Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use Aedancodes/monet_text_encoder_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aedancodes/monet_text_encoder_trained with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Aedancodes/monet_text_encoder_trained", dtype=torch.bfloat16, device_map="cuda") prompt = "in the style of monet" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- c6edfda043061d4d2c7b41022eb3321b8bde036d0ed34cecf6646eb701ac3f55
- Size of remote file:
- 492 MB
- SHA256:
- b4380bf70aefdfa1bc80eb48662c0f563f8827d722e4054fb499f8a0323add27
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