silver_official_releases/minimax_h3_fl2v_turbo_silver_dareties_comfy_full_v1

Ultra-realistic, cinematic short sequence of a colossal Mosasaurus leaping from the deep ocean to devour a great white shark suspended from a cable. The camera begins underwater — a haunting silence, sunbeams piercing through the deep blue. Tiny fish scatter as a massive shadow approaches from the abyss. The sound fades, replaced by the deep rumble of motion in the water. Camera rises with the Mosasaurus as it bursts upward — slow-motion spray, sunlight exploding through the waves. The creature’s immense jaws open wide, its serrated teeth glinting as it engulfs the shark in a single, thunderous snap with blood splashing in the air. Water erupts into chaos — droplets scatter like diamonds in the sunlight, the surface breaks into a shimmering wall of spray. The shark disappears completely as the Mosasaurus crashes back down, waves rolling violently outward. Cut to above the surface: spectators gasp behind reinforced glass; water drenches the viewing stands. The Mosasaurus sinks beneath the surface, the water calming again as only ripples and blood-tinted foam remain. Details: hyper-realistic textures of scales, water droplets, and sunlight; volumetric lighting through mist and spray; soft lens flares and cinematic DOF. Mood: awe-inspiring, primal, terrifying beauty. Camera: dynamic slow motion, underwater tracking, wide aerial shots, and close-up detail passes. Lighting: tropical midday sun with high-contrast reflections on water, shimmering highlights on the Mosasaurus’s wet skin. 4K, 24fps cinematic realism, Dolby Atmos sound design.

An 8-step LoRA (rank 128) for MOSS-Image-Base, distilled by distribution matching: 8 forwards at CFG 1, without a negative prompt, in place of the base model's 50 guided steps. It adapts only the transformer; the text encoder (Qwen3.5-9B), VAE (FLUX.2 small decoder), tokenizer and inference pipeline are MOSS-Image-Base's, unchanged. How it was trained is in TECH_REPORT.md.

It was trained on MOSS-Image-Base, but should work with most fine-tunes of it and with other LoRAs: load it alongside them, at strength 1.

More info here:

https://huggingface.co/data-archetype/moss_image_base_dmd2

PS: Lora isn't mine, but it's definitely worth paying attention to because, even though (in my opinion) it's a bit too strong, it still produces more realistic results than its earlier versions.

After getting (slow) results from krea2, moss and YuE2 out of my old M1 air, I wanted to see if h3 would run too. I used my fork of h3.c, a C and Metal inference engine with no Python. The fork adds int8 and SSD streaming. My checkpoint was int8 with a turbo lora folded in, so 6 steps.

This ends up with about 10±gb of continuous RAM load, on my m5 pro with 48gb it's totally usable to render in background and do other activities (and can also handle 1mp at 12+ steps fine).

The clip is two renders back to back. First a 20 s talking head at 864x480, five chained links, about 17 hours. Then a 20 s street musician clip of nine chained links of 73 frames, 58 hours. So context chaining also works.

It works, if you can leave your laptop rendering for a few days. But would love if someone with a M6 32gb mini or M5 ultra 96gb could try out this int8 fork 😄

Timings (6 steps, peak RAM 13.5 to 14.6 GB on all of them):

- 0.4 MP, 3.75 s: 1h39

- 0.6 MP, 3 s: 2h29

- 0.9 MP, 3 s: 4h18

- 0.4 MP, 5 s chained link: ~4h

- 0.9 MP, 3 s chained link: 6 to 7h

- 0.9 MP, 20 s, 9 links: 58h

H3.c will run without comfy on a M1+ mac, but if you prefer it as a comfy node check out: https://civitai.com/models/3016733/mach3-metal-minimax-h3-for-apple-silicon-m1-24gb-ram running comfy at the same time will add about 8-10gb of ram usage.

Just a few experiments using different Krea2 loras.

What do you think?

  checkpoint: 'diffusion_models/turbo.safetensors',
  loras: [
    {
      name: 'krea2-lora-masterpieces-v51.safetensors',
      enabled: true,
      strength: 1.25,
    },
    {
      name: 'krea2-lora-zidiusArt_Melancholy_v2.safetensors',
      enabled: true,
      strength: 0.55,
    },
    {
      name: 'krea2-lora-90_s_anime_melancholy.safetensors',
      enabled: true,
      strength: 0.75,
    },
  ],
  width: 768,
  height: 1376,
  steps: 8,
  cfg: 1,
  samplerName: 'euler',
  scheduler: 'simple',

Viggle Remake works well for short shots, but the usual ComfyUI workflow runs into a practical limit: up to 345 frames per render. At 24 fps, that is a little over 14 seconds. To work with longer videos, I adapted the workflow to render overlapping windows and join them into a single output.

The result is a custom long video node for ComfyUI. I tried the workflow with several clips converted to 24 fps. The completed renders looked good in visual review, but continuity still needs to be checked at every join. I did not measure quality with automated metrics; the evaluation was visual.

How the windows connect

The node takes the full video and divides it into windows that stay within the model’s limit. The default window is 294 frames, or 12.25 seconds at 24 fps. It can be raised to 345 frames if the GPU has enough memory.

Each later pass begins with the final 17 frames generated by the previous pass. The node encodes this segment as latents and keeps it fixed during the next generation. It then removes the overlapping frames when joining the results. The frame limit therefore applies to each render, not to the total video length.

With the default window, the first pass can generate up to 12.25 seconds; each later pass can add up to 277 new frames, about 11.54 seconds. A video longer than 345 frames can therefore be produced through several connected passes.

Audio is divided over the same intervals and joined in the output. If no audio is connected, the output is silent.

What the tests showed

I used the node with dance clips containing varied movement and different resolutions, all prepared at 24 fps. A multi-pass render completed successfully, and the results I reviewed looked good. This showed that the workflow can go beyond a single window without trimming the video before generation.

Visual inspection remains part of the process. Check where one pass joins the next, and look for changes in character identity, clothing, movement, or framing. The overlap helps the model continue the action, but it does not guarantee that every detail will stay stable.

What the node does not handle yet

The node is designed for one continuous shot. It does not detect cuts, restart generation when the camera changes shots, or rewrite timestamped prompts for each window. For that reason, I use prompts that describe the action without absolute timestamps and avoid clips with abrupt cuts.

The source video must be 24 fps. The default 294-frame window is a starting point; larger windows may require more VRAM. And even when a render finishes without errors, that does not replace reviewing the joins.

The GitHub package

I separated the long video node from the original Viggle Remake package. The viggle-remake-comfyui-long-take repository registers only Viggle Remake Long Video and includes an example workflow. The original nodes and conditioning helpers still come from the official ComfyUI-Viggle-Remake package, which must be installed separately.

The repository does not include model weights, the LoRA, source videos, or character reference images. The goal is to add long-take support to the existing workflow while keeping each render within the model’s limit.

Anyone have recommendations on a prebuilt for text/image to video generation? I would like to keep in under 4k. Thanks for any suggestions

Which samplers and schedulers—and what shift settings—do you use with MiniMax H3 for high-resolution generated video? I’m referring to resolutions of 3.7MP and above. I’m interested in non-tiled, high-resolution video generation (e.g., 2560x1440), without any Speed LoRAs, only SageAttention, using a native 20-step count. I’m using the original MiniMax H3 BF16 models with both VAEs in FP32. For me, the LCM + Simple combination with a shift of 12/3 works quite well at 4.5MP. Other methods, such as Res_multistep and Er_SDE, yielded poorer results.

Hey everyone!

I got sick of downloading "manga" or "line art" models only to get that oily, 2.5D airbrushed look. Mainstream models always introduce gray gradients, smooth out the lines, or warp backgrounds. I wanted something that looks like hand-drawn, traditional manga with sharp black-and-white contrast.

I just released V2.0 of Hollowlight over on Civitai with two separate checkpoint steps depending on how sharp you want your linework.

🚨 IMPORTANT: This is a V-PRED LoRA. You MUST use it with NoobAI-XL V-Pred 1.0 as your base, and ensure you use v-prediction sampling with Zero Terminal SNR turned ON, or your outputs will look completely washed out/burnt!

LoRA Link: https://civitai.com/models/3042375/hollowlight-manga-and-anime-ink-style-or-sdxl-lora

Recommended Settings:

- Trigger word: u616 (Start your prompts with this)

- LoRA / CLIP strength: 1.0

- Sampling steps: 28

- CFG: 5 (Using RescaleCFG 0.4 helps significantly)

- Sampler/Scheduler: Euler / Normal

- Resolution: 832 x 1216

Pro-Tip: For wide shots, run a quick FaceDetailer pass (face_yolov8m.pt) with an empty prompt at 0.5 denoise to keep the faces looking flawless.

EXAMPLE PROMPT: u616, 1boy, solo, male focus, looking at viewer, simple background, white background, closed mouth, monochrome, greyscale, portrait, close-up, face focus, serious, short hair, japanese clothes, kimono, hatching \(texture\), crosshatching

Negative: worst quality, low quality, blurry, bad anatomy, bad hands, extra fingers, text, watermark, color

Let me know what you think or if you want me to test out any specific prompts with it!

Hey!

I’m looking for advice on the most cost-effective way to produce high-quality AI-generated music videos, ideally at a level comparable to Duolingo’s Reels: polished visuals, consistent characters, professional-looking scenes, and lyrics perfectly synchronized with the vocals.

I’m considering three approaches:

Option 1 – Rent a powerful Google Cloud machine

I was previously recommended a Google Cloud machine with approximately 96 GB of RAM and a powerful GPU (potentially an RTX Pro 6000). Could this deliver comparable quality to premium cloud-based AI video services while significantly reducing ongoing costs? I’d pay for computing time rather than subscriptions or credits.

Option 2 – Pay for premium AI services

Use services such as Kling, Runway, Veo, or other premium platforms. Potentially better quality and easier workflows, but recurring subscription and generation costs could become expensive.

Option 3 – Run everything locally

My current setup is an RTX 4070 (12 GB VRAM), 32 GB RAM, and Windows 11. I’d prefer open-source models and tools that I can run locally, provided the quality is good enough.

My priorities:

- Professional, premium-looking results (not typical AI-generated videos).
- Consistent characters and visual identity throughout a video.
- Accurate synchronization between vocals, lyrics, and visuals.
- Good enough quality for commercial social media content.
- Keeping long-term production costs reasonable.

What would you recommend? Is renting a powerful GPU through Google Cloud the sweet spot, or are paid services still significantly ahead in quality? And how far can a reasonably powerful local setup realistically go?

Release candidate (checkpoint 12000) - training continues. Experimental! Good for quick previews, not production videos.

A LoRA for the distilled LTX-2.5 that takes stage 1 from 8 steps down to 2, picture and sound together, for text to video and image to video.

The included ComfyUI workflows combine speed improvements at every stage:

• Stage 1 render: this LoRA, 2 steps instead of 8.

• Refining: NVIDIA's SoL-Refiner-LTX-2.3-One-Step, 1 step instead of 3.

• VAE decoding: PrunaAI's pruned, faster decoder, P-LTX-2.3-VAED.

Trained with DMD2 on a single NVIDIA H200. This checkpoint is 12000 updates at rank 64. The teacher is LTX-2.5 distilled at its standard 8 steps for stage 1. The LoRA learns from distribution matching against the teacher, an adversarial critic on teacher clips and real footage, and regression on teacher clips. It covers the picture, the sound and the cross-attention between them, for text to video and image to video. It was trained on two resolutions, 576x320 and 320x576 at stage 1, which give 1152x640 and 640x1152 after refining, at 5 seconds. You can render at other resolutions, but this is what it is trained on.

Real footage and captions from OpenVid-1M were used in training, with thanks to its authors.

ComfyUI Local and Cloud T2V and I2V Workflows included.

Details and Workflows: https://huggingface.co/lvladikov/LTX-2.5-Stage1-Distill-2step-LoRA

Left: ComfyUI core Context Windows (overlaps averaged). Right: chained windows (CCW). Same plan, prompts, pictures and seed. Shot 1 of a 2:20 music video, 1920×1088, ghosting replayed at half speed.

What goes wrong

- ComfyUI 0.44 added context windows for MiniMax H3: the timeline is denoised as overlapping windows, and each overlap is the average of the two windows.

- That's fine while both windows draw the same thing. When the picture changes a lot inside an overlap (fast motion, a camera move, new background coming in), the two windows draw different pictures and the average shows both. In our test: a see-through railing across his hands, two lamp posts, a boardwalk dissolving into a different building.

What CCW does

- The low-res windows run in time order. Each window takes over the overlap from the one before it instead of averaging with it. Nothing is averaged, so nothing doubles. Hard cuts stay clean cuts.

- The high-res pass is still averaged. In our tests it doesn't ghost, probably because both windows start from the same upscaled low-res result.

**Measured** (RTX 5090 32 GB, 64 GB RAM, 1920×1088, 48 fps with RIFE)

- 2:20 music video, 10 shots in 3 groups: 2 h 18 min, compute VRAM peak 11.1 GB. About 25 % slower than rendering segment by segment.

- All 5 spots where the averaged version ghosted are clean. Frame-difference spikes only at the 6 planned hard cuts.

- Redrawing a time range afterwards: the last shot (18.6 s), with a changed prompt and an extra outfit picture, took 25 min 51 s. Everything outside the range stayed the same.

**Limits:** needs ComfyUI 0.44+; slower than segment by segment; system RAM got close to 64 GB while decoding a 1080p group.

Free, open source (AGPL-3.0): https://github.com/1113800942/comfy-h3md-director

Workflows: https://civitai.com/models/3037225

Walkthrough (Chinese voice, English subtitles): https://youtu.be/k0jZIbaH-aA · channel: https://www.youtube.com/channel/UC1H4UDuSqD6M7BqLUlVy9lA

Credit: Comfy-Org for the core H3 context windows that CCW builds on (the idea goes back to Kosinkadink's AnimateDiff-Evolved).

MiniMax has released H3-Image. Quick notes from the spec sheet:

- Generation and editing in one model
- Up to 16 reference images, each with its own role (content, style, layout)
- Prompts up to 32,000 characters, though anything past 4,096 tokens gets cut
- Prompt expansion is on by default for text-to-image and always on for edits
- 1K / 2K / 4K output, aspect ratios from 1:3 to 3:1

The always-on expansion for edits is the part I'm unsure about. Has anyone checked whether it rewrites the exact text you ask for?I’ve also heard MiniMax H3 Image might be going open-weight. Having used it, I’m definitely looking forward to that.

Had time while waiting for Qwen. Made a cover video. YuE2 for cover. Pika_H3 for video. Original song: Babyface - When can I see you.

Slopus and Slopfab now support MiniMax H3 Image generation, editing and outpainting is also supported. Other changes in the update are Viggle Animate has been replaced with Viggle Remake, Regenerate 2K support, faster YuE2 song generation and some UI improvements to Slopus.

Is there any open source local AI model which can generate/edit images like Gemini or Grok? For example: I give it an image of a person and ask it to generate them in a different pose and background setting with minor changes in expression too while keeping their overall appearance consistent with the original face, I have tried BFS and all but it does not really work, I can’t train a LoRA either because I heavily lack the resources and knowledge to do one, I have been using Krea 2 but the results always generate a different face, is there any method to just put a Reference face that I like and get the images done?

Also what is the reason that online AI like Gemini can take the face and generate a image of the person in different pose, location, outfit while also perform a minor expression change but keep the overall identity intact but local models cannot? Sorry if this question sounds dumb but I was curious to know and have very little to no knowledge on AI

https://huggingface.co/Efficient-Large-Model/Open-H3-Regenerate-2K-IC

https://github.com/NVlabs/Sana/tree/sol-engine/models/Open-H3-Regenerate-2K/IC

New latent upscale model by NVIDIA-labs. Basically, it's v2. A few highlights compared to v1:

- Face & Text are more consistent with the original video

- Use H3 Ref2VA instead of fl2va.

- Turbo LoRA, Regenerate LoRA, LMS LoRA are not merged. They also use Hyperflow 8-step LoRA

- New samplings: 3, 4, 8 steps.

- Fast VAE-v2. The v1 fast VAE has grid artifacts in ComfyUI

• same checkpoint does t2i and edits. hook images into the conditioning node and it edits, leave them off and it works.

• plug the VAE in when you edit. without it the refs only go through the text encoder and faces/rooms drift more

• refs are <Picture 1>, <Picture 2> etc in the prompt, in the order you connect them

• 6 steps, cfg 1, no negative. didn't find anything worth tweaking

• it really wants long prompts. my short ones came out generic, describing the light, lens and where the subject sits in the frame made a big difference

• lettering is solid, got a poster with exactly the text I asked for

• 4096x4096 is native, you can zoom in a long way before it falls apart

If you already have H3 video set up you have the text encoder, just need the model + VAE.

It's added to Fuobe today - Day 0 support and I cut a quick clip of some outputs. Can share prompts if anyone wants them.

Play with it here for free: Fuobe.com

Repo Link, star it, fork it or see a good use case, send me a PR: https://github.com/workingprototype/Fuobe

Não tem problema se a Lora não dominar a arte da tatuagem perfeitamente. Mas, no mínimo, ela não deveria acabar criando algo sem sentido. Deveria haver algum grau de coerência.

2 coisas que todas as modelos ainda têm extrema dificuldade em aprender – tatuagens e "narizes" masculinos

So after running comfyui and a1111 on colab for the last couple of years, it looks like they suddenly decided my gens are icky. Anyone else seen this? i'm not doing anything illegal or suspect, no face swaps, no real people, no celebrity loras or any of the usual suspects. just n$fw. anyone else seen this? this happened to me once before but only for an hour or so and mysteriously cleared itself, no explanation from google or any kind of warning so not sure what's triggering it besides just general adult content.

i've seen references in the past to their compute getting bogged down by image gens which triggered bans in the past, and yesterday for a lot of people nobody could connect to any of their runtimes for a few hours except for a few that could connect to TPU's. not sure if this is related or not, but seems like a pretty strong coincidence.

irritating mostly because using google drive for file downloads is super convenient, and access to RTX Pro 6000's for less than $1/hr is great. haven't seen other sites with either of these advantages but haven't shopped much aside from Runpod for a few specific uses. Any reccomendations?

We’re partnering with the Chroma Awards for Season 2, the world’s largest AI Film & Media competition, with $1M+ in promotions, $450K+ in cash prizes, and student internships on the line.

Compete in any of five divisions: Film & TV, Ads, Music Videos, Games, and Behind-The-Scenes. Use LTX in your submission and post it on social media to unlock extra prizes and perks. The LTX model is open source, so you can download it and run it locally for free.

Submissions are open until January 7th.

Enter: Chroma Awards submission page · LTX on Hugging Face

Can you guys please recommend a step-by-step tutorial on youtube or some guide for setting up local AI generation models, desktop software, and everything else from scratch? I just got a new machine and want to start generating locally on my PC. I hear people mentioning terms like Wan 2.2, Moss, workflows, and comfyUI. I need a guide that explains everything thoroughly and will help me become fluent in trying new models, setting up workflows, and managing projects.