Text-to-Image
Diffusers
TensorBoard
PyTorch
StableDiffusionPipeline
stable-diffusion-v2-1-base
diffusion-models-class
Instructions to use CSAle/DilbertDiffusion2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CSAle/DilbertDiffusion2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CSAle/DilbertDiffusion2", dtype=torch.bfloat16, device_map="cuda") prompt = "dilbert walking his dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
File size: 545 Bytes
e5a62fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"_class_name": "StableDiffusionPipeline",
"_diffusers_version": "0.13.0.dev0",
"feature_extractor": [
"transformers",
"CLIPFeatureExtractor"
],
"requires_safety_checker": false,
"safety_checker": [
null,
null
],
"scheduler": [
"diffusers",
"PNDMScheduler"
],
"text_encoder": [
"transformers",
"CLIPTextModel"
],
"tokenizer": [
"transformers",
"CLIPTokenizer"
],
"unet": [
"diffusers",
"UNet2DConditionModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
}
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