Image-to-Image
Diffusers
Safetensors
English
FluxKleinPipeline
image-editing
instruction-based-image-editing
4k
distillation
dcae
Instructions to use openvivo/BluePixel-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use openvivo/BluePixel-Turbo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("openvivo/BluePixel-Turbo", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download tokenizer/tokenizer.json from openvivo/BluePixel-Turbo: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/openvivo/BluePixel-Turbo/resolve/main/tokenizer/tokenizer.json
- Command line
-
hf download hf://openvivo/BluePixel-Turbo/tokenizer/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/openvivo/BluePixel-Turbo/resolve/main/tokenizer/tokenizer.json
11.4 MB
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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