Text-to-Image
Diffusers
Chinese
AltDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
multilingual
English(En)
Chinese(Zh)
Spanish(Es)
French(Fr)
Russian(Ru)
Japanese(Ja)
Korean(Ko)
Arabic(Ar)
Italian(It)
Instructions to use BAAI/AltDiffusion-m9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BAAI/AltDiffusion-m9 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9", torch_dtype=torch.bfloat16, device_map="cuda") 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
File size: 573 Bytes
f3ebb1d 5b8e1a6 f3ebb1d 7f212fe f3ebb1d 5b8e1a6 f3ebb1d 5b8e1a6 114d700 5b8e1a6 f3ebb1d 7f212fe | 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 | {
"_class_name": "AltDiffusionPipeline",
"_diffusers_version": "0.8.0.dev0",
"feature_extractor": [
"transformers",
"CLIPImageProcessor"
],
"safety_checker": [
"stable_diffusion",
"StableDiffusionSafetyChecker"
],
"scheduler": [
"diffusers",
"PNDMScheduler"
],
"text_encoder": [
"alt_diffusion",
"RobertaSeriesModelWithTransformation"
],
"tokenizer": [
"transformers",
"XLMRobertaTokenizer"
],
"unet": [
"diffusers",
"UNet2DConditionModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
}
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