CVL-UESTC/Internal-Guidance
CVPR 2026-Guiding a Diffusion Transformer with the Internal Dynamics of Itself (IG)
This project helps researchers and practitioners in generative AI create highly realistic images from scratch, particularly for large datasets like ImageNet. By providing a guidance mechanism for Diffusion Transformers, it enables faster training and improved image quality. The primary user would be an AI/ML researcher or practitioner focused on advancing state-of-the-art image generation models.
Use this if you are developing or training diffusion models and need to achieve state-of-the-art image generation quality with improved training efficiency.
Not ideal if you are looking for an off-the-shelf image generation tool for end-user applications rather than a research framework.
Stars
60
Forks
2
Language
Python
License
MIT
Category
Last pushed
Feb 26, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/CVL-UESTC/Internal-Guidance"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
UCSC-VLAA/story-iter
[ICLR 2026] A Training-free Iterative Framework for Long Story Visualization
PaddlePaddle/PaddleMIX
Paddle Multimodal Integration and eXploration, supporting mainstream multi-modal tasks,...
keivalya/mini-vla
a minimal, beginner-friendly VLA to show how robot policies can fuse images, text, and states to...
adobe-research/custom-diffusion
Custom Diffusion: Multi-Concept Customization of Text-to-Image Diffusion (CVPR 2023)
byliutao/1Prompt1Story
🔥ICLR 2025 (Spotlight) One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation...