---
license: mit
language:
- en
pipeline_tag: text-to-image
tags:
- openvino
- text-to-image
---

Model Descriptions:

This repo contains OpenVino model files for [SimianLuo's LCM_Dreamshaper_v7](https://huggingface.co./SimianLuo/LCM_Dreamshaper_v7).

Generation Results:

By converting model to OpenVino format and using Intel(R) Xeon(R) Gold 5220R CPU @ 2.20GHz 24C/48T x 2 we can achieve following results compared to original PyTorch LCM.

Results time includes first compile and reshape phases and should be taken with grain of salt because benchmark was run using 2 socketed server which can underperform in those types of workload. 

Number of images per batch is set to 1

|Run No.|Pytorch|OpenVino|Openvino w/reshape|
|-------|-------|--------|------------------|
|1      |15.5841|18.0010 |13.4928           |
|2      |12.4634|5.0208  |3.6855            |
|3      |12.1551|4.9462  |3.7228            |

Number of images per batch is set to 4

|Run No.|Pytorch|OpenVino|Openvino w/reshape|
|-------|-------|--------|------------------|
|1      |31.3666|33.1488 |25.7044           |
|2      |33.4797|17.7456 |12.8295           |
|3      |28.6561|17.9216 |12.7198           |


To run the model yourself, you can leverage the 🧨 Diffusers/🤗 Optimum library:
1. Install the library:
```
pip install diffusers transformers accelerate optimum 
pip install --upgrade-strategy eager optimum[openvino]
```

2. Clone inference code:
```
git clone https://huggingface.co./deinferno/LCM_Dreamshaper_v7-openvino
cd LCM_Dreamshaper_v7-openvino
```

2. Run the model:
```py
from lcm_ov_pipeline import OVLatentConsistencyModelPipeline
from lcm_scheduler import LCMScheduler

model_id = "deinferno/LCM_Dreamshaper_v7-openvino"

scheduler = LCMScheduler.from_pretrained(model_id, subfolder = "scheduler")
pipe = OVLatentConsistencyModelPipeline.from_pretrained(model_id, scheduler = scheduler, compile = False) # Enable if you don't plan to reshape and recompile

prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

# Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.

width = 512
height = 512
num_images = 1
batch_size = 1
num_inference_steps = 4

# Reshape and recompile for inference speed

pipe.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images)
pipe.compile()

images = pipe(prompt=prompt, width=width, height=height, num_inference_steps=num_inference_steps, guidance_scale=8.0, output_type="pil").images
```