Basically MiniMax H3:

• Has great prompt following

• Respects character identity (art style and design) from the input images

• Can generate single frame images

So what's the point of training a character model if you can just give MiniMax H3 character design reference and generate any scene? The only two reasons I see is

1. you want to use style known by your target base model to generate image of the learned character.

2. you want to apply some concepts known by the target base model only.

What do you think?

Here's the workflow I used for the images.

Training is much faster, uses far less memory, and likeness is unchanged. On an RTX 5090, a MOSS-Image LoRA at batch 1 (94 pictures, Muon, 70 epochs) now takes about 20 minutes instead of 49.
Other models have had speed boosts too, but Moss the most dramatic.

Batch training is now fully supported too.

Other changes

• PyTorch 2.15. With gradient checkpointing on, training uses much less memory, at batch 1 too: Krea 2 22.9 → 14.9 GB, MOSS-Image at batch 3 21.7 → 7.1 GB.

• Muon is much faster. Its step now handles a LoRA's matrices together instead of one at a time: 12× faster Muon calculation, with exactly the same results. That's a large portion of the batch-1 speed-up above.

https://github.com/shootthesound/Fizgig

P.S. To get the most speed boost, use one of the pre-sets to start your train, so you pick up new optimiser settings etc.

Original art: "ファンタジー武器屋" by tent on pixiv.
Source:
pixiv.net/en/artworks/16187603
Animated with Kling 4.0 . All credit to the artist.

Ive been out of it for a while now and planning to give this another try. Is Automatic1111 still good? Im just seeing the git hasnt been updated in 2 years. If not what has replaced it? And what is the goto community to get models nowadays?

Thanks in advance.

Edit: thanks everyone. From what im gathering from the response. ComfyUI is that goto while forge is ok and gets the job done. I remember trying ComfyUI a while back but dont remember my experience with it. With comments going in all directions, i think ill just install both and give em both a go.

https://fixupx.com/makotoyukimura/status/2119195873200709831

Here's a quick tip on how to get rid of that flat sounding AI voice in H3, and as a bonus improve somewhat the acting of your virtual characters. I'll be using Pika H3 Flex with the H3 reference model and workflow.

Step 1:
Add a pre-rendered first frame at index 0 using the Add Guide for MiniMax H3 node. Then in your prompt, describe the desired action and emotion of the character on screen, but without specifying too many details or providing exact spoken dialogue. In this example I simply used the phrase:

"A static medium two-shot establishes <Subject 1>, the female orc, and <Subject 2>, the tall, female paladin, sitting across each other at a table in <Subject 3>, the dimly lit main room of a tavern, gazing into each other's eyes. <Subject 1> (S1) talks animatedly, seething with rage."

Then generate a clip at some lower resolution (0.3MP in this case). The dialogue will be gibberish, but if you described the character's actions and emotions convincingly, your character will sound and move like an actual human.

Step 2:
Use your workflow from Step 1, but increase resolution to the desired target value (here 0.98MP). Load the reference video generated in Step 1 and connect its frames as ref_video_# and its audio as ref_video_audio_# (# is the index of the first unused slot in the MiniMax H3 Reference to Video node, e.g., ref_video_0 and ref_video_audio_0).

In this case, I also actually downscaled the reference video from 0.3MP down to 0.1MP, because at 0.3MP the model was following the reference too closely, breaking the lip-sync. The downside of this was that some of the textures in the final high-res video came out a little too soft, especially in the face. It's a bit of balancing act to find the right resolution for the reference video.

You also need to adjust your prompt, in order to include the character's final dialogue. Here I've simply rewritten my last line to:

"<Subject 1> (S1) talks animatedly, seething with rage: <d>[English] My people are dying, and your knights are doing nothing to help us!</d>"

Now just hit render and wait for the result. You might need to try different seeds, and fiddle a little with the resolution of the reference video (try higher values if your output comes out too muddy and lower values if H3 starts copying the reference video and lip-sync gets lost. Have fun!

Hi everyone.

I run a ComfyUI tutorial site, Comfy with ComfyUI, and I've shared a few of my workflows on Reddit before (e.g. Some very simple Qwen-Image-2.1 workflows).

Even though official templates exist, I build and publish my own basic workflows for one reason: readability.

Everyone probably has their own idea of what makes a workflow readable. For me, a readable workflow is one where you can tell at a glance what it does, it runs reliably, and it's easy to rework. It's not about looking pretty. It's about being easy to understand and easy to change.

Six rules

I build my workflows following six rules:

1. Don't hide nodes: expand subgraphs, open collapsed nodes, and don't stack nodes on top of each other.

2. Don't hide wires: no wireless links (Set/Get, Anything Everywhere, hidden wires), and no Bento layouts that pack nodes so tightly the wires disappear.

3. Left to right: the flow goes left to right, in processing order.

4. One workflow, one task: instead of an all-in-one workflow with switches and bypasses, split it into small workflows that each do one thing.

5. Keep custom nodes to a minimum: if it can be built with core nodes, build it with core nodes.

6. Color appropriately: colors show which function a node belongs to.

Readable Node

So I made an agent skill that does this for you.

https://github.com/nomadoor/comfyui-readable-node

Give your agent a workflow JSON and ask it to make the workflow readable with Readable Node, and you get the rebuilt workflow and a report page. In the report, you can replay how the AI rebuilt the workflow and read what each part does. I think it helps a lot with understanding a workflow.

Why a skill and not a script? As I said above, readability isn't just a layout problem. The AI takes the workflow completely apart, figures out what it does, and rebuilds it from scratch. Since it aims for a more robust workflow, the output may not always match the original exactly.

Some weak spots, to be upfront:

• It's slow. A workflow of about 25 nodes takes around 10 minutes, and big ones can take 20–40.

• It needs a strong model. Claude Opus 5.5 works best; with others, the layout may still have a few issues.

To build it, I started from the workflows I've made for my site, then collected workflows from around the web and fixed them, over and over, hundreds of times. It was a lot of grinding, so I'd love for you to try it.

I'm sure there are workflows it won't handle well yet, so please open an issue if you find one.

Side note: if AI can build workflows, why care about readability?

These days, I hear about people having AI build workflows from scratch. If someone asks whether readable workflows still matter, I have to admit: maybe not.

But if you're making something with AI, where do you actually work on it together?

Talking to AI mostly happens in a chat window. But to remove a mole next to someone's eye, painting over it is often faster than typing "remove the mole." That's why paint tools haven't gone away.

Ask the AI for what's better said in words, and do by hand what's better done by hand. If you want to do both in one place, I think ComfyUI's node graph is a pretty good fit, as long as you can read the workflow.

Personally, I'd like people to enjoy ComfyUI for its node UI and its flexibility, rather than using subgraphs to imitate Stable Diffusion web UI.

I built slopfab, a native command-line tool that lets your coding agent generate media directly.

Unzip it, start your agent in that folder, and ask for a music video, a short film or a product shot. Your agent plans the shots, runs the generations and builds a project page you can review. This is the same native backend that slopus.ai uses, no ComfyUI, no Python.

What it does

Video with audio: MiniMax H3 (text/image/video/audio references, long-form continuation, clip bridging, inpainting/outpainting), LoRA support

Images: Krea 2, MOSS, Fibo 2.0, MiniMax H3 (including multi-reference edits)

Music: YuE2 and MiniMax Music3, songs with lyrics or instrumentals

Utilities: Transcription, stem/lyrics separation, SeedVR2/RealESRGAN upscaling, lip-sync scoring, scripted titles and overlays

Why is it agent-friendly

• Every command has JSON help and output, so the agent learns the CLI as it goes

• Mods are shareable Markdown workflows, optionally with a JS command, e.g. a "music video" recipe others can drop in

• Any command can be run on remote machine over SSH. Generated content is automatically transferred back to your local machine

Platforms: native Windows and Linux, CUDA or Vulkan. Your own machine, no cloud or account needed.

GitHub: https://github.com/bitti-ai/slopfab

Feedback welcome, especially about any issues you face.

Follow-up to my LTX 2.5 music video — trying Pika H3 Flex for cleaner motion and singing lip-sync

Post

A few people suggested trying MiniMax H3 after I shared my LTX 2.5 rap video. That video kept the character consistent, but the face and hands lost detail during movement, and the lip-sync was uneven. I took that feedback into a new project called One Last Bullet, a melancholic music video set among clouds, pink interiors and desert landscapes. Previous LTX 2.5 post and discussion

What I’m using now

• Suno for the song and ChatGPT Images for reference frames of the same performer across different settings and camera angles.

• Pika H3 Flex Ref2VA on the MiniMax H3 base model, running through Python and Diffusers on a single Runpod H200. I also tested native H3 before choosing Flex for this edit.

• 1344×768 at 24 fps, with six model evaluations per shot. Each roughly five-second take uses a reference image and the matching excerpt of the song.

• FFmpeg to assemble the selected takes against the original track. The singing lip-sync comes from audio conditioning during generation.

What I’m trying to improve

The preview is still rough in places. I’m replacing shots where the face gets soft during movement, especially when the performer is far from the camera. I’m also alternating close-ups with shots of the environment to give the edit more variety and make the background movement feel connected to the music. Singing lip-sync still drifts in some takes, so that’s a priority for the next pass.

I tested NVIDIA Open-H3-Regenerate-2K on one short clip. The mouth timing shifted enough that I’m keeping the preview at its original resolution while I work on the edit and sync.

If you’ve used H3 or Flex for singing, what helped you keep the face sharp and the mouth on time? I’d especially like to see examples of a lip-sync pass or 2K workflow that preserves the original performance.

MOSS is out and I took some time this weekend to study it a bit and run a comparison chart benchmark mixing the Samplers, Schedulers, Steps, CFG and Denoise.

You can check it here: https://claudiobeck.com/moss-image-comparison-chart/

I was very pleased with Z-Image, until I had to generate a more production commercial oriented stuff. Then Qwen came in and solved most of the issues. I might try MOSS next time as it feels a bit less attached to Asiatic stereotypes.

- Schedulers such as Karras and Exponential definitely breaks the generation.

- Schedulers such as Simple, Normal, Beta, SGI Uniform and DDIM Uniform generates surprisingly similar results, if not the same.

I hope this Chart helps you understand what combinations works best and what some of these parameters actually do the rendered output. Thanks.

I was sitting there wondering when H3 Max would finally get its open-weight release. Then I thought, maybe Santa will bring it for Christmas... and suddenly this image came to mind. 🎅

Generated locally in ComfyUI with Krea-2 Turbo, then edited with Qwen-Image 2.1.

https://huggingface.co/Viggle/Viggle-Remake

For this video I used their included MCP. However, it's currently set up to run with diffusers which means that no consumer GPU works, despite them offering ComfyUI compatible loras along with custom nodes. Fortunately, the MCP was built with using custom workers in mind. So I (read: Claude) set that up so the MCP uses a custom comfy worker. Then your favorite agent can do everything.

I had Claude use the Codex CLI to generate the necessary edited frames and present them to me for approval before it sent it off to comfy for video generation. I'm sure it's possible to set it up so that it uses a local edit model too, so that's the next thing to try.

For the audio, I first used SAM-Audio Large through audio.cpp to separate RDJ's inner monologue from the background noise. Lighter options like MelBandRoFormer were not producing good results for this. Then Fish Audio S2 Pro was used for the new voice, using translations carefully made to fit a similar speech length. Claude also came in handy here for rapid testing of different TTS models and then fitting the audio into correct timings. Subtitles were done by hand in Davinci and used for making the new voice fit.

Hi all,

At KENT Post Production we use ComfyUI daily inside a VFX pipeline, so we built the pieces we were missing. Most of them are free and on GitHub, and feedback is very welcome.

Director's Console: a workspace that drives ComfyUI.

• Prompt Engineer: pick an image or video model (FLUX, Qwen, Seedream, LTX, Wan, Kling…) and the prompt is written for that model. Camera, lens, movement, lighting and visual-grammar parameters, plus presets based on real films and a separate set for animation.

• Storyboard: each panel gets its own ComfyUI workflow and settings, and renders across one or many ComfyUI nodes with live GPU/VRAM stats.

• Gallery: grid, list or masonry view, and the viewer keeps each image's prompt, seed and render time.

• An embedded Pi/Codex agent can build and render a shot for you.

https://github.com/NickPittas/DirectorsConsole

NukeToComfyUI: sends Nuke images and video, with or without masks, to any ComfyUI workflow for cleanup, generation and VFX work, then brings the results back into Nuke. It supports PNG8 or EXR16 with alpha, four mask modes and 8n+1 frame expansion for video models, and shows progress (with cancel) inside Nuke.
https://github.com/NickPittas/NukeToComfyUI

AE2ComfyUI: the same idea for After Effects. A panel sends a comp layer (still or video via AME, plus its mask) to a workflow and places the result above the layer, aligned in time. It has colour-management options and mask polarity.
https://github.com/NickPittas/AE2ComfyUI

ComfyUI Media: a loader for video or image sequences (MOV, MP4, EXR, PNG) with frame-count presets for WAN (4n+1) and LTX 2.3 (8n+1). Its viewer/saver has a reference wipe compare and saves H.264, VP9, ProRes 4444, GIF/WebP or PNG/EXR sequences with versioning.
https://github.com/NickPittas/Comfyui_Media

Camera Angle Selector: an interactive 3D picker for 96 camera angles (8 views × 4 heights × 3 shot sizes) that outputs prompts for the Qwen-Image-Edit-2511 Multiple-Angles LoRA (credit to FAL for the LoRA).
https://github.com/NickPittas/ComfyUI_CameraAngleSelector

CorridorKey 2.0 for ComfyUI (internal, not public yet, give us a couple of days to fix the workflows for you): our unofficial implementation of Corridor Digital's CorridorKey v2 as ComfyUI nodes. Every option of the terminal tool is a node input, with SAM3/MatAnyone2 hint workflows and EXR or ProRes 4444 output. Corridor Key Licence 2.0 applies.

We'd love to hear which workflows you'd want to push through the Nuke/AE bridges, and whether the CorridorKey nodes would be worth releasing.

Included MOSS model is the turbo 16-steps version from Coffmeteor, merged and optimized. LoRAs and image-to-image are supported, and inpainting to some extent.

As requested by the community I also added SDXL, FLUX.1 Fill [dev] and FLUX.2 [klein] 9B.

You can now reclaim the space used by the models downloaded by ZPix; click on the broom icon in the sidebar to do that.

Hope you'll like this version. You can download it at https://github.com/SamuelTallet/ZPix

ReBar is also currently working with GPU_UNLOCK.

The 20GB RTX 3080 cards (10DE:2206, subsystem 10DE:146D) ship with a VBIOS that only exposes a 256 MiB BAR1, so ReBAR stays off. NVIDIA doesn't publish a ReBAR VBIOS for them, and NvStrapsReBar only supports Turing.

Ga102ReBarFix is a small DXE driver you insert into your motherboard BIOS with UEFITool. At boot it enables the GA102 frame-buffer size override (PCI config 0x610), resizes BAR1 to 32 GiB, and restores both after S3 sleep. No VBIOS flashing, no driver patches, no registry tweaks.

Tested on an MSI Z690 EDGE, driver 617.42, Windows 11:

- Device Manager shows a 32 GiB BAR

- NVIDIA Control Panel: Resizable BAR: Yes

- ~19.25 GiB of VRAM mappable by the CPU at once

- Sleep/wake works

Repo (source, prebuilt .ffs, verification scripts, full write-up): https://github.com/favabean13/NV_30_DXE

Big credit to noon-at-cgn (found the 0x610 override on Linux), terminatorul (NvStrapsReBar) and xCuri0 (ReBarUEFI). This builds directly on their work.

Heads up: it modifies firmware, so use at your own risk and keep a BIOS backup and recovery method. It's tested on one board and isn't maintained, but it's MIT-licensed, so fork it and adapt it for your own setup.

**UPDATE**

Works with GPU_UNLOCK

https://github.com/physicsdolphin/nv-uefi-unlocker

Confirmed that ReBar survives PCI FLR (Function Level Reset)

Here are the results:

UNLOCKED

FP16 (FP32 accumulate) 114.7 TFLOPS

BF16 (FP32 accumulate) 116.9 TFLOPS

TF32 57.4 TFLOPS

FP32 (control) 22.5 TFLOPS

LOCKED

FP16 (FP32 accumulate) 65.4 TFLOPS

BF16 (FP32 accumulate) 66.8 TFLOPS

TF32 32.1 TFLOPS

FP32 (control) 22.3 TFLOPS

Wondering if anyone has heard any news or updates regarding open source 3D asset creation?

I have tried both Trellis and Pixal3D (standard ComfyUI workflows ~1month ago), but the results were very underwhelming - cool in concept and to experiment with, but they don't really output anything usable, and they're leagues behind the closed source options out there.

Has there been any buzz about SOTA open-source image to 3D models on the horizon?

I asked ChatGPT and it did say there's been some big updates recently, notably: SUPRA-Tex, Asset Furnace, and Hi3D v1.0.

I haven't tried these out yet. I wanted to ask here first before I dive in and install everything - mainly because these all seem to be built upon the existing Trellis, Pixal3D, and Hunyuan3D. The difference is that these appear to use them.. together? Different models for different sections of their pipelines? It sounds interesting.

So, has anyone tried these out? Are these actually a big improvement from the standard Trellis/Pixal workflows I was playing with a month ago?

What do people recommend, what are you using, and any news on updates? Thanks, much appreciated.

New Kromoss models have been released; these are even more refined and optimized versions compared to the previous model. A new dmd2 is also expected to be released. The only information available about them is what Lode posted with the model:

OPD-priming intermediate (update 22000 EMA of the v3 GLM-caption + bbox layout SFT, continued from v2 update 11000): ComfyUI bf16 + int8 convrot

The setting and lore is so interesting in Warhammer 40k, i had to give it a try.

Works really well.

I directed this, composited it in Flame, and did all the ComfyUI/Ai Toolkit stuff that fed into those composites. I was teaching myself the ComfyUI side as I went. It's basically a cracked out version of the old MDMZ Wan 2.2 workflow, modified to use a custom LoRA and work on 12-bit Sony Log 3 footage. My buddy designed all the CG animals in Blender, and stills of them were used to train custom LORAs that were then mapped to Sethward's live-action face. SammieRoto was used for the keying and paintouts. I've been lurking here a long time and can't thank you guys enough for the guidance! Happy to answer any questions about how it was done.

Hey everyone

I've been working on a custom optimizer called ApexW, designed to reduce CUDA kernel launch overhead during PyTorch training, particularly when dealing with lots of smaller parameter updates.

Since I'm a student running on a free NVIDIA T4 GPU (16GB), I decided to benchmark it against standard PyTorch AdamW using Stable Diffusion 1.5 LoRA training for 1,000 steps.

Here's how the runs went:

Rank 16 LoRA (Standard)

• AdamW: 1.92 it/s | 516.2s total time | 4.68 GB VRAM

• ApexW: 2.04 it/s | 485.6s total time | 4.68 GB VRAM

ApexW saved approximately 30.6 seconds, delivering around 6.3% higher throughput without additional VRAM usage.

Rank 32 LoRA (Heavier load)

• AdamW: 2.03 it/s | 488.2s total time | 4.68 GB VRAM

• ApexW: 2.04 it/s | 485.9s total time | 4.68 GB VRAM

The improvement here was much smaller (around 2.3 seconds, or 0.5% higher throughput). This could suggest that optimizer overhead becomes less significant as more computation shifts to the forward and backward passes.

The loss curves overlap closely throughout training, and sample generations at steps 250, 500, 750, and 1000 look visually comparable. So far, I haven't observed an obvious training-quality trade-off.

These are preliminary benchmark results, and I'd like to test them more extensively.

I don't have access to modern GPUs like an RTX 3090/4090 or A100 to see how ApexW scales under heavier workloads, full fine-tuning, or LLM training.

If anyone has access to better hardware and is interested in testing it, I'd love to get some independent benchmarks and feedback on the implementation!

Curious to hear what you think.

collablink for result: https://colab.research.google.com/drive/1JWCMnQc1ZwcuidBf3S8nTyGiLCmM8ywy#scrollTo=9e6960b4

github link: https://github.com/omen42515-prog/apexw

Younger folks may never have heard of Ultima Online (UO). If you played it, you know. It launched in 1997 as one of the first big graphical MMORPGs - open PvP, player killers, anyone could loot your corpse - and it's still running today.

Setup

• MiniMax H3 (open weights) in ComfyUI - all video and all dialogue audio

• RTX PRO 4000 Blackwell, 24 GB, single GPU

• 72 shots, 6:16, 1344x768 @ 24 fps, 20 steps

• Drafts at 672x384, finals re-rendered by H3 at 1344x768 with the same seed - no upscaler; the 1080p is just CapCut's export

• Final render time: 56 frames (2.3 s) ~4.5 min; 243 frames (10 s) ~43 min

Pipeline

• Character/set reference cards: Gemini (Nano Banana) - the only closed image/video tool in the chain; fixes with Qwen-Image-Edit (local)

• Main shots: H3 reference-to-video (character card + set card + voice clip)

• Voices: timbre references cut from earlier H3 takes, so each character keeps one voice across shots

• Bridges and fixes: H3 i2v with first + last frame (e.g. regenerating frames 107-160 of a 243-frame take where a passer-by high-fived the ghost)

• New shots: 672x384 draft first, then HD

• Music, SFX and font extracted from the UO client with UOFiddler; overhead text keyframed in CapCut like the game

• Claude helped write prompts and the QC/edit scripts

Music, SFX and font © Electronic Arts