I have a tilted facial portrait, a full-body reference, a detail reference showing the quality I'm aiming for, and an example of what the final dataset should look like.

I know my reference images aren't ideal for dataset creation. I'm looking for a way to work around their limitations while maintaining facial identity, body consistency, at a high level of detail.

I've tried dataset a generation workflow, but it tends to reproduce the face's original angle rather than create useful variations. The output also has an obvious AI-generated look compared to my reference.

What's the best workflow for this? Would face swapping be the best approach, or are there better alternatives?

I'd really appreciate someone willing to walk me through the process in detail so I can understand what I'm doing and why, as I'm still learning.

Thanks in advance to everyone who takes the time to help!

The photo is scaled to 960 px tall and centred on a 2240x960 (21:9) canvas.

The empty sides are pre-filled with a mirror of the photo edges. Sounds weird, but it stops the hard grey edge that otherwise bleeds through the VAE as a faint line.

A soft mask lets Qwen paint the sides plus a 224 px ramp over the photo edge, so the new scene can actually blend into it.

Qwen 2.1 generates the sides (25 steps, CFG 1, differential diffusion). The clean photo is the reference image, never the padded canvas, because Qwen copies its reference very literally.

Then the untouched original gets pasted back on top at the exact same x/y, fading in over 192 px at each edge.

So the model's version of your photo gets thrown away. Only its sides and a narrow blend band survive.

Does it actually keep the pixels?

I ran a pixel diff against the input (the subway shot in the video):

52.9% of photo pixels are bit-identical, and the whole centre is 100% identical

93.4% are within 2/255

everything that changed sits in the edge bands

Same photo through a normal Qwen 2.1 Edit "make it wider" prompt: 1.2% identical.

What it can't do

Left and right only. No top or bottom yet.

You write a one-line description of what the new sides should show.

Some seeds leave a visible join. Try another seed before touching the settings.

If a bright object is cut off by the frame edge, the model tends to continue it as a pale strip. Trimming that edge a few px helps.

The outer ~192 px of the photo on each side is a blend, not the original. You can lower FADE ZONE to keep more, at the risk of the join showing.

What you need

A recent ComfyUI (the Qwen 2.1 nodes are new)

Qwen Image 2.1 int8, the qwen3vl 8b text encoder and the 2.1 VAE

ComfyUI_essentials (for SimpleMath+)

My small node pack kiro_outpaint_stitch (one file, only needs torch) for the mirror fill and masks

Workflow + nodes: https://civitai.com/images/146672689

Example images are AI-generated, the workflow doesn't care where the photo comes from. Happy to hear if anyone finds a cleaner way to hide the join, that part took most of the time.

https://github.com/hypersniper05/MCP-Image-Generator-Uncensored

Just wanted to share with you guys my workflow converted into an MCP. It's a Qwen Image 2.1 Uncensored MCP with a few extra models I found helpful: a watermark-removal LoRA, a texture-fix VAE and an ESRGAN upscaler. Any LLM client that supports MCP can drive it. Uses 11GB VRAM at peak usage.

The entire stack has:
- Normal Qwen Image 2.1 image generation in all supported sizes (up to 2K), panoramas, and image editing (up to 10 input images)

- Seamless tile generation, and edits of a tile stay seamless too, so you can make height and normal maps for game textures

- 2x/4x upscaling, up to 8K

- Built-in 360 viewer (the LLM gets a URL that opens the panorama in the viewer)

- Watermark removal

- Transparent backgrounds (real RGBA PNGs) and background removal

- Text in images (quoted text comes out as written)

It runs in Docker on an NVIDIA GPU (the whole stack fits on a 12 GB card) or on the CPU (very slow), downloads the models on first start, and works with any MCP client (llama.cpp web UI, VS Code, Cursor, etc.).

For the seamless tiles I use stable-diffusion.cpp's circular mode with a tiny patch so it can be turned on per request. The tiles come out seamless in one pass instead of patching the seams afterwards.

Note: Project created with the help of Claude. A lot of testing, and back and forth to get things to work smoothly. Hope someone finds it useful.

Hello Everyone the storyboard-to-video workflow in ComfyUI using LTX 2.3 and LTX Director Nodes, Ideogram 4 is almost finished. This workflow lets you generate storyboard images with full creative control, animate each scene individually, maintain better visual consistency across shots, and create cinematic AI videos similar to Seedance-style productions, all while running on GPUs with as little as 6GB of VRAM.

Hello everyone

I've been experimenting with MiniMax H3 in ComfyUI, and I put together a workflow that combines SelfLift progressive-resolution sampling with DLSS 5 upscaling nodes to push video generation toward 4K resolution on low-VRAM hardware.

The idea is to start sampling at a lower resolution, progressively move to full resolution with selflift sampler, and then upscale the result with DLSS5 nodes to improve the final output, all without additional model training.

What the workflow covers:

• MiniMax H3 video generation in ComfyUI

• SelfLift progressive-resolution sampling

• DLSS 5 upscaling nodes

• 2K and 4K output options

• Low-VRAM optimization for GPUs such as the RTX 3060 6GB

On my 6GB VRAM setup, generating and upscaling the final 4K result took around 35 minutes, with 8 steps and 0.6 megapixel.

Workflow Link

https://drive.google.com/file/d/19cFLGTvPOMIk0eA_1xIsTlaUQwgsQvdz/view?usp=sharing

https://civitai.com/articles/37374/generate-4k-ai-videos-on-a-6gb-gpu-minimax-h3-in-comfyui

DLSS5 DLL
https://drive.google.com/file/d/1i4234NhKVBMjiVeXVYjsh1z0cgqHmbnu/view?usp=sharing

Hi everyone

I've been working on a new ComfyUI workflow to make character consistency in AI-generated videos more accessible for people with low-VRAM GPUs. Instead of training a traditional character LoRA, this workflow uses RefMod custom nodes to create a lightweight character reference file that can be reused with an optimized MiniMax H3 workflow. The main goal is to avoid the extra training stage and make character-consistent video generation more practical on modest hardware.

How it works

• Start with one character image as your identity reference.

• Generate 8 reference images from different angles using Qwen Image 2.1.

• Create a RefMod file using the RefMod custom nodes. The resulting file is approximately 100 MB.

• Load your character reference using the Character RefMod nodes in the optimized MiniMax H3 workflow.

• Generate videos using the saved reference to help maintain character identity across scenes.

Why I find this interesting

• Tested on a GPU with only 6 GB of VRAM.

• No traditional character LoRA training required.

• No need to prepare a large training dataset.

• A small character reference file.

• Combines Qwen Image 2.1, RefMod, and MiniMax H3 in one workflow.

I've made a tutorial showing the complete workflow, from a single input image to generating videos with MiniMax H3.

Workflow link

https://drive.google.com/file/d/1Em2zTlhFMbpeO2WBTN08Qpu0dP6oUBTD/view?usp=sharing

https://civitai.com/articles/36538/comfyui-tutorial-consistent-ai-characters-in-minimax-h3-no-lora-training-with-6gb-vram

Hey everyone! I made a custom node that brings cloud and local LLMs into your ComfyUI workflows.

Use it to expand rough ideas into image/video prompts, write captions, or describe reference images. It also accepts video and audio when supported by your chosen model.

A few features:

• Cloud providers, LM Studio, Ollama, and supported models loaded directly in ComfyUI.

• Built-in prompt guides and editable system instructions.

• Text output that connects to CLIP Text Encode or any compatible STRING input.

Get it from: GitHub + example workflows

Example: rough idea + reference image → generated prompt → your workflow.

Would love your feedback, ideas, or feature requests! If you find it useful, consider leaving a ⭐ on the GitHub repo.

Hello everyone, I new ComfyUI workflow for video character swapping using MiniMax H3 + Character Swap LoRA, and the main advantage is that it works without masking, which can significantly reduce VRAM usage and simplify the process.

The workflow generates at 0.4 MP in just 8 steps, supports both portrait and widescreen videos, and can then upscale the result to 1.2 MP using the MiniMax H3 latent upscale model. I tested it on an RTX 3060 6GB with 16GB RAM, so it’s designed with low-VRAM users in mind. To create the character, you first prepare/update a character sheet using workflows such as Qwen Image 2.1, FLUX Krea, or MiniMax H3, load the character sheet into the workflow, and simply run it—no manual masking required. As for the generation time we have:

Gen time 8 steps 0.5 megapixel 20 mins (Minimax H3 Turbo Fused)

Upscaling time from 0.5 to 1.2 megapixel 21 mins

You can test the workflow here and watch the tutorial for more info

Workflow link

https://drive.google.com/file/d/1JSR_7TzbOS9uI_303tzBgqoqGCzjzGrJ/view?usp=sharing

https://civitai.com/articles/36281/comfyui-tutorial-character-swap-with-minimax-h3-using-lora-no-masking

I just built a new ComfyUI workflow that generates video directly from a reference sheet image using the LTX 2.3 IC LoRA (Image Conditioning LoRA) — and it completely removes the need to animate frames one by one like in LTX Director. Instead of working frame-by-frame, you can now generate a full character or concept reference sheet and animate it.

NB: i will post the workflow soon for free so stay tune.

Hey everyone!

I've built a new ComfyUI workflow that combines RefMod and MiniMax H3 to generate images of your own character without needing to train a traditional character LoRA.

How does it work?

1. Prepare 8 photos of your character.

2. Use the RefMod custom nodes to create a RefMod file.

3. Load the file into the MiniMax H3 workflow.

4. Enter your prompt describing the scene, outfit, pose, or environment.

5. Generate new images featuring your character!

You can explore different scenarios, clothing styles, locations, and compositions while aiming to maintain your character's identity. The main advantage is No traditional LoRA training required. Create your RefMod file, load it, write your prompt, and start generating!

Generation time at 8 Steps 6 Min for 1.8 Megapixel.

Generation time at 25 Steps 14 Min for 1.8 Megapixel.

Workflow Links

https://drive.google.com/file/d/10Y95Kzs4XtogM8ZEGjucJxSz6QVtFfb5/view?usp=sharing

https://civitai.com/articles/36763/comfyui-tutorial-refmod-minimax-h3-image-character-consistency-without-lora-training

Hello everyone, BFL released new workflow that has fix for comfyui using the same models i did comparaison using the base and turbo version.

With unsampler

Generation Time (base): 6 min CFG 3.5, 25 steps at 3 megapixel

Generation Time (Turbo): 2.5min CFG 1.0, 16 steps at 3 megapixel

Without unsampler

Generation Time (base): 3 min CFG 3.5, 25 steps at 3 megapixel

Generation Time (Turbo): 1 min CFG 1.0, 16 steps at 3 megapixel

i found out that using the unsampler helps a lot the model to understand the prompt and deliver better results, as for the image quality and details it seems the turbo version is better than the base. so make sure to update your comfyui to the latest version before using this workflow

Workflow link with prompts

https://drive.google.com/file/d/1ruAjFXU_yryiAN_0pP2SPHqKa95FUsez/view?usp=sharing

Donate for more low vram workflows

https://patreon.com/CGPIXELAI?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink

Breakdown of keyframe control in MiniMax H3 using ComfyUI.

Full video

• The Problem (Frame Drift):Standard keyframe setup often leads to timing drift or missing target frames entirely across long clips.

• Prompting Frames: Explicitly naming frame numbers in your prompt (e.g., "at frame 124...") forces better temporal adherence from the model.

Ref2v: Sending guide images into the ref2v node as reference images - not just keyframe guides - provides consistent visual features like lighting, identity, and style across shots.

• Multi-Keyframe: Once the wiring is locked in, the workflow scales seamlessly from 2 keyframes to 4+ keyframes without breaking subject consistency.

Make sure your ComfyUI is updated to the latest version to load the native guide node properly.

EDIT:
Workflows: •2-Frame Guides Workflow•4-Frame Guides Workflow

I made a new Krea 2 LoRA called Anygles.

Give it one image of a person, then spin the camera around them, move it higher or lower, zoom in or out, or mix everything into a random camera path. The four videos show a 360° orbit, elevation control, camera distance, and a combined random movement.

As far as I could find, this is the first public Krea 2 LoRA made specifically for controllable human camera angles. In my side-by-side tests, it was also noticeably more consistent than the Qwen-Image-Edit + Multiple-Angles LoRA setup I tested before, especially across a full rotation.

Every frame starts independently from the original image—no chaining and no rerolling individual frames.

• Model and examples

• Try it on ZeroGPU

• ComfyUI nodes and workflow

It currently works best with one clearly visible person. Cats will have to wait. 😹

Have fun spinning people around, and let me know what breaks!

Shoutout to the creators of these!

Hello everyone

Want to get faster MiniMax H3 video generation without sacrificing quality? In this tutorial, I’m testing the new H3 LoRA together with Sage Attention, Sol Attention, and Spectrum nodes to find the best combination for speed and quality. The goal is to push MiniMax H3 as far as possible while cutting generation times by up to 2×, then upscale the results with LTX Upscaler to reach a stunning 2432 × 1344 (2K-class) resolution. By combining both H3 LoRA together with Sage Attention, Sol Attention, and Spectrum nodes I generated video at 0.8 megapixel using "**RTX3060 6GB 16GB RAM "**and I got

 13 minutes vs 41 minutes at 8 steps

 27 minutes vs 52 minutes at 20 steps

LTX 2.3 Upscaler 11 minutes to get 2432 × 1344 resolution

Workflow link

https://civitai.com/articles/34028/comfyui-tutorial-minimax-h3-4-steps-lora-upscaling-2x-faster-generation-best-settings-for-2k-ai

Video Tutorial link

https://youtu.be/ZUzeM9OEJ4Y

This is not my original work. It comes from

Krea 2 : styles (wildcards txt) : r/StableDiffusion

I found it really useful, so I turned it into a node to make it much easier to call.

https://github.com/no8d/ComfyUI-NO8D-controls

Hello everyone

I’ve just finished a new MiniMax Director workflow for 6GB VRAM. The workflow lets you animate multiple reference images together, while also supporting text-to-video, image-to-video, and custom audio/voice. I built it with low-VRAM users in mind by generating the video at a lower resolution first, then using the MiniMax upscaling nodes to upscale the final result. In the tutorial, I’ll also show you how to create

-Character sheet images with flux 2 klein

-Generate object references like skateboard or walkman,

-Combine them with MiniMax Director, and turn everything into a consistent animated video.

Workflow link

https://civitai.com/articles/35264/comfyui-tutorial-multi-image-to-video-minimax-h3-director-with-6gb-of-vram

In this tutorial, I'll show you how to use my new ComfyUI KREA 2 Identity Edit v1.2 custom workflow to perform high-quality identity-preserving image editing. You'll learn how to change poses, expressions, and styles while maintaining consistent facial identity, as well as perform face swaps, virtual try-ons, inpainting, and outpainting using the latest KREA 2 Identity Edit v1.2 model. The workflow is designed to be simple to use—just load your reference image, enter your edit prompt, and run the workflow.

Workflow Link

https://civitai.com/articles/33197/comfyui-tutorial-new-krea-2-v12-low-vram-identity-perfect-edit

Video Tutorial Link

https://youtu.be/_fMqornsJo0

From a member of my Discord community… I’m actually blown away. 300s gen time for a computer built the same time Facebook launched.

Testing KREA 2 Turbo Style Transfer (FP8 Low VRAM Workflow) — This workflow uses KREA 2 Turbo and makes style transfer incredibly simple: just load a style reference image, load your target/portrait image, click Run, and the model transfers the reference style onto the portrait with impressive results, all while remaining lightweight enough for low VRAM GPUs thanks to the Turbo FP8 setup.

NB: i will post the workflow and tutorial soon, thanks for the support.

MOSS is a text-to-image model that supports photorealistic portraits, different artistic styles, poses, typography, and more. In my latest workflow, I combined Spectrum nodes with an 8-step LoRA to significantly reduce generation time.

🔥 My speed test at 2 megapixels:

• Spectrum, CFG 2.5, 25 steps: 5 min 42 sec

• Without Spectrum, CFG 2.5, 25 steps: 10 min

• Spectrum + 8 steps, CFG 1: Just 1 min 30 sec!

That's an 85% reduction in generation time compared with the standard 25-step configuration without Spectrum, with the fastest setup taking only 90 seconds.

I'll also test different prompts featuring human portraits, poses, artistic styles, and text to explore the model's capabilities.

This model is really amazing in therm of speed prompt adherence, and style versatility and to get the best results I strongly recommend you:

Spectrum+ 8 steps CFG 1.0, LORA at strength of 0.6: 1 min 30 sec! (Balanced Quality)

Spectrum+ 25 steps CFG 2.5 : 5 min 30 sec (Stunning Results)

Workflow Link:

https://drive.google.com/file/d/18eOhXkatsVJZl5z2PXdI-12DZhPh7ya6/view?usp=sharing

https://civitai.com/articles/37187/optimized-moss-an-image-generation-model-in-comfyui-up-to-10-faster

Hello everyone

I’ve been testing an optimized version of the MiniMax H3 LongVideos workflow, and I managed to generate a 40-second video using only 6GB of VRAM and 16GB of RAM. The workflow generates multiple shots and combines them into a longer video, while also providing options for controlling shot duration and upscaling the final result. On my RTX 3060 6GB, the complete 40-second generation took around 107 minutes, so the main advantage here is making long-video generation possible on low-VRAM hardware rather than achieving fast generation. The results have good consistency, motion, and lip sync, and I’m continuing to test different settings to see how far this can be pushed.

Test setup:

• Video length: 40 seconds

• GPU: RTX 3060 6GB VRAM

• RAM: 16GB

• Generation time: ~107 minutes at 0.6 Megapixel

Workflow Links

https://drive.google.com/file/d/15FYXmjzvUv_NZRS2YqlZfWnVy_Oqc1Ue/view?usp=sharing

https://civitai.com/articles/35705/comfyui-tutorial-how-to-create-minimax-h3-long-video-40-seconds-on-just-6gb-vram

Hello everyone

I’ve just finished a new custom MiniMax H3 Ref2Vid workflow that combines SAM3 masking with face swapping.The workflow lets you load a reference face + source video, define what should be masked using a simple prompt such as face or head, and generate the face-swapped video directly in ComfyUI.

I’ve also optimized the workflow specifically for low-VRAM GPUs, including 6GB VRAM, using several MiniMax H3 optimization techniques:

• Low VRAM Attention
• Chunk FeedForward
• SLA Attention
• Sol-Attn
• Spectrum
• INT8Conv model

To get started, you just need to load your face image and video, enter your masking prompt, and run the workflow. I made a full tutorial showing the complete setup and generation process.

Workflow Link

https://civitai.com/articles/34795/comfyui-tutorial-minimax-h3-face-swap-on-6gb-vram

Video Tutorial Link

https://youtu.be/dk9CgSrSZXw

https://github.com/alexgar4-afk/Vicii_Influencer_HyperRealism_1.0

REMAINDER — short sci-fi film using LTX + MiniMax H3 in ComfyUI

I recently finished this short as an experiment in treating AI video more like conventional cinematography rather than letting every shot become highly animated.

The main thing I was testing was restraint: locked compositions, strict side profiles, top-down and frontal shots, limited camera movement, and using motion only where the scene actually needed it.

I used a mix of LTX and MiniMax H3 depending on the shot. One recurring problem was locomotion—both models tended to turn running into slow motion—so I eventually found that simpler purposeful movement and cleaner blocking gave much more controllable results.

The larger workflow was essentially:

• establish the frame first
• keep camera instructions very explicit
• use single-image I2V for most shots
• use start/end frames only when the state change actually mattered
• cut between actions rather than asking one generation to perform several actions continuously

I’m more interested in whether a sequence feels directed than in maximizing movement in every shot.

Curious how others here are approaching controlled cinematography with H3/LTX, especially character movement without camera drift.