material_stable_diffusion
Tileable Stable Diffusion - Cog model (by TomMoore515)
sd-xy-studies
Examining parameter variation in Stable Diffusion. (by bitRAKE)
| material_stable_diffusion | sd-xy-studies | |
|---|---|---|
| 3 | 6 | |
| 231 | 8 | |
| - | - | |
| 0.0 | 10.0 | |
| over 1 year ago | over 1 year ago | |
| Python | ||
| - | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
material_stable_diffusion
Posts with mentions or reviews of material_stable_diffusion.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-11.
-
How to train a SD model to generate tiled images?
I have seen some work generateing tiled images using SD mode, such as `https://github.com/TomMoore515/material_stable_diffusion`.
- where is the noise function?
-
List of Stable Diffusion systems - Part 2
(Added Sep. 7, 2022) Web app material_stable_diffusion by tommoore515. GitHub repo. "for generating tileable outputs".
sd-xy-studies
Posts with mentions or reviews of sd-xy-studies.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-11.
- How to train a SD model to generate tiled images?
- Helpful X/Y Plots I created to visualize parameter impact
-
What is the difference between Samplers? Which do you prefer?
It's best to see the difference with X/Y graphs - that's why I created this: https://github.com/bitRAKE/sd-xy-studies
- Stable Diffusion links from around October 10, 2022 that I collected for further processing
-
Only 20 steps!
They perform differently: LMS has a very small stable region; "DPM2 a" oscillates like the other "A" types; DPM2 converges -- all of them are exponentially better at prompt comprehension. See for yourself: https://github.com/bitRAKE/sd-xy-studies/tree/main/platonic
-
X/Y graph studies, CFG, steps, sampler, ...
https://github.com/bitRAKE/sd-xy-studies https://github.com/bitRAKE/sd-xy-studies/tree/main/platonic
What are some alternatives?
When comparing material_stable_diffusion and sd-xy-studies you can also consider the following projects:
stability-sdk - SDK for interacting with stability.ai APIs (e.g. stable diffusion inference)
Diffusion-WebUI - One-click run on Colab for all major models (NovelAI, Stable Diffusion V1.5) [Moved to: https://github.com/acheong08/Diffusion-ColabUI]
cog-stable-diffusion - Diffusers Stable Diffusion as a Cog model
stable-diffusion
inpainter - A web GUI built with Next.js for inpainting with Stable Diffusion using the Replicate API.