Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use Aedancodes/picasso_text_encoder_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Aedancodes/picasso_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/picasso_text_encoder_trained", dtype=torch.bfloat16, device_map="cuda") prompt = "in the style of picasso" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-1000/optimizer.bin from Aedancodes/picasso_text_encoder_trained: direct link, hf CLI and curl.
- Browser
- Download file 1.97 GB
-
https://huggingface.co/Aedancodes/picasso_text_encoder_trained/resolve/main/checkpoint-1000/optimizer.bin
- Command line
-
hf download hf://Aedancodes/picasso_text_encoder_trained/checkpoint-1000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Aedancodes/picasso_text_encoder_trained/resolve/main/checkpoint-1000/optimizer.bin
1.97 GB
- Xet hash:
- 12859a48c3bdfc77330dc9d59147f4b12b1468dce8ac3bc4ec520d7d352a0738
- Size of remote file:
- 1.97 GB
- SHA256:
- 2e9cf1963342ef6cbc09821e17b146b3e9464cd2ff7a23957aeb7b0363a90412
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