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Score_sde_pytorch Alternatives
Similar projects and alternatives to score_sde_pytorch
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Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
[ECCV 2022] Compositional Generation using Diffusion Models
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SaaSHub
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Magic123
Discontinued [ICLR24] Official PyTorch Implementation of Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors
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DiffEqFlux.jl
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
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dpm-solver
Official code for "DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps" (Neurips 2022 Oral)
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Self-Attention-Guidance
Official implementation of the paper "Improving Sample Quality of Diffusion Models Using Self-Attention Guidance" (ICCV 2023)
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score_sde
Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
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DiffusionFastForward
DiffusionFastForward: a free course and experimental framework for diffusion-based generative models
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better score_sde_pytorch alternative or higher similarity.
score_sde_pytorch discussion
score_sde_pytorch reviews and mentions
Posts with mentions or reviews of score_sde_pytorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-26.
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[D] score based vs. Diffusion models
there's an implementation of score-based models from the paper that showed how score based models and diffusion models are the same here: https://github.com/yang-song/score_sde_pytorch
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Machine learning and black box numerical solver[D]
Someone has already mentioned Neural Ordinary Differential Equations, which is also the first thing that came to mind. There are also extensions to it, where one can use PDEs(Neural Hamiltonian Flows) or even stochastic DEs(Score-Based Generative Models) in the model. All of them covering different but overlapping use cases.
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[Discussion] Could someone explain the math behind the number of distinct images that can be generated with a latent diffusion model?
I was considering an unconditional latent diffusion model, but for conditional models, the computation becomes much more complex (we might have to use bayes here). If we use Score-Based Generative Modeling (https://arxiv.org/abs/2011.13456), we could try to find and count all the unique local minima and saddle points, but it is not clear how we can do this...
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[D] Machine Learning - WAYR (What Are You Reading) - Week 138
You can find an implementation here: https://github.com/yang-song/score_sde_pytorch/blob/main/models/ddpm.py
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Stats
Basic score_sde_pytorch repo stats
4
2,123
0.0
about 2 years ago
yang-song/score_sde_pytorch is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of score_sde_pytorch is Jupyter Notebook.
Popular Comparisons
- score_sde_pytorch VS Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- score_sde_pytorch VS diffusion_models
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- score_sde_pytorch VS Magic123
- score_sde_pytorch VS LenslessPiCam
- score_sde_pytorch VS dpm-solver
- score_sde_pytorch VS Self-Attention-Guidance
- score_sde_pytorch VS DiffEqFlux.jl
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