latent-diffusion
latent-diffusion
latent-diffusion | latent-diffusion | |
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1 | 70 | |
0 | 10,681 | |
- | 3.3% | |
1.8 | 0.0 | |
almost 2 years ago | 3 months ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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latent-diffusion
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Run Your Own DALL·E Mini (Craiyon) Server on EC2
We’re referencing our three models directly as git repos (because they’re not available as packages in PyPI) in model_requirements.txt, but we had to make a few changes to make these repos work as pip packages. Pip looks for a setup.py file in the git repo to figure out which files from the repo need to be installed into the environment, as well as what the dependencies of that repo are. GLID-3-xl and latent-diffusion (another diffusion model that GLID-3-xl depends on) had setup.py files that needed tweaks to include all of the code needed to run the models. SwinIR didn’t have a setup.py file at all, so we added one. Finally, all of these setup.py files needed additional dependencies, which we just added to the model_requirements.txt file.
latent-diffusion
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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?
SwinIR - SwinIR: Image Restoration Using Swin Transformer (official repository)
disco-diffusion
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
dalle-mini - DALL·E Mini - Generate images from a text prompt
glid-3-xl - 1.4B latent diffusion model fine tuning
hent-AI - Automation of censor bar detection
dalle-2-preview
stable-diffusion
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
stable-diffusion - A latent text-to-image diffusion model
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image