Best open source Image to Video CogVideoX1.5-5B-I2V is pretty decent and optimized for low VRAM machines with high resolution - native resolution is 1360px and up to 10 seconds 161 frames - audios generated with new open source audio model
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Best open source Image to Video CogVideoX1.5-5B-I2V is pretty decent and optimized for low VRAM machines with high resolution - native resolution is 1360px and up to 10 seconds 161 frames - audios generated with new open source audio model
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Full YouTube tutorial for CogVideoX1.5-5B-I2V : 
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Full YouTube tutorial for CogVideoX1.5-5B-I2V : 
https://youtu.be/5UCkMzP2VLE
https://youtu.be/5UCkMzP2VLE
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1-Click Windows, RunPod and Massed Compute installers : 
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1-Click Windows, RunPod and Massed Compute installers : 
https://www.patreon.com/posts/112848192
https://www.patreon.com/posts/112848192
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https://www.patreon.com/posts/112848192
https://www.patreon.com/posts/112848192
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 - installs into Python 3.11 VENV
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 - installs into Python 3.11 VENV
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Official Hugging Face repo of CogVideoX1.5-5B-I2V : 
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Official Hugging Face repo of CogVideoX1.5-5B-I2V : 
https://huggingface.co/THUDM/CogVideoX1.5-5B-I2V
https://huggingface.co/THUDM/CogVideoX1.5-5B-I2V
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Official github repo : 
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Official github repo : 
https://github.com/THUDM/CogVideo
https://github.com/THUDM/CogVideo
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Used prompts to generate videos txt file : 
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Used prompts to generate videos txt file : 
https://gist.github.com/FurkanGozukara/471db7b987ab8d9877790358c126ac05
https://gist.github.com/FurkanGozukara/471db7b987ab8d9877790358c126ac05
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Demo images shared in : 
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Demo images shared in : 
https://www.patreon.com/posts/112848192
https://www.patreon.com/posts/112848192
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I used 1360x768px images at 16 FPS and 81 frames = 5 seconds
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I used 1360x768px images at 16 FPS and 81 frames = 5 seconds
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+1 frame coming from initial image
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+1 frame coming from initial image
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Also I have enabled all the optimizations shared on Hugging Face
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Also I have enabled all the optimizations shared on Hugging Face
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pipe.enable_sequential_cpu_offload()
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pipe.enable_sequential_cpu_offload()
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pipe.vae.enable_slicing()
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pipe.vae.enable_slicing()
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pipe.vae.enable_tiling()
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pipe.vae.enable_tiling()
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quantization = int8_weight_only - you need TorchAO and DeepSpeed works great on Windows with Python 3.11 VENV
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quantization = int8_weight_only - you need TorchAO and DeepSpeed works great on Windows with Python 3.11 VENV
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Used audio model : 
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Used audio model : 
https://github.com/hkchengrex/MMAudio
https://github.com/hkchengrex/MMAudio
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1-Click Windows, RunPod and Massed Compute Installers for MMAudio : 
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1-Click Windows, RunPod and Massed Compute Installers for MMAudio : 
https://www.patreon.com/posts/117990364
https://www.patreon.com/posts/117990364
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https://www.patreon.com/posts/117990364
https://www.patreon.com/posts/117990364
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 - Installs into Python 3.10 VENV
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 - Installs into Python 3.10 VENV
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Used very simple prompts - it fails when there is human in input video so use text to audio in such cases
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Used very simple prompts - it fails when there is human in input video so use text to audio in such cases
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I also tested some VRAM usages for CogVideoX1.5-5B-I2V
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I also tested some VRAM usages for CogVideoX1.5-5B-I2V
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Resolutions and here their VRAM requirements - may work on lower VRAM GPUs too but slower
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Resolutions and here their VRAM requirements - may work on lower VRAM GPUs too but slower
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512x288 - 41 frames : 7700 MB , 576x320 - 41 frames : 7900 MB
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512x288 - 41 frames : 7700 MB , 576x320 - 41 frames : 7900 MB
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576x320 - 81 frames : 8850 MB , 704x384 - 81 frames : 8950 MB
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576x320 - 81 frames : 8850 MB , 704x384 - 81 frames : 8950 MB
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768x432 - 81 frames : 10600 MB , 896x496 - 81 frames : 12050 MB
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768x432 - 81 frames : 10600 MB , 896x496 - 81 frames : 12050 MB
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896x496 - 81 frames : 12050 MB , 960x528 - 81 frames : 12850 MB
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896x496 - 81 frames : 12050 MB , 960x528 - 81 frames : 12850 MB
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Best open source Image to Video CogVideoX1.5-5B-I2V is pretty decent and optimized for low VRAM machines with high resolution - native resolution is 1360px and up to 10 seconds 161 frames - audios generated with new open source audio model

Full YouTube tutorial for CogVideoX1.5-5B-I2V : https://youtu.be/5UCkMzP2VLE

1-Click Windows, RunPod and Massed Compute installers : https://www.patreon.com/posts/112848192

https://www.patreon.com/posts/112848192 - installs into Python 3.11 VENV

Official Hugging Face repo of CogVideoX1.5-5B-I2V : https://huggingface.co/THUDM/CogVideoX1.5-5B-I2V

Official github repo : https://github.com/THUDM/CogVideo

Used prompts to generate videos txt file : https://gist.github.com/FurkanGozukara/471db7b987ab8d9877790358c126ac05

Demo images shared in : https://www.patreon.com/posts/112848192

I used 1360x768px images at 16 FPS and 81 frames = 5 seconds

+1 frame coming from initial image

Also I have enabled all the optimizations shared on Hugging Face

pipe.enable_sequential_cpu_offload()

pipe.vae.enable_slicing()

pipe.vae.enable_tiling()

quantization = int8_weight_only - you need TorchAO and DeepSpeed works great on Windows with Python 3.11 VENV

Used audio model : https://github.com/hkchengrex/MMAudio

1-Click Windows, RunPod and Massed Compute Installers for MMAudio : https://www.patreon.com/posts/117990364

https://www.patreon.com/posts/117990364 - Installs into Python 3.10 VENV

Used very simple prompts - it fails when there is human in input video so use text to audio in such cases

I also tested some VRAM usages for CogVideoX1.5-5B-I2V

Resolutions and here their VRAM requirements - may work on lower VRAM GPUs too but slower

512x288 - 41 frames : 7700 MB , 576x320 - 41 frames : 7900 MB

576x320 - 81 frames : 8850 MB , 704x384 - 81 frames : 8950 MB

768x432 - 81 frames : 10600 MB , 896x496 - 81 frames : 12050 MB

896x496 - 81 frames : 12050 MB , 960x528 - 81 frames : 12850 MB





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