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README.md

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 license: apache-2.0
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+Motion LoRA Model Overview for Vertigo Effect Simulation
+This document provides a comprehensive overview of a specialized Motion LoRA model designed for simulating dynamic scenes with an emphasis on the vertigo effect using a dolly zoom technique. This model is built for researchers and developers with interests in advanced motion models, particularly for cinematic effects like the one famously used in "Jaws".
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+License and User Agreement
+Apache License 2.0: This model is released under the Apache License 2.0, and users must comply with its terms.
+Commercial Usage Disclaimer: Users assume responsibility for any commercial legal disputes related to the use of this model.
+Consent by Installation: By installing this model, users agree to the stated terms and conditions.
+Model Demonstrations
+Showcase: Demonstrations of the model's capabilities are available on YouTube and Twitter.
+Specific Configuration for the Vertigo Effect
+Model Training Setup:
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+Resolution: 512x384, optimized for capturing the intricate details of the vertigo effect.
+LoRA Rank: Utilizes a 64 LoRA rank to enhance learning efficiency and model adaptability.
+Training Duration: Implements 16 frames across 5 sequences with a progression marked by 200 and 300-step checkpoints for detailed refinement.
+Video Input: Trained with a singular clip, focused on accurately reproducing the vertigo effect through detailed visual inputs.
+Training Configuration:
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+Training Model: realisticvisionv51, a highly advanced base model known for generating realistic and detailed visuals.
+Training Prompt: "A man, vertigo effect, dolly zoom," directing the model to specifically learn and replicate the dynamic dolly zoom effect associated with the vertigo sensation.
+Text Configuration: Utilizes tailored text configurations to precisely guide the model in generating footage that showcases the vertigo effect with a dolly zoom on a man.
+Model Specifications and Optimization:
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+Inference Model: Consistent with the training model to ensure high fidelity in output.
+Additional Parameters: use_offset_noise is activated to introduce realistic variations and enhance the authenticity of the vertigo effect.
+Learning Rate Adjustments: The learning rate, spatial learning rate, and Adam weight decay are dynamically adjusted between 5x to 20x the original rate based on the dataset size, ensuring optimal learning efficiency.
+Workflow Compatibility: Designed to integrate seamlessly with the Animatediff workflow, with recommended weights set between 0.5 and 1 to balance between visual quality and computational demand.