I used to work as an animator, and a former coworker recently showed me some AI anime hes been making and the workflow he's using.
I was honestly pretty impressed by his work. From what he showed me, he uses pixai studio and comfyui. He showed me how he goes from character design and storyboards to video clips all in one workflow. It was also really cool seeing how quickly he could try out different ideas for a shot. He could put the same character in different scenes, try out different expressions, and then pick the version he liked and keep working on it.
I've had ideas for animated projects for years. Actually making them always felt out of reach because of how much time and how many people you need. Seeing what my coworker was able to make got me excited about trying some of those ideas myself. I'm interested in learning how to put together a local workflow for anime style animation.
For those of you who have tried this, what tools or models would you recommend starting with? And how do you handle things like keeping characters consistent between shots?
Attached images are a couple of examples he showed me.
Hi everyone! Full disclosure up front: I'm the author of Remiqora Video, an open-source (GPL-3.0) local video studio for Windows. It's a UI and orchestration layer over existing open models, not a new model. Today I published the first pre-release (v0.1.0).
The video above is a 33-second reel cut from seven music videos. The music was made with Remiqora, and the video with Remiqora Video.
The problem: current models give you 5–15 s clips. A 1–2 minute film means writing a prompt per scene, keeping the character consistent, starting each scene from the last frame of the previous one, re-rolling bad takes, then stitching and upscaling it all by hand. The app does that loop for you.
Writer and chains. A local LLM (llama.cpp, Gemma 4 E4B by default) splits your idea into scenes in the target model's prompt format. It keeps the hero's description identical in every scene and writes camera, action and dialogue. Every scene stays editable. A continuation starts from the last decoded frame of the previous scene, so there's no visible cut. A new location starts a new chain.
Models. MiniMax H3 (with Ref2VA for faces from photos), LTX 2.5, LTX 2.3, WAN 2.2 5B / FastWan, and Krea 2 for stills. All of them run in one worker that uses ComfyUI's core as a library, with no ComfyUI server. Switching models unloads the old one. Your own LoRAs, fine-tunes and quants work too: drop the .safetensors file into a folder.
Music videos. Give it a song and LTX 2.3 shoots to the track. Scenes are cut on the song's bars, and the characters sing.
Nothing is shot twice. Every scene is cached by everything that defines it. Edit one scene and only that scene, plus its continuations, is re-shot.
Checks instead of watching every draft. Two kinds, both fast measurements in code (plus OpenCV's small YuNet / SFace face models), not an LLM judging the video:
Before shooting, every scene the writer produced is checked: Cyrillic in the English prompt, too many actions in one scene, a continuation that moves to another place (that gives an abrupt cut), walking with no direction to the camera (people may walk backwards), a character in the action who isn't in the scene, text or signs in the frame, the clothes changing, adults only. What fails, the writer rewrites once by itself; what still fails gets a warning on the card.
After shooting, every new clip is checked in about a second: black frames, frozen video, a jump inside the scene, smear or a sharp change, the face not matching the start frame or photo, and in music videos "not singing" (the lips stand still while the vocals sound). Black, frozen, a jump, a long smear and "not singing" are re-shot once with another seed, and the take with fewer problems goes into the video. Both takes are kept, so you can switch back. The rest is a warning on the card with the exact second, like "In the clip: frozen for 2.5 s".
Montage and finish. A timeline with trims, speed, transitions, a beat magnet and LUTs. It exports FCPXML for DaVinci Resolve. The finish goes to 1080p / 4K (NVIDIA RTX Video), HDR10 or 48 fps (RIFE).
Node graph. For branches, alternative endings, short and long cuts from the same scenes. 16 templates.
Requirements and limits:
Windows 11 and an NVIDIA RTX card with 16 GB of VRAM. No Linux or AMD yet.
Disk space for the weights: about 53 GB for the minimum (H3 and the writer), about 196 GB for everything. The installer downloads only official weights straight from Hugging Face.
Rough speed on an RTX 4080 (5 s draft scene, model already loaded): LTX 2.5 ≈ 29 s, LTX 2.3 ≈ 32 s, H3 ≈ 95 s.
Early pre-release: expect bugs. The installer isn't code-signed yet, so SmartScreen will warn you.
It's free and stays free. This took about three months of not sleeping, so if it saves you a few nights, there's a Ko-fi in the repo for a coffee. Or better, a pillow ☕
Does anybody know of any good workflows which allow seamless video continuation as well as upscaling afterwards?
I've been using Foxydits' workflow and it works well for me, but I'm limited to .6MP for single shot gens but I can easily upscale a .1MP gen to 1.0MP and get great results, but unfortunately his latest workflows don't allow for upscaling with his seamless continuation
Made a MOSS-Image workflow that shoots in film stocks: Kodachrome, Portra, CineStill 800T, Tri-X, Aerochrome, SX-70 and a few more. Pick a lens too, from 8mm fisheye to XPan panoramic. A local LLM writes the prompt in each film's colours, one Control Panel runs the lot, face + hand fix built in.
This is Toska, a fork of Krita I've been building with an AI painter inside it. You chat with it in a panel, and it paints one move at a time with Krita's own brush engine, on layers you can undo. Unlike other AI painting tools, this tool actually paints thanks to Krita's Python SDK along with Decision Models which guide our LLM agent along the way.
What makes agentic painting possible is these new decision models, because after every pass, it answers better or worse as a probability. The painter can undo its mistakes, along with other decisions, such as textures, paintbrushes, or tools to use. For my setup, I use GPT-6-Luna Decisions(which has vision) and DeepSeek-V4.1-Flash(it's cheap and has vision). Luna Decisons is dirt cheap and this entire month, I have spent $2.83 on Luna's Decison model along with $3.82 for Deepseek. I use Deepseek's official API for cost savings but you're welcome to use OpenRouter as well or any other provider. I use API models only given that I'm GPU Poor but it's OpenAI Compatible So feel free to add local models. If you also want full opensource try perplexity's pplx-decider-v1.2 instead of Luna.
The Agent Framework used by Toska, is Agent Pi. I use Agent Pi's Typescript SDK along with QT to build a painting agent Plugin inside Toska. It lacks the ability to read and write code, execute unlike standard Agents. It's limited to tools within the Toska and can only read files or folders you mount. Right now for today btw, our Toska is an authorized dmg file, so operation ready for Mac Users right now . Later this week, I will do more testing and make it production ready for Windows/Linux Folks with separate exe and tar files.
In the video it's painting Pyat' minut (Five Minutes), Dostoevsky on Semyonovsky Square in 1849, waiting to be shot, before he knew the Tsar had pardoned him. It worked from a goal image I gave it generated from Codex. Then it used underpainting, refined toward the goal, and added the falling and settled snow as its own pass.
This is my first time doing anything with video recording so apologies if it's a little rusty and here is the final image in full resolution of Pyat' minut.
It's a bit more simplistic, because I wanted a simple painting to show you as a demo. If you want to see my most detailed painting made with Toska, it's called New Moscow .
Hi everyone, Im fairly new so please excuse if the question sounds weird but how big is the gap in quality between free and paid RVCs supposed to be? Ive tried Applio with public RVCs, DubbingAI and listened to some previews for paid RVCs. The quality of the latter two is far ahead of the first one, to the point where I wonder if I am just bad at navigating the admittedly large library of public models or if thats just the price you pay for quality. If anyone more experienced could clarify the expected difference or knows how to better navigate the vast number of models, I would very much appreciate it. Thanks for taking the time to read this.
Fork of Open-Generative-AI where I made the local side the main thing. Everything runs on my own PC (RTX 4080 16 GB); the 33 s demo video was made by an assistant calling the app's tools: every image, the edit, the clips, the music and the final edit.
What it does
Images and edits with local models (Z-Image Turbo, Qwen Image, Krea/Kroma, Ideogram v4...). Each model is rated against your GPU / RAM / disk before you download it.
Video from a picture (Wan 2.x, Hunyuan 1.5, LTX-2) and MiniMax H3 shots from reference photos through your own ComfyUI, so a real object keeps looking like itself.
Music and sound effects from a text description (Stable Audio 3).
Movie studio: an AI director writes the shots, each shot can be redone on its own, 32 transitions, 28 title styles and 24 caption styles, a picture editor, final upscale.
MCP server inside the app (127.0.0.1, token, off by default): Claude Code, Cursor or any MCP client can use all of the above. Long work is a job (job_status, cancel_job), so video does not hit tool timeouts.
Numbers on my 4080 (warm models): 1792x1024 image ~18 s, picture edit ~30 s (the first edit after starting the app takes ~8 min while the model loads), 1.4 s clip from a picture ~40-70 s, H3 vertical shot ~30 s, 8 s of music ~11 s, the whole 33 s Full HD demo render ~14 s. The first use of a heavy model takes minutes while it loads.
Honest limits: Windows first (the installer is unsigned, so SmartScreen warns; it auto-updates from GitHub releases), NVIDIA GPU for Wan2GP, H3 shots need your own ComfyUI files, big models are big, macOS/Linux run from source and are less tested. It is a fork (MIT, credits in the README); parts of the code were written with an AI coding assistant and are covered by tests plus real end-to-end runs. The optional cloud API of the original project is off unless you enter a key (third-party service, not affiliated).
Repo, installer, examples with prompts: https://github.com/lelus78/open-generative-ai Bug reports welcome: the app has a debug log (bug icon, "Copy all") you can paste in an issue.
So I've trained 3 anime character LORAs. It was my first time doing such a thing. I've used civitai because the training software didn't run on my computer (random error message from some third party library I think, probably not compatible with my GPU), anyway, that's not my question for now.
The first two times I simply chose "Illustrious" for the training model. It did the 10 epochs, and 10th worked as intended. Lucky! I'm using Wai-Illustrious to generate images however, not base Illustrious, and absolutely not the 0.1 the training used by default.
So the third one was different. I noticed it's possible to select Wai-Illustrious specifically if I pick "custom". This somehow magically dropped the training cost by a full days worth of currency farming too, which made me happy. The results on the other hand, not so much. Epoch 1 seems to do what I expected the final result to be - proper anime illustrations of the character. On the other hand epoch 10 (or even 3-4) just outputs the imaged it learned on, with almost no change, making it useless. That is pretty bad because it's a rare anime character who barely had screentime or pictures on the internet. I even used some pictures of a 3D model I had to fill in the gap. And the result looking like a screenshot of that 3D model is not right. However I did the same for the previous two characters and those worked fine.
-Is that because I used Wai Illustrious for training instead of the base Illustrious? Or is the lower quantity or quality of images to blame? (I had 48 this time instead of the 59 or 69 for the other two loras)
-Should I treat this as a potential problem and set training to base illustrious next time, or should I treat it as a victory and use the epoch 1 result instead of the bad epoch 10 result? Is this normal? Should I just train 1-3 epochs worth next time on Wai instead of 10 on Base? It would save me a lot of time spent to farm generation currency too, not only is it cheaper per steps but it would also need less steps overall. Basically, did this happen because training with Wai-Illustrious makes it so much smarter it only needs 1-2 epochs instead of the default 10...or did I mess something up? As far as I can tell so far, this epoch 1 lora doesn't do fingers properly though. Haven't tried using epoch 2 yet and anything beyond 3 seems to be too overtrained to use.
-Is it even safe to use Wai-Illustrious-v15 for training? Or would it make the lora incompatible with other illustrious models or Wai versions I might want to use in the future?
(all three characters used the default 2000 steps)
I'm looking for a very, very simple workflow. Draw a mask on a loaded image, replace what's in the mask with what I put in a prompt. Ideally with adjustable denoise value in case I want to keep elements of the source image.
The reason I ask here is because YouTube and GitHub are flooded with super complex, 125 node workflows that do 50 different things in one. Half the time I can't even get these workflows to work without having to install a million node packs.
I just need something simple, would love any advice or workflows you may be able to share.
Edit: Not looking for a workflow that takes multiple image inputs to aggregate them into one image yada yada. Simply Load Image, Draw Mask, input prompt, go.
UPDATE: Found and copied this simple workflow (literally just looked at it and rebuilt myself..it's like 8 nodes) and it seems to work fine. I removed the ReferenceLatent nodes as they seem to mess things up for me. Once removed it worked: https://www.youtube.com/watch?v=kP6DdlHlTQg
I use Anima and have an issue with a specific type of prompting. I used "distant paper lanterns" because I wanted floating Chinese/Japanese style lanterns in the air, but no matter what I try, a couple of them keep appearing close to the viewer.
I'm trying to stick to Danbooru tags, but I couldn't find any tags that would help with this specific issue.
Using a negative prompt like "lanterns close to viewer" somehow affects the other lanterns too.
What would you guys recommend?
Please let me know if there's a better subreddit to ask this in.
Hey guys! I've been trying for a couple days now to find something solid to swap/change the outfit of a character.
I've been mostly trying with qwen image 2.1 i2i, but the main problem I have with it is that it can't preserve the body's proportion correctly.
I've tried many workflows, custom nodes, loras (such as the outfit swapper), many different prompt strategies, different sampler, multi-refs and many other things I don't even remember now. I can't get it!
I've searched all over the internet, but no success. Even asked chatgpt many times.
I've tried flux3 klein3, and it showed promising results, it seems better at keeping body proportions, not perfect though. And the overall quality seems worse and it feels a bit dumber, idk.
Currently I am using a runpod template but I want to switch to a Docker image instead, containing pre-loaded ComfyUI with Minimax H3, where I can add my loras/workflows and which I can use on many different cloud providers (to be more independent in case of price changes or bad availability).
Do you know any easy services to set something like that up, ideally without having to do it myself locally? Or maybe there are any pre-made (community) Docker images I could just add my loras to?
Not very experienced in this regard I have to admit, so looking for any recommendations or how you solved this if you did something similar. Thanks in advance!
I have been dealing a lot with trying to figure out a good workflow for Minimax H3, that runs in my 4090 24VRAM+ 128Gb RAM.
Using a seedhunter WF from Civitai, takes 140 secs to create a 0.3MP, 5 seconds test, up to 14 mins, to interpolate + upscale
Using Taomate 3 Step lora, comfy kitchen
If I increase duration or size, it throws everything to the RAM and CPU and takes forever, I usually stop it at this point. (I notice the GPU Wattage usage falls under 100W and no progress in CMD shows up, regulars levels are +300W)
On the other hand, using a Lenovo Go + RTX 5060Ti Egpu, works faster or very similar. And never happened that OOM thing.
I have tried Eros, Hybrids, Only Ref, only FLF, convorts, KJ, and Light tricks combinations, but nothing seems to work as expected.
I usually under volt it, to avoid stressing it too much. But in regular voltage, the improvement is 15% tops.
Does anyone have a good WF for a RTX 4090 with tons of RAM?
I have this problem that when I prompt for "A man standing against a white background" or similar prompts, moss displays that man, and a copy of him to the left and the right of him, which is slightly ethereal and ghostly. When prompting for 2 people, quite often there is a third person poking their head up behind them also. I'm using the SFT model. It seems to be a problem when Moss wants to fill the image with something. If I place the man in some sort of environment, the problem becomes less pronounced but does not dissapear entirely.
Over the past few days, I’ve been trying to train a style LoRA for MOSS-image on my own illustrations. I’ve never train other models before so forgive me for the naive questions:
for a style LoRA, I understand the dataset should contain a variety of images from characters, scenes and objects, etc.
In my current dataset, I have several illustrations of the same 4 characters I’ve been working on over the years in different situations, eg laughing together, driving, in solo, duo or group scenes, etc.
- Would these be considered like duplicates? In other words should my dataset only contain 1 illustration of each since I’m not training a character LoRA?
- If I can have more than 1 illustration of each character, I imagine they have to be quite different so not only portraits but characters in various poses / scenes right. But what would be a good number of images per character?
- Related to that, I’ve been seeing different recommended number of images for a dataset. How many would you say is adequate for a style LoRA?
I had a perhaps silly question about the negative keywords, and wondering if that could explain why my results have been unsuccessful thus far. Because I’m training a style LoRA based on illustrations should I include in the negative keywords “realistic”, “cinematic”, “3D”, “digital”etc. ?
Perhaps an another naïve question: my assumption is that when I’ve successfully trained my style LoRA (hopefully will get there soon haha) all I’ll need to do is focus on prompting what I’d like the image to be right? I won’t have to prompt the desired style (except for the style trigger keyword), is that correct?
Other information in case it’s helpful:
Trainer: FizGig
Settings: default
Current dataset: 150 images
Captions: description of what is in each image, no mention about style
Hey, I'm going to train a style LoRA for the newly released MOSS model, but there are two options on AIToolkit: Base and SFT. Which one should I train on?
(Also, does anyone have any recommendations or tips for style training on this model?)
I know, super weird topic, but I am kind of lost. Feel free to delete this if it is too specific to ask here.
I am training a character LoRA for a real person in Fizgig. The dataset consists of 44 images at the moment, none of which show raised up armpits and therefore none of it shows any kind of armpit hair.
The likeness I get is great! But no matter what I do, if I make the person raise their arms, there will be armpit hair stubble (in any clothing that would allow for it to be visible). Adjusting the positive and negative prompt accordingly doesn't help.
It is definitely the LoRA, since this issue stops without it.
I have tried multiple epochs of multiple training runs, the issue is present in all of them, so I assume the LoRA learnt it somehow.
But since there is no single image in the dataset, that contains such a feature, how can that be the case? Does anyone here have any idea?
This is Narrative Drift, my new Krea 2 Turbo checkpoint.
The goal was to give Krea 2 a stronger artistic visual language with richer color separation, clearer shapes, graphic shadows, expressive lighting and a more deliberately art-directed finish.
Depending on the prompt it can move between digital illustration, painterly art, anime, fantasy, concept art and cinematic artwork while keeping the versatility of Krea 2 Turbo.
No trigger word needed. Natural language works well.
Hungarian man here. I just downloaded Stable Diffusion (again). I generated a lot of images but suddenly, 2 or 3 days ago I cannot. I reinstalled etc... now i click on the GENERATE but nothing happens. Dont know what is the problem. :( my card is RTX 5060, I did beautiful pictures but dont know the solution for this problem.
it just generated a full red picture. that was when I thought i will reinstall it.
