
## yujiepan/dreamshaper-8-lcm-openvino

This model applies [latent-consistency/lcm-lora-sdv1-5](https://huggingface.co/latent-consistency/lcm-lora-sdv1-5)
on base model [Lykon/dreamshaper-8](https://huggingface.co/Lykon/dreamshaper-8), and is converted as OpenVINO **FP16** format.

#### Usage

```python
from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionPipeline
pipeline = OVStableDiffusionPipeline.from_pretrained(
    'yujiepan/dreamshaper-8-lcm-openvino',
    device='CPU',
)
prompt = 'cute dog typing at a laptop, 4k, details'
images = pipeline(prompt=prompt, num_inference_steps=8, guidance_scale=1.0).images
```

![output image](./assets/cute-dog-typing-at-a-laptop-4k-details.png)


#### Scripts

The model is generated by the following codes:

```python
import torch
from diffusers import AutoPipelineForText2Image, LCMScheduler
from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionPipeline

base_model_id = "Lykon/dreamshaper-8"
adapter_id = "latent-consistency/lcm-lora-sdv1-5"
save_torch_folder = './dreamshaper-8-lcm'
save_ov_folder = './dreamshaper-8-lcm-openvino'

torch_pipeline = AutoPipelineForText2Image.from_pretrained(
    base_model_id, torch_dtype=torch.float16, variant="fp16")
torch_pipeline.scheduler = LCMScheduler.from_config(
    torch_pipeline.scheduler.config)
# load and fuse lcm lora
torch_pipeline.load_lora_weights(adapter_id)
torch_pipeline.fuse_lora()
torch_pipeline.save_pretrained(save_torch_folder)

ov_pipeline = OVStableDiffusionPipeline.from_pretrained(
    save_torch_folder,
    device='CPU',
    export=True,
)
ov_pipeline.half()
ov_pipeline.save_pretrained(save_ov_folder)
```

{"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["text-to-image"], "base_model": "runwayml/stable-diffusion-v1-5", "inference": true}
diffusers text-to-image base_model:runwayml/stable-diffusion-v1-5 license:creativeml-openrail-m has_space diffusers:OVStableDiffusionPipeline region:us