The SDXL-Lightning model is converted to OpenVINO for fast inference on CPU.
Original Model : SDXL-Lightning
You can use this model with FastSD CPU.

To run the model yourself, you can leverage the 🧨 Diffusers library:
pip install optimum-intel openvino diffusers onnx
from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionXLPipeline
pipeline = OVStableDiffusionXLPipeline.from_pretrained(
"rupeshs/SDXL-Lightning-2steps-openvino-int8",
ov_config={"CACHE_DIR": ""},
)
prompt = "A dolphin leaps through the waves, set against a backdrop of bright blues and teal hues"
images = pipeline(
prompt=prompt,
width=768,
height=768,
num_inference_steps=2,
guidance_scale=1.0,
).images
images[0].save("out_image.png")
8 commits
The SDXL-Lightning model is converted to OpenVINO for fast inference on CPU.
Original Model : SDXL-Lightning
You can use this model with FastSD CPU.

To run the model yourself, you can leverage the 🧨 Diffusers library:
pip install optimum-intel openvino diffusers onnx
from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionXLPipeline
pipeline = OVStableDiffusionXLPipeline.from_pretrained(
"rupeshs/SDXL-Lightning-2steps-openvino-int8",
ov_config={"CACHE_DIR": ""},
)
prompt = "A dolphin leaps through the waves, set against a backdrop of bright blues and teal hues"
images = pipeline(
prompt=prompt,
width=768,
height=768,
num_inference_steps=2,
guidance_scale=1.0,
).images
images[0].save("out_image.png")
8 commits