Instructions to use XXX2333/OranAI_realistic_quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use XXX2333/OranAI_realistic_quality 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("XXX2333/OranAI_realistic_quality", 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
from openvino.runtime import get_version
print(get_version()) from optimum.intel import OVStableDiffusionXLPipeline from diffusers import DiffusionPipeline, LCMScheduler import time
openvion版本要求高,但是不要用最新的
model_path = './ov-dmd-1step' pipeline = OVStableDiffusionXLPipeline.from_pretrained( model_path, ov_config={"CACHE_DIR": "."}, )
pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config) prompt = "a close-up picture of an old man standing in the rain"
image = pipeline(prompt = prompt, num_inference_steps=1,guidance_scale=0, timesteps=[399]).images[0] image.save("ovgenerated_ship.png")
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