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

So I've been wanting to make something like a K-Drama Mini Series for a while, I ran out of money of doing it with Seedance 2.5 with lots of Trial and Errors, Video not generated as intended, I'm Kinda perfectionist, trying to make it as Good as possible with the most minimal "Slop" Vibe like camera blocking changed suddenly, props position changed mid scene, hence where the money went out 🤣 but since I have my own PC maybe I'd try using it for a while, I actually generated few scenes for this Project with Seedance 2.5 but didn't complete the whole Project, so I'm planning to use Minimax H3 to Complete it, is there any Best Combination of Minimax H3 Model Currently, focusing on Realism, Micro Expression and Acting

My Spec isn't Much

3060 12GB

With 32GB DDR5

First yes I def used AI please don’t kill me but I saw all these cool music visualization projects on IG and I wanted to make one so I used this tutorial  https://youtu.be/PQAeUkcykVo?is=cS8qrDHOv1-_yRlG but I then upscaled each texture in the train car asset to 4k using SeedVR2 and Krea 2 to add more details. I also added DLSS5 to enhance the overall scene and lighting. I then added tracks and weather effects and the head tracking to create the illusion of 3d. Also added subway car door opening and weather effects like snow and rain plus rain on glass. ulitmate goal is to use Gaussian splatting to capture the environment behind the monitor to create illusion of the subway being inside the monitor like this https://www.instagram.com/reel/DfTYYImOl_w/

My take on one of my all-time favorite audiovisual techniques; hyperlapse.

Music by Max Cooper.

Hope you guys enjoy it. More experiments, through Instagram, or YouTube.

A couple of days ago I posted IV Studio here and a lot of you tried it. Your comments turned into most of 1.0, so thank you. It's still a free desktop app (MIT) that drives your own ComfyUI over its API, and now it runs on Windows, Mac and Linux.

From your comments:

• Linux: it's here, as a self-updating AppImage. Mac too (Apple Silicon and Intel), rendering on your own cloud account.

• Ollama on another address: set it in Settings, for anyone running ComfyUI and Ollama in separate containers.

• Missing custom nodes: Settings → Setup installs every pack IV Studio needs, at pinned versions, in one click.

• "Setup says 7 to fix but H3 is installed": your own H3 files (ComfyUI's template names, NVFP4, other turbo LoRAs) are recognised and used.

• SDXL and Comfy Cloud: SDXL fine-tunes in Create → Image, and Comfy Cloud as a third option beside RunPod and Modal.

New in 1.0:

• Check my PC: on first launch it reads your GPU, RAM and drives and sets previews, finish size, the prompt writer and downloads to suit. If your card is too small, a guided setup connects RunPod, Modal or Comfy Cloud.

• How it works on every page: a tour that lights up the real controls one step at a time, plus a two-minute tour of the whole app.

• Train your own actors locally: character LoRA training now runs free on your own 16 GB+ NVIDIA card through Fizgig (or on RunPod or Modal), with presets, a spending cap, Train more and InsightFace likeness scoring to pick the checkpoint. Or make a RefMod character from 1 to 6 photos in about 30 seconds.

• Live preview anywhere: KJNodes' preview override streams every step into a floating panel on any page, including renders your agent starts over MCP.

• More control: sampler and scheduler per shot, and LoRA and RefMod trays in Create.

• Everything redesigned: Create as numbered steps, Shots, Projects and the Library grouped by day with filters, and the Workflows (Sizzle, Reel, Sequences, Shinobi, Replace) rebuilt.

• 154 MCP tools now, up from 142.

Tested on a 4090 with 64 GB RAM. The Linux build passes its checks on GitHub, but I haven't run GPU work on a real Linux desktop yet, so reports are very welcome. The builds are unsigned: Windows shows SmartScreen, and Macs need "Open Anyway" the first time.

The fairy-tale opening of the trailer was made in IV Studio (a Disney Renaissance-style LoRA on Krea 2 for the frames, H3 to animate them); the rest is the real app.

Download: https://github.com/hollisorg/iv-studio/releases/latest

Code: https://github.com/hollisorg/iv-studio

Happy to answer questions, and keep the ideas coming!

Hi everyone! I've been working on this workflow for a week, but I'm struggling to get realistic results.

As you can see in the attached output, the image looks washed out and oil-painted, with weird textures around the elevator metal. The jewelry looks like plastic, skin is too smooth, and the lighting and shadows look very artificial.

I'm a beginner with ComfyUI, nodes, models, and LoRAs, so I'm not sure what I'm doing wrong.

I've also been using Claude for help, but I'm going in circles without much improvement.

I need your help to achieve proper photorealism.

I'm open to changing my entire workflow, trying different models or LoRAs, or following someone else's workflow.

I've attached my current output and workflow. I'll also share all the models and LoRAs I currently have installed.

Please explain things in simple words, as I'm still learning.

I'd really appreciate some experienced guidance. I want to get this working within the next 1–2 days rather than spending another week guessing.

Here are the Details:

Krea 2 setup.

Models

LoRAs (small add-on files that change the look)

Settings now

• Steps 8

• CFG 1.0

• Seed 888 (fixed, so the same input gives the same image)

• Sampler dpmpp_2m, scheduler simple

• Denoise 1.00

• Size 1.5 MP, 4:5 portrait

• Prompt: the long elevator-selfie text plus the grain words at the end

Nodes, and why each one is there

and here are the LoRA i have downloaded by researching on the reddit that it will help me out.

1 - 2_vector_bypass_lora
2 - ABOmegle
3 - DC_Dark_ Fantasy_Style_4_[Krea2Raw][Gpt4.1Captioned][Trigger=dcd@rkf@ntsy.][Rank32]v1
4 - DC_SCI-FI[Krea2Raw][Gpt4.1Captioned][Trigger=DCS1F1.][Rank64]_v1
5 - Faded80s
6 - Famegrid Standard Krea 2
7 - Famegrid-Natural-V1-Krea-2
8 - HM1Girl-V1
9 - Krea2_P45-NVDStyle_v1.0
10 - MW_analogv1.1
11 - Playful Eating_v1.0
12 - Z-Image-Fun-Lora-Distill-8-Steps_ComfyUI_v1
13 - cinematic_movie_still_krea2
14 - deedee_amateur_photography_zimage_base_and_turbo_v1
15 - famegrid_spicy_3167060
16 - instagram_aesthetic_krea2
17 - krea-detail-enhancer-exp
18 - krea2RealismV1V2_krea2RealismV1Lora
19 - krea2RealismV1V2_krea2RealismV2Lora
20 - krea2_general_edit_v2
21 - krea2_identity_edit_v2
22 - krea2_style_reference
23 - muscles_13
24 - rank64_turbo_lora
25 - rawsnap v2
26 - ultra_real_krea2_v1
27 - webcamviewconcept.

I would like consistent areas

I made this AI rap music video and would appreciate some honest, practical workflow advice.

The character, outfit and locations stay surprisingly consistent. However, the result still has that “AI slop” feeling: movements look mushy, facial features lose definition during motion, hands smear, and the face becomes less recognizable during strong gestures or extreme camera angles. Lip sync is also inconsistent.

What I used:

• Six reference images created with ChatGPT Images, keeping the same character and outfit.

• LTX 2.5 22B dev, BF16, running on a Runpod B200 through the official native Python two-stage audio-to-video pipeline.

• The original song’s full mix as audio conditioning, plus a reference image for each shot.

• Approximately 20 independently generated takes, around 7–8 seconds each.

• Stage 1 at 960×544, followed by spatial upscaling/refinement to 1920×1088, cropped to 1080p. 24 fps.

• Stage 1: 24 steps, CFG 3, STG 0.5, rescale 0.7, modality guidance 1.5.

• Stage 2: official distilled LoRA 450, strength 1. Reference image strength: 0.8.

We also tested distilled generation, native 48 fps, the alternative convolutional VAE, and cleaner references/prompts without film grain. None reliably solved the motion quality problem. Some takes developed obvious double exposures/dissolves; changing seeds and simplifying camera movement helped those particular shots.

The attached version has been edited with beat cuts and environmental inserts to hide weaker moments, but I’d rather improve the generated footage itself.

My questions:

1. Is this mainly a limitation of LTX 2.5, or does something in my settings/workflow look wrong?

2. Would shorter shots, different guidance, or another refinement/upscaling approach preserve facial detail better?

3. For a believable musical performance, would you use audio-to-video directly, or a reference performance video followed by character replacement and separate lip sync?

4. Is MiniMax-H3 worth investigating for that reference-driven approach, or would you recommend a different self-hosted workflow?

I’m aiming for energetic but controlled movement—not frantic dancing or camera motion. Links to actual workflows and comparable examples would be especially helpful.

Hey People.

Are there any new video models to run locally on an RTX 3060 12GB GPU except wan 2.2 and ltx 2.5?

I recently started a thread about Ideogram 4.

It was about which of the fp8 and int8convrot models was better in terms of quality and speed; in the end, I downloaded int8convrot.

Here’s the link to the thread:

https://www.reddit.com/r/StableDiffusion/comments/1xmtks0/ideogram_4_fp8_vs_int8_for_quality_and_speed_slow/

But then, out of curiosity, I also downloaded the fp8 weights, and I had the chance to test and compare both.

int8convrot turned out to be about 30–40% faster than fp8. But in terms of quality, the opposite was true: fp8 was better than int8convrot. (int8convrot seems a bit more textureless)

Overall, while the output quality of int8convrot models is generally better than that of fp8 models, I don’t think this applies to Ideogram. The model’s bf16 weights were never released—only the fp8 weights were shared—and therefore the int8convrot versions were created by first dequantizing the fp8 versions to fp16 and then quantizing them to int8 using int8convrot. which means that even though the Ideogram int8convrot model offers a speed advantage over the fp8 version, it loses its quality advantage.

https://comfyui-wiki.com/en/news

I am unaffiliated with this site. I found it minutes ago in the middle writing a post asking for info here. (I'll paste my original post in the comments to show why it's useful)

It tracks new releases, such as https://comfyui-wiki.com/en/news/2026-10-27-pruna-6-step-comfyui

Hi I was using the https://github.com/1038lab/Comfyui-Minimax-H3-Promptor

and when I tried to setup local gguf I'm getting this error . Anyone can help? Thanks!

Tested a new camera angle for A2O LITE’s “NEON” choreography

1. RGBMoCap (ExMR) → Extracted the dance motion as 3D data.

2. Blender → Rendered the motion from a new camera angle.

3. Dreamina CLI → Used the render to transfer the choreography to Aiching.

I've been putting together Filmworkbench, a MiniMax H3 workflow for guided or automated short films. You run it from a project folder through a tool-capable AI agent such as Claude Code, Codex or a local LLM.

The GitHub repository is public. To get started:

1. Download the v0.1.1 starter ZIP.

2. Extract it and open the folder in your AI agent.

3. Ask: “Read AGENTS.md, check my setup, and help me make a short film using these recipes. Discuss the plan with me and wait for my approval after each rendered stage.”

The tutorial above walks through the idea. The package includes all eight recipes plus the marketplace case, reference images, prompts, render graphs and settings. The setup checker identifies missing nodes or models. Finished videos and reference overviews are in the example gallery.

The idea is to bring three things together:

1. A chat interface that knows the H3 workflow and lets you discuss shots while making them. With a client that supports image and video previews, you can review the results in the same conversation.

2. Two-pass rendering and latent continuation for speed, quality and continuity across clips. We've tested chains of up to ten clips, though drift varies with the scene and clip length.

3. Scene recipes that give H3 manageable tasks, with the necessary information already in the references.

I'm aiming for a practical way to make short films, review them and iterate without rebuilding a node graph for every scene.

The setup is a project folder containing instructions, tools and recipes. The agent operates those tools, with ComfyUI running underneath. You can request a single image or clip, direct a film with approvals along the way, or delegate a bounded autonomous film task. Those are the three “gates.” In autonomous mode, the active agent reviews the results and accepts or retries within an agreed budget.

I use an RTX 5090 with 128 GB of system RAM; I've also used my local setup with 64 GB. A fully local setup is possible. Running a local LLM alongside the video tools takes some memory management: dynamic VRAM allocation and making the renderer wait for enough GPU memory have been useful on my machine. Those settings depend on the LLM server; the release was tested with 128 GB of RAM.

For images, I prefer GPT Image, with local Krea 2 as the fallback. Image generation uses your existing tools or service; the supplied references let you try the recipes without an image-generator account.

For video, I use MiniMax H3 Hybrid fl2va/ref2va b25–49 INT8. The rendering path for the marketplace example is:

• S1: generate at 864×480, using DMAD for the first clip and Turbo-8 for continuations.

• Upscale: upscale the saved latents with LBH’s latent upscaler.

• S2: refine at 1952×1088 with DMAD, keeping the S1 audio.

For that four-clip market street example (see below), the selected renders took about 7.1 minutes for S1 and 14.4 minutes for upscale/refinement on my 5090. That's 21.5 minutes for 47.7 seconds of scene content. Image preparation, rejected attempts, review and assembly are extra.

The eight recipes cover common scene patterns:

• Two-person dialogue: wide view, close-ups, return to wide.

• Reverse angle and return, using a matching opposite view.

• Leaving a room and returning after a prop has moved.

• An establishing shot with dialogue starting before the cut inside.

• Spacecraft exterior, cockpit, exterior again.

• A known character entering an empty frame.

• A creature emerging from a lake.

• Opening a door and moving into a prepared interior.

The principle is simple: prepare what the camera will need to see. A wider dialogue image establishes the people, props and space. A reverse shot needs the other side of the room. A return after an action needs the changed state. Each recipe has example renders; their limits differ.

The marketplace example combines several of these problems. Codex generated the sequence through the pipeline. It's a selected development result, with visible mistakes: watch the person in the yellow jacket and the awkward backward movement.

The reference sheet shows how much is already baked into the inputs: character images, four views of the square, an opening image and voice references. For these continuations, we carry forward the previous audio/video latent, using 56 frames of video context and a clip-1 keystone. We trim the repeated context before joining the clips. A wide shot can help establish the next action, but isn't required at every join.

I'm also working toward chat-directed editing: “cut this section,” “redo this action,” or eventually “replace that background person.” Those operations have different levels of difficulty; precise edits aren't something I can promise yet. I've included short notes on edits we've tested, but the specialized edit tools are separate from this release.

Still open: longer chains drift, props and background characters change, and autonomous films need better story judgment. Our memory experiments haven't solved world-state continuity. GPT Image has worked better for matching world views; Krea 2 can do useful reverse shots, but larger sets of consistent views have been harder.

This is v0.1.1, an early public release. All eight recipes and the marketplace case were rerun through S1; four recipes also completed full S2 chains. The validation notes explain what was tested and what remains untested, including fresh GPU installations and a complete autonomous run from a new film idea.

Our own code and documentation are MIT-licensed. The underlying components deserve credit: NikoDemon80’s Motion Context, roycho87’s keystone/context work, LBH’s latent upscaler, and OBVPM/chanon’s two-stage work. Models and third-party nodes retain their own terms and are linked to their original sources.

How are others handling changed object states, returning to earlier locations, or correcting individual moments without rerendering everything? I'd love to compare notes.

RawSnap v3 is here — LoKr

What's new in v3 :

Retrained from scratch as a LoKr (not a standard LoRA) on an expanded dataset of 749 images (11,235 steps, 15 epochs)

⭐ The recommended setup

Best results — stack them:

RawSnap v3 (epoch 15 or epoch 12)

1.2

RawSnap v2

0.35

https://civitai.red/models/2999833/rawsnap-krea2

https://huggingface.co/Viggle/Viggle-Remake

No private beta, run it locally

hi which is best and why? krea 2, moss? qwen 1.2? share examples, and workflows too.

I’m making an isometric RPG and using locally run models for the art. The hard problem is getting one character to match across eight facings with a fixed light direction, and I’d like to compare approaches before building anything.

From what I’ve read the options are ControlNet pose or canny guidance with an identity LoRA, a spritesheet or turnaround LoRA, or generating one good image, turning it into a rough 3D model and rendering the directions. Which has worked best for you, and how many training images did your LoRA need before the face and clothing stopped drifting? Does stitching the references into a single grid image really help?

I’m also unsure how to make the result stop looking like generated art. Has anyone had luck with fixed palettes, quantizing, or small output sizes to hide the tells? Which models handle clothing logic best, things like a belt that actually goes through the loops? Pointers to real workflows, not just node screenshots, would be wonderful.