
the pipeline is available from github.com and is called in the usual way. The Technique requires the DDIM scheduler.
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Img2Img as a side-scrolling enhancer - more pictures in the comments
https://github.com/thekitchenscientist/sd_lite is where the code is. Version 1 of the multi-pipe is limited to images 512 high or wide but any number on the other dimension
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You to can create Panorama images 512x10240+ (not a typo) using less then 6GB VRAM (Vertorama works too). A modification of the MultiDiffusion code to pass the image through the VAE in slices then reassemble. Potato computers of the world rejoice.
Not to be deterred I hacked together some code to blend it all back together after the VAE but before the final colour balance. The pipe code is available on github. thekitchenscientist/sd_lite