Instructions to use pseudopsych/openingdrawer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pseudopsych/openingdrawer with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pseudopsych/openingdrawer", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from pseudopsych/openingdrawer: direct link, hf CLI and curl.
- Browser
- Download file 988 Bytes
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https://huggingface.co/pseudopsych/openingdrawer/resolve/main/README.md
- Command line
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hf download hf://pseudopsych/openingdrawer/README.md
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curl -L -o README.md https://huggingface.co/pseudopsych/openingdrawer/resolve/main/README.md
988 Bytes
| license: creativeml-openrail-m | |
| base_model: CompVis/stable-diffusion-v1-4 | |
| training_prompt: Someone opening a drawer with their left hand, only their hand the drawer visible and from the the perspective of them | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - text-to-video | |
| - tune-a-video | |
| inference: false | |
| # Tune-A-Video - openingdrawer | |
| ## Model description | |
| - Base model: [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) | |
| - Training prompt: Someone opening a drawer with their left hand, only their hand the drawer visible and from the the perspective of them | |
| ## Samples | |
| Test prompt: Someone opening a drawer | |
|  | |
| ## Related papers: | |
| - [Tune-A-Video](https://arxiv.org/abs/2212.11565): One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation | |
| - [Stable-Diffusion](https://arxiv.org/abs/2112.10752): High-Resolution Image Synthesis with Latent Diffusion Models | |