Crash Zoom Out Camera Motion (Wan2.1 I2V LoRA)
8144°
<crash-zoomout-wan>
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by Remade - The video begins with a close-up on a man's face, his hands tied with rope, and an anxious expression. Then, a cr34sh crash zoom out effect reveals a dark and obscure room, the man is still tied up and two men wearing balaklavas and holding guns appear to be standing behind him.
Creator notes
Generate videos with 100+ camera control and VFX LoRAs for free on the .
Wan2.1 14B I2V 480p v1.0:
Trained on 50 seconds of video comprised of 10 short clips (each clip captioned separately) of scenes that used the crash zoom out camera motion.
The trigger phrase is: 'cr34sh crash zoom out effect'
See below for some prompt examples that worked well for me. You can also check the videos I've posted here for the captions that were used to generate them. For each video the input image is just the first frame.
Recommended Settings:
LoRA strength = 1.0
Embedded guidance scale = 6.0
Flow shift = 5.0
Here's a link to the Wan2.1 I2V LoRA inference workflow I used to generate these videos:
This is a slight modification to Kijai's version, with the main difference being the addition of the WanVideo Lora Select node, connected to the 'lora' field of the WanVideo Lora Select node.
Prompt Examples:
The video begins with a close-up on a man's face, his hands tied with rope, and an anxious expression. Then, a cr34sh crash zoom out effect reveals a dark and obscure room, the man is still tied up and two men wearing balaklavas and holding guns appear to be standing behind him.
The video begins with a close-up on the man's face, with ice covering his beard and eyelashes. He has a concerned or startled expression, his eyes are a vivid blue. A cr34sh crash zoom out effect rapidly pulls the camera back, revealing the man in a yellow jacket set in a icy landscape. The cr34sh crash zoom out effect shows his position: standing on the edge of the sea with icebergs in the background.
Let me know if there are any questions, I'll be happy to help!
You can also join our to request LoRAs and generate videos with them for free!
Trigger words
cr34sh crash zoom out effect
SHA256
5274A80437775AC894F2CBC6501D2154DAB1796A262F2398CD1FFC9F2946ECA6
Ranking badge
Updated Oct 22, 2026
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Also ranked: #18 Hottest Wan model
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Crash Zoom Out Camera Motion (Wan2.1 I2V LoRA) Popularity Trend
Heat score, last 12 months
Heat score
8,144°
This week
▼
0.5%
8,147° then, 8,144° now
This month
▼
2.1%
8,155° then, 8,144° now
Past year
▼
23.5%
8,276° then, 8,144° now
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+1,046°
Civitai downloads
2,938 downloads of this exact version on Civitai. Downloads are the heaviest ingredient and are counted on a log scale: ten times the downloads adds the same 300°, so a model with a million downloads is not a thousand times hotter than one with a thousand. The six download sources share that one term, split between them in proportion to their counts.
+345°
Likes
200 likes and thumbs up across the sources. A like weighs half a download, on the same log scale.
+11°
Adoption rate
About 0 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.
−258°
Age
−258° because the Wan weights came out in 2025. Every model inherits the age of the weights it is built on: 150° a year, up to 750°, and it keeps growing until then. Without it the top of the list would be 2022 and 2023 checkpoints on the strength of downloads that had years to pile up.
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 8,144°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
- 1Start at the floor.Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.7,000°running total
- 2Add the downloads.2,938 download-equivalents in all, from Civitai downloads. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.+1,046°8,046°running total
- 3Add the likes.200 likes and thumbs up, at half the weight of a download.+345°8,391°running total
- 4Add the adoption rate.About 0 downloads a day right now, measured between two readings, at a quarter of the weight.+11°8,402°running total
- 5Take off the age.The Wan weights came out in 2025: 150° a year, up to 750°, growing until then.−258°8,144°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.8,144°
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
Example renders
Prompts and settings shared by the people who made these renders on Civitai. Pick a render to see how it was made.
Settings
Size
688x368
Prompt
The video begins with a close-up on the man's face, with ice covering his beard and eyelashes. He has a concerned or startled expression, his eyes are a vivid blue. A cr34sh crash zoom out effect rapidly pulls the camera back, revealing the man in a yellow jacket set in a icy landscape. The cr34sh crash zoom out effect shows his position: standing on the edge of the sea with icebergs in the background.
Settings
Size
496x720
Prompt
The camera pulls back, and we see the girl knight with dagger bending down to the (red-haired witch with roped hands). Girl knight cutting the ropes with dagger, then red-haired witch kisses gratefully the girl knight.
Settings
Size
488x720
Prompt
The Ferris wheel, spinning rapidly, winds a cloud around itself and flies into the sky like a rocket.
Settings
Size
496x720
Prompt
The camera pulls back, and we see the girl knight with dagger bending down to the (red-haired witch with roped hands). Girl knight cutting the ropes with dagger, then red-haired witch kisses gratefully the girl knight.
Settings
Size
496x720
Prompt
The camera pulls back, and we see the girl knight with dagger bending down to the (red-haired witch with roped hands). Girl knight cutting the ropes with dagger, then red-haired witch kisses gratefully the girl knight.
No prompt was shared with this render.
Settings
Sampler
Euler
Steps
4
Guidance
1
Size
480x720
Prompt
A gothic girl in a hood poses and twirls in front of the camera in a winter forest, languidly slightly opening her mouth and throwing daring glances at the viewer. The camera is slightly zoomed out.
Negative prompt
static, immobilized, low detailed, pixelized, out of focus, 色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走
Settings
Sampler
Euler
Steps
4
Guidance
1
Size
480x720
Prompt
A gothic girl in a hood poses and twirls in front of the camera in a winter forest, languidly slightly opening her mouth and throwing daring glances at the viewer. The camera is slightly zoomed out.
Negative prompt
static, immobilized, low detailed, pixelized, out of focus, 色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走
Run Locally
How to install
How to install a LoRA: folders, file checks and first use in ComfyUI, Forge, Automatic1111 and Draw Things
Sep 24, 2025
crash-zoom-out.safetensors
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Crash Zoom Out Camera Motion (Wan2.1 I2V LoRA) is a LoRA for the Wan family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Wan models, check its heat score to see how it is trending, and open it on PirateDiffusion or BitVector to try it.
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