Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusion3Pipeline
diffusers-training
template:sd-lora
sd3
sd3-diffusers
Instructions to use MoritzAMLLaura/trained-sd3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MoritzAMLLaura/trained-sd3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MoritzAMLLaura/trained-sd3", 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: 870 Bytes
58465db | 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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | {
"_class_name": "StableDiffusion3Pipeline",
"_diffusers_version": "0.34.0.dev0",
"_name_or_path": "stabilityai/stable-diffusion-3-medium-diffusers",
"feature_extractor": [
null,
null
],
"image_encoder": [
null,
null
],
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"CLIPTextModelWithProjection"
],
"text_encoder_2": [
"transformers",
"CLIPTextModelWithProjection"
],
"text_encoder_3": [
"transformers",
"T5EncoderModel"
],
"tokenizer": [
"transformers",
"CLIPTokenizer"
],
"tokenizer_2": [
"transformers",
"CLIPTokenizer"
],
"tokenizer_3": [
"transformers",
"T5TokenizerFast"
],
"transformer": [
"diffusers",
"SD3Transformer2DModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
}
|