You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hi, thanks for sharing the inference code for lambda-ECLIPSE; nice work!
Do you plan to release the training code for the lambda-ECLIPSE? Such resource-efficient training would be very useful for many low-resource groups. I'll be looking forward to trying this.
The text was updated successfully, but these errors were encountered:
Hello @j-min, thanks for showing interest in our work.
TL;DR: We are building an end-to-end project that can help us utilize the true potential of CLIP models for T2I. But public release may take some time.
Long answer:
Current progress/hype in T2I is largely due to three works: SDXL-Turbo (inference time efficiency), DALL-E 3 (SOTA), and Playground v2.5 (highly aesthetic). Majority of the such works use cleaver tricks (like synthetic captions, DPO, etc.) which we haven't explored yet in ECLIPSE/UnCLIP settings. Hence, the true potential is still a mystery to us!!
Due to the efforts involved in building the end-to-end system, we are projecting the partial release in May. We are also looking for help/collaborations to speed up the process and explore its use cases in unknown territories. If you are interested please reach out to me at: [email protected]
Alternative: The training is pretty straightforward and uses a mix of contrastive learning and projection loss as described in the paper.
Hi, thanks for sharing the inference code for lambda-ECLIPSE; nice work!
Do you plan to release the training code for the lambda-ECLIPSE? Such resource-efficient training would be very useful for many low-resource groups. I'll be looking forward to trying this.
The text was updated successfully, but these errors were encountered: