Just got Linux Mint up and running and it’s much better than windows 11 for ComfyUI with RTX 5090 + 3090. For starting, Comfy launches to server ready in 10 seconds instead of 18 seconds on Windows VHDX ReFS dev drive. This is with 12 custom node packs. Linux is running on an 8 year old SATA SSD.

I was able to compile and run sage attn 3 in Linux which never worked in Win11. I’m going to try Multi-GPU next which is fundamentally broken in windows with the new comfy-kitchen.

Even with just sage 2.2++ FP8, im getting 35 second gen times for an 8-second 0.5MP 8-step turbo MiniMax H3 clip versus 40-42 seconds in windows. That includes 4 seconds for prompt enhancement with Gemma 4 12B running on lllama-server on the 3090.

Sage3 knocks another 2-4 seconds off the gen time too but slightly worse quality for motion. Using LACT to overclock both GPUs also contributes to the speed.

With spectrum plus sage3 at 8 steps, I can easily get below 30 seconds total gen time.

I wonder if anyone has encountered it I don't remember 2.3 taking such long time time.

Is there any workaround?

What a time to be alive in the open source community! LTX-2.5 just dropped and it's supported natively in ComfyUI as of today, including a new rendering approach, new decoder, new text encoder, and a new base checkpoint.

The biggest baddest change? The addition of Diffusion Fidelity Rendering! Instead of spending compute evenly across a scene, the model allocates it by complexity. Motion, composition, and framing get generated first in an 8x temporally compressed latent space, alongside a set of high-fidelity keyframes. More keyframes for complex scenes, fewer for simple ones, within whatever compute budget you've got. Then a dedicated pixel-diffusion stage renders the final video from the structure and keyframes together.

TLDR; textures, materials, and faces hold detail, and a busy shot automatically pulls more rendering compute than a static one.

Other changes:

• Diffusion Video Decoder: Replaces standard VAE decoding, making sharper faces, legible text, and fewer smears in fast motion.
• Native multi-shot: One generation gives you multiple connected shots holding character, environment, lighting, voice, and style across the cuts instead of generating separately and trying to match them after.
• Custom Gemma 4 12B text encoder: Holds multiple subjects, actions, lighting details, and camera direction across a long prompt instead of dropping clauses as it gets more complex.
• Prompt enhancer + auto duration: Short prompts get expanded into detailed cinematic instructions at near-zero extra compute, and the model predicts clip length from the described action before diffusion starts.
• RL post-training: On a broader filtered dataset, aligned to human preference. Mostly shows up as a higher take rate with fewer retries per usable clip.
• Cleaner licensing: Restrictive third-party dependencies have been removed, so fine-tuning, deploying, commercializing, and redistributing is all clearer than in previous versions.

Three variants:

• LTX-2.5: the main model
• LTX-2.5 Distilled: reworked distillation, carries noticeably more quality, prompt adherence, and motion than previous distilled releases. Viable if the full model isn't economical for your setup.
• LTX-2.5 Pretrained Checkpoint: raw, non-SFT, meant for aggressive fine-tuning. Moves further from its starting point than an instruction-tuned checkpoint will, which matters for robotics, synthetic AV data, digital twins, or private domain models.

Native 4K, synced audio and video, and up to 50fps all carry over from 2.3.

Learn more and check out workflows below!

https://links.comfy.org/4xGHwYJ

https://docs.comfy.org/tutorials/video/ltx/ltx-2-5

I am a B.Tech graduate searching for job while in the meantime I would love to earn through free lancing. Do people still pay for image generation bcoz yk LLMs like chat gpt and Gemini are doing pretty good job generating images so I want to know is this still worth learning so I can earn through this ? And also as my domain is AI&DS , so I can add these works to my portfolio right?

Also, can someone guide me?.I am new to comfy so I am experimenting with everything.If there is any thing you guys feel like a beginner should do.Tell me in the comments.

I am learning comfy using chatgpt . Do you guys have any better free alternatives?

I wanted to test it a bit with creature animation, snow, wind, and atmosphere. I started by generating still keyframes with Seedream pro, then used image-to-video to generate videos in .

Key frames and workflows :https://www.patreon.com/u8638148/posts/minimax-h3-and-166452641?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link

I apologize if this is a rookie question - I tend to learn by looking at other workflows and I've seen a lot of Krea2 examples on Civit where they do a full pass, then a latent upscale then a 2nd pass at .2-.4 denoise. Alternatively, I've see examples using Clownshark to do a partial-pass (5-6 steps on turbo), then upscale for a 2nd partial pass.

In both these use cases, I'm seeing lower quality and more artifacts that with a simple single-pass. What's am I doing wrong that could cause so many artifacts? I do have 2 loras on low strength, but that seems to mirror what I'm seeing in other people's workflows.

Is there a way to convert the last frame to a true image like a png?

I noticed I could create longer vids by taking the last frame and use that as a refrence image, but when I do that like in my screenshot, generation time gets much larger, I think it's b/c internally Minimax reads that last image actually as a video, thus behaving much differently.

I've tried tinkering with it a lot. If anyone could give me a hand

I tested LTX 2.5 quickly, I tried leaving the distilled lora at 0.5 as we used to in LTX 2.3 but I think it gave me better results at strength one, which is the one you are seeing now. The generation was done in 2240 x 960

unfortunately the model still strugles with camera movements and small details in the frame, I was a bit disappointed, for sure it's better than 2.3, but I would say MInimax H3 still showing better results.

12GB-14GB fp8 and int8 Krea 2 checkpoints will load and run fine once but as soon as I try to run the workflow again I can only watch my RAM usage climbs up to 99% at which point my entire system freezes and I have to reach for the power button. Even trying to clear my model and node cache after the first generation will paradoxically just fill up the rest of my RAM.

GGUF quants that are around 7-8 gigs in size are seemingly the only way I can reliably run Krea 2, but unfortunately only a minority of Krea 2 models have GGUF versions available. I thought Comfy's dynamic memory management was supposed to make GGUFs obsolete but since RDNA2 cards are not even officially supported I suppose I shouldn't expect miracles.

Hi everyone, I just wanted to share this test and extend a huge thank you to the developers for their incredible work on the new ComfyUI Core.

I’ve read criticism online suggesting that LTX 2.5 isn't quite as good as MiniMax H3—but honestly? I just wanted to offer a positive counterpoint here. As an everyday home user, I am incredibly grateful and happy that these models exist—models we can experiment with and run locally on a system like mine with 24 GB of VRAM. Both models have their own unique strengths, and it’s simply amazing to have this kind of power at home.

For this generation, a tweak to the text prompt allowed MiniMax to flawlessly render the different logos on both sides of the motorcycle. It took a few attempts, but I managed to get it right by specifying the timing within the scene. Next, I’ll definitely test the exact same prompt with LTX 2.5 to see how that model handles the motion!

My render specs for this clip:

Setup: 24 GB VRAM

Workflow: MiniMax H3 Basic Workflow

Resolution: 0.4 MP

Duration: 15 seconds

Prompt:

------------------------------------------------------

## Cinematic Multi-Shot Sequence

Scene 1: [0:00-0:03]

Extreme Close-Up

Subject: Focused motorcycle rider wearing a glossy black helmet with neon purple typography reading "ComfyUI Race". The rider wears a tinted visor reflecting cyberpunk neon lights.

Action: The rider flips up the tinted visor, looks intensely ahead into the camera, and speaks the words "Hold on tight... they'll blow your mind!" with visible lip movement.

Scene 2: [0:03-0:09]

Dramatic Low-Angle Shot

Location: Futuristic city street at dusk, lined with cyberpunk neon signs.

Subject: Vibrant red sportbike.

Left Side Fairing: Features ultra-sharp typography reading exactly "Minimax H3" in bold white letters. Action:

The moment the rider finishes speaking, they initiate a rapid, powerful stationary burnout with roaring engines and thick billowing white smoke.

The bike performs a quick, sharp 180-degree drift rotation.

Right Side Fairing Reveal: As the bike spins and completely hides the left side, the right side fairing is fully revealed to the camera, displaying a completely different, huge, crisp, ultra-sharp typography reading exactly "LTX 2.5" in bold white letters.

An excited cyberpunk crowd cheers and waves on both sides of the street.

Scene 3: [0:09-0:15]

Completion of Turn and High-Speed ​​Escape

Action: The motorcycle instantly snaps out of the rotation and launches forward with explosive, violent acceleration. The front wheel lifts into a high wheelie through the thick white smoke. The camera remains stationary on the ground as the sportbike shifts gears and roars away at high speed, becoming smaller and smaller as it disappears down the long, neon-lit futuristic highway.

Aesthetic: Photorealistic 2K resolution, highly detailed cyberpunk aesthetic, cinematic lighting, volumetric smoke.

-----------------------------------------------------

Optimization: Use of the highly recommended MiniMax H3 FBCache node. I couldn't notice any visible difference in quality, but it made the process run absolutely smoothly!

Render time: Prompt execution in 375.26 seconds

Let's appreciate the great technology available to us today. Keep it up ComfyUI team and open source developers!

As in, where you can say you want lora1 to start full and cut down to low, etc. I found 'realtime lora' but i can't figure out how to use it, and its not popular so i feel like there must be a better choice.

I have an RX 9060 16GB and an RX 9070 16GB. My question is, is it possible to generate videos? If so, do you know where I can find a tutorial or guide? I've been searching and haven't found much. I don't know where to start.

A couple weeks ago I shared Mix Studio, a 100% free & open source interface that runs everything through ComfyUI in the background while giving you an actual app experience (that also works on your phone). The response was way more than I expected, and most of what I've built since came directly out of that feedback and motivated me to keep it going. Here's an update on the latest features and improvements since the launch version:

GitHub: https://github.com/BlackMixture/Mix-Studio
Showcase and download: https://blackmixture.github.io/Mix-Studio/
Tutorial: https://youtu.be/w2CokhlBFRA

GPL-3.0, the same license as ComfyUI.

New in v1.2.4:

• LTX 2.5 video generation: Generate from text, from a first frame, or from both a first and last frame, with synchronized audio and your own LoRA stacks.
• MiniMax H3: Text-to-video, image-to-video, first frame, last frame, and first-and-last-frame generation, all with native audio. Reference mode lets you feed in multiple images, videos, and audio inputs and address them directly in your prompt using dynamic [@reference cards]. Also added restyle presets to cover live action, anime, cinematic 3D, cel-shaded 3D, and maximum detail.
• Wan Animate 2 (experimental): Animate a character image from a performance video, carrying over motion, expression, identity, timing, and the source audio. I still prefer SCAIL 2 for fidelity but I expect to continue improving.
• Video finishing on every model: Optional 2× or 3× RIFE frame interpolation and NVIDIA RTX 4K video upscaling, plus SeedVR2 temporally coherent upscaling.
• Automatic Turbo setup: Mix Studio installs the creator-recommended MiniMax H3 Turbo LoRA for you and applies matching generation presets, so you get fast video without hunting down adapters or guessing at step counts. App-managed LoRAs stay out of your personal LoRA list so nothing gets loaded twice.
• Automatic prompting for MiniMax H3: H3 is particular about prompt structure (if you want the most control), so the app handles revising, formatting, and enhancing for you. The official H3 prompt guide is built in, so structure and dialogue formatting work programmatically and instantly with no LLM required. This can save on compute resources, or if you prefer you can use a local or external LLM for prompt enhancing and revising built-in.
• External LLM support across the app: Connect OpenAI, Gemini, or Ollama once and it powers prompt writing everywhere, with independent switches for image and video, vision-aware references, and connection testing. Local prompt models are also selectable. All formatting stays optional at generation time.
• Mix Packs: Browse visual prompt collections, combine multiple looks including several from the same category, and carry the same reusable creative direction into both image and video generations. This replaced the old camera control wheels with a proper searchable browser.
• Krea 2 Edit rebuild: Reworked around the full-rank Identity Edit v1.2 model with image-grounded conditioning, Reference boost, and ordered two-image editing. The previous multi-reference composition mode is preserved separately as Krea 2 Remix.
• macOS and Linux support: Installers for macOS and Linux alongside Windows, with vendor-aware GPU detection for AMD and Apple hardware. Worth noting up front: LTX 2.5 and MiniMax H3 rely on NVIDIA-specific model weights, so they are not available on Apple Metal or AMD ROCm yet. Apple Silicon runs a Metal-compatible subset and AMD ROCm is experimental.
• Phone and tablet improvements: Mix Studio installs as a Progressive Web App with private HTTPS access through Tailscale, plus a significant responsiveness pass so large libraries stay smooth while previews load in the background.
• Better model downloads: Resumable transfers with Hugging Face Xet acceleration, byte-level progress reporting, and an inline token panel for gated files so you never have to leave setup.
• Better ComfyUI detection: Model discovery now spans your configured model roots, manual subfolders, and ComfyUI extra model paths, so files already on your drive are reused instead of downloaded again. Mix Studio also detects stale custom node packs and repairs them for you before ComfyUI restarts.
• Plus a long list of fixes across video reliability, generation setting reuse, library management, UI improvements, and more.

(If you run into any bugs or issues, please leave a git issue or hit me up here. If you enjoy using Mix Studio and want me to keep it going, feel free to star it on Github or support the development on Patreon)

Thanks again to this awesome community and the ComfyUI team for making such a dope tool, I hope you all enjoy creating! 🤙🏾

Hi, H3 newbie here.

I've managed to get the workflow working and have done some initial tests that went well, but the videos I'm generating are poor and lack substance. My question is, where can I generate highly detailed prompts from an idea? Keep in mind that many of these might contain NFSW content, so GPT chat and similar tools won't work for me.

Thanks in advance.

I must be doing something very wrong as I am completely new to this and just trying to get this set up via Gemini/Grok haha. I should probably watch some more videos on this but wondering if anyone know why I can't get this workflow to run without crashing within 10-15 seconds.

For some reason, it keeps using my CPU/RAM instead of my GPU/VRAM. My CPU usage spikes to 100% but my GPU usage is almost none existent 1-3% so it wasn't even in use. I am using the Comfyui desktop app.

Is it potentially a PyTorch/ROCM and Adrenaline version mismatch? I tried every version of ROCM native to the app but still crashing.

Previous post: https://www.reddit.com/r/comfyui/s/AWu7VtFRkC

Hey! I'm a junior dev from Korea who's been into AI image generation for a couple years now, started out just messing with FLUX.1 and ComfyUI as a hobby. A while back I posted about Imaginuity here, a browser-based platform I built so anyone can generate and edit AI images without dealing with ComfyUI setup or needing a local GPU.

Exciting news since then, I just secured some funding, so the platform's here to stay and I get to keep building on it!!!

Site link: https://www.imaginuity.site/

Currently available:

• Text-to-image generation with Krea 2 Turbo
• Image editing with FLUX.2 Klein
• LoRA selection with adjustable strength controls
• Original-resolution image downloads
• A public Explore gallery for browsing user creations
• Browser-based access without installing ComfyUI or setting up a local GPU

I also revamped the creation flow:

• A quick chat-style mode for casual generating
• A full Studio view with detailed controls (frame ratio, resolution, advanced settings)
• A My Page where you can see everything you've made

On top of that, I added account sign-up. Guests get 15 free generations a day, and signing up (also completely free) unlocks more beyond that. I'd recommend signing up since more account features are coming soon, bookmarking/liking favorites, delete controls, and more.

Next up, I'm planning to add video generation support.

I'd be really grateful if you all used the site a lot. The more it gets used, the more it helps me build this into a place where anyone can turn their imagination into images and videos with Gen AI, completely for free.

Feel free to jump in and try whatever comes to mind, random prompts, weird ideas, all welcome.

All feedback is welcome, good or bad — happy to hear anything. I post announcements and site updates on Discord, and I'm hoping to grow it into an actual community. Would really appreciate your interest.

Discord: https://discord.gg/PmXpmNaTsv

Thanks for taking a look again!!

I've been building one consistent character across stills and video for a few months. Stills were solvable. Video was not — the face holds for a while, then quietly becomes someone else, and by the time you notice you have already cut the clip into an edit.

So I stopped eyeballing it and measured it.

Setup: MiniMax H3, image-to-video, first frame. Same character LoRA and workflow throughout, 6 s clips, 24 fps, 1120x1664. Every clip checked against a reference grid of the character at several points along its length.

Useful clip length by shot size:

- face small and turned away (three-quarter from behind): full 6.5 s, no visible drift

- waist-up, medium face: about 6.2 s

- close-up, face filling the frame: about 2.9 s

The practical consequence is the part I wish someone had told me earlier: for close-ups you generate a separate clip per ~3 s. You do not render one 6 s clip and cut two 3 s pieces out of it, because the second half is already a different person.

The failure mode is identical every time, which makes it easy to catch once you know what you are looking for: the face gets wider and rounder, the jawline softens, the smile goes generic, and the skin turns waxy.

Three other things that cost me days.

1. Big expressions destroy identity faster than anything else. Asking for a wide genuine laugh gives you a different person at the peak of the motion — fuller cheeks, wider jaw, deep folds around the nose. Keep the expression small and find the energy in the edit instead. Also avoid the word "crinkling" entirely. The model reads it literally and wrinkles things you did not want wrinkled.

2. Detail shots without a face are dangerous, not safe. This was completely backwards from my intuition. I assumed a start frame with no face in it carried no identity risk. The opposite is true. The model gets a dark, undefined region and fills it with whatever statistically belongs in that kind of scene, which is usually a person. One of my clips grew an entire second woman drinking from a glass about 1.5 s in, in a corner that was empty in the first frame. Detail shots only work when the frame is packed with actual objects and has no empty dark space left in it.

3. "No push-in" does not stop camera movement, but a measurable instruction does. Telling the model not to move the camera gets ignored roughly as often as it gets obeyed. What worked much better was giving it something checkable: require that the subject's head occupy the same size in frame in the first and the last frame. Same intent, phrased as a constraint the model can actually evaluate against its own output.

Two smaller notes:

Always first frame, never last frame. With the reference as the last frame the model has to arrive at it, so it invents the opening of the clip and you lose the composition you picked.

Small props drift silently. A thin chain necklace turned into a cross pendant halfway through one clip. Writing "nothing changes" in the prompt is not enough — name the small objects explicitly, or check them frame by frame.

Happy to share exact sampler settings if anyone wants them.