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"""This is an experimental pipeline used to test AI PC NPU and GPU"""
from pathlib import Path
from diffusers import EulerDiscreteScheduler,LCMScheduler
from huggingface_hub import snapshot_download
from PIL import Image
from backend.openvino.stable_diffusion_engine import (
StableDiffusionEngineAdvanced,
LatentConsistencyEngineAdvanced
)
class OvHcStableDiffusion:
"OpenVINO Heterogeneous compute Stablediffusion"
def __init__(
self,
model_path,
device: list = ["GPU", "NPU", "GPU", "GPU"],
):
model_dir = Path(snapshot_download(model_path))
self.scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
)
self.ov_sd_pipleline = StableDiffusionEngineAdvanced(
model=model_dir,
device=device,
)
def generate(
self,
prompt: str,
neg_prompt: str,
init_image: Image = None,
strength: float = 1.0,
):
image = self.ov_sd_pipleline(
prompt=prompt,
negative_prompt=neg_prompt,
init_image=init_image,
strength=strength,
num_inference_steps=25,
scheduler=self.scheduler,
)
image_rgb = image[..., ::-1]
return Image.fromarray(image_rgb)
class OvHcLatentConsistency:
"""
OpenVINO Heterogeneous compute Latent consistency models
For the current Intel Cor Ultra, the Text Encoder and Unet can run on NPU
Supports following - Text to image , Image to image and image variations
"""
def __init__(
self,
model_path,
device: list = ["NPU", "NPU", "GPU"],
):
model_dir = Path(snapshot_download(model_path))
self.scheduler = LCMScheduler(
beta_start=0.001,
beta_end=0.01,
)
self.ov_sd_pipleline = LatentConsistencyEngineAdvanced(
model=model_dir,
device=device,
)
def generate(
self,
prompt: str,
neg_prompt: str,
init_image: Image = None,
num_inference_steps=4,
strength: float = 0.5,
):
image = self.ov_sd_pipleline(
prompt=prompt,
init_image = init_image,
strength = strength,
num_inference_steps=num_inference_steps,
scheduler=self.scheduler,
seed=None,
)
return image
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