Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use noamaz/dog_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use noamaz/dog_example with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("noamaz/dog_example", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 618 Bytes
e2dca7e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export INSTANCE_DIR="/dt/shabtaia/dt-sicpa/noam/diffusers/examples/dog_example/images"
export OUTPUT_DIR="/dt/shabtaia/dt-sicpa/noam/diffusers/examples/dog_example"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=400 \
--push_to_hub |