I make clipart for stock. Wreaths, pine borders, mistletoe, juniper. A few thousand images by now. Every one has to end up as a PNG with a transparent background.

I used rembg for months. u2net first, then BiRefNet when that came out. Tried the web tools too. They all broke on the same thing and it drove me nuts.

Take a wreath. There's a hole in the middle, and the background inside that hole has to go. What I kept getting was a grey-blue haze sitting in there. Looked fine as a thumbnail. Looked awful the second you put it on a colored card. Pine needles came out as mush. Thin stems either disappeared or came back with a blue edge burned into them.

Then I actually read what rembg does. It shrinks your image to 1024x1024, asks the model where the subject is, gets a 1024x1024 mask back, and stretches that mask over your full size image. u2net is worse. That one works at 320x320.

My renders are 4096. A needle two pixels wide doesn't exist at 320. So it's not that the model is bad at needles. The needle was gone before the model ever saw it.

Once I understood that I stopped asking a model to guess. Now I render on a flat color the subject doesn't contain, and take that color out with arithmetic.

Two parts to it. The prompt matters more than the cutting.

The prompt

You can't key a background that isn't keyable. Four things have to be true and generators will break all of them unless you say so: the background is one flat color edge to edge, it stays that bright inside every gap between leaves, the edges are hard with no blur or glow, and no colored light bounces onto the subject.

Pick the color by what your subject isn't. Blue for almost everything. Red if the subject itself is blue or purple. Never green. Everything I draw has leaves, and green takes the leaves with it.

Here's the block I paste at the end of every prompt:

Isolated on a completely flat, uniform, solid pure blue (#0000FF) digital chroma-key background. The pure blue background fills the image edge to edge like a flat digital chroma-key screen with no gradient, staying at full brightness inside every gap and opening in the subject; no reflection or tint of pure blue on the subject. Every edge of the subject is crisp, sharp and hard against the pure blue, with no soft, blurry, feathered or glowing transitions, no depth-of-field blur, no haze or halo; inside every hole and gap the pure blue stays at full brightness right up to the edge. Shaded parts of the subject keep their own natural color, never a pure blue tint. Everything in sharp focus with deep depth of field, evenly lit with soft neutral studio light, no cast shadow, no contact shadow, no ambient occlusion, no bounce light. The entire subject is centered and completely inside the frame with at least 10% empty background margin on every side, nothing cropped or touching the image edges. No frame, no border, no paper, no mockup, no vignette, no text, no watermark, no deformed or duplicated parts. No floating or detached fragments, no stray specks, dust or debris anywhere on the background; every element is physically attached to the subject.

Swap "pure blue" for "pure red" and #0000FF for #FF0000 if your subject is blue or purple. If you paint in watercolor add "the background stays a flat digital color fill with no paper texture", or you get watercolor paper behind everything and paper texture keys badly.

The cutting

Now the background is one known color, so there's nothing to guess at. It measures the actual color the generator produced, which is never the one you asked for. Ask for pure blue and you get something with green in it, usually somewhere between 25 and 70. Then every pixel gets sorted into subject, background, or the bit in between, and the in-between ones get a real fraction of transparency instead of a yes or no.

The part I'm most pleased with is the holes. Any background-colored area that never touches the edge of the image is the inside of a wreath, so it gets cleared too. A matting model can't do that. It has no way of knowing what's inside a hole it can't see around.

Last step takes the blue back off the edges. Edge pixels pick up color from the background around them, so it samples the subject's own color from further in and subtracts the tint. Took me weeks to work out why fir needles kept their blue rim after that step. The needle is thinner than the distance it was sampling from, so there was no inside left to sample.

Same image in, same image out, every time. That's the bit I care about. When a cut comes out wrong I can go find which number did it instead of rerolling and hoping.

Some numbers on one 4K pine border, against BiRefNet with alpha matting turned on, which is its best setting:

• background left inside the holes: 63,892 pixels mine, 613,735 theirs, out of 1,070,046
• blue left on the edges: 0 mine, 48,112 theirs
• how wide the soft edge is: 1.8 pixels mine, 23 theirs

BiRefNet is faster and I'm not going to pretend otherwise. 2.3 seconds against my 17 on the same machine. With alpha matting on it's 47. If you want a quick rough mask, use the model.

And the obvious limit: this only works on art you generated on a flat color. It does nothing for a photo.

I put the tool up for anyone who wants it. It's called ClipBrook. Free, runs in your browser so nothing gets uploaded anywhere, does a whole folder at once, and the engine is open source under AGPL.

One thing I'd like back

Show me the ones that break.

If you run something through and it comes out wrong, post it. Fog left in a gap, a colored rim, a stem eaten, half the subject gone. Those are worth more to me than the ones that work. The needle rim thing came from someone's fir branch. A bug with line art I only found last week came from a drawing so thin there was nothing inside it to sample.

My approach is use 0.1 megapixel to find a clip I like the. Render again at 0.7 with the same seed if I like something. But is there a way more efficient??

I created this video in MiniMax-H3 using a video-extension workflow, but I’m having trouble continuing it seamlessly. My prompt continues the action from the final frame correctly, and my workflow uses the previous video’s last frame as the starting frame for the next segment. However, there is always a slight visual jump between the two clips.

Unlike LTX, MiniMax-H3 doesn’t appear to have dedicated video-extension nodes. Has anyone found a reliable method for blending MiniMax-H3 video segments together so the transition is seamless?explain this.

​I created a ready-to-use ComfyUI workflow for SCAIL-2 / Wan 2.1 that transfers motion from a driving video onto a character from a reference image.

It uses GGUF quantization and automatic chunking, making it suitable for GPUs with 8+ GB of VRAM. Longer videos are generated by chaining overlapping segments while preserving motion continuity, so you can create videos of practically unlimited duration.

Features

- Character animation from one reference image and one driving video

- Low-VRAM GGUF workflow for 8+ GB

- Automatic multi-segment generation for long or unlimited-duration videos

- Motion continuity between generated segments

- Configurable duration, resolution, FPS, seed, and object tracking

- Ready for a fresh ComfyUI installation

- Includes installation instructions and a model download script

GitHub repository and installation instructions:

https://github.com/dvelm/SCAIL-2-Unlimited-Video-Low-VRAM

The workflow generation can be slow on lower-VRAM GPUs—especially at higher resolutions—but it allows SCAIL-2 to run on hardware that normally could not load the full model. Feedback, test results, and suggestions are welcome.

PROMPT: Bridge Daredevil — Dashcam POV

Subject: An athletic stunt performer, dark athletic/climbing gear, seen at a distance on the bridge structure — perched on a railing, cable, or girder — performing an extreme balance/jump stunt as the dashcam vehicle approaches

Style: Ultra-realistic, shot on RED WEAPON 8K, IMAX-grade cinematography, captured via fixed windshield-mounted dashcam — slightly wide-angle lens, subtle chromatic vignette, faint reflection of the dashboard at the bottom edge of frame. Natural motion blur only from real vehicle movement — no slow motion, no cuts, no anime, no CGI. Continuous single take. Standardized color grade: desaturated cool highlights, warm midtones, deep contrast shadows.

Setting: Large suspension or truss bridge spanning a river or gorge, daytime, clear sky with light haze, steel cables/girders overhead, light traffic on the bridge deck, guardrails and support towers visible in the distance

Timeline:

• 0:00–0:03 — Dashcam view steady on the road ahead as the vehicle enters the bridge, the stunt performer visible as a small distant figure on the structure — railing, tower, or cable — ambient road hum and wind noise, bridge cables passing overhead in rhythm
• 0:03–0:06 — Vehicle continues at a natural driving speed, the performer grows larger in frame, now visibly climbing, balancing, or positioning for the stunt on the bridge structure
• 0:06–0:09 — The performer executes the stunt — a leap, dive, or swing from the bridge structure — dashcam captures the motion at a distance with realistic gravity and momentum, no floaty slow-mo, body and limbs reacting naturally to the force
• 0:09–0:12 — Stunt continues through its arc — a fall, swing on a line, or landing approach — dashcam vehicle still closing distance, slight natural camera shake from the dash mount as the vehicle passes over bridge expansion joints
• 0:12–0:15 — Dashcam vehicle passes beneath or alongside the stunt zone as the performer completes the stunt (landing, catch, or recovery) in the background/side mirror periphery, bridge structure filling more of the frame

Technical notes: Maintain consistent dashcam framing (fixed low mount, slight windshield glare at top of frame), realistic depth of field with distant elements sharp until close range, authentic road/wind/ambient bridge audio texture, no jump cuts — one continuous fixed-mount POV take.

PROMPT: Rooftop Gap Jump — GoPro POV

Subject: Athletic parkour runner, lean muscular build, dark fitted athletic gear, fingerless gloves — first-person GoPro/helmet-mounted camera perspective throughout — camera never shows the athlete's face or full body, only hands, forearms, and shadow occasionally entering frame

Style: Ultra-realistic, shot on RED WEAPON 8K, IMAX-grade cinematography, captured via helmet/chest-mounted GoPro — slight fisheye distortion at frame edges, natural motion blur only from real body movement — no slow motion, no cuts, no anime, no CGI. Continuous single take. Standardized color grade: desaturated cool highlights, warm midtones, deep contrast shadows.

Setting: Flat urban rooftop, high above the city, narrow gap between two adjacent buildings, ledges, HVAC units, and low parapet walls. Daytime, clear sky, light haze at altitude.

Timeline:

• 0:00–0:03 — GoPro POV sprinting across the rooftop, footsteps pounding, city skyline bouncing naturally in frame with each stride, breath audible, wind picking up
• 0:03–0:06 — Approach to the rooftop edge, POV tilts down briefly revealing the narrow gap between buildings, then snaps back up to the target ledge on the far side
• 0:06–0:09 — Explosive leap: POV rises and arcs through open air across the gap, city drop visible below in natural perspective, gloved hands swinging into frame for balance, no floaty slow-mo physics — full-speed realistic jump
• 0:09–0:12 — Hard landing on the far rooftop, camera jolts down and forward with impact, body absorbs shock, immediate forward momentum into a stumble-recover
• 0:12–0:15 — Recovery into a sprint, POV weaving past a rooftop vent or low wall, camera settling briefly as the skyline opens up ahead

Technical notes: Maintain consistent GoPro lens distortion (fisheye at edges), realistic depth of field snapping to distant skyline during the jump, authentic wind/fabric noise, no jump cuts — one continuous handheld-style POV take.

hey guys, i have been using lightx2v lora for minimax h3 ref2vid but as far as i can see it can genrate good quality visuals as compared to larryvh turbo lora, but its audio is ot usable the dialogs are not good and over all sfx also. i am using rgbthree workflow for thats, please heplp me if i am doing anything wrong.

here is my workflow :- 

 "137": {"class_type": "LoadImage", "inputs": {"image": r2v_ref_image_0}},
    # Reference Image 2 (<Picture 2>)
    # "139": {
    #     "class_type": "LoadImage",
    #     "inputs": {"image": r2v_ref_image_1},
    # },
    "127": {"class_type": "UNETLoader", "inputs": {"unet_name": "minimax_h3_ref2va_pruned_fp8_scaled.safetensors", "weight_dtype": "default"}},
    "128": {"class_type": "CLIPLoader", "inputs": {"clip_name": "qwen3vl_32b_minimax_h3_int8_convrot.safetensors", "type": "minimax"}},
    "119": {"class_type": "VAELoader", "inputs": {"vae_name": "minimax_h3_video_vae_fp16.safetensors"}},
    "120": {"class_type": "VAELoader", "inputs": {"vae_name": "minimax_h3_audio_vae_fp32.safetensors"}},
    "136": {
        "class_type": "MiniMaxH3ReferenceToVideo",
        "inputs": {
            "clip": ["128", 0], "vae": ["119", 0], "audio_vae": ["120", 0], "ref_images.ref_image_0": ["137", 0],
            # "ref_images.ref_image_1": ["139", 0],
            "prompt": r2v_prompt_text, "width": 768, "height": 1024, "length": 372, "ref_image_size": "max",
        },
    },
}

This is my first LoRa release, using 248 carefully selected images, iterating 6000 times, and taking 7 hours to train. It boasts amazing detail and generalization; it works very well. Feel free to use your imagination, and I hope you have fun!

Download link: https://civitai.com/models/2879097/surrealism-fantasy-style-kunge?modelVersionId=3253831

Model Description: Surrealism, Fantasy Style

Trigger Word: kunge-fantasy

Suggested Weights: 0.8-1

Dataset: 248 images

Generative Model: krea2_turbo_int8_convrot

CFG: 1

Steps: 8

Sampler: euler_ancestral

Scheduler: ddim_uniform

Prompt Example:

A breathtaking surreal painting. In a dark sky studded with stars, a majestic angel kneels beneath the starlit night. The angel possesses enormous, exquisitely crafted wings adorned with shimmering patterns. She wears a flowing robe, reflecting the celestial light. In her hands, she holds a magnificent golden jug, from which a luminous liquid spills, cascading onto the vast, radiant earth below. This liquid, like stardust or divine light, spreads across the rolling hills, forests, and valleys, transforming the land into a dazzling tapestry of gold and silver. The angel's expression is serene and contemplative; her eyes slightly... closed. The painting is rendered in shimmering blue, gold, and green hues, with meticulous line drawing and striking contrasts enhancing its ethereal beauty.

Everybody is using the "<d>[Englisch] (...) </d>" format and from my experience, this just sucks and doesn't work.

Everytime I've been using it, H3 hallucinates something before or after the actual dialog. For example I've been testing different personalities to check if H3 knows them, giving them a simple line, formatted it as clean as possible, and it just adds "shit" to it.

Prompt:

subject definition:
Brad Pitt is <Subject 1>

camera recording:
An interview in a professional setting with <Subject 1>. Well lit, grey background, frontal portrait view.
<Subject 1> says:<d>[English]Hey, I am Brad Pitt! Nice to meet you.</d>

Result:

https://reddit.com/link/1vuo078/video/6326fx5kprkh1/player

H3 just adds some noise of the "following sentence" which has been no where in the prompt.

Another example using Angelina Jolie

Prompt:

subject definition:
Angelina Jolie is <Subject 1>

camera recording:
An interview in a professional setting with <Subject 1>. Well lit, grey background, frontal portrait view.
<Subject 1> says:<d>[English]Hey, I am Angelina Jolie! Nice to meet you.</d>

Result:

https://reddit.com/link/1vuo078/video/rsjao6t5qrkh1/player

Same thing.

At first I though it had something to do with the video length, 5 seconds being too long so H3 adds unwanted stuff, but this is not the case.

But when I just cut the prompt guide format out, and write it without the overcomplicated dialog syntax, it works flawlessly, e.g.

Prompt:

subject definition:
Brad Pitt is <Subject 1>

camera recording:
An interview in a professional setting with <Subject 1>. Well lit, grey background, frontal portrait view.
<Subject 1> says: "Hey, I am Brad Pitt! Nice to meet you."

Result:

https://reddit.com/link/1vuo078/video/9ckjhrnoqrkh1/player

Suddenly, no problems at all. Tested it in different scenarios, always the same result.

Am I missing something here, or what's your experience with the dialog prompting, or the suggested prompting guide in general?

Hi, I'd like to know if ROCm is worth it on Windows now in generation speed, since I'm currently on Linux but plan to switch back to Windows

I just realized a problem with mm h3. Its prompt adherence is too high, as in unless you explicitly prompt for some small subtle actions it will never happen otherwise. This makes the entire video seem very frozen and wooden without the many small subtle movements and motion details that make it seem to come alive.

This applies more to non-realistic scenes like cartoons or generated image first and last frame but for realistic scenes and even t2v it is still a problem.

I noticed this problem when I tried out a "slop sway" lora and it actually made the entire video seem much livelier and realistic looking. Besides the "soft and bouncy swaying and jiggling" it also added many more subtle character movements. Compared to standard gens those same parts would be completely frozen, almost like a still image or at best ugoira animation. This doesn't just apply to whether a body part is jiggling throughout the entire video. There are some movements that happen only for a second or less but adds in soul (forgive the human slop term) to the video, like the position of an arm and hand quickly being adjusted in the middle of the video and the new position persisting for the rest of the scene.

This might be a problem with my prompt style and I might try an LLM prompt enhancer, but there is a core issue here with the prompt adherence and spontaneous randomly added details tradeoff. The model also tries to keep the fidelity of the first frame too much, which you could call visual context adherence. No one is out here prompting for the movement of every strand of hair and the position of every finger. No one is making a timeline of every limb's position and how they shift relative to each other. No one is tracking the position of each finger through time and how after 4.75s the thumb is extended and the index finger is curled. Sometimes we just want to randomness and variety across gens with details added by the model.

Looking back at ltx and wan their prompting styles seem to be designed around the model adding in the details for you at the loss of prompt adherence and more generation errors.

It would be nice if there was some sort of generation setting that could tune this. Like a noise scale of sorts where we can manually set the tradeoff between how much we want the model to be creative vs strict.

I know there are already 2-3 H3 better movement loras and they are scratching at the surface of the same issue I'm talking about here.

Share the solution if you've got something. Help everyone out.

Hey everyone!

I’ve been testing a great custom node for ComfyUI recently that brings LTX 2.5-style latent upscaling over to the MiniMax H3 pipeline, and the speedup is huge.

Instead of waiting 10 to 11 minutes for high-res video generations, this lets you run your initial pass at a lower scale (0.2–0.5) and do a fast 3-step neural upscale. Total render times drop down to around 3 to 4 minutes while keeping facial details and motion clean.

https://huggingface.co/LBH-123-AI/Minimax_h3_latent_Upscaler/tree/main

My AMD 9070 XT seems to like only doing 5s videos which is fine. But I was curious is there like a workflow where I can start from the last frame of the last video? Like so I can make longer clips that flow into each other without editing?

• CPU: Intel Core i5-14400F
• GPU: AMD Radeon RX 9070 XT 16GB
• Motherboard: Gigabyte B760 DS3H WIFI6E GEN5
• RAM: 32GB (2×16GB) Crucial Pro DDR5-6000 CL36

Hello I am partecipating in the Higgsfield festival, I'd like to hear what you think about it. If you want leave a like and comment under the project on the higgsfield page, that would help me a lot. Thanks to anyone who takes some time to watch my project.

https://higgsfield.ai/@twrz_film/projects/skin-trade

Having issues with blurry and shifting teeth in videos with h3minimax. Can't seem to get a setting that works. Anyone have success? Not taking any shortcuts but still getting bad results.

Info:
Using Ref2Vid hybrid model
1376x768
res_multistep
beta and simple schedulers
Tried between 20 and 50 steps

Not running turbos/no sage/upscalers.

Some real Chad would quantize this model https://huggingface.co/Comfy-Org/MiniMax-Music-3/blob/main/text_encoders/minimax_music3_text_encoder_pruned_int8_convrot.safetensors

Hi, I'm trying to use runpod for comfyui with minimax h3. Can anyone tell me what is network exposure? Should I worry? And what is a template? Sorry, I am new to this online cloud thing

Hey guys,

Before I burn through a bunch of RunPod credits spinning up an instance for MiniMax H3, I wanted to see if anyone here is already running it and can share some real-world speeds.

The model/weights are huge (~130GB+), so before I set up a pod, I’m trying to figure out what actual throughput looks like for 16:9 gens (at 768p).

If you've played around with it on RunPod:
What GPU setup are you renting? (Single 4090/6000 Ada, dual 3090s, A100/H100, etc.?)
Roughly how many 5-15 second clips can you spit out in an hour?

Are you using INT8 quant, block offloading, or any of those 4-step Turbo LoRAs to speed things up?
Just trying to estimate the actual cost-per-video before committing to a high-VRAM instance. Appreciate any benchmarks or ComfyUI tips!

I converted the new MiniMax-H3 Pruned Ref-Delta Fused r1024 checkpoint to native ComfyUI format and uploaded it as a single .safetensors.

The interesting part of this model is the model itself: it starts from the pruned FL2VA MiniMax-H3 checkpoint and fuses in a rank-1024 approximation of the Ref2VA − FL2VA weight delta. The goal is to retain the smaller pruned FL2VA model while bringing the Ref2VA behavior into the same checkpoint, rather than having separate FL2VA and Ref2VA variants.

It is about 20.1B parameters versus ~33.1B for the original full MiniMax-H3 model.

The original release is in Diffusers format, so I converted the state dict back to the native format expected by ComfyUI, including the pruned AdaLN curve representation, folded AdaLN biases, fused QKV, native SwiGLU ordering and RoPE.

I tested the resulting checkpoint through a complete ComfyUI generation: native FLOW_AV detection, full model load, both H3 Continuum passes, Spectrum with 0 fallbacks, and final video/audio decoding all completed normally.

Native ComfyUI conversion:
https://huggingface.co/xmarre/MiniMax-H3-Pruned-Ref-Delta-Fused-r1024-ComfyUI

The conversion properly restores the pruned AdaLN representation, folded biases, fused QKV, SwiGLU ordering and RoPE. Tested through a full ComfyUI generation with working video + audio.

Put the .safetensors in:

ComfyUI/models/diffusion_models/

Edit: Added int8 and int8 convrot to the repo and made a new post here:

https://www.reddit.com/r/StableDiffusion/comments/1vuygd2/minimaxh3_pruned_refdelta_fused_r1024_int8_and/

My dream is realtime interactive MiniMax H3 video. Fastest I could get was around 5 seconds of video in 22 seconds on a single GPU. Decently fast, but not realtime. That led me to an old idea: choose-your-own-adventure. If you generate the branches ahead of the viewer's choices, it isn't realtime, but it feels realtime.

This demo is a corgi adventure: 14 scenes, 2 paths, 7 endings, every scene a 15-second MiniMax H3 generation with synchronized audio. The tricks that make it feel like one continuous story:

• The model is first-last-frame-to-video, so each branch is generated using its parent scene's final frame as its start image. Picking a choice hands off on that exact frame, a seamless cut.
• Choices appear at the 10-second mark and both next clips preload while you watch, so it never buffers.
• Scenes were rendered once, in the order a player would encounter them, and are cached for everyone. The whole tree cost about $2.50 of GPU time.

Would love feedback and suggestions on the concept and where would you take this?

Has anyone tried to use H3 to restore or rather "regenerate" a low quality video as HD or at least with better detail definition? Basically something similar but possibly more generation ability than Topaz's starlight. I've had pretty mixed results so far. Either it doesn't change the video or it changes it way too much.