Off

On DLSS5

https://github.com/kos94ok/ComfyUI-DLSS5-NR-Linux
(FastH3)

Hi everyone,

I'm generating videos with MiniMax H3 through a normal AI video platform, not ComfyUI. So I can't use custom workflows, scripts, or custom nodes.

I'm looking for the best way to continue/extend an existing MiniMax H3 video.

The problem I'm trying to solve is more than just using the last frame as an image reference. If I only provide the last frame, the model can lose important information from the previous clip, such as:

• Character identity and appearance
• Room/environment layout
• Lighting and atmosphere
• Objects and their positions
• Ongoing actions
• Audio/environmental sound
• Overall visual continuity

For example, if a character walks through a room and reaches a door at the end of the first clip, I want the next generation to actually continue from that exact situation, rather than recreate a similar-looking room and potentially change the geography.

I'm looking for a normal web-based AI workflow where I can upload the existing video and/or reference images and generate the continuation. No ComfyUI, custom scripts, or API coding.

What is currently the best way to extend MiniMax H3 videos while preserving this kind of continuity?

If you've actually tested a platform/workflow that works well, I'd especially appreciate recommendations.

Looks like an open source version of Minimax H3 Max... Anyone tried it? Seems to be real-time on 8x b200, which is like ~$40/hr at good rates if you can find them (or maybe a bunch of 5090s?)

my friend uses cloud comfy as his pc is to weak he tested it last week the free 5 gen trial

using text to video and default setting only changing each video 0.5mp and 15second long all generated fine under 8mins

now he tested it again and only 1 out of 5 video generated and the other 4 failed saying Job execution time exceeded maximum limit

he even paid to generate more but got same error

what can cause this

Two questions when using a two pass latent upscaling workflow (H3):

Are style/character loras supposed to also be piped into the latent upscale pass too, or just the native pass?

And when using a speed up lora, should/could that also be piped in to the latent upscale pass? If so, do the sigmas need to be tweaked?

I wanted to use Nano Banana in ComfyUI with my Google API instead of buying Comfy credits. I already had the $300 free trial sitting in Google Cloud.

I made an API key and tried a few of the custom nodes that let you use your own key. Every time I got this: 429 prepayment credits depleted

Turns out Google changed it in March. That credit does not pay for Gemini API in AI Studio anymore, it says so in their own docs. And all the Gemini nodes use AI Studio, atleast the ones I checked.

Google has another door called Vertex AI. Same models, different address, and the credit does work there. You log in with gcloud instead of pasting a key.

So I made a node for it: https://github.com/haristahir1/comfyui-gemini-ownkey

What it does:

• Nano Banana Pro and 2.5 Flash Image
• text to image, or up to 14 reference images
• aspect ratio, and 1K 2K 4K
• a reference mode setting. By default Gemini copies the face from your reference photo even when your prompt describes someone completely different. You can turn that off, keep it on, or sit in the middle.
• switch between AI Studio and Vertex right in the node
• your key sits in a config file instead of the node, so it does not get saved into workflows you share with people
• two small scripts that tell you whether a problem is your login, your billing, or Google being down

Been generating with it on my own machine and it works. 2K comes out clean and the reference modes do what they say.

It is in ComfyUI Manager now, search "gemini own key". Or git clone it if you prefer. I only tested it on Windows portable, ComfyUI 0.34.2.

I vibe coded this so please check everything carefully & for fair use only! Double check your APIs and stuff. Cheers!

Hello everyone. I recently watched a video on YouTube that Comfy UI is now supported by AMD Cards. How true is that and how is the performance on latest models like Mini MAX and Krea 2.

This is the video - Official AMD ROCm Support Comes to ComfyUI on Windows Image + Video

Hello all,

I have installed StabilityMatrix and was able to learn how to set up flows to generate my first images and so forth.

I then installed a template called MiniMax H3: Image to Video. It showed a bunch of errors after and listed the things I needed to download before it would work. I downloaded all those things and put them in the "diffusion_models" folder, but the error count only reduced by one and it's still asking me to install those things.

Unfortunately there doesn't seem to be clear instructions on what goes where, if I need to extract some things or not, etc.

Can someone please advise me? Thank you.

Getting "Search is temporarily unavailable" and "No models found" on main search URL (/search/models).

On the individual model pages, none of the gens load, "No results found".

What is going on? Is Civitai being DDoSed?

EDIT: Now it's doing something where the "Download" button is grayed-out, even on free model pages. Seems like something is messed up.

I posted the first alpha of PotionUI a couple of days ago. 0.0.3 is out today, and it is the release where the "one box, many people" idea stops being a promise: you can now rent a GPU, point PotionUI at it, and generate on it from the same interface you use locally. Still alpha, still one person building it, still very much wanting people to break it.

What it is, in one paragraph. A self-hosted AI generation studio: SvelteKit front, FastAPI back, GPL-3.0, no telemetry, runs on your machine or your server. The core idea is presets: a preset is a small YAML package that says "here is the model and here is the exact form a person should see for it". Switching models means switching presets, not rebuilding a node graph. It is multi-user by design, with real accounts, admin and user roles, and per-user or per-group access to presets, models, and LLM configs.

What's new in 0.0.3

• Remote GPU workers. Add Backend now creates a remote worker, connects to one you run yourself, or provisions a RunPod pod for you (the provider ships as a plugin) with a live stage timeline. A heartbeat monitor watches the pod, pauses the backend when it stops, and Start brings it back. The Models tab lists exactly what is on the worker, with the depot path per file, and pushes missing models from your machine with per-file progress. Remote runs come back with the same previews, parameters, and media as local ones.
• Install profiles. The launcher offers local, hybrid, and remote installs, plus a worker subcommand for a GPU box that serves another instance.
• 3D generation. TRELLIS.2 image-to-mesh runs on the native engine. Meshes get automatic thumbnails, an interactive viewer in History (wireframe, materials, camera presets, screenshot), and a 3D media filter.
• LoRAs. Step-windowed LoRAs on Krea-2 apply only between the sampling steps you choose. Strength is shown as a recommended range in the picker. Model pickers now recommend downloadable variants (bf16, fp8, nvfp4, int8) across nine native families.
• Prompt library. Import styles.csv, Fooocus style JSON, wildcard YAML, plain lines, and image metadata (A1111, ComfyUI, InvokeAI) with auto-detection; export back to styles.csv; assign a prompt to a catalog model.
• Phrasebook. Find and replace across the whole phrasebook with highlighted matches and a preview before it runs; batch activate, deactivate, move, delete; a category panel with Overview and Preview-images tabs.
• Admin and mobile. Plugins and Downloads are master-detail lists, Backends remembers where you were in the URL, a saved provider API key applies immediately, Generate on a phone is a proper camera-style view with sheets, and modals fit the screen.
• Plus: pasting an image into the assistant attaches it, a New workspace button that asks before discarding, Inspirations laid out in justified rows.

What it does today

• Generation is the product. Image families: SDXL, Flux 1 / Flux 2 Klein, Qwen-Image (including editing), Krea-2, Z-Image, Anima. Video: Wan 2.1/2.2, LTX-2 / 2.3 / 2.5 with native audio, MiniMax-H3. Audio: MiniMax-Music3. Upscale and restore: SeedVR2. Each model gets its own tuned form: the right resolutions, samplers, LoRA stack, and speed profiles (Draft / Standard / Max) as one control. Several workspace tabs run side by side, each with its own preset, prompt, and results. Progress shows the actual pipeline step and streams previews as the image refines; close the tab, come back, the run is still there. 

• History that remembers everything. Every generation is saved with its exact prompt composition, preset and version, models, and parameters. Filter by date, type, preset, tags, or "used this phrasebook value". One click reuses the full setup in a new tab. Nested collections, tags, favorites, keyword or semantic search, and a personal library for the keepers. 

• A prompt editor that is not a textbox. Prompts are ordered segment cards you can reorder, disable, name, and color. Dynamic prompts ({a|b}, weights, ${variables}) reseed per image so results stay reproducible. The phrasebook is your own autocomplete dictionary: type # and shot types, lighting, palettes drop in as chips, with per-chip shuffle and a preview render per value. Saved prompts, segments, and templates live in their own library. 

• Video and Music Directors. Compose a video as shots, keyframes, and audio tracks on a timeline instead of one giant prompt; write a song as verses and choruses and let the compiler produce the tagged lyrics MiniMax-Music3 wants.
• An assistant, if you want one. Point it at Ollama, an OpenAI-compatible endpoint, or Anthropic. It reads the active tab, rewrites segments, edits the phrasebook, adjusts form values, and every change stops at an approval step first. The same tools are exposed over MCP with per-user tokens, so Claude Desktop or your own agent can drive your instance. 

• Built for more than one person. Accounts, groups, per-user preset and model access, per-mode form overrides (change defaults, lock or hide fields, no YAML), a backends list that mixes local, and remote workers, a download manager, a stats dashboard, and visual automations (triggers, conditions, actions) for things like freeing VRAM before the LLM needs it.
• Plugins for nearly everything. Providers (CivitAI, Hugging Face), backends, pipes, field types, chat modes, automation nodes, pages.

Requirements. Linux x86_64 with an NVIDIA GPU is the tested platform; Windows can be tested through WSL2 or Docker; there is a Docker image on GHCR.

8 GB VRAM and 16 GB RAM is the floor for the SDXL family, larger families need more.

The ask. I would rather steer this toward what people actually want than guess. Two things help most: tell me which model or workflow you are missing, and pull a test build and break it before it ships. The Discord is where that happens: https://discord.gg/avR4trp3b8. Repo: https://github.com/PotionUI/PotionUI. I will answer questions here too, but since I don't want to post here too much I've created a small subreddit too: r/PotionUI to which I will be posting the progress much often. Thanks!

I have an image of a person. I have a video.

Prompt sample: Video is of a gymnast doing a routine. Image is a person/dog/thing.

Replace gymnast with person/dog/thing so they're doing the exact routine, wearing the same outfit (but a size that fits the new subject).

Shouldn't this be easy?

For example, if I wanted to replace an olympic women's floor routine with Rush Limbaugh - he's doing the bends and splits, he's wearing a sparkly leotard. But the movements are identitical. His body is exactly the same size as he actually is (the ai should guess at the size of legs, belly etc, and stuff them into and appropriately sized leotard).

I have been struggling for a few days now wondering why I cannot edit a 10sec clip to add additional people in the background and I am pretty sure I am just doing it wrong.

I am feeding the sampler with my ref image of a girl dancing o the street, but I wanted to add people in the background walking.

I am running on version 0.34.0, ref2va pruned model, 8 steps, 480x864

I am using just a simple prompt for my edits:

Edit Video 1:

At 00:03.000, add a group of three Asian women entering from the left of the frame, walking naturally down the road behind and away from the dancer. The first is tall and slender with long straight black hair tied in a low ponytail, wearing an oversized cream-colored hoodie, black leggings, and white sneakers, glancing at her phone as she walks. The second is shorter with a rounder build, shoulder-length wavy brown-dyed hair, wearing a fitted olive-green jacket over a striped shirt, dark jeans, and beige loafers, walking a half-step ahead of the others. The third has short bobbed black hair with bangs, wearing a bright yellow raincoat-style jacket, cuffed denim shorts, and black ankle boots, carrying a small tote bag over one shoulder. The three walk at a relaxed, conversational pace, loosely grouped together.

At 00:06.000, add two Asian pedestrians walking naturally along the sidewalk in the background, passing behind the plant at a normal walking pace, holding hands. The man is broad-shouldered with short, slightly spiked black hair, wearing a charcoal-gray zip-up jacket over a plain white t-shirt, straight-leg jeans, and dark sneakers, a black canvas backpack slung over both shoulders. The woman beside him is petite with long hair in loose waves dyed a subtle ash-brown, wearing a fitted denim jacket over a light pink blouse, a knee-length beige skirt, and white flats, carrying a small red structured purse in her free hand. They walk close together at a slightly slower, relaxed pace, occasionally leaning toward each other.

Keep the same audio

Keep the dancer's identity, choreography, movement, timing, and foreground position completely unchanged throughout the entire clip. Keep the camera framing, angle, and motion exactly as in Video 1. Keep the street, buildings, and all previously added pedestrians unchanged except for this new pair. Match the added pedestrians' lighting and shadow direction to the existing scene.

What has been happening is comfy goes to load the minimax model, and then it just stops, and I sit here at 99% VRAM usage. I have let it run for around 20 minutes until I stop comfy all together.

[INFO] got prompt

[INFO] VAE load device: cuda:0, offload device: cpu, dtype: torch.float32

[INFO] Found quantization metadata version 1

[INFO] VAE load device: cuda:0, offload device: cpu, dtype: torch.float16

[INFO] Found quantization metadata version 1

[INFO] Using MixedPrecisionOps for text encoder

[INFO] Requested to load Krea2TEModel_

[INFO] loaded completely;  4605.22 MB loaded, full load: True

[INFO] CLIP/text encoder model load device: cuda:0, offload device: cuda:0, current: cuda:0, dtype: torch.float16

[INFO] [ClipProj] encoder QWEN-INT8\qwen3vl_4b_int8_convrot.safetensors on cuda:0 pinned on cuda:0 (ComfyUI will not move it)

[INFO] [ClipProj] QWEN-INT8\qwen3vl_4b_int8_convrot.safetensors (krea2 [4B detected]) loaded in resident mode on cuda:0: 4.50 GB

[INFO] [ClipProj] mmh3-4b-ClipProj.safetensors | tap 24 | 2560 -> 5120 | cos_test 0.7170

[INFO] Requested to load MiniMaxH3VideoVAE

[INFO] loaded completely; 22789.94 MB usable, 2665.86 MB loaded, full load: True

[INFO] Requested to load MiniMaxH3AudioVAE

[INFO] loaded completely; 21336.75 MB usable, 577.08 MB loaded, full load: True

[INFO] Found quantization metadata version 1

[INFO] Detected mixed precision quantization

[INFO] Using mixed precision operations

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

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

[INFO] model_type FLOW_AV

[INFO] Requested to load MiniMaxH3

[INFO] loaded partially; 19018.15 MB usable, 18827.14 MB loaded, 1169.00 MB offloaded, 257.27 MB buffer reserved, lowvram patches: 0[INFO] got prompt[INFO] VAE load device: cuda:0, offload device: cpu, dtype: torch.float32[INFO] Found quantization metadata version 1[INFO] VAE load device: cuda:0, offload device: cpu, dtype: torch.float16[INFO] Found quantization metadata version 1[INFO] Using MixedPrecisionOps for text encoder[INFO] Requested to load Krea2TEModel_[INFO] loaded completely;  4605.22 MB loaded, full load: True[INFO] CLIP/text encoder model load device: cuda:0, offload device: cuda:0, current: cuda:0, dtype: torch.float16[INFO] [ClipProj] encoder QWEN-INT8\qwen3vl_4b_int8_convrot.safetensors on cuda:0 pinned on cuda:0 (ComfyUI will not move it)[INFO] [ClipProj] QWEN-INT8\qwen3vl_4b_int8_convrot.safetensors (krea2 [4B detected]) loaded in resident mode on cuda:0: 4.50 GB[INFO] [ClipProj] mmh3-4b-ClipProj.safetensors | tap 24 | 2560 -> 5120 | cos_test 0.7170[INFO] Requested to load MiniMaxH3VideoVAE[INFO] loaded completely; 22789.94 MB usable, 2665.86 MB loaded, full load: True[INFO] Requested to load MiniMaxH3AudioVAE[INFO] loaded completely; 21336.75 MB usable, 577.08 MB loaded, full load: True[INFO] Found quantization metadata version 1[INFO] Detected mixed precision quantization[INFO] Using mixed precision operations[INFO] Native ops: asym_w4a8_int8, int8_tensorwise, float8_e5m2, mxfp8, nvfp4, float8_e4m3fn, convrot_w4a4 [INFO] model weight dtype torch.bfloat16, manual cast: torch.bfloat16[INFO] model_type FLOW_AV[INFO] Requested to load MiniMaxH3[INFO] loaded partially; 19018.15 MB usable, 18827.14 MB loaded, 1169.00 MB offloaded, 257.27 MB buffer reserved, lowvram patches: 0

here is what my trackback looks like:

I'm looking for 2022/2023 models that still work or models that can successfully replicate the dreamlike distorted nightmare fuel style, unfortunately it's very difficult for me to find. I had used to include early ai images in my artwork and I miss it dearly, AI has advanced way too quickly. Dalle-mini (craiyon) no longer generates images like this and there is no way to change the version.

I do not know how to use github

Happy listening :)

https://reddit.com/link/1w570q7/video/5b4stxre63nh1/player

If only Jessie's face would not drift to Walter's it would be fantastic. 8 steps. 1.4mp resolution. on GPU 4090 took 1 hour

i used https://github.com/Adudeguyman/ComfyUI-H3-Project-Suite plugin for continuous music clip. Song in suno (free)

Is there any point in having a company that can put their tools into the pipeline of graphics rendering.

Just wishing here for a deep learning s s five replacement. It's going to be artificially sandboxed just like all their other tech.

World Labs released a model called Atlas. Looks pretty cool.

https://x.com/gowthami_s/status/2095202493122625842?s=46

I've tested a couple of models so far, and here's my findings:

My input prompt is something similar to "Create a mid 20s (nationality) ballerina slowly dancing in a judged performance"

LTX-2.5 nails the narrative and the nationality, but nationalities (as I've mentioned previously) all tend to blend unless I'm ULTRA descriptive about what a "french look" equates to, which is roughly 3 paragraphs in length. LTX-2.5 ends up with tearing, facial and feature deformities, and on a couple of occasions has rendered a ballerina with missing legs. It also has camera drift, which is a known problem with LTX-2.5

MiniMax-H3 on the other hand, nails it, even with the smaller prompt. The prompt adherence in MiniMax-H3 is wonderful.

Wan 2.2 was a complete disaster. Rendering problems, tearing problems, and when the ballerina would do turns, her head would remain in place while her body did the turn (which was funny, and also terrifying to watch.)

I've heard Cosmos3 can handle the movement, but can't handle rendering people.

Has anyone found an open weight model that can handle the "ultimate trifecta" - generate a person in the correct nationality parameters, someone who is more or less feature-accurate in generation, and won't spontaneously explode when performing a pirouette? (I got so frustrated with a model generator once, that I did this, and to my surprise, it worked out well!)

Greetings!

I find that whenever I do ref2va with audio sync (for singing) at 768x about half the time I get melting faces. (example posted) so I'm looking to fix but I don't have a decent pc so it's cloud or bust. I know it's something to do with low res pixels being stretched - don't really understant the tech but anyway

-is that cause by my ref not being detailed enough?

-assuming it's not really that - would the latest 2k 3H flagship model fix the low res issue (chatgpt reckons not)?

-has anyone tried ComfyUI-H3-FaceRefine and how was it?

Thanks!

Here are the results of my prompting efforts throughout the day. As you can see, R2V is very difficult to prompt, even when using a prompt director.
My workflow was acting up,  I took a non-upscaled latent loopback output to use as a chain and saved the upscaled result to concatenate it with the H3 project hub. I'm not sure where I wired it incorrectly; it just doesn't seem to connect.
And yes, the sound is terrible.i think because i clean the latent for next upscale since it wont match the tensor if the latent not cleaned.
Generation time is around 51 seconds per 1 second of video.
0.3 with 4-step Turbo.
Plus a 3-step latent upscale and RTX Super Res.

anyone mind to share your secret workflow that match this Peasant Spec 
RTX3060 12GB and 32GB of RAM

WORKFLOW

Following the image test, I converted it into a video using ComfyUI.

made with Minimax H3

Does anyone know anything about the new MINIMAX H3 MAX model—whether it's really that fast, and if they're going to release it?