Text-to-Image
Diffusers
UniDiffuserPipeline
image-to-text
image-captioning
image-variation
text-variation
multi-modality
generative model
Instructions to use dg845/unidiffuser-diffusers-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dg845/unidiffuser-diffusers-v0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dg845/unidiffuser-diffusers-v0", 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: 694 Bytes
69a6a81 | 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 | {
"_class_name": "UniDiffuserPipeline",
"_diffusers_version": "0.17.0.dev0",
"clip_tokenizer": [
"transformers",
"CLIPTokenizer"
],
"image_encoder": [
"transformers",
"CLIPVisionModelWithProjection"
],
"image_processor": [
"transformers",
"CLIPImageProcessor"
],
"scheduler": [
"diffusers",
"DPMSolverMultistepScheduler"
],
"text_decoder": [
"unidiffuser",
"UniDiffuserTextDecoder"
],
"text_encoder": [
"transformers",
"CLIPTextModel"
],
"text_tokenizer": [
"transformers",
"GPT2Tokenizer"
],
"unet": [
"unidiffuser",
"UniDiffuserModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
}
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