This LoRA is trained on top of Lightricks/LTX-2.3 and is built with a custom training paradigm tailored for high-consistency video generation.

It was originally optimized for first-frame / last-frame guided transition videos, but the same training strategy also gives it strong generalization in:

• First-to-last frame video generation
• Text-to-video
• Image-to-video
• Stylized transformation and scene transition generation

Compared with a narrowly tuned transition LoRA, this version focuses more on motion continuity, semantic stability, prompt responsiveness, and cross-scene transformation quality, so it remains effective even outside the strict start-end frame setup.

LTX-2.3 is a strong base for controllable video generation, with improved visual quality and prompt adherence. On top of this foundation, this LoRA further enhances transformation-style motion, visual coherence, and scene-to-scene continuity.

The result is a practical LoRA that can handle both:

• precise transition tasks driven by start and end frames
• open-ended prompt-driven generation
• image-conditioned motion generation

• Excellent first-last frame transition quality
  Produces smoother semantic and visual interpolation between two target states, reducing abrupt jumps and broken motion.

• Works beyond transition-only scenarios
  Even without explicit start/end frame constraints, it performs well in text-to-video and image-to-video generation.

• Custom training paradigm
  Trained with a dedicated methodology designed to improve controllability, temporal coherence, and subject consistency across changing scenes.

• Strong prompt adaptability
  Handles character change, style morphing, object transformation, scene switching, and cinematic motion prompts well.

• Wide subject coverage
  Effective on humans, animals, animation characters, environments, and mixed-concept prompts.

This LoRA is especially suitable for the following workflows:

1. First-to-last frame generation Smoothly bridge two highly different frames while preserving motion logic and visual readability.

2. Text-to-video generation Improve dynamic transformation prompts, scene evolution prompts, and narrative transition prompts.

3. Image-to-video generation Add stronger motion intent and more expressive transformation capability to single-image driven video generation.

4. Creative transition design Useful for transformation clips, cinematic cuts, identity morphs, object swaps, and surreal scene transitions.

File	Recommended Strength (alpha)
ltx2.3-transition.safetensors	1.0

Setting	Value
LoRA Strength	1.0
Embedded Guidance Scale	1.0
Classifier Free Guidance	4.0

You can start from the settings above and then make small adjustments depending on:

• how strong you want the transition effect to be
• whether the prompt is more cinematic or more literal
• whether the task is first-last frame, text-to-video, or image-to-video

Recommended trigger phrase:

zhuanchang

If needed, place the trigger word near the end of the prompt so the base prompt still clearly describes:

• subject
• scene
• camera movement
• transformation behavior
• atmosphere

For best results, prompts should usually contain:

1. Shot description
   Example: close-up, medium shot, wide shot, low-angle, tracking shot.

2. Subject and environment
   Describe the character, object, or scene as clearly as possible.

3. Motion or transformation process
   Explain what changes over time: identity, style, object form, scene layout, or camera trajectory.

4. Visual details
   Add texture, lighting, color, material, and spatial cues.

5. Ending trigger
   Add zhuanchang when you want the LoRA behavior to activate more strongly.

[shot type and camera language]. [subject and scene description]. [describe the motion, transformation, or transition process in detail]. [add lighting, texture, atmosphere, and composition cues]. zhuanchang

A low-angle wide shot establishes a winding, wet asphalt road flanked by a dense, dark forest where heavy fog clings to the mossy tree trunks. The glistening surface of the road reflects the dim, moody light, highlighting the vibrant double yellow lines that curve into the misty distance. The camera glides forward smoothly at a low height, tracking the damp texture of the pavement as droplets of moisture fall from the overhanging emerald canopy. Suddenly, the camera tilts upward and accelerates, piercing through the thick, grey veil of the forest ceiling and ascending rapidly into a dense layer of rolling white clouds. As the camera breaks through the cloud deck, it reveals a breathtaking vista of a sharp, snow-dusted mountain peak piercing a brilliant, clear blue sky. The jagged rock textures and icy ridges of the summit are illuminated by crisp, high-altitude sunlight while soft clouds drift slowly around the mountain's base. zhuanchang

• This LoRA is trained on the LTX-2.3 foundation and is intended to complement the base model rather than replace prompt quality.
• Best results usually come from clear temporal instructions instead of short keyword-only prompts.
• For transition-heavy tasks, stronger scene descriptions and explicit motion language generally improve stability.
• For text-to-video and image-to-video tasks, keeping the prompt visually focused usually leads to better composition and cleaner motion.

👉 Click here to watch the full video tutorial 👈

Weights for this model are available in Safetensors format.

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This model was trained by the AI Laboratory of Chongqing Valiant Cat Technology Co., LTD(https://vvicat.com/).Business cooperation is welcome