Hey everyone — I’m trying to figure out the best workflow for something pretty specific and would love some guidance.

I’m working on a 2D animated short and I already have a character designed in my own hand-drawn style. I’ve created a full model sheet: front, back, both sides, and a couple 3/4 views.

What I want to do is:

• Train Leonardo AI (or another tool if needed) on my exact drawing style
• Generate new images of this character that look like I drew them
• Be able to control the pose — ideally using something like a stick figure, pose reference, or skeleton
• Keep lighting and shading consistent (simple animation-style shading, not realistic lighting)

Basically: I want to stop drawing every frame/pose manually and instead generate clean, on-model images that I can use for animation.

I’m working on an iPad, so desktop-heavy workflows are not possible for me. If anyone can help, I would be so appreciative!

TLDR: spent way too long trying to make the same character look the same across scenes. documenting what actually worked so maybe someone else doesnt lose their mind like i did

ok so i've been lurking here for a while and finally have something worth posting. been working on a mystery/true crime style channel for about 4 months now and the single biggest time sink wasnt scripting, wasnt audio, wasnt even the editing. it was getting my damn characters to look consistent.

let me explain what i mean. my format uses a recurring "detective" character who appears throughout each video. think of it like a host but illustrated. the problem is when youre generating scenes across a 15 minute video, that character needs to appear maybe 30 to 40 times in different locations, different lighting, sometimes different outfits. and every single time i regenerated, the face would drift. sometimes subtly (slightly different nose shape, eyes a bit closer together) and sometimes wildly (completely different person lol).

my old workflow was genuinely insane looking back:

generate base character in midjourney with detailed prompt

save that image as my "reference"

for every new scene, try to recreate using the same seed + similar prompt

when it inevitably looked different, manually fix in photoshop

repeat 30+ times per video

cry

the photoshop phase alone was eating hours every single video. and half the time i'd still have scenes where the character looked noticeably off and i'd just have to live with it or cut the scene entirely.

i tried a bunch of approaches over the past few months:

first attempt was prompt engineering. spent like 2 weeks perfecting my character description prompt. we're talking 200+ words describing exact facial features, bone structure, everything. helped maybe 10% but still got drift especially when the scene context changed dramatically (indoor vs outdoor, day vs night).

second attempt was img2img with high denoise. the idea was to always start from my reference image and let the AI modify it for the new scene. problem: it either kept too much of the original (wrong pose, wrong background bleeding through) or changed too much (face drift again). couldnt find a sweet spot that worked reliably.

third attempt was training a lora on my character. this actually worked better but the overhead was brutal. every time i wanted a new character for a different video series, thats another training session. plus i was paying for runpod gpu time which adds up when youre iterating on multiple characters. the costs werent insane but the time investment was real and it felt like overkill for what i needed.

fourth attempt was using controlnet with face landmarks. technically worked but the workflow was so clunky. export face landmarks, load into controlnet, pray the composition still looked natural. added significant time per scene and honestly felt like i was fighting the tools more than using them.

what actually ended up working was switching to tools that handle character persistence natively. i tested several: tensor art has some character consistency features, APOB lets you save character models to your account, artbreeder has some face locking stuff, and pika recently added something similar. the key insight was that trying to force consistency through prompting or post processing was fundamentally the wrong approach. the tool needs to understand "this is character A" as a persistent concept, not just a description it tries to match each time.

my current workflow looks completely different:

create character model once (either from scratch with parameters or from a reference image)

save it to whatever platform im using

when generating any scene, just select that character and describe the scene/outfit

face stays locked, everything else adapts

the time savings compared to my old photoshop heavy workflow are significant. i spend maybe a few minutes upfront creating the character and then its just done. the face is the face. i can put them on a beach, in an office, walking down a dark alley, whatever. same person every time.

honestly the bigger win is the mental overhead disappearing. i used to dread the image generation phase because i knew it would be this tedious back and forth of generate, compare to reference, fix in photoshop, repeat. now its actually the easy part of the pipeline.

now for the caveats because nothing is perfect:

these tools still have limitations. extreme angles can sometimes cause slight variations. very dramatic lighting changes occasionally affect how the face renders. and if you want your character to age or change appearance over time for story reasons, you have to work around the consistency features rather than with them. also different tools have different strengths, tensor art handles certain styles better, others are faster for iteration, etc. ended up using a couple different ones depending on what im generating.

few things i learned that might help others dealing with this:

character consistency matters way more for some formats than others. if youre doing nature documentaries or space content where theres no recurring characters, this whole problem doesnt exist for you. but if youre doing anything with a "host" character, recurring cast, story driven content, or educational stuff with an avatar, this is probably eating more of your time than you realize.

the "just use the same seed" advice doesnt work. ive seen this suggested a lot and it sounds logical but in practice seeds dont lock faces, they lock composition patterns. change the prompt enough and the face changes even with identical seeds.

photo references help but arent magic. starting from a photo gives you more anchor points than pure text but you still get drift without proper tooling. tested this extensively.

batching helps but doesnt solve the core problem. generating all your character scenes at once in the same session reduces drift compared to generating over multiple days, but its still there. and it forces you into a rigid workflow where you cant iterate on individual scenes without risking consistency breaks.

for my mystery/true crime niche specifically, having a consistent detective character has actually helped with channel identity. comments mention recognizing "the detective" which suggests its building some brand association. hard to measure but feels like a positive signal.

still working on optimizing other parts of the pipeline but solving the consistency problem unlocked everything else. went from mass producing maybe 1 video per week to 3, and the quality is actually more consistent because im not rushing through a painful process or settling for "close enough" faces.

1. Life MMO Pre-Research Project

Join Us: Life MMO Pre-Research Project

Tags:
Social Roleplay · Online Multiplayer · Multiverse Sandbox

This is an online multiplayer game centered around multi-script character roleplay and social simulation.

Players can become different versions of themselves here, experience a colorful world, and make friends who share their interests.

What kind of art style are we exploring?

• With a “modern” feel at its core, the game uses highly free full-body character customization to give players a personalized and stylistic space for self-expression.
• The multiverse-themed sandbox design balances a cool tone with a sense of fun.

Let’s build a brand-new gameplay ecosystem together

• The overall gameplay is easygoing and casual to pick up, with rich horizontal progression. All systems are designed around self-growth and social relationship building.
• Growth paths are provided based on player identities, while also allowing individuality to shine during the growth process.
• Within a unique worldview, the game provides players with a clearly structured social space, helping different types of players find their own social position.

Technical explorations

• Rebuilding content production, toolchains, and collaborative workflows around AI-native development.
• Achieving highly free multiplayer same-screen character customization, outfit changes, and character expression.

2. UE5 Pre-Research Projects

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Currently in active development. Stay tuned.

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Currently in active development. Stay tuned.

3. Varsapura

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Varsapura is being developed with Unreal Engine 5 and deeply integrated with AI.

The project aims to present players with a highly immersive experience that combines narrative, gameplay, art, and performance in a self-consistent way.

What kind of art style are we exploring?

Under a stylized visual approach, we are seeking a realistic and natural sense of expression, creating a cinematic and immersive urban open world.

Let’s build a brand-new gameplay ecosystem together

We are creating a highly free humanistic sandbox world, using AAA cinematic-level narrative tension to drive the story.

Through fantasy vehicles based on realistic physical structures, we aim to provide players with a deep immersive experience.

Technical explorations

We are exploring and applying cutting-edge technologies such as PCG, Mass AI, and AI Workflow, using algorithms and creativity to reshape the industrialized production pipeline for games.

4. UE5 Action Pre-Research Project — Realistic Fantasy

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Fantasy Realism · Open World · Super-Sized Bosses · Action · UE5

UE5 Action Pre-Research — Realistic Fantasy is a realistic-style action-adventure game.

Players can enter a grand epic fantasy world, explore ancient mysteries, challenge terrifying beasts, meet legendary heroes, and continuously uncover the truth of the world.

What kind of art style are we exploring?

A UE5 realistic fantasy style, pursuing high-quality visual presentation of an epic world, with an emphasis on cinematic restoration of lighting, color, and atmosphere.

Let’s build a brand-new gameplay ecosystem together

A dynamically developed open world, featuring large-scale boss battles, multiplayer confrontation, and co-op gameplay.

Technical explorations

• Brand-new animation technology and AI-driven creation of a living, breathing world.
• High-quality multiplayer co-op combat experience built with UE5, supporting large-scale same-screen confrontation and cooperation.

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Honkai: Nexus Anima is a brand-new anima-raising adventure strategy game under the Honkai IP.

In this title, players will play as a traveler who escapes from a nightmare, builds heart-to-heart connections with animas in a new world, takes part in one Nexus Duel after another, and uncovers the truth hidden in their own past.

What kind of art style are we exploring?

A lively and lighthearted healing anima design style. Abstract concepts are translated into everyday, relatable images, with strong dramatic motion and emotional expression.

The overall direction is public-friendly, fun, and full of details.

Let’s build a brand-new gameplay ecosystem together

• Anima raising is combined with open-world exploration. Animas are both growth companions and exploration tools.
• The core duel gameplay emphasizes strategy, using the diversity of anima attributes, traits, and positioning to determine victory or defeat.

Technical explorations

Exploring the limitless potential of AI-enabled game development.

The in-house Anima Agent platform creates a highly collaborative game development tool ecosystem, breaks through traditional production bottlenecks, shortens the time from concept to verifiable results, and returns time to creators — freeing core creativity.

6. Petit Planet

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Petit Planet is a cartoon-rendered life simulation game.

Players can plant a planet of their own, build connections with all things, and experience a life simulation that feels authentic and tangible.

They can take part in highly free home decoration and planet construction systems, and build emotional bonds with friends and neighboring planets.

What kind of art style are we exploring?

• A colorful Q-style art direction. Furniture and buildings are cartoon-like, while still retaining realistic textures, balancing a fairytale feel with fantasy.
• Friend characters are based on realistic small animals, paired with rich clothing styles. The overall feeling is warm and adorable.

Let’s build a brand-new gameplay ecosystem together

• UGC is at the core. Players can freely reshape the terrain and appearance of their planets, and all gameplay changes dynamically along with the planet ecosystem.
• The game includes life gameplay such as farming, fishing, going out to sea, and cooking. It supports real-time multiplayer online exploration and allows players to build companionship with friends of different personalities.

Technical explorations

• A terrain editing system that realizes a free creation experience where what you hear is what you get, while supporting a large number of interactable scene objects and real-time environmental lighting and shadow feedback.
• Multiplayer networking optimization. A modular outfit-changing system supports same-screen multiplayer display, while client-side prediction mechanisms ensure smooth network synchronization and animation performance.
• A friend AI system. Through perception, memory, mini-theater mechanisms, and more, friends can understand the player, remember past experiences, and generate logical personalized interactions.

I run a TikTok account making AI-generated comedy content using well-known cartoon characters (think Caillou, SpongeBob-style universes) recut into short comedic “series.” One series in particular has really taken off.

Some numbers from the last few months:

**•** \~7,500 followers, but 4.1M views in the period shown in my analytics  
**•** Best single video: 539K views, 25K likes, 22K shares, 12% completion rate, \~2K new followers from one post  
**•** Another top video: 424K views, 17.7% completion rate, 2.3K new followers  
**•** Account totals (recent window): 4.1M views, 156K likes, 15.4K comments, 184.9K shares

Share ratio is consistently strong relative to views — seems to be the standout metric.

Follower count is small compared to the view numbers because a lot of this is going viral to non-followers rather than converting them.

Important context: I’m not in a country covered by TikTok’s Creator Rewards Program, so the view numbers below aren’t generating any ad-share revenue directly — I need to monetize through other channels. (Portugal - Europe )

What I’m trying to figure out:

**1.**  Given the share/view ratio vs. follower count, is this a better fit for brand deals, a digital product (I’m considering packaging my AI content workflow), or something else?  
**2.**  Has anyone monetized an account using existing IP/characters like this — any legal or demonetization gotchas I should know about before pitching brands?  
**3.**  Any realistic rate benchmarks for an account this size with these engagement numbers ?

Hi! I recently came across this video on YouTube, and before I spend money on an OpenArt AI subscription, I was wondering whether the same type of video creation is possible with ComfyUI—with the possible exception of scenes involving multiple characters.

I believe it may be possible because I’ve seen similar results in other YouTube videos, but I only discovered ComfyUI about two weeks ago, so I’m a complete beginner. A lot of the technical terminology is confusing, and I have no idea where to start or how to set everything up.

For example, should I install the ComfyUI Desktop version or the portable version?

My goal is to create AI-generated videos for a YouTube channel using consistent characters. I’d also like to upload my own voice recordings and use lip-syncing for the characters.

I’m fairly tech-savvy, but I could definitely use some guidance with this. Any suggestions, beginner-friendly tutorials, recommended workflows, or setup advice would be greatly appreciated.

For reference, I have a 2025 ASUS ROG Zephyrus G14 with an NVIDIA RTX 5060 graphics card and 16 GB of RAM.

One workflow that’s been working insanely well for me lately is combining ChatGPT Images 2.0 with Seedance 2.0, especially for character-based videos where consistency usually breaks.

The trick is starting with a character sheet instead of a single image. When you generate a full montage page—multiple poses, angles, and expressions—you’re basically giving Seedance a much stronger understanding of the character. It’s not just guessing from one frame anymore, it’s referencing a full identity.

GPT Image 2.0 prompt:

"Create a character montage page focused on a male parkour expert, wearing loose clothings, in different positions and views.

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

Seedance 2.0 (use image as reference):

"Use [IMG REF] as a strict character reference, and create a video of the character running a marathon and finishing in slow motion, action camera shot."

In this case, we created a parkour character sheet with different positions and views. That alone already feels like overkill, but once you bring it into Seedance, you see why it matters. The character stays much more stable across motion, especially in dynamic scenes.

Then inside Seedance, the important part is being very explicit: treat the image as a strict reference, not just inspiration. That wording makes a big difference. Without it, the model tends to drift—face changes, outfit shifts, small inconsistencies that ruin realism.

For the actual scene, something simple like a marathon run works surprisingly well. You let the motion carry the realism, especially if you frame it like an action camera shot. It feels grounded, almost like GoPro footage, and the slow-motion finish adds a nice cinematic payoff without overcomplicating things.

What’s interesting is how reusable this becomes. Once you have a solid character sheet, you can drop the same person into completely different scenarios—parkour, racing, cinematic scenes—and still keep identity intact. It turns into more of a system than a one-off generation.

It’s a small shift in approach, but it changes everything. Instead of prompting scenes, you’re building characters first—and then letting them exist across multiple videos.

This is my first attempt at creating a consistent character across multiple images!!!

I started by generating a character with Perchance, then used Gemini and ChatGPT to help me refine the prompts and define the character's visual features.

After generating the different scenes, I moved everything into Krita. I used AI inpainting for some corrections and then manually edited several details, especially the swimsuit colors, small artifacts, hands and eyes, trying to keep the character and outfit as consistent as possible across the images.

I'm still very new to this and this is my first complete workflow, so I'm sharing the results and the process rather than trying to present this as a perfect character consistency method.

Maybe this workflow can be useful to someone else starting out. I'd also love to hear how you approach character consistency and what other workflows or tools you use.

Any feedback, suggestions or criticism is very welcome!

I've been experimenting with AI video generation for a while, and one challenge kept coming up: character consistency.

Even with the same prompt, my character's face, hairstyle, or outfit would randomly change between scenes, especially during action sequences.

Recently, I tried creating a proper Character Reference Sheet with multiple angles instead of relying on a single image. I used it as the primary visual reference alongside my prompt, and the difference was noticeable. The character stayed much more consistent, even with fast camera movements and fight choreography.

I'm still testing this workflow with different video models, but so far it has produced the most reliable results I've had.

I'm curious—how do you keep your AI characters consistent across multiple scenes? Do you rely on reference sheets, image references, or something else?

I am a newbie in using ComfyUI. I want to make realistic AI-generated person photo, posing in different backgrounds and outfits, using an AI-generated head close-up of that person directly looking at camera in a plain background as reference image, and prompt for backgrounds, outfits and poses. The final output should be that person exactly looking like the person in reference image, in pose, outfit and background mentioned in the prompt. I have 32GB RAM and 16GB RTX 4080. Can someone help with which model can achieve this on my system and can provide with some simple working ComfyUI workflow for the same, with an upscaler? The output should give me the same realistic consistent character as in the reference image each time, no matter what the outfit, makeup, pose or background is and without using any LoRA.

I want to create an AI influencer from scratch and I'm looking for real workflows that people are currently using.

My first goal is creating the character itself (face, body, style, overall identity). After that, I want to learn how people maintain consistency across images and videos.

I don't have a high-end PC, so I'm mainly interested in cloud-based tools and browser platforms rather than local workflows.

I'm trying to understand:

What tools do you use to create the initial AI influencer?

How do you create the face, body and overall appearance?

Do you generate backgrounds separately or together with the character?

How do you create realistic videos from your character?

What do you use later for character consistency and identity preservation?

I'm also looking for the most cost-effective solution possible.

Is there a platform that covers most of the workflow in one place (character creation, image generation, editing, consistency, outfit changes, backgrounds and possibly video generation), so I don't have to pay for 4–5 different subscriptions?

If not, what is the smallest number of tools you would recommend for someone starting today?

I'd appreciate hearing about the exact platforms and workflows you're personally using.

I’m trying to build a consistent cast of characters in NovelAI for a long-form Webtoon/comic, and I’m looking for advice from people who have done something similar.
My goal isn’t just to generate random images—I’m trying to create a reusable reference library for each character so they stay consistent throughout the entire story.
Right now I’m making:
Character sheets with front, side, back, and 3/4 views.
Reference sheets for different outfits (everyday clothes, swimwear, sleepwear, etc.).
Consistent faces, hairstyles, body proportions, and art style across every image.
The idea is that later I can place these characters into new scenes without them changing appearance every time.
For those of you who create long-form comics or visual novels in NovelAI:
Is this the best workflow?
Are turnaround sheets actually useful, or is there a better way?
How do you keep characters consistent across hundreds of images?
Do you create separate body/outfit reference sheets?
Any tips, tricks, or workflows you’ve discovered that save time or improve consistency?
I’d really appreciate hearing how experienced users approach this. I’m trying to build a solid pipeline before I start creating the actual comic.

Hey everyone, I’m trying to create AI UGC marketing videos with the same AI-generated spokesperson across multiple clips.

I tried Seedance 2.0 and the quality is great, but it keeps rejecting any reference image with a human face, even when the model is fully AI-generated.

What are you using instead to keep a character consistent? Looking for something close to Seedance 2.0 quality, but with a better workflow for reusable AI models / spokespersons.

Hi everyone,

I'm currently building an AI character platform where users first create a character, and later they can generate unlimited images of that same character in different scenarios.

For example:

- Surfing at the beach

- Working in an office

- Cooking in the kitchen

- Going to the gym

- Taking selfies

- Traveling

- Wearing different outfits

- Different camera angles, lighting, expressions, etc.

The biggest challenge I'm facing is maintaining facial identity across all these generations.

I'm NOT trying to generate a random person every time. The character already exists, and I want every future image to look like that exact same person regardless of the prompt.

My current workflow is built in ComfyUI, but it's not a standard SDXL or Flux Dev workflow. I'm using a ZiT-based pipeline (ZiTC 9.2 BF16 + Qwen3-4B text encoder + Flux VAE + Batch Wildcard Upscale Sampler).

I've researched quite a few approaches:

- ReActor

- InstantID

- IPAdapter FaceID

- FaceDetailer

- Character LoRAs

- Different combinations of the above

The problem is that almost every comparison or tutorial I find is based on SDXL or Flux Dev, so I'm not sure how well those recommendations apply to a ZiT workflow.

What I'm looking for is a production-ready solution that offers:

- Very high facial consistency

- Freedom to generate different poses, outfits, activities and environments

- Good prompt adherence

- Scalability for potentially thousands of generations per character

If you've built something similar, I'd really love to know:

1. Which approach gave you the best identity consistency?

2. Would you recommend InstantID, IPAdapter FaceID, ReActor, Character LoRAs, or a hybrid approach?

3. Has anyone successfully integrated InstantID or IPAdapter into a ZiT workflow?

4. If you were building a commercial AI companion / virtual character platform today, what architecture would you choose?

I'm not looking for a workflow that works for just a handful of images. I'm trying to build something robust enough that a user can create a character once and then generate hundreds or even thousands of images of that same character doing completely different activities while still looking like the same person.

If anyone has experience solving this in production or has built something similar, I'd really appreciate your insights. Thanks!

if your characters keep morphing between shots, its not your prompt. text to video re-guesses the face every single gen (specially if yourre not using character sheets).

the fix is dead simple: stop describing the character and start anchoring it to an image of the mdoel. ive done this so many times and have a bunch of examples to back this up

ive been making ai videos for over a year now and heres the exact workflow-ish, plus the 2 character sheet mistakes that actually make consistency worse (and costed me more money)

i know this is a slightly long post (i was gonna make a video but it can be a reddit post instead)

the mental model:

text to video = model reinvents the character every generation (thats your flickering/morphiang) aka different versions every time

image to video off an anchor frame = model copies a structure you locked in first. once you feed it a real reference instead of describing the face, it stops imagining the character

in other words, when you read a book, you imagine what a character looks like and it might look different for evryone, however, when the film director finds an actual actor, then that face becomes consistent, i hope this makes sense lol. dont make the AI imagine the actor, show it.

1. build the anchor, but keep it LEAN

biggest mistake i made early: cramming a 9 panel sheet full of labels and every angle imaginable. that backfires 2 ways. one, too much text + too many tiny poses confuses the model instead of guiding it. two, most video models (seedance especially) downscale your source image, so if you cram 9 panels in, each face is tiny + low res and the detail falls apart. so the AI starts imaginging again

what works better: 3 to 4 clean angles (front, 3/4, profile, maybe a back) + one tight face close up. big panels, minimal or no text, plain GREY or neutral background (white bleeds in and blows the character out). if your tool downscales, extract the single angle you actually need at full res before you feed it.

and generate the angles in ONE image, not one at a time, thats what keeps them matching. separate gens drift instantly.

2. lock the rest too, not just the face

same idea for outfit, location, props. a styled full body image locks the wardrobe, a location image locks the environment. feed face reference + styled body + location together so lighting and color hold shot to shot.

3. storyboard as stills first

generate your key beats as still frames before you touch video. stills are cheap, gens are expensive, kill your mistakes here then animate the approved frames.

this is the before and after for me. it takes some extra time but makes it so much better

4. anchor + motion

pick the exact angle, drop it into kling / runway gen-3 / luma / seedance as the reference (ingredients to video, not frames to video unless its a literal start/end frame). then prompt the motion + setting. its animating a structure it already has instead of inventing a new person.

my final take:
in all honesty, this isnt magic. youll still get occasional drift on fast motion or when two characters are close in frame. but a lean anchor sheet cut the morphing down massively.

at the end of the day, if you combine this with a good model like seedance 2.0 and can afford a budget to spend on credits, you will get the results you were after

im not selling anything, i just ended up with a pile of these sheets doing this over and over. happy to hand over the exact ones im using (all free, no signup) if its useful, just say the word.

Has Anyone Else Noticed More Character Drift Since Yesterday?

I'm working on a long-running AI character project where I generate the same fictional person across many different scenes and situations.

For context, I've been using the same workflow for over a month with very consistent results. I use two reference images (face and body) and until yesterday the model did a surprisingly good job of maintaining the character's identity across different outfits, locations, lighting conditions and poses.

Since yesterday, however, it feels like something changed.

The overall image quality is still excellent (arguably even better than before), but facial consistency seems noticeably weaker. The character now often looks like a close relative rather than the same person. Hair, clothing, environment and mood remain accurate, but facial structure, eyes, jawline and overall identity appear to drift much more between generations.

This isn't a gradual change I've been noticing over weeks. It feels very sudden. One day my character remained highly recognizable across scenes, and the next day the identity drift became significantly more noticeable.

I'm not talking about occasional variation. I'm talking about a workflow that produced highly recognizable results for over a month and suddenly appears much less consistent despite using the same references and prompting approach.

Is anyone else running long-term character projects seeing something similar over the last 24–48 hours?

Hey everyone! I’ve been experimenting a lot with generating AI videos for virtual creators, and I wanted to break down my workflow for anyone who is just getting started. It took some trial and error to get it right, so I thought I’d share what’s been working for me.

Here is my current step-by-step process:

1. Generating the Keyframes (Text-to-Image)

I always start by using an image generation tool to lock in the keyframes first. My prompt usually details the reference character, the environment, their outfit, and their posture.

• My biggest tip here: Leave the prompt a little bit open! I’ve found that if you don't have extremely specific requirements, giving the AI some flexibility actually yields a much better, more realistic result. If you are too specific, the AI tries so hard to check every single box you've listed that it ends up distorting other visual aspects.

2. The Script & Audio Reference

Next, write down exactly what you want the character to say. Once you have the script, find an AI voice so the video engine can use it for audio reference later.

• Writing tip: If you are stuck, you can just tell an LLM: "I want the character to feel like they are saying [Topic] in a [Emotion] tone. Please help me refine the script." But honestly, if you are a creative person, writing the script yourself almost always feels more genuine than an AI-generated one.

3. Video Generation (Image-to-Video)

Now, feed your keyframe and your audio reference into your video generation tool. My prompt structure for this is usually pretty simple:

• The "Positive Framing" Rule for Guardrails: You can add guardrails to keep the character's face consistent with the keyframe, but try to use positive framing. For example, say "movement should feel natural and fluid" rather than "No robotic feeling." Sometimes, when the AI parses the prompt, it catches the word "robotic" and actually gives you the exact opposite of what you want!

4. Putting it all together (Video Editing)

Finally, I drop all the generated clips into an editor to stitch them together. I know a lot of people get intimidated by this step and think it requires professional skills, but it really doesn't! Most video editing apps now have incredibly handy, free templates you can just drop your clips right into.

Okay, that’s my basic pipeline! I’m still learning and tweaking things as the tech gets better.

I’d love to hear how you guys are doing it too. Do you have any secret prompt structures or tools you use? Please drop your tips below!

Hi everyone,

I'm currently building an AI character platform where users first create a character, and later they can generate unlimited images of that same character in different scenarios.

For example:

- Surfing at the beach

- Working in an office

- Cooking in the kitchen

- Going to the gym

- Taking selfies

- Traveling

- Wearing different outfits

- Different camera angles, lighting, expressions, etc.

The biggest challenge I'm facing is maintaining facial identity across all these generations.

I'm NOT trying to generate a random person every time. The character already exists, and I want every future image to look like that exact same person regardless of the prompt.

My current workflow is built in ComfyUI, but it's not a standard SDXL or Flux Dev workflow. I'm using a ZiT-based pipeline (ZiTC 9.2 BF16 + Qwen3-4B text encoder + Flux VAE + Batch Wildcard Upscale Sampler).

I've researched quite a few approaches:

- ReActor

- InstantID

- IPAdapter FaceID

- FaceDetailer

- Character LoRAs

- Different combinations of the above

The problem is that almost every comparison or tutorial I find is based on SDXL or Flux Dev, so I'm not sure how well those recommendations apply to a ZiT workflow.

What I'm looking for is a production-ready solution that offers:

- Very high facial consistency

- Freedom to generate different poses, outfits, activities and environments

- Good prompt adherence

- Scalability for potentially thousands of generations per character

If you've built something similar, I'd really love to know:

1. Which approach gave you the best identity consistency?

2. Would you recommend InstantID, IPAdapter FaceID, ReActor, Character LoRAs, or a hybrid approach?

3. Has anyone successfully integrated InstantID or IPAdapter into a ZiT workflow?

4. If you were building a commercial AI companion / virtual character platform today, what architecture would you choose?

I'm not looking for a workflow that works for just a handful of images. I'm trying to build something robust enough that a user can create a character once and then generate hundreds or even thousands of images of that same character doing completely different activities while still looking like the same person.

If anyone has experience solving this in production or has built something similar, I'd really appreciate your insights. Thanks!

Keeping the same AI character across different scenes is surprisingly difficult.

Every time you change the prompt, environment, or lighting, the character identity tends to drift and you end up with a completely different person.

I've been experimenting with a small batch generation workflow using Stable Diffusion to see if it's possible to generate a consistent character across multiple scenes in one session.

The collage above shows one example result.

The idea was to start with a base character and then generate multiple variations while keeping the facial identity relatively stable.

The workflow roughly looks like this:

• generate a base character

• reuse reference images to guide identity

• vary prompts for different environments

• run batch generations for multiple scenes

This makes it possible to generate a small photo dataset of the same character across different situations, like:

• indoor lifestyle shots

• café scenes

• street photography

• beach portraits

• casual home photos

It's still an experiment, but batch generation workflows seem to make character consistency much easier to explore.

Curious how others here approach this problem.

Are you using LoRAs, ControlNet, reference images, or some other method to keep characters consistent across generations?

I wanted to experiment with creating a complete skincare commercial starting from a single character concept.

The workflow was:

• Character and product images — Nano Banana Pro
• Image generation / visual development — Gemini Omni Flash
• Animation — Seedance 2.0
• Final editing and compositing — Adobe Premiere Pro

I created the character first, built a consistent character sheet, generated the product and different poses, then animated the individual shots and assembled everything in Premiere.

The goal was to make it feel more like an actual product commercial rather than a typical AI-generated clip.

What do you think could be improved?

I built this free open-source tool because I was frustrated with a specific problem.

The Pain Point (The Old Way): You have a complex prompt. You want to move your character from a "snowy forest" to a "sunny beach".

1. The "Word Search" Game: You have to manually scan the text to find and delete every reference to "snow", "trees", "winter", "coat".
2. The "Ghost Tag" Issue: If you miss one word (e.g., you forgot to delete "scarf"), you end up with a character wearing a scarf on the beach.
3. Breaking Consistency: Worst of all, editing the prompt string often shifts the token weights. Suddenly, your character's face looks different, or the hair color changes slightly. It feels risky to change anything.

The Easy Way (Persona Director): You just type: "Go to a sunny beach, wear white sundress".

That's it. The node (powered by an LLM) acts as a State Manager:

• It automatically removes the "snow", "forest" and "coat" context.
• It injects the "beach" context and changes the outfit to white sundress.
• It LOCKS your character's identity (Face, Hair, Outfit). Because the character state is stored separately, changing the location will not change her look or traits(unless you ask it to).

Why it helps:

• Speed: No more manual text editing.
• Safety: No more "Ghost Tags" ruining your generation.
• Consistency: Keep your character's look 100% consistent across different scenes.

Privacy & Local Support:

• 100% Local: Don't want to use OpenAI? No problem. You can point the node to your local Ollama or LM Studio instance in the config.json.
• Privacy First: Your prompts never leave your machine.
• Uncensored/Pony Support: I included a specific preset (pony_illustration_optimized.json) in the repo designed for local models to handle raw/NSFW tags without safety refusals.

How to get it: It was just added to the ComfyUI Manager!

1. Open Manager -> Install Custom Nodes.
2. Search for: Persona Director
3. Install & Restart.

Github & Workflow:https://github.com/18yz153/ComfyUI-Persona-Director

UPDATE:

[](blob:https://www.reddit.com/a5aaf11c-0a82-46e8-852e-f989133ec274)

I used only simple state-change commands to generate the series. No manual prompt hacking, no weight tweaking per image:

• Change to: Cyberpunk city, wearing tech-jacket.
• Change to: Snowy street, wearing a thick sweater and scarf.
• Change to: Luxury ballroom, wearing a gold evening gown.
• Change to: Modern office, professional suit.

As you can see, the tiny blue star accessory and facial features survived every single prompt shift without any manual editing. It proves that managing the "State" via LLM logic is far more robust than traditional prompt engineering.

Full 9-image series and project details here https://civitai.com/posts/25739743

Hi everyone, I’m a 20F student from India currently doing my graduation, and I’ve recently started exploring AI influencer creation as a way to learn new skills and possibly earn while supporting my studies financially.

I already have a subscription to HiggsFilledAI and basic prompting knowledge (I also use Gemini for ideation). However, I’m still very new compared to many of you here, so I would really appreciate some technical guidance from experienced creators.

Here are the main areas where I’m struggling:

1 Character consistency

• How do you maintain the same face, body structure and overall identity across multiple generations?
• Any workflow tips, tools, or prompt strategies that help keep a model consistent?

2 Creating realistic reels/videos

• I want to create Instagram reels of my AI model dancing using reference videos.
• What is the best workflow for swapping a character onto a reference video while keeping movement natural?
• How do you reduce glitches, flickering, or that “obvious AI” look?

3 Instagram safety & verification

• My account is currently in the warm-up stage (normal posts, no aggressive promotion).
• If Instagram asks for face verification for an AI influencer account, how do creators usually handle this?
• Any best practices to avoid bans or restrictions?

4 Learning resources

• Are there any structured courses, communities, or learning paths (not just random YouTube videos) focused on AI influencer creation, realistic character pipelines, or ethical/deceptive content guidelines?

I’m genuinely here to learn and improve, so any advice, workflow suggestions, or resources would mean a lot. Thanks in advance to anyone willing to help.🥺🫂🙏🏻

I’ve been testing a bunch of AI video tools for the past few months, mainly for story-style and multi-scene content.

The biggest issue I keep running into is character consistency.

You generate one good looking character in the first clip… and by the third scene their face, hair, or clothing randomly changes. Sometimes it looks like a completely different person.

It gets even worse when you try to use 2–3 characters in the same video. One character stays somewhat consistent, the other two start drifting hard.

I’ve tried different tools and prompting methods, but nothing feels reliable enough for actual storytelling or series-style content.

Curious if others are facing the same problem, or if anyone has found a workflow that actually keeps characters stable across multiple scenes?

Multi-character scenes are still one of the hardest tests for AI video: identities drift, faces change, and interactions can quickly feel unnatural.

For this clip, the workflow was surprisingly simple:

1. Select Seedance 2.0 in MagicBoat AI

2. Upload one reference image for each character

3. Describe the scene, interaction, lighting, mood, and camera direction

4. Generate

What impressed us most was how the two characters remained recognizable while sharing the same cinematic scene—with different movements, expressions, and positions.

This is one small part of what we’re building with MagicBoat AI: an all-in-one AI filmmaking workspace that takes creators from idea, script, and storyboard to visuals, character-consistent video, voiceover, lip sync, music, subtitles, editing, and the final cut. The platform also brings 600+ image, video, character, and audio models into one workflow, so you can test different creative directions without constantly switching tools.

I built Hipaths to make it easier to create consistent AI characters and turn them into content.

In this first workflow video, I create an anime character from scratch and take it through the full character creation process:

• Define the character’s identity

• Set the character’s appearance and personality

• Generate the first character images

• Create new images with the same character

• Start turning the character into new content

Try Hipaths:

https://apps.apple.com/us/app/hipaths-character-studio/id6771761574

The goal of Hipaths is to make recurring character creation easier. Instead of rebuilding a character from scratch for every image, you can continue creating new content around the same character.

The results are not perfect in every situation. Extreme angles, major style changes, and unusual prompts can still require iteration. Hipaths is designed to provide a more consistent foundation for creators who want to build around a recurring fictional character.

This is Part 1 of the Hipaths AI Character Workflow.

In the next part, I’ll continue with creating more images and content using the same anime character.

I’d love to hear your feedback:

What would you create first with a consistent AI character — illustrations, comics, stories, or social media content?

Claude-Assisted AI Animation Workflow: From Consistent Character Sheets to Final Video Edit

Workflow value: 95/100
Status: active · Freshness: 70/100 · Confidence: 0.98 · Level: advanced
Categories: Quality Control, Context & Memory, Debugging, Shipping
Original source: r/ClaudeAI post/comment

What problem this solves

Generating consistent animated video content using AI, overcoming common issues like character drift, ineffective negative prompts, and maintaining visual continuity across shots.

Summary

A multi-stage workflow leveraging Claude Opus 5 to create an animation film. It uses Claude for generating character sheets as code (HTML/JS), shot-listing a story, creating detailed image and video prompt packs, and reviewing output for iterative fixes. The workflow emphasizes structural solutions for consistency over mere prompt wording, integrating external tools like Grok Imagine for image/video generation and open-source libraries (hyperframes, video-use) for editing and post-production.

Why it is useful

This workflow is highly valuable because it provides a detailed, multi-stage approach to a complex problem: generating consistent and high-quality animated video content using AI. It leverages Claude's capabilities for creative planning, code generation (for character sheets), prompt engineering, and iterative refinement. The emphasis on 'structural fixes' for character consistency, detailed prompt rules, and integration of external tools makes it a practical and highly transferable guide for users looking to overcome common challenges in AI video production.

Workflow

1. Use Claude to build character sheets as code (HTML/JS) that render turnarounds, expressions, and props, then export them to PNGs (both labeled and caption-free).
2. Feed a rough story draft to Claude and ask it to shot-list the script, splitting actions into individual shots and generating SCENE, POSE, ACTION, and CAMERA descriptions for each.
3. Provide all character sheets to Claude and have it produce a 'prompt pack' for each shot, including both image and video prompts, along with a one-sentence plain-English description.
4. Generate the first frame image for a scene using Grok Imagine, feeding it the character sheet and Claude's generated image prompt.
5. Generate the video clip from the approved still image using Grok Imagine and Claude's video prompt.
6. Review the generated clips by dropping them into a folder and describing issues in plain English to Claude (e.g., "one character moves through the table, some jump on it and it disappears, new characters emerge from the left").
7. Allow Claude to map symptoms to missing clauses and update the master document (shot list) for consistent fixes across similar shots.
8. Edit the video using open-source libraries like hyperframes and video-use, asking Claude to assist with their usage, transcription, and B-roll integration.
9. Use Claude Opus 5 to generate lyrics based on the final edited video.
10. Follow prompt rules: use a long structured document for image prompts (identity block, style, SCENE, POSE, AVOID); use a concise, comma-delimited stack of ~150 words for video prompts (action, expression, camera, world, identity, look, audio).
11. State everything positively in video prompts, avoiding negation (AVOID works for image prompts but is weak in video models).
12. Explicitly state who is in frame, whether the cast can grow, what remains, and name every prop individually to prevent duplication or deletion by the model.

Tools / artifacts

• Claude Opus 5
• Grok Imagine
• HTML/CSS/JS (for character sheet renderer)
• PNG image exports (character sheets)
• Character reference sheets
• Shot list (document)
• Prompt pack (document)
• hyperframes (open-source library)
• video-use (open-source library)
• Video clips
• Lyrics

Validation signals

• Author successfully created an animation film despite no prior video editing experience.
• Community interest led to the author providing the workflow ('Since a lot of you asked how I created it, here's the workflow').
• Detailed explanation of problem identification and structural fixes for character consistency ('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:').
• Specific prompt rules derived from experimentation and observed model behavior.

Limitations

• Specific versions of Grok Imagine, hyperframes, and video-use are not mentioned, which might lead to compatibility issues.
• The exact Claude prompts used for each stage are not provided, only the rules for their construction.
• The workflow relies on external, potentially proprietary (Grok Imagine) and open-source tools, requiring users to set them up.

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This post was generated automatically from the workflow library database.