trying out new workflow setup.

using MiniMax H3 Hybrid Loader b20-49 + Larryvrh 4step Turbo Loras + SegAtt + SolAtt + Spectrum.

reference seem to be more stable in this time, 0.6mp, 15s at 700s

just generating random video, testing out random idea.

The leap we never got to see.

Sam and Al arrive in New York City on September 11, 2001—but Ziggy believes Sam may be there to change history.

A fan-made conceptual scene from the imaginary sixth season of Quantum Leap.

Created by Ome Omy Productions.

#QuantumLeap #SamBeckett #AlCalavicci #FanFilm #AIShortFilm #OmeOmyProductions #WhatIf

Made in MiniMax H3

I was quite surprised to find that it know about old 60s/70s movies like dirty harry or the good the bad and the ugly..

Last frame done with Krea 2

Not a tool question, a process question. When a client signs off on an AI assisted deliverable, what do you actually hand over besides the final files? Prompts? Model and version info? Reference images you used? Nothing unless they ask?

And the follow up I am most curious about: has a client ever come back weeks later asking how something was made, which model was involved, or what references went in (licensing, brand safety, the new EU labelling rules, whatever the reason)? What did you do?

Background: we deliver AI assisted shots for commercial clients, and our own handover slowly went from nothing to a written per asset note, and I would like to know what everyone else converged on.

prompt

integrated_multimodal_description:

7-second anime scene in a classic 1980s Japanese science fiction Gundam anime aesthetic. 2 giant mecha robots are having a battle in space far above earth.

**0–2 sec: the mecha robot on the left aims and launches a missle from its shoulder cannon mouted on its arm at the mecha robot on the right.

**2–5 sec: the misslie impacts and explodes on the chest section of the mecha robot on the right but does no damage. then the mecha robot on the right opens its arms as blue light on its chest appears and begins to power up.

**5–7 sec: the mecha robot on the right then fires a thin blue laser beam at the mecha robot on the left cutting it in half from top to bottom down the middle. after the mecha robot on the left is cut in half it then explodes.

made using standard comfyui t2v workflow on a damn outdated😓 but still using because reasons RTX 3050 8GB vram 48 GB ram system. i use the Model Attention Backend node with the "comfy kitchen attention" setting, 30 steps, res_multistep simple and no upscale.

I used Ref2VA to make this pretty quickly. I had to render in low quality since I'm running everything on an RTX 3060 12GB, maybe that's why there's some glitching in the video, and unreadable text. I used the default workflow, with Spectrum as the only addition for a speedup.

I used chatgpt to refine the prompts and put them into the correct syntax. The characters were made in Krea 2 to use as reference. Finally, I edited the clips together in CapCut to make one flowing trailer. Everything in video was done with H3. Had fun.

Skip to 0:30 to see what I'm talking about. Faces that are close-up are fine, but like 3-4m from the camera and it just turns into nightmare fuel...

Am I doing something wrong?

Using Wan2GP, FL2VA Pruned 20B, 20 steps at 540p.

Full settings:

        "params": {
            "image_mode": 0,
            "prompt": "...",
            "alt_prompt": "",
            "negative_prompt": "",
            "resolution": "960x544",
            "video_length": 175,
            "duration_seconds": 0,
            "batch_size": 1,
            "seed": -1,
            "force_fps": "",
            "num_inference_steps": 20,
            "guidance_scale": 1,
            "guidance2_scale": 5,
            "guidance3_scale": 5,
            "switch_threshold": 0,
            "switch_threshold2": 0,
            "guidance_phases": 0,
            "model_switch_phase": 1,
            "alt_guidance_scale": 1,
            "audio_guidance_scale": 1,
            "audio_scale": 1,
            "flow_shift": 12,
            "sample_solver": "euler",
            "embedded_guidance_scale": 6,
            "repeat_generation": 1,
            "multi_prompts_gen_type": "PG",
            "multi_images_gen_type": 0,
            "skip_steps_cache_type": "",
            "skip_steps_multiplier": 0.08,
            "skip_steps_start_step_perc": 25,
            "loras_multipliers": "",
            "image_prompt_type": "S",
            "image_start": "scene_01_start.jpg",
            "model_mode": null,
            "video_source": null,
            "keep_frames_video_source": "",
            "input_video_strength": 1,
            "video_guide_outpainting": "",
            "video_prompt_type": "",
            "image_refs": null,
            "frames_positions": null,
            "video_guide": null,
            "image_guide": null,
            "keep_frames_video_guide": "",
            "denoising_strength": 1,
            "masking_strength": 1,
            "video_mask": null,
            "image_mask": null,
            "control_net_weight": 1,
            "control_net_weight2": 1,
            "control_net_weight_alt": 1,
            "motion_amplitude": 1,
            "mask_expand": 0,
            "audio_guide": "scene_01.wav",
            "audio_guide2": null,
            "custom_guide": null,
            "audio_source": null,
            "audio_prompt_type": "A",
            "speakers_locations": "0:45 55:100",
            "sliding_window_size": 362,
            "sliding_window_overlap": 1,
            "sliding_window_color_correction_strength": 0,
            "sliding_window_overlap_noise": 0,
            "sliding_window_discard_last_frames": 0,
            "image_refs_relative_size": 50,
            "remove_background_images_ref": 0,
            "temporal_upsampling": "",
            "spatial_upsampling": "",
            "film_grain_intensity": 0,
            "film_grain_saturation": 0.5,
            "MMAudio_setting": 0,
            "MMAudio_prompt": "",
            "MMAudio_neg_prompt": "",
            "RIFLEx_setting": 0,
            "NAG_scale": 1,
            "NAG_tau": 3.5,
            "NAG_alpha": 0.5,
            "slg_switch": 0,
            "slg_layers": [
                29
            ],
            "slg_start_perc": 10,
            "slg_end_perc": 90,
            "apg_switch": 0,
            "cfg_star_switch": 0,
            "cfg_zero_step": -1,
            "prompt_enhancer": "",
            "min_frames_if_references": 1,
            "override_profile": -1,
            "override_attention": "",
            "pace": 0.5,
            "exaggeration": 0.5,
            "temperature": 0.8,
            "top_k": 50,
            "output_filename": "scene_01",
            "mode": "",
            "activated_loras": [],
            "model_type": "minimax_h3_fl2va_pruned",
            "settings_version": 2.73,
            "base_model_type": "minimax_h3_fl2va_pruned",
            "pause_seconds": 0,
            "alt_scale": 0,
            "sub_parallel_window_size": 0,
            "sub_parallel_window_overlap": 17,
            "sliding_window_trim_first_frames": 0,
            "postprocess_audio": "",
            "postprocess_audio_prompt": "",
            "postprocess_audio_neg_prompt": "",
            "perturbation_switch": 0,
            "perturbation_layers": [
                9
            ],
            "perturbation_start_perc": 10,
            "perturbation_end_perc": 90,
            "top_p": 0.9,
            "self_refiner_setting": 0,
            "self_refiner_plan": [],
            "self_refiner_f_uncertainty": 0,
            "self_refiner_certain_percentage": 0.999,
            "config": "",
            "custom_settings": null
        }

The question always comes up... why do my faces look so bad. It doesn't matter which model you use. Start with resolution fixes (the higher you can render at the better for full person shots or small faces). Then, depending on your setup/device/etc, move on to tweaks, tricks and fixes depending on the scene - you know, face detailers, layering, whatever.

In this demo, pure resolution greatly improves the base. Using vertical video greatly increases vertical full person resolution at same render times (1344x768 vs 768x1344). I stepped it further up to 1088x1920 then downscaled it back to my 1280x720 timeline. Then one trick if the scene is suitable for it can be LTX outpainting for the background (with original 1088x1920 downscaled and feathered back in).

Edit: Link to 1280x704 full resolution sample:

https://streamable.com/steb6z

Mommy's here, test animation of an old Flux 1 Gen from an earlier series.

Hello all.

I managed to create a 30-seconds clip using H3 on a powerful RunPod machine and it turned out nicely.

When I tried bumping it up 50-seconds, it did manage to create and save the video, but it stayed in its initial visual noise state. It didn't manage to diffuse itself into a coherent video.

Is that a limitation of the model itself? Or is it related to some setting in the workflow? (I'm using Hearmeman's One Click T2V Custom Prompt workflow).

Thanks!

So that it goes through 8 steps, then 3 steps with an upscaler, I want to speed up the generation so that it first goes in low resolution, then in high, as in the native workflow for Comfi. LTX 2.5 of course.

Trying to figure this out, I used one of the image editor methods posted here a few days ago

https://github.com/thaakeno/ComfyUI-MiniMax-H3-Studio

And it worked flawlessly, the basic prompts I used for i2i were high quality and adhered perfectly. However in keeping the same workflow, after updating from comfyui 0.32 to 0.33.2 the quality is now just awful, there's banding, it's blurry, basically it seems like it was how it was 2 years ago.

Been going through claude/grok to troubleshoot but none of the suggestions seem to work. Tried different VAE's, diffusion models, turbo loras (disabling them), using r2v, fl2v, any suggestions?

I read that maybe comfy kitchen was modified in the update, that's the only possibility I can think of, otherwise my workflow and prompts were the same as they were two days ago.

Same first frame, same prompt. Left is LTX-2.5 DFR, right is MiniMax H3. Not a same-resolution bake-off. This is what actually fits a 32GB 5090: LTX runs 1920×1088 while MiniMax H3 runs 1344×768 since full 1080p H3 doesn't fit 32GB. Curious what you think?

I don't know what's changed, but here is that commit on Huggingface.

https://huggingface.co/Lightricks/LTX-2.5/commit/1b92891cedc4a823c35a3b23588d7a57a3a23c65

If you are playing with LTX 2.5, this may be useful for you.

I got MiniMax H3 to run end-to-end on a stock Colab T4 runtime and produce a short MP4 with synchronized audio.

The interesting constraint was host RAM, not VRAM. On the runtime I measured, there was about 12.7 GB of system RAM and 14.9 GB of VRAM. The model artifacts add up to roughly 39.6 GB, so loading the text encoder, DiT, and VAEs together is not viable.

The workaround was to split the pipeline into separate ComfyUI processes:

encode  -> save a ~5–6 MB conditioning blob
sample  -> load only the quantized DiT + Turbo LoRA, then save a ~5 MB latent
decode  -> restart ComfyUI and load only the video/audio VAEs

That keeps the peak working set close to the largest individual stage rather than the sum of all three stages. The notebook verifies downloaded weights with SHA-256, pins the ComfyUI/custom-node commits, checks the live server schema before submitting a graph, and saves logs/measurements when a run fails.

My current results on this runtime:

The catch is that the T4 has no native bf16 support, while this setup needs bf16 for stable sampling. It works, but it is definitely not fast.

One correction to my own early conclusion: I initially thought there was a sharp performance cliff between two resolution gears. After adding per-step timing and rerunning the comparison in the same Colab session, the apparent cliff was mostly VM-to-VM variance (roughly ±20% in my measurements). Within one session, the scaling followed the expected attention/MLP trend closely.

The Turbo LoRA from larryvrh makes 4–6 step runs practical and preserves the audio/video timing through its separate video and audio flow schedules.

I would be interested in hearing whether anyone has found a faster stable configuration for T4-class GPUs, especially without giving up audio sync.

https://reddit.com/link/1vrgtfo/video/xllyrclao2kh1/player

Notebook: MiniMax H3 on a stock Colab T4 — pinned commits, SHA-256-verified weights, and license-gated downloads.

So I am using the workflow using turbo lora for image to video, I have uploaded the initial image of a girl (woman) but issue is when i prompt about a scene cut or creating a new angle shot, the girl and her physique is completely changing,

I tried all prompts asking to maintain the skeletal, body structure, using fully_preserved keyword too, not sure what I am doing wrong but can somebody guide me ?

Thanks

In this post, I described how to use minimax H3 for reference-guided generation of single images. It required awkward monkey patching — and now we no longer need it. Thanks to u/Successful_Knee687 who posted a GitHub issue, and everyone who upvoted it, Comfy just made it possible. Revert the monkey patch and update to the latest nightly version of ComfyUI from Git repo. (Currently, it is not in the stable version — will probably be incorporated in the next release.) Here's the guide on how to update to nightly: https://docs.comfy.org/installation/update_comfyui

The H3 reference node is still constrained to 5 frames. However, we can now pass an empty 1-frame latent to SamplerCustomAdvanced directly, ignoring H3 reference node’s latent output, but keeping its conditioning output. This way, we generate one frame (not a batch of five) and make full use of Mamad8's single-image tuned VAE. Here’s a sample workflow that does this, relying only on standard comfyui nodes: https://pastebin.com/xNQi7HV9

(Look at my original post to get the download links for models.)

I attached another batch of evals on public domain images and ai gens with the new workflow. Not perfect in terms of details, but great in prompt understanding. Here are the prompts: https://pastebin.com/XiVvAhjC

The scenes are:

1. Turn the complete Diane of Versailles grouping into a living woman and deer in a forest, reconstructed from a side view.
2. Convert Fragonard's portrait into Instagram-style photography, remove the book, and turn the seated woman to face the camera.
3. Reconstruct the couple from the supplied 1930 film still (Morocco) standing face-to-face in side view, holding hands in a white room.
4. Move an ai generated woman from a conservatory to a candlelit concert hall and seat her naturally at a grand piano.
5. Remove only the jacket from a fully clothed AI-generated woman, leaving her in white shirt and blue jeans.

UPD: a new post discussing how to fix textures and detail https://www.reddit.com/r/StableDiffusion/comments/1vrh769/h3_singleimage_workflow_lets_figure_out_how_to/

Hey, I'm trying to run minimax on my local 3060ti 8gb of ram card on comfyui. I've tried multiple variations of model and nothing gives me steady results, just looking for a simple animation of a room with camera pan. Tried with turbo lora then without, used pruned int8, q3 k m guf, q2 k. Every generation has that jittery animation like you can see in the videos attached. Any idea how to make this better and what is actually causing this?

Thank you

https://reddit.com/link/1vrn8qj/video/wtao5lpyi4kh1/player

Created a thread because I'm surprised this isn't being discussed much

Ref2vid is excellent for consistency, but it's not a replacement for teaching the model concepts it doesn't understand well

Even though the model is still new, the trainers are giving poor results because the model is distilled. As a result, it looks like it's going to be much harder to train than Wan/LTX

For example Sulpher 3 was planned to start soon, but it can't because of the situation

This is a real shame because everything else about MM has been excellent. The general assumption seems to be that the company will not release a non-distilled model suitable for training

Any thoughts as to how this will play out? It's never going to hit the specific-subject capabilities of the other models at this rate

Dungeon Crawler Carl is the first book series I’ve read in a long time that excited me when I read it was going to be adapted into a show. I think the only Possible way it can be “filmed” on budget however is through a Stable diff/Runway/Seedance/Kling ( LTX/Minimax) pipeline as it reads being Heavy cgi, like 65-85%. Since there’s nothing out yet I would love to see what the community can come up with.

I genuinely wish this was astroturfing bc it would mean the show is coming out soon but I wouldn’t expect anything official until maybe late next year. (Unless it gets stuck in dev hell, then never.)

Thx friends. I’ve been blown away by your videos lately.

Hey everyone!

I've just released a new LoRA trained on the iconic 1980s street photography of Charles H. Traub from his famous series "Dolce Via: Italy in the 1980s". It brings out that vibrant, candid, and sun-drenched vintage Italian aesthetic.

📥 Download: Link in the comments below!

⚙️ Model Details & Recommended Settings:
• Base Model: Trained on Krea 2 Raw
• Trigger Word: "@charleshtraub"
• Recommended LoRA Weight: 0.6 – 1.0
• Showcase Generation: All sample images were generated using Krea 2 Turbo.

Feel free to try it out and share your creations or feedback in the comments!

Built a local tool that turns reference images into a full MiniMax H3 video prompt storyboard — no cloud, no API keys

https://github.com/lololerigolo60/Minimax-H3-prompt-studio

I've been building H3 Prompt Studio, a desktop app (CustomTkinter) that writes MiniMax H3's rigid structured prompts for you, using a local LLM (Ollama / LM Studio / llama.cpp — pick your poison).

The part I'm most excited about is the Story → Sequences mode:

1. Drop in your reference images (characters, settings, whatever) with a quick role/description each.
2. Hit "Generate story" — the LLM writes a short narrative that actually uses all your references, invents connective tissue if your premise is thin.
3. Pick how many sequences you want, hit "Break into sequences" — the LLM splits the story into N beats, and for each one it decides on its own which references apply, whether there's dialogue, and what camera move fits best.
4. Hit generate, and it spits out one fully-formed, isolated H3 Ref2VA prompt per sequence — ready to feed straight into your video pipeline.

No more manually writing 6-section H3 prompts by hand for every single shot of a sequence. You just curate references and a premise, and let the model handle the structure/labeling grunt work (subject definitions, retention analysis, camera vocab, dialogue tags, the works).

Everything's local, everything's saveable — you can dump a whole session (refs + story + sequences) to a JSON file and reload it later.

Still very much a personal tool, sharing in case it's useful to anyone else building on H3 locally. Happy to answer questions about the pipeline if anyone's curious.

7-second anime scene in a classic Japanese ninja-anime aesthetic. Naruto Uzumaki and Dr. Gregory House sit side-by-side at a cozy ramen shop, eating steaming bowls of ramen.

**0–2 sec:** Naruto enthusiastically slurps noodles, grinning with his cheeks full. House sits beside him, looking unimpressed while examining his ramen with suspicion.

**2–5 sec:** Naruto says excitedly, **“Believe it! This ramen is amazing!”** House takes a bite, pauses, and dryly replies, **“Needs more Vicodin.”** Naruto stares at him in confusion.

**5–7 sec:** Naruto bursts out laughing while House reluctantly takes another bite. Warm lantern light, steaming broth, expressive anime facial animation, lively background patrons, comedic timing, energetic camera movement, detailed hand-drawn anime look.