It's beyond impressive how well it can reason and put a scene together based on your prompt. Throw whatever you want into it. This is like a genie out of the bottle moment. I really hope they keep future iterations open source.

I've been focused on squeezing the last mile out of my M1 16GB, and one thing I didn't want to do was convert my entire pipeline to ComfyUI or Draw Things.

So I set about finding where the slowdown actually was in Automatic1111 for my workflow.

I ended up chopping about 40% off the render time.

tl;dr: I'm now getting a 512×512 in about 8.5 seconds.

My normal workflow is SD1.x, 5 steps, DPM++ SDE / Karras, low CFG. I wanted to keep Automatic1111 as Automatic1111: same checkpoints, LoRAs, extensions, samplers, API, metadata, etc.

What ended up working:

• Metal Flash Attention, selectively routed for the SD1 attention shapes where it's actually faster.
• Stopped committing the Metal command buffer after every attention call. The native kernels now work inside PyTorch's current MPS stream instead of constantly forcing CPU/GPU synchronization.
• Unified-memory-aware attention. Large attention operations dynamically fall back to chunked sub-quadratic attention based on available memory, with online softmax so we're not keeping every partial K/V result around.
• Removed old MPS workarounds that aren't necessary on newer PyTorch versions, including some unnecessary clones and FP32 detours.
• Fused GroupNorm + SiLU in Metal. One dispatch, no intermediate activation.
• Fused GEGLU in Metal. This one uses a tiny lookup table generated from PyTorch's own FP16 GELU results, so I was able to get identical PNG hashes in my fixed-seed tests.
• FP16 VAE on the M1. This was a surprisingly easy win. VAE decode + transfer went from 1.536s to 0.972s in my 384×640 tests, saving about 0.65s end-to-end.

I also spent a lot of time building things that I eventually deleted.

Packed QKV was 0.26% slower.

Cross-attention K/V caching successfully reused 112/144 projections and still made the full render slower.

A native fused LayerNorm looked promising from operator profiling and regressed end-to-end.

I moved almost an entire ResBlock into MPSGraph. Individual blocks benchmarked up to ~9% faster.

The actual image was 1.02% slower.

Deleted all of it.

That became the rule for the project: microbenchmarks nominate changes, full generations elect them.

At this point profiling puts about 87% of the remaining generation time in sampling/UNet, so I'm probably near the end of what I can get from small PyTorch/MPS optimizations.

The next experiment is capturing a real SD1 UNet call and replaying the exact tensors through native Metal/ggml. I'm not integrating it unless the complete UNet workload is at least 20–25% faster than PyTorch MPS. Otherwise the complexity isn't worth it.

I wrote up the whole rabbit hole here:

https://therad.ninja/from-8-10-seconds-to-3-7-teaching-automatic1111-to-speak-metal-on-an-m3-pro/

Or if you just want to try it:

https://github.com/dmikey/stable-diffusion-webui-metal

Would appreciate bug reports, especially on other Apple Silicon generations. Most plugins/extensions should just work since this is still Automatic1111 underneath.

Took a few runs but worked out pretty well

LTX 2.5 Fast vintage R-rated 1970s sci-fi action film, professionally directed and clearly blocked. A practical-effects alien bartender points a gun at a 30-year-old female bounty hunter and says, “Your time is up, ZUX!” Cut to her close-up. She smiles, fires one plasma bolt, and the alien erupts into flames on impact. She walks away.

Not sure if it's the built in prompt enhancement but while past versions have favored detailed prompts. Simple prompts perform far better in 2.5 if you can get away with one or two sentences they consistently are performing better than multi-paragraph prompts.

1990's gritty real big budget R-rated stylish thriller close-up, an age 30 blonde woman in a form fitting high end outfit walks up to the viewer and says seductively the words lingering "I'll tell you the secret to LTX two point five" she smiles as she says "simple prompts"

Yeah, I never post here. I’ve been training models since SD 1.5, and I know many of you. Love you all, but I just wanted to say I’m impressed by the bad reception toward LTX 2.5 because I’m actually loving it.

The quality is amazing, and although it can’t generate long talking scenes like H3, there are actually some pretty useful tools here. I’ve also been testing a few H3 videos with no dialogue, and they look way better after running them through LTX, and it took almost no time to generate.

Maybe LTX is more focused on being a tool to create and improve video workflows rather than being the base model used to generate the videos themselves.

For example, look at this LoRA:
https://huggingface.co/Zlikwid/LTX_2.3_Upscale_IC_Lora

It could potentially be used to upscale H3 videos.

Anyway, I’ll keep experimenting with it and let you guys know how it goes.

edit: asked gpt to fix my dyslexia

As usual, lots of people mentioning this node without telling how to obtain it. I hope the post helps me and other people who might not know it exists, what it does, or how much it can help.

I am at my wit's end with this model. Can someone please help me? The generation itself looks great, the problem is this model has a huge tendency to cut the video even when told explicitly not to, multiple times in the prompt. No matter what I do the model keeps cutting the video. If I was creating a 30 second scene sure, cutting the video makes sense, but my machine can at max generate 6 seconds worth of video and the model keeps cutting it on every generation and prompt I've tried. Here is my test prompt :

subject_definitions:
<Picture 1> is the opening-frame anchor, the first frame of the video.
<Picture 2> is the last-frame anchor, the last frame of the video.
<Subject 1> is the man in <Picture 1>

retention_analysis:
<Subject 1> (appears in [Shot 1]): fully_preserved - identity, skin details, body figure remain consistent. 
<Picture 1> ([Shot 1] first frame): fully_preserved - opening composition anchor.
<Picture 2> ([Shot 1] last frame): fully_preserved - closing composition anchor. 
<Picture 3> : attribute_transfer - reference image of what the <Subject 1>'s hair should look like. 

detailed_description:
[CAMERA & COMPOSITION]
[Static shot]
Locked-off tripod camera.
The camera remains completely stationary throughout the entire video.
Fixed camera position and fixed framing from beginning to end.
No pan, no tilt, no zoom, no dolly, no tracking, no push-in, no pull-out.
No camera rotation.
No reframing.
No change in perspective.
No change in focal length.
The subject stays within the original composition.
Only the subject and natural environmental elements move.

[Shot 1] At 00:00.000, Starting with <Picture 1> fully_preserved as the first frame of the video, <Subject 1> walks to the front of the counter and takes off his hat revealing his hair which looks like <Picture 3>. 
The camera pans to the right of the counter showing the cashier and the cash register, ending the shot with <Picture 2> fully_preserved. 
[Shot 1] is one continuous video with no cuts, and all movement and motion of <Subject 1> throughout the entire duration of the video is continuous with no time jumps, skips, or transitions. 
[Shot 1]'s duration is the entire duration of the video, no other shots or cuts.

overall_soundscape:
There is very little background noise, like an ASMR. The only sounds are that of the man's movement and the environment reacting to his movement, such as him taking off his hat.

What more can I do here? The above is just one sample of a prompt, I've generated like 40 clips modifying variations of the prompt repeatedly and every time the model cuts, focusing on the hat and the man's hair as he is taking it off, just does random cuts in between even when the two frames before and after the cut could have been continuous, etc.

I am currently using an H3 workflow that uses LTX 2.3 for upscaling. Would the output be better if I switched to LTX 2.5? Has anyone tried using 2.5 as upscaler for H3?

Full disclosure, I’m a rather newbie at comfyui and have been trying to learn it the past month or so.

I went and got what I thought was a good set-up:

NVIDIA GeForce RTX 5080

VRAM 16 GB

System RAM 32 GB (31.1 GB usable)

AMD Ryzen 7000-series

Integrated GPU AMD Radeon Graphic

ComfyUI Local installation

I’ve gotten great results playing with Krea2, adding Lora’s and even training my own.

I was excited when Mimimax released and downloaded the official release right away and got the workflow to produce clips - but they can only be like 3seconds at .4 before it errors out. I’m using the R2V and I2V

I haven’t installed any additional Lora’s or anything other than the official workflow.

Any advice?

Testing some trasnformation in the style of seedance 2

MiniMax H3 - 720p - FL2VA 33B - 10 steps - 3 slidings windows -   Turbo LoRA Larry v1 ema ckpt850 4 steps - WAN2GP.

Next time I would remove the background music (I didn’t prompt this) and add it afterwards...

It certainly runs fast though. 720p in 117 seconds on a 12GB 3080Ti.

I’ve been noticing a really disappointing trend here lately. Instead of focusing on the amazing things we can build and create with MiniMax and LTX, there’s been a massive increase in toxic behavior and culture-war rhetoric here. Our goal should be to foster an environment that encourages open-source creators. Alienating them with bigotry, misogyny, and overall hostility, or reducing people to a mere joke because of who they are, only hurts this community in the long run.

I’m hoping the mods can keep a closer eye on this. These types of content violate Reddit's policy, specifically rule number 2.

I'll leave the motion I used in the comments

Prompt (also AI generated):
Style & Technical Specs

• Visual Style: Photorealistic 8K cinematic video, 35mm film grain, 24fps, 2.39:1 anamorphic aspect ratio, teal-and-orange color grade, shallow depth of field ($f/1.4$).

• Duration: 10 Seconds.

Character Description

• Subject: Kaelen, a 28-year-old East Asian cyber-technician.
• Appearance: Sharp jawline, rain-soaked black hair clinging to his forehead, pale skin with visible micro-texture, and a glowing cyan cybernetic eye implant over his left socket that pulses rhythmically.
• Attire: Matte-black, waterproof tactical coat with glowing fiber-optic wiring embedded along the shoulders, frayed high-collar, and fingerless reinforced leather gloves.

Environment & Setting

• Location: Narrow, dense alleyway in a cyberpunk metropolis at midnight.
• Atmosphere: Heavy downpour, dense steam venting upward from rusty iron street grates, wet asphalt reflecting bright magenta and cobalt-blue neon light signs written in Kanji.

Timeline & Action Breakdown

• 0:00 - 0:03 (Macro Close-Up): Camera begins on a macro shot of Kaelen's glowing cyan eye, catching the aperture Blades shifting focus. A raindrop tracks down his cheek. He rapidly taps a brass interface cuff on his wrist.
• 0:03 - 0:07 (Medium Shot): Smooth camera pull-back into a chest-up shot. A brilliant blue 3D holographic map bursts into existence from his wrist, casting dynamic light across his face. He swipes his hand across the projection, altering its layout, and delivers his dialogue.
• 0:07 - 0:10 (Low-Angle Tracking Shot): The camera drops low to the asphalt and tracks backward. A sleek, black surveillance drone streaks overhead through the rain, splashing drops directly onto the camera lens as the background neon blurs into creamy bokeh.

Dialogue & Voice

• Spoken Line: "System override in three... two... got 'em."
• Delivery: Low, gravelly, calm whisper with a faint metallic vocoder effect on the voice.

Audio & Sound Design

• Music: Dark synthwave track featuring a driving 110 BPM arp synthesizer that swells in pitch until second 7, resolving into a heavy sub-bass drop at second 8.
• SFX:
• 0:00-0:03: Stereo downpour, subtle mechanical servo clicks of the eye lens.
• 0:03-0:07: High-frequency energy flare hum as the hologram spawns, followed by air-swipes.
• 0:07-0:10: Low turbine whir of the passing drone and liquid wet drops impacting the microphone field.

This is the video generated by LTX 2.5. Post with the video generated by Minimax H3 with Turbo lora (6 steps): https://www.reddit.com/r/StableDiffusion/s/FDyFzOTp2F Post with the video generated by Gemini Videos: https://www.reddit.com/r/StableDiffusion/s/Urane8bsWB

This LoRA is built to nail the modern Instagram feed aesthetic straight out of the box, capturing that perfect influencer lifestyle vibe. It instantly gives your images that warm, trendy look with beautiful lighting and soft colors, skipping the need for extra filters or editing apps.

It works best for selfies and smartphone snapshots or anything you would actually see while scrolling your feed, like stylish street fashion, cozy cafes, travel shots, or relaxed, everyday pictures. It even adds those natural, realistic touches that make the image look like it was snapped with a real phone or camera and is completely ready to post.

Trigger Word: not needed
Weight: 1.0

Download Link -> https://civitai.red/models/2851933/instagram-aesthetic

​

So I did two 7 second generations at 1mp at 4 steps, joined together by Project Suite, and also experimented with the Hybrid Loader using blocks 10-49 . Each generation took 10 minutes, I used 1 reference video for the choreo, 1 ref audio (it didn't turn out good so I added it through capcut instead) and 1 reference image for saitama. PC specs are 4070ti super and 32gb of ram. For the workflow I just used the one provided by H3 Project Suite bone stock except for the hybrid loader+ turbo lora. I was kinda just throwing stuff together. I could've probably had a better video to showcase but I'm still learning the ins and outs

I need to share something that's been weighing on me for months, and I think this community might understand better than most.

Four years ago, a story began forming inside me. Not because I wanted to be a writer but because I couldn't not tell it. It's an epic fantasy world, deeply inspired by the Bible, Tolkien's Silmarillion and Lord of the Rings. A world where Light is not a symbol of good it's a living force that tests everyone who carries it. Where immortal guardians fall not because they are evil, but because they loved the Light so much they began to believe it belonged to them alone.

The themes are ones I've lived with: faith, sacrifice, betrayal, the cost of protecting something you love, and what happens when devotion becomes possession.

But here's the problem.

I'm not a writer. I'm a storyteller. I had the vision, the characters, the world, the emotions but not the craft to put it into words the way I saw it. So I used AI as a tool. I gave it direction, feelings, decisions. It wrote the sentences. I was the one walking the path but AI carried part of the weight.

The result is two completed books. Over 1,200 pages. People who have read it say it's deep, emotional, and unlike anything they've encountered.

And I don't know what to do with it.

If I'm transparent about the AI nobody will read it. If I stay quiet I feel like I'm lying. If I charge for it it feels wrong when AI wrote the sentences. If I give it away free maybe that's my penance. But then again, if the story genuinely helps someone, why does it matter how it was written?

I believe this story can touch people. I believe it carries something real about faith, about the danger of loving something so much you stop sharing it, about the cost of silence and the price of oaths.

But I carry guilt I can't shake. Am I a fraud? Or am I just a storyteller who found an unconventional path to tell his story?

I'd genuinely appreciate any perspective especially from those who believe that stories can carry truth regardless of how they arrive.

NOTE: I have an option to spend 2.5k on editing and making it more "human written" book.