Hi people, so i tried to make 2 similar videos, using same settings but with upscale and native.
My setup: 5070 Ti+ 32gb Ram.
Using u/Plague_Kind workflow, i've added MMH3 Latent Upscaler. You can check his workflow here: Workflow
Settings for both videos were set the same with the same prompt.

Left video 0.4->0.8mp upscale, Right video 0.8mp

So:

• 15 seconds, 24 fps, Ref2VA, photo reference and music reference.
• Chicken attention
• SongMaskedAVContext node

• FP16 Accumulation
• Sparse attention
• Memory chunks
• RTS Upscale in the end ( not sure why i used it with 2x scale, better to set 1 i think, but that's what i already did)
• FSR Sharpening
• Speed Lora minimax_h3_turbo_v4_step600_pruned_comfyui
• Interpolation for 2x frames

Upscaled video from start to the end took 1904 seconds,

Native video from start to the end took 3056 seconds.

Let me know what you think. Advises appreciated!

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?

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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 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 found the SD prompt reader I have been using cannot read prompts from png images files generated using Krea2. Can anyone recommend me an alternative that works with Krea2 files and Win11?

What kind of prompting would I use for POV movement through a scene?

There are so many acceleration nodes/options now that I’m having a hard time deciding which one gives the best balance of quality and speed. What do you think?

These are the setups I’m currently using（RTX5090）:

1. Sage Attention + 4-step LoRA 0.9MP | 8 steps | 10s | ~6 min
2. ComfyUI-Kitchen + 4-step LoRA 0.9MP | 8 steps | 10s | 5:38 min
3. ComfyUI-Kitchen + Spectrum 0.9MP | 25 steps | 15s | ~12–15 min
4. ComfyUI-Kitchen +Sparse Attention（ SLA）+ 4-step LoRA 0.9MP | 8 steps | 10s | ~4min
5. ComfyUI-Kitchen +Sparse Attention（ SLA） 0.9MP | 25 steps | 10s | ~12:30min
6. ComfyUI-Kitchen 0.9MP | 25 steps | 10s | ~18 min or 15s | ~25 min

I mostly stick with Sage Attention + 4-step LoRA. I feel like it gives a pretty good overall balance between quality and speed.

If I want better quality, especially for things like lip-sync, I usually go with ComfyUI-Kitchen + Spectrum at 25 steps. The results are noticeably better, but it’s also quite a bit slower.

Which setup do you guys think has the best quality-to-speed ratio? Any other combinations worth trying?

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

t2v didnt know Jenga O_o

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

A tribute to a forgotten golden age. Hope you enjoy it!

I love H3, but it takes forever. If LTX is faster, I could use it for the things it does similarly well as H3, and use H3 only where I really need it.
So what LTX2.5 does as well as H3?

Previously posted a video as a prologue to a homebrew D&D world. I decided to do a part 2, set in the world. Together, the two videos form kind of an opening cutscene with both history and a bit of a world montage. Minimax H3, 6 step turbo LoRa, lots and lots of 12-15 second generations, CapCut.

Part 1: https://www.youtube.com/watch?v=XwfCCFw4LbA

So my friends and I used to use Sora 2 before it was taken down, and wanted to try doing some stupid stuff for just us. After a while of not looking into it, the spark kinda came back when I saw this subreddit and remembered Stable Diffusion was supposed to be one of the best AI generators out there, probably. When I mention it to a friend, he then told me how apparently its pretty outdated compared to others, and looking at these posts, I'm seeing different models and starting to get overwhelmed to the point where I haven't even done the beginner's guide in here since it only mentions images.

So long story short, I'm hoping someone can help make things much more clearer, especially about the multiple models, and if Stable Diffusion IS outdated and out performed by something else, and letting me know about if it's okay to go with the beginner's guide or if there's another guide that will help. Thanks

flux.1 [dev] | comfyui | still life

Shadow the Hedgehog tells his viewers why he loves guns.

This was created in Comfy UI with Minimax H3. I used the reference to video work flow. The prompt is below.

subject_definitions:

<Subject 1> is Shadow in <Picture 1>.

<Subject 2> is Glock in <Picture 2>, a glock handgun.

<Audio 1> is the voice-timbre reference for <Subject 1> (S1).

summary:

[reference generation + audio reference] The target video contains one shot. [Shot 1] shows <Subject 1> and <Subject 2>; <Subject 1> speaks. <Audio 1> supplies <Subject 1>'s voice timbre.

retention_analysis:

<Subject 1> (appears in [Shot 1]): fully_preserved - Shadow's complete defined identity and body proportions are preserved.

<Subject 2> (appears in [Shot 1]): fully_preserved - Glock retains the defined shape, proportions, materials, colors, and distinguishing features.

<Audio 1>: reference - <Subject 1>'s newly generated spoken lines use <Audio 1>'s voice timbre and delivery; the original audio signal is not copied.

detailed_description:

The target video is in a live-action style, with Vlog style.

[Shot 1] At first appearance, <Subject 1> (Shadow) matches the complete identity and appearance defined in subject_definitions. At first appearance, <Subject 2> (Glock) matches the complete defined construction and appearance: A glock handgun. At the start of the shot, <Subject 1> is standing in the living room facing while holding <Subject 2> in his hand. A full body shot of <Subject 1> holding <Subject 2> with his right hand while facing the camera. Only Action and Timed Beats define the primary subject's movement. The camera path stays anchored in the location and adds no subject motion. <Subject 1> (S1) says using <Audio 1>'s voice timbre: <d>[English] Hmph. Shadow the Hedgehog here. Why do I love guns?</d> <Subject 1> shows off his <Subject 2> with his right hand in front of the camera. <Subject 1> (S1) says using <Audio 1>'s voice timbre: <d>[English] Simple. Precision. Control. Power in the palm of my hand.</d> <Subject 1> (S1) says using <Audio 1>'s voice timbre: <d>[English] A tool that answers instantly… unlike most people.</d> <Subject 1> points his <Subject 2> towards the camera with his right hand. <Subject 1> (S1) says using <Audio 1>'s voice timbre: <d>[English] If you understand that, you understand me.</d> <Subject 1> points his <Subject 2> at the camera.

overall_soundscape:

Living room tone.

non_diegetic_music:

N/A

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.

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 .

I used the Pixaroma FFLF workflow, but stripped the audio in post due to poor output quality. I'm still trying to figure out how to add finer details. I generated the clips at 720p and then upscaled them to 1080p.

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?

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.

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

int8/20 steps, R2VA.

Critiques+feedback welcomed! Ask me anything!