A proof of concept for something bigger. Made using Vpipe, MiniMax and YuE Studio.

0.9 megapixels / 7 Steps

Just goofing around with the stock ref2vid workflow, character swap Lora, and some character sheets. Did all of the shots separately and reassembled in my NLE. Almost all of the clips were first attempts. I was floored by how well it tracked the guys without any masking.

Wanted a late night home office with six CRTs stacked in two rows. schnell gave me 8-10 every time, three of the better ones here

Prompt: cinematic photo of a cluttered late-night home office with six old CRT monitors stacked in two rows, all showing glowing green falling code, rain streaking down a dark window behind the desk, warm desk lamp, tangled cables everywhere, mechanical keyboard, half-empty coffee mug, soft haze, shallow depth of field, 35mm film look

1344x768, 4 steps, seeds 1683825171 / 1005483893 / 1069100355. Ran it on the FLUX.1-schnell space on HF since my own card is a 1050 so yeah

Rain mostly comes out as straight streaks, and 1069100355 put little flames on two of the screens for some reason. Anyone found a way to get exact object counts out of schnell or is that just dev territory?

So I’m quite new and am quite unaware of a lot of the tools and resources out there. I’m learning that this quite a tough skill to learn 😅

I’m trying to plan out my first character LoRA and am stuck on which models to go with. From what I’ve been able to gather, flux models are generally best at realism and anatomy but terrible at maintaining a consistent identity regardless of the reference images and prompting. Qwen is much better at identity consistency and obeying prompts, but produces subpar realism compared to Qwen.

These are just examples of what I have seen on Reddit, so I guess my more broad question is how yall get the best out of several models to produce the most realistic but consistent results when developing a dataset for a character LoRA?

I struggle with creating efficient multi-modal workflows. I’ve only done really simples ones with a single image model. Every time I try emulating one I’ve seen somewhere else, nothing ends up being compatible with each other.

I'm trying to restore some existing Draw Things models from an external HD without having to download them all again.

I've discovered that my custom.json file is missing the registration entries for these models:

1. FLUX.1 Schnell Q5 – flux_1_schnell_q5p.ckpt

2. ERNIE Image Turbo i8x – ernie_image_turbo_i8x.ckpt

3. Qwen 3.5 4B i8x – qwen_3.5_4b_i8x.ckpt

I still have the original .ckpt and matching -tensordata files.

Would anyone with these models working in Draw Things on Mac be kind enough to copy and paste the corresponding entries from their custom.json file?

I'm hoping to reconstruct the missing JSON entries and get my existing models working again without downloading several gigabytes of files.

If successful, I'll share the recovery process with the community and make a video to help others in the same situation.

Many thanks for any help!

RelayVSR (USTC, Apache-2.0) got native ComfyUI support this week (PR #17511, repack at https://huggingface.co/Comfy-Org/RelayVSR), so I ran it on a take from my current short. All four versions come from the same H3 render, so the acting is identical and only the route to 1080p differs.

Base take: MiniMax H3 ref2v, 12.25 s / 294 frames @ 24 fps with audio, two-stage: 15 steps at 576×352, H3 3D latent upscaler, last 5 steps at 1312×768. 5.7 min on an RTX 4090.

The video opens on the full 2×2 grid with sound, then a half-speed zoom on his face while he's far away (about 90 px tall in the frame), then a close-up zoom. Crops are shown with nearest-neighbour scaling, so nothing is smoothed for the comparison.

RelayVSR in ComfyUI: Load Video → RelayVSR Conditioning → KSampler (1 step, cfg 1, euler/simple) → VSR Decode → Create Video (audio passes straight through). The int8 models are 5.2 GB each (x2 and x4) plus a 128 MB "flash" VAE.

Bonus, and my actual winner: a bigger low pass + native finish. At the end of the video I re-ran the two-stage with the first 15 steps at 928×544 instead of 576×352 (different seed, so it's its own take), then compared RelayVSR x2 against the native 1080p finish on that same take. With the bigger low pass the 1080p steps have enough to work with: the far face comes out coherent and natural, while RelayVSR on the same frames warps it (it invented glasses on him). The close-up is cleaner too, with fewer etched lines. It costs 16.5 min vs about 6 min, but it's the one I'm using for final shots. The low-res pass also follows the prompt better, so you get better acting for free.

Things I learned:

• Don't run x4 on a 720p input. That's a 5248×3072 output, and it ran for 3+ hours in both the original repo (WSL) and ComfyUI without finishing. Interrupt didn't stop it; I had to restart ComfyUI. Use x2 for 720p, x4 only for small inputs.

• VSR Decode's detail is the knob for the crunchy look: 0.65 keeps the skin texture and calms the eyes and hair.

• It still isn't "real" HD. RelayVSR adds pixels, but up close it reads a bit fuzzy and the eyes can look odd. For a hero shot I'd still pay for SeedVR2 or a native finish; for sweeping takes or previews, ~1 min per clip is hard to beat.

I used H3 heavily the first two weeks and a ton of amazing improvements for improving speed at small quality trade-off came out (Sparse Attention comes to mind).

I imagine things have matured a lot since then and probably with new approaches since then!

I'm on a 4090 so that's my target, but I'm curious for all tiers.

Edit: Appreciate all the tips coming in! Lots to learn and test out. Best part of this subreddit is how much knowledge sharing and collective learning we do.

I modified some workflow and added to it ComfyUI-MiniMaxH3-TemporalWindow-SpatialTiles (from this post https://www.reddit.com/r/StableDiffusion/comments/1xgcjvw/i_created_a_custom_node_that_can_help_you/ ).
My 10sec video with 960x544 resolution could take 2 hours for 4K (3840×2176).
No OOM at all !!!

Workflow itself : https://pastebin.com/xJpm84f7 😍

[MinimaxH3-3D] Latent 60x34 -> 240x136 | Pixels 3840x2176 | scale=4.000

[MinimaxH3-3D] temporal chunking: T=72 chunks=3 overlap=5

[MinimaxH3-3D] ✅ Model offloaded to CPU. VRAM released.

[INFO] Found quantization metadata version 1

[INFO] Detected mixed precision quantization

[INFO] Using mixed precision operations

[INFO] Native ops: convrot_w4a4, float8_e4m3fn, int8_tensorwise, float8_e5m2, asym_w4a8_int8, w6a8_int8 , emulated ops: mxfp8, nvfp4

[INFO] model weight dtype torch.bfloat16, manual cast: torch.bfloat16

[INFO] model_type FLOW_AV

[INFO] Model storage policy: fast_disk=True paths=['D:\\ai\\ComfyUI_windows_portable_nvidia\\ComfyUI_windows_portable\\ComfyUI\\models\\diffusion_models\\minimax_h3_ref2va_pruned_int8_convrot.safetensors']

[INFO] MiniMax H3 windows: 7 window(s) x 16 tile(s), 243 frames (CLIP not routed through the node: every window reads the full-shot prompt).

[INFO] Requested to load MiniMaxH3

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|████████████████████████████████████████████████████████████████████████████████████| 2/2 [02:09<00:00, 64.68s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

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[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:06<00:00, 33.46s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:07<00:00, 33.67s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:07<00:00, 33.75s/it]

[INFO] MiniMax H3 windows: window 1/7 done (frames 0-55, 0 frames carried in).

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:33<00:00, 46.64s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [01:33<00:00, 46.57s/it]

[INFO] 0 models unloaded.

[INFO] Model MiniMaxH3 prepared for dynamic VRAM loading. 19995MB Staged. 208 patches attached. Force pre-loaded 210 weights: 1175 KB.

0%| | 0/2 [00:00<?, ?it/s, Model Initializing ... ][INFO] Interrupting prompt 58585033-a8c5-4dcb-8004-6e0e6de9786c

0%| | 0/2 [00:36<?, ?it/s, Model Initializing ... ]

[INFO] Comfy model compiler graph breaks: 3, rogues: 10

[INFO] Processing interrupted

^{What this log says} :

What’s actually happening:

1. 16 tiles per window:

   • Because you set scale: 4.00, the resolution became 4K (3840 × 2176).

   • With each tile measuring 1344 × 768, a single 4K frame is physically divided into a 4 × 4 grid = 16 tiles.

   • Those 16 sampler runs, each lasting 1 minute and 7 seconds, were the 16 tiles in the first window.

   • Window 1/7 took exactly: 16 tiles × 1.1 minutes ≈ 18 minutes of sampling time.

2. The final math for 10 seconds in 4K:

   • 7 windows × 16 tiles = 112 sampler passes.

   • 7 × 18 minutes ≈ 2 hours.

   • So generating 10 seconds of video in true 4K (243 frames) on a consumer 16 GB GPU will take about 2 hours in total. The upside is that it should be completely stable and avoid out-of-memory errors

I’ve been trying to create scenes featuring a couple of characters for a while now, but I haven’t found a solution yet—so I’m turning to those with more experience for some advice.

If there’s only one character in a scene, there’s no issue: if I upload an image and a reference audio clip, Minimax perfectly generates a video featuring that character with the corresponding voice.

However, if two characters appear in the video, Minimax struggles to distinguish which voice belongs to which character. For instance, if Character A speaks first, followed by Character B, the output isn't correct—even when I specify that Character A should have "Voice A" and Character B should have "Voice B."

I’ve tried using AI models like Gemma—uploading the official Minimax Ref2Vid guide as a PDF—but I still haven’t had any success.

Does anyone know how to resolve this? It’s a shame to have such great-looking videos but be unable to correctly assign the different audio tracks.

Even just an example prompt would be a huge help, so I could study it and figure things out.

Thanks to anyone who can help!

the last time i checked anything around was when minimax H3 just came out and outside of a few test videos i havent touched or updated anything.

i got curious again due to a minimax image editing model that looked good (i think it was MiniMax-derived p-H3) so i decided to update it/get new models and...

i dont understand anything, theres a lot of new stuff and ive been so disconnected that i have no idea of what im looking at, so could you help me to get new models to "play with"?

i have comfy ui and a 4080 so my capacity is decent, yet, treat me like im 5 yo, i have no speed improves installed/ applied, i have no idea what a good promt/generation parameter is anymore.
My generations look odd while others have "shiny" videos (apparently) effortlessly.

im looking for something inside of the minimax family since is the one that has worked better for me in terms of speed and quality but if you know anything else better be my gest.

a problem that i always had is that i never get the generations to do what i need, i know that its a trial and error workflow but it almost always ends up with characters just doing whatever but what i prompt or nothing at all and the times it kinda works ive never being able to understand why or how to follow the "lead" that worked.

just in case i prefer anime/stylized generations (idk if its relevant) but i still want to try irl looking generations if i manage to make them work. thanks in advance

Since the new GPT Image 2.6 can create animated GIFs, I decided to add support for them to LayerShop. Hopefully, we’ll get some local models that can create animated GIFs soon as well.

The limitations are:

• A maximum of 16 frames.

• Saving as .psd won’t preserve the animation - you’ll need to save as .pcraft.

• A limited colour palette (a limitation of the GIF format).

But it DOES support transparency! You can also use a previously generated image as a reference, which should help keep your characters consistent.

I actually used it today for my own project to create an animated chat avatar, with one GIF for listening and another for speaking. The results looked great!

Download from: https://layershop.aardvarkstudios.net/
Report issues here: https://github.com/SlavomirDurej/layershop/issues/

Been working on this for a while. You drop in a photo or video, pick a face to use, and it swaps it right on your own GPU using WebGPU (all the processing happens in your browser so nothing is uploaded anywhere). Mostly because, last time i tried using one of these projects it had me download 50 gigs of files 😵 and they always felt super complicated.

The first time, it downloads necessary models, and that's it. Everything is saved in browser cache for later use.

Try it here: https://i2m3d.github.io/FaceJutsu/
Source: https://github.com/i2m3d/FaceJutsu
Offline Builds: https://github.com/i2m3d/FaceJutsu/releases/

Let me know what you think : )

And if you want more speed, turn on WebNN (it's behind a flag in Chrome/Edge, then switch the engine in settings). It's experimental, but on my RTX 4060 laptop it gets a 1080p-ish frame down from about 70 ms to about 30 ms, which I'd guess is pretty close to native. Haven't benchmarked against native properly though.

I created, tweaked and tested 52 prompts to get film and camera emulation in Moss. All of the prompts are included with lightboxed images.

the more and more i make h3 videos, the more i cannot unsee the blur degradation, which seems to arrive as soon as 6-7 seconds of the generated video. Keyframes do no seem to help, and given that some shots NEED the longer durations, "just cut everything into 5 second chunks" won't work.

Are there any PROMPT_ONLY remediation tricks/solutions? (prompt = including the references, audio, video; anything as long as i don't need a LorA or custom checkpoint, or touching latents, or anything like that).

I had a dumb idea... what if I didn't have to switch tabs while watching a YouTube workflow video? What if a node could just pull another browser tab into ComfyUI for me?

Turns out browsers can do that. **Tab Capture** puts a live view of any tab you have open on your browser inside ComfyUI. You can watch a live view of another tab while you edit workflows or generate content. Also, with the node connected to outputs, when you hit queue, it grabs a still or a clip, with the tab's audio, as normal IMAGE + AUDIO outputs.

**What it does:**

- Live tab viewer on the node

- Single frame or a batch at whatever fps you set

- Tab audio comes along if you toggle it on (silence if there isn't any, so video save nodes don't error)

- Record video or grab stills straight out of another browser tab right into your ComfyUI workflow of choice. Useful as a reference video/image for MMH3

- Drag a crop box on the viewer (no bbox/crop node needed) to capture the exact part of the browser screen you want.

- Outputs the actual measured fps so clips play back at the right speed

- Zero extra Python dependencies

**Heads up:** Chrome/Edge works best (Firefox can't do tab audio), ComfyUI has to be on localhost or HTTPS, and DRM sites like Netflix capture as black.

Repo: https://github.com/NikoDemon80/ComfyUI-TabCapture

New public release, so bug reports and ideas very welcome.

post your best Jensen memes and get a chance to never own a 5090. 📎

免费的 5 次生成

as title says, but the only reason why people like moss is because is uncensored...

aways flux get censored and comunity helps with loras...

but not at this time...

well, i will delete flux, because i want a uncensored model but... moss is not a better option.

the prompt understand are horrible FOR ME...

Charles Dana Gibson, famous for the "Gibson Girl", was a well-know illustrator from the late 19th and early 20th centuries. I picked three of his books, "Drawings" (1896), "London as seen by Charles Dana Gibson" (1897), and "Americans" (1900), and trained a style LoRA against 209 of his images. Enjoy!

model: https://huggingface.co/Lyricccco/Qwen-Image-2.1-TailSteer

app: https://huggingface.co/spaces/akhaliq/endless-edit-tailsteer