# Class-conditional image generation

**URL:** <https://discuss.huggingface.co/t/class-conditional-image-generation/42536>\
**Category:** 🧨 Diffusers\
**Created:** [June 8, 2023, 12:14pm UTC](https://discuss.huggingface.co/t/class-conditional-image-generation/42536 "2023-06-08T12:14:39Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![mario-dg](https://avatars.discourse-cdn.com/v4/letter/m/3be4f8/32.png) [@mario-dg](https://discuss.huggingface.co/u/mario-dg)\
**Post date:** [June 8, 2023, 12:14pm UTC](https://discuss.huggingface.co/t/class-conditional-image-generation/42536/1 "2023-06-08T12:14:39Z")

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Hey everyone,  
for a university project I want to play around with class-conditional DDIM image generation based on a custom created dataset of microscopy images. Basically I want to generate synthetic microscopy images of a specific biological application based on a set of features that should be visible.

I have a dataset of roughly 3400 images that I tagged manually with 4 different features.  
I.e. for each image I have a vector of length 4, with 1 indicating that the specific feature is visible in the image. I want this to be necessary to control what kind of images are generated, like in stable diffusion but without a whole text prompt.

I need ideas or implementation hints on how to implement that using huggingface and accelerate.  
I dont how how to add the feature vector in the pipeline for training and inference.

Thanks for any kind of help! 🙂

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**Author:** ![WiNE-iNEFF](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/wine-ineff/32/30221_2.png) [@WiNE-iNEFF](https://discuss.huggingface.co/u/WiNE-iNEFF)\
**Post date:** [October 12, 2023, 9:28am UTC](https://discuss.huggingface.co/t/class-conditional-image-generation/42536/2 "2023-10-12T09:28:03Z")

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Have you found a way to add the feature vector in the pipeline for training and inference?