Instructions to use yqi19/genie_envisioner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yqi19/genie_envisioner with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yqi19/genie_envisioner", 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
| script_path=${1} | |
| echo $script_path | |
| config_path=${2} | |
| echo $config_path | |
| if [ -z $WORLD_SIZE ]; then | |
| NGPU=`nvidia-smi --list-gpus | wc -l` | |
| echo "Training on 1 Nodes, $NGPU GPUs" | |
| torchrun --nnodes=1 \ | |
| --nproc_per_node=$NGPU \ | |
| --node_rank=0 \ | |
| $script_path \ | |
| --config_file $config_path | |
| else | |
| echo "Training on $WORLD_SIZE Nodes, 8 GPU per Node" | |
| NGPU=`nvidia-smi --list-gpus | wc -l` | |
| torchrun --nnodes=$WORLD_SIZE \ | |
| --nproc_per_node=$NGPU \ | |
| --node_rank=$RANK \ | |
| --master-addr $MASTER_ADDR \ | |
| --master-port $MASTER_PORT \ | |
| $script_path \ | |
| --config_file $config_path | |
| fi | |