The first image has the workflow embedded.

Does anybody have any idea why the image I generated has that yellow/greenish color shift?

without loras, I've tried 50 steps, 3 cfg but the image looked so sloppy. It looked really bad. With 8 step lora, it looks really good but I get that weird color problem.

I have tried with/without kitchen, without canon lora, without sft lora (nightmare lol), disabling the degrid node, with the regular load diffusion node etc. None of these has fixed the problem, yet.

Moss Image might be the fastest T2I so far on my end. On 3060ti, it takes 19 seconds for 2mp, and 40 seconds for 3mp. Really amazing.

About a thousand Qwen-Image 2.1 renders in ComfyUI over a week, on a 16 GB consumer card, same seeds across settings, to find out what the settings actually do. Short version, times are per image on that card:

Guidance

The official template runs at CFG 1 with 25 steps.
At CFG 3 with a negative prompt plus the APG and FreSca patches on the model path, the composition stays the same (same seed, same size) and the rendering gains what CFG 1 flattens: embroidery stitches, rivets, pupils on cartoon faces, contrast.
It costs a second pass per step. 4 MP, 20 steps: 120 to 135 s at CFG 1 vs 255 to 275 s at CFG 3.
On ten test pairs the slower one won every time.
Pushing CFG 1 to 30 or 40 steps brings sharpness back but not the materials.

Size and steps change the image, not its resolution

Like any diffusion model, the same seed at 1 MP and at 4 MP gives two different pictures.
Between 2.5 and 4 MP the composition held in 8 of 10 pairs; from 1 MP it never did.
20 and 25 steps give nearly the same picture, 16 drifts.
So: pick the size first, then hunt seeds.
For a gallery served at 1024 px, 2.5 MP at 20 steps was the sweet spot (120 to 135 s).
4 MP at 20 steps for prints (255 to 275 s). 1 MP at 25 steps is about 40 s.

Turbo

Qwen-Image-2.1-Turbo (8 steps, CFG 1, fixed sigmas) runs 9 to 10 s at 1 MP and 38 to 39 s at 4 MP, seven times faster.
Clean images, objects of the prompt often better placed, but it drifts to photo realism: an embroidery becomes a photo of a crocodile, charcoal loses its grain.
The LoRA version on the base checkpoint gives the same images, slower, so the checkpoint is the one to download.

Guided Turbo, the unexpected one

Keep the Turbo checkpoint and its 8 sigmas, but set the CFGGuider to 3, fill the negative prompt, and insert APG then FreSca on the model path. 66 to 71 s at 4 MP, same composition as plain Turbo, and the fine detail, contrast and saturation come back.
It still does not recover the medium (the embroidery stays a photo).
Do not give it 20 steps with the simple scheduler: it overcooks, 169 s for a worse image.

Traps

The 2025 Qwen-Image-Lightning LoRA does not load on 2.1 (3072 vs 4096 attention width); ComfyUI logs one error per layer and quietly renders the bare model in 8 steps.
Shopfronts grow invented lettering unless the prompt says "no lettering".
Characters the model does not know are swapped for a lookalike.

Full write-up with the same-seed comparison crops, the exact node graph, the negative prompt and the prompt structure that worked best: https://stablediffusion.fr/guide/qwen-image-2-1

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

See title. So the European AI-model following the latest EU-laws for AI generation is terrible and about to bomb? GOOD.

Can't wait for hypocritical EU politicians to reverse their new laws once they realize nobody uses their AI-models. Heck, the EU is probably using American models themselves because they know their own suck.

So I have had reasonable success crafting scenes in MiniMax H3 but in some situations it just comes up short. Recently I was trying to make a video of a group of people sitting around a campfire having a conversation. So video side of the generation was fine as was the dialog. However the way the dialog was spoken was not realistic at all. I am not talking about the delivery (which at times sounds stilted). No, I mean like the dialog didn't sound like it was coming from an outdoor environment by a person talking from the left side of the screen. It sounded like a booming voice over. All my dialog comes out sounding like this.

I am no sound designer or engineer so I don't know the right way to describe what I want. I guess I am talking about sound space. I am not sure if I am explaining myself. Let me use a different example. If a person is talking in a room while pacing, there should be a little echo from the sound bouncing off the interior walls as well as the sound should pan left to right as the person walks left to right. Not be so right up in front of you.

I am sure people have encountered this before. How do you overcome it? I assume you would have to process the sound in some sort of outside manner because I have not been able to achieve that effect inside H3.

Is it more precise to write a full description in text, or just putting the tags?

Also, how can I do to put 2 or more characters in the same picture without them getting mixed?

Edit: I'm using WAI-illustrious SDXL v16

Low res image:

And since Flux 3 can generate up to 4096x4096px, "Generative" upscale works surprisingly well in i2i workflows: Flux 3 Upscale.

At least it works similarly if not better than SeedVR2_7B

Prompt: "photorealistic image."

Some test with 4 step lora.

I've added tcg layout to test his prompt understanding and text rendering.

I'm impressed by the quality of this model.

How to:
For who want to try 4 step lora, i'm using this but i've use only the lora.safetensor loaded as default lora with clip and weight set to 1 (with CR lora loader).

Image are done at 2400x1792, 4 step, beta scheduler, er_sde sampler, cfg 3.

I also using dual cfg guider: positive prompt to both cond, and negative to negative.

Use default sft model, clip and vae.

Use last version of ComfyUI, or update to last.

After many generation, The model seem do better results on landscape image, and seem really like anime.

Actually i've not tested on realistic render, test on it later.

What your impression about this model? i've see many post, some like, some not because is not an edit model, so evalute it for what is it: a text to image model.

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

Hi everyone!
I’m looking for an AI tool that can take two inputs: a photo of my character and a reference video of someone dancing. I want the AI to generate a new video in which my character performs the same dance as the person in the reference video, following the original choreography and movements as accurately as possible.
I’ve already tried a few AI video generators, but the results weren’t very good. Some generated completely different dances instead of copying the movements from the reference video.
I’m mainly looking for a tool that offers a reasonable number of video generations without being too expensive. Free options would be great, but I’m also willing to pay for a subscription if it’s worth the price.
The problem is that many services offer something like 500 credits per month, while generating a single video can cost around 60 credits. That means I can only create about 8 videos per month, which is far too limiting for the kind of content I want to make.
**Does anyone know a good AI tool that can accurately transfer dance movements from a reference video to a character from a photo and offers enough generations for a reasonable monthly price?**
I’m also interested in open-source tools that can be run locally if they provide good results.
I’d really appreciate recommendations based on your own experience, including the service’s pricing and how many videos you can realistically generate.
Thanks in advance!

Flux3 Klein Bf16 in ComfyUi, after the update of the huggingface repo.

First test: a 2x2 character sheet form source image.

Image 1 is source. Image 2 is Qwen-Image 2.1, Image 3 is Flux3 from same source.

Qwen Image is the clear winner IMO.

Second test: a simple color swap on the shirt, from blue to red. Result speaks for itself. Good color swap, but hue shift and introduction of smudging on walls etc.

https://github.com/SpendinFR/H3STUDIOFILM

Hi,

What are your tips to prompt for realistic scenes. Not fake looking scenes but every day life scenes. Any particular words to use? I figured out using “GoPro video“ as a prompt can really have a big influence e.g.

Any tips are appreciated!
Best

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

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.

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 ?

I am working to build out additional workflows in Minimax H3, and I'm trying to figure out the best way to tightly control dialogue. My initial thought was to use TTS engines to develop the dialogue and achieve the correct "performance," then use an Image and Audio to Video workflow that would lipsync using the dialogue I already generated. However, finding a workflow that actually works and also does not have 800 extra nodes that I don't need has proven difficult.

What methods/workflows are the best to achieve this? Are people more so leaning on using an audio reference for the voice (whether as a direct reference or as part of a refmod or .char file), and thus letting Minimax generate the "performance?" If so, how have the results been? I feel like the dialogue generated can be too stiff most of the time, but I have only tried letting the model generate it without a reference so far.

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 :)).

*2.25MP, 16steps, distilled model.

First i tried: Woman lying on grass. High quality. 

Second: Woman lying on grass, arms crossed. Perfect anatomy. High quality. Top-down camera perspective.

Then, a challenge: Woman wearing red bikini lying on sand, arms crossed. Beach settings. High quality. Perfect anatomy. Perfect facial anatomy. Top-down camera perspective.

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.

I feel like I'm reading conflicting opinions on the scope and complexity of training LoRAs for Anima with no particularised reasoning from users who find it easy. When training 'styles' to 'characters', what were the aspects of the LoRA that you'd noticed became better or worse relative to you would of trained on something like Illustrious? Did you use the same dataset also?

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

Testing a 45 second workflow. looks seemless?

P.S. this isn't C136, it's Alchemist Rick. Still curious how good he looks; like the hairshape and color and clothing; trying to make it look like Rick and Chris Loyd and a medieval Alchemist. probably I'd replace the flask with a potion vial.

Hey all, ive been using the Comfyui MH3 Ref2va template workflow with 1 addition of a second lora (ive tried them chained and added sequentially). I was having no issues running 10-15 sec generations with 3 ref images and MP counts as high as 0.8mp until yesterday. I updated Comfyui to help with a different issue and now ever since I run into tensor shape errors that identify dimensions that dont work. Im generating at a 1:1 aspect ratio and my ref images match that. I can only get generations to work if i use 2 or less ref images and only at 0.3mp at 5 seconds. Any other ref images, quality increases or duration increases error out in the sampler at step 2. Anyone have ideas on what I can try?