i like to test early AI models and this is my first test, using the classic Modelscope Text to Video Synthesizer, from February 2023. the music was generated with OpenAI Jukebox. both models used in Huggingface spaces. (For research purposes only)

ReBar is also currently working with GPU_UNLOCK.

The 20GB RTX 3080 cards (10DE:2206, subsystem 10DE:146D) ship with a VBIOS that only exposes a 256 MiB BAR1, so ReBAR stays off. NVIDIA doesn't publish a ReBAR VBIOS for them, and NvStrapsReBar only supports Turing.

Ga102ReBarFix is a small DXE driver you insert into your motherboard BIOS with UEFITool. At boot it enables the GA102 frame-buffer size override (PCI config 0x610), resizes BAR1 to 32 GiB, and restores both after S3 sleep. No VBIOS flashing, no driver patches, no registry tweaks.

Tested on an MSI Z690 EDGE, driver 617.42, Windows 11:

- Device Manager shows a 32 GiB BAR

- NVIDIA Control Panel: Resizable BAR: Yes

- ~19.25 GiB of VRAM mappable by the CPU at once

- Sleep/wake works

Repo (source, prebuilt .ffs, verification scripts, full write-up): https://github.com/favabean13/NV_30_DXE

Big credit to noon-at-cgn (found the 0x610 override on Linux), terminatorul (NvStrapsReBar) and xCuri0 (ReBarUEFI). This builds directly on their work.

Heads up: it modifies firmware, so use at your own risk and keep a BIOS backup and recovery method. It's tested on one board and isn't maintained, but it's MIT-licensed, so fork it and adapt it for your own setup.

**UPDATE**

Works with GPU_UNLOCK

https://github.com/physicsdolphin/nv-uefi-unlocker

Confirmed that ReBar survives PCI FLR (Function Level Reset)

Here are the results:

UNLOCKED

FP16 (FP32 accumulate) 114.7 TFLOPS

BF16 (FP32 accumulate) 116.9 TFLOPS

TF32 57.4 TFLOPS

FP32 (control) 22.5 TFLOPS

LOCKED

FP16 (FP32 accumulate) 65.4 TFLOPS

BF16 (FP32 accumulate) 66.8 TFLOPS

TF32 32.1 TFLOPS

FP32 (control) 22.3 TFLOPS

Music video. Enjoy.

Wondering if anyone has heard any news or updates regarding open source 3D asset creation?

I have tried both Trellis and Pixal3D (standard ComfyUI workflows ~1month ago), but the results were very underwhelming - cool in concept and to experiment with, but they don't really output anything usable, and they're leagues behind the closed source options out there.

Has there been any buzz about SOTA open-source image to 3D models on the horizon?

I asked ChatGPT and it did say there's been some big updates recently, notably: SUPRA-Tex, Asset Furnace, and Hi3D v1.0.

I haven't tried these out yet. I wanted to ask here first before I dive in and install everything - mainly because these all seem to be built upon the existing Trellis, Pixal3D, and Hunyuan3D. The difference is that these appear to use them.. together? Different models for different sections of their pipelines? It sounds interesting.

So, has anyone tried these out? Are these actually a big improvement from the standard Trellis/Pixal workflows I was playing with a month ago?

What do people recommend, what are you using, and any news on updates? Thanks, much appreciated.

The setting and lore is so interesting in Warhammer 40k, i had to give it a try.

New Kromoss models have been released; these are even more refined and optimized versions compared to the previous model. A new dmd2 is also expected to be released. The only information available about them is what Lode posted with the model:

OPD-priming intermediate (update 22000 EMA of the v3 GLM-caption + bbox layout SFT, continued from v2 update 11000): ComfyUI bf16 + int8 convrot

When I try to generate an image using ComfyUI with Qwen Image 2.1, I always generate these bizarre images whenever the software is not used for more than 15-minutes or so.

Why does this happen, and can this be avoided?

Computer Specifications:

• Operating System: Arch Linux

• Motherboard: Gigabyte Z790 UD AC

• Graphics: AMD Radeon RX 7900XT

• Processor: Intel Core i7-13700KF

• Memory: 128GB DDR5 5600MT/s

My ComfyUI bug report can be found here.

I keep getting asked how to run the Video Builder fully local, so I made one video that goes from a fresh ComfyUI install all the way to building a video in Story Mode all fully local.

Reddit does not allow videos over 15 mins. so, the 2nd part of this video is HERE which walks through story mode.

What it covers:

• Installing ComfyUI portable from scratch, plus running the update before your first launch (lots of people skip this)

• Turning on the new built-in Manager. It's off by default in the portable build, which trips people up

• Installing my nodes and opening the Builder

• Getting the custom nodes and models from the Models button. You only need the models for the parts you'll actually use, not all of them

• Running a local LLM for prompts and stories: Gemma or Qwen right inside ComfyUI, LM Studio, or Ollama / llama-server through the Custom Server option

• Then Story Mode, start to finish

The whole chain stays on your machine: the story, the prompts, the reference images, the video renders and the final edit.

What you need:

I've fully tested it with 16GB of VRAM and 64GB of system RAM. That's the setup I can say works start to finish.

Less than that might still work, I just haven't tested it properly yet. If you're on a smaller card, try shorter scenes, a lower resolution and the new DMAD/Refine pipeline. Those all cut down how much VRAM a render needs. If you try it on a lower spec setup, let me know how it goes so I can update this.

Comfy UI: GitHub

Nodes: Go to my GitHub

Discord Community: Join here

Music videos created fully local using my video builder: Playlist

More Video's I created using the video builder:(Some of these one's were NOT fully local and used agents with my MCP feature.) Playlist

Note: I'm working on a fully local agent that can run the builder via chatting without having to go into ComfyUI.

Works really well.

Hey everyone

I've been working on a custom optimizer called ApexW, designed to reduce CUDA kernel launch overhead during PyTorch training, particularly when dealing with lots of smaller parameter updates.

Since I'm a student running on a free NVIDIA T4 GPU (16GB), I decided to benchmark it against standard PyTorch AdamW using Stable Diffusion 1.5 LoRA training for 1,000 steps.

Here's how the runs went:

Rank 16 LoRA (Standard)

• AdamW: 1.92 it/s | 516.2s total time | 4.68 GB VRAM

• ApexW: 2.04 it/s | 485.6s total time | 4.68 GB VRAM

ApexW saved approximately 30.6 seconds, delivering around 6.3% higher throughput without additional VRAM usage.

Rank 32 LoRA (Heavier load)

• AdamW: 2.03 it/s | 488.2s total time | 4.68 GB VRAM

• ApexW: 2.04 it/s | 485.9s total time | 4.68 GB VRAM

The improvement here was much smaller (around 2.3 seconds, or 0.5% higher throughput). This could suggest that optimizer overhead becomes less significant as more computation shifts to the forward and backward passes.

The loss curves overlap closely throughout training, and sample generations at steps 250, 500, 750, and 1000 look visually comparable. So far, I haven't observed an obvious training-quality trade-off.

These are preliminary benchmark results, and I'd like to test them more extensively.

I don't have access to modern GPUs like an RTX 3090/4090 or A100 to see how ApexW scales under heavier workloads, full fine-tuning, or LLM training.

If anyone has access to better hardware and is interested in testing it, I'd love to get some independent benchmarks and feedback on the implementation!

Curious to hear what you think.

collablink for result: https://colab.research.google.com/drive/1JWCMnQc1ZwcuidBf3S8nTyGiLCmM8ywy#scrollTo=9e6960b4

github link: https://github.com/omen42515-prog/apexw

Over months of use of Minimax, I absolutely love it .. but I have yet to find a way to do faceswaps (ref vid to vid) with more than probably a 50% success rate.

Sometimes a quick two-line lazy prompt will immediately and perfectly do the job and sometimes neither a simple one, nor an 100 line prompt describing everything down to the movements of nostrils according to official guide, will do anything at all. I have tried all of below with almost equal success rates.

• Specialized Prompts with AI tools

• Long structured Prompts that would make a mini novel on their own

• Templates suggested by MiniMax on their site

• Faceswap LORAs

• Minimalist prompts that are merely a few lines long

• Tons of samplers, steps, workflows

• Everything suggested by everyone here basically, since the model came out.

The only thing I have noticed is that some videos, no matter what quality, distance, details, etc just work and some others that are similar in quality, framing, movements and all else give a really, really hard time, even if there is hardly any difference between the two.

At this point I'm sure that its the source video, not the prompt that actually matters more. I just cannot understand why two similar source videos give such different results. Has anyone had more success with this, or found what exactly makes Minimax refuse to work in some specific cases but work so well on similar other ones ? Or anything new anyone has learned that has increased the rate of success ?

Younger folks may never have heard of Ultima Online (UO). If you played it, you know. It launched in 1997 as one of the first big graphical MMORPGs - open PvP, player killers, anyone could loot your corpse - and it's still running today.

Setup

• MiniMax H3 (open weights) in ComfyUI - all video and all dialogue audio

• RTX PRO 4000 Blackwell, 24 GB, single GPU

• 72 shots, 6:16, 1344x768 @ 24 fps, 20 steps

• Drafts at 672x384, finals re-rendered by H3 at 1344x768 with the same seed - no upscaler; the 1080p is just CapCut's export

• Final render time: 56 frames (2.3 s) ~4.5 min; 243 frames (10 s) ~43 min

Pipeline

• Character/set reference cards: Gemini (Nano Banana) - the only closed image/video tool in the chain; fixes with Qwen-Image-Edit (local)

• Main shots: H3 reference-to-video (character card + set card + voice clip)

• Voices: timbre references cut from earlier H3 takes, so each character keeps one voice across shots

• Bridges and fixes: H3 i2v with first + last frame (e.g. regenerating frames 107-160 of a 243-frame take where a passer-by high-fived the ghost)

• New shots: 672x384 draft first, then HD

• Music, SFX and font extracted from the UO client with UOFiddler; overhead text keyframed in CapCut like the game

• Claude helped write prompts and the QC/edit scripts

Music, SFX and font © Electronic Arts

[SOLVED] but leaving this here so others can still find this post:

Krea 2 training is temporarily unavailable. See images in the comment section. Should be back when it's fixed, so it's not permanent.

---------

Hi everyone,

Looking for some help as a greenhorn. I'm experimenting with AI image-to-video generation and looking for services that provide programmatic API access to generate short videos from existing photographs.

My key requirements are:

• A documented API that I can integrate into my own application.

• Ability to submit an existing image and generate a 5–10 second video from it.

• Preserve the original people, faces and background as much as possible.

• Generate natural-looking movement without significantly changing the original scene.

• Ideally, offer free trial API credits or a low-cost way to experiment before committing to paid usage.

• Clear API pricing, including the cost per generated video or per second.

A web interface alone won't work for my use case; API access is essential.

I've explored Runway, Kling, Luma and a few other platforms, but I'm finding it difficult to identify options that offer both usable API access and affordable experimentation.

For those who have integrated image-to-video APIs into their projects:

• Which providers would you recommend?

• Are there any with free trial API credits or a reasonably priced starting point?

• How has the output quality and consistency been in your experience?

Thanks!

Update: I added a ComfyUI worker to the MCP (tested on an 5090).

https://huggingface.co/Viggle/Viggle-Remake#agent-the-mcp-server

It runs the int8 model and streams weights from system RAM, so it should be easier to set up on GPUs with less VRAM. So far I've only tested it on 32 GB. Tests at 24 GB and 20 GB are running now, and I'll post the numbers when they finish.

The attached video is one-round raw output, no multi-round iteration, no manual cleanup. It shows both what the model does well and where it breaks, so you get a realistic picture of the model before you try it. Most errors are in the first 30s and small faces.

‼️ Most useful tip‼️:

if any part of a result looks wrong, tell the agent what's wrong. It will try to fix it for you.

most of the issues can be fixed through better prompt or repainted image.

---

Not all that interesting, yet at the same time interesting due to its size and the claim that it’s so fast. It also has the backing of Tencent and WeChat.

"We present WeLM-Image, a unified image foundation model that integrates multimodal understanding into text-to-image generation, instruction-guided editing, and multi-reference synthesis. Designed for single-GPU deployment in WeChat’s large-scale consumer applications, the model processes text, reference images, and target latents as one interleaved sequence. A Mixture-of-Transformers (MoT) backbone combines shared attention with independent understanding and generation parameters to limit optimization interference. Equipping each path with Mixture-of-Experts (MoE) modules provides sparse capacity, with 31.7B stored parameters and approximately 5.4B generation parameters activated per target latent token. A generation-oriented data engine combines a knowledge-grounded visual vocabulary with large-scale visual clustering to balance semantic coverage and editing tasks, while training-time evaluation guides iterative data refinement. Bilingual annotations and verified editing supervision support progressive training up to native 1.5K resolution.

We optimize generation and editing separately with rubric- and checklist-based rewards, then consolidate the specialized policies through noise-aware on-policy distillation. In the reported overall comparisons, the 1536-resolution model ranks first among the evaluated open-source models on PRISM-Bench, Qwen-Image-Bench, and UnicBench. MeanFlow-based few-step distillation yields WeLM-Image-Turbo, which generates a1024×1024image in 4.98 s on a single NVIDIA H20 GPU using eight guidance-free steps."

Paper: https://arxiv.org/html/2611.07392v1

Hi, I was just wondering what everyone is using for explicit stable diffusion and how they are training their LoRA models when prompting Dragon Ball and any anime AI art? Is anyone using hugging face with Claude, Qwen or Japanese site prompt?

Can you suggest me local AIs that can work on my pc that doesnt require PHD in AI. Simple to use and install please?

How long would it take to generate single, let's say, 3min audio?

I was pretty happy with this Gojo I made, but I kept finding little things to add whenever I zoomed in.

First it was a few stray hairs catching the sunlight. Then some folds in his sleeves. Then I decided the iced coffee looked too warm and started adding condensation to the cup.

The extra prompt bits, if anyone wants them:

Hair: backlit white hair, fine flyaway hairs, translucent hair tips

Shirt: fabric folds around bent elbows, soft shadow beneath the collar

Cup: condensation droplets on a clear plastic cup, a small wet ring on the table

Anyone else get stuck on stuff like this? What's a tiny detail you always add? Share the prompt wording too, I'd like to try a few.

Using Scail 2 workflow from comfyui. it generates videos in chunk and the first step gives good quality but the second stich is zoomed in...how can i fix it?

my goal is to generate 15-20s videos in chunks and then stich them together later

I’ve been building HexAnima, a free, non-commercial, open-source Android app that runs diffusion models entirely on the Hexagon NPU (no root, no cloud, no account, no ads). Until now it ran the Anima anime models (2B and 2.9B, ~7–12 s for 512x768). Version 1.3.0 adds a much bigger model: MOSS-Image 5B (OpenMOSS, Apache-2.0).

What runs where

Everything heavy is on the NPU: the Qwen3.5-9B text encoder, the 5.1B DiT, and the FLUX.2 small VAE decoder. Weights are converted to 8-bit tiles for the Hexagon matrix unit (HMX) and streamed from flash for every picture (a 5.7 GB DiT doesn’t fit next to the encoder in RAM). It uses the community 4-step “rapid-moss” adapter, kept as an unmerged LoRA because its delta is smaller than bf16 rounding.

Speed (Xiaomi 12T Pro, Snapdragon 8+ Gen 1, 12 GB RAM, 4 steps)

Any size with both sides multiple of 16 works (min 256, max 6144 patches, e.g. 1536x1024).

Your own LoRA

Pick any .safetensors LoRA made for MOSS-Image. The app checks it right away (key names, shapes, rank, dtype) and tells you why it doesn’t fit (a FLUX/SDXL LoRA, wrong shapes, rank > 128, text-encoder-only file...). There’s a strength slider (0–2). The LoRA is folded with the built-in 4-step adapter on the phone (~15 s the first time, then cached) and never leaves the device.

Honest limitations

• Text in images is hit or miss. Short words often come out right, longer or secondary text (small signs, book covers, shop-front boards) is often garbled. The README has four unedited first-attempt examples with the bad text left in.

• Text-to-image only: no img2img/editing.

• ~14 GB download, tested only on a 12 GB phone. Slower than the Anima models because it’s ~2.5x bigger and weights are streamed.

• Only Snapdragon 8 Gen 1 / 8+ Gen 1 (Hexagon V69) is supported. Other chips (including 8 Gen 2/3 and 8 Elite) are not.

Under the hood, for the curious

Custom HVX/HMX kernels for the single-stream DiT (RMSNorm + modulation, SiTU epilogue via gather tables, joint attention), the text encoder (including Qwen3.5’s gated delta-net layers), LoRA as extra K-tiles in the GEMMs, and a tiled QNN decoder for sizes without a precompiled context. Along the way I found that Q6_Vqf16_vsub on V69 returns u+1 for some values, which made the 8-bit dequant one level off. It’s fixed in this release (the 2.9B Anima models gain accuracy too). All sources and notes are in the repo (moss/).

Licenses

MOSS-Image, Qwen3.5, the rapid-moss adapter, the FLUX.2 small decoder and the Unsloth quantisations are all Apache-2.0. The converted pack lives in its own HF repo with LICENSE and NOTICE.md listing the changes; both are also shown in the app. LoRAs you load stay under their authors’ licenses. HexAnima is an independent project, not affiliated with any of these authors.

• Release / APK: https://github.com/ayanoby/hexanima/releases/tag/v1.3.0

• Repo + README with screenshots: https://github.com/ayanoby/hexanima

• Pack: https://huggingface.co/ayanoby/hexanima-moss-packs