deinferno/LCM_Dreamshaper_v7-openvino

Model Descriptions:

This repo contains OpenVino model files for SimianLuo's LCM_Dreamshaper_v7.

Hugging Face Demo on CPU:

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:

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")

# Use "compile = True" if you don't plan to reshape and recompile model after loading
# Don't forget to disable OpenVino cache via "ov_config = {"CACHE_DIR":""}" because optimum won't use it anyway and it will stay as dead weight in your RAM when loading pipeline again
pipe = OVLatentConsistencyModelPipeline.from_pretrained(model_id, scheduler = scheduler, compile = False, ov_config = {"CACHE_DIR":""})

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