Instructions to use neph1/framepack-camera-controls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use neph1/framepack-camera-controls with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("neph1/framepack-camera-controls", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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README.md
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Loras mentioned in this article: https://huggingface.co/blog/neph1/framepack-camera-control-loras
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Trigger word for each is "cam_control".
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If the training video bleeds through (like a grey cube appearing in the generation), lower the strength. Between 0.5 and 1.0
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---
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Loras mentioned in this article: https://huggingface.co/blog/neph1/framepack-camera-control-loras
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Trigger word for each is "cam_control".
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Trigger and training prompts:
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<li>rotate_360_500.safetensors - "cam_control", "The camera rotates 360 degrees around the subject."
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<li>hero_zoom_500.safetensors - "hero_zoom", "the camera zooms in on the subjects face."
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<li>cam_control_rotate_left_600.safetensors - "cam_control", "The camera rotates around the subject.
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<li>cam_control_zoom_out.safetensors - "cam_control", "The camera zooms out while focusing on the subject."
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If the training video bleeds through (like a grey cube appearing in the generation), lower the strength. Between 0.5 and 1.0
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