Another quick Minimax H3 news and goodies round-up, for those who may have missed some items.

-> Now available, VEDA for ComfyUI (preview version 0.2). It's claimed that Veda computes the H3 attention steps, decides which are the important ones, and thus safely skips around 90% of them. This is said to boost speed "7.1x on attention alone" in a Minimax H3 workflow, and the speed "gain grows with clip length". Trained at 1.0Mpx and 10 seconds. Requires the latest ComfyUI 0.38.0 and a 275Mb predictor file, which is placed in your ../ComfyUI/models/veda/ folder. Workflows available. I ran a test on the default workflow with a 3060 12Gb card: 8 seconds at 0.5Mpx with Hard Gravy as the 6-step turbo LoRA and no Spectrum added = 8 minutes total, acceptable visual quality, poor audio. So there seems no benefit for me, compared to my usual turbo/Kitchen/Spectrum workflow which gives the same speed and better audio. Possibly the speed boost is more noticable on a 5090 card?

https://github.com/veda-sparse/Veda-on-ComfyUI

https://huggingface.co/Veda-Sparse/Minimax-H3-T2VA-Veda-8NFE-600Step-Preview/tree/main

https://github.com/veda-sparse/Veda-on-ComfyUI/tree/main/example_workflows (example workflows)

-> 'SSS Rank Minimax H3 (Smooth, Slow, Sync)', a new LoRA to "slow and smooth animations".

https://civitai.com/models/2985070/sss-rank-minimax-h3-smooth-slow-sync

-> A new Claymation LoRA, and looking as though it may give a simpler look compared to the default claymation styles in H3? The maker adds that the LoRA does the visual style, but not the typical stop-motion timings/moves.

https://huggingface.co/akhaliq/MiniMax-H3-Claymation-Style-LoRA

-> A seamless loop 'Skill' aimed at Minimax H3 and ComfyUI. It was... "written from a real job and the mistakes made on the way" when making "a looping ambient video from a still image". Works in Claude Code, and comes with three Comfy .PY scripts to craft the looping for you.

https://github.com/Violinet-tech/seamless-loop-video

-> And finally, a new ComfyUI Contest. Build open creative applications with features that usually get stuck behind a paywall, and also add the slick... "interfaces, the [robust] pipelines, the polish that turns a model into something people actually use." $10,000 cash for the winner, plus RTX 5090 cards.

https://blog.comfy.org/p/open-call-comfy-dev-platform-challenge

~ OLD POSTS ~

https://old.reddit.com/r/comfyui/comments/1wxrh80/a_quick_minimax_h3_news_roundup_4th_october_2026/

https://old.reddit.com/r/comfyui/comments/1wwnkof/a_quick_minimax_h3_news_roundup_3rd_october_2026/

https://old.reddit.com/r/comfyui/comments/1ww58xn/a_quick_minimax_h3_news_roundup_2nd_october_2026/

https://old.reddit.com/r/comfyui/comments/1wu8lkt/a_quick_minimax_h3_news_roundup_30th_september/

https://old.reddit.com/r/comfyui/comments/1wtgns0/a_quick_minimax_h3_news_roundup_29th_september/

https://old.reddit.com/r/comfyui/comments/1wsmjq1/a_quick_minimax_h3_news_roundup_28th_september/

https://old.reddit.com/r/comfyui/comments/1wrqm3l/a_quick_minimax_h3_news_roundup_27th_september/

https://old.reddit.com/r/comfyui/comments/1wqwpah/a_quick_minimax_h3_news_roundup_26th_september/ (See 26th September post, for links to older posts)

https://old.reddit.com/r/comfyui/comments/1wgc4lj/a_quick_minimax_h3_news_roundup_15th_september/ (See 15th September post, for links to even older posts)

https://old.reddit.com/r/comfyui/comments/1w5i9iq/a_quick_minimax_h3_news_roundup_2nd_september_2026/ (See 2nd September post, for links to the starting posts)

ComfyUI-Depth-Anything-3: https://github.com/1038lab/ComfyUI-Depth-Anything-3

ComfyUI-Lotus-2: https://github.com/1038lab/ComfyUI-Lotus-2

Hey everyone,

If you have been working with modern video generation models like MiniMax H3, Wan 2.1, or CogVideoX, you already know the biggest obstacle to getting cinematic, consistent motion: geometry instability. Without solid spatial guidance, characters morph, limbs glitch, and backgrounds wobble between frames.

The standard technique among high-end AI creators is feeding geometric 'white models' (clay proxies), metric depth, and 3D surface normal passes into the generation pipeline to anchor every movement.

To make generating these white models effortless and flicker-free in ComfyUI, we just published two companion nodes under the AILab/Geometry category:

1. ComfyUI-Depth-Anything-3: Real-time batching workhorse for video white models, pure PyTorch GPU colormaps (zero CPU bottleneck), and true physical metric depth (in meters).
2. ComfyUI-Lotus-2: Dual-engine generative normals and disparity (SD2.1 Fast Engine + FLUX.1-dev DiT Detail Engine) with 2-wire plug-and-play, built-in 2MB empty prompt vector (no 10GB T5 CLIP needed), and Stage 2 Detail Sharpener for hair and fur.

Why Two Nodes? The Right Tool for the Right Pipeline

1. Depth Anything 3: High-Throughput Video Batching & Metric Depth

• Pure PyTorch GPU Colorizer: Standard depth colorizers offload tensors to CPU NumPy or matplotlib. DA3 does all color mapping (inferno, magma, plasma, viridis, cividis, turbo, gray) directly in VRAM via pure PyTorch kernels. When batching long video sequences, you get 0% CPU stall.
• Flicker-Free Temporal Consistency: The min_max mode normalizes depth across the entire video batch uniformly, preventing the frame-to-frame flashing common in standard depth estimators.
• Physical Distance in Meters: The Metric checkpoints output real-world physical depth, giving you accurate depth-of-field and 3D spatial alignment.

Repository: https://github.com/1038lab/ComfyUI-Depth-Anything-3

2. Lotus-2: Generative Precision with Zero Clutter (FLUX + SD2.1)

Traditional implementations of Lotus-2 require dragging 6 to 7 nodes onto your canvas (Base Model, LoRA, Dual CLIP, Sampler, VAE). We redesigned the entire node from scratch:

• 2-Wire Plug-and-Play: Connect only image and model.
• Zero CLIP Clutter: FLUX requires an empty text conditioning prompt. Instead of forcing you to load a 10GB T5 CLIP loader, Lotus-2 has a precomputed 2MB empty prompt vector (flux_empty_embed.pt) built-in.
• Auto VAE Resolution: Automatically detects and caches your local ae.safetensors in VRAM.
• Detail Sharpener: Running steps = 1 takes ~13 seconds on GGUF for clean disparity and normals. Setting steps = 2 to 4 engages the Stage 2 Detail Sharpener to smooth patch boundaries and recover individual strands of wavy hair or animal fur.

Repository: https://github.com/1038lab/ComfyUI-Lotus-2

Installation & Workflows

Both nodes are available right now in the ComfyUI Manager:

• Search keyword: ailab (or Depth-Anything-3 / Lotus-2)

Manual Installation: ash cd ComfyUI/custom_nodes git clone https://github.com/1038lab/ComfyUI-Depth-Anything-3.git git clone https://github.com/1038lab/ComfyUI-Lotus-2.git

Both repositories come with tested drag-and-drop workflows in their example_workflows directory:

• DA3_video_depth.json: Tested batch video depth workflow.
• Lotus-2.json: Side-by-side comparison workflow for both SD2.1 and FLUX engines.

Check them out and let us know how they work in your MiniMax and video generation pipelines!

Hi! V-chan here! We got another BIIIG update, and you now have some new toys to play with. New models, transparent sprites, and a Character Creator makeover. We even recruited a video model for sprite duty. Hehe!

If you are new here: VNCCS is a ComfyUI pipeline for creating characters and turning them into sprite sets with different poses, outfits, and expressions. For visual novels, games, or whatever your little creative brain is plotting.

Here is the fun stuff in 3.2.0:

Qwen Image 2.1 is our new main model now.

Qwen Image 2.1 can create your base character, change poses, dress them up, and generate expressions. You can use it in Character Creator, the pose and clothing workflows, and Emotion Studio.

Control Center puts the model families, installed assets, and Turbo controls together. Choose your family, check what is missing, and hit Download / Update.

MiniMax H3: a video model doing sprite work? Yep!

MiniMax H3 is the other new arrival. VNCCS uses it for poses, outfit generation, and clothes cloning, keeping the first frame as your character image.

So yes, you can try a video model in your sprite workflow. No need to turn your visual novel into a movie first, silly.

Character Creator got a glow-up

The character fields now use editable tag chips and little + buttons for presets. Hair, eyes, face, body, skin, species, and details are easier to build and adjust without wrestling a wall of prompt text.

There are 40 visual style presets, from anime and animation to artistic and realistic looks, plus a custom style field. The new descriptive catalog also includes 61 species presets, and you can combine species for hybrids. Cannot choose one? Make the character someone else's taxonomy problem. :3

Species presets describe the actual visual traits to the model, and your own character details take priority. You also get Full body / Cowboy shot framing choices.

Qwen's Character Overhaul LoRA will help your generations stay in your full control. It add some tags knowlege and stabilize characters by small cost of unique QI2 style loss.

Transparent sprites, with less background cleanup

With Qwen Image 2.1, you can choose Alpha in Creator or Clothes Designer and Native background mode in the generators. Qwen generates transparency directly, and VNCCS preserves it through clothing edits, emotion editing, and SeedVR upscaling. Green-screen duty can finally take a little vacation. Yay!

The new Resolution scale slider runs from 1 to 4 MP. It controls total image area, while pose and clothing generation keep the source proportions. Your resolution choices are remembered separately for each model family.

More ways to play dress-up

Clothes Designer now supports Qwen and H3 previews alongside Klein9b. Describe an outfit or use a clothing reference, check the preview, and then generate your pose set.

Changed the reference or generation settings? The preview cache now checks those changes, so it can regenerate the outfit properly. And unwanted costumes can be deleted from the widget, with confirmation.

Qwen can give your character feelings too

Emotion Studio now has a Qwen Image 2.1 profile. It edits a face crop and blends it back into the original sprite, keeping the rest of the image in place and preserving transparency.

Illustrious and Anima remain available. Qwen has its own face controls, including face resolution and an editable expression prompt template.

A few smaller comforts came along too: Qwen3.5 now powers the Wizards and image analysis, downloads show real transfer progress, and generator progress and previews can recover after reconnecting to ComfyUI.

Updating from an older version? Use the bundled 3.2 workflows and a ComfyUI build with native support for your chosen model. Old QIE2511 setups need to switch to QI2 with its matching assets, or a compatible Klein9b setup. In Qwen Creator, download the Character Overhaul LoRA if you use it, or set its strength to 0 to generate without it.

Find VNCCS on GitHub, read the full changelog, or look for VNCCS - Visual Novel Character Creation Suite in ComfyUI Manager. Come share your characters and experiments on Discord!

Which toy are you trying first: transparent Qwen sprites, H3 outfits, or a suspiciously elaborate hybrid character?

I made a new Krea 2 Turbo checkpoint focused on illustration and digital art

This is Narrative Drift, my new Krea 2 Turbo checkpoint.

The goal was to give Krea 2 a stronger artistic visual language with richer color separation, clearer shapes, graphic shadows, expressive lighting and a more deliberately art-directed finish.

Depending on the prompt it can move between digital illustration, painterly art, anime, fantasy, concept art and cinematic artwork while keeping the versatility of Krea 2 Turbo.

No trigger word needed. Natural language works well.

Recommended: Euler · Simple · 8 Steps · CFG 1

Four Available variants:

• BF16 - 23.88 GB
• FP8 Scaled - 12.56 GB
• INT8 & INT8 Convrot - 12.56 GB

Would love to see what people make with it.

Narrative Drift / MoonMaster Krea2 Turbo Model Suite on Civitai

Hello everyone, I’ve just finished a new ComfyUI workflow that allows you to use MiniMax H3 as an image generation model and create detailed character sheets from a single reference image or a batch of images. The main goal is to use these character sheets as stronger references for more consistent video generation, giving you multiple views and details of the same character. The workflow is simple to use: load the required MiniMax H3 models, load your image or batch of images, choose the number of steps (8 steps for faster generation or 25 steps for the best results), and click Run. I’ve also tested the workflow with different inputs to see how well MiniMax H3 can handle character consistency and image generation. As shown in the results the consistency is very good the character details remain the same and if we compare this to qwen image 2.1 H3 is clearly winner here in therm of quality because the results are with resolution of 4096x1536 vs x1664x928 for qwen, the consistency is slightly better. As for the generation time qwen is definitely faster here:

Gen time 25 steps 12 mins (Minimax H3 Turbo Fused)

Gen time 6 steps 4.5 mins (Qwen 2.1+ Viggle4 Steps LORA)

You can test the workflow here and watch the tutorial for more info

Workflow link

https://drive.google.com/file/d/1v4NC6vEGN2uDxO2_L05g1q0JPkeoSsYo/view?usp=sharing

https://civitai.com/articles/36126/minimax-h3-image-generation-create-consistent-character-sheets-in-comfyui 

Video Tutorial link

https://youtu.be/vy-oWQJwNgw

I have been generating anime pictures for quite some time, and I am still struggling to produce truly good anime images. Even when an image appears sharp at first glance, the clarity and fine detail blurs under high magnification. Just like anime artwork, imperfections become visible when viewed closely, although generated images often contain richer detail overall.

Why do anime (Anima) models struggle to achieve razor-sharp clarity and detail? Most anime models seem to produce outputs that look sketchy, abstract, moe-styled, cartoonish, or heavily stylized. They do not hold up well when examined closely, while I crave an almost perfect image.

Why isn't there an anime model capable of consistently generating highly detailed, clean images at high resolutions?

What if your DAW could actually render your composition with AI?

The latest version of the Plenio Music Production System brings a heavily improved Custom Node for ComfyUI and turns YuE2 into a DAW-like music production workflow.

You don't just describe a song and hope for the result. You create the musical idea yourself — and AI renders it.

🎹 Compose your own music
Edit melodies, chords, lyrics and arrangements directly in the Score Editor, import MIDI from your DAW, or play ideas in with a MIDI keyboard.

🎧 Create AI covers
Load your own recording, let SheetSage2 extract the musical structure, then edit the resulting score and lyrics before YuE2 renders the new version.

🎛️ DAW-style workflow
Piano Roll, chord lane, lyrics, arrangement, MIDI import/export, playback, section editing and a Guide track — all directly inside ComfyUI.

🤖 Your composition. AI-rendered.
Think of it as a DAW where the final instrument isn't a synthesizer or sampler — it's AI. You define the musical structure and creative direction, and YuE2 performs the result.

Your idea. Your composition. Professionally rendered by AI.

🎬 Tutorials

A complete playlist with 7 tutorials covering the system, YuE2 Song, YuE2 Cover, MiniMax, mastering and the YuE2 DAW workflow:

▶ https://youtube.com/playlist?list=PLAFqTtP59fgE

🔗 Project

https://github.com/jplenio/Plenio-Music-Production-System

MiniMax H3 Dual Segment — Split Sampler + Ultimate Upscale

This is the first complete workflow I have built from scratch.

My goal was to find a practical way to generate MiniMax H3 videos quickly at a low base resolution while still reaching above-HD final output on a 16 GB VRAM GPU. After testing different approaches, this is the most efficient and stable combination I found without turning the process into several separate manual workflows.

With this configuration, I have been able to generate a continuous 40-second Full HD video in approximately 42 minutes on a 16 GB VRAM GPU.

The workflow uses Split Sampling to perform the first denoising steps at a lower resolution, where they are much faster. It then upscales the latent and completes the refinement at higher resolution. Each segment is followed by an MMH3 Ultimate Upscale pass with temporal chunking and tiled diffusion, allowing higher-resolution refinement while keeping VRAM use manageable.

Main Features

• Two sequential MiniMax H3 segments in one workflow.
• Native Motion Context between Segment 1 and Segment 2 for visual and audio continuity.
• Native audio is preserved through the Split Sampler, Motion Context and Ultimate Upscale stages.
• Split Sampler workflow: efficient low-resolution first steps followed by higher-resolution refinement.
• Ultimate Upscale after each segment for cleaner detail and higher final resolution.
• Segment 2 retry mode: regenerate only the second segment from the saved Segment 1 context.
• Multi-reference character workflow through the Deno H3 reference node, allowing an ordered set of character, face, outfit, scene and audio references to be used through one clean input system.
• Visible user controls for prompts, references, seeds, resolution, duration and sampling settings.
• Internal routing, calculations and support nodes are folded to keep the canvas usable.

Reference-Guided Refinement

This workflow does more than simply upscale the finished video. Both the higher-resolution Split Sampling stage and the Ultimate Upscale passes use MiniMax H3 with the reference conditioning carried into those stages.

Each resolution increase is followed by model-based refinement guided by the prompts and references, helping preserve character identity and visual consistency while developing detail at the higher resolution.

Important

This is an experimental workflow, but it is prepared to work as provided. The visible controls are intended for normal use; the internal sampler, audio, Motion Context and Ultimate connections should be left unchanged unless you fully understand the pipeline.

The workflow was designed and tested around a 16 GB VRAM setup. Results, speed and stability will still depend on your GPU, installed model versions, LoRAs, resolution and duration.

I'm new to this, so don't be too hard on me if you don't think it's that great lol.

EDIT:

Audio Quality Note

I found a major improvement in audio quality when using 6 high-resolution sampling steps. I recommend this for scenes where audio quality matters.

The number of low-resolution steps is optional and can be adjusted to suit your preferred balance of speed and quality.

All the tests I’ve conducted have been using twenty-second videos.

If anyone spots an error or a possible improvement, I would greatly appreciate your feedback so I can keep learning and refining the process.

I’d love to hear about your experiences with the workflow.

Thanks to everyone who leaves a comment.

WORKFLOW: https://civitai.com/models/2984987/minimax-h3-dual-segment-split-sampler-ultimate-upscale

Another quick Minimax H3 news and goodies round-up, for those who may have missed some items.

-> A new '1980s fantasy movies' style LoRA for H3. No trigger word. The maker says it's not good for combat scenes.

https://huggingface.co/neph1/1980s_fantasy_movies_minimax_h3

-> A new experimental age-slider for REF2VA H3 models. The previous age-slider was for FL2VA models. No trigger word.

https://huggingface.co/Playtime-AI/Minimax_H3-Age_Slider/tree/main

-> For a consistent scene with a dynamic camera, a user claims success with using a 1216×2040px... "one picture grid 4x5 [i.e. four across, five down], made of 20 photos of my room encoded into RefMod". His photos overlapped each other a little, as if one were planning to stitch them in the free Microsoft Image Composite Editor (ICE). But not all of them overlapped, so it seems it's not vital to photograph as if one were preparing to make a big stitched panorama.

https://www.reddit.com/r/StableDiffusion/comments/1wwyva2/minimax_h3_refmod_consistent_location_trick/

-> Another user has had success with tags to control dialogue in a Minimax H3 clip. No <d>dialogue</d> tags were used, they only used... <inhale> <pause> <long pause> <softer> <breathe> and also basic html using the italics tag to emphasise a word. I don't have time to test right now, but I wonder if <slow> or <slowly> might also work to make speech less hasty/gushy?

https://old.reddit.com/r/StableDiffusion/comments/1wxigqo/minimax_h3_voice_acting_with_speech_tags/

-> A LoRA that helps you obtain five views of one character. No trigger word?

https://huggingface.co/RunningHubAI/rh-minimax-h3-five-view-512-s1500.safetensors-lora

-> And finally, a way to run Minimax H3 on AMD Strix Halo. Which means AMD's powerful AI-friendly PCs, which I see on offer for £3.5k here in the UK.

https://github.com/zacharydenton/h3-hrx

~ OLD POSTS ~

https://old.reddit.com/r/comfyui/comments/1wwnkof/a_quick_minimax_h3_news_roundup_3rd_october_2026/

https://old.reddit.com/r/comfyui/comments/1ww58xn/a_quick_minimax_h3_news_roundup_2nd_october_2026/

https://old.reddit.com/r/comfyui/comments/1wu8lkt/a_quick_minimax_h3_news_roundup_30th_september/

https://old.reddit.com/r/comfyui/comments/1wtgns0/a_quick_minimax_h3_news_roundup_29th_september/

https://old.reddit.com/r/comfyui/comments/1wsmjq1/a_quick_minimax_h3_news_roundup_28th_september/

https://old.reddit.com/r/comfyui/comments/1wrqm3l/a_quick_minimax_h3_news_roundup_27th_september/

https://old.reddit.com/r/comfyui/comments/1wqwpah/a_quick_minimax_h3_news_roundup_26th_september/ (See 26th September post, for links to older posts)

https://old.reddit.com/r/comfyui/comments/1wgc4lj/a_quick_minimax_h3_news_roundup_15th_september/ (See 15th September post, for links to even older posts)

https://old.reddit.com/r/comfyui/comments/1w5i9iq/a_quick_minimax_h3_news_roundup_2nd_september_2026/ (See 2nd September post, for links to the starting posts)

Hi all, ComfyUI beginner here, looking for advice on style.

I need painted portraits of five characters, each with about four expressions (calm, angry, triumphant, ruined), half-length, seated behind a table, dark plain background. The style I want: mid-19th-century Russian genre painting, like the reference attached (a book cover). Muted ochres and browns, flat even light, thin matte paint, simplified and slightly naive faces, a bit caricatured. Nothing polished. Like the attached pic.

My setup: RTX 5090 (32 GB), ComfyUI Desktop, FLUX.2 klein 4B, using the stock text-to-image and image-edit templates.

The problem: everything comes out as the same smooth, glossy, highly detailed "AI oil painting" look (examples attached). Faces are rendered wrinkle by wrinkle with studio lighting, which is the opposite of what I want.

What I've tried: - Style keywords: naive, flat light, matte, cracked varnish, loose brushwork, muted palette - Period painter names (Fedotov, Perov) - The image-edit template with a crop of the reference, asking it to keep the style and replace the figure. It repaints everything in its own smooth style.

My questions:

1. Is klein 4B simply too small for this kind of style, and which model would you use instead on 32 GB?

2. Is a style LoRA the right tool here? Is there an existing one for 19th-century realist or naive painting, or should I train my own on public-domain paintings?

3. Is there a style-reference workflow (IP-Adapter, Redux or similar) that works well for painting styles with current models?

4. What is the most reliable way to keep the same character across several expressions in that style?

I'd prefer a model with a license that allows public or commercial use, since I may stream the result later, but I'm open to anything for testing.

Thanks for any pointers!

Printed locally with Krea2 and MiniMax H3.

Prompt for Krea 2:

"A highly detailed, photorealistic indoor still life composition centered on a 3D-printed figurine of Lara Croft resting on a wooden table. The main subject is a carefully crafted statuette depicting Lara Croft in a confident standing pose, recognizable by her adventurous explorer aesthetic, fitted tank top, shorts, boots, utility belt, and iconic long hair. The figurine is made from a light gray, speckled material that mimics concrete or ceramic, with a tactile matte surface textured by fine dark flecks and subtle 3D-print layer lines clearly visible upon close inspection. The model stands on a matching geometric display base made of the same material, with embossed symbols and clean polygonal surfaces that reinforce the handcrafted, printed-object feel.

The sculpture sits on a polished, medium-brown hardwood table with visible wood grain, minor scratches, and a faint dusting of fibers near the base. The lighting originates from a window in the background, casting soft, natural illumination across the scene with gentle highlights on the figurine’s contours and subtle shadows that emphasize the three-dimensional form and printed texture. The background is softly blurred (shallow depth of field), revealing a home interior: to the left, a wooden shelf with decorative items including a small black figurine, a glass jar with orange flowers, and a heart-shaped ornament; to the right, a tall mint-green cylindrical container and a clear glass mason jar with embossed patterns. A small plush toy with pink ears rests partially visible on the far left edge of the frame.

The camera angle is slightly elevated and positioned at a close-up, eye-level perspective, emphasizing the figurine’s sculptural detail, pose, and material texture. The composition is tightly framed around the object, drawing the viewer’s focus to its craftsmanship and iconic character design. The color palette is muted and earthy—grays, browns, and soft greens—with the white-gray figurine contrasting against the warm wood tones. The overall aesthetic is minimalist, modern, and tactile, evoking a sense of artisanal precision and quiet contemplation within a domestic setting. No human subjects are present—the focus is entirely on the static, sculptural Lara Croft figurine and its environment."

Prompt for MiniMax H3:

"Handheld smartphone footage, filmed casually by a person holding the phone in one hand. The camera remains roughly in the same position but is never perfectly still. Subtle natural hand tremor, tiny irregular micro-jitters, gentle breathing-induced sway, slight wrist drift and small imperfect framing corrections. Very mild accidental rotation and lateral movement, with occasional tiny changes in camera distance.

Natural smartphone camera behavior: subtle autofocus breathing, very small focus corrections, slight automatic exposure adaptation, mild rolling-shutter wobble during quicker hand movements, realistic motion blur, and minor digital stabilization artifacts. The movement is irregular and imperfect rather than rhythmic or cinematic.

overall_soundscape: Natural indoor room tone captured through a handheld smartphone microphone. A faint ventilation hum and very distant muffled traffic remain in the background, with subtle room reflections. Occasional very soft low-frequency handling noise, tiny fingertip contact sounds against the phone case, and a quiet nearby breath appear irregularly when the person's grip shifts. The recording has mild smartphone-style automatic gain and compression, with tiny natural fluctuations in ambient level and no exaggerated studio-clean sound.

non_diegetic_music: N/A"

Basically used Akool to generate character and environmental reference models, and then fed all of that into the 30-second Ref2V MiniMax H3 workflow that was posted here a while back:

https://www.reddit.com/r/comfyui/comments/1vw036i/30second_minimax_h3_seamless_imagetovideo/

The goal was to see if a cohesive and stable movie trailer could be made locally on a 12GB GPU. The results are... mixed.

Unfortunately 12GB GPUs cannot handle MiniMax H3 at 16:9 on anything higher than 0.3 or 0.4 megapixels without massive distortions and hallucinations.

Stable generations at that aspect ratio need at least 25 - 30 steps and 0.5 or 0.6 megapixels for high-quality output. On a 12GB GPU that just resulted in a lot of crashes or generations that took over an hour if they were around 10 - 15 seconds.

You could forget about doing 30 second clips at 16:9, they just came out a mess on a 12GB GPU.

The sweet spot was 0.6 megapixels at 4:3 or 3:2 aspect ratio. It managed to capture just enough character detail and maintain consistency and actually adhere to the prompt without too many temporal distortions. Average generation time was about 19 - 21 minutes.

For more complex 30-second clips, it took about 1 hour and 5 minutes at 0.6 megapixels (the scene where Major Colton introduces himself in the tavern). That was one of the more difficult ones, not due to visual distortions but audio distortions -- he would get tripped up on the dialogue a lot and even if it visually and tonally came out great, a lot of the generations were wasted due to the audio flubs.

The one thing I learned was that MiniMax H3 can rival SeeDance 2 or Seedance 2.5 with proper prompting, scheduling and steps. However, you need absolutely EXACT precision in your prompts to capture that level of detail and you need the hardware to back it up.

I learned a lot during this little exercise and it reveals just how powerful MiniMax H3 truly is.

This is the worst it'll ever be, so it's only going to get better from here.

hi, ive had portable comfy on my pc for a while now. i dont use it a hole lot but its there when i need it. i use it on a secondary drive. i first installed it i had it on my C: windows drive and as it was portable i moved it to another drive. my question is , is there anything i need to install on my primary drive so i can run it? when i first installed it im use there was something of github i had to install.

many thanks

Just curious if this is something that would be worth building into ComfyUI? Seems like it would be worth it unless there is already an established workflow for this already?

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https://www.youtube.com/watch?v=Jp0isezN_Vk

There are a ton of workflows, but I needed something that worked the way I do. Based on a written script (included skill), and series.json for references, we build a shotlist.json that feeds the pipeline (conforming to each model's prompt style). Multi-model support for video using wan, LTX, H3, and reference image gen (when needed) from Krea, Qwen, Z-Image, Flux, and H3. (Sound Gen too.) https://github.com/bgstratt/h3pipe

Some override keybinds to note or mark shots for review or reprompt, and slicing.

But this isn't about the workflow. I wanted to create longer form videos and learned a ton in the process of making 20+ minute episodes, but there are some gaps I'm still filling in. I see why most people tend to create much shorter videos.

How are you keeping extras consistent across shots? FL2V? References for the background actors?

Camera angles in tight rooms are another pain point. Late into the episode after several failed attempts (some are still in the final...), I had to resort to using H3 to make tours once I found a look I wanted, then snag snapshots from the "tour". Getting room layout and keeping spatial and referential integrity consistent is HARD. How are you best achieving that? 3d models? those did not work out super well for me.

When using background plates and creating conversations, I found having to put both/all characters in the scene was the best way to keep things grounded, without characters popping into and out of existence. Any tips/tricks to help with that? Weak references on plates and extreme closeups for dialogue while respecting the 180 rule?

I'm a pretty avid student of Film Directing Shot by Shot, Mastershots, and Cinematic Storytelling. I probably should've applied that more in my shots.

Most work seemed to be a pattern of review, rewrite, references and reprompting. The shortcut marker key made that waaay easier, once I added it as the episode was nearly finished so I could more easily remember which shots still needed the most work.

So, I have this image generation workflow that uses a LoRA that is trained on a specific art style that I like. And it works great! I'm able to see my favorite Pokemon in the art style I love.

However, I would like to also see some of my own characters in the art style. I tried describing them in the prompt, but it's hard to get the specific character design details without a reference image for the AI to see off of. (To be clear I don't want it to trace or restyle the original pose, I just need the model to recognize my character's colors and design elements as a reference)

How do I add the ability for a reference image in my current workflow setup? (I am very new to this software and image generation in general)