Instructions to use benjamin-paine/vidxtend with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use benjamin-paine/vidxtend with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("benjamin-paine/vidxtend", 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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"benjamin-paine/vidxtend",
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torch_dtype=torch.float16,
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variant="fp16",
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```py
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pipeline = VidXTendPipeline.from_single_file(
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"benjamin-paine/vidxtend",
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torch_dtype=torch.float16,
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variant="fp16",
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```
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Usage is as follows:
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"benjamin-paine/vidxtend",
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torch_dtype=torch.float16,
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variant="fp16",
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)
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```
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Or, as a single file:
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```py
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from vidxtend import VidXTendPipeline
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pipeline = VidXTendPipeline.from_single_file(
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"benjamin-paine/vidxtend",
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torch_dtype=torch.float16,
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variant="fp16",
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)
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```
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Use these methods to improve performance:
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```
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pipeline.enable_model_cpu_offload()
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pipeline.enable_vae_slicing()
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pipeline.set_use_memory_efficient_attention_xformers()
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```
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Usage is as follows:
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