Row 57907

Row ID: 57907 | Dataset Entry | Axioma AXP Content Repository

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Cool. I started a similar project about 8 months ago with the goal of learning about diffusion models and trying to generate plausible SNES music.

[This](https://developer.nvidia.com/blog/generative-ai-research-spotlight-demystifying-diffusion-based-models/) is a really helpful blog post if you want to take your model beyond generating nightmare fuel and actually generate some cats.

[Here](https://drive.google.com/drive/folders/1aCs0HWvocO1-EN2dXhEa3Hwcje1xeO24?usp=drive_link) are some of the samples I've been able to generate with models in various states of training progress. [Here](https://github.com/parlance-zz/dualdiffusion) is the link to the project github if you're interested. I'd highly recommend adding classifier free guidance using some kind of conditioning, even simple class labels work great. CFG is basically a cheat code to massively improving sample quality and is pretty simple to get going.

I noticed in your [code](https://github.com/Null-byte-00/Catfusion/blob/cbbd4dbca1f2588e7cb189520f731fd31d70e74b/model.py#L146) you're taking the loss as an L1 loss. I guarantee this is hindering and not helping performance; the stereotypical blurriness of MSE loss is a _feature_ in diffusion models, as it enables the statistical guarantees required to allow the model to actually properly anneal over the different noise levels in the sampling process. Just like in the blog post the way I think about it is the ideal model output should be a superposition of all the possible training samples that could be underneath the target noise level. This means blurry outputs are desirable as the number of possible training samples underneath the noise expands to the size of entire dataset at the highest noise levels.

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text Cool. I started a similar project about 8 months ago with the goal of learning about diffusion models and trying to generate plausible SNES music. [This](https://developer.nvidia.com/blog/generative-ai-research-spotlight-demystifying-diffusion-based-models/) is a really helpful blog post if you want to take your model beyond generating nightmare fuel and actually generate some cats. [Here](https://drive.google.com/drive/folders/1aCs0HWvocO1-EN2dXhEa3Hwcje1xeO24?usp=drive_link) are some of the …
label r/machinelearning
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communityName r/MachineLearning
datetime 2024-05-23
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  "text": "Cool. I started a similar project about 8 months ago with the goal of learning about diffusion models and trying to generate plausible SNES music.\n\n[This](https://developer.nvidia.com/blog/generative-ai-research-spotlight-demystifying-diffusion-based-models/) is a really helpful blog post if you want to take your model beyond generating nightmare fuel and actually generate some cats.\n\n[Here](https://drive.google.com/drive/folders/1aCs0HWvocO1-EN2dXhEa3Hwcje1xeO24?usp=drive_link) are some of the samples I've been able to generate with models in various states of training progress. [Here](https://github.com/parlance-zz/dualdiffusion) is the link to the project github if you're interested. I'd highly recommend adding classifier free guidance using some kind of conditioning, even simple class labels work great. CFG is basically a cheat code to massively improving sample quality and is pretty simple to get going.\n\nI noticed in your [code](https://github.com/Null-byte-00/Catfusion/blob/cbbd4dbca1f2588e7cb189520f731fd31d70e74b/model.py#L146) you're taking the loss as an L1 loss. I guarantee this is hindering and not helping performance; the stereotypical blurriness of MSE loss is a _feature_ in diffusion models, as it enables the statistical guarantees required to allow the model to actually properly anneal over the different noise levels in the sampling process. Just like in the blog post the way I think about it is the ideal model output should be a superposition of all the possible training samples that could be underneath the target noise level. This means blurry outputs are desirable as the number of possible training samples underneath the noise expands to the size of entire dataset at the highest noise levels.",
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