Instructions to use zzinuzz/nackedei with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zzinuzz/nackedei with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zzinuzz/nackedei", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
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README.md
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license: cc
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---
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license: cc
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datasets:
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- fka/awesome-chatgpt-prompts
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- gopipasala/fka-awesome-chatgpt-prompts
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- amphion/Emilia-Dataset
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metrics:
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- bertscore
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base_model:
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- nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
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- stabilityai/stable-diffusion-3.5-large-turbo
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new_version: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
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pipeline_tag: image-to-video
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library_name: diffusers
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tags:
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- art
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- music
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- text-generation-inference
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- merge
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