Text-to-Image
Diffusers
TensorBoard
Safetensors
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
Instructions to use riversnow/doodler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use riversnow/doodler with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("riversnow/doodler") 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
Download checkpoint-2000/optimizer.bin from riversnow/doodler: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/riversnow/doodler/resolve/main/checkpoint-2000/optimizer.bin
- Command line
-
hf download hf://riversnow/doodler/checkpoint-2000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/riversnow/doodler/resolve/main/checkpoint-2000/optimizer.bin
6.59 MB
- Xet hash:
- fa58681cae1226ed23867c66617b68bd00c577821b5bb877c81b4da1972cfd15
- Size of remote file:
- 6.59 MB
- SHA256:
- 5ee524867152258e46f0b1d517d275c99cfc803d7ca535e120bed11762359809
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