Instructions to use hf-internal-testing/tiny-IFPipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-IFPipeline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-IFPipeline", 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
- Draw Things
- DiffusionBee
File size: 517 Bytes
9a99f87 | 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": "IFPipeline",
"_diffusers_version": "0.39.0.dev0",
"feature_extractor": [
null,
null
],
"requires_safety_checker": true,
"safety_checker": [
null,
null
],
"scheduler": [
"diffusers",
"DDPMScheduler"
],
"text_encoder": [
"transformers",
"T5EncoderModel"
],
"tokenizer": [
"transformers",
"T5TokenizerFast"
],
"unet": [
"diffusers",
"UNet2DConditionModel"
],
"watermarker": [
"deepfloyd_if",
"IFWatermarker"
]
}
|