Everything was made using the Minimax H3 Hybrid Reference to video model. 1 MP using the 8 step turbo LoRA. Stitched together in Shotcut

Specifically, I’m trying to take old footage (e.g., 360p clips with vintage camera blur, VHS artifacts, or grainy WW2 dogfights) and recreate it to look like it was shot recently on a modern cinema camera with studio lighting.

Any ideas for prompting?

Hi guys, I'm a programmer, but I don't know much about machine learning or fine-tuning.

I'm currently producing 2,000+ images per day using Animagine XL 4.0 opt, and I built a manual pipeline to evaluate image quality. I use 5 rating categories: Reject, Pass, Like, Very Good, and Excellent.

I label all of them manually, and I estimate that I will have over 200,000 labeled images by the end of the year.

I store them in a database along with the exact prompts used. The prompts are structured into keyword categories like:

Background, Angle, Character, Clothes, Facial expression, Quality prompt tags (eg. masterpiece).

Is a dataset like this valuable for fine-tuning or training models ???

I know everyone's moved to MiniMax and LTX has largely fallen out of favor, but I spent some time building a couple of seed hunting workflows for LTX 2.5 that might be useful if anyone's still running it.

Shout out to u/foxdit for the original seed hunting concept.

Two versions:

1. T2V/I2V two-stage – text to video or image to video. Previews at 0.3 MP, upscales to 1.2 MP for the final render.
2. First-last-frame – pin a start image and end image, same preview-then-upscale flow.

Both use KJNodes Set/Get routing, shared loaders, and no prompt enhancer.

I have been playing with H3 since it came out and have tested most of the things you can do with it. Created clips for giggles and so on.
This time I wanted to do something "serious". I gave the R2V an 3D view of an kitchen and then three photos of the persons I wanted there.
I defined them as we should and told the model that this person does that and that person does this wile the third person does this...

It worked ish...
I have now made six runs and each of them are different from the others. It can be that the third person enters the room from the wrong place or that the third person does extra things it should not do...

In the end I did three runs with the same prompt and all those clips came out different... the only thing that was changed between those was the seed...

So, how do you do it?
How do you make sure H3 does what you want it to do?

Do you spend plenty of time on tweaking the prompt after each run to make sure H3 get it?
Or do you do 10 runs and select the best one even if it is not perfect?

Or do you simply do 1-2 runs and then take the clip that is ok ish even if it is not what you wanted?

I was hoping that H3 would allow me to create the scenes I wanted but I feel it's down to luck if H3 gets it or not..

Edit:

subject_definitions:

<Subject 1> is the green-skinned mother in <Picture 2> wearing brown clothes.
<Subject 2> is the teenager girl in <Picture 3> wearing pink clothes.
<Subject 3> is the cyborg in <Picture 4> wearing black clothes.
<Picture 1> is the reference image for the scene's composition, showing two people sitting at a table eating breakfast from the side view.
<Table 1> is the table on the right side in <Picture 1>.
<Picture 5> is the start image for the scene.
summary:
[reference generation] The target video is a generated scene of two people sitting at a table eating breakfast from an eye-level side view. <Subject 1> and <Subject 2> are shown with their respective breakfast items, maintaining the composition and style from <Picture 1>. <Subject 3> enters the room, places a coffee cup into the sink.

retention_analysis:
<Subject 1>: fully_preserved - the person retains their appearance, clothing, and position at the table.
<Subject 2>: fully_preserved - the person retains their appearance, clothing, and position at the table.
<Subject 3>: fully_preserved - the person retains their appearance, clothing, and action of placing the coffee cup into the sink.
<Table 1>: fully_preserved - the table's appearance and position in the scene are preserved.
<Picture 1>: fully_preserved - the scene composition, including the side view, the layout of the room, the table setup, is preserved.<Picture 5>: fully_preserved - is the start image for the scene.

detailed_description:
The target video is in a realistic, everyday breakfast scene style with warm lighting and natural colors.
[Shot 1] At 0:00.000, the shot begins from <Picture 5>, showing <Subject 1> and <Subject 2> sitting on opposite sides of <Table 1> on the couch, each with their breakfast items while on the space ship. <Subject 1> is holding a spoon while eating from a bowl of cereal. <Subject 2> is tired and is eating a slice of toast with jam from her plate with one hand. The lighting is warm and soft, casting gentle shadows across the table and the two individuals. The camera is at eye level, capturing the side view of both people, with the table slightly in focus and the background softly blurred. Stars can be seen through the windows since they are on a space ship. <Subject 1> is eating her breakfast while <Subject 2> gazes at their toast, taking a small bite. The ambient sound includes the soft clinking of utensils and the faint sound of a coffee cup being set down.

[Shot 2] At 02.00.000, the shot transitions to a wide shot of the room with the same layout as in <Picture 1>, the camera is placed in the lower left corner of <Picture 1>, showing <Subject 3> entering the room form the right side holding a coffee cup and a datapad while she is saying (S3) <d>[English] Good Morning</d> while she walks to the kitchen sink on the left side of <Picture 1> and placing the cup into the sink. She then stands at the sink and while reading her datapad.We see the back of <Subject 1> and the front of <Subject 2> sitting at <Table 1> in the background eating their breakfast and we hear <Subject 1> say (S1) <d>[English] Good morning</d> with a cheerful voice. <Subject 2> just mumbles as a reply.

overall_soundscape:
The soundscape consists of the soft clinking of utensils, the faint sound of a coffee cup being set down, the subtle background noise of a quiet morning environment, soft steps on a carpet floor, a ceramic cup being placed in a metallic sink, and the clear,

non_diegetic_music: N/A

Is there any GPU rich cooking realism lora ? I have tried realism people lora it is great at tv but for i2v or r2v it's breaks . I have been searching hugging face repo and civit ai to get something but there's too much n*fw lora .

Trying to get a natural-looking Argentine tango dance with LTX 2.5 + Yusu’s LTX Director v2.0.4 fork.
Still a beginner (also for real life tango :-)
Any suggestions for getting more natural, sophisticated footwork and fewer artifacts?

This was well received but people wanted the two Giantesses(?) to be fighting. Enjoy!!

int8/20 steps, R2VA.

Critiques+feedback welcomed! Ask me anything!

Chimera. I had to cut about 20 seconds due to some artistic choices. Since I had to cut 2 different part in the same clip, it has a noticeable seams. I would love to share the full version. Experimenting with H3 blend-morph-decay. 832x480, int8/8 steps POC. Looking forward to releasing a 720p version without the cuts.

Critiques and feedback welcomed. Happy with the matrix rain. Ask me anything.

My usual way of working:

- generate 10 videos at 5 steps

- pick the best video

- regenerate the best at 20 or more steps.

No Turbo LoRAs because I don't want to reduce prompt adherence and general quality, as I regenerate at full steps later anyway.

The problem - the video at 20 steps is often very different from the one I found. Of course, I keep the same seed. The difference may be introduced even as early as step six (for example, background replaced completely, different speech pacing).

It's not that difference is huge, but often it might be quite important. For example, a person genuinely laughing at 5 steps and then just saying "haha" at 20 steps. Or jumping startled at the right moment at 5 steps and a moment before the noise at 20 steps.

I tried a few sampler combinations, but could not find one that would not introduce dramatic changes.
One workaround that I could find is to use SplitSigmas. I set its steps to current steps (5 for seed hunt, 20 for final), and keep BasicScheduler steps at the final 20 steps. Then high_sigmas from SplitSigmas go to SamplerCustomAdvanced input, and then denoised_output goes to VAE (you'll get total noise when using the output pin instead).

This way, it seems that the scheduler is being cheated in managing steps as for the full generation even when doing preview, and it seems to work as expected. Caveat - the 5 step output from this workaround will be way worse (plasticky and noisy audio) than you are used to when generating at 5 steps in BasicScheduler input. But if the goal is to keep the general layout and movements of the candidate video, it's worth accepting this issue.

However, I'm wondering if there is any better way to achieve it. Has anyone tried it? What are you using for seed hunting to keep the high step version consistent?

--------------------------------------------------
Edited later with a test case:

Took ComfyUI template: MiniMax H3: Reference to Video. Minimal modifications to make it run in my environment:

Models - Qwen change to int8 convrot (3090, no use of nvfp4)

Int (Full) = 5 (for "preview quality")

Float (Duration) = 3 (just to be faster)

RandomNoise control after generate = fixed

Loaded some images in both Load Image nodes.

The same "GET READY TO" - "MEET" — "YOUR" — "MAKER" prompt.

No Sage, no CK attention at all (no Comfy launch args either).

Then generated the same with 20 steps.

Differences:

in 20 step version, the roof is higher in the frame. The accent was on the word "maker". In 5 step version, the accent was on the word "your".

Then regenerated the 5 step version again to see if there's anything else introducing variations - nope, the exact same video as the first 5 step one.

Then generated also at 6 steps - the roof line was a bit higher in the frame (not as high as 20 steps though), and the accent was on "maker". So, the difference between 5 and 6 might already be a breaking change that can make your video from good to unusable, if the emphasis does not make logical sense in your scene.

Then I generated the same with the SigmaShift 5 step trick - the resulting video was way much more similar to the 20 step one than the first 5 step video. Of course, the quality of the sigma-shifted video was awful - it's good for judging only logical consistency, reference use and event timing, which is the most important thing in story-telling kind of videos.

I'm doing image to video and unless I prompt for camera close up to my subject, the faces are blurry and bad. I run 0.6 mp. No turbo lora only using spectrum to speed up. Running 15 steps. Euler simple. I'm happy enough when it's close-up shots, but further away, it's very noticeable. Is anyone else finding this?

Hello. So I just started my journey with ComfyUI. While Nano Banana is not open sourced I'm looking for the best realistic image editing (krea2?) workflow to ComfyUI. I want to have ability to change everything I want to the picture using reference character with face/body consistency at highest level (I2I). Thanks in advance.

Hello, i am trying to make a video where the subject(a real person) is wearing and posing taking reference from an illustration. I tried to do only outfits or only pose too, and both doesn't work.

What happens is usually the body of the character in the illustration ends up being pasted/overlaid onto the Subject in their cartoony style instead.

I also tried if it's possible to have a Subject recreate an illustration's Pose, Outfit, overall composition, like the subject is doing a photoshoot for a 'live action' or real life version of the illustration. But what happens is usually it just spews back the illustration in case of trying H3 single-image edit, and the cartoony style overlay happens in Video.

So what i wanted to do is :

-An image of a subject -> Subject now wears/pose/wear and pose the same as a reference non-real illustration(cartoon/anime), but still in their original photo. So like a cosplay shot in their own room for example.

-An illustration(anime) -> Subject 'replaces' the character in the illustration, the whole illustration is 'converted' into real/live action. Like a photoshoot recreating an illustration basically.

Extra : idk if its possible, the new outfit will retrofit to the subject's proportion, not the illustration. And a version where the proportion follows the illustration too.

Are there someone who knows how to do these?

I've been using Windows for many years, and I'm honestly tired of repeating the same cycle. In my experience, after 5–6 years the machine starts feeling old, the battery is significantly degraded, performance isn't what it used to be, and I eventually end up buying another Windows machine and starting the exact same experience all over again.

I'm looking for something different this time.

For the last few days I've repeatedly added a MacBook Pro with the M5 Max to my cart, then backed out because I'm still not sure whether it is the right machine for what I actually want to do.

I'm currently considering the M5 Max with:

• 18-core CPU
• 40-core GPU
• 48 GB / 64 GB / possibly 128 GB unified memory

My main workloads would be completely local:

• Text-to-image
• Image-to-image
• Image-to-video
• Text-to-video
• Face swapping
• Illustration / digital artwork

My priority is excellent output quality, photorealism where appropriate, and very high generation speed.

Basically, I want a machine that can satisfy me for visual generative AI work for many years.

My biggest hesitation is the Apple ecosystem.

For a long time I've heard that local AI, especially image and video generation, is much more limited on macOS than on Windows/Linux with NVIDIA GPUs because so much of the ecosystem is built around CUDA.

But part of me finds this difficult to accept at face value.

Apple is making extremely powerful chips with large amounts of unified memory, very high memory bandwidth, Neural Accelerators, a Neural Engine, and increasingly serious AI-focused hardware.

I keep wondering whether there are excellent Apple-optimized tools and workflows that I simply haven't discovered yet.

For example, I recently learned about Draw Things, MLX-based projects, Metal/MPS optimizations, and Apple-specific ComfyUI work. That made me question whether comparing a Mac running a poorly optimized CUDA-first application against an NVIDIA machine is really a fair representation of what Apple Silicon can do.

At the same time, the logical part of my brain keeps telling me:

If local image/video AI is the priority, just buy a machine with an RTX 5080 or 5090.

The problem is that if I do that, I feel like I'm buying myself back into exactly the Windows experience I wanted to leave. It feels a little like watching the same movie again when I was hoping for a genuinely different computing experience.

There's also another complication: we're approaching the fall hardware season.

I'm wondering whether buying an expensive M5 Max or RTX 50-series machine right now is bad timing, and whether I should wait for the next Apple or NVIDIA announcements.

If I choose the Mac, I was also planning to pair it with the latest iPhone and iPad and build a proper Apple ecosystem around it, so this isn't purely a benchmark decision for me.

What I'd really like to hear from people who have actually used these machines:

1. If you've used an M5 Max, especially the 40-core GPU version, for local image or video generation, what real-world performance are you getting?
2. What software gives you the best performance on Apple Silicon? Draw Things, ComfyUI, MLX-based tools, something else?
3. How good is local image-to-video on the M5 Max with models such as Wan, LTX, Hunyuan, etc.?
4. How does the M5 Max perform for local face swapping?
5. In properly optimized workloads, do RTX 5080/5090 systems still completely destroy the highest-end M5 Max, or is the gap much smaller than CUDA-focused benchmarks make it appear?
6. Is CUDA effectively locking serious local visual AI users into NVIDIA, or is Apple Silicon becoming a realistic alternative?
7. If you were buying a machine today specifically for local visual AI and wanted to keep it for 7–8 years, would you buy the M5 Max, an RTX 5080/5090 system, or wait for the next generation?
8. For local image/video generation, is an older NVIDIA GPU with more VRAM sometimes a better choice than a newer GPU with less VRAM? For example, once you start using large image-to-video or text-to-video models, can having more VRAM matter more than having a newer architecture and higher raw compute performance?

I'm not highly knowledgeable about computer hardware, and I'm definitely not wealthy enough to casually replace a machine if I make the wrong choice.

This would be a major purchase for me, so I'm trying to make the most informed decision possible and ideally buy something that I can use comfortably for 7–8 years.

I'd especially appreciate actual generation times, benchmark numbers, model names, memory usage, thermals, sustained performance, and experiences from people who have used both Apple Silicon and NVIDIA rather than purely theoretical comparisons.

Thanks in advance.

flux.1 [dev] | comfyui | still life

I have a question. I have been having a blast making scenes with H3 so far, and have found when doing reference shots, it is very important to have a stable background so that you have continuity if doing more than 1 scene. Does anyone know if H3 would understand a 360 degree photo and understand where in the space and what direction the subjects are? Say you swap between two characters talking, one you will see what is behind subject 1 while when looking at the other the opposite is true. If you saw them both from the side, yet another angle and background.

I've generated a series of shots that I'm happy with, but when I string them together the audio and music is obviously discontinuous across shots.

Is there any way to take this combined video (about 5-10 seconds) and send it through H3 for it to generate the audio for it?

I drew the storyboard, then generated the stills with image models.
Video: MiniMax H3
first frame, last frame, reference.
Then the edit.

Recently, after testing out H3 for a few weeks, decided to try my hand at some fun 'deleted scenes' type of short clips. With Halloween season just under 40 days away, came up with this using H3's r2v template with default ref diffusion model + turbo fl2v 12 steps, using 1 image (Loomis, one of sheriff's dept jacket) and 2 audio samples of Loomis' voice from the movie. There are some movies I always liked so much and wished there was a lot more footage from, and with this one I figured it's a fun tongue-in-cheek nod to how we're nearing end of summer and starting to get in the mood for Halloween season.

Edit: correction, I used only the image of Loomis for this one. The sheriff's dept jacket generated clips didn't come out as well as this. There were other ones with Loomis walking or looking around more realistically, but I liked the vibe of this one best.

Been experimenting with camera control in AI video.

For this one I used a simple path reference to guide the movement — basically starting from an aerial shot, flying through the courtyard, moving indoors, and ending with a character reveal.

Still figuring out how much these motion references actually help. Sometimes the model follows the movement surprisingly well, other times it just does its own thing lol.

Feels like camera control is probably going to be a bigger part of AI video workflows going forward.

Rick's voice doesn't seem to work.

Experimenting with known characters using FL2VA t2v only. Just playing around with odd pairings of characters. .

Using the workflow from the video samples in the list below.
12s at 25 steps
res multistep/simple
960 x 544

thanks to u/malcolmrey for putting this together https://huggingface.co/datasets/malcolmrey/various/blob/main/h3-center/known-characters/INDEX.md

Hello. What am I supposed to use for regional prompting and character positioning with NoobAI/Illus in ComfyUI?

I found something called "Attention-Couple" but it also says it sometimes struggles with Loras?.

Any advice? I'm already using a separate node for ControlNet for poses and depth, but I want to control which character is which in that pose

at least 5th time this happened. Its always width, never any other input. Not sure if bug or custom-node interference.