I've been doing some testing to check the creative variety of MOSS image. It has got quite some cinematic looks in it.

Nothing fancy about the workflow, just straight from the comfy template.

diffusion model: moss_image_sft_int8_convrot.safetensors
text econder: qwen3.5_9b_int8_convrot.safetensors
vae: flux2-vae
steps: 50
cfg: 3
sampler: euler
scheduler: sgm_uniform / simple

With these settings 4k res is rendering in about 55 sec on a GTX5090 with peak VRAM 19,5 GB.

Great fun so far!

Flux 3 is heavy censored to the point of breaking the anatomy.

Moss is crazy uncensored. It can do anything without a lora. Anything. Insane.

Every image was made using base model at cfg4 and 39 steps and at around 1.5MP

Here's a one click workflow so you can create Seamless Vlogs.

https://github.com/roycho87/Seamless_vlog_workflow

https://github.com/roycho87/seamless_video_combiner

https://github.com/roycho87/minimax_continuous_audio_splitter

Also, here's two useful nodes you might like used in the workflow.

https://github.com/roycho87/h3_clown_anchoring

Clown anchoring, in this workflow, uses a method i learned making pull forward that takes the latent from the video in front of your generation, unpacks it, then places it at the end if your video then masks it. This essentially creates context motion for the end of your video.

It also has a switch so you can drop the first 17 frames of that latent. So for this method, it will help the movement become more natural since you’re creating fresh generations and the first couple of frames may appear still if you sync directly to them.

https://github.com/roycho87/comfyui-load-audio-batch-from-dir

This just batch loads audio from a directory.

Edit:

There was a small error with clown anchoring that caused it to go out of sync if you had the “drop 17 switch” enabled. That should be fixed now

Fizgig LoRA Isolation: train 2 identities into one LoRA and keep everyone else looking like themselves.

A normal LoRA of a person spreads their face onto everyone else in the picture: strangers in the background, a friend beside them, a crowd. LoRA Isolation stops that. Your subjects can stand together in one picture, or next to someone the model already knows, and every face stays its own.

The numbers (Krea 2, same pictures, same settings): with a normal LoRA, 48 of 50 strangers came out looking like the trained person. With LoRA Isolation, 0 of 50. Likeness of the trained person stayed the same (ArcFace 0.78 vs 0.77).

Demo video: (same as above) https://youtu.be/CNh4sN5vN_8

A second person in a LoRA is usually the problem. Here it's part of the fix: learning two identities together is what teaches the LoRA that each face belongs to its own trigger. They don't need to be the same gender, and yo uegt a great multicharacter LoRA. In the demo the second person was a woman, and every stranger was a man, demonstrating it did not have to be the same gender as me to preserve the isolation (that bit was a surprise to me tbh).

How it works: DOP at the base, with Fizgig's own layers on top.

• DOP (Differential Output Preservation), from Ostris's AI-Toolkit
• Multi-subject identity anchoring
• Fused-token class decoupling (gwelt, a man trains as gweltman)
• In-frame negative-pair compositing with causal-order captions (stranger first)
• Per-sample LR scaling, strided DOP and balanced sampling
• Class-aware training paths for people, animals and things, and styles

Tips:

• Train two people even if you only want yourself. The second face is part of what keeps strangers clean.
• Two trained people in one picture, plus someone the model already knows, works well. Three or more trained people in one picture is less reliable.
• Prompt with the joined word (gweltman man at a cafe) and describe the other people.

The side-by-side training pictures use 32 generated strangers with natural skin texture, so they don't pull your LoRA towards poor skin and no real person's photo is used. They download with your models.

It's all set up on one card on the Training tab, and works on every model in Fizgig except ACE-Step (the music model). Tested on Krea 2, with more model testing to follow.

*And before anyone points it out, its not perfect, I know but I think its progress and its fully documented in my github, and opensource. One thing I'd like to improve is less lip similarities - but maybe some ideas will come from people using it *

How to set it up (video): https://youtu.be/8uW5NdL5b20

Release notes: https://github.com/shootthesound/Fizgig/releases/tag/v8.4.0

Fizgig on GitHub: https://github.com/shootthesound/Fizgig

I wanted my finished illustrations to look like an actual manga page: crisp ink lines, blank paper for light areas, solid black for dark hair and clothes, and screentone only where it’s needed.

It uses Qwen-Image 2.1 Turbo (edit) to decide what should be black, white or toned, then a small finishing node prints it with real halftone dots. There’s also a lite workflow without the edit model.

The node is a single small Python file (numpy/opencv/torch only), MIT licensed.
GitHub: https://github.com/ashukae/ComfyUI-MangaScreen
Also in ComfyUI Manager, search “Manga Screen”.

Have fun experimenting with it! If you run into any problems, let me know here or open an issue on GitHub. Thanks!

Same prompt, same seed, no lora

I tested a few samplers and schedulers on the MOSS Image model in addition to Euler. These are worth using if you want to try out realistic images, I got pretty good results with them even at cfg 3:

samplers: heun, heunpp2, exp_heun_2_x0, dpmpp_sde, dpmpp_2m_sde, res_2s, (res_2m)

schedulers: simple, sgm_uniform, beta, normal, linear_quadratic, beta57, sigmoid_offset, power_shift, radiance_shift, bong_tangent

I noticed that the euler_ancestral_cfg_pp, lcm, and er_sde schedulers primarily generated Asian women, in case anyone is interested in that.

Based on my very first tests, the pairs that followed prompts the most closely were dpmpp_sde/radiance_shift and dpmpp_2m_sde/radiance_shift, but overall, radiance_shift did a better job of keeping the prompt consistent.

In my test, I used SFT model with the 8-step SFT lora_latest with a strength of 0.7 and a resolution of 1536x1536. Generating one image took 28 seconds on a 3090 graphics card.

The original workflow includes a shift node; it’s best to delete or disable it, because it only generated noise for me when used with the comfyUI models (Thanks to Kijai for the models!). The models and the good basic workflow I used to generate the images can be found here:

https://huggingface.co/Comfy-Org/MOSS-Image

Update: I created a plot using the combinations above and Euler. I've posted it here, the prompt is included in the image, the image contains some adult content:

https://huggingface.co/buckets/Mykee/MOSS-Plots/tree/Moss%20best%20sampler-scheduler%20combos.png

https://reddit.com/link/1x8dj76/video/3gf1felnt1wh1/player

I've updated the ComfyUI Drama-3 nodes and they're at least 3x faster now! Works with 8GB VRAM.

Nodes: https://github.com/PedroMarinhoDev/ComfyUI-Drama3

this is the easiest model to get a new face with a new seed value that I have used. what other models and methods are people having an easy time to change faces after fixing certain basic characteristics of a person ?

base model with turbo lora , 8 steps 1 cfg euler/simple

tried different things , its got some variety , can do a lot of different things for a small model,anatomy is really good unlike some other models

neat model.

https://huggingface.co/akhaliq/MOSS-Image-SFT-4-steps

View on YouTube while video is pending here if needed. https://youtu.be/acmAZolUw68

I did not do any Post edits on this. At all. This was straight from the video builder.

• The song, made with Suno, lyrics created by Claude.

• I came up with the story and the band's character sheets, and the locations. Used GPT image 2 to create them.

Model settings: MiniMax H3 Singularity, 25 steps on pass 1, then the 8-step 768p turbo LoRA at 4 steps and 0.35 denoise on pass 2 with the latent upscale model.

This used my ComfyUI video builder which has MCP/API support so you can have an agent run it for you and then open up the created project later to see it and play around with it.

The video builder can create all the prompts for you but for this Video I did have Claude use the API / MCP stuff to help create the prompts. You can use any LLM agent to do this.

What Claude did on its own:

• Planned 28 scenes and wrote every H3 prompt shot by shot, with per-shot lyric cues and emotion tags

• Wrote and voiced the spoken intro and outro with ElevenLabs Eleven v4 plus ElevenLabs sound effects (I gave it my API key. I'm only on the $5 plan FYI)

• Rendered overnight through the VRGDG Video Builder's API: 45 renders, about 8 GPU hours on a 5090 and this included re-takes and alternate scene versions.

• Checked every take frame by frame and re-shot the problems.

• Checked every cut, and the audio sync of all 28 scenes came out at 0 ms

• Made optional versions where scenes continue straight from the previous one using latent continuation

You can find my Video builder on GitHub https://github.com/vrgamegirl19/comfyui-vrgamedevgirl
We are working on a new story mode which will allow you to create video's just like this one when it comes to the story flow - But no need for Claude!

You can find a 2-pass workflow in my workflows folder.

Workflows\MiniMaxH3_2Pass_LatentUpscale_Shareable.json

LoRA for reducing the soft & smooth AI look and adds more texture and details to images.

Dataset was high-qulality SFW Professional Photography images with wide variety like portraits, lifestyle, fashion, wildlife, sports fitness, architecutre, nature and more.

It is trained on sft version of moss. Samples are generated with Lodestone's turbo (which he said is still in training) lora 14 steps and 1.0 CFG, works very well with it.

Still looking for better turbo worfklow this is the best I had.

LoRA Link - https://civitai.red/models/2462105/ultrareal-moss-krea2-klein9b
Workflow - https://github.com/vizsumit/comfyui-workflows
Prompts are from this free prompt website - https://promptdexter.com

Okay, sorry, but I’ve been busy since last Friday and haven’t had a chance to check Reddit. Now I’m seeing people talking about Flux Klein 3? And apparently it’s terrible (wait, seriously??). And then there’s MOSS Image??

We already have Qwen Image 2.1 and Krea 2, so how do QI 2.1 and Krea 2 against Flux Klein 3 and MOSS Image? Can these two new models actually replace the ones we already have? Too many models, too little HDD space, and too little time, but I am happy :)).

Hey guys,

I have been working on the consistent character portable format (.char) for a while & couple of reddit users asked me if multi character is possible for image & video generation.

I have enabled the same in Omnichar with upto 4 characters in the same video or images.
Here I have created 3 workflows:

1. Encode Characters (one workflow, which builds .char with MMH3 & Flux support)
2. Use multi .char with MinimaxH3
3. Use multi .char with Flux2 Klein 9B(4b also supported)

Detailed guide on building full consistent character(workflow included): Previous reddit post

Official repo: https://github.com/omnichar/OmniChar (Opensource)
ComfyUI Node: https://github.com/omnichar/ComfyUI-Omnichar/tree/main (Opensource, Multi character support is not yet available)

Next up, I will be enabling the same via ComfyUI node.

Hope it's helpful.

AcademiaSD LoRAlab Trainer Studio now supports MOSS-Image, the new open text-to-image model (Apache 2.0). It is the 12th model family in the Studio, next to Qwen-Image 2.1, FLUX.2 Klein 9B, FLUX.3 Image Klein, Krea 2, Z-Image, ERNIE-Image, Ideogram 4, Anima, SDXL, LTX-2.3 and MiniMax-H3.

What you get:

- Train on MOSS-Image Base (recommended for LoRAs) or on MOSS-Image SFT. Both share the text encoder, so one pre-cache works for either.

- The 5B transformer runs in NF4. In my tests at 512x512, rank 8, it trains at about 1 s/step on an RTX 5080 using under 6 GB of VRAM.

- Text encoder (Qwen3.5-9B, used only during the pre-cache): BF16 CPU offload by default (exact, works on any GPU), or INT8 / NF4 if you prefer a smaller download.

- Fast previews with darask0's 4-step turbo adapter: about 4 s per preview instead of about 43 s with 28 steps and CFG. It is added unmerged, so it never touches the LoRA you are training.

- Auto-captions with Qwen3-VL-8B: detailed prose, with any visible text copied exactly.

- The LoRA loads directly in ComfyUI (nightly, which now supports MOSS-Image natively) with the standard LoRA loader.

- Defaults: 500 steps, LR 0.0004, cosine with 100 warmup steps, rank 8 / alpha 8. In my tests it already looked good at around 300 steps, so experiment.

And everything the other trainers already have: the same web UI, rsLoRA / LoRA+ / LoKr, 5 LR schedulers, settings you can change without stopping the run, exact-step resume, one-click export to ComfyUI, remote access from your phone or another PC, a RunPod template, Windows and Linux, and 8 interface languages.

Download size: 9.9 GB to 20.2 GB depending on the text encoder you pick (the original model is 27.2 GB).

Already installed? Just run Update_LoRAlab-TrainerStudio.bat and the MOSS-Image card appears in the launcher.

GitHub: https://github.com/AcademiaSD/AcademiaSD_LoRAlab-TrainerStudio

NF4 model: https://huggingface.co/AcademiaSD/MOSS-Image-NF4-for-LoRA-Training

Credits: darask0 for the 4-step adapter and lodestones for the 8-step DMD it builds on.

https://huggingface.co/Abiray/MOSS-Image-SFT-INT8-convrot

I recommend the INT8 as a start. nVidia RTX20x+ & RDNA2 (RX6000)+ are good. ARM needs fairly recent GPUs to have the right OpCodes; Raspberry Pi 5 & M5/A17 Pro+ for NE support.

If those seem too slow, or if really VRAM constrained, then try a GGUF quant.

Also there is a SFT banding fix LoRA.

render speedups: https://huggingface.co/darask0/MOSS-Image_4-step_adapter_lora

upgrade your Flux VAE : https://github.com/ClownsharkBatwing/RES4LYF

OSS for the win!

edit: early LoRAs & LoRA training in comment.