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1from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionPipeline
2pipeline = OVStableDiffusionPipeline.from_pretrained(
3 'yujiepan/dreamshaper-8-lcm-openvino',
4 device='CPU',
5)
6prompt = 'cute dog typing at a laptop, 4k, details'
7images = pipeline(prompt=prompt, num_inference_steps=8, guidance_scale=1.0).images
1import torch
2from diffusers import AutoPipelineForText2Image, LCMScheduler
3from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionPipeline
4
5base_model_id = "Lykon/dreamshaper-8"
6adapter_id = "latent-consistency/lcm-lora-sdv1-5"
7save_torch_folder = './dreamshaper-8-lcm'
8save_ov_folder = './dreamshaper-8-lcm-openvino'
9
10torch_pipeline = AutoPipelineForText2Image.from_pretrained(
11 base_model_id, torch_dtype=torch.float16, variant="fp16")
12torch_pipeline.scheduler = LCMScheduler.from_config(
13 torch_pipeline.scheduler.config)
14# load and fuse lcm lora
15torch_pipeline.load_lora_weights(adapter_id)
16torch_pipeline.fuse_lora()
17torch_pipeline.save_pretrained(save_torch_folder)
18
19ov_pipeline = OVStableDiffusionPipeline.from_pretrained(
20 save_torch_folder,
21 device='CPU',
22 export=True,
23)
24ov_pipeline.half()
25ov_pipeline.save_pretrained(save_ov_folder)