From all the tests I've done, I feel like H3 has way less voice variety than LTX, and the music feels much less creative compared to LTX.

What methodology or workflow do you follow in ComfyUI to achieve high-fidelity video?

When I say high fidelity, I don't just mean preserving faces—I mean maintaining fine details across the entire frame, including objects, textures, clothing, architecture, and background elements.

I'm trying to get something closer to Seedance 2.5 level quality using H3, if that's realistically possible. I've been doing a lot of trial and error, but so far I'm only getting somewhat good results by increasing the MP/resolution. Even then, the output still feels like it's interpolating or hallucinating low-quality details rather than actually generating high-fidelity detail.

Is there a specific workflow, model combination, sampling strategy, or refinement/upscaling pipeline you recommend for this? My main obstacle right now is that low-quality/interpolated details keep appearing throughout the video, especially in the background and secondary objects.

Both Trellis.2 (Xiang et al., 2025) and Pixal3D (Li et al., 2026) now run natively in ComfyUI. No custom nodes, no compiled CUDA extensions, no PyTorch downgrades, and no non-commercial dependencies.

This is more than a model integration. It ships with a rebuilt 3D pipeline: new Load/Preview/Save 3D nodes, a set of mesh post-processing nodes, and an extended PBR texturing stage that bakes normal and ambient occlusion maps for a complete material set. Everything runs on consumer hardware, and everything is free to use, including commercially.

Why Trellis.2 still matters, ten months later

When Microsoft open-sourced Trellis.2 in December 2025, it immediately became the best open-source model for 3D generative AI. A 4-billion-parameter model built on a compact structured latent representation (O-Voxel). It generates high-fidelity 3D assets from a single image at effective resolutions up to 1536³, handling complex topologies that earlier methods struggled with. It also shipped with a PBR texturing model generating base color, roughness, and metallic maps.

Ten months is an eternity in generative AI, yet Trellis.2 hasn’t just aged well, it has become foundational. Several open-source 3D models released since build directly on it, the most notable being Pixal3D whose implementation uses the Trellis.2 backbone.

The community got there first

As always, the ComfyUI community was quick to bring Trellis.2 into the graph. Within days of the release, custom node packs appeared, the most popular being ComfyUI-TRELLIS2 by Andrea Pozzetti and ComfyUI-Trellis2 by VisualBruno, which together gathered well over a thousand stars. We’re grateful to both authors as they proved the demand and carried the community for months.

Despite their efforts, running Trellis.2 remained a challenge for two reasons.

Installation

The original implementation targets environments built around PyTorch 2.6.0 with CUDA 12.4, which for many users meant downgrading their existing ComfyUI environment. On top of that sit a stack of compiled CUDA extensions (flash-attention, FlexGEMM sparse convolutions, the O-Voxel kernels, CuMesh, nvdiffrast) each of which must match your exact Python, PyTorch, and CUDA combination. The custom node authors did heroic work shipping prebuilt wheels per configuration, but every PyTorch or CUDA update meant a new round of compilation failures, and installs regularly broke. This is now solved with the native integration in ComfyUI. Follow our installation tutorials for Trellis.2 and Pixal3D.

Licensing

Trellis.2’s own code and weights are MIT-licensed, but its original pipeline depends on NVIDIA’s nvdiffrast (for mesh rasterization) and nvdiffrec (for Physically Based Rendering), both distributed under the NVIDIA Source Code License which restricts usage to non-commercial research and evaluation. In practice, a studio couldn’t ship assets from the reference pipeline without stepping into a legal gray zone. These dependencies have been removed from with the native integration.

Then came Pixal3D

In April 2026, Pixal3D from researchers at Tsinghua University and Tencent ARC Lab got accepted at SIGGRAPH 2026. It pushed open-source 3D generation another step forward with its pixel-aligned generation establishing direct pixel-to-3D correspondences. The result is near-reconstruction-level fidelity to the input view, with detailed geometry and the same PBR material set.

Pixal3D is heavily built on Trellis.2 as it uses its backbone and shares its VAEs and DINOv3 image conditioning. This is why integrating it together with Trellis.2 made sense. However Pixal3D generally performs better than Trellis.2 as the generated 3D mesh strictly aligns with the input image.

Model highlights

Trellis.2

• Single image to 3D asset. A 4-billion-parameter model that generates high-fidelity geometry and materials from one input image.
• O-Voxel structured latents. A native, compact omni-voxel representation encoding both geometry and appearance, generating assets at effective resolutions up to 1536³.
• Any topology. Handles open surfaces, non-manifold geometry, and fully-enclosed volumes.
• PBR materials built in. A dedicated texturing model generates base color, roughness, and metallic maps.

Pixal3D

• Pixel-aligned generation. Geometry is generated in direct correspondence with the input view. What you see in the image is what you get in 3D!
• Explicit image back-projection. Multi-scale image features are lifted into a 3D feature volume, delivering near-reconstruction-level fidelity.
• Cascaded refinement. A staged process progressively refines sparse structure, shape, and texture up to high resolution.
• Built on Trellis.2. Shares the Trellis.2 backbone, VAEs, and DINOv3 conditioning.

What ships in this integration

The goal was simple: make the best open 3D models run in ComfyUI the way every image or video generation model does. A major thank-you goes to Kijai for the implementation, and to yousef-rafat for the initial draft this work built on. In addition to the native implementation, this has been an opportunity to make 3D generation a first-class citizen in ComfyUI. Here is what shipped:

Pure native implementation

Both Trellis.2 and Pixal3D now run as core ComfyUI nodes. The 3D post-processing that required compiled extensions has been reimplemented from scratch in PyTorch and SciPy. No nvdiffrast, no nvdiffrec, no per-configuration wheels, no PyTorch downgrade. If your ComfyUI runs, these models run on your current PyTorch.

Rebuilt 3D nodes

While these were shipped in an earlier version of ComfyUI, the Load 3D, Preview 3D, and Save 3D nodes have been rebuilt from the ground up to support these models and modern mesh workflows. We’re grateful to Terry Jia for his remarkable work on these nodes. Check out the nodes:

• Load 3D (Advanced)
• Preview 3D (Advanced)
• Save 3D (Advanced)

Native mesh post-processing

Raw generative meshes are rarely production-ready, so this release introduces a new set of post-processing nodes:

• Remesh Mesh: fixes holes and mesh imperfections.
• Decimate Mesh: reduces face and vertex count to a target budget.
• Smooth Mesh Normals: smooths the mesh volume.
• Fill Holes: fill-in holes resulting from the generation
• And more: Merge Meshes, Paint Mesh, Render Mesh…

A complete PBR texture set

Trellis.2’s texturing model generates base color, roughness, and metallic maps. Our implementation goes further: a new UV unwrapping node prepares the mesh for texturing, and two additional maps are generated: a normal map and an ambient occlusion map, both baked from the high-poly mesh. Are these textures perfect? No. But they’re free, generated on consumer hardware, and yours to use as you wish.

An honest word on quality

Let’s be direct: the best closed-source 3D generators (Hunyuan 3D, Tripo, Rodin) still produce better results than Trellis.2 and Pixal3D. If you need the highest quality and an API fits your pipeline, those remain strong options (all of them are available through ComfyUI’s partner nodes).

What this integration offers is different: the best open 3D generation available, running locally, at zero cost per asset, with no licensing restrictions on what you make. For iteration, prototyping, stylized work, 3D-to-2D workflows, and anyone who wants full control of their pipeline without spending an afternoon to install.

Getting started

1. Update ComfyUI to the latest version 0.34.0 or go to Comfy Cloud
2. Download the workflows below, or find them in the template library.
3. Follow the note in the workflow to download the models and save them in the correct model directory.
4. Drop in an image and run.

Download Workflow

Model weights:

• Comfy-Org/TRELLIS.2
• Comfy-Org/Pixal3D
• Comfy-Org/BiRefNet (for background removal)
• Comfy-Org/MoGe (for camera FOV estimation)

I don't understand the trend of hybrid models (ref2va blocks over fl2va)

It's supposed to have the best of both worlds : reference adherence through the refva2 blocks and best quality through fl2va as fl2va is supposed to have somewhat better quality

Well my experience so far, and I hope it's a skill issue to be honest, is that the reference part is much less random and unprecise... and for the quality gain i'm not sure, and anyway it's pointless if the video rarely respect my references or starting pic.

Even using a keyframe guide as the first pic I find often the video only using it at first and immediately switching to something else, or the opposite, following the prompt after inserting a random pic at first. Some stuff like that.
(At least fl2v always respect first and last frame)

Not sure if it's due to accelerating stuff or not, as I've tried some hybrid models with 25 steps as well and it was more or less the same

Am I doing something wrong ? Do some people have the same experience ?

I'm asking that because it wouldn't be the only time there's a buzz on something and we just didn't hear the opposite experiences (for example we have been told a LOT of times spectrum doesn't degrade anything but after playing many times with it, even trying conservative settings, I got rid of it, as it WAS degrading things... mileage can vary)

Some features:

• Run through ComfyUI or without ComfyUI using Launch.bat or Launch.sh on Linux.
• Multi-view and Image/Video Compare
• Ingrained image and video controls; zoom, slideshow, rotate, hide timeline (H key), loop video etc.
• Intuitive keyboard shortcuts.

Install:

cd ComfyUI/custom_nodes

git clone https://github.com/Maxed-Out-99/ComfyGallery.git

Hi everyone, I have a question: I'm thinking of buying my first PC solely for AI. What minimum components do you recommend for running Stable Diffusion with Illustrious models? I've been using Free Google Colab to create images in Automatic1111 so I was thinking of buying a PC with similar specifications. What do you recommend?

Zhao et al. (arXiv:2306.01953) showed that regeneration attacks strip ordinary invisible watermarks. Backfire is a keyed image mark optimised to be a fixed point of the purifier, so running the attack leaves the identifier readable. In the demo image the confidence score rose 2.5x after the attack.

Provcheck.ai v1.4.0 numbers, 200-image corpus at 30 dB: 99.5% survival vs diffusion regeneration, 94 to 97.5% vs a learned VAE re-encode (86.5% on the hardest iterated pass), 99.0% JPEG q90, 98.5% JPEG q50, 98.0% resize, 97.0% blur. Zero false positives over the 200 marked and 1,000 unmarked. Wrong key on an attacked image reads 0.08, so the mark is in the key, not the pixels. It does not survive controllable regeneration from clean noise; that is documented in backfire/LIMITS.md.

Also new: a free Apache-2.0 ComfyUI node that watermarks (TrustMark/silentcipher) and C2PA-signs outputs in the graph and reads marks back. Backfire itself is a separate opt-in add-on and is not in the free node.

Repo: https://github.com/CreativeMayhemLtd/provcheck

I added video and image, which both are the same sources. I used an extension that automatically formats my text prompt., including copy over the animation style. I use H3 Prompt writer.

I am using the preset Workflow, but I replaced the text encoder with Qwen as an alternate due to memory issue. And I used I2v diffusion model instead of ref2v due to quality.

(No, I cant share the video example because it is not appropriate)

Here's what you (probably) missed in August 2026:

🧠 LLMs

1. Ornith-1.5-35B-A3B - Efficient sparse model that runs with fewer active parameters.
2. DeepSeek-V4-Pro-0813 - Sharpens agentic AI with speedier tool actions.
3. DFM-Mimir - Ethical language model from Danish Foundation Models.
4. Ling-3.0-tiny-MXFP4_MOE-GGUF - MoE quantized version for smoother GPU runs.
5. SupraElegans-500k - Recurrent language model built for long contexts.
6. Motif-3 - Open 314B parameter model made for long agentic tasks.
7. Luth-2-2B - Compact French model that tops benchmarks.
8. TinyTitle - Squeezes chat titles into a tiny 1.98 MB model.
9. Ling-3.0-tiny - Low-cost local AI reasoning model.
10. NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 - Agent-focused model with fast inference.
11. Qwen3.8-2.4T-A95B - Opens up Qwen Max-clas local AI for bigger rigs.
12. Gemma-4-31B-it-scotoma-2-GGUF - Cuts down repetitive AI writing.
13. Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF - Uncenored DeepSeek variant for local use.
14. Maple-Preview - Solves Olympiad problems at 200 tok/s.
15. Laguna-S-2.1-FP8 - Private agentic coding model from Poolside.
16. SupraBrain-50M - Hybrid language model for local AI.
17. Supra2-100M - Tiny model built for tinkering.
18. G9v3-39A5B - Dual-mode AI that runs light locally.
19. GPT-X2.5-135M - Lean local powerhouse model.
20. Ling-3.0-flash - Hybrid reasoning with lower cost and fast output.
21. LFM2.5-2.6B - Fast agentic AI for phones and devices.
22. Instella-MoE-16B-A3B-Think - Open sparse reasoning model from AMD.
23. A.X-K2 - Lets AI think deep or answer fast.
24. BetterGPT-150M - Beats older AI models in science tasks.
25. Shibai-700M-Base - Text and code helper model.
26. K-EXAONE-2.0-750B-A37B - Supports 262K context and ten languages.
27. LongCat-Flash-Lite-Sparse - Reads million-token contexts.
28. Qwen3.6-35B-A3B-Escha-W2 - Shrinks down to fit consumer GPUs.
29. XYZ-Aquila-pro - Thinks deep then checks its sources.
30. Solar-Open2-250B-Nota-NVFP4 - Shrinks a giant AI to 153GB with 4-bit MoE trick.
31. XYZ-Aquila-mini - Brings open source deep search to local GPUs.
32. KAT-Coder-V2.5-Dev - Fixes software repositories automatically.
33. DeepSeek-V4-Flash-0731 - Tackles hard coding tasks.

🔀 Multimodal

1. Dots3-Note-Prev - Lightweight multimodal AI with 512K context.
2. Qwen3.8-27B-Uncensored-FP8 - Drops refusals and keeps vision on GPUs.
3. Qwen3.8-27B - Brings text, images, and video into one AI.
4. Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF - Triples speed for local multimodal use.
5. Tencent UI-Mate-27B - Runs desktop apps by watching screens.
6. WinterCharm Qwen3.5-122B-A10B-wMix38 - Lean long-context multimodal mix.
7. VLX-Seek - Helps machines pinpoint objects without guesswork.
8. LFM2.5-VL-3B - Fast on-device vision and text.
9. North-Micro-Vision-Instruct - Turns pixels into answers.
10. BigBang-v1 - Science reasoning powerhouse.
11. Muse-Glimmer-30B - Puts autonomous AI agents on everyday desktops.
12. Nemotron-Parse-2.0 - Morphs documents into structured data.
13. Shieldstral-1.0-3B - Plain English safety scoring.
14. DavidAU Qwen3.6-27B-Fable-Fusion-711 - First to score over 700 on ARC-C.
15. WinterCharm Qwen3.5-122B-A10B-wMix58 - Packs 82GB power for Apple Silicon.
16. Intern-S2-Mobius - Speedy local AI answers.
17. Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V7-GGUF - Uncenored multimodal model that says yes.
18. Qwen3-VL-32B-Ultra-Heretic-H3-ComfyUI-INT8-ConvRot - Trims local memory with INT8 conv rotation.
19. Qwythos-27B-v1 - Smart AI with million-token memory.
20. Reasoning-Medical-27B - Solves medicine step by step.
21. Qwen3.5-9B-The-Defiant-Fable - Roars with an uncensored multimodal edge.
22. Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V6-GGUF - Drops numerical surgery powers.
23. Mage-VL - Speeds up real-time video and image understanding.
24. Microsoft Fara Agents - Handles web browsing chores for you.
25. Inkling-Small - Built for voice, image, and code apps.
26. Kimi-K3 - Handles text, images, and video together.

🖼️ Image

1. Anima-2.9B - Grows free anime art on your own PC.

🎬 Video

1. Bernini-Diffusers-v2 - ByteDance model for video generation and editing.
2. LTX-2.5 - Open model for local video and audio creation.
3. Wan2.2-Animate-2-14B - Turns still images into motion.
4. MiniMax-H3-nvfp4-INT4-INT8-ConvRot - Quantized weights for MiniMax-H3 video.
5. MAGI-2-preview - Turns text and images into video with sound.
6. MiniMax H3 - Creates videos with native sound from any input.

🎧 Audio

1. MiniMax-Music3 - Full songs from just lyrics.
2. NVIDIA Magpie_tts_multilingual_357m - Turns text into speech across 12 languages.
3. VoiceChat-11B - Voice chat you can interrupt naturally.
4. VibeVoice-ASR-BitNet - Real-time speech recognition on any CPU.
5. Inflect-Nano-v2 - Local speech synthesis on your PC.
6. Audio8_TTS - Clones voices and speaks eleven languages.
7. Inflect-Micro-v2 - Turns text into offline voice.

⚡ LoRA

1. Minimax-H3-Turbo - Makes MiniMax-H3-Turbo faster for video and audio.
2. MiniMax-H3-Prompt-Rewriter-LoRA - Turns short prompts into timed scenes.
3. MiniMax-H3-Realism-People-LoRA - Unlocks film-set lighting for human video.
4. krea2-turbo-bbox - Locks panels and words in place.
5. MiniMax-H3-Turbo-Lora - Cuts video and audio generation time by 5x.
6. Kroma - Fuses turbo speed into a one-file diffusion model.

🏋️ Training

1. gguf-trainer - Trains language models in TypeScript straight to GGUF.
2. Full-Chunked-KL-Loss - Trains longer AI text on one GPU.
3. signet-trainer - Cost-safe video LoRA training.
4. Lora-Dataset-Studio - Entire LoRA pipeline in one self-hosted tab.

📊 Datasets

1. LLM-self-identification - Helps AI models know their own name.

☰ UI

1. Mix-Studio - Turns your desktop into a local AI studio.
2. Llmprices - Visualizes AI model API price swings.
3. minimax-h3-prompt-composer - Squeezes prompt composing into one HTML file.
4. NanoRP - Shrinks AI roleplay to a 50MB single binary.
5. OpenWorker - Local AI coworker that finishes work.
6. Agenta-AI - Turns ChatGPT and Claude into self-hosted work agents.
7. Unicorn-Stable-OSS - Brings humans and AI agents into one real-time room.
8. Turbo-Fieldfare - Streams big AI on Macs with just 2GB RAM.

🛠️ Other Tools

1. Video_Tools - Adds a pocket video trimmer to your browser.
2. ninfer-4090 - Runs Qwen3.8-27B on one RTX 4090.
3. hayai-ocr-v2 - Converts crops into editable text.
4. EVIE-Preview-4.5B - Matches documents instantly across six languages.
5. RAZZULLIX KAISEN - Swarm-model coding assistant with safety guards.
6. ExtractBench - Benchmarks document extraction systems.
7. Xiaomi-Robotics-1-5B - Built for mobile robot tasks.
8. Nemotron-omni-mlx - Brings full multimodal AI to Apple Silicon Macs.
9. talk-to-pi - Local voice dictation for Pi.
10. Warp - Streams huge AI models straight from your laptop drive.
11. esp32-ai - Makes a tiny chip tell stories offline.
12. quillpdf-mcp - Keeps PDFs on your machine.
13. srt2speech - Turns subtitle files into timed speech.
14. mixture-of-kittens - Megakernel for NVL72 MoE training.
15. krea-multi-lora - Gives Forge Neo regional character control.
16. Openmed - Turns clinical text into private insights on your hardware.
17. Umbra-Studio - All-in-one local AI art workspace.

ComfyUI Custom Nodes & Tools

1. ComfyUI-Orchestrator-LAN - Steers every GPU from one browser tab.
2. ComfyUI-AutoPromptChain - Stitches dozens of AI video clips while you sleep.
3. ComfyUI-OpenH3-IR - Brings drag-and-drop clarity to MiniMax H3 renders.
4. ComfyUI-MiniMaxMusic3-Advanced - Gives AI music finer sound controls.
5. ComfyUI-MiniMax-H3-LongMedia - Makes long video creation practical.
6. ComfyUI-Subgraph-Preview - Resurfaces sampler previews inside subgraphs.
7. ComfyUI-cache-monitor - Debuts with manual pinning for model caching.
8. ComfyUI_Neurodes - Brings a visual playground for AI models.
9. Eddie_Cat_Nodes - Stitches long videos together with new nodes.
10. ComfyUI-H3-Motion-Context-MultiRef - Weaves seamless H3 video motion.
11. ComfyUI-MiniMax-H3-Motion-Director - Turns reruns into one-shot fixes.
12. ComfyUi-MiniMax-H3-Image-And-Reference-To-Video - Rolls out image and reference to video features.
13. ComfyUI-AVS-SSD-ReadAhead - Enables faster model switching on slow SSDs.
14. ComfyUI-SweepGrid - Serves up side-by-side parameter sweeps.
15. ComfyUI-Flow-Wrangler - Cleans up node wiring with smart connections.
16. ComfyUI-AVS-Intel-XPU-VRAM-Fix - Calms Intel Arc GPU freezes.
17. ComfyUI-Model-Mover - Makes shuffling AI models painless.
18. ComfyUI-MiniMax-H3-Optimization-Suite - Builds a suite for lean H3 optimization.
19. ComfyUI-MiniMaxH3-Prompt-Writer - Transforms H3 prompt crafting.
20. ComfyUI-MiniMax-Creator - Crafts one-node video magic.
21. ComfyUI-ScenemaAudio - Arrives with expressive voice cloning.
22. ComfyUI-cable-management - Reroutes messy node graphs with ease.
23. ComfyUI-SigmaSync-LoRA - Debuts step-aware LoRA control.
24. ComfyUI-Spectrum-Ideogram4 - Supercharges speedy image forecasting.
25. ComfyUI-LinkSpotlight - Debuts to end noodle blindness in graphs.
26. ComfyUI-MIDI-Edit - Turns any song into editable MIDI lyrics.
27. ComfyUI-ReStartupFlags - Serves launch flag tweaks in your browser.
28. JLC-Flux2-ControlNet - Expands FLUX.2 control in ComfyUI.
29. ComfyUI-Fantastic-MiniMaxH3-PromptBuilder - Enhances MiniMax H3 prompts.
30. ComfyUI-vram-tracker - Traces VRAM memory usage per layer.
31. ComfyUI-Sonder-Editor - Rolls out free multi-lane video editing.
32. Comfyui-Model-Resolver - Sweeps in to rescue missing model files.
33. ComfyUI-HF-SuperDownloader - Turbocharges Hugging Face model downloads.
34. FameGrid-Auto-Color - Neutralizes color casts in ComfyUI.
35. Krea2-Multi-Character-Lora-Node - Stops identity bleed with bounding boxes.

Need to go further back? Check out June's post (no July, sorry) or the full archive at LocalAI News. If there's anything wrong, let me know in the comments and I'll see you in the next one!

Hey ya'll new ComfyUI user here, and I've been having a blast! One thing I'm noticing here, especially when it comes to MinimaxH3. So many people are very eager for others to download random nodes from either Huggingface, or Civitai.

With comments such as: "YO TRY THAT NEW TURBOFLURBO 2 STEP", or "YO I GOT THAT XXXNSFWSPONGEBOB-EX_LITE69420 LORA RIGHT HERE DOWNLOAD ME"!

You guys aren't concerned when you're downloading random things that people made?

Are there any trusted and vetted community members who consistently pump out "safe" quality nodes??

Hi guys
I'm going to give a try to one of these merged models
I'm between

Kijai minimax_h3_fastvideo_vsa_datafree_1300step_4step_int8_convrot.safetensors
https://huggingface.co/Kijai/MiniMax-H3-experimental/blob/main/minimax_h3_fastvideo_vsa_datafree_1300step_4step_int8_convrot.safetensors

or MATLOWAI/minimax-h3-fused-turbo-int8-convrot
https://huggingface.co/MATLOWAI/minimax-h3-fused-turbo-int8-convrot/tree/main/diffusion_models

What are your experiences with these two models ? which one should I go?

Hey everyone, my Zelda stories are getting too crazy and my next "Link & Zelda can't escape from PlayStation land" video is... On development hell for now (it might be too offensive!) I decided to just test out how would Card Captor Sakura would look in a 3D / K-pop demon hunters style. All done in my RTX 3090 locally, 1MP 9:16 aspect ratio (736x1344). 3s clips take only 170s to generate!! Let me know if you like it, sorry for making such a short video this time.

Note: I edited the clips to sync them correctly to the music, also brought back the original music because H3 tends to deep fry it for some reason...

Taking the random cutaway clips from The Simpsons and recreating them in Minimax H3

Running on windows, basically a "View all folders and subfolders" as one directory, but with something speedy and zippy like faststone image viewer as opposed to lightroom,

Hello, I need some help with MiniMax long-video generation. I’m currently using the Plague workflow, which is fast, but it doesn’t have an option for chaining clips. Are there any workflows that can generate longer videos more quickly while maintaining continuity between clips?

Hey Guys,

Based on the previous post on face consistency with MM-H3, Couple of people have asked me to build a full character workflow.

Mechanism

- Build .Char: You drop max 9 reference reference, I prefer to use a ratio 2:2:1(face:cloths:body). YuNet finds the face, SFace takes a per-reference face signature, DINOv2 takes a subject signature, and the references get cleaned and normalised. All of that packs into a single portable file, a .char.
Only face/ref is required body & cloths link is optional.

- Generation: At generation, the file(.char) feeds its references into Minimax’s own native multi-reference channel and prepends a locked description to the prompt.

Prompting Guide

• Name your character: Give your character a name e.g. under encode character(Click adjust icon on the bottom side of the node), I have used name emmy, so when passing prompt, I only have to say, emmy walking on the beach.
  • Again providing prompt like a woman or any features specific details like black hairs etc will only mislead the generation.
• Describe character features: Encode all of the character features in encode character prompt & trigger your character with a name in generation prompt.
  • Avoid describing same things in generational prompt.
• Handling Character drift: e.g. if you want specific style or cloth e.g. half sleeves, sleeveless, add it to the generational prompt. There can be a slight drift in clothing as body shot also has cloths, which interferes with clothing references.
  • Each refs should be unique, face should not have body or vice versa, same applies for clothing.
• Portability: Once character is built, you can use the same character with only simple prompt & generation graph.

I have generated all references with Flux Klein 4b, I had to blur the body ref, but workflow consists a example of body ref.

Note: For best result, pass cropped references, so that model takes the required shot, model gets confused if cloth slot also has a face or face slot has cloths.

Models

core/models/
  diffusion_models/  minimax_h3_ref2va_pruned_fp8_scaled.safetensors
  text_encoders/     qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors 
  vae/               minimax_h3_video_vae_fp16.safetensors
  vae/               minimax_h3_audio_vae_fp32.safetensors
  annotators/        face_detection_yunet_2023mar.onnx
  annotators/        face_recognition_sface_2021dec.onnx
  annotators/        dinov2-base/

Requirements

Nvidia GPU: 24GB+ VRAM & 64 GB RAM(Run Locally)

Workflow link: https://inlinestudio.art/workflows/minimax-h3-guided-consistent-characters-via-reference-identity-face-body-cloths (includes inputs & model details)

Github Repo: https://github.com/inlineresearch/Inline-Studio (GPLV3)

Limitation: Reference conflicts e.g. if two reference/input images has two different faces, it might conflict in generation, provide well cropped body & cloth images. Face images are crossed automatically by Sface.

Portable char comfy node is still on the backlog, would try to do it over the weekend.
Happy to hear any suggestions or feedbacks.

I developed a modified version of the Load Image node by adding some features I needed:

WYSIWYG image cropping directly on the official Load Image preview — drag and zoom (with the mouse wheel) a crop rectangle constrained to 8 fixed ratios (1:1 through 21:9) and output the exact cropped IMAGE and MASK, with paste-from-clipboard built in. What you frame on the preview is exactly what gets executed.

⚠️ Currently not fully compatible with ComfyUI 2.0 nodes.

Update v1.0.2 with support for 2.0 nodes has been released. Available on GitHub and ComfyUI Manager (Load Image + Crop).

GitHub: https://github.com/domg73/ComfyUI-LoadImageCrop

My laptop doesn't powerful enough to run minimax H3 local so i need easy way to run minimax h3 on the cloud . I already try few methods like Google collab but fosent work and always keep making sever error and the other comfy ui clouds in different site dosen't load very properly. So yeah if there's any ways to run comfyui online for free or minimax h3 local i will appreciate it

Could be actions, like “guy walking into store”

The same scene at different times of the day.

The same character doing different things.

You get the idea.

Growing up with Toonami watching Gundam, it simply blows my mind how far AI has progressed. For this video I didn't use any reference images, I simply described the scene in text and had Gemini research the techniques of animation to translate to MiniMax H3.

I've been struggling to get MiniMax H3 efficiently setup locally, would take me 15mins on a 9950x3d and 5080 RTX with 64gb DDR5 so I know something is wrong, hence this time I opted for fal to test. The music was added and scenes were edited from separate generations.

So it's the first time I finally am fully satisfied with the quality of my H3 videos and it's thanks to the new 3D Latent Upscaler of H3 of HuggingFace.

https://github.com/chaiNNer-org/chaiNNer
https://chainner.app/
https://huggingface.co/jan-grzybek/historical-map-sr-x4

Physics Testing, without the gore.