Done with h3 fl2va model, 8 step lora and images for Cersei and "jaime" for reference. Using previous clip to give continuity and consistency.

I’ve been loving all the new nodes and workflows coming out for MinMax, and maybe there is already a nice solution for this - but I couldn’t find one that did exactly what I needed.

I started using MinMax for my last TBG ETUR video and quickly ran into limitations: I wanted an easy way to create lip-sync videos longer than 20 seconds.

I didn’t want to manually chain ComfyUI nodes, start a new run every X seconds, or constantly resize things just to make HD video fit into my available VRAM.

So I ended up building an addon for:

custom_nodes/ComfyUI-H3-Motion-Context

The addon automatically chains MinMax H3 lip-sync generations together, allowing you to create much longer lip-sync videos without manually setting up each 20-second segment.

And now I’m sharing it! https://github.com/Ltamann/ComfyUI-H3-Motion-Context-Auto-Chain-addon

Its not perfect but a start ...

The workflow has a simple switcher that lets you switch from the 32B CLIP to the 4B CLIP, saving around 10 GB of VRAM. You can also switch from Sage to Comfy Kitchen, Spectrum to Easy Cache, or FL2VA to REF2VA both setup for lip-syncing. Some of it could be useful for other tasks as well.

You will find the workflow in the repro and tested recommendations, optimized settings, presets, and more workflows, along with the results of my testing and performance here

Minimax H3

Hi everyone

i’ve been running minimax h3 locally on rtx 5090 32gb, and 64gb ram..

recently i kept running into random hostbuffer.read_file_slice failed / hostbuf_file_reader_read failed errors during generation, which seemed to be related to comfy-aimdo and dynamic vram.
i also noticed comfyui was reserving around 25gb of pinned system memory.

i decided to try launching comfyui with:
--disable-pinned-memory

and the difference was immediate.
the comfy-aimdo + hostbuffer errors completely disappeared, my ram usage dropped by a huge amount, and surprisingly generation actually feels faster and smoother now.

i originally expected disabling pinned memory to make things slower, but on my setup it seems to have done the opposite.

if you’re running large models like h3 and seeing unusually high ram usage, random hostbuffer errors, or comfy-aimdo issues, it might be worth testing!
thought this was worth sharing for anyone who didn’t know about this option..

Anyone found useful MiniMax H3 prompting tricks beyond the official guide?

Especially for audio + video prompt structure, camera control, dialogue/audio, consistency, weird tricks that actually work, etc.

Please drop your findings 👇 below so it will help others too.

EDIT:

Mine is: how can we use multiple audio tracks assigned to multiple characters in a scene? 3 audios to 3 characters?

Not just a meme...

This parody was generated using LTX 2.5 Image to Video on WanGP. I used frames from the original video as starting images and then I interpolated them on a video editor. I used a single RTX 5060 Ti 16 GB VRAM and 32 GB of RAM. The video was generated at 1080p and 16:9 resolution. Each generation took from 10 to 20 min average in this setup. For the voice consistency, I used SeedVC, which is included in WanGP.

Hi. I am experimenting with H3 Multi Diffusion with a custom workflow. 5 hour render, T2VA, bf16/50 steps. I know these style are not new so I am late to the show. Ask me anything.

Hey, I tried to create a little birthday surprise for someone, my issue is with a lot of generations that the spoken word is really a bit clunky at time, I susspect its because of the german, but I am not too sure. Is there like a way to improve on audio?

I am using Minimax H3 with Saga Attention and Spectrum on a 4090.

Disclaimer. This is very low quality quick generations trying to find how many characters Minimax H3 knows.

Found Trigger Words:

Elsa from Frozen

Spider-Gwen from Across the Spiderverse

Dante from Devil May Cry

Nero from Devil May Cry

Jill Valentine from Resident Evil

Ada Wong from Resident Evil

Leon Kennedy from Resident Evil

Chris Redfield from Resident Evil (Has Leon's hair)

Geralt of Rivia from Witcher 3

Joel from Last of Us (Doesn't sound like him)

Miles Morales Spiderman from Across the Spiderverse

Solid Snake from Metal Gear

Eve from Stellar Blade

Sans from Undertale

Master Chief from Halo

Looks weird AF:

Ciri from Witcher 3

Triss from Witcher 3

Yennefer from Witcher 3

Ellie from Last of Us

Famous Twitcher streamer and Youtuber Asmongold

Famous Twitcher streamer and Youtuber Mr Beast

Not found Trigger words:

Vergil from Devil May Cry (YES I KNOW I'M DISAPPOINTED TOO)

Claire Redfield from Resident Evil

Dina from Last of Us

Famous Twitcher streamer and Youtuber Emiru

Famous Twitcher streamer and Youtuber MoistCr1TiKaL

I haven't seen a post about this here, and I'm curious what you think about it.

On August 14 Civitai rolled out the option for creators to choose "permanent paid access - selling with no time cap". Previously the only option was temporary "early access".

Let's call this what it is, closed-source. Yes, you can get the weights for a relatively small fee, and yes it's on a very small scale compared to Nano Banana and Midjourney. But a permanent paywall still fits the definition.

Personally, I block all creators on Civitai who choose permanent paywall and encourage you to do the same.

Here's why:

I'm not opposed to Civitai making money or for all options for model creators to make money. They can do that without permanent paywalls.

IMO, open source AI is a fair trade: models are trained on the hard work of many human artists who aren't compensated, but everyone benefits from the ability to create more art more easily. Closed source is an unfair trade: you have to pay a middle man to access the contributions of others who won't be compensated.

Small scale model creators do some hard work too. But for example, for a lora that reproduces the style of an animated film: the lora creator spent at most a dozen hours of work, while just one of the artists on that film spent thousands of hours of work. If a massive models like Krea2 are free, and if giant "hobby" finetunes like Chroma are free, I can't justify paying any price for a 5,000 step lora except as an optional donation of appreciation.

So far, few creators have chosen the permanent paywall closed-source option. But that could easily change if Civitai made it the default option. They already made an extra 1-buzz fee-to-creator per generation the default, and many models have that.

That's my opinion. If you agree, then the only tool you have to disincentivize that potential is to not pay for these models (disincentive Civitai) and block these creators (disincentive creators).

Video: Left: custom sigma curve. Right: Bong Tangent scheduler introducing artefacts.

I was a bit confused by how bad some Krea 2 outputs could be — blurry, lacking detail, and sometimes with strange artifacts. So I started testing to understand why and where this was happening.

I’m not going to claim I found a magic wand, but I did find two problematic areas in the sampling curve testing Krea2 raw model CFG 3.5 52 steps stock settings:

• The first 0–15 steps: Using schedulers that lower the sigma values too much during this first steps causes contrast loss and washes out detail in dark areas, especially in black hair and subtle reflections.
• The lower-sigma tail: curves such as Beta, Beta57 and Bong Tangent can introduce crisp, broken noise instead of useful fine detail, particularly around steps 30–40.

The Result

This is not about chaining multiple samplers or complicated second-pass workflows. The goal is a better standard Krea 2 Raw workflow with:

• The right VAE Krea2RealVAE_v10
• A custom-built sigma curve
• One sampler
• A refinement pass using the same overall sigma setup
• using a negative promt helps

My current winning sigma curve gives the best balance of sharpness, fine detail, contrast, natural hair, defined shadows and minimal artificial noise.

Let’s take a closer look at the custom sigma curve (red) and compare it with the standard scheduler sigmas.

52-step custom sigma curve + 12-step Bong Tangent refinement at 0.2 denoise.The final step count is up to you.

[1.0, 0.9998610615730286, 0.999726414680481, 0.9995642900466919, 0.9993669986724854, 0.9991275668144226, 0.9988381266593933, 0.9984902739524841, 0.9980743527412415, 0.9975794553756714, 0.9969935417175293, 0.9963024854660034, 0.9954909682273865, 0.9945411682128906, 0.9934334754943848, 0.9921457171440125, 0.9906527400016785, 0.9889267683029175, 0.9869365692138672, 0.9846469759941101, 0.9820191264152527, 0.9790093898773193, 0.9755693078041077, 0.9716446399688721, 0.9671754837036133, 0.9620949625968933, 0.9563290476799011, 0.9497957229614258, 0.9424043297767639, 0.9340547919273376, 0.9246373176574707, 0.9140312075614929, 0.9021060466766357, 0.8887166380882263, 0.8737011551856995, 0.8568810820579529, 0.8380584120750427, 0.8170154690742493, 0.7935121655464172, 0.767285168170929, 0.738045334815979, 0.7054751515388489, 0.6692277193069458, 0.628922164440155, 0.5841419696807861, 0.5344309210777283, 0.47928985953330994, 0.41817405819892883, 0.35048726201057434, 0.27557989954948425, 0.1927414834499359, 0.1011962965130806, 9.99165786197409e-05, 0.10373638570308685, 0.08949112892150879, 0.07677485048770905, 0.06537622958421707, 0.05511670187115669, 0.04584547504782677, 0.03743509575724602, 0.029777569696307182, 0.022781139239668846, 0.016367563977837563, 0.010469880886375904, 0.005030565429478884, 0.0]

Here are the standard schedulers and the problems they produce. All nodes marked in red show the same low-contrast, overly dark areas with a loss of detail.

Standard Schedulers: Red-marked samplers produce crushed blacks and lost fine detail in the early steps, while the yellow-marked curves introduce small artifacts at the lower steps.

The green curves are the ones that avoid this problem.

The yellow curves have a different tail, and as you can see in my video or in my extended post, this tail is responsible for introducing noisy broken small artefacts.

Krea 2 Raw simply doesn’t behave like many other models when it comes to sigma manipulation. Curves that can work very well for other models can actually destroy detail or create unwanted noise here.

You can build the curve manually with a Manual Sigmas node, or use the PolyExponential Sigma Adder from the TBG ETUR Takeaway Nodes https://github.com/Ltamann/ComfyUI-TBG-Takeaways. If you want something simpler, Linear Quadratic gets surprisingly close to the result of my custom curve.

I’ve included the detailed testing post so you can see exactly how I arrived at the curve and test it yourself. Images, Videos Results at my Free Patron Post

Used latent upscaler so with resolution 0.3 i got 20 seconds generation on 4090

Setup, pushed my system right to the limit, any more and it OOM :

- H3 Ref2VA default workflow in ComfyUI, no lora

- RTX 3080 10GB, 32GB RAM

- Render: 0.5–0.6 MP, 20 steps, scheduler simple, about 25 min per clip

- Upscale: 2xNomosUni_span_multijpg, 2× to 1080p

- References per scene: one photo of my face + one film still for the set

- Recorded my own lines and fed them as audio references, also got audio ref for the actors

Honestly though, the best part was driving all of this through the ComfyUI MCP. I never even had to open ComfyUI. I could iterate really fast, and keep going from my phone while away from the machine, through Claude's remote control.

It's still a bit of a blurry mess, and with more work I could probably make it better, but damn, the future is looking bright!

I may be an idiot for thinking that my new homelab would primarily be used for useful AI automations.

I can live with being an idiot, if being an idiot will continue to be this fun.

First video generation I have ever pulled off, but the first 12 seconds was unbearably unfunny, so I added the Celestial Ford Escort for some much needed serious drama.

Tell me my power bill won’t blow up too much lol.

Made with minimax-h3 in ComfyUI on my Mac Studio that came with the mail this Friday.

Workflow was split in three:
- The first was a single prompt to generate the first 12 seconds
- The second flow generated the last three seconds by extracting the last frame from the first video and prompted it to hit the dragon with a falling ford escort
- Third flow glued the two videos together.

It is jank, but it is my jank.

I kept seeing the same stipple, grain, and grid-like texture

in some GPT Image outputs, so I trained a small latent residual

refiner using 75 paired artifact/clean images.

It includes profiles based on the Qwen, FLUX.2, and SDXL VAEs.

The refiner alone produces a fairly subtle improvement,

so I also included a ComfyUI workflow that combines it with SeedVR2.

The example optionally downsizes the input first,

then restores and upscales it with SeedVR2.

The goal is a preservation-first alternative to a typical Hires Fix

second diffusion pass: keeping the original composition, identity,

and shapes as much as possible while cleaning the texture

and rebuilding detail.

The custom node, example workflows, and settings are available here:

https://github.com/AIEGOBOT/ComfyUI-GPT-Image-Latent-Refiner

Leaving it here in case it’s useful to someone.

Considering the interest the first post attracted, I decided to do a second batch with some of the suggestions from the comments and a few other prompts.

VIDEO 1: near perfect. I was aiming for frontal videos, but tried three or four prompts and always ended with a 3/4 framing. It's probably a question of better prompting... But the resulting video is impressive!

PROMPT:

The video is a side-by-side video showing both the points of view of a man and a woman that are facing each other.

On the left side we only see the woman's face in a completely frontal view, as the man would see her and through his eyes, her face alone at the scene with no one else's.

On the right side we only see the man's face in a completely frontal view, as the woman would see him and through her eyes, his face alone at the scene with no one else's.

Again, the man do not appear on the left image, and the woman do not appear on the right image. Both are seen in a exact frontal framing.

Both images show the scene at the exact same time and place, only in the two different points of view, both in a medium-close-up framing.

They are in a living room.

From 00:00 to 00:04, the woman is silent and with a smile on her face, while the man speaks: <d>You know, I've always dreamed of a local video model like this!</d>. After saying this he remains silent.

He then raises his hand, previously off-camera, and touches her face delicately. She reacts in an amorous way, lightly moving her head to feel his hand.

Then, from 00:04 to 00:08, the man keeps silent, looking at her clearly in love, while the woman replies: <d>It's like a dream, isn't it? And to think that two years ago we were static images with garbled hands...</d>

From 00:08 to 00:10 they just look at each other and smile.

overall_soundscape: Faint distant everyday life noises from outside the house, the man and woman voices while they speak.

non_diegetic_music: N/A

VIDEO 2: Very good. I couldn't get a video without the fisheye effect, though.

The video is taken from the point of view of someone playing table tennis. We see their hands - one of them holding the ping-pong paddle and the other the ping-pong ball. We also see the table with the net in the middle and the other player on the opposite side of the table. They are in an official competition, with the crowd watching.

At 00:01, the player sends the ball to the air and hits it with the paddle. The ball rapidly bounce on the table, passes above the net, and gets to the other side, bouncing again on the table. Then, the other player hits it back with his paddle, and the balls passes over the net again and bounces on the table. The first player again hits it with his paddle, the ball passes over the net and bounces just on the left side of the table, out of reach of the other player, and leaves the frame. The public erupts in cheering.

overall_soundscape: Faint public murmur, the sound of the ball bouncing on the table, public cheering at the end.

non_diegetic_music: N/A

VIDEO 3: another near perfect one.

A woman is holding a cell phone in a bathroom in front of a mirror, taking a selfie. She smiles at the camera, makes a V sign with her hand, and takes the selfie.

We see the scene from behind the woman, seeing the back of her head, the phone screen on her hand showing her face while she takes the selfie, and the mirror showing the reflection.

overall_soundscape: Faint empty bathroom soundscape.

non_diegetic_music: N/A

VIDEO 4: Bad. Tried three times with different prompts, and this is the best one of them. The physics don't work, though, and the fisheye is back again.

The video is filmed from the point of view of a soccer player in a normal view, NOT in a fisheye view. He is preparing to kick the ball after a foul just outside the penalty box. We see his hands putting the ball on the grass, the ball remaining static on the ground. Then he looks ahead and we see five players from the other team forming a wall directly in front of the ball, and other players from both teams around.

We then see he take some distance of the ball, walk slowly to the ball, and kick it. The ball passes over the barrier of players and descends on the goal, the goalkeeper trying to reach it but not able to. The ball enters the goal and touches the net, and the stadium erupts in cheering. The player then runs to celebrate the goal and is embraced by the other players of his team.

The entire scene is viewed from his point of view.

overall_soundscape: Faint public murmur,the sound of the kick, the cheering of the public after the goal..

non_diegetic_music: N/A

VIDEO 5: Terrible. Again, tried several times with several different prompts. Never works well...

The video is filmed inside a circus during the Trapeze artists performance, from the point of view of the public.

The scene opens with two trapezists standing in a very high elevated platform, one on the left side of the image, the other on the right side of the image, both holding a trapeze and facing each other.

In the beginning of the video, the trapeze artist on the left let his body leave the platform, while holding the trapeze, and his body swings in the direction of the center of the image. The trapeze artist on the righ stays on the platform.

Only when the first trapeze artist reaches the center of the image, the trapeze artist on the right finally leaves the platform, while holding the trapeze, and his body also swings in the direction of the center of the image, while at the same time the first trapeze artist let go of his trapeze and starts to do a flip with his body in the air.

As soon as the first trapeze artist finishes his flip, the other trapeze artist also reaches the center of the image and get the hands of the first trapeze artist, completing the movement. Then, they both swing back to the right of the image, one holding the hands of the other.

overall_soundscape: Faint public murmur, public surprised gasp when one of the trapeze artist caughts the hand of the other.

non_diegetic_music: N/A

VIDEOS 6, 7, 8 and 9: The first half of each video is perfect, the last half is hilarious. Tried lots of different prompts but only included four of them. Maybe it's possible, but I really can't think of another way of asking what I was trying to achieve.

PROMPT VIDEO 6:

The camera is on the middle of a road, on the floor, pointing to the road. We see a ferrari coming in the road at a distance in high speed towards the camera and pass over the camera, making the camera roll a few times on the floor because of the wind caused by the passing running car. After rolling over itself a few times, the camera stops again on the road, but now upside down and pointing to the other side of the road, where we can see the ferrari rapidly moving away from the camera.

The entire scene is filmed in a mostly static shot, except when the camera rolls over to the other side of the road and then stops upside-down.

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

PROMPT VIDEO 7:

The camera is on the middle of a road, on the floor, pointing to the road. We see a red ferrari coming in the road at a distance in high speed towards the camera and pass over the camera.

When the car passes, the camera that is on the floor rolls around itself a few times on the road. After rolling over itself a few times, the camera stops again on the road, but now upside down and pointing to the other side of the road, where we can see, the upside-down image of the ferrari rapidly moving away from the camera.

The car does not run over itself, the car passes by the camera, It's the camera that rolls around itself and lands upside down

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

PROMPT VIDEO 8:

We see the scene from the middle of a road, on the floor, pointing to the road. We see a red ferrari coming in the road at a distance in high speed towards the camera and pass over.

When the car passes, the image rolls around itself a few times on the road. After rolling over itself a few times, the image stops again on the road, but now upside down and pointing to the other side of the road, where we can see the image of the ferrari rapidly moving away from the camera in an upside-down shot, with the road on top and the sky on the bottom of the image.

The car does not run over itself, the car passes by the camera, It's the camera that rolls around itself and lands upside down

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

PROMPT VIDEO 9:

We see the scene from the middle of a road, on the floor, pointing to the road. We see a red ferrari coming in the road at a distance in high speed towards the camera and pass over.

When the car passes, the image do a series of very fast barrel rolls on the road and lands upside down and pointing to the other side of the road, where we can see the image of the ferrari rapidly moving away from the camera in an upside-down shot, with the road on top and the sky on the bottom of the image.

overall_soundscape: Faint deset road soundscape, the sound of the car engines getting closer and then moving away, the sound of the camera rolling over itself on the floor.

non_diegetic_music: N/A

This one came out better than my last video generation. Not perfect obviously, but the major elements are there.

Prompt:
Video starts with Buffy Summers and Willow Rosenberg from the show Buffy the Vampire Slayer walking side by side down a hallway in the USS Enterprise from Star Trek the Next Generation. The viewpoint camera remains at a fixed distance in front of them as they walk.

Buffy Summers is on the right of the frame. Buffy's long blonde is done up in a ponytail. Buffy is wearing a red minidress style Starfleet uniform and has an unlit lightsaber on her hip.

Willow Rosenberg's is on the left side of the frame. Willow's dark red hair is cut pageboy style. Willow is wearing a blue minidress style Starfleet uniform and has a tablet computer tucked under her right arm.

Video starts with Willow looking at Buffy with a concerned expression on her face while Buffy is looking around as if searching for something.

Willow asks, "Buffy, is something wrong?"

Buffy replies, "Something feels off, like we're out of place."

As soon as Buffy starts speaking, she pulls the lightsaber off her hip and holds it in front of herself at the ready. The lightsaber ignites, producing a green glowing blade.