app.py

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
from diffusers import EulerAncestralDiscreteScheduler, LCMScheduler

#LCMScheduler 產生垃圾
#EulerDiscreteScheduler 尚可
#EulerAncestralDiscreteScheduler 很不錯chatgpt推薦


#model_id = "hsuwill000/LCM-anything-v5-openvino"

model_id = "spamsoms/LCM-anything-v5-openvino2"
#adapter_id = "latent-consistency/lcm-lora-sdv1-5"

#1024*512 記憶體不足
HIGH=512
WIDTH=512

batch_size = -1

pipe = OVStableDiffusionPipeline.from_pretrained(
        model_id, 
        compile = False, 
        ov_config = {"CACHE_DIR":""},
        torch_dtype=torch.int8, #快
        #torch_dtype=torch.bfloat16, #中
        #variant="fp16", 
        #torch_dtype=torch.IntTensor, #慢,
        safety_checker=None,
        use_safetensors=False,
        )
print(pipe.scheduler.compatibles)
#pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
#pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

#pipe.load_lora_weights(adapter_id)
#pipe.fuse_lora()


pipe.reshape( batch_size=-1, height=HIGH, width=WIDTH, num_images_per_prompt=1)
#pipe.load_textual_inversion("./badhandv4.pt", "badhandv4")
#pipe.load_textual_inversion("./Konpeto.pt", "Konpeto")
#<shigure-ui-style>
#pipe.load_textual_inversion("sd-concepts-library/shigure-ui-style")
#pipe.load_textual_inversion("sd-concepts-library/ruan-jia")
#pipe.load_textual_inversion("sd-concepts-library/agm-style-nao")


pipe.compile()

prompt=""
negative_prompt="(worst quality, low quality, lowres, ), zombie, interlocked fingers, large breasts, username, watermark,"

def infer(prompt,negative_prompt):

    image = pipe(
        prompt = prompt, 
        negative_prompt = negative_prompt,
        width = WIDTH, 
        height = HIGH,
        guidance_scale=1.0,
        num_inference_steps=8,
        num_images_per_prompt=1,
    ).images[0] 
    
    return image


examples = [
    "Sailor Chibi Moon, Katsura Masakazu style,close-up,",
    "1girl, silver hair, symbol-shaped pupils, yellow eyes, smiling, light particles, light rays, wallpaper, star guardian, serious face, red inner hair, power aura, grandmaster1, golden and white clothes",
    "masterpiece, best quality, highres booru, 1girl, solo, depth of field, rim lighting, flowers, petals, from above, crystals, butterfly, vegetation, aura, magic, hatsune miku, blush, slight smile, close-up, against wall,",
    "close-up,(illustration, 8k CG, extremely detailed),(whimsical),catgirl,teenage girl,playing in the snow,winter wonderland,snow-covered trees,soft pastel colors,gentle lighting,sparkling snow,joyful,magical atmosphere,highly detailed,fluffy cat ears and tail,intricate winter clothing,shallow depth of field,watercolor techniques,close-up shot,slightly tilted angle,fairy tale architecture,nostalgic,playful,winter magic,(masterpiece:2),best quality,ultra highres,original,extremely detailed,perfect lighting,",
]

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"""
        # anything-v5-openvino {WIDTH}x{HIGH}
        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,
            fn = infer,
            inputs = [prompt],
            outputs = [result]
        )

    run_button.click(
        fn = infer,
        inputs = [prompt],
        outputs = [result]
    )

demo.queue().launch()