meadowrun
Meadowrun makes it easy to run your code on the cloud (by meadowdata)
latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models (by CompVis)
meadowrun | latent-diffusion | |
---|---|---|
2 | 70 | |
93 | 10,681 | |
- | 3.3% | |
9.1 | 0.0 | |
10 months ago | 3 months ago | |
Python | Jupyter Notebook | |
MIT License | 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.
meadowrun
Posts with mentions or reviews of meadowrun.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-07-26.
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Run Your Own DALL·E Mini (Craiyon) Server on EC2
If you’re anything like us, though, you’ll feel compelled to poke around the code and run the model yourself. We’ll do that in this article using Meadowrun, an open-source library that makes it easy to run Python code in the cloud. For ML models in particular, we just added a feature for requesting GPU machines in a recent release. We’ll also feed the images generated by DALL·E Mini into additional image processing models (GLID-3-xl and SwinIR) to improve the quality of our generated images. Along the way we’ll deal with the speedbumps that come up when running open-source ML models on EC2.
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Why Starting Python on a Fresh EC2 Instance Takes Over a Minute
So it is more reasonable to cache the download locally for up to 4 hours. That saves us 5–10 seconds on every run.
latent-diffusion
Posts with mentions or reviews of latent-diffusion.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-21.
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SDXL: The next generation of Stable Diffusion models for text-to-image synthesis
Stable Diffusion XL (SDXL) is the latest text-to-image generation model developed by Stability AI, based on the latent diffusion techniques. SDXL has the potential to create highly realistic images for media, entertainment, education, and industry domains, opening new ways in practical uses of AI imagery.
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Is it possible to create a checkpoint from scratch?
Here's a link to the early latent-diffusion git, that might be able to create a blank model (I haven't tested it): https://github.com/CompVis/latent-diffusion
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Anything better than pix2pixHD?
Latent diffusion could work for you: https://github.com/CompVis/latent-diffusion (https://arxiv.org/abs/2112.10752)
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Image Upscaler AI
There are a lot but the one implemented as LDSR in most stable guis is this one. https://github.com/CompVis/latent-diffusion
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I've been collecting millions of images of only public domain /cc0 licensing. I'd like to train a stable diffusion model on the collection. Could some one share their knowledge of what this would take? Otherwise, simply enjoy my library.
CompVis/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models (github.com)
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Run Clip on iPhone to Search Photos
The "retrieval based model" refers to https://github.com/CompVis/latent-diffusion#retrieval-augmen..., which uses ScaNN to train a knn embedding searcher.
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Class Action Lawsuit filed against Stable Diffusion and Midjourney.
Stability is basically https://github.com/CompVis/latent-diffusion + training data.
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[D] Influential papers round-up 2022. What are your favorites?
Found relevant code at https://github.com/CompVis/latent-diffusion + all code implementations here
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Can anyone explain differences between sampling methods and their uses to me in simple terms, because all the info I've found so far is either very contradicting or complex and goes over my head
DDIM and PLMS were the original samplers. They were part of Latent Diffusion's repository. They stand for the papers that introduced them, Denoising Diffusion Implicit Models and Pseudo Numerical Methods for Diffusion Models on Manifolds.
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AI art is very dystopian.
yes, https://github.com/CompVis/latent-diffusion
What are some alternatives?
When comparing meadowrun and latent-diffusion you can also consider the following projects:
dalle-playground - A playground to generate images from any text prompt using Stable Diffusion (past: using DALL-E Mini)
disco-diffusion
glid-3-xl - 1.4B latent diffusion model fine tuning
dalle-mini - DALL·E Mini - Generate images from a text prompt
dalle-flow - 🌊 A Human-in-the-Loop workflow for creating HD images from text
hent-AI - Automation of censor bar detection
warehouse - The Python Package Index
dalle-2-preview
glid-3-xl - 1.4B latent diffusion model fine tuning
stable-diffusion
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
meadowrun vs dalle-playground
latent-diffusion vs disco-diffusion
meadowrun vs glid-3-xl
latent-diffusion vs dalle-mini
meadowrun vs dalle-flow
latent-diffusion vs hent-AI
meadowrun vs warehouse
latent-diffusion vs dalle-2-preview
meadowrun vs glid-3-xl
latent-diffusion vs stable-diffusion
meadowrun vs jax
latent-diffusion vs DALLE2-pytorch