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license: apache-2.0 |
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Motion LoRA Model Overview for Vertigo Effect Simulation |
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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 |
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Apache License 2.0: This model is released under the Apache License 2.0, and users must comply with its terms. |
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Commercial Usage Disclaimer: Users assume responsibility for any commercial legal disputes related to the use of this model. |
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Consent by Installation: By installing this model, users agree to the stated terms and conditions. |
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Model Demonstrations |
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Specific Configuration for the Vertigo Effect |
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Model Training Setup: |
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Resolution: 512x384, optimized for capturing the intricate details of the vertigo effect. |
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LoRA Rank: Utilizes a 64 LoRA rank to enhance learning efficiency and model adaptability. |
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Training Duration: Implements 16 frames across 5 sequences with a progression marked by 200 and 300-step checkpoints for detailed refinement. |
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Video Input: Trained with a singular clip, focused on accurately reproducing the vertigo effect through detailed visual inputs. |
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Training Configuration: |
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Training Model: realisticvisionv51, a highly advanced base model known for generating realistic and detailed visuals. |
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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. |
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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. |
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Model Specifications and Optimization: |
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Inference Model: Consistent with the training model to ensure high fidelity in output. |
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Additional Parameters: use_offset_noise is activated to introduce realistic variations and enhance the authenticity of the vertigo effect. |
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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. |
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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. |
