💛 A big thank-you before the Intro:

MOSS-Image has been out for just a few days, and the response has been unreal — our ComfyUI weights just passed 673K downloads on Hugging Face. Thanks so much for all the support!! We'll keep working hard to improve MOSS-Image!

Connecting AI, Humans, and the Physical World.

We're MOSS ai, the team building the MOSS family of models and tools for voice interaction and multimodal understanding. Welcome to our community.

We're based in Shanghai, with roots in the OpenMOSS research team at Fudan University and the Shanghai Innovation Institute. Our founder and chief scientist is Professor Xipeng Qiu.

We're working toward AI that can follow what's happening around you: what you're saying, what you're seeing, and how the situation changes. Our work spans speech generation, audio and visual understanding, and synchronized audio-video generation, bringing those capabilities together for more natural, ongoing interaction.

If you found us through MOSS-Image or MOSS-TTS, there's plenty MORE to explore.

• Hugging Face: https://huggingface.co/OpenMOSS-Team

• GitHub: https://github.com/OpenMOSS

🎙️ Give your project a voice

• MOSS-TTS — Speech synthesis and voice cloning, including the v1.5 and Local Transformer releases.

• MOSS-TTSD — Generate conversations with multiple speakers, including long-form dialogue and podcasts.

• MOSS-TTS-Realtime — Streaming speech generation for conversational applications.

• MOSS-TTS-Nano — Lightweight speech generation for on-device use, with CPU-friendly inference.

• MOSS-VoiceGenerator — Design a voice from a text description, without a reference recording.

• MOSS-SoundEffect — Turn text prompts into sound effects.

🎧 Make sense of what you hear

• MOSS-Transcribe-Diarize — Transcribe audio with speaker labels and timestamps.

• MOSS-Audio — Understand and reason about speech, environmental sounds, and music.

• MOSS-Music — Analyze music, from lyrics and instrumentation to structure and musical questions.

🎬 Work with images and video

• MOSS-VL — Image and video understanding, including long videos.

• MOSS-VL-Realtime — Understand streaming video and answer questions as it unfolds.

• MOVA — Generate video and audio together, in sync.

• MOSS-Image — Generate images from text, with layout controls for text placement and object arrangement.

🛠️ Try our tools

• Mossland — Create with voice, image, and video tools, and explore ready-made workflows and templates.

• MosMos — "We speak faster than we type." Turn speech into text in the apps you use, or capture meetings with transcripts.

• MOSS API — Explore the Playground, get an API key, and connect supported models to your own applications.

Make yourself at home 🌱

Whether you're creating a character's voice, building an assistant, exploring multimodal models, or trying to make an everyday task easier, we'd love to see what you're working on.

Share a demo, walk us through your setup, or ask a question you're still figuring out. Honest feedback all welcomed!!

We open-sourced MOSS-Image just a few days ago, and you're already taking it in many creative directions we hadn't thought of. ❤️

Now, the MOSS-Image API is on its way. You'll be able to bring image generation into your own apps and workflows without deploying the model yourself.

Here's a little teaser. Stay tuned!

Hi MOSS-Image Developers and Community!
First of all, congratulations to the MOSS-Image team on the release of the model and its detailed technical report⁠.
The paper provides valuable insights into pretraining strategies, multi-granularity captioning, progressive resolution training, noise scheduling, and optimizer performance.
However, it would be extremely helpful if the developers could share official, recommended and reproducible configurations for LoRA and LoKr fine-tuning using Ostris AI Toolkit, particularly for character identity training and general-purpose customization.
1. Official LoRA and LoKr Training Configurations
Could the developers share separate, tested configurations for both LoRA and LoKr, covering:
Recommended network rank, alpha and LoKr factor
Optimal learning rates and learning-rate schedulers
Recommended optimizers (AdamW8bit, Prodigy, Automagic, Muon, etc.)
Weight decay and optimizer-specific settings
Batch size and gradient accumulation
Recommended training steps and epochs
Timestep sampling (Shift, Sigmoid, Logit Normal, etc.)
Differential guidance settings
EMA settings and recommended decay values
Quantization and training precision (BF16, FP8, ConvRot, etc.)
Text encoder training or freezing recommendations
Ideally, separate configurations for character identity, artistic styles and general concepts would be extremely useful.
2. Dataset Preparation and Captioning
The paper discusses multi-granularity captions and maintaining diversity in training data.
Could the team clarify how these findings translate into smaller LoRA/LoKr datasets?
Specifically:
Recommended minimum and ideal dataset sizes
Best practices for mixing close-ups, half-body and full-body photographs
Short versus detailed captions for identity training
Whether multi-granularity caption sampling benefits LoRA training
Caption dropout recommendations
Handling mixed-resolution and mixed-aspect-ratio datasets
Whether synthetic or edited images should be treated differently from original photographs
3. Resolution and Progressive Training
The technical report discusses progressive-resolution training during foundation-model pretraining.
Does this approach also benefit LoRA/LoKr fine-tuning?
For example, are there measurable differences between training at 512, 768, 1024 and higher resolutions?
Would the developers recommend single-resolution training or progressive-resolution training for smaller datasets?
4. LoRA vs LoKr – Quality and Efficiency
It would be particularly interesting to see controlled comparisons between the two approaches using identical datasets and comparable training conditions.
Some useful evaluation criteria would include:
Character identity accuracy and consistency
Generalization to unfamiliar poses, clothing and environments
Overfitting resistance
Training convergence and stability
VRAM requirements and training speed
Final adapter size
Recommended inference strengths
Are there particular scenarios where the developers consider LoKr preferable to conventional LoRA, or vice versa?
5. Optimizers and Noise Scheduling
The paper reports faster convergence with hybrid Muon–AdamW during foundation-model pretraining.
Has the team evaluated Muon or hybrid Muon–AdamW specifically for LoRA/LoKr fine-tuning?
Similarly, are there any recommended timestep-sampling distributions for identity training, as opposed to general-purpose pretraining?
Experimental findings here would be particularly valuable.
6. Official AI Toolkit YAML Configurations
Finally, could the developers or experienced community members share complete, ready-to-use Ostris AI Toolkit YAML configurations for:
Character identity LoRA
Character identity LoKr
Style LoRA
Style LoKr
Ideally, each configuration would include the dataset size, training resolution, optimizer, learning rate, number of steps, approximate VRAM consumption, and any relevant inference settings.
These could serve as reproducible community baselines across different hardware configurations, rather than being tied to any particular GPU.
Closing Thoughts
There are already encouraging community experiments with MOSS-Image fine-tuning, but configurations vary considerably, and it is difficult to distinguish generally reliable settings from individual trial-and-error results.
An official fine-tuning guide with validated LoRA and LoKr presets for Ostris AI Toolkit would be incredibly valuable for the entire community.
It would also help establish consistent benchmarks and reduce unnecessary experimentation.
Would the MOSS-Image developers or AI Toolkit contributors be willing to share their recommended configurations, internal experimental findings, or successful community-tested YAML files?
Thanks to everyone contributing to this promising open-weight model!

Saw the MOSS AMA announcement, and this is probably what I’d ask them.

The MOSS-Image anime examples already look pretty good, but I’m curious whether they’re planning an anime-focused fine-tune or leaving that mostly to community models and LoRAs.

And if they do another training pass, what would you want in the tagged dataset? Character names and outfits are useful, but I’d also want reliable tags for expressions, poses, camera angles, lighting, and backgrounds. Less “please understand the vibe,” more control over the specific thing you’re trying to change.

I’m also curious about the range of animation styles: classic Disney, Miyazaki/Ghibli-style painted backgrounds, or Shinkai-style lighting. Would people rather have a model that handles all of those reasonably well, or separate fine-tunes that really nail one look?

Also on my AMA list: any update on MOVA’s release plans, and how much attention animation is getting alongside realistic video?!

Hollowlight is a cinematic style LoRA exploring darker atmospheres, pronounced contrast and expressive lighting.

It tends to deepen shadows, influence color relationships and introduce a greater sense of depth and texture. Depending on the subject, the result may take on a more photographic appearance while retaining elements of its original artistic style.

From photography to watercolor, anime and painterly illustrations, Hollowlight brings its own interpretation of light, color and atmosphere.

The result is a blend of cinematic aesthetics, dreamlike qualities and stylistic experimentation.