The goal of this lora is to reproduce the video style similar to live wallpaper, for those who play league of legends remember the launcher opening videos, that's the goal, but you can also use it to create your lofi videos :D enjoy.
[Wan2.2 TI2V 5B - Motion Optimized Edition] Trained on 51 curated videos (24fps, 96 frames) for 5,000 steps across 100 epochs with rank 48. Optimized specifically for Wan2.2's unified TI2V 5B dense model and high-compression VAE. Delivers superior motion quality compared to larger model variants while maintaining consumer-GPU efficiency.
My Workflow (It's not organized, the important thing is that it works hahaha): 🎮 Live Wallpaper LoRA - Wan2.2 5B (Workflow) | Patreon
Trigger word: l1v3w4llp4p3r
Note: since civitai does not have Wan2.2 models, I will set this model here as Wan2.1, do not use civitai's creation mode because you will probably lose your credits.
[Wan2.2 I2V A14B - Full Timestep Edition]
Trained on 301 curated videos (256px, 16fps, 49 frames) for 24 hours using Diffusion Pipe with Automagic optimizer, rank 64. Uses extended timestep range (0-1) instead of standard (0-0.875), enabling compatibility with both Low and High models despite training only on Low model.
Trigger word: l1v3w4llp4p3r
Works excellently with LightX2V v2 (256 rank) for faster inference - recommended starting strength: 2.0 for both LoRAs to avoid artifacts. Loop workflows not yet tested.
[Wan I2V 720P Fast Fusion - 4 (or more) steps]
Wan I2V 720P Fast Fusion combines 2 Live Wallpaper LoRA (1 Exclusive) with Lightx2v, AccVid, MoviiGen and Pusa LoRAs for ultra-fast 4+ steps generation while maintaining cinematic quality.
🚀 Lightx2v LoRA – accelerates generation by 20x through 4-step distillation, enabling sub 2-minute videos on RTX 4090 with only 8GB VRAM requirements.
🎬 AccVid LoRA – improves motion accuracy and dynamics for expressive sequences.
🌌 MoviiGen LoRA – adds cinematic depth and flow to animation, enhancing visual storytelling.
🧠 Pusa LoRA – provides fine-grained temporal control with zero-shot multi-task capabilities (start-end frames, video extension) while achieving 87.32% VBench score.
🧠 Wan I2V 720p (14B) base model – providing strong temporal consistency and high-resolution outputs for expressive video scenes.
[Wan I2V 720P]
The dataset used consists of 149 videos (each one hand-selected) in 1280x720x96 resolution but was trained in 244p and 480p and 64 frames with 64 dim (L40s).
Trigger word was used so it needs to be included in the prompt: l1v3w4llp4p3r
[Hunyuan T2V]
The dataset used consists of 529 videos (each one hand-selected) in 1280x720x96 resolution but was trained in 244p and 72 frames with 64 dim (multiple RTX 4090).
No captions or activation words were used, the only control you will need to adjust is the lora strength.
Another important note is that it was trained in full blocks, I don't know how it will behave when mixing 2 or more loras, if you want to mix and are not getting a good result, try disabling single blocks.
I recommend using lora strength between 0.2 and 1.2 maximum, resolution 1280x720 or generate at 512 and upscale later, minimum 3 seconds (72 frames + 1).
[LTXV I2V 13b 0.9.7 – Experimental v1]
The model was trained on 140 curated videos (512px, 24fps, 49 frames), using 250 epochs, 32 dim, and AdamW8bit.
It was trained using Diffusion Pipe with support for LTXV I2V v0.9.7 (13B).
Captions were used and generated with Qwen2.5-VL-7B via a structured prompt format.
This is an experimental first version, so expect some variability depending on seed and prompt detail.
Recommended:
Scheduler: sgm_uniform
Sampler: euler
Steps: 30
⚠️ Long prompts are highly recommended to avoid motion artifacts.
You can generate captions using the Ollama Describer or optionally use the official LTXV Prompt Enhancer.
For more details, see the About this version tab.
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For more details see the version description
Share your results.
Description
🧪 Overview
This is an experimental LoRA trained for the LTXV I2V (Image-to-Video) model, version 0.9.7, with 13B parameters. It transforms static images into fluid, seamless animated loops, with natural motion applied only to flexible or dynamic elements — like hair, clothing, particles, and ambient light — while preserving rigid structure stability (e.g., armor, weapons, mechanical parts).
This is the first version of this LoRA, and results may vary depending on the prompt quality and seed. Better versions may be released in the future as training techniques are refined.
My Workflow
⚙️ Training Details
- 🧠 Base Model: LTXV I2V v0.9.7 (13B parameters)
- 🎞️ Video Dataset: 140 short clips
- ⏱️ Frame Rate: 24 fps
- 🧮 Frames per Video: 49
- 🖼️ Resolution: 512px
- 🔁 Epochs: 250
- 🧮 Total Training Steps: ~35,000
- 📉 Learning Rate: 1e-4
- 📦 Batch Size: 1
- 📐 LoRA Dimension: 32
- ⚙️ Optimizer: AdamW8bit
- 🛠️ Trainer Used: Diffusion Pipe (by tdrussell)
- 🚫 Not using official trainer: LTX-Video-Trainer (by Lightricks)
- Layer Coverage:When trained using Diffusion Pipe, all layers were updated during LoRA training.In contrast, the official trainer from LightTricks (LTX-Video-Trainer) by default only updates attention layers (e.g., to_k, to_q, to_v, to_out.0), making it possible to use higher dim (e.g., 128) while still keeping the file size low (~700MB).
- Initial Loss: High — LTXV I2V is known to require many steps before reaching stability
⚠️ The I2V 13B model begins with a very high initial loss, and convergence is slow — requiring many steps to stabilize below 0.1. Training this architecture is not plug-and-play and takes persistence.
⚠️ Prompting Recommendations
This LoRA is very sensitive to prompt quality and seed variation.
Using short or unclear prompts often causes:
- Rigid elements like weapons or chairs to appear soft or rubbery
- Unintended motion of static parts (e.g., armor bending, background flickering)
These artifacts are not due to the LoRA itself but rather to a lack of motion guidance in the prompt or an unsuitable seed.
✅ To get the best results:
- Use long, detailed prompts that clearly separate moving vs non-moving parts
- Try changing the seed if you're seeing unwanted distortion
You can generate prompts automatically using my custom ComfyUI node:
🔧 Ollama Describer
This node uses a vision-capable LLM to generate motion-aware captions. In my case, I used Qwen2.5-VL-7B to generate all motion prompts during training and testing.
💡 Alternatively, the LTXV Prompt Enhancer from Lightricks' custom node set may also be used for prompt conditioning.
🧠 Recommended Prompt Template
Use this with any vision-enabled LLM like Qwen-VL, Gemini, or GPT-4o:
You are an expert in motion design for seamless animated loops.
Given a single image as input, generate a richly detailed description of how it could be turned into a smooth, seamless animation.
Your response must include:
✅ What elements **should move**:
– Hair (e.g., swaying, fluttering)
– Eyes (e.g., blinking, subtle gaze shifts)
– Clothing or fabric elements (e.g., ribbons, loose parts reacting to wind or motion)
– Ambient particles (e.g., dust, sparks, petals)
– Light effects (e.g., holograms, glows, energy fields)
– Floating objects (e.g., drones, magical orbs) if they are clearly not rigid or fixed
– Background **ambient** motion (e.g., fog, drifting light, slow parallax)
🚫 And **explicitly specify what should remain static**:
– Rigid structures (e.g., chairs, weapons, metallic armor)
– Body parts not involved in subtle motion (e.g., torso, limbs unless there’s idle shifting)
– Background elements that do not visually suggest movement
⚠️ Guidelines:
– The animation must be **fluid, consistent, and seamless**, suitable for a loop
– Do NOT include sudden movements, teleportation, scene transitions, or pose changes
– Do NOT invent objects or effects not present in the image
– Do NOT describe static features like colors, names, or environment themes
– The output must begin with the trigger word: **lvwpr**
– Return only the description (no lists, no markdown, no instructions)
🧪 Experimental Status
This is the first public version of this LoRA for LTXV I2V.
If I discover new training techniques, better captioning strategies, or improvements in convergence, future versions will be released with higher quality and better performance.
🙌 Feedback Welcome
If you create something interesting with this LoRA, feel free to share what you’ve made.
I’ll be checking community uploads — and if I find your results particularly impressive, I’ll help give them a boost of Civitai buzz 😉