Üretim4
görsel·reddit-stablediffusion·1 saat önce
If you've used MOSS-Image SFT you may have noticed faint vertical colour stripes in skies, fog, walls and studio backdrops. They're easy to miss until you boost the colour, then you can't unsee them. I made a small LoRA that removes them and leaves everything else alone. Hugging Face: https://huggingface.co/nynxz/MOSS-Image Civitai: https://civitai.com/models/3008520/sft-destripe How to use Add LoraLoaderModelOnly to an SFT workflow. Strength 1.0 at 768 px , 1.5 at 1024 px . No trigger words. It's for SFT only, not Base. Results (bandviz score, lower is cleaner, 12 test images not used to…
görsel3D·reddit-stablediffusion·6 saat önce
At this point I’ve trained 2 character Lora’s using datasets that worked good on other models such as Krea 2 and Qwen 2.1. However with MOSS the identity is only about 70-80% there. Have others experienced the same issues? I used AI toolkit both the base and sft model. submitted by /u/Bostonparis [link] [comments]
araçvideo·hf-papers·5 gün önce·21 oy
Interactive video generation commonly relies on small bidirectional chunks to reduce response latency. Yet our preliminary experiments show that larger chunks improve video quality and better utilize GPU parallelism, allowing more latents to be sampled with only a modest increase in sampling time. Motivated by these observations, we propose Speculative Sampling, a strategy that combines the modeling power of large bidirectional windows with the low-latency interaction of small ones. It expands the modeling window by appending noise latents as padding and caches the resulting predictions for…
3D·hf-papers·4 gün önce·67 oy
We study how vision-language models (VLMs) can learn to think and actively explore in 3D space. Instead of providing multi-view images, videos, or 3D representations to the model, we preserve the native visual encoding of VLMs, i.e., a single image, one at a time, just like humans, and treat the 3D representation only as an environment with which the model can interact through actions (e.g., move left) and observations (rendered images). Each rollout begins from an initial first-person view and a visual question. The model can then iteratively reason, navigate, and reorient its viewpoint…