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import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel, OVStableDiffusionPipeline
import torch
from huggingface_hub import snapshot_download
import openvino.runtime as ov
from typing import Optional, Dict
model_id = "Disty0/LCM_SoteMix"
batch_size = -1
class CustomOVModelVaeDecoder(OVModelVaeDecoder):
def __init__(
self, model: ov.Model, parent_model: OVBaseModel, ov_config: Optional[Dict[str, str]] = None, model_dir: str = None,
):
super(OVModelVaeDecoder, self).__init__(model, parent_model, ov_config, "vae_decoder", model_dir)
pipe = OVStableDiffusionPipeline.from_pretrained(model_id, compile = False, ov_config = {"CACHE_DIR":""})
taesd_dir = snapshot_download(repo_id="deinferno/taesd-openvino")
pipe.vae_decoder = CustomOVModelVaeDecoder(model = OVBaseModel.load_model(f"{taesd_dir}/vae_decoder/openvino_model.xml"), parent_model = pipe, model_dir = taesd_dir)
pipe.reshape( batch_size=-1, height=512, width=512, num_images_per_prompt=1)
pipe.compile()
def infer(prompt,negative_prompt):
image = pipe(
prompt = prompt+"score_8_up,score_7_up,score_6_up,score_9,score_8_up,score_7,masterpiece,best quality,source_anime,bangs,",
negative_prompt = "SimpleNegative_AnimeV1, SimpleNegativeV3, an12, rfneg, verybadimagenegative_v1.3, easynegative, text, watermark, censored, (mosaic censoring:1.3), teeth, (animal ears:2)",
width = 512,
height = 512,
guidance_scale=1.0,
num_inference_steps=8,
num_images_per_prompt=1,
).images[0]
return image
examples = [
"A cute kitten, Japanese cartoon style.",
"A sweet family, dad stands next to mom, mom holds baby girl.",
"A delicious ceviche cheesecake slice",
]
css="""
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
power_device = "CPU"
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Disty0/LCM_SoteMix 512x512
Currently running on {power_device}.
""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
result = gr.Image(label="Result", show_label=False)
gr.Examples(
examples = examples,
inputs = [prompt]
)
run_button.click(
fn = infer,
inputs = [prompt],
outputs = [result]
)
demo.queue().launch() |