Create README.md

README.md

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+---
+tags:
+- lora
+- diffusers
+- template:diffusion-lora
+- image-to-video
+- i2v
+
+
+widget:
+- output:
+    url: https://cdn-uploads.huggingface.co/production/uploads/653cd3049107029eb004f968/wELqO6i8Hc_ZxjUbmhfqs.mp4
+  text: '-'
+- output:
+    url: https://cdn-uploads.huggingface.co/production/uploads/653cd3049107029eb004f968/uHMdR_l6NTjJvCujpLsBv.mp4
+  text: '-'
+- output:
+    url: https://cdn-uploads.huggingface.co/production/uploads/653cd3049107029eb004f968/ubGisPVxx6txg82hbeHNV.mp4
+  text: '-'
+  
+base_model:
+- Wan-AI/Wan2.2-I2V-A14B
+instance_prompt: null
+---
+# Quick cuts Lora Wan2.2 I2V 14B
+
+Mirror of: https://civitai.com/models/2113025/cinematic-quick-cuts
+
+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.
+
+Consider it experimental, as the dataset is quite limited at the moment.
+
+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".
+
+The format is:
+
+A series of quick cuts:
+
+    [shot one]
+
+    [shot two]
+
+    [shot three]
+
+...
+
+Each cut has (about) one sentence description. You may specify angle, too.
+
+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.
+
+Tested both with T2V and I2V (but trained on I2V).
+
+Only high noise required.