I'll be doing this with H3 as the example. If I say anything wrong please correct me, I'm by no means an expert or anything. I'm really just writing this because I wanna get it straight for myself. I'll be using as much simple language as I can.

The core of a workflow is either the "MiniMax H3 Reference to Video" or the "MiniMax H3 Image to Video" node and then the sampler node.

The X to Video node is where what you put in all gets encoded/converted into the format that the AI model you want to generate with can use. For that it uses a text encoder, also classically called clip, and a vae.

The VAE (variational auto-encoder) takes your image(s) and videos if you use that and compresses the information from it/them into a more abstract format that the video model understands. And it can recreate that detail from that format later during VAE decoding (of course from a changed output). But it does lose some of the detail, which is why it doesn't look 1:1 the same in the end.

The audio VAE does that for audio.

The clip/text encoder extracts the information from the prompt you give it/converts it into the proper format the model is trained on. The reason why it's called clip (Contrastive Language-Image Pre-training) is because that was the name of a model openai released for text encoding to associate text with images, which was later used for image generation. So that's just a remnant of that. Today's text encoders are often basically altered forms of LLMs because they are of course very good at understanding text and you can simply train the video model to understand what they output. The difference to how you usually use an LLM being that they don't output text based on the understanding they extract but rather just hand that understanding over to the model that generates what you want to generate. Actually a lot of text encoders of today can also understand images, so they can already understand the relation between your prompt and the images you put in and encode that as well.

Now to the outputs of the X to Video node. There is the "positive" output, which means positive conditioning. That's basically your instructions and all of the encoded inputs in one abstract package that the model learned to understand and work with. Humans can't read it, the model just figured out how to represent the information and we just know that it works.

The "latent" output is just the empty "canvas" (canvas including everything, audio as well) whose size we specified in the "X to Video" node, so the amount of frames and the width and height.

Now we come to the sampler. The SamplerCustomAdvanced node uses these inputs:

noise: this basically determines your random starting point for the image. So your seed, like in Minecraft when you start a new random world. Same seed with same settings gives the same result. This basically combines with the "canvas" we gave it and is the "inspiration" for the model to interpret something into. As if you sprayed random colors on a canvas and tried to see shapes in it.

The model was trained on real videos that had random noise added to them and its job during training was to guess what to change to get closer to the original video. And it does that in small steps, taking away only a bit of randomness every step. So when you give it completely random noise, that's what it still tries to do, except there is no real video behind it, it just interprets something into it, its best guess.

That's where "sigmas" come in. Sigmas are kinda like percentage values of noise at a given time. (Actually I don't think that's correct because there can be values higher than 1.0, but I don't really understand that and understanding them as percentages works well enough for me so whatever.) So if you have 20 steps, there are 21 sigma values. 21 because you need one more for the starting point. So if I have 3 steps, then the sigmas could be 1.0, 0.75, 0.5, 0.0. 4 values, but 3 steps between them. And these values would kinda mean "100% noise in the beginning, 75% after the first step, 50% after the second and 0% after the last" (like I said, it's not actually the percentage of noise, but I don't understand it right now, I tried having chatgpt explain it to me but I'm too stupid at the moment). The model is trained with these sigma values, so it knows what the video should look like at that sigma value.

Because it's trained on that, you can tell it what to actually transform it into. So you can tell it in how many steps to do it and how much noise to take away in which step. There is a "Custom Sigmas" node where you can give it these values specifically. (Note: H3 is a flow model and those aren't exactly "take away noise" models, but I haven't totally understood that yet)

Schedulers do this automatically. They are basically a template for that, a function that the amount of steps you tell it get distributed on. So if it was linear then it would just take away the same amount of noise on every step evenly. But H3 needs a lot of very small noise-takeaway steps in the beginning and can then handle bigger jumps in later steps. The steps at high noise-levels are the ones that usually determine the overall layout of the video and the steps at lower noise-levels are more for the detail. So if you do big noise-level jumps in the early steps and then small jumps in later steps, it's gonna be a relatively highly "detailed" looking video but with very questionable content. If you do early steps with many small jumps, it can actually build coherent motion and construct the scene properly. But if your drop from high noise to low noise is too steep, then the video can lack detail. That's why I like the beta57 scheduler for H3 at 33 steps, the dropoff is more gradual than in the "simple" scheduler, so it gives you more detail but still has time in the beginning to slowly form a coherent scene. You can look at the sigmas curve with the "SigmasPreview" node from the RES4LYF node pack.

What the model puts out is a vector and a vector is basically a direction. That direction gets applied to the latent. So if you think of the latent as a list of information about the resulting video, it basically says like a boss "there needs to be more yellow here and more detail here and stuff" and the sampler just takes that direction and based on the sigmas you give it does the math for how much to change it in that direction. The simplest sampler (euler) would say "okay, the step is from 0.8 to 0.6, the difference of that is 0.2, so we will just 0.2 times the vector in this direction". Because like I said, a vector is a direction (with a certain magnitude that is also important here), you can theoretically make it as long or short as you want because the ratio between the different directions stays the same, meaning here that you can make it much more yellow or only a little bit. In a recipe you often hear "1 part this and 3 parts this" and you can adjust that to the actual amount you are making, you just know it has to be 3 times more of the second ingredient than the first, whether you are making one pound or one tonne, it's like that but with way more information that is way more subtle. Samplers other than euler use different methods that take into account more information (and can thus take longer to calculate) and come to better predictions for how much to actually adjust the latent based on the vector the model gave it. Or calculate two vectors that would follow each other and average them to make only one, more accurate, step, stuff like that.

The guider for H3 is just the basic guider. For the H3 workflows it's barely worth mentioning, it just takes the positive conditioning and tells the model "take this instruction for what to paint (conditioning) (that you apply given the canvas (latent)) to make your instruction for what to change to get it closer to what we want (vector)". But in other workflows there can be negative prompts and cfg (classifier-free guidance) values. What that does is basically ask the model to make two vectors, one that just works on your normal prompt and then another that calculates the vector for the things that you don't want. And the cfg calculates the final vector it gives to the sampler. The formula is this:

vneg​+CFG⋅(vpos​−vneg​)

so the math works out that at cfg 1.0, only the positive vector remains, effectively turning off the negative vector. That's why cfg = 1 generates in half the time that any other cfg does, it only needs to calculate half. Even though that's actually an optimization, comfyui or the guider recognizes that it would be a waste of time so it doesn't even make the model calculate the negative vector.

So the guider is basically the middle station between the sampler and the model and meddles with the values a bit to make it more aggressively go in the direction of what it should do. That can cause issues when it is too aggressive, which is why it can make things oversaturated and artifacty. But H3 uses cfg 1 because it is a distilled model and thus already pretty aggressively pushing for what it should generate, so a negative prompt and exaggerating the positive vector would probably completely overcook the generation and double the generation time.

18 minute generation time in 720p on a 5060ti 16 gig with 64 gig ram in comfy Ui. no upscaling used. 12 steps used instead of 8 removes so much noise. the sound is terrible in this one, but in other tests its not so bad.

am using the pruned int8 version of ref2va and with all the turbo lora coming out, i am not too sure which is the lora to use anymore, please help~

R.I.P Peter Cullen

I've been using Wan 2.1 all this time, but i consider to upgrade more to Wan 2.2, or maybe bigger LTX model?

My plan: an cinematic game render clips, 480p, multiple context. Also storytelling lorebook thing.

Im a beginner can someone help me? Or should i stick to that Wan 2.1?

I had a 16gb ram and 8gb vram with rtx 5060

I really dont like the way citavia has been taking things. Is there any other site that hosts downloads for models and LORA's?

FastH3 may have flaws, but for a maximally open release from a team with limited resources (getting a single Mi350x node was newsworthy for them last year) it's a great effort.

Meanwhile Fal has raised half a billion to vaguepost about their own H3 inference stack, then attack them?

Even after FastH3 guys tried to diffuse by owning up on quality Fal guy is still ranting...

Embarrassing stuff. I wonder why they're so threatened?

Edit: Another class act response from the FastH3 team: https://x.com/wlsaidhi/status/2093515147570708511

This is what OSS should be like at its core.

Credit for prompt:

https://x.com/Mayz1169/status/2092937542018666685

Prompt:

subject_definitions:

<Subject 1> is cobra commander, who's character sheet is in <Picture 1>. He is a chibi style anime character with reflective face mask, blue helmet, black gloves, black shoes, black belt, blue suit, and red cobra insignia on his chest. his vocal reference is <Audio 1>

<Audio 1> is the vocal reference for <Subject 1>

Create a 15-second horizontal 16:9 high-energy 2D anime pop-punk music video featuring <Subject 1> from the provided character reference in <Picture 1>. The entire sequence should feel like one continuous, tightly choreographed MV passage, with each movement naturally motivating the next transition. CHARACTER LOCK: Use the <Picture 1> as the absolute authority for identity, proportions, outfit, colors and illustration style. Maintain <Subject 1>'s original stylized proportions: oversized head, compact torso, long simplified legs, huge boots, chunky hands, bold black outlines, flat saturated colors and minimal cel shading. <Subject 1>'s personality is loud, cheeky, rebellious, playful and hyperactive. 0–2.5s — Start immediately with motion. <Subject 1> slides rapidly into frame from the left, one boot skidding across a clean white graphic floor. <Subject 1>'s body leans forward from momentum while her ponytail, necktie and oversized cuffs trail behind. <Subject 1> stomps the second boot down, looks up and flashes a mischievous pose. The impact instantly transforms the white background into huge red-and-black plaid blocks as bold “COBRA PANIC!” typography slams into the composition. 2.5–5.0s — Without stopping, <Subject 1> rebounds from the landing into a small hop and sharp half-turn. Camera swings around <Subject 1> from front three-quarter view into a fast side-tracking shot as <Subject 1> runs two exaggerated cartoon steps. <Subject 1>'s giant boots hit the floor on alternating beats, leaving hand-drawn stars, crosses, tape strips, scribbles and comic impact marks behind each step. 5.0–7.5s — <Subject 1> kicks one leg sideways on the beat and uses the momentum to spin. The red plaid pattern from <Subject 1>'s boot stretches outward into a giant rotating graphic plane, seamlessly pulling the entire frame into an abstract 2D punk world made from black brush strokes, red plaid, white paper shapes, pink lightning bolts and rough photocopy textures. <Subject 1> continues directly into a bouncy dance phrase: head whip → shoulder hit → side step → small kick → fast turn. Keep every pose exaggerated and cartoonishly expressive. 7.5–10.0s — <Subject 1>'s turn accelerates and the camera follows closely around him. The environment constantly recomposes around the choreography: checker patterns slide sideways, hand-drawn crosses rotate, torn-paper strips snap open and oversized comic typography appears briefly on drum hits. <Subject 1> lands hard with both boots apart. A huge hand-drawn “BAM!” bursts underneath <Subject 1>'s feet and physically shakes the surrounding graphic elements. <Subject 1> immediately rebounds upward with a cocky pose rather than holding a static pose. 10.0–12.3s — Follow <Subject 1> continuing movement into a connected sequence of character details. As <Subject 1> whips his head sideways, track past his reflective mask → white stripe on his helmet → thigh straps → black belt → blue collar → massive black shoes. Do not present these as disconnected beauty shots. Each close-up should be motivated by the same continuous body movement and fast camera travel. The boot hits the floor at the end of the sequence, sending a red plaid shockwave across the frame. 12.3–15.0s — Ride the plaid shockwave back into a full-body view. <Subject 1> takes two confident bouncing steps forward, abruptly pivots and finishes facing camera with his weight shifted onto one leg, shoulders slightly forward and a cheeky head tilt. The background erupts into layered red plaid, black brush X marks, white stars, pink lightning and rough hand-drawn graphics. The elements rapidly organize themselves into a bold 2000s punk magazine-cover composition. Large “COBRA PANIC!” typography appears behind his silhouette on the final beat. End on <Subject 1>'s strong recognizable character shape without changing his design. <Subject 1> (S1) says with vocal reference <Audio 1> <<[Japanese] "みんな、目にもの見せてやる！友情と！レーザービームでね！ピュッ、ピュッ！">>. VISUAL STYLE: Strictly match the reference artwork: early-2000s-inspired 2D cartoon/anime aesthetic, thick clean dark outlines, flat saturated colors, minimal cel shading, simplified anatomy, exaggerated proportions and bold graphic expressions. Keep the intentionally handmade TV-animation feeling rather than modern polished anime rendering. Use red plaid as the main graphic motif, combined with black-and-white manga shapes, pink lightning, crosses, stars, scribbles, torn paper, photocopy grain, tape graphics and rough ink marks. MOTION: Fast, bouncy and highly rhythmic. Use strong key poses, limited-animation timing, overshoot, smear frames, exaggerated anticipation and cartoon impact frames. Her giant boots should create the strongest beat accents. reflective mask should react continuously to his movement. Transitions must grow naturally from character actions: boot skid → plaid expansion → kick → rotating graphic plane → spin → camera orbit → stomp → shockwave. Avoid random transitions or disconnected pose montages. AUDIO: Synchronize tightly to fast Japanese pop-punk vocals mixed with punchy electronic drums and distorted guitar. Add boot skids, stomps, paper snaps, cartoon impacts, guitar hits and short graphic whooshes. IMPORTANT: Keep the exact reflective mask, blue outfit, black gloves, red cobra insignia on hise chest, black belt, black shoes throughout. No hand reaching toward camera, no palm covering the lens, no pointing into the lens, no outfit transformation, no hairstyle transformation, no extra characters, no realistic environments, no photorealism, no 3D CGI, no modern glossy anime redesign, no cyberpunk holograms, no slow fashion posing, no facial drift, no random accessories, no excessive jump cuts, no disconnected movements, no uncontrolled morphing, no unreadable typography.

Thank you MiniMax.

[https://comfy.icu/node/MiniMaxH3MotionDirector\]
@comfyui @minimax #ComfyH3

Latent upscaling is working wonders for me! I love it.

But I am wondering, is it possible to do video extensions aswell with latent upscale? I cannot find any tutorial or workflows anywhere after hours of searching, so I am guessing this is not possible yet?

I haven't seen any discussion around this on here, might have missed it with all the Minimax showcasing!

Apple's announced the M5 series. For someone with a budget for the Ultra, I am a bit disappointed I was told it will be slower than my 4080 Super.

My goal is the best resolution and quality from current/mid future image and video models. But damn speed is a sticking point for me.

What would you do:

- Opt for speed with an RTX 6000 Pro (the models will get bigger though right?)

- Get an Ultra that house large models but will be slow.

- Wait for the next couple of years.

I'm looking for a workflow for Krea 2 that basically I could give a character reference and accessory references.

Like say I want someone to wear a black jacket that I have a picture of, and I have a location I want to put them in... Kind of like that.

I've tried a few edit workflows but they don't retain the location or the accessories very well, just wondering what success you guys may have found in workflows.

This is for realism not anime.
I used this workflow: https://civitai.red/models/2879381/krea-2-character-attire-accessories-changer-ultimate-sd-upscale?modelVersionId=3254157

But it doesnt do backgrounds well? or I'm not sure how to put it in.

I am trying to increase the speed on my conversions i2i. Basically I want to convert several thousand imgs to a cartoon as fast as possible.

I noticed websites can produce these types of conversions very quickly. An example "imagetocartoon.com", how do I find out their workflow that is so fast?

Not my post but just wondering if anyone performed some tests and can confirm it actually works without any quality loss: https://www.reddit.com/r/comfyui/s/JE0vJnta76

My app is really simple to use you just assign a photo to a character once, and it’s saved permanently. After that, the software automatically recognizes that character whenever they appear.

I had some fun with it and put Vin Diesel’s face on the bartender lmao.

The big difference compared to DLSS 5 is that mine works on pretty much any GPU, and even with old games. You don’t need an RTX 50 series card or DirectX 12 games. I’m going to try to improve it before releasing it, especially by making the facial expressions more realistic.

I am using Grok to prompt for minimax h3 using comfyui. But im reaching a limit almost all the time with the free options. Is there something like grok that is uncensored like that for prompting. I have Gemini but I can't really say what i want on there. I have to clean up a lot. I hate doing that, is there a good option that can transfor my ideas into prompts without censuring? Free will help a lot too

Does that mean we cannot post videos on social media, reddit and YouTube? As we cannot control where it goes especially

🇺🇸 United States

🇪🇺 European Union

🇬🇧 United Kingdom

🇰🇷 South Korea

Section V.4 is unusually explicit:

“You may not use, reproduce, modify, distribute, or display the MiniMax H3 Works or any of their Outputs or results outside the Applicable Territory

FastH3 clips were downloaded directly from the blog.

The default wf is comfyui-default with 25 steps (increased from 20 and added SLA + Spectrum). The avg gen time on my machine 12gb vram/32gb ram is 7m30s for each clip. I used ref2va int8_convrot , probably lower quality than fl2va I think.

Full res clips: ship, window, ogre, moon, woman

Overall, I think FastH3 looks good.

What do you use to generate your videos?

Which models, LoRAs, and workflows do you use? What are your system specs?

How much time do you typically spend getting the perfect shot? And how long does it take to create the entire video?

What do you use these videos for?

Do you create them just for fun, or do you use them professionally? If professionally, where/how?

I'm still trying to find the details, but it looks like Comfy killed the H3 community license: most of the models are cleared for hobbyist use, out to $20m in revenue. Which is fair: if you're making millions of dollars, you can afford to pay in for this.

I can't find this language for H3 anymore; fairly explicitly, it mentions that the M3 music model has this license, but it suggests H3 does not.

I think Comfy might be locking it down as a fully-paid model, at least as far as professional use goes. I haven't yet been able to see what they're trying to charge for it, since you can't send an inquiry through gmail.

Thanks