metadata
license: mit
pipeline_tag: text-to-image
tags:
- openvino
- text-to-image
inference: false
Model Descriptions:
This repo contains OpenVino model files for madebyollin's Tiny AutoEncoder for Stable Diffusion.
Using in 🧨 diffusers:
To install the requirements for this demo, do pip install "optimum-intel[openvino, diffusers]".
from huggingface_hub import snapshot_download
from optimum.intel.openvino import OVStableDiffusionPipeline
from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVModelVaeEncoder, OVBaseModel
# Create class wrappers which allow us to specify model_dir of TAESD instead of original pipeline dir
class CustomOVModelVaeDecoder(OVModelVaeDecoder):
def __init__(
self, model, parent_model, ov_config = None, model_dir = None,
):
super(OVModelVaeDecoder, self).__init__(model, parent_model, ov_config, "vae_decoder", model_dir)
class CustomOVModelVaeEncoder(OVModelVaeEncoder):
def __init__(
self, model, parent_model, ov_config = None, model_dir = None,
):
super(OVModelVaeEncoder, self).__init__(model, parent_model, ov_config, "vae_encoder", model_dir)
pipe = OVStableDiffusionPipeline.from_pretrained("OpenVINO/stable-diffusion-1-5-fp32", compile=False)
# Inject TAESD
taesd_dir = snapshot_download(repo_id="deinferno/taesd-openvino")
pipe.vae_decoder = CustomOVModelVaeDecoder(model = OVBaseModel.load_model(f"{taesd_dir}/vae_decoder/openvino_model.xml"), parent_model = pipe, model_dir = taesd_dir)
pipe.vae_encoder = CustomOVModelVaeEncoder(model = OVBaseModel.load_model(f"{taesd_dir}/vae_encoder/openvino_model.xml"), parent_model = pipe, model_dir = taesd_dir)
pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
pipe.compile()
prompt = "plant pokemon in jungle"
output = pipe(prompt, num_inference_steps=50, output_type="pil")
output.images[0].save("result.png")