| Idea | Status | References/Papers |
|---|---|---|
| FIFO CogVideoX | In Progress | https://jjihwan.github.io/projects/FIFO-Diffusion |
| Flux Image Inversion using RNRI | Blocked. Understanding of prior for flow matching too low | https://barakmam.github.io/rnri.github.io/ |
| CogVideoX Attention Scaling | Paused. Need to recheck for higher res | https://arxiv.org/abs/2306.08645 |
| RB Modulation for FLUX | Planned. SOC is straightforward. AFA needs to be seen in detail. Planning to replace CSD with other image opeerators for different manifold explorations. Soft histograms for relative color palette retention ? | https://rb-modulation.github.io/ |
| CogVideoX distillation using FineVideo and PeRFlow | Planned. Needs compute grant. May be scrapped once BFLs video model is out. | https://arxiv.org/abs/2405.07510 |
| Underwater Image Colorization as an Inverse Problem | Planned. Needs better underestanding of inverse problems | https://github.com/LituRout/PSLD |
| Flux generation steering using SAE for CLIP | Planned. Need better understanding of SAEs & apply them to T5 as well | https://www.lesswrong.com/posts/Quqekpvx8BGMMcaem/interpreting-and-steering-features-in-images |
| LoRA (move to MoRA ?) ControlNet layer | Planned. Compute ∆W for Flux dev & its controlnet layer. Decompose to LoRA and see decomposition error. If its low enough, LoRA should be enough | ChatGPT conversation |
| MoRA finetuning Flux | I have a hypothesis: MoRA might give better samples than LoRA for Flux. I'll try it out sometime next week maybe. TLDR: 1. Full finetuning > LoRA for personalization. 2. Full finetuning > MoRA > DoRA > LoRA. 3. MoRA should converge fast like LoRA but give better quality/diversity like finetuning. There should be no free lunch though. Hmm | 1. MoRA: High-rank PEFT Approach 2. Full Finetuning of Flux 3. GitHub: MoRA |
| Transformer layers as Painters for DiTs | Complete. Results published here | https://arxiv.org/abs/2407.09298 |
Also, Will update the empty pinned repos by 26/09/2024 :)