app.py

import gradio as gr
import torch
from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel, OVStableDiffusionPipeline
from huggingface_hub import snapshot_download
import openvino.runtime as ov
from typing import Optional, Dict
#from diffusers import DiffusionPipeline, EulerDiscreteScheduler, KDPM2DiscreteScheduler

model_id = "hsuwill000/AngelBreed-v4-openvino-8bit"

#只有512x512 否則一直重畫
HIGH = 512
WIDTH = 512

batch_size = -1  # Or set it to a specific positive integer if needed

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)




examples = [
    "(Digital art, highres, best quality, 8K, masterpiece, anime screencap, perfect eyes:1.4, ultra detailed:1.5),1girl,flat chest,short messy pink hair,blue eyes,tall,thick thighs,light blue hoodie,collar,light blue shirt,black sport shorts,bulge,black thigh highs,femboy,okoto no ko,smiling,blushing,looking at viewer,inside,livingroom,sitting on couch,nighttime,dark,hand_to_mouth,",
    "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,",
    "((colofrul:1.7)),((best quality)), ((masterpiece)), ((ultra-detailed)), (illustration), (detailed light), (an extremely delicate and beautiful),incredibly_absurdres,(glowing),(1girl:1.7),solo,a beautiful girl,(((cowboy shot))),standding,((Hosiery)),((beautiful off-shoulder lace-trimmed layered strapless dress+white stocking):1.25),((Belts)),(leg loops),((Hosiery)),((flower headdress)),((long white hair)),(((beautiful eyes))),BREAK,((english text)),(flower:1.35),(garden),(((border:1.75))),",
]



pipe = OVStableDiffusionPipeline.from_pretrained(
    model_id,
    compile=False,
    ov_config={"CACHE_DIR": ""},
    torch_dtype=torch.uint8,  # More standard dtype for speed
    safety_checker=None,
    use_safetensors=False,
)

#pipe.scheduler = KDPM2DiscreteScheduler.from_config(pipe.scheduler.config)



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
       

                                   )

print(pipe.scheduler.compatibles)

pipe.reshape(batch_size=batch_size, height=HIGH, width=WIDTH, num_images_per_prompt=2)

pipe.compile()

prompt = ""
negative_prompt = "Easy Negative, worst quality, low quality, normal quality, lowers, monochrome, grayscales, skin spots, acnes, skin blemishes, age spot, 6 more fingers on one hand, deformity, bad legs, error legs, bad feet, malformed limbs, extra limbs, ugly, poorly drawn hands, poorly drawn feet, poorly drawn face, text, mutilated, extra fingers, mutated hands, mutation, bad anatomy, cloned face, disfigured, fused fingers"

def infer(prompt, negative_prompt):
    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        width=WIDTH,
        height=HIGH,
        guidance_scale=7.5,
        num_inference_steps=30,
        num_images_per_prompt=1,
    ).images[0]
    
    return image

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"""
        # {model_id.split('/')[1]} {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=1)
        
        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()