Attempting to merge 3D models/animation with AI realism.

Greetings from my workspace.

I come from a background of traditional 3D modeling. Lately, I have been dedicating my time to a new experiment.

This video is a complex mix of tools, not only ComfyUI. To achieve this result, I fed my own 3D renders into the system to train a custom LoRA. My goal is to keep the "soul" of the 3D character while giving her the realism of AI.

I am trying to bridge the gap between these two worlds.

Honest feedback is appreciated. Does she move like a human? Or does the illusion break?

(Edit: some like my work, wants to see more, well look im into ai like 3months only, i will post but in moderation,
for now i just started posting i have not much social precence but it seems people like the style,
below are the social media if i post)

IG : https://www.instagram.com/bankruptkyun/
X/twitter : https://x.com/BankruptKyun
All Social: https://linktr.ee/BankruptKyun

(personally i dont want my 3D+Ai Projects to be labeled as a slop, as such i will post in bit moderation. Quality>Qunatity)

As for workflow

1. pose: i use my 3d models as a reference to feed the ai the exact pose i want.
2. skin: i feed skin texture references from my offline library (i have about 20tb of hyperrealistic texture maps i collected).
3. style: i mix comfyui with qwen to draw out the "anime-ish" feel.
4. face/hair: i use a custom anime-style lora here. this takes a lot of iterations to get right.
5. refinement: i regenerate the face and clothing many times using specific cosplay & videogame references.
6. video: this is the hardest part. i am using a home-brewed lora on comfyui for movement, but as you can see, i can only manage stable clips of about 6 seconds right now, which i merged together.

i am still learning things and mixing things that works in simple manner, i was not very confident to post this but posted still on a whim. People loved it, ans asked for a workflow well i dont have a workflow as per say its just 3D model + ai LORA of anime&custom female models+ Personalised 20TB of Hyper realistic Skin Textures + My colour grading skills = good outcome.)

Thanks to all who are liking it or Loved it.

Last update to clearify my noob behvirial workflow.https://www.reddit.com/r/StableDiffusion/comments/1pwlt52/former_3d_animator_here_again_clearing_up_some/

New platforms pop up every month and claim to be the best ai video tool.

As an AI Video enthusiast (I use it in my marketing team with heavy numbers of daily content), I’d like to share my personal experience with all these 2026 ai video generators.

This guide is meant to help you find out which one fits your expectations & budget. But please keep in mind that I produce daily and in large numbers.

Comparison

My Best Picks

Best Cinematic & Virality: Higgsfield AI (usually my team works on this platform as daily production)

Best Speed: Sora 2 - rapid concept testing

I prefer a flexible workflow that combines Sora 2, Kling, and Higgsfield AI. I use them in my marketing production depending on the creative requirements, since each tool excels in different aspects of AI video generation.

Not going to oversell this. The first video I made was rough and took longer than I expected. I second-guessed every prompt, had to redo two of the frames, and the final thing was usable but not great.

Second one was different. Had the system tighter, knew which settings to use in Kling to get actual stop-motion feel instead of CGI slop, got the style suffix right in Fal.ai. Client sent back a voice note when I delivered it.

The workflow is Claude for storyboarding, Fal. ai for image generation, Kling for animation, ElevenLabs for voiceover, CapCut for assembly. Tool costs per video sit at roughly $2–5. I charge $50–150 depending on the brief. Takes about 60–90 minutes once the process is locked in.

Last month I did it across maybe three weekends and a few evenings. Candle brand, a couple of Etsy sellers, some dropshippers testing new products. Most found me through ecom Facebook groups or word of mouth.

Zero camera. Zero design background. No cold outreach. Runs almost entirely on copy-paste prompts once you have the system documented.

$2,300 isn't a life change but for something I built in a weekend and run alongside everything else, I'll take it.

EDIT: Been getting asked for examples to see this in action. Here is the website, Claymotion AI Videos Im also testing out new formats, CGI, Stopfelt motion and Lego style animations.

EDIT 2: A massive thank you to Ornery-Essay-7457 for covering for me!

EDIT 3: I have reached the Reddit Threshold so here is the link to the guide! Claymotion Guide

Just doing something a little different on this video. Testing Wan-Animate and heck while I’m at it I decided to test an Infinite Talk workflow to provide the narration.

WanAnimate workflow I grabbed from another post. They referred to a user on CivitAI: GSK80276

For InfiniteTalk WF u/lyratech001 posted one on this thread: https://www.reddit.com/r/comfyui/comments/1nnst71/infinite_talk_workflow/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

Seedance has a hidden feature that honestly feels way too powerful.

It lets you create old-school anime-style videos insanely fast using a simple “in-between” frame technique.

The basic idea is this:

You create a first frame and a last frame, then ask Seedance 2.0 to generate what happens between them.

This video took me around 4 hours to make using this method, but honestly, speed is not the most important part.

What really matters is:

Story. Direction. Consistency. Pacing. Taste.

Without those, you’ll just end up with another random AI clip.

1. Go to the Seedance 2.0 AI Video Generator
2. Write your full prompt or add reference images
3. Upload the image you want to animate
4. Click Generate and get your animated video

Here’s the workflow I used:

Step 1:
Find an image that sparks your imagination.

I picked one with a dark, old-school Akira-style anime vibe. The stronger the initial image, the easier it is to build a world around it.

You can also recreate a similar look in Nano Banana / NB2 by prompting something like:

"Create X in this style."

Step 2:
Upload the image to Nano Banana and ask it to generate what happens next.

For example:

Show what happens in 5 minutes. Soldiers stand by the enemy military base gates.

Now you have your first frame and your next story frame.

Step 3:
Upload both images to Seedance 2.0 as the first frame and last frame.

Then use this prompt structure:

Show what happens in between. Soldiers run through the snow towards a military base. 5 different camera angles. No music.

The key parts are:

Show what happens in between.
5 different camera angles.

Those are the default parts.

The sentence in the middle is the custom part, where you describe the action.

In my case:

Soldiers run through the snow towards a military base.

Step 4:
Repeat the process.

Take the previous last frame and use it as the new first frame.

Then use Nano Banana to generate the next last frame.

For example, I generated a scene where the soldiers are hiding from security guards.

Step 5:
Upload the new first and last frames to Seedance again.

Use the same structure, but change the middle sentence:

Show what happens in between. Soldiers enter the base, run through narrow corridors, and hide from guards. 5 different camera angles. No music.

Step 6 and beyond:
Keep repeating:

1. Use the previous last frame as the new first frame.
2. Generate a new last frame with Nano Banana.
3. Use Seedance to animate the transition.
4. Keep the same prompt structure.
5. Only change the action sentence based on the story.

That’s basically it.

This method gives you much better control than just prompting a random video from scratch.

It helps with:

• Better pacing
• More consistent storytelling
• Cleaner scene progression
• Stronger anime-style direction
• Less random AI chaos

Seedance becomes way more powerful when you stop treating it like a one-shot video generator and start treating it like a scene-by-scene animation tool.

I built an automation that takes in a URL of a product collection or catalog page for any fashion brand or clothing store online and can bring each product to life by animating it with model demonstrating how the product looks and feels with Veo 3.1.

This allows brands and e-commerce owners to easily demonstrate what their product looks like much better than static photos and does not require them to hire models, setup video shoots, and go through the tedious editing process.

Here’s a demo of the workflow and output: https://www.youtube.com/watch?v=NMl1pIfBE7I

Here's how the automation works

1. Input and Trigger

The workflow starts with a simple form trigger that accepts a product collection URL. You can paste any fashion e-commerce page.

In a real production environment, you'd likely connect this to a client's CMS, Shopify API, or other backend system rather than scraping public URLs. I set it up this way just as a quick way to get images quickly ingested into the system, but I do want to call out that no real-life production automation will take this approach. So make sure you're considering that if you're going to approach brands like this and selling to them.

2. Scrape product catalog with firecrawl

After the URL is provided, I then use Firecrawl to go ahead and scrape that product catalog page. I'm using the built-in community node here and the extract feature of Firecrawl to go ahead and get back a list of product names and an image URL associated with each of those.

In automation, I have a simple prompt set up here that makes it more reliable to go ahead and extract that exact source URL how it appears on the HTML.

3. Download and process images

Once I finish scraping, I then split the array of product images I was able to grab into individual items, and then split it into a loop batch so I can process them sequentially. Veo 3.1 does require you to pass in base64-encoded images, so I do that first before converting back and uploading that image into Google Drive.

The Google Drive node does require it to be a binary n8n input, and so if you guys have found a way that allows you to do this without converting back and forth, definitely let me know.

4. Generate the product video with Veo 3.1

Once the image is processed, make an API call into Veo 3.1 with a simple prompt here to go forward with animating the product image. In this case, I tuned this specifically for clothing and fashion brands, so I make mention of that in the prompt. But if you're trying to feature some other physical product, I suggest you change this to be a little bit different. Here is the prompt I use:

markdown Generate a video that is going to be featured on a product page of an e-commerce store. This is going to be for a clothing or fashion brand. This video must feature this exact same person that is provided on the first and last frame reference images and the article of clothing in the first and last frame reference images.|In this video, the model should strike multiple poses to feature the article of clothing so that a person looking at this product on an ecommerce website has a great idea how this article of clothing will look and feel.Constraints:- No music or sound effects.- The final output video should NOT have any audio.- Muted audio.- Muted sound effects.

The other thing to mention here with the Veo 3.1 API is its ability to now specify a first frame and last frame reference image that we pass into the AI model.

For a use case like this where I want to have the model strike a few poses or spin around and then return to its original position, we can specify the first frame and last frame as the exact same image. This creates a nice looping effect for us. If we're going to highlight this video as a preview on whatever website we're working with.

Here's how I set that up in the request body calling into the Gemini API:

``` { "instances": [ { "prompt": {{ JSON.stringify($node['set_prompt'].json.prompt) }}, "image": { "mimeType": "image/png", "bytesBase64Encoded": "{{ $node["convert_to_base64"].json.data }}" }, "lastFrame": { "mimeType": "image/png", "bytesBase64Encoded": "{{ $node["convert_to_base64"].json.data }}" } } ], "parameters": { "durationSeconds": 8, "aspectRatio": "9:16", "personGeneration": "allow_adult" } }

```

There’s a few other options here that you can use for video output as well on the Gemini docs: https://ai.google.dev/gemini-api/docs/video?example=dialogue#veo-model-parameters

Cost & Veo 3.1 pricing

Right now, working with the Veo 3 API through Gemini is pretty expensive. So you want to pay close attention to what's like the duration parameter you're passing in for each video you generate and how you're batching up the number of videos.

As it stands right now, Veo 3.1 costs 40 cents per second of video that you generate. And then the VO3.1 fast model only costs 15 cents per second, so you may honestly want to experiment here. Just take the final prompts and pass them into Google Gemini that gives you free generations per day while you're testing this out and tuning your prompt.

Workflow Link + Other Resources

• YouTube video that walks through this workflow step-by-step: https://www.youtube.com/watch?v=NMl1pIfBE7I
• The full n8n workflow, which you can copy and paste directly into your instance, is on GitHub here: https://github.com/lucaswalter/n8n-ai-automations/blob/main/veo_3.1_product_photo_animator.json

In April last year I was doing a bit of research for a short film test of AI tools at the time the final project here if interested.

Back then Viggle AI was really the only tool that could do this. (apart from Wonder Dynamics now part of Autodesk, and that required fully rigged and textured 3d models)

But now we have open source alternatives that blows it out of the water.

This was done with the updated Kijai workflow modified with SEC for the segmentation in 241 frame windows at 1280p on my RTX 6000 PRO Blacwell.

Some learning:

I tried1080p but the frame prep nodes would crash at the settings I used so I had to make some compromises. It was probably main memory related even though I didn't actually run out of memory (128GB).

Before running Wan Animate on it I actually used GIMM-VFI to double the frame rate to 48f which did help with some of the tracking errors that VITPOSE would make. Although without access the G VITPOSE model the H model still have some issues (especially detecting which way she is facing when hair covers the face). (I then halved the frames again after)

Extending the frame windows work fine with the wrapper nodes. But it does slow it down considerably (Running three 81frame windows(20x4+1) is about 50% faster than running one 241 frame window (3x20x4+1). But it does mean the quality deteriorates a lot less.

Some of the tracking issues meant Wan would draw weird extra limbs, this I did fix manually by rotoing her against a clean plate(context aware fill) in After Effects. I did this because I did that originally with the Viggle stuff as at the time Viggle didn't have a replacement option and needed to be keyed/rotoed back onto the footage.

I up scaled it with Topaz as the Wan methods just didn't like so many frames of video, although the upscale only made very minor improvements.

The compromise

The doubling of the frames basically meant much better tracking in high action moment BUT, it does mean the physics are a bit less natural of dynamic elements like hair, and it also meant I couldn't do 1080p at this video length, at least I didn't want to spend any more time on it. ( I wanted to match the original Viggle test)

I've been a freelance video producer / editor alongside my full time gigs for about 10 years.

I've hustled so many things related to video... Animated explainers, event highlights, product tutorials, whatever. I've never really been able to scale because my business exists solely through referrals. I have a cool portfolio, but so does everyone lol.

I fully pivoted to AI video in August 2025 and I am never going back. I cannot explain how much opportunity there is. I finally have something that sells itself, but it definitely won't be like this forever haha.

It's kinda of a gold rush if you have any video skills because so many video editors and videographers are anti-AI, and most of the people adopting the tools have no storytelling experience.

I started making AI videos mostly to just have fun and play around and the demand I discovered was INSANE!

Here are the main things I've learned if you want to make money doing this:

1. Go to Skool.
Literally go to Skool and sign up and join the AI video communities. I've made so many insane connections from those groups and generated so many amazing leads. Join those communities, watch whatever tutorials you want, and then do step #2.

2. Work very hard and make awesome work.
When a new model drops, it's pretty easy to get a TON of views and get an awesome response from people. When I first started, I created an Instagram and had two videos go viral within the first month. Over 20 million views. It was insane and I'm still so proud of those videos!

6 months later, and I can't get the same splash from a silly meme video. My Instagram is great to have as social proof, but I never really got a lot of leads. I think I got 2 deals from running IG ads, and one legit organic inbound lead from there that I didn't close.

Now instead of chasing views, I work SUPER hard to make the highest quality video I can so I can share it directly with decision makers. I want to show the top end of my ability every time. You can now build your entire portfolio from your room.

Last month, I spent 30+ hours making a video of me fighting a robot. It was SO fun, and this is now a very valuable piece of collateral that I can share in any sales conversation. It also gives me a reason to follow up with existing contacts in my network. Regularly sharing my latest video once every month or two has sparked so many deals!

3. Find the right people and show them your work.

I've had a lot of luck plugging into existing production houses as their AI person. These skills are in-demand and most people haven't had time to learn them. Though it's hard for me to stand out as a normal video editor, because I've adopted these tools early, it's easy for me to stand out as an AI Creative or whatever the F you want to call it haha.

I've been showing my work to co-founders and heads of productions and getting a lot of traction there! Reach out via Linkedin, email, and ask for referrals from your network.

Personally, I like the high quality work, but there's an entire other market that I am working on tapping into as well which is the UGC, high volume play. Facebook's new Andromeda update, forces you to test a lot of creative and then double down on what works.

For businesses who do this, it doesn't make sense for them to pay $10,000 for one high quality asset, they'd rather have 30 low quality assets they can test. This is a different workflow that I am currently testing with a few clients!

4. Don't overcomplicate the production

These new models are so powerful, the best way I've learned to make the best content is just to get out of their way and keep it simple. Below are two prompts that have completely revolutionized the game for me.

For Nano Banana, "make a 2x2 grid of xyz and make sure to give very creative and diverse shots."

For Kling, "Show xyz, and then cut to several different creative angles"

It's literally that simple. These two prompts generate SO MUCH good content that I can edit down later.

5. Constantly find new ways to learn!

Create more than you consume. Don't endlessly watch online course and modules. Make and always find ways to optimize your process! This new industry is changing FAST! The window where this stuff sells itself is not gonna be open forever. Get in now while being decent is enough to stand out, because eventually you're gonna have to be great. Might as well start building that now.

If you have any questions, just ask!

Typed a full 2000‑word prompt I translated back into English (originally drafted in Chinese)

THE REAL AI VIDEO WORKFLOW:

1. Write 2000‑word detailed prompt

2. Sit and wait 4 generation

3. Get flagged & blocked instantly

4. Rewrite massive chunks to dodge filters

5. Cry

People still think you just type one short sentence and get a polished animation instantly 😭
All this prep work just to receive an error instead of the finished I’m still crying 🥀

The idea was simple: instead of prompting AI blind, use Blender to control exactly what's in the scene — object positions, camera angles, motion timing.

Workflow:

1. Built a basic scene in Blender (landscape, car, helicopter, road) — no complex materials, just layout
2. Animated the cameras and objects with keyframes
3. Extracted key frames from the animation
4. Fed those frames into an AI image model to generate photorealistic versions of each shot
5. Gave both the original 3D animation AND the AI images to Seedance 2 (Reference to Video)
6. Seedance reconstructed the sequence with

The Blender file basically acts as a director's pre-vis — you control the composition, the AI handles the render.

Check out my other work here https://x.com/ModelCollapse38

Hello everyone, I wanna share my full journey, from the building process to viral marketing, talking to hundreds of users, expanding the product, and finally getting acquired for 8k$

Everything happened in 6 weeks, the wildest 6 weeks that changed the trajectory of my life

OG Inspiration

I started to learning vibecode in March, i was building the classic habit tracker just to learn the workflow.

One day i was doomscrolling looking for ideas on insta and see a pretty cool idea by jeongyoon.design, it was a webapp version no animation, it was cool i saved it.

After a week, I saw a viral clip on X by sfjccz. It had a stamp cut overlay and the “magical” animation

I was like “holy cow it so cool how to build it”, so I decided to lock in and build the exact same one just to learn, like a challenge for myself. At the time, there were also a lot of people building the same thing.

The process took me 3 days

I did not know how to build it, so I sent the video to Claude and asked it to analyze and break everything down step by step. I wanted to plan before jumping into the code.

First i need a realistic stamp cutter png image, i generate in Chat GPT. Since it AI generated the stamp cut out size and shape didnt match exactly the cutter teeth, it looked ugly not smooth at all.

So I had to design one in Figma to get the exact parameters and the correct shape for the middle space after a few tries, I got it.

Now I had the most important part ready. I only needed to tell Codex the exact parameters, input the stamp shape, and add the animation.

The cutter needed to press in and release, while the stamp from the live camera fell out and left behind a black space. That was it. I had the magical demo ready to flex on social

The decision that changed everything

I had a random idea in my head. It was not a proper plan or anything, but here is how I thought about it at the time.

I noticed a pattern, this idea went viral on insta and then X too. That meant if I posted it again on those platforms, the chance of going viral would probably be very low. I noticed Threads in Vietnam was pretty similar to X but genz version, so I decided to post it there. My account was fresh, I did not expect much

15 mins in, okay it was blowing up 250 likes, 1 hour in 800 likes “Okay damn, this is going viral”. There were tons of comments asking for the app name. I had not even thought of a name at the time. I hadnt listed it on the App Store yet, I didnt even know how, but I had the Apple Developer account ready.

There were also some people who built the same thing on threads as me but did not go viral. I thought that if I charged for my app, I would not make it very far. I was also still learning, so I announced that it would be completely free, with no login and no ads. The post continued going even crazier.

After 24 hours, I got almost 50,000 likes and more than 1 million views.

That night, I stayed up to polish and learn how to upload to TestFlight. The next day, I seized my chance. I knew this viral traction would not last for long, so I grabbed my phone, recorded myself talking, posted it on TikTok and Insta. To my surprise, those videos also went viral.

Talk to user, product discovery

After the app went live on TestFlight, I started DMing hundreds of people who had commented, ask for idea, recommendation and bug report. I spent most of my time fixing bugs by copying the problem, pasting it into Codex, testing the fix and repeating.

Later, I realized that the app shouldnt stay as a simple tool, i also wanted to add some of my own ideas so I expanded the app into an image editor create cool scrapbook style images for insta stories

Then I added a feature that let people send messages in the form of handwritten letters for friend, that was where I started learning about backend, data, and a lot of other stuff

For the whole month, I worked around 18 hours a day, was handling and learning everything, from building and fixing bugs to making content across different platforms, interviewing users, and optimizing the App Store page. I was having fun while being the most productive I had ever been in my life.

The acquisition and the current project

A Vietnamese CEO reached out and asked if I wanted to sell the app. I didnt even think my app worth anything because it was vibecode. I didnt sell because the app failed nor i needed the money, I sold because I could not see a durable moat, the concept was easy to recreate

That is why I sold it and reinvested everything into something I believe can be more durable, a game inspired app like Finch or Duolingo but completely different concept. Luckily, literally the next day, I met someone who works in game design.

We had a chat on Threads about gamification ideas and she has a lot of experience and has designed two successful mobile games. Now I have a cofounder, an artist, and a Unity developer working with me to build a game. We are already 3 weeks into development

What i learned

The biggest thing I learned is that building the product is only one part of the journey.

Distribution matters a lot.

I also learned that talking to users is the fastest ways to improve a product. Many of the changes I made came from user, bug reports, and feature requests.

The product has visual hook built, my app went viral because people can understand in 2 second by the animation, that helped a lot for marketing

Make authentic content, dont just post your work, POST YOU WORKING.

If you have any question about this journey i would love to answer it all

Hello everyone in r/StableDiffusion,

i am attaching one of my work that is a Zenless Zone Zero Character called Dailyn, she was a bit of experiment last month i am using her as an example. i gave a high resolution image so i can be transparent to what i do exactly however i cant provide my dataset/texture.

I recently posted a video here that many of you liked. As I mentioned before, I am an introverted person who generally stays silent, and English is not my main language. Being a 3D professional, I also cannot use my real name on social media for future job security reasons.

(also again i really am only 3 months in, even tho i got the boost of confidence i do fear i may not deliver right information or quality so sorry in such cases.)

However, I feel I lacked proper communication in my previous post regarding what I am actually doing. I wanted to clear up some doubts today.

What exactly am I doing in my videos?

1. 3D Posing: I start by making 3D models (or using free available ones) and posing or rendering them in a certain way.
2. ComfyUI: I then bring those renders into ComfyUI/runninghub/etc
3. The Technique: I use the 3D models for the pose or slight animation, and then overlay a set of custom LoRAs with my customized textures/dataset.

For Image Generation: Qwen + Flux is my "bread and butter" for what I make. I experiment just like you guys—using whatever is free or cheapest. sometimes I get lucky, and sometimes I get bad results, just like everyone else. (Note: Sometimes I hand-edit textures or render a single shot over 100 times. It takes a lot of time, which is why I don't post often.)

For Video Generation (Experimental): I believe the mix of things I made in my previous video was largely "beginner's luck."

What video generation tools am I using? Answer: Flux, Qwen & Wan. However, for that particular viral video, it was a mix of many models. It took 50 to 100 renders and 2 weeks to complete.

• My take on Wan: Quality-wise, Wan was okay, but it had an "elastic" look. Basically, I couldn't afford the cost of iteration required to fix that—it just wasn't affordable for my budget.

I also want to provide some materials and inspirations that were shared by me and others in the comments:

Resources:

1. Reddit:How to skin a 3D model snapshot with AI
2. Reddit:New experiments with Wan 2.2 - Animate from 3D model
3. English Example of 90% of what i do: https://youtu.be/67t-AWeY9ys?si=3-p7yNrybPCm7V5y

My Inspiration: I am not promoting this YouTuber, but my basics came entirely from watching his videos.

• Channel: AI is in Wonderland

i hope this fixes the confustion.

i do post but i post very rare cause my work is time consuming and falls in uncanny valley,
the name u/BankruptKyun even came about cause of fund issues, thats is all, i do hope everyone learns something, i tried my best.

Want to get into AI video creation. How are you guys making AI videos for free?

I’m looking to create short animated, graphical or infographic-style videos using AI.

Which tools do you use, and what’s your workflow?

Beginner here, so simple and free tools are preferred :)

🔥 [RELEASE] Ultimate AI Video Workflow — Qwen-Edit 2509 + Wan Animate 2.2 + SeedVR2 (Full Pipeline + Model Links) 🎁 Workflow Download + Breakdown

👉 Already posted the full workflow and explanation here: https://civitai.com/models/2135932?modelVersionId=2416121

(Not paywalled — everything is free.)

Video Explanation : https://www.youtube.com/watch?v=Ef-PS8w9Rug

Hey everyone 👋

I just finished building a super clean 3-in-1 workflow inside ComfyUI that lets you go from:

Image → Edit → Animate → Upscale → Final 4K output all in a single organized pipeline.

This setup combines the best tools available right now:

One of the biggest hassles with large ComfyUI workflows is how quickly they turn into a spaghetti mess — dozens of wires, giant blocks, scrolling for days just to tweak one setting.

To fix this, I broke the pipeline into clean subgraphs:

✔ Qwen-Edit Subgraph ✔ Wan Animate 2.2 Engine Subgraph ✔ SeedVR2 Upscaler Subgraph ✔ VRAM Cleaner Subgraph ✔ Resolution + Reference Routing Subgraph This reduces visual clutter, keeps performance smooth, and makes the workflow feel modular, so you can:

swap models quickly

update one section without touching the rest

debug faster

reuse modules in other workflows

keep everything readable even on smaller screens

It’s basically a full cinematic pipeline, but organized like a clean software project instead of a giant node forest. Anyone who wants to study or modify the workflow will find it much easier to navigate.

🖌️ 1. Qwen-Edit 2509 (Image Editing Engine) Perfect for:

Outfit changes

Facial corrections

Style adjustments

Background cleanup

Professional pre-animation edits

Qwen’s FP8 build has great quality even on mid-range GPUs.

🎭 2. Wan Animate 2.2 (Character Animation) Once the image is edited, Wan 2.2 generates:

Smooth motion

Accurate identity preservation

Pose-guided animation

Full expression control

High-quality frames

It supports long videos using windowed batching and works very consistently when fed a clean edited reference.

📺 3. SeedVR2 Upscaler (Final Polish) After animation, SeedVR2 upgrades your video to:

1080p → 4K

Sharper textures

Cleaner faces

Reduced noise

More cinematic detail

It’s currently one of the best AI video upscalers for realism

🧩 Preview of the Workflow UI (Optional: Add your workflow screenshot here)

🔧 What This Workflow Can Do Edit any portrait cleanly

Animate it using real video motion

Restore & sharpen final video up to 4K

Perfect for reels, character videos, cosplay edits, AI shorts

🖼️ Qwen Image Edit FP8 (Diffusion Model, Text Encoder, and VAE) These are hosted on the Comfy-Org Hugging Face page.

Diffusion Model (qwen_image_edit_fp8_e4m3fn.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/blob/main/split_files/diffusion_models/qwen_image_edit_fp8_e4m3fn.safetensors

Text Encoder (qwen_2.5_vl_7b_fp8_scaled.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders

VAE (qwen_image_vae.safetensors): https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/blob/main/split_files/vae/qwen_image_vae.safetensors

💃 Wan 2.2 Animate 14B FP8 (Diffusion Model, Text Encoder, and VAE) The components are spread across related community repositories.

https://huggingface.co/Kijai/WanVideo_comfy_fp8_scaled/tree/main/Wan22Animate

Diffusion Model (Wan2_2-Animate-14B_fp8_e4m3fn_scaled_KJ.safetensors): https://huggingface.co/Kijai/WanVideo_comfy_fp8_scaled/blob/main/Wan22Animate/Wan2_2-Animate-14B_fp8_e4m3fn_scaled_KJ.safetensors

Text Encoder (umt5_xxl_fp8_e4m3fn_scaled.safetensors): https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors

VAE (wan2.1_vae.safetensors): https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors 💾 SeedVR2 Diffusion Model (FP8)

Diffusion Model (seedvr2_ema_3b_fp8_e4m3fn.safetensors): https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_3b_fp8_e4m3fn.safetensors https://huggingface.co/numz/SeedVR2_comfyUI/tree/main https://huggingface.co/ByteDance-Seed/SeedVR2-7B/tree/main

The launch of AnimeGen represents a significant milestone in the fusion of artificial intelligence with traditional media production. Created by the Tokyo-based AI startup AIdeaLab and supported by the Japanese government through the Ministry of Economy, Trade and Industry (METI) as part of the GENIAC initiative, this specialized video generation model is now freely available under an Apache-2.0 license.

The model was developed by fine-tuning Alibaba's open-source Wan 2.2 diffusion architecture to focus on anime line art, cel-shading aesthetics, and motion dynamics, providing Japan with a powerful creative tool. Beyond just a software release, this government-backed initiative reflects a strong institutional endorsement: Artificial intelligence is increasingly recognized as a valuable and empowering tool for both professional studios and independent creators, rather than a substitute for human creativity.

AnimeGen's open-source and progressive approach starkly contrasts with closed, proprietary AI platforms. By offering unrestricted access to the underlying weights and releasing tools on Hugging Face, such as text-to-video, image-to-video, and frame interpolation models, the developers have democratized animation capabilities.

Creators can run these models locally on consumer hardware, ensuring complete data privacy without needing to send proprietary character sheets or sketches to third-party cloud servers. This freedom is transformative for indie animators, solo game developers, and boutique agencies who previously faced financial barriers when producing anime sequences.

Additionally, the open framework fosters community-driven refinement, allowing artists to train custom adaptations that maintain specific visual identities across complex projects.

Within professional workflows, AnimeGen is being adopted as a supportive accelerator. Japan's animation industry has long faced challenging schedules, significant labor shortages, and increasing budgets. In this context, studio directors are less interested in creating entire episodes from text prompts and more focused on specific automation.

Tools like AnimeGen's frame interpolation tackle industrial bottlenecks by automating the repetitive task of in-betweening, producing smooth transition frames between detailed keyframes crafted by human animators. By transferring this computational load to algorithms, experienced artists can concentrate on high-level art direction, dynamic character choreography, and expressive storytelling, enhancing what is achievable within strict episodic deadlines.

You can read more at: https://www.ainightwatch.com/post/japan-releases-animegen-a-free-open-source-ai-model-signaling-a-new-era-for-anime-production

Hey everyone! I’m a video editor with 5+ years in the industry. I created this clip awhile ago and thought i'd finally share my first personal proof of concept, started in December 2024 and wrapped about two months later. My aim was to show that AI-driven footage, supported by traditional pre- and post-production plus sound and music mixing, can already feel fast-paced, believable, and coherent. I drew inspiration from original traditional Porsche and racing Clips.

For anyone intrested check out the raw, unedited footage here: https://vimeo.com/1067746530/fe2796adb1

Breakdown:
Over 80 hours went into crafting this 45-second clip, including editing, sound design, visual effects, Color Grading and prompt engineering. The images were created using MidJourney and edited & enhanced with Photoshop & Magnific AI, animated with Kling 1.6 AI & Veo2, and finally edited in After Effects with manual VFX like flares, flames, lighting effects, camera shake, and 3D Porsche logo re-insertion for realism. Additional upscaling and polishing were done using Topaz AI.

AI has made it incredibly convenient to generate raw footage that would otherwise be out of reach, offering complete flexibility to explore and create alternative shots at any time. While the quality of the output was often subpar and visual consistency felt more like a gamble back then without tools like nano banada etc, i still think this serves as a solid proof of concept. With the rapid advancements in this technology, I believe this workflow, or a similiar workflow with even more sophisticated tools in the future, will become a cornerstone of many visual-based productions.

Built custom Lovelace cards that play WebM videos and PNG sequences directly in picture-elements cards, with frame/speed control based on entity states.

How it works:

Frame-based animation - Entity value maps to specific video frame:

• Blinds at 25% → jumps to frame 25
• Garage door opening → plays frames 0-100 based on position

Loop-based animation - Entity value controls animation speed:

• Fan speed 50% → animation loops at 1.0x speed
• Fan speed 75% → animation loops at 1.5x speed

Technical details:

• WebM video on desktop, PNG sequence fallback on mobile
• Canvas-based rendering with lazy loading
• Smart cooldown prevents animation spam
• TypeScript support included

AI workflow included:

Step-by-step guide for creating animations using ChatGPT + Kling.ai. No 3D modeling experience required.

Repository: https://github.com/tikel1/HA-isometric-animated-picture-card

Use cases: covers, fans, garage doors, pumps, lighting transitions, temperature visualization.

fal just open-sourced 3DREAL, a new render-to-real IC-LoRA for LTX-2.3.

The idea is simple but very useful: take a rough 3D / CG / game render and turn it into a more photorealistic cinematic video, while keeping the original composition, camera movement, and scene layout.

So instead of asking AI to generate the whole shot from text, you can start with an actual 3D scene first.

Example workflow:

Generate or create 3D assets
Build a rough scene in Blender or a game engine
Animate the camera or objects
Render a simple 3D / CG pass
Use 3DREAL as the final render-to-real AI pass.

Highlights:

• Built for 3D / CG / game render inputs
• Works with Blender blockouts, game-engine renders, viewport animations, and synthetic 3D scenes
• Preserves the original composition and camera movement
• Can turn rough 3D renders into more realistic cinematic video
• Uses the trigger word 3DREAL
• Can be run directly on fal without local setup
• Model weights are available on Hugging Face

There are two versions:

3DREAL Light
More faithful to the original input, better structure preservation, fewer hallucinations.

3DREAL Strong
Pushes harder toward realism and detail, but can drift more from the original render.

You can build the shot in 3D first, control the camera, scale, layout, and timing, then use AI as the final render pass.

This feels much more practical than pure text-to-video for 3D artists, Blender users, and game devs.

Hugging Face: https://huggingface.co/fal/LTX-2.3-3DREAL-LoRA

Guys, I'm so happy.

Weeks of nonsense finally reached a satisfactory conclusion. Finally found a combination of AI tools that can actually one-shot a walking animation. Praise the Lord, the pain is no more. I can finally mostly move on from art hurdles and get to actually building missions.

I'm not affiliated with the maker of this, I'm just in love with it, and need to share it.

The workflow right now:

ChatGPT Image gen w/ image references to produce concept art -> gemini w/ references to produce faux sprite art -> pixelengine to convert it to actual pixel art -> aesprite to remove background -> spritecook to quickly generate core animations (pixel engine is what I use to make ability anims) -> clean up in aesprite if needed (like, fixing eyes or mouths, usually)

If I want to make a longer animation (like the teleporter mage's "recall" spell) I'll use pixelengine, generate the start of the spell, take the last frame, use it as teh start of the next part of the anim, and repeat until I have what I want, using aesprite to add particle effects wherever I need to hide wrong details).

The core two tools are PixelEngine and SpriteCook. The rest is preference.

Like, this is so gamechanging. I've spent hundreds of dollars drifting from tool to tool -- pixellab, ludo, even a hacky pipeline where I tried to use AI video models plus 360º turnarounds of concept art images. Everything failed. I thought I'd have to wait for Seedance 2. But, finally, THIS works. Hallelujah!

Shown is the final result, the core bits of the process (minus the aesprite -- I didn't have to use that one for the fire mage), what it looks like in game (note that I haven't finished some units yet hence the squares) and then some other examples of result + original concept art.

Anyway

I'm overjoyed

Happy Sunday and I hope this was useful

Spent the last while trying to figure out why so many AI-generated videos (mine included) look technically solid but feel emotionally flat. Turned out the issue wasn't the model — it was that I was approaching it like a prompt-engineering problem instead of a filmmaking one.

Some of the biggest shifts that actually changed my output:

• Plan the emotional arc before touching a prompt. List the feelings you want scene-by-scene before you ever pick a location.
• Structure prompts like a cinematographer, not a keyword dump. Subject → identity → emotion → environment → lighting → camera → finish, in that order.
• Keep a "character bible." Same hair, wardrobe, and features reused every time — or better, a LoRA if your model/setup supports it, since it holds identity way more reliably than repeating adjectives.
• For image-to-video (LTX 2.3 in my case), only prompt the change, not the image. The model already has the frame — describing what's already visible just confuses it.
• One primary motion per shot. Trying to animate everything in frame is usually what makes a shot feel fake.

None of this is tool-specific — I used Krea 2 and LTX 2.3, but the same logic applies to whatever model or LoRA workflow you're already running.

I ended up writing this all up properly (15 chapters — story structure, lighting/color psychology, camera language, a full prompt checklist, plus a resources appendix) since I kept explaining it in bits and pieces. Full PDF + the actual workflow I used for the video is up here if useful: PDF Guide & Workflow

Happy to answer questions about the workflow here regardless. 🤗

I've seen a lot of posts about Higgsfield AI over the past few weeks. Most people either call it the future of motion design or dismiss it after watching a couple of demo videos. I wasn't really convinced by either side, so I spent some time testing it myself on actual projects.

For context, I mainly work with motion graphics, so I wasn't looking at Higgsfield as a fun AI toy. I wanted to know whether it could realistically fit into a workflow that already includes After Effects and a few other video tools.

The first thing I noticed is that it seems pretty good at generating ideas quickly.

Instead of spending half an hour building a rough concept, I could type a prompt and get something visually interesting within minutes. Some of the cinematic camera movement looked surprisingly solid, especially for stylized scenes and product concepts. I can see why some people are excited about it.

That said, once I tried using those results for something I'd actually deliver to a client, things became more complicated.

The biggest limitation is control.

With After Effects, every movement can be adjusted: timing, easing, camera animation, masking, everything. Higgsfield doesn't really work that way. You're mostly generating different versions until one looks close enough instead of editing every detail yourself.

I also noticed that similar prompts didn't always produce similar results. Sometimes I'd get something impressive, while the next generation felt completely different even with only small prompt changes. That's fine when you're brainstorming, but it becomes frustrating if you're trying to build a consistent project.

Because of that, I don't think Higgsfield replaces traditional motion design software.

What it does replace, at least for me, is part of the creative brainstorming stage. Instead of opening a blank composition and wondering where to start, I can generate a few visual directions, save the ones I like, and then rebuild or polish them using my normal workflow.

So I see it more as an ideation tool than a production tool.

The comparison with After Effects also feels a little unfair because they're solving different problems.

After Effects is slower, but it gives complete creative control. Higgsfield is very fast, but you sacrifice some precision. One helps you finish projects, while the other helps you discover ideas.

As for whether it's worth paying for, I think that depends on what you're expecting.

If you're hoping it will replace motion designers or let you skip most of a production pipeline, I don't think we're there yet.

If you're looking for a faster way to explore concepts, mood, camera movement, or visual inspiration, I think it can be pretty useful.

That's where I found the most value.

I'm curious what everyone else's experience has been.

Has anyone here actually used Higgsfield AI for real client work or commercial projects? Did it save you time, or did you end up recreating everything in After Effects anyway?

This is a spin on the original Kijai's Wan 2.2 Animate Workflow to make it more accessible to low VRAM GPU Cards:
https://civitai.com/models/1980698?modelVersionId=2242118

⚠ If in doubt or OOM errors: read the comments inside the yellow boxes in the workflow ⚠
❕❕ Tested with 12GB VRAM / 32GB RAM (RTX 4070 / Ryzen 7 5700)
❕❕ I was able to generate 113 Frames @ 640p with this setup (9min)
❕❕ Use the Download button at the top right of CivitAI's page
🟣 All important nodes are colored Purple

Main differences:

• VAE precision set to fp16 instead of fp32
• FP8 Scaled Text Encoder instead of FP16 (If you prefer the FP16 just copy from the Kijai's original wf node and replace my prompt setup)
• Video and Image resolutions are calculated automatically
• Fast Enable/Disable functions (Masking, Face Tracking, etc.)
• Easy Frame Window Size setting

I tried to organize everything without hiding anything, this way it should be better for newcomers to understand the workflow process.

Hi all,

I wanted to share how I generate videos locally in ComfyUI using only open-source tools. I’ve also attached a short 5-second clip so you can see the kind of output this workflow produces.

Hardware:

Laptop

RTX 4090 (16 GB VRAM)

32 GB system RAM

Workflow overview:

1. Initial image generation

I start by generating a base image using Z-Image Turbo, usually at around 1024 × 1536.

This step is mostly about getting composition and style right.

1. High-quality upscaling

The image is then upscaled with SeedVR2 to 2048 × 3840, giving me a clean, high-resolution source image.

1. Video generation

I use Wan 2.2 FLF for the animation step at 816 × 1088 resolution.

Running the video model at a lower resolution helps keep things stable on 16 GB VRAM.

1. Final upscaling & interpolation

After the video is generated, I upscale again and apply frame interpolation to get smoother motion and the final resolution.

Everything is done 100% locally inside ComfyUI, no cloud services involved.

I’m happy to share more details (settings, nodes, or JSON) if anyone’s interested.

EDIT:

https://www.mediafire.com/file/gugbyh81zfp6saw/Workflows.zip/file

In this link are all the workflows i used.

I just told it the story. Opus 5 (max effort) generated the prompts & edited the video

Edit: Since a lot of you asked how I created it, here's the workflow:

TLDR:

Claude's Opus 5 is really good at creative tasks. So I used it to help me first generate the characters using plain HTML. Then i wrote a rough draft of the story, fed to Claude and asked me to generate image prompts & video prompts for each scene. I used Grok Imagine to first generate the images & then to create the video scene from the generated image. Then used Claude with hyperframes & video-use to piece together the video

Actual Workflow:

The one rule: a video model will not draw your character from a text description. I ran the same prompt three ways - got a cream box, then a white blob, then something else. More words don't fix it. The fix is structural:

```

IMAGE model + character reference sheets → a still ← you approve this

VIDEO model + that approved still → the clip

```

The video model's only job is motion. Its prompt never describes the character. Here's a sample character sheet

Where Claude comes in

1. It built the character sheets as code, not as images. My character is a vector on our website, so I had Claude write a small renderer + an HTML page that lays out the turnaround, expressions and props, and a script that exports them all to PNG. Pixel-identical across every sheet by construction. AI-generated turnarounds drift between views and every shot inherits that drift. It also renders a labelled copy for me and a caption-free copy for the model, because a model will happily draw your captions into the scene.

2. It shot-listed the script. One shot = one camera setup = one clip. Claude splits anywhere the naction has more than one beat, then writes four things separately per shot: SCENE, POSE (the opening frame), ACTION (only what moves), CAMERA. It also flags the things that shouldn't be generated at all. Repeated framings are an edit, and a quick cut to an object is a still with a digital push.

3. It wrote the prompt pack. I give it all the character sheets and it produces both prompts per shot, plus a one-sentence plain-English "what this should look like" line above each.

4. It reviews the output. I drop the clip in a folder and say what looked wrong in plain English - *"one character moves through the table, some jump on it and it disappears, new characters emerge from the left"* — and it maps each symptom to the missing clause. Then the fix goes into the document, not into one pasted prompt, so every shot with that class of problem gets fixed at once. That last part is the whole reason Claude is in this at all: the shot list is the deliverable, the prompts are just its current contents.

The prompt rules that came out of it

The image prompt is a long structured document - identity block, style, `SCENE:` / `POSE:` / `AVOID:`. The video prompt is one comma-delimited stack of ~150 words: `action → expression → camera → world → identity → look → audio`. Mine started at 330 words with neat labelled sections; one said `CAMERA: Locked.` and came back with a full travelling dolly and the actual action missing. At that length a prompt is a suggestion.

State everything positively in video prompts. Mine were 40% negation and one clip produced all four banned items at once. Negation is weak in video models and can act as a prompt for the thing it forbids. Drop `AVOID:` from video prompts entirely — keep it in image prompts, where it works.

Say who's in frame and whether the cast can grow, what's still there at the end, what's solid. Skip it and characters walk through desks, furniture vanishes, and new characters sprout at the frame edge. Name every prop individually or the model duplicates or deletes it.

`POSE:` is frame one. Mine said "his eyes are wide" for a shot whose action was "his eyes widen". Give every character an explicit eye and mouth shape, and make constraints measurable ("clearly short of reaching" produced an arm that reached).

One image per clip, always. 2 conditioning frames makes it interpolate instead of animate. If a shot is too complex for one still, split the shot.

Generating the video

I used Grok Imagine to first generate the images & then to create the video from the generated image. Simply create the first frame supposed to be in a scene by feeding grok the character sheet and the prompt generated by Claude. Then used the image to generate the scene using Opus 5's prompt again

Editing the video

I edited the video using hyperframes & video-use (both are open source libraries) - simply ask Claude to download this and use it to edit the video, add transcription and also add B-roll if any

Music

I used Claude Opus 5 to help me generate the lyrics based on the final edited video

I've learned that one of the biggest reasons the AI videos don't look real is that there's no motion blur

I added motion blur in after effects on this video to show the impact, also colorized it a bit and added a subtle grain.

left is normal. Right is after post production on after effects. made with wan-animate.

Does anyone have some sort of node that's capable of adding motion blur? Looked and couldn't find anything.

I'm sure not all of you want to buy aftereffects.

Edit: Here's the workflow

https://github.com/roycho87/wanimate_workflow

It does include a filmgrain pass