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--- |
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tags: |
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- lora |
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- diffusers |
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- template:diffusion-lora |
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- image-to-video |
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- i2v |
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widget: |
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- output: |
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url: https://cdn-uploads.huggingface.co/production/uploads/653cd3049107029eb004f968/wELqO6i8Hc_ZxjUbmhfqs.mp4 |
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text: '-' |
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- output: |
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url: https://cdn-uploads.huggingface.co/production/uploads/653cd3049107029eb004f968/uHMdR_l6NTjJvCujpLsBv.mp4 |
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text: '-' |
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- output: |
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url: https://cdn-uploads.huggingface.co/production/uploads/653cd3049107029eb004f968/ubGisPVxx6txg82hbeHNV.mp4 |
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text: '-' |
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base_model: |
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- Wan-AI/Wan2.2-I2V-A14B |
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instance_prompt: null |
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--- |
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# Quick cuts Lora Wan2.2 I2V 14B |
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Mirror of: https://civitai.com/models/2113025/cinematic-quick-cuts |
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This lora is trained on "quick cuts", an editing technique that tells the story of a whole scene in a couple of seconds. I figured it would be suitable for the constrained context window local video producers have to work with. |
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Consider it experimental, as the dataset is quite limited at the moment. |
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It's trained on shot concepts like "wide-angle shot", "mid-shot", "close-up shot" and (which is often used for quick cuts, "extreme close-up shot". |
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The format is: |
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A series of quick cuts: |
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[shot one] |
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[shot two] |
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[shot three] |
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... |
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Each cut has (about) one sentence description. You may specify angle, too. |
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It's trained on between 3 and 5 shots, over a very short time. Going for the full 81 frames might make it lose strength. |
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Tested both with T2V and I2V (but trained on I2V). |
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Only high noise required. |
