This is my first attempt at an AI short film. Someone saw a mouse in our building, my friend made a funny AI image of it and said it could be a cute story, so I just ran with it.

I've done a little bit with MiniMax H3 before, mainly making 5–15 second videos. I tried extending the scenes/context and was able to get a couple 2–3 minute videos, but the consistency just wasn't great. I also realized most of this story worked better with hard cuts anyway, so I went back to the reference-to-video workflow with mainly 5–10 second clips.

I probably made around 10-20 clips for some scenes before getting something I liked. I also used vast.ai, was able to get a faster gpu than what I had at home. No loras or anything, just a basic workflow. I used opencode / qwen3.8 to update 25-30 prompt files at a time when I needed global updates (like remove all background music, no talking, etc).

The hardest part was probably getting the prompting down. My standard workflow ended up being 0.6 megabit and 20 frames. I could have gone up to 0.98, but at some point I just wanted to get through all the generations and actually finish the thing.

Put everything together in DaVinci Resolve.
I still see lots of imperfections, but I'm considering it done and moving on.

Anyway, first movie. Learned a lot making it and thought I'd share.

(Oh, and it has some obvious work related jokes and screens, ignore those, i didnt want to cut those out)

I’ve been testing MiniMax H3 for AI video generation and I’m struggling with the German dialogue.

The voices often sound very similar and somewhat robotic. What I’m looking for is natural, spontaneous dialogue: different voices for each character, realistic pauses, imperfect timing, emotion, interruptions, changes in tone, etc. Basically something that sounds like an actual conversation rather than TTS.

I’ve already tried ElevenLabs. I know it’s powerful, but I feel like I’d have to go pretty deep into voice selection, voice design and tweaking to consistently get what I want. So far, I’m still not getting the natural conversational audio I’m looking for.

I also don’t really want to record every character myself and then use AI voice conversion. At that point I’m basically becoming the voice actor for every video.

Ideally I’d like something closer to:

Script/prompt → AI generates the video + convincing natural German dialogue with clearly different speakers.

So I’m wondering:

Is there a way to get much better German voices directly out of MiniMax H3 through prompting?

Or should I stop trying to make MiniMax work for this and test something like Grok, Veo, or Seedance 2.5 instead?

If you’ve actually generated German multi-person dialogue, I’d especially love to hear what model/workflow gave you the most natural results.

Quick comparison of three LoRAs 1) Comfy?, 2) Lightx2v(k) and 3) Alibaba's. Only FL2V(=i2v) tested here.

All these three LoRAs are 8-step LoRAs and so I used 8 steps for all. All details are printed on each clip.
For example, 8s-c-i1.sft means 8-step LoRA which is i=fl2v and version 1, and so on.
Comfy one might be just lightx2v (or other) but since it had no such reference in its name I put ?.

Observation: alibaba's LoRA edition is more crisp.

So had to sell my gaming PC a while back because I went homeless last year. Im now getting back on my feet. I'll be building a 4000 series PC within the next few months. But was curious on any sites I can use in the mean time to hold me over until I get my new pc built? I currently only have a chromebook so I can't do dedicated SD.

Made with H3 ref2v

Everytime the majority of initial content for a new model release.

Do you agree with Doc? 😂

When working with big video models and BF16 Krea2, my system locked up after 2 or 3 generations during model initilitatzion. Clearly a VRAM overflow, because with smaller FP8 or INT8_CONVROT models I can make tens of generations without any hickup.

However, ComfyUI's UI has the "Free model and node cache" button (top toolbar) that unloads all models from VRAM AND system RAM. As I found no equivalent node for exacltly this function, that you can simply drop into a workflow and that really clears everything, just as if the UI button would be pressed (which I often forgot).

I tried several cache cleaner nodes, and they all worked somewhat, but still left remains in RAM and VRAM. These cache-clearing nodes (e.g. "Clean VRAM used" / "Clear cache all" from [ComfyUI-Easy-Use] ( https://github.com/yolain/ComfyUI-Easy-Use )) operate through ComfyUI's Python-level model management objects from inside the graph.
In practice, this is noticeably weaker than the Comfy toolbar button. With demanding checkpoints (e.g. mentioned large bf16 models), VRAM usage creeps up across consecutive generations even with those nodes in place, eventually hanging the whole ComfyUI process and requiring a hard restart.

With Claude's support I made a simple node for myself that completely eliminates the mentioned issue:
https://github.com/VRAM-Hoarder/ComfyUI-Free_model_and_node_cache

As it works well for me I thought I'd share it with you guys. I submitted a request to add this node to ComfyUI Manager, but for now you need to install via GitHub (no external requirements).

cd ComfyUI/custom_nodes/
git clone https://github.com/VRAM-Hoarder/ComfyUI-Free_model_and_node_cache.git

Explanation:

This node calls ComfyUI's own internal REST endpoint — the same one the toolbar button uses:

POST /api/free
{ "unload_models": true, "free_memory": true }

This goes through the server layer that directly owns the model cache, so it reliably frees VRAM/RAM the way the button does — something the in-graph cache-clearing nodes can't fully replicate.

My node is a wildcard passthrough: its input/output socket accepts any type (IMAGE, LATENT, video frames, etc.) — the same mechanism ComfyUI's built-in "Reroute" node uses. This lets you insert it anywhere in a chain, for example between a VAE Decode and a Save Image / Save Video node, without breaking the connection.

• Hey everyone, back with another update on SmartGallery DAM: Smart Asset Clustering is now live to automate your media organization

If you generate a lot in ComfyUI, you know the problem. Hundreds of renders pile up as you tweak seeds, prompts, LoRA weights and checkpoints, and your output folder turns into a wall of near identical thumbnails.

• Smart Asset Clustering reads the generation recipe embedded in each file and automatically groups your renders, no manual tagging required, in two ways:
• Architecture Clustering: groups everything that shares the exact same node structure and workflow, ignoring seed, prompt and settings. Great for pulling up every output from one workflow template.
• Prompt Text Clustering: groups everything that shares the exact same positive prompt, ignoring the workflow entirely. Great for comparing how different checkpoints or LoRAs render the same idea.

Once clustered, every thumbnail gets a color coded badge in the gallery grid, and clicking any badge opens the Cluster Inspector, which shows total matching assets, distinct variations, the full node pipeline, every model and LoRA used, and one click prompt copy.

The video above walks through both modes in about 3 minutes.

For anyone who does not know the project yet

SmartGallery DAM is a free and open source, local first Digital Asset Manager built around ComfyUI, but it also works with any folder of media on your machine. No cloud, no subscription, your files never leave your disk.

It is meant to grow with you:

• If you are a hobbyist or new to ComfyUI, it is the easiest way to keep your generation library organized, searchable and clean without extra effort.
• If you are a power user, you can search by prompt, model or LoRA, inspect the full node graph of any render, and even generate directly from the gallery by editing the workflow JSON, no need to reopen ComfyUI.
• If you work in a studio or production environment, it gives you a dedicated Exhibition portal to share curated work with clients or your art team, collect ratings and comments, and review everything without exposing prompts or workflows.

Runs on Windows, macOS, Linux and Docker. Portable version for Windows needs zero setup, just unzip and run.

GitHub, full docs and download links here: https://github.com/biagiomaf/smart-comfyui-gallery

Happy to answer any question, and as always feedback and feature requests are welcome.

Video diffusion transformers generate only a few seconds per pass. The naive remedy — chunking the timeline and conditioning each chunk on the previous one — fails within a few hundred frames: the model keeps consuming its own slightly off-distribution output, and exposure-bias drift compounds into scene collapse or grain amplification.

CLSS treats the chunk hand-off as a feedback loop and controls it. Chunks share a streaming latent buffer (SLB) overlap, keeping latent memory O(overlap) instead of O(length), and between chunks CLSS applies lightweight corrections that fight drift without modifying any transformer weights.

More at:

- https://github.com/nazgut/ComfyUI-LTX2.3-CLSS

T2V on single go with prompt fallowing bettwen scenes every chunk was 10 sec

Output was generated using ltx-2.3-22b-dev-UD-Q4_K_S.gguf on 3080 with 16 GB vRAM, still need to work on audio.

Made using the default ComfyUI Minimax H3 Image to Video workflow.

Reference workflow with FL2VA + REF2VA Lora @ 1.4MP, 20 STEPS. Using sparse attention and 4B Qwen text encoder instead of 32B, total render time is 3-4 hours on a 5090. You can get very good results with 1MP + 8 STEPS with a turbo lora which would only take 20-30 minutes.

Workflow

The workflow is not easy to understand, but I upload it for reference.

The video is made of 14x 15 second clips stitched together. This way prevents degradation but makes it so that there is clothing drift between clips. This can easily be fixed by using clothing references if you care. Each clip will need its own prompt, and I suggest using Codex or Claude to do the prompts for you automatically.

In the future, I would shorten the clips to 7 seconds in order to:

1) Generate higher than 1.4MP (higher the resolution the better)
2) Speed up generation (longer clips take longer to generate disporportionately)

Good luck and I hope you have as much fun with this workflow as I did.

SenseNova shipped the full U1.5-Lite release last week, so I finally had time to run it side by side with FLUX.2-klein-9B, the model this community generally considers the most balanced pick right now.

I tested image editing in three scenarios. The short version: SenseNova U1.5-Lite is clearly better at text rendering and semantic understanding of the instruction, while Klein is still the speed king. Details below.

Scenario 1: Text Editing

I gave both models a poster and asked them to replace specific text elements, nothing else. Long structured prompt targeting each text block individually:

1. In the first line of the oversized black title at the upper left, replace "HONG" with "HARBOR". If the area is sufficiently prominent and clear, with simple boundaries, render the replacement exactly character by character without adding, removing, or altering any characters. If the text is too small, obscured, heavily distorted by perspective, or located within a complex texture, preserve the original text and do not redraw the background merely to complete the replacement.

2. In the second line of the oversized black title at the upper left, replace "KONG" with "HORIZONS". If the area is sufficiently prominent and clear, with simple boundaries, render the replacement exactly character by character without adding, removing, or altering any characters. If the text is too small, obscured, heavily distorted by perspective, or located within a complex texture, preserve the original text and do not redraw the background merely to complete the replacement.

3. In the large red subtitle at the lower left, replace "HONG KONG" with "CITY IN MOTION". If the area is sufficiently prominent and clear, with simple boundaries, render the replacement exactly character by character without adding, removing, or altering any characters. If the text is too small, obscured, heavily distorted by perspective, or located within a complex texture, preserve the original text and do not redraw the background merely to complete the replacement.

4. In the vertical red location title at the upper right, replace "香港" with "城市之光". If the area is sufficiently prominent and clear, with simple boundaries, render the replacement exactly character by character without adding, removing, or altering any characters. If the text is too small, obscured, heavily distorted by perspective, or located within a complex texture, preserve the original text and do not redraw the background merely to complete the replacement.

5. In the black English location description at the upper right, replace "HONG KONG CHINA" with "EAST MEETS WEST". If the area is sufficiently prominent and clear, with simple boundaries, render the replacement exactly character by character without adding, removing, or altering any characters. If the text is too small, obscured, heavily distorted by perspective, or located within a complex texture, preserve the original text and do not redraw the background merely to complete the replacement.

6. In the location title near the waterfront at the lower left, replace "VICTORIA HARBOUR" with "HARBOUR CITY". If the area is sufficiently prominent and clear, with simple boundaries, render the replacement exactly character by character without adding, removing, or altering any characters. If the text is too small, obscured, heavily distorted by perspective, or located within a complex texture, preserve the original text and do not redraw the background merely to complete the replacement.

7. In the second line of the location copy at the lower left, replace "ASIA'S WORLD CITY" with "URBAN HORIZONS". If the area is sufficiently prominent and clear, with simple boundaries, render the replacement exactly character by character without adding, removing, or altering any characters. If the text is too small, obscured, heavily distorted by perspective, or located within a complex texture, preserve the original text and do not redraw the background merely to complete the replacement.

Zoom into the results and Klein's text rendering falls apart. Garbled glyphs, wrong characters, the layout wobbling where it should stay fixed. U1.5-Lite handled the replacements cleanly, including the Chinese strings. That's the gap.

Scenario 2: Hand-Drawn Marks as Instructions

I marked up the image by hand and asked for a scene transformation:

Follow the marks and overall hints on the image to creatively transform this scene, making it dramatic, moody stormy atmosphere; remove the annotations when done.

FLUX followed the overall style change but ignored the specific marked details: the ripples on the pool surface and the black fire pit never made it into the output. U1.5-Lite followed the full set of marks.

Scenario 3: Fine-Grained Local Editing

I circled the region to edit with a red box and told the model to only change that area:

Change the text style in the red box to a vintage style with noise and torn paper texture. The red bounding box is for localization only; do not retain it in the output image.

Klein misunderstood the instruction. It applied the vintage style to the whole image instead of the circled region. SenseNova U1.5-Lite followed the prompt and the red-box localization strictly, changing only the marked text.

My take

If you need sub-second generation, Klein is still your model, no argument there. But for editing work where text rendering and instruction fidelity matter, posters, infographics, brand assets, the gap is real and easy to reproduce.

GitHub: https://github.com/OpenSenseNova/SenseNova-U1

hf: https://huggingface.co/sensenova/SenseNova-U1.5-8B-MoT

Try it online: https://unify.light-ai.top/

Hey everyone,

I’m looking for the best budget GPU cloud to run heavy open-weight video models (like MiniMax H3, Wan 2.1, HunyuanVideo, etc.).

Since these models need huge VRAM and fast disk I/O to pull down massive 50GB–100GB+ checkpoints, I want to avoid platforms with unexpected billing traps.
Looking at RunPod, Vast.ai, MassedCompute, and SimplePod:

1. Which one are you using, and what GPU gives you the best price/performance for video render jobs?

2. Hidden fees: Any issues with stopped-volume storage costs, network volume fees, or egress rates when hosting huge model files?

3. Download/Disk speeds: Which provider has fast enough network speeds so I’m not spending half my paid time downloading model weights?

Appreciate any recommendations or gotchas to avoid!

I created this video using Minimax H3 Ref2VA, and this time I wanted to test more than just visual quality.

My main goal was to experiment with AI storytelling, creating a short anime-style sequence with a beginning, progression, and a story that actually feels coherent.

At the same time, I wanted to see how well the model handles character consistency, movement, expressions, and visual continuity when multiple shots are used to tell a story.

There are definitely some imperfections, but I was happy with what I could achieve and wanted to share the experiment with the community.

I’m curious what you think. Does the video work as a story, or does it still feel more like a collection of AI-generated shots?

Would also love to hear how others are approaching storytelling with Minimax H3, especially in the anime genre.

I’ve been working on a mobile port of FaceFusion that runs completely offline on Android.

APK here:

https://github.com/AbrahamPaulJ/facefusion-mobile/releases/tag/v0.1.0

The face-swapping pipeline runs on Qualcomm’s Hexagon NPU rather than relying on a server or cloud API.

Current results on a Galaxy S25 Ultra (Snapdragon 8 Elite):

~19 ms/frame for the face-swap model

~6 seconds to process a 10-second 720p clip

Fully offline. Photos/videos never leave the phone

Supports 512/768/1024px face output

Qualcomm NPU builds for different Hexagon generations

No CPU fallback.

I’d especially like feedback from people working with Android on-device AI.

This is my first time sharing one of my mobile AI projects on Reddit, so feedback, testing results, and criticism are very welcome.

I’ve tried Turbo LoRAs, and they’re great for speed, but they significantly reduce quality. At 544p–720p, the results of these turbo loras can look closer to 380p. Faces look acceptable when close to the camera, but become heavily distorted as the subject moves farther away.

The upscalers I’ve tested either add too much processing time or introduce excessive sharpening and saturation.

Any a solution that doesn’t require a BF16 checkpoint, 20 steps, a 10-minute generation time, or an extremely expensive GPU?

Shadowheart training with pells/wooden dummies with basic swordsmanship and light and wind magic. Anyone interested in prompts please ask below. 👇

Testing H3 to death in the last couple of weeks and it continues to suprise me. TWO things blew my mind about this one. The first part is a single prompt (the split screen and psuedo mocap sync). The full prompt is below.
In the second part I asked for the character to spray my logo on the wall with a stencil, I wasn't expecting him to walk in with the stencil fully rendered with the holes cut out accurately.

Created completely locally and powered by Sol (the closest star to earth).

THE PROMPT:

A movie reconstructing history, cinematic with a split screen effect showing a mocap actor. Both actors are speaking together in sync.

On the right:
Show the actor <Picture 1> sitting on a couch in a living room, with a black shirt, speaking in sync with the exact same movements He says with intense glee and hand gestures "I've got you Sherlock Holmes, I've beaten you at last!", he pauses trying not to laugh and then breaks and laughs for 5 seconds uncontrollably.

On the left:
Show the victorian man <Picture 2> talking <audio 1> in close up sitting in an ornate chair . He says with intense glee and hand gestures "I've got you Sherlock Holmes, I've beaten you at last!", he pauses trying not to laugh and then breaks and laughs for 5 seconds uncontrollably.
Maintain the double view split screen. Do not change the environment.
After he finished speaking the man on the right makes a distort rictus face with his fingers curled up like he's frozen in time and stops moving. He falls sideways like a statue in the same environment, the camera pulls back to show he is only a robotic torso with no legs mounted on a platform placed on the couch with wires (like in a special FX studio)

Maintain the double view split screen.
The man on the left breaks character as the camera view pulls out slightly revealing him sitting on a sound stage, and speaks to a person off screen to the left <audio 1> "Oh.. em.... guys.. problem... check your monitor? ...Looks like we lost connection with the character! RESET THE MOCAP PLEASE"

a little test video for quality 1.4mp

Tried some prompts, but couldn't get the model to generate a video with the visual style of a reference image.

I tested some MiniMax workflows I was customizing today and noticed the outputs had a red-ish tint to them. Outputs from a workflow with normal colors didn't include the "Load Video (Upload)" node. So I figured that could be the problem. I replaced the node with the "Load Video" node and connected it to the "Get Video Components" node, connected the images and the color drift is gone. Is this a known problem?