Contains Flux Fill, Canny, and Dev checkpoints in NF4.
Sayak Paul PRO
sayakpaul
AI & ML interests
Diffusion models, representation learning
Recent Activity
replied to
their
post
1 day ago
Diffusers supports a good variety of quantization backends. It can be challenging to navigate through them, given the complex nature of diffusion pipelines in general.
So, @derekl35 set out to write a comprehensive guide that puts users in the front seat. Explore the different backends we support, learn the trade-offs they offer, and finally, check out the cool space we built that lets you compare quantization results.
Give it a go here:
https://lnkd.in/gf8Pi4-2
updated
a dataset
3 days ago
huggingface/diffusers-metadata
upvoted
a
paper
6 days ago
FineVision: Open Data Is All You Need
Organizations
Provides a list of papers focusing on optimizing T2I diffusion models, targeting fewer timesteps, architecture optimization, and more.
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Progressive Distillation for Fast Sampling of Diffusion Models
Paper • 2202.00512 • Published • 1 -
On Distillation of Guided Diffusion Models
Paper • 2210.03142 • Published -
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
Paper • 2309.06380 • Published • 32 -
Consistency Models
Paper • 2303.01469 • Published • 8
Contains Flux Fill, Canny, and Dev checkpoints in NF4.
Provides a list of papers focusing on optimizing T2I diffusion models, targeting fewer timesteps, architecture optimization, and more.
-
Progressive Distillation for Fast Sampling of Diffusion Models
Paper • 2202.00512 • Published • 1 -
On Distillation of Guided Diffusion Models
Paper • 2210.03142 • Published -
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
Paper • 2309.06380 • Published • 32 -
Consistency Models
Paper • 2303.01469 • Published • 8