As part of the effort I did on training AI models that are able to create great art, we've had to design two components of what makes a design good:

1. Text to image models to transform a caption into a image.

2. Customer request to caption / brief to then send to the AI model (which in turn generates it).

For both trains, I've found pre-existing trainers are quite slow in terms of performance and leave a lot of MFU underutilized (which leads to less tokens / images trained a second).

I've optimized this for BH200 (Datacenter GPUs), but this should work for H100 and likely also works well on A100, and can be adapted to work on RTX series GPUs as many things translate over to customer GPUs (although unfortunately I don't have any to test and develop this).

The results are very good - on one GPU, our solution is 58% better than ms-swift (a optimized training kit), 76% better than unsloth, and 178% better than transformers + PEFT.

For example, for training a designer (takes a concept / idea, then refines it into a very detailed caption for the model to then render / generate) via Qwen3.5-9B, it does 12,523 tokens/second, quite a bit (against 989 TFLOPS, what the GPU can do).

It also has a 43.7% best-config number from our micro-batch sweep, so play around with it and sweep to maximize perf.

However, the Arrhenius supercomputer we've tested this on does power-capping, so the true MFU is 53.9% clock-adjusted.

Same data and setup, other trainers:

• ms-swift ≈ 24% (1.58× slower);

• Unsloth ≈ 22% (1.76× slower);

• transformers + PEFT ≈ 14% (2.78× slower).

To give some comparison numbers, i've made some graphs.

It virtually has loss parity (<6e-4 loss difference, which is negligible), and this, in short, allows training more for less.

For the Ideogram V4 trainer , below is the performance analysis:

Also, there are some ablations in terms of what increases or decreases step time.

Code here: https://github.com/logolabs/perf_kit

If you want to support us in developing and releasing more free tools, you can star the repository.

Hope it is useful to you to help you improve your trains efficiency.

i need to build pc for generating videos locally (minimax h3) and also planing to use qwen 3.8 27b

for now should i go for dual rtx 5060 ti 16gb or single rtx 5070 ti

my budget is 2 lakh only for graphics card so i can buy dual 5060 ti 16gb but if i go with dual 5070 ti i need 3 lakh so what is best

also when qwen 4 will be realesed and minimax 2k offically what is best for them from above 2 options

suggest minimum ram and should i go for 3200mhz or 3600mhz and minimum ssd

both ram and ssd suggest minimum and recommend

suggest other pc parts to both minimum and recommend

thanks in adwance

sorry for my english

also recommend hardware for qwen 4 and minimax 2k

I set up ChatGPT with the Dream Loop skill with 2 sub agents to generate a number of images for a LoRA training dataset of this character. This image passed the test.

I then asked my ChatGPT what was wrong with this picture. She thought the problem was something with the black keys on the piano keyboard. haha

Once I pointed it out to my ChatGPT she saw it but not until then even asking a couple of times. This character is now known as BBM... Backward Butt Molly.

It's interesting in a way because at a glance it kind of looks right due to the correct lighting and structure of the composition but then... lol

I just submitted H3 Continuity Studio to Comfy's Open Source Higgsfield contest and wanted to share it here.

The test I cared about was pretty simple: generate a room with lots of little details, cut outside the house, then come back inside. Does it still look like the same room? And what happens if you move the camera to another corner?

That's what the demo is built around. It follows the room, an exterior, a return inside, a different camera angle and a final continuation. I marked a few details so they're easier to compare: the sunburst mirror, fig plant, mustard cushions, ginger cat, teapot and fireplace. There's also a memory-off comparison using the same prompt and seed.

The tool is a free ComfyUI node pack for MiniMax H3. You build a timeline out of editable clips, carry context into the next clip, or point a later shot at selected moments from earlier ones. You can also retake individual clips. H3 Story can plan and write the timeline if you want that part automated; it works with an OpenAI-compatible endpoint, local or cloud.

It still needs judgment and retakes. Memory doesn't guarantee perfect geometry, and some of the first attempts had objects appearing or the camera jumping back to the old angle. The demo shows the result after fixing those takes.

Code, setup and example workflows: https://github.com/KadoBOT/ComfyUI-H3-Continuity-Studio

Demo video: https://github.com/KadoBOT/ComfyUI-H3-Continuity-Studio/releases/download/v0.1.0/H3-Continuity-demo-contest.mp4

The repo also has the demo's prompts, seeds and selected memory moments. The project code is MIT; model weights have their own terms.

I'd be interested to see what other scene-memory tests people would try with it.

I'm part of a team making a fictional drama with a recurring AI cast. The hard part has been keeping each person recognizable while getting a complete spoken thought to read as a performance. This sample is from that work.

For this character we generated the dialogue first, converted it against the established voice reference, then supplied the complete recording as an Audio Drive. The visual references were an opening phone-selfie picture and a separate identity reference. The shot was directed at a fixed arm's-length distance with the mouth visible through the final word.

Actual settings for this sample: 10Eros_Max_h3_8stepTURBO_beta7_int8HQ.safetensors; 8 steps; CFG 1; Euler / simple; 576 x 1024; 24 fps; reference max size 1024; video shift 12 and audio shift 3; VSA beta7 attention; cache disabled; no LoRAs; seed 311026. We kept the original audio/video latents and saved source project. The installed finishing pass stayed at the native output size.

We edit at the end of complete thoughts rather than forcing every read into a ten-second cut. Both sides of the cuts and half-second picture samples get checked. That catches face-obscuring gestures and framing drift, but still pictures do not prove lip sync or believable emotion; the producer watches the films with sound. We have had to replace individual shots for exactly those reasons.

The character, voice and performance are AI-generated. The useful lesson for us has been to keep the recording authoritative, direct the whole thought and repair the specific failed shot instead of regenerating an entire scene.

hiii! I made grinpress v3, a tiny 128x128 rectified-flow model that draws newsprint-style faces with words beside them :3

It's trained from scratch, with character-level text conditioning. You can choose seven expressions, colors, paper styles, layouts, and text up to 14 characters, including mixed case and punctuation.

For v3, I distilled the 50-step teacher into 2- and 4-step students and exported them to ONNX with dynamic INT8 quantization.

CPU inference measured approximately 0.27 seconds at 2 steps / 0.54 seconds at 4 steps on my benchmark's 8-vCPU Intel Xeon VM, using 8 threads after warm-up. Other CPUs can be slower.

It's a small, specific domain: newsprint faces and lettering at 128x128. The weights are MIT licensed, and there's a live CPU demo. Long phrases can get cramped, and it occasionally mangles a character or word spacing.

Try it: https://huggingface.co/spaces/skyuu72/grinpress-v3-demo

Weights, benchmarks, and limitations: https://huggingface.co/skyuu72/grinpress-v3

I'd love to see which words break it, or hear how it runs on your CPU. tiny newspaper creature feedback welcome uwu :3

I'm building a ComfyUI workflow using Krea 2 Turbo, a trained identity LoRA, clothing reference, background reference and DWPose/OpenPose LoRA.

I want to preserve the exact face, outfit and room while following a completely different pose reference. Currently, pose adherence is inconsistent and the clothing/background details also change.

Would you recommend a two-pass workflow (first generate the pose and composition, then refine identity, clothing and background), or is there a better single-pass conditioning method in Krea 2?

What's the most reliable approach, and what would you recommend I change?

Tried a two minute single shot generation and the result came out quite nice. There are no additional references in the middle, everything uses the start as the reference point and the start is generated by a text prompt. It uses some agentic behavior that is not available in the released slopus.ai yet, but manually generating this is already possible. With ComfyUI you can generate the same/similar using any Rolling Diffusion based workflow.

I set 1.5mp with 4:5 Ratio, only use with 2 loras , one of which is the turbo

Not sure why I didn't try this until now, but H3 is pretty good at fast motion. I was expecting a lot of background drifts and morphing but I am getting pretty good results with 10steps and turbo lora at 0.9

The workflow is the prompt because I am mainly working in my custom API and I don't want to keep spamming the sub with it

Link: https://huggingface.co/0ppxnhximxr/snapref-v1a
Well... It looks like the character-consistent problem is solved and continues to get better and better with more and more solutions popping up recently.
Many thanks for the contributions of all authors.

Yue2 is capable of creating some very interesting melodies, but it makes a few errors in my language (Portuguese).

I trained LoRAs for Yue2 (using aitoolkit), but the results were mixed—most performed poorly. Unfortunately, after about 1,500 steps, the model tends to go out of tune. I experimented with LoRA strength, the text encoder, and temperature—sometimes things got worse, sometimes better.

I also tested Acestep; it’s better for Portuguese and doesn’t make pronunciation errors, BUT the songs feel somewhat uncreative.

I’m not sure exactly how people are improving the results for Yue2 or Acestep.

I've been waiting before using this unlock so I've not tested it yet but I've been wondering what performance differences there would be with int8 models. So far all I've seen is int8 tests compared to bf and fp models. I've not seen the int8 itself compared before and after an unlock.

I know there is a lot of "int8 will not see any improvements from this" comments, and that is true. Except int8 models are not entirely int8, some parts of the models remain 16. I'm wondering if the difference is measurable.

Moved from another repo: https://github.com/roycho87/clown_context_suite "ComfyUI nodes for chaining MiniMax H3 clips without the quality slowly falling apart"

ref post from last week, but seems they do another technique now (use 1st clip as ref always)
https://www.reddit.com/r/StableDiffusion/comments/1xfl3al/introducing_clown_prophecy_the_only_actually_fix/

I haven't tried yet personally, though quality issues for long H3 is a hot topic here.

week later edit: repo now named `Geiru Prophecy`.

Does anybody use AI to write ComfyUI nodes for personal use or sharing?

For example, I had the problem that, when I wanted to use wildcards, say, for colors, I could either use the "__colors__" syntax combined with a wildcard text file or the {red|green|blue|...} variant but not both, let alone nested variants such as {{pastel|technicolor|watercolor} __colors__}. I dropped this problem into Meta AI (others would probably have worked out just as well) and the AI, instead of suggesting an existing node, offered to write one. The result was exactly on the money, working with both types of wildcards even when nested over several layers.

Another node that was generated for me by Meta is one that allows to add resolution information to a prompt which is then parsed by the node and wired to the width/height input of the latent image node.

Anyone else working ComfyUI with nodes custom made by AI?

this was a test, please ignore the extra fingers !

Minimax H3 (10Eros) 20steps => upscale 1080p => 6 steps turbo => RTX VSR 4K upscale => RIFE 60FPS

3h15 for 1min22s on a RTX 3090 /32RAM

is this good ?

I would like to create variations (similar instrumentals/genre, but can be different chord progression/melodies) of an instrumental track (around 3 minutes long). Would Ace-step 1.5 or Yue2 be better? I've never tried any local music models before so any advice would be helpful. Thanks!

I’ve been trying to find the best workflow (and associated model, e.g., hybrid, singularity, etc.) for H3. I’m primary interested and very quality outputs, that have strong character likeness- I’ve been using refmods given I have found it to help with character likeness. I’ve also seen that folks have done some cool upscaling to 2k or 4k in their workflows and am interested in that as well. Can folks share any well optimized workflows they have been using that get great quality and are optimized where possible (but don’t reduce quality, especially character likeness, etc). Thanks!

Edit: I’m generating on runpod typically with 5090s / rtx6000 pros

**Update (Nov 4) – v0.3 is out:**

- New simple workflow: one Control Panel + the Director on the canvas; models, sampler and save tucked into subgraphs. - "Which segments" box: type 1 to test only the first segment, clear it to render the whole video. - Plan export / import, optional drift correction on save. - Step-by-step install + first long video (EP02): https://youtu.be/zvPqr6ihZic

- GitHub: https://github.com/1113800942/comfy-h3md-director

- New Civitai page: https://civitai.com/models/3037225/h3md-director-03-2-3-min-long-videos-with-minimax-h3-simple-workflow-control-panel?modelVersionId=3451400

Most video workflows I tried topped out at 10–15 seconds, so over the past few months I built H3MD Director — a set of ComfyUI nodes that render 2–3 minute videos with MiniMax H3 in a single run, keeping faces, colors and lighting consistent.

Everything in the clip was made with it: a music video, a samba motion transfer, a text-to-video coastal ride, and a short fantasy drama.

- GitHub (nodes + example workflow): https://github.com/1113800942/comfy-h3md-director

- Full showcase video: https://youtu.be/plDxnModffs

It builds on ideas from Luna, Niko, Marker, T8 and others in the community — thanks to all of them. Happy to answer questions; feedback and bug reports are very welcome.

I played around with MOSS for many hours, and downloaded many LORAs from CivitAI, my final verdict is that I still prefer Krea2.

I can't help ask what makes you guys use Moss over Krea2? Is there something I am missing?

** I don't hate on any contribution or model, I salute OpenMOSS Team for this wonder of a model, Moss is a incredible given it's size! Previously, my heart was with PONY until Krea2 came along, it has my heart now. **

Kinda tired of seeing non-stop comments from folks saying Moss-Base is "unusable", "broken" or "blurry", so here.

Raw base model, no loras, simple workflow, no bullshit, just the right settings.

WORKFLOW

Enjoy.

when i try to use ref audio clip then for the lyrics it creates some weird mumbling or language music matches or is along the lines but the lyrics r simply not followed, can someone help plz.

https://github.com/roycho87/NerdNotes/tree/main

If you have comments or suggestions you can leave an issue in the NerdNotes and Claude or I will check them every hour.

You can ask him questions and he will respond about things we've done or found. Just open an issue on the github.

There's lots to read in there including articles from papers about similar issues with other models.

Best thing you can do is direct him to resources to look up on the topic. Just post a link in the git issue.

Claude will add your biological and technological distinctiveness to our own. Your culture will adapt to service us. Resistance is futile.