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
-
https://huggingface.co/pseudopsych/openingdrawer/resolve/main/README.md
- Command line
-
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
metadata
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
- 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: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
- Stable-Diffusion: High-Resolution Image Synthesis with Latent Diffusion Models
