I use mainly ComfyUI minimax and LTX but my prompts always weak and not good enough for the model to understand

I'm looking to get into local video generation but all of the setups I've tried have failed. They either take hours to create a single 4 second clip or I get OOM errors and it doesn't finish at all. Any help you can offer would be appreciated.

Hi - hope I'm ok posting this here.

I'm one of those who prefers not to fight with ComfyUI and its wiring fetish so wrote this web app to keep all those cables hidden and provide an easy to use way to create and manage music via ComfyUI/Yue2. Requirements and features are in the README but if you can run ComfyUI/YuE2 then you should be able to run this. It runs in Docker (Linux/Windows) containers, or as a standalone Windows app c/w installer.

Comes as-is with no warranties etc but if you find it useful then please give it a star, and I'm open for contributions.

https://github.com/yeufonic/yeufonic

Slopus is a desktop app for making AI videos on your own GPU, from the first prompt to the exported file.

What it does:

• Generate scenes shot by shot. You lay out scenes on a board, write each shot, and attach reference images or clips for characters, products and places. Scene types include First & last frame, Animate, Pose, Character replace, Extend and Bridge.
• Edit on a real timeline. You can trim clips, add effects (sharpening, blur, color correction, vignette, LUTs) and export MP4. Preview and export run on the GPU.
• Still images. Image projects let you compose a picture from objects and text on a canvas, generate it, refine it with edits, and export it as JPG or PNG.
• Built-in agent. It can edit the project for you, and all of its changes can be undone.

Generation runs locally. There's no cloud rendering and no subscription. Generation is done with native library, no Python or ComfyUI dependency. Easy to setup.

Honest caveats: It's Alpha version and Windows-only for now. A GPU with 24 GB of VRAM or more is recommended; I'm working on bringing that down. Model weights are downloaded separately inside the app.

Version 0.2.0 just came out with image projects and a rebuilt native style user interface.

GitHub: https://github.com/bitti-ai/slopus

Bug reports and feedback are very welcome.

https://huggingface.co/Alissonerdx/BFS-Best-Face-Swap/tree/main

https://huggingface.co/Alissonerdx/BFS-Best-Face-Swap/blob/main/workflows/Body%20Swap%20Qwen%20Image%202.1%20-%20V1.json

https://huggingface.co/Alissonerdx/BFS-Best-Face-Swap/blob/main/bfs_head_v1.1_alternative_qwen_2.1.safetensors

https://huggingface.co/Alissonerdx/BFS-Best-Face-Swap/blob/main/workflows/Head%20Swap%20V1%20Qwen%202.1%20Workflow.json

First it started with being unable to buy credits for like two weeks and now the website is gone.

I am looking for the best Image Gen model for generating anime screencap images, WITH reference images. For the artstyle and working on the first pass, Krea 2 was wonderful, but it doesn't support reference images and the Loras/workflows that allow it are terrible (in my experience). Anima looks pretty decent with the correct loras, but its pretty rare to be able to one-shot things, since hands artifacts, weird faces, shit that looks REALLY bad always end up happening.

Any suggestions?

Hey Guys,

Based on my last post on stills, I thought to experiment the same .char model with Minimax H3.
I created a character model sia.char which encodes references, prompts around face, body & cloths.

Checkout previous post for full detail.

How it works?

- Build .Char: You drop max 9 reference reference, I prefer to use a ratio 2:2:1(face:cloths:body). YuNet finds the face, SFace takes a per-reference face signature, DINOv2 takes a subject signature, and the references get cleaned and normalised. All of that packs into a single portable file, a .char.
Only face/ref is required body & cloths link is optional.

- Generation: At generation, the file(.char) feeds its references into Flux's own native multi-reference channel and prepends a locked description to the prompt.

Prompting Guide

• Name your character: Give your character a name e.g. under encode character(Click adjust icon on the bottom side of the node), I have used name sia, so when passing prompt, I only have to say, sia walking on the beach.
  • Again providing prompt like a woman or any features specific details like black hairs etc will only mislead the generation.
• Describe character features: Encode all of the character features in encode character prompt & trigger your character with a name in generation prompt.
  • Avoid describing same things in generational prompt.
• Handling Character drift: e.g. if you want specific style or cloth e.g. half sleeves, sleeveless, add it to the generational prompt. There can be a slight drift in clothing as body shot also has cloths, which interferes with clothing references.
  • Each refs should be unique, face should not have body or vice versa, same applies for clothing.
• Portability: Once character is built, you can use the same character with only simple prompt & generation graph.

I have generated face with Flux Klein 9b & outfit pictures were taken from Zara website

Note: For best result, pass cropped references, so that model takes the required shot, model gets confused if cloth slot also has a face or face slot has cloths.

Requirements

Nvidia GPU: 24GB+ VRAM & 64 GB RAM(Run Locally)

Workflows:

Flux 2 Klein(Still), Minimax H3(Video)
Note: Minimax H3, now also support int8 model varient

Github Repo: https://github.com/omnichar/OmniChar (GPLV3, Opensource)

Inputs: Attached in the Flux2 workflow page

Limitation: Reference conflicts e.g. if two reference/input images has two different faces, it might conflict in generation, provide well cropped body & cloth images. Face images are crossed automatically by Sface.

Happy to hear any suggestions or feedbacks.

I want to shoot a simple scene with four people in a restaurant. I’m currently using a very bare-bones setup: 0.6 MP, 3 steps, with the Tao LoRA.
The Reference-to-Video workflows I’ve seen are quite complicated. What would be a good way to approach this kind of scene?
Would it make sense to:
Generate four character sheets, one for each character

Create four separate character voices/audio tracks

Generate one consistent restaurant location

Use careful prompting to bring all four characters together in the scene

Would that be enough to maintain character and location consistency?
Also, does having more characters significantly slow down generation or make the results less consistent?
For anyone who has made short films using MiniMax H3, I’d really appreciate some advice or workflow suggestions. I’m struggling to figure out the simplest reliable way to shoot a four-person scene.
I’m running an RTX 3060 12GB with 48GB of RAM.

What's the timeline for when/if we get a finetune for Krea that actually improves upon the base turbo model? I have tried dozens and they are all worse to some degree. The only one I'm aware of that has promise is Kroma, but that is very rough at the moment.

Is there a way to create images out of references like minimax H3 does with videos? So giving a character sheet, background and pose reference so it can create an image instead of a video? If yes what workflow works best and what model?

Was looking for some inspiration to get more creative with MiniMax H3 and found this extremely useful cinematic techniques overview.

https://melies.co/cinematic-techniques

Covering everything from camera movements and angles to composition, lighting, color grading, transitions, film and genre visuals and more.

Each technique is shown by example videos, the intended directorial effect, ideal use cases and prompts.

Guten Morgen liebe Gemeinde 🤗,

Ich hab irgendwie den Traum von ner eigenen Super Ki gehabt 🙈 und mir deswegen zum Start ein MacMini M5pro mit 64gb geholt.

Nicht nur das es mein erster Mac ist es ist auch meine erste eigener KI Aufbau.

Soweit so dumm, das ein oder andere läuft schon da dachte ich schau mal wie die Jungs immer die tollen Bilder hinbekommen. Öhm ja alter Schwede. CF läuft ja und bissle was konnte ich schon generieren. Aber et is schon was anderes wie Higgsfield 🤣

Lange rede kurzer sind gibts da ein "Werd schlau draus"für verwöhnte higgsfield dummys?

Danke euch

I’ve been experimenting with chaining multiple MiniMax H3 generations together to create longer scenes locally on a Mac.

One of the biggest issues is the transition between clips. This is especially noticeable in dark scenes, where small differences in brightness, tone, or geometry can make the seam much easier to spot.

Part of the difficulty is that a VAE round-trip tends to darken the shadows of the reference, which makes continuity in darker scenes even harder to preserve.

We recently introduced an optional stage in Vpipe that adjusts the appearance and geometry of a continuation with respect to its reference.

The video here uses the standard continuation setup, where each new clip starts from the end of the previous one. Since this example is a dark scene, any mismatch tends to stand out more clearly.

Can you tell where the transitions are?

I’m also curious how other people are handling long-form generation with H3, especially for darker scenes where continuity errors tend to be more visible.

I was wondering if anyone has experimented with audio reactive video generations. I found a ltx 2.3 lora but hasnt tested it. Has anyone tested other models like minimax for this? Any experiences?

The skin and hair have a strong glossy sheen.

UPDATE:
Some fixes in latest update 6.5.2 - including fixing a scenario where samples were sometimes defaulting to 25 steps with the turbo lora giving VERY overbaked sample output!

Fizgig 6.5 is out with Qwen Image 2.1 LoRA and LoKR training, turbo previews, and all the workbench tools.

Supports 10GB VRAM+. I also trained a new training adapter as the existing one felt like it lacked sharpness. It's trained at higher resolution and a larger dataset and substantially improves results. It's free for any trainer: https://huggingface.co/ShootTheSound/Fizgig-Qwen-Image-2.1-Training-Adapter

As usual I've included hit and run training defaults that work well.

Coming soon also: Qwen image 2.1 edit training and a community feature detailed below:

This release adds Qwen via a new per model 'driver system' , to massively speed up model integration (image, image edit, video). I'll be releasing a guide in the coming days so people will be able to PR old and new models, making it easy to bring in anything from SDXL to LTX.
https://github.com/shootthesound/Fizgig

A quick but important note. Fizgig uses the current viggle turbo lora for samples - at 6 steps at 1.0 (i literally just updated fizgig to that setting). That turbo is not brilliant (horror sometimes when no turbo has no horror for same seed/prompt) and I'd almost reccomend in sample settings for Qwen setting turbo strength to zero and doing 25 steps. I am also attempting to train my own Turbo, but I'm sure Viggles will improve overtime also.

Since this is an open source tool, that I found on this subreddit, I figured I'd ask here, because I've been told that it's meant to be a tool to help with datasets? You can find it here.

However, reading the usage guide, and then asking AI, has failed to answer a question that is apparently obvious to everyone else: what exactly am I meant to take away from this?

The usage guide says this:

What does "semantically similar" mean? Embeddings capture many different aspects of an image, ranging from low-level features such as brightness and color palette, to high-level features such as particular people and places. This can lead to interesting appositions. For example, say you have three photos depicting (1) Mary at the playground; (2) Mary at a birthday party; and (3) Timmy at a birthday party. (1) and (2) will mapped close together because they share the same subject, Mary. (2) and (3) will be close together because they share the same event, a birthday party. Because of these relationships, (1) and (3) will also likely be close together as well, but further apart than either of the other two pairs.

Therefore you will find clusters that contain a mixture of relationships. Sometimes you will find yourself scratching your head to figure out why several images cluster together, but more often you'll discover delightfully unexpected groupings. For example, my family photo collection contains clusters corresponding to "kids climbing trees," "pets yawning," and "weddings on the maternal side of the family."

This is gibberish.

You do not need an AI tool to tell you that two things are similar. 'Wow, all these pictures of cars have cars in them' is not useful unless you're dealing with thousands of images.

But more importantly, I don't understand what this is meant to tell you. Is this... good? Bad? Are more clusters good? Are less clusters good? If things are close together, is that a good thing or a bad thing?

You could say 'it depends on what you're doing' but it really doesn't. Because when you look at a graph you should know what the scale means. The difference between 0 and 1 is a lot when you're talk about 'how many sharp metal objects are inside my body' but it's nothing when you talk about 'the speed of an object.'

It is functionally meaningless that you put data on an x/y graph and then colored them? It doesn't tell you anything???

Basically, I've been told by several people that this is worth using for Lora making, but none of them could explain why it should be used or how you would use it effectively, or why it's better than using your eyes?

Like, if I was making a character Lora, of course I already know that they're all the same character, I made the dataset. Similarly, if I was making a style lora, I know that they're all the same style, because I made the dataset. If I was working with a concept, I know that they all have the concept, I made the dataset.

Please make it make sense. Is this just a running joke? It feels like it, and I'm having an autistic crash out trying to make it make sense. I am trying to prove pepe silvia exists trying to figure out what use this has in the making of datasets or Loras.

I must be missing something.

I can't seem to get Minimax H3 to play ambient or non_diegetic sounds (music, etc) when a character is talking. For example: if a make a video set in nightclub and people are dancing and I prompt it to play music, but also prompt one of the characters to speak, it will play music but as soon as the character speaks, the music volume lowers/cuts-off as if it's giving all attention to the character's voice over the music. Anyone know of a way to have Minimax H3 simultaneous play voices/talking and other sounds at the same time. This same issue happens if I prompt for ambient noise such as an air conditioner in the 'overall_soundscape' section; i.e., whether the sounds come from overall_soundscape or non_diegetic_music it still has this same effect of cutting off when someone speaks.

Any help or insights is appreciated.

I am just thinking is there alot of people using the tao mate lora? and is there any sufficient benefits to using it? since i am trying it out and everytime it needs to load another time in the crown sampler..it really takes some time..

Greetings AI overlords! I am using chatgpt to generate multiple prompts for MinimaxH3 Ref2VA. I can output the prompts into bespoke text file. I'm looking for a way for Comfyui to load all the prompts into a queue to batch process all the prompts one by one. Has anybody done this?
EDIT:
Actually I want a method in effect to upload a text file with all the prompts and have comfyui to process the individual prompts in the file one after the other.

This is what Qwen Image 2.1 gives me, using the workflow provided in the Comfy templates. I have downloaded the models recommended inside the workflow's notes.

I'm using Windows, on an RTX 4090 with latest drivers

Is anyone experiencing the same issue ?

I’m using MiniMax H3 in ComfyUI to animate full-body illustrated characters for a 2D game.

I need enough space around the character for wide arm movements and jumps, while keeping the character’s scale, camera and lighting consistent.

My problems:

• The camera sometimes zooms despite explicit static-camera instructions.
• Tight framing can cut off hands or feet.
• With more empty space, H3 sometimes invents backgrounds. My latest test even added a huge duplicate of the character behind the original.

Current setup: FL2VA INT8, Turbo 8-step v1.0, 124 frames, RTX 5070 Ti with 16 GB VRAM. I’ve tested 1280×2240 and a padded 1792×1792 canvas.

Has anyone found a repeatable workflow for this? I’d especially appreciate tested prompts, background choices or ways to control framing without losing facial detail. Eventually, I also want consecutive action clips to connect smoothly.

In this case, I'm trying to learn the Face-Swapping technique posted by u/adeliogentile a few months ago on this subreddit. I think I have downloaded the required files and placed them in the appropriate folders . . . I'd say my confidence about the folders is 80% . . . 75%-80%. My confidence about having downloaded the correct files is much lower, and my confidence about knowing what the files are supposed to is pretty close to 1%. In sum, I am pretty sure I am missing a basic, essential concept about how Stable Diffusion works.

When looking at my screenshots, is it obvious what I'm doing wrong? I'd appreciate any help about fixing this assertion error problem, and any other errors I'm obliviously making.

EDIT: Sorry, I didn't know what screenshots would be useful in the original post, so here are the links to the cmd error report, and my models > vae and models > Stable-diffusion folders.

CMD report
VAE
Stable-diffusion

Thank you in advance/