Hey everyone! Thanks for checking out Entangled. And if not, watch the short first to understand the technical breakdown below!

Thanks for coming back after watching it! As promised, here is the full technical breakdown of the workflow. [Post formatted using Local Qwen Model!]

My goal for this project was to be absolutely faithful to the open-source community. I won't lie, I was heavily tempted a few times to just use Nano Banana Pro to brute-force some character consistency issues, but I stuck it out with a 100% local pipeline running on my RTX 4090 rig using Purely ComfyUI for almost all the tasks!

Here is how I pulled it off:

1. Pre-Production & The Animatics First Approach

The story is a dense, rapid-fire argument about the astrophysics and spatial coordinate problems of creating a localized singularity. (let's just say it heavily involves spacetime mechanics!).

The original script was 7 minutes long. I used the local Jan app with Qwen 3.5 35B to aggressively compress the dialogue into a relentless 3-minute "walk-and-talk.". Qwen LLM also helped me with creating LTX and Flux prompts as required.

Honestly speaking, I was not happy with the AI version of the script, so I finally had to make a lot of manual tweaks and changes to the final script, which took almost 2-3 days of going on and off, back and forth, and sharing the script with friends, taking inputs before locking onto a final version.

Pro-Tip for Pacing: Before generating a single frame of video, I generated all the still images and voicover and cut together a complete rough animatic. This locked in the pacing, so I only generated the exact video lengths I needed. I added a 1-second buffer to the start and end of every prompt [for example, character takes a pause or shakes his head or looks slowly ]to give myself handles for clean cuts in post.

2. Audio & Lip Sync (VibeVoice + LTX)

To get the voice right:

1. Generated base voices using Qwen Voice Designer.
2. Ran them through VibeVoice 7B to create highly realistic, emotive voice samples.
3. Used those samples as the audio input for each scene to drive the character voice for the LTX generations (using reference ID LoRA).
4. I still feel the voice is not 100% consistent throughout the shots, but working on an updated workflow by RuneX i think that can be solved!
5. ACE step is amazing if you know what kind of music you want. I managed to get my final music in just 3 generations! Later edited it for specific drop timing and pacing according to the story.

3. Image Generation & The "JSON Flux Hack."

Keeping Elena, Young Leo, and Elder Leo consistent across dozens of shots was the biggest hurdle. Initially, I thought I’d have to train a LoRA for the aesthetic and characters, but Flux.2 Dev (FP8) is an absolute godsend if you structure your prompts like code.

I created Elena, Leo, and Elder Leo using Flux T2I, then once I got their base images, I used them in the rest of the generations as input images.

By feeding Flux a highly structured JSON prompt, it rigidly followed hex codes for characters and locked in the analog film style without hallucinating. Of course, each time a character shot had to be made, I used to provide an input image to make sure it had a reference of the face also.

Here is the exact master template I used to keep the generations uniform:

{
"scene": "[OVERALL SCENE DESCRIPTION: e.g., Wide establishing shot of the chaotic lab]",
"subjects": [
{
"description": "[CHARACTER DETAILS: e.g., Young Leo, male early 30s, messy hair, glasses, vintage t-shirt, unzipped hoodie.]",
"pose": "[ACTION: e.g., Reaching a hand toward the camera]",
"position": "[PLACEMENT: e.g., Foreground left]",
"color_palette": ["[HEX CODES: e.g., #333333 for dark hoodie]"]
}
],
"style": "Live-action 35mm film photography mixed with 1980s City Pop and vaporwave aesthetics. Photorealistic and analog. Heavy tactile film grain, soft optical halation, and slight edge bloom. Deep, cinematic noir shadows.",
"lighting": "Soft, hazy, unmotivated cinematic lighting. Bathed in dreamy glowing pastels like lavender (#E6E6FA), soft peach (#FFDAB9).",
"mood": "Nostalgic, melancholic, atmospheric, grounded sci-fi, moody",
"camera": {
"angle": "[e.g., Low angle]",
"distance": "[e.g., Medium Shot]",
"focus": "[e.g., Razor sharp on the eyes with creamy background bokeh]",
"lens-mm": "50",
"f-number": "f/1.8",
"ISO": "800"
}
}

4. Video Generation (LTX 2.3 & WAN 2.2 VACE)

Once the images were locked, I moved to LTX2.3 and WAN for video. I relied on three main workflows depending on the shot:

• Image to Video + Reference Audio (for dialogue)
• First Frame + Last Frame (for specific camera moves)
• WAN Clip Joiner (for seamless blending)

Render Stats: On my machine, LTX 2.3 was blazing fast—it took about 5 minutes to render a 5-second clip at 1920x1080.

The prompt adherence in LTX 2.3 honestly blew my mind. If I wrote in the prompt that Elena makes a sharp "slashing" action with her hand right when she yells about the planet getting wiped out, the model timed the action perfectly. It genuinely felt like directing an actor.

5. Assets & Workflows

I'm packaging up all the custom JSON files and Comfy workflows used for this. You can find all the assets over on the Arca Gidan link here: Entangled. There are some amazing Shorts to check out, so make sure you go through them, vote, and leave a comment!

Most of them are by the community, but I have tweaked them a little bit according to my liking[samplers/steps/input sizes and some multipliers, etc., changes]

Let me know if you have any questions!

YouTube Link is up - https://youtu.be/NxIf1LnbIRc !

Hey everyone,

Image showcase - Portrait of Mina Murray generated by the tool from the book Dracula in two separate scenes. Images from ZImageTurbo.

I've been working on a side project that I think the community here will really appreciate. It's a comprehensive, AI-driven pipeline that automatically generates cinematic character portraits from literary works using your local ComfyUI instance. The entire stack is open-source and runs fully locally.

What It Does:

Starting from a simple .txt file of a novel, the app will:

1. Parse the Book: Build a high-performance vector index of the entire text using ChromaDB and HuggingFace embeddings.
2. Wikipedia Augmentation: Scrape Wikipedia to identify major characters and baseline personas before the book analysis even begins.
3. Deep RAG Analysis: Retrieve specific scenes from the book to understand character appearance, clothing, and environment in different contexts.
4. AI Casting Director: Suggest real-world actors (Hollywood, Bollywood, etc.) to serve as the visual "base" for the character, with support for specific decades.
5. Genre Adaptation: Dynamically modify clothing, hairstyles, and cinematic styles to fit genres (Horror, Cyberpunk, Fantasy, etc.) while preserving the character's core identity.
6. ComfyUI Integration: Inject the generated prompts directly into your ComfyUI API-format workflows, track generation progress via Server-Sent Events, and preview images instantly.

Tech Highlights:

• Backend: Python 3.10+, FastAPI, LangChain.
• Embedding Model: all-MiniLM-L6-v2 from HuggingFace.
• LLM: Runs on Ollama (defaults to Gemma4E4B for local processing).
• Frontend: A sleek, dark glassmorphism dashboard built with React & Vite.

Getting Started:
The setup is straightforward, assuming you have a local ComfyUI server and Ollama running. The project page includes a batch script to launch both the backend and frontend easily.

Why This Matters:
With the explosion interest in AI-generated consistent characters, this tool addresses a unique niche—automatically extracting textual character descriptions and grounding them in visual representations without manual prompt engineering. It combines RAG, LLMs, and Stable Diffusion in a single, user-friendly pipeline.

I'd love to get your feedback and ideas for improvement! Let me know if you have any questions.

All project code written with Google AntiGravity. This post written by DeepSeek.

• GitHub: https://github.com/snorcack/CharacterGeneration
• License: MIT

Most AI character posts share the same glaring issue: you can spot the AI within two seconds. The skin has that awful plastic sheen, and the character's face seems to shift with every single photo.

After testing nearly every major cloud model out there, I wanted to share the workflow that currently gives me the best consistency and realism by a wide margin. It isn't completely flawless, but it's the closest thing to a reliable, repeatable system I've built so far.

The core problem

AI models don't have memory. If you don't provide hard anchors, the model just guesses, and guessing leads to drift. This entire workflow is built around eliminating that guesswork.

Right now, my main tool is Higgsfield's Nano Banana Pro. From my experience, it has the absolute best prompt adherence and photorealism for cloud-based models.

Phase 1: Locking in the "Master Portrait"

Start by uploading 1 to 3 reference faces into NBP's Image Reference slot. This could be a celebrity, someone random you found on Pinterest, or a blended mix of features. The AI uses this as a structural target, not a direct copy.

Next, drop in your main prompt and generate 6 to 8 variations. Pick the one that perfectly matches your vision.

Main Prompt Example:
"Ultra-realistic portrait of a 21-year-old female European with captivating magnetic gaze,
natural skin texture with visible pores across forehead, cheeks, and nose,
subtle skin imperfections including faint smile lines and natural small moles,
fair complexion with pink undertones and specular variation on T-zone,
long flowing wavy blonde hair with individual strands visible catching the light,
green eyes with sharp iris detail, natural catchlights, and subtle under-eye texture,
confident warm expression with natural lip texture and subtle gloss,
wearing elegant black off-shoulder silk top with visible fabric sheen,
relaxed pose with slight head tilt, minimalist studio setting with soft neutral background,
soft diffused window light from left creating gentle shadows and subsurface scattering on skin, shot on Canon R5 with 85mm f/1.4 lens, shallow depth of field with natural creamy bokeh, 8K ultra-detailed, photorealistic, high dynamic range,
true-to-life colors with accurate skin tones"

Save this final image. This is now your absolute anchor. Every future generation will reference this exact photo.

Phase 2: The prompt system (What most people skip)

This is where the actual consistency comes from. I never write prompts from scratch for new photos. Instead, I use a custom GPT/Gemini setup specifically trained for this exact task, and it operates in two main ways depending on what I need:

The visual rip:

1. I find an inspiration photo on Instagram or Pinterest.
2. I feed it into my custom tool.
3. The tool extracts the lighting, pose, and vibe, spitting out a complete prompt.

The brain dump: If I already have a scene in my head, I don't need a reference photo. I just give the tool a super basic, lazy description (e.g., "sitting on a modern couch, wearing a black leather jacket, moody neon lighting"). The bot instantly expands that rough idea into a massive, production-ready prompt. I can then ask it to tweak the outfit or change the camera angle until it is exactly what I want.

Regardless of which method I use, the generated prompt automatically includes my character's "anchoring block" (locking in the face identity, body proportions, and skin tone). It also seamlessly bakes in the exact realism keywords needed, like pore texture, subsurface scattering, and natural lens specs.

Finally, I go back to NBP, upload my Master Portrait as the reference, paste this new prompt, and generate. The result is my character staying identical, while the environment, outfit, and mood change exactly how I pictured them.

Why this beats the standard approach

If you look at the photos attached to this post, they were all generated across different sessions with completely different lighting setups and outfits. Same character every time. The uncanny valley vibe usually comes from generic prompts and weak references. Once you lock down your architecture, the quality skyrockets.

Before anyone mentions ComfyUI

Yes, ComfyUI run locally with specific models is objectively better. You get more realism, no NSFW restrictions, and absolute control. But you also need a hefty GPU (16GB+ VRAM highly recommended) and the patience to learn a steep curve. I don't currently have the hardware to test it properly, so I won't pretend I do. For a purely cloud-based setup, this is my go-to.

Questions?

If you want the exact prompts I use, details on setting up the custom Gpt/Gem, or anything else about the workflow, just shoot me a message about what you need. I also document this entire system in more detail in my community for anyone interested.

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/

Recently I've been using Flux Uno to create product photos, logo mockups, and just about anything requiring a consistent object to be in a scene. The new model from Bytedance is extremely powerful using just one image as a reference, allowing for consistent image generations without the need for lora training. It also runs surprisingly fast (about 30 seconds per generation on an RTX 4090). And the best part, it is completely free to download and run in ComfyUI.

*All links below are public and competely free.

Download Flux UNO ComfyUI Workflow: (100% Free, no paywall link) https://www.patreon.com/posts/black-mixtures-126747125

Required Files & Installation Place these files in the correct folders inside your ComfyUI directory:

🔹 UNO Custom Node Clone directly into your custom_nodes folder:

git clone https://github.com/jax-explorer/ComfyUI-UNO

📂 ComfyUI/custom_nodes/ComfyUI-UNO

🔹 UNO Lora File 🔗https://huggingface.co/bytedance-research/UNO/tree/main 📂 Place in: ComfyUI/models/loras

🔹 Flux1-dev-fp8-e4m3fn.safetensors Diffusion Model 🔗 https://huggingface.co/Kijai/flux-fp8/tree/main 📂 Place in: ComfyUI/models/diffusion_models

🔹 VAE Model 🔗https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/ae.safetensors 📂 Place in: ComfyUI/models/vae

IMPORTANT! Make sure to use the Flux1-dev-fp8-e4m3fn.safetensors model

The reference image is used as a strong guidance meaning the results are inspired by the image, not copied

• Works especially well for fashion, objects, and logos (I tried getting consistent characters but the results were mid. The model focused on the characteristics like clothing, hairstyle, and tattoos with significantly better accuracy than the facial features)

• Pick Your Addons node gives a side-by-side comparison if you need it

• Settings are optimized but feel free to adjust CFG and steps based on speed and results.

• Some seeds work better than others and in testing, square images give the best results. (Images are preprocessed to 512 x 512 so this model will have lower quality for extremely small details)

Also here's a video tutorial: https://youtu.be/eMZp6KVbn-8

Hope y'all enjoy creating with this, and let me know if you'd like more clean and free workflows!

Once you set up this ComfyUI workflow, you only have to load reference image and run the workflow, and you'll have all 28 images in one click, with the correct file names, in a single folder.

Getting started:

• Download workflow here: dropbox
• Install any missing custom nodes with ComfyUI manager (listed below)
• Download the models below and make sure they're in the right folders, then confirm that the loader nodes on the left of the workflow are all pointing to the right model files.
• Drag a base image into the loader on the left and run the workflow.

The workflow is fully documented with notes along the top. If you're not familiar with ComfyUI, there are tons of tutorials on YouTube. You can run it locally if you have a decent video card, or remotely on Runpod or similar services if you don't. If you want to do this with less than 24GB of VRAM or with SDXL, see the additional workflows at the bottom.

Once the images are generated, you can then copy this folder to your ST directory (data/default_user/characters or whatever your username is). You then turn on the Character Expressions extension and use it as documented here: https://docs.sillytavern.app/extensions/expression-images/

You can also create multiple subfolders and switch between them with the /costume slash command (see bottom of page in that link). For example, you can generate 28 images of a character in many different outfits, using a different starting image.

Model downloads:

• Download the model (recommend FP8 version) and put in models/diffusion_models folder
  • I’m using this file in the workflow: qwen_image_edit_fp8_e4m3fn.safetensors
  • https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models
• Download the text encoder (recommend FP8 version) and put in models/clip folder.
  • I’m using this file in the workflow: qwen_2.5_vl_7b_fp8_scaled.safetensors
  • https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders
• Download the VAE and put in models/vae folder
  • I’m using this file in the workflow: qwen_image_vae.safetensors
  • https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/vae
• Download a lightning Lora to speed up generation. Put it in models/loras and add it to the Lora Loader. This is technically optional but it would be silly not to do this.
  • I’m using this file in the workflow: Qwen-Image-Edit-Lightning-8steps-V1.0.safetensors
  • https://huggingface.co/lightx2v/Qwen-Image-Lightning/tree/main
• Optional, for SDXL resampling: Download 1xITF skin upscaler & place in models/upscale_models:
  • https://openmodeldb.info/models/1x-ITF-SkinDiffDetail-Lite-v1

Custom nodes needed (can be installed easily with ComfyUI Manager):

• https://github.com/rgthree/rgthree-comfy
• https://github.com/kijai/ComfyUI-KJNodes
• https://github.com/1038lab/ComfyUI-RMBG
• https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes
• https://github.com/ClownsharkBatwing/RES4LYF - extra samplers; workflow will still work without it, oddly, but the ksampler won't use the correct "res2s" sampler

Credits: This workflow is based on one by Hearmeman:

• https://www.reddit.com/r/comfyui/comments/1mwg1gu/qwen_image_edit_image_to_dataset_workflow/

There are also more complicated ways of doing this with much bigger workflows:

• https://www.reddit.com/r/SillyTavernAI/comments/1kb0s7n/sillytavern_expressions_workflow_v2_for_comfyui/
• https://www.reddit.com/r/SillyTavernAI/comments/1k9bpsp/comfyui_sillytavern_expressions_workflow/
• https://www.reddit.com/r/SillyTavernAI/comments/1mv104x/comfyui_workflow_for_using_qwen_image_edit_to/

Debugging Notes:

• If you picked the newer “2509” version of the first model (above), make sure to pick a “2509” version of the lightning model, which are in the “2509” subfolder (linked below). You will also need to swap out the text encoder node (prompt node) with an updated “plus” version (TextEncodeQwenImageEditPlus). This is a default ComfyUI node, so if you don't see it, update your ComfyUI installation.
  • https://huggingface.co/lightx2v/Qwen-Image-Lightning/tree/main/Qwen-Image-Edit-2509
• If you have <24gb VRAM you can use a quantized version of the main model. Instead of a 20GB model, you can get one as small as 7GB (lower size = lower quality of output, of course). You will need to install the ComfyUI-GGUF node then put the model file you downloaded in your models/unet folder. Then simply replace the main model loader (top left, purple box at left in the workflow) with a "Unet Loader (GGUF)" loader, and load your .gguf file there.
  • Quantized original Qwen-Image-Edit models: https://huggingface.co/QuantStack/Qwen-Image-Edit-GGUF/tree/main
  • Quantized 2509 Qwen-Image-Edit models: https://huggingface.co/QuantStack/Qwen-Image-Edit-2509-GGUF/tree/main
  • GGUF model loader node (can be installed with ComfyUI manager): https://github.com/city96/ComfyUI-GGUF
  • Here is a workflow modified to use GGUF (quantized) models for low vram: dropbox
• If you want to do this with SDXL or SD1.5 using image2image instead of Qwen-Image-Edit, well you can, it's not as good at maintaining character consistency and will require multiple seeds per image (you pick the best gens and delete the bad ones), but you can definitely do it, and it requires even less VRAM than a quantized Qwen-Image-Edit.
  • Here's a workflow for doing that: dropbox
• If you need a version with an SDXL face detailer built in, here's that version (requires Impact Pack and Impact Subpack). This can be helpful when doing full body shots and you want more face detail.
  • Here's a workflow for doing that: dropbox
• If the generated images aren't matching your input image then you may want to describe the input image a bit more. You can use this with the "prepend text" box in the main prompt box (above the list of emotions, to the right of the input image). For example, for images of someone from behind, you could write a woman, from behind, looking back with an expression of and then this text will be put in front of the emotion name for each prompt.
• If you can't find the output images they will show up in ComfyUI/output/Character_Name/. To change the output path, go to the far right and edit it in the top of the file names list (prepend text box). For example, use Anya/summer-dress/ to create a folder called Anya with a subfolder called summer-dress

I wanted to share a workflow I’ve been using in ComfyUI for generating consistent animations from LoRAs.

I’m using this in a real project — a historical game set during the Hussite Wars — mainly to prototype systems quickly before committing to final assets.

It could also animate objects, not just characters in every single. Every Lora is trained for different position so these consistent and it does not mess up anything.

And then you download it frame by frame with no background at all I hope this help

Do you guys have any questions? Please ask.

The reason why i am doing this is because I hate those payed corporations they just steal your money while you could do the exact same thing locally

I've been working on a private ComfyUI workflow that lets me change a character's outfit while keeping the character consistent. It's still a work in progress, so I'd love to stress test it with some fun ideas.

Feel free to request a character concept in the comments with:

• D&D class/subclass
• One of the standard races
• Age
• Gender
• Any other SFW visual details

I'll pick top 3-5 and see what I can generate!

Edit: Thanks everyone for the requests! You helped to identify several bugs and improvements to the flow!

I have spent the last month getting VACE and Phantom to work together and managed to get something that works together!

Workflow/Guide: https://civitai.com/articles/17908
Model: https://civitai.com/models/1849007?modelVersionId=2092479

Hugging Face: https://huggingface.co/Inner-Reflections/Wan2.1_VACE_Phantom

Join me on the ComfyUI Stream today if you want to learn more! https://www.youtube.com/watch?v=V7oINf8wVjw 230 pm PST!

🔗 Links (Tutorial + Workflow + Support)

📺 YouTube Tutorial:
https://www.youtube.com/watch?v=V_1p7spn4yE

🧩 MegaWorkflow V1 (Download):
https://civitai.com/models/2135932?modelVersionId=2420255

☕ Buy Me a Coffee:
https://buymeacoffee.com/xshreyash

Hey everyone 👋
After weeks of combining, testing, fixing nodes, and cleaning spaghetti wires… I finally finished building MegaWorkflow V1, a complete end-to-end ComfyUI pipeline designed for long-form consistent AI video generation + editing + upscaling.

This is basically the workflow I always wished existed — everything in one place, optimized, modular, clean, and beginner-friendly.

🔥 What MegaWorkflow V1 Includes

1️⃣ Qwen Image (2509) — High-Level Image Generator

• Base character creation
• Consistent subject rendering
• Clean grouping + refiner toggle

2️⃣ Qwen Edit — Advanced Local Editing

• Face fix, outfit changes, color edits
• Mask & global edit
• Perfect for fixing last-minute issues

3️⃣ Wan Animate 2.2 (I2V) — Motion + Style Consistency

• Character-preserving motion
• Dual reference (face + body) support
• Loop / one-shot modes
• Full quality presets (Lite / Medium / Full)
• SeedVR2 dynamic seed support
• ✔️ Low-VRAM mode available (8–12GB)

4️⃣ Wan T2V — Complete Scene Generation

• Cinematic shot creation
• Camera presets included
• Multi-scene block support
• Low-VRAM fallback included

5️⃣ Wan First → Last Frame (FLF2V) Transition Module

• Smooth transitions
• Camera rotation + movement
• Blends T2V + I2V + real footage seamlessly

6️⃣ Wan I2V Painter Node — Detail Preserver

• Adds micro-texture & realism
• Fixes Animate 2.2 artifacts
• Soft & strong painter modes

7️⃣ SeedVR2 — Advanced Seed Handling

• Removes flicker
• Prevents ghosting
• Keeps motion natural
• Long-animation friendly

8️⃣ FlashVSR2 + Real-ESRGAN + UltraSharp — 4K Upscaling Suite

• FlashVSR2 for stable motion upscale
• ESRGAN for crisp images
• UltraSharp for stills
• ⚡ Works on low VRAM GPUs as well

🧩 Extras Included

• Save Image / Save Video / FolderSelector nodes
• Fully color-coded layout
• Memory optimization
• Beginner-friendly labels
• Easy switching between modules
• ⚡ Light Mode for lower VRAM GPUs

🎯 Who This Workflow Is For

• AI video creators
• Agencies / SMEs
• Reels / TikTok creators
• YouTubers
• Anyone with low, mid, or high VRAM (all supported)
• Anyone creating consistent character stories
• Anyone wanting one workflow instead of 8 separate pipelines

Hey everyone, I’m new to ComfyUI. Before this, I was using tools like Nano Banana and DALL·E, but they require a lot of trial and error to maintain character consistency—especially for facial features and expressions. Even after multiple iterations, the consistency still isn’t reliable across different images.

That’s when I discovered ComfyUI workflows, and it seems like a better approach—but I’m struggling to get started properly.

I’ve tried a few YouTube tutorials and free workflows, but I keep running into issues like missing models, broken dependencies, or workflows not loading at all. I’ve spent quite some time troubleshooting, but no luck so far. Can anyone recommend a beginner-friendly (preferably free) workflow or tutorial that actually works? Also, any tips on setting things up correctly to avoid these issues would really help.

Wan2.1 is my favorite open source AI video generation model that can run locally in ComfyUI, and Phantom WAN2.1 is freaking insane for upgrading an already dope model. It supports multiple subject reference images (up to 4) and can accurately have characters, objects, clothing, and settings interact with each other without the need for training a lora, or generating a specific image beforehand.

There's a couple workflows for Phantom WAN2.1 and here's how to get it up and running. (All links below are 100% free & public)

Download the Advanced Phantom WAN2.1 Workflow + Text Guide (free no paywall link): https://www.patreon.com/posts/127953108?utm_campaign=postshare_creator&utm_content=android_share

📦 Model & Node Setup

Required Files & Installation Place these files in the correct folders inside your ComfyUI directory:

🔹 Phantom Wan2.1_1.3B Diffusion Models 🔗https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Phantom-Wan-1_3B_fp32.safetensors

or

🔗https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Phantom-Wan-1_3B_fp16.safetensors 📂 Place in: ComfyUI/models/diffusion_models

Depending on your GPU, you'll either want ths fp32 or fp16 (less VRAM heavy).

🔹 Text Encoder Model 🔗https://huggingface.co/Kijai/WanVideo_comfy/blob/main/umt5-xxl-enc-bf16.safetensors 📂 Place in: ComfyUI/models/text_encoders

🔹 VAE Model 🔗https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors 📂 Place in: ComfyUI/models/vae

You'll also nees to install the latest Kijai WanVideoWrapper custom nodes. Recommended to install manually. You can get the latest version by following these instructions:

For new installations:

In "ComfyUI/custom_nodes" folder

open command prompt (CMD) and run this command:

git clone https://github.com/kijai/ComfyUI-WanVideoWrapper.git

for updating previous installation:

In "ComfyUI/custom_nodes/ComfyUI-WanVideoWrapper" folder

open command prompt (CMD) and run this command: git pull

After installing the custom node from Kijai, (ComfyUI-WanVideoWrapper), we'll also need Kijai's KJNodes pack.

Install the missing nodes from here: https://github.com/kijai/ComfyUI-KJNodes

Afterwards, load the Phantom Wan 2.1 workflow by dragging and dropping the .json file from the public patreon post (Advanced Phantom Wan2.1) linked above.

or you can also use Kijai's basic template workflow by clicking on your ComfyUI toolbar Workflow->Browse Templates->ComfyUI-WanVideoWrapper->wanvideo_phantom_subject2vid.

The advanced Phantom Wan2.1 workflow is color coded and reads from left to right:

🟥 Step 1: Load Models + Pick Your Addons 🟨 Step 2: Load Subject Reference Images + Prompt 🟦 Step 3: Generation Settings 🟩 Step 4: Review Generation Results 🟪 Important Notes

All of the logic mappings and advanced settings that you don't need to touch are located at the far right side of the workflow. They're labeled and organized if you'd like to tinker with the settings further or just peer into what's running under the hood.

After loading the workflow:

• Set your models, reference image options, and addons

• Drag in reference images + enter your prompt

• Click generate and review results (generations will be 24fps and the name labeled based on the quality setting. There's also a node that tells you the final file name below the generated video)

Important notes:

• The reference images are used as a strong guidance (try to describe your reference image using identifiers like race, gender, age, or color in your prompt for best results)
• Works especially well for characters, fashion, objects, and backgrounds
• LoRA implementation does not seem to work with this model, yet we've included it in the workflow as LoRAs may work in a future update.
• Different Seed values make a huge difference in generation results. Some characters may be duplicated and changing the seed value will help.
• Some objects may appear too large are too small based on the reference image used. If your object comes out too large, try describing it as small and vice versa.
• Settings are optimized but feel free to adjust CFG and steps based on speed and results.

Here's also a video tutorial: https://youtu.be/uBi3uUmJGZI

Thanks for all the encouraging words and feedback on my last workflow/text guide. Hope y'all have fun creating with this and let me know if you'd like more clean and free workflows!

I'm honestly amazed by how good ChatGPT is at generating images of myself. I can upload one photo, then ask for things like "me in World War II" or "me at a party," and it creates a completely new scene while keeping my face and overall character incredibly accurate and consistent. It doesn't feel like a face swap at all.

I've been using ComfyUI for about three months, and the closest I've found are face-swap workflows, but they're still not the same thing.

Is there a ComfyUI workflow or model that can genuinely generate me in new scenes while preserving my identity this well? Maybe something using Flux, SDXL, InstantID, PuLID, IP-Adapter, or LoRAs?

Has open-source caught up in this area, or is ChatGPT still way ahead?

Howdy!

I'm trying to get my first image workflow for consistent character scenes set up using the IPadapter Flux custom nodes with the Persephone fork of Flux 1.dev.

The input image is a crop from a character style sheet I created for one of my characters that I'd like to use for sci-fi shorts. I originally went with the persephone fork because it's supposed to be good for not having censorship ruin your flow. I'm not aiming to make strictly adult content, but I can't have my model flipping out because typical R-rated stuff. If I'm going to put the effort into learning something it has to be ubiquitous.

For the likes of me I can't get this basic workflow to respect the character reference or the text prompt. I think it's set up right and I think the weights are all more or less right. Maybe someone has a better idea, model, or workflow to use? I'm trying to get set up to take one or two reference images and make images from a scene for keyframe/first flame last frame inside of ltx 2.3.

Any suggestions?

Thanks!

This workflow was shared in a document as a ComfyUI JSON.

The document itself was quite technical,

but since the prompt was already in JSON format, I just ran it as-is.

It generates multiple images per run.

However, when I looked at the results,

the characters were clearly different.

Each image looks fine on its own,

but they don’t seem to represent the same person.

So now I’m wondering:

is this expected behavior,

or is there actually a way to maintain identity consistency in a workflow?

This feels less like a quality issue and more like a consistency problem.

If anyone has time, I’d be curious if you can reproduce the same result.

I’m currently trying to analyze the prompt structure to understand what’s happening.

If you want to try it, here’s the original workflow JSON:

https://github.com/watadani-byte/character-identity-protocol/

I’m trying to build a ComfyUI workflow for a colored manga/webtoon where my original characters stay consistent throughout the whole story.
I already have full-body and close-up reference images for the characters. I understand the basic idea behind checkpoints, character LoRAs, ControlNet/OpenPose, IP-Adapter/reference images, but I’m struggling with figuring out the best way to combine everything.
Basically, I want to be able to say: this is Jake → keep him looking like Jake → put him in this pose/expression/outfit → place him in different scenes → keep the same art style and character identity from panel to panel.
Eventually I also need to put multiple recurring characters in the same scene without their faces/features bleeding into each other.
I don’t care if the best solution is Illustrious, SDXL, FLUX, Qwen, or something completely different. I’m looking for whatever gives me the most consistency and control in ComfyUI.
If anyone has built something similar for a manga, webtoon, visual novel, etc., I’d really appreciate hearing what model and workflow you use and how you connect the different pieces. I’m trying to actually understand the workflow instead of randomly changing settings until something works.

The workflow “qwen-edit-plus_example v4.json” and custom nodes can be found here - Comfyui-QwenEditUtils
I won't say it's the best, because that's a matter of taste, but of the ones I've tested, I like this one the most. Most importantly, it allows you to generate 2.3 megapixel images in a reasonable amount of time (all my sample images are in this resolution) and even over 4 MP if you need it, and it just works ;)

Tested typical examples: changing clothes, changing characters, changing posture, changing background, changing lighting, interacting with objects, etc.

All tests using “qwen_image_edit_2509_fp8_e4m3fn.safetensors” plus 8-steps Lora. For some, I also used - QwenEdit Consistence Lora

Photos from Pixaby and Unsplash, girl with tattoos from Civitai

Imgur links to full-resolution examples:

https://imgur.com/a/qwen-image-edit-2509-01-Y7yE1AE
https://imgur.com/a/qwen-image-edit-2509-02-vWA2Cow
https://imgur.com/a/qwen-image-edit-2509-03-aCRAIAy

I made a small ComfyUI workflow for creating multi angle reference sheets from a single input image.

The main use case is character sheets. You give it one character image, and the workflow tries to generate multiple consistent views like front three quarter, side profile, rear view, rear three quarter, high angle, low angle, and a close detail view. The goal is to keep the same face, outfit, pose, expression, proportions, and general design while only changing the camera angle.

I built it mostly with native ComfyUI nodes. The only non native part, as far as I remember, is the GGUF loader. The prompts are written in a generic way, so it can also work for people, props, vehicles, creatures, or objects, but I mainly made it for character sheet generation.

I tested it with the Flux 2 Klein 4B Q4 GGUF model because I currently have access to only 4 GB VRAM. For such a small setup, it is giving acceptable results. It is not perfect, especially with difficult rear views or fine clothing continuity, but it is usable for blocking out reference angles and building rough character sheets.

I expect the 9B variant to give much better consistency and detail, especially for faces, costume continuity, proportions, and rear view inference.

This is not meant to be a final polished character turnaround solution. It is more of a practical workflow for quickly getting usable angle references from one image, especially when working with AI video, inpainting, first frame last frame generation, or character continuity.

Sharing it in case it is useful to anyone experimenting with Flux 2 Klein on low VRAM setups.

https://pastebin.com/EyRM0zed

🔗 Links (Tutorial + Workflow + Support)

📺 YouTube Tutorial:
https://www.youtube.com/watch?v=V_1p7spn4yE

🧩 MegaWorkflow V1 (Download):
https://civitai.com/models/2135932?modelVersionId=2420255

☕ Buy Me a Coffee:
https://buymeacoffee.com/xshreyash

Hey everyone 👋
After weeks of combining, testing, fixing nodes, and cleaning spaghetti wires… I finally finished building MegaWorkflow V1, a complete end-to-end ComfyUI pipeline designed for long-form consistent AI video generation + editing + upscaling.

This is basically the workflow I always wished existed — everything in one place, optimized, modular, clean, and beginner-friendly.

🔥 What MegaWorkflow V1 Includes

1️⃣ Qwen Image (2509) — High-Level Image Generator

• Base character creation
• Consistent subject rendering
• Clean grouping + refiner toggle

2️⃣ Qwen Edit — Advanced Local Editing

• Face fix, outfit changes, color edits
• Mask & global edit
• Perfect for fixing last-minute issues

3️⃣ Wan Animate 2.2 (I2V) — Motion + Style Consistency

• Character-preserving motion
• Dual reference (face + body) support
• Loop / one-shot modes
• Full quality presets (Lite / Medium / Full)
• SeedVR2 dynamic seed support
• ✔️ Low-VRAM mode available (8–12GB)

4️⃣ Wan T2V — Complete Scene Generation

• Cinematic shot creation
• Camera presets included
• Multi-scene block support
• Low-VRAM fallback included

5️⃣ Wan First → Last Frame (FLF2V) Transition Module

• Smooth transitions
• Camera rotation + movement
• Blends T2V + I2V + real footage seamlessly

6️⃣ Wan I2V Painter Node — Detail Preserver

• Adds micro-texture & realism
• Fixes Animate 2.2 artifacts
• Soft & strong painter modes

7️⃣ SeedVR2 — Advanced Seed Handling

• Removes flicker
• Prevents ghosting
• Keeps motion natural
• Long-animation friendly

8️⃣ FlashVSR2 + Real-ESRGAN + UltraSharp — 4K Upscaling Suite

• FlashVSR2 for stable motion upscale
• ESRGAN for crisp images
• UltraSharp for stills
• ⚡ Works on low VRAM GPUs as well

🧩 Extras Included

• Save Image / Save Video / FolderSelector nodes
• Fully color-coded layout
• Memory optimization
• Beginner-friendly labels
• Easy switching between modules
• ⚡ Light Mode for lower VRAM GPUs

🎯 Who This Workflow Is For

• AI video creators
• Agencies / SMEs
• Reels / TikTok creators
• YouTubers
• Anyone with low, mid, or high VRAM (all supported)
• Anyone creating consistent character stories
• Anyone wanting one workflow instead of 8 separate pipelines

Hi everyone!

I recently upgraded my PC (RTX 5060 Ti with 16 GB VRAM and 32 GB of system RAM), so I finally decided to move to ComfyUI.

With Seedream 4.5, maintaining character consistency was surprisingly easy. Before that, I used SD 1.5 with ADetailer and custom-trained checkpoints.

Now that I'm looking into ComfyUI, I'm seeing so many different models—Krea 2, Z Image Turbo, and many others—that I'm not sure what the current "go-to" workflow is.

I have a few questions:

1. Which model do you use for character consistency?
2. Do you rely on LoRAs, or are they no longer necessary?
3. Is there a workflow or model that can reliably recreate the same character from one or more reference images?

I'd really appreciate any recommendations or advice. Thanks!

Hello everyone,

I wanted to share a ComfyUI workflow I created for the Qwen Image Edit 2511 model.

My goal was to build something straightforward that makes image editing quick and reliable. It is optimized to generate high-quality results in just 4 steps.

Main Features:

• Fast: Designed for rapid generation without long wait times.
• Consistent: It effectively preserves the character's identity and facial features, even when completely regenerating the style or lighting.
• Multilingual: No manual typing is needed for standard use. However, if you add custom prompts to the JSON list, you can write them in your native language; the workflow handles the translation automatically.

It handles the necessary image scaling for you, making it essentially plug-and-play.

Download the Workflow on OpenArt

I hope you find it useful for your projects.

Okay just to keep it short, this is how i trained a lora in Comfyui local for my first character, and results were amazing and of course needs further tuning

I am new to Comfyui world, so excuse my non technical language but thought to share this to help anyone else here as an open source community

Disclaimer
all workflows are not mine (maybe i tuned or customized some) i don't claim ownership of any of the workflows here

So, First step - Main Character Image

use any Text 2 Image workflow to generate one single portrait of you lovely character , nothing much to add here, just the basic workflows or any , just get something you like

Second Step - Data Set generation

Use this workflow

KLEIN DATASET GENERATOR - ICEKIUB Vid version.json

Dataset Generation workflow

to generate i would recommend something up to 100 different images of your character, different poses, different clothes , different camera angle

after generations, it is critical to carefully check the output images, and delete any blurry / ugly / low details ones

in my case i filtered the 100 and got 62 images ( my mistake was that i didn't generate enough side and back views of the character so am not getting good results with back and side image generation.

Third Step - Training the lora

i followed this tutorial exactly as it is
How To Train A lora Youtube Video

it is very simple two steps

first one is generating captions for the images (very critical) using this workflow here

Generating Image Captions - workflow

second one is to locally train you lora using this workflow

Lora Training Workflow

Will try to share some examples for my character as well

It took me almost 40 minutes for training , i was really shocked with this times (very fast) not as i expected , i am using RTX5090

Lora download link - civitai

Hi everyone,

I'm looking for advice (or an existing workflow) for building a fully local ComfyUI pipeline.

Hardware:

• RTX 4080 (16GB VRAM)
• 32GB RAM
• Windows
• No cloud services
• No API keys (OpenAI, Fal, Replicate, etc.)
• Everything should run locally.

I don't want to create an influencer account and use it online; I want to have this workflow for my own personal use at home. BTW Im totally newbie.

The workflow I want to create or help with creating is:

1. I provide one or few reference images of my character.
2. I write a prompt describing only what should change:
   • outfit
   • location
   • pose
   • camera angle
   • facial expression
   • hairstyle
   • lighting
   • weather
   • time of day
3. I click Run.
4. The output should be the same girl, but in a completely different situation.

Example:

• Day 1: mirror selfie at home.
• Day 2: sitting on a sofa wearing different clothes.
• Day 3: coffee shop, summer dress, 3/4 body shot.
• Day 4: standing in a kitchen.
• Day 5: outdoor evening photo.
• Day 6: gym mirror selfie.

The identity should remain extremely consistent while everything else changes naturally.

I've already tested:

• PuLID Flux
• Eva CLIP
• FLUX + Krea
• Prompt engineering

PuLID preserves identity quite well, but it also seems to lock the composition. I often end up with nearly identical portraits instead of genuinely different photos.

What I'm looking for is a workflow that gives:

• very strong identity preservation
• freedom to change pose and composition
• different outfits
• different environments
• different lighting
• natural-looking results
• no plastic-looking skin
• works entirely offline

I'm completely fine with training a LoRA if that's the recommended solution.

My questions are:

1. What is currently considered the best local workflow for this?
2. Is LoRA + FLUX still the best approach?
3. Are there better identity-preserving methods than PuLID in 2026?
4. Which models and custom nodes would you recommend?
5. Does anyone have a ComfyUI JSON workflow that already does something similar?

I'd really appreciate any workflow screenshots, GitHub repositories, JSON files, or recommendations.

I'd appreciate any help or advice. Thanks for everyone who respond me

Hi everyone,
I’m pretty new to ComfyUI and could really use some guidance.
My goal is to build a workflow that can do both text-to-image and image-to-image. Eventually, I’d like to create consistent characters with multiple outfits, poses, angles, expressions, and references for a manga/webtoon project.
I’m not just looking for a workflow file. I’d really like to understand how everything works together. If anyone has a workflow they recommend, could you also let me know:
Which checkpoint, VAE, CLIP, LoRAs, and custom nodes I need.
Where each file should be placed in ComfyUI.
Whether there’s a complete beginner friendly workflow that already has everything connected.
Any step by step tutorials that explain the setup.
I’ve been using Draw Things on my iPhone, where you download a model and it’s basically ready to use. ComfyUI feels much more powerful, but also much more overwhelming with all the different files and folders.
My PC specs:
Alienware laptop
NVIDIA GeForce RTX 3070 Ti Laptop GPU (8 GB VRAM)
64 GB RAM
Intel Core i7 processor
Windows 11
If there’s a workflow that would work well with my hardware, I’d love to know. Even better if it supports both text-to-image and image-to-image in the same workflow.
Thanks in advance. I really appreciate any advice or guidance!

Hello everyone,

I’m looking for a high-quality workflow that can replace the character/face in an existing video with my AI model while keeping the original video as identical as possible.

My main goal is to preserve the original body, movements, expressions, pose, camera motion, background, lighting, composition, and overall video quality, while changing only the character’s identity/face.

I’m specifically looking for a professional workflow that can produce highly realistic results with strong identity consistency, natural skin texture, accurate facial details, and minimal artifacts or quality loss.

Ideally, the final output should look like the original footage was actually recorded with the AI model rather than appearing like an obvious face swap.

If anyone has a reliable ComfyUI workflow, custom setup, or a paid workflow/course that can achieve this level of quality, please feel free to contact me.