app.py

import gradio as gr
from PIL import Image
import diffusers
from diffusers.models import AutoencoderKL

vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")


def read_content(file_path: str) -> str:
    """read the content of target file
    """
    with open(file_path, 'r', encoding='utf-8') as f:
        content = f.read()

    return content


def predict(prompt, negative_prompt, guidance_scale, num_inference_steps,model, scheduler, lora, lora_weight):
    pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", vae=vae).to("cuda")
    pipeline.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
    if model == "Realistic_V5.1":
        pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", vae=vae).to("cuda")
    if model == "Realistic_V5.0":
        pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE", vae=vae).to("cuda")
    if model == "EpicRealism":
        pipeline = diffusers.DiffusionPipeline.from_pretrained("emilianJR/epiCRealism", vae=vae).to("cuda")
        pipeline.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
    
    scheduler_class_name = scheduler.split("-")[0]
    add_kwargs = {}
    if len(scheduler.split("-")) > 1:
        add_kwargs["use_karras_sigmas"] = True
    if len(scheduler.split("-")) > 2:
        add_kwargs["algorithm_type"] = "sde-dpmsolver++"
    scheduler = getattr(diffusers, scheduler_class_name)
    
    pipeline.scheduler = scheduler.from_pretrained("emilianJR/epiCRealism", subfolder="scheduler", **add_kwargs)
    
    
    if lora == "nayanthara":
        lora = "profaker/Naya_lora"
    if lora == "saipallavi":
        lora = "profaker/saipallavi_lora"
    if lora == "shobita":
        lora = "profaker/Shobita_lora"
    if lora == "surya":
        lora = "profaker/Surya_lora"
    if lora == "vijay":
        lora = "profaker/Vijay_lora"
    if lora == "None":
        images = pipeline(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=int(num_inference_steps),
        guidance_scale=guidance_scale,
        clip_skip=1
        ).images[0]
        print("Prompt", prompt)
        print("Negative", negative_prompt)
        print("Steps", num_inference_steps)
        print("Scale", guidance_scale)
        print("Scheduler", scheduler)
        return images
        
    pipeline.load_lora_weights(lora)
    
    images = pipeline(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=int(num_inference_steps),
        guidance_scale=guidance_scale,
        cross_attention_kwargs={"scale": lora_weight}
    ).images[0]
    
    print("Prompt", prompt)
    print("Negative", negative_prompt)
    print("Steps", num_inference_steps)
    print("Scale", guidance_scale)
    print("Scheduler", scheduler)

    return images


css = '''
.gradio-container{max-width: 1100px !important}
#image_upload{min-height:400px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
@keyframes spin {
    from {
        transform: rotate(0deg);
    }
    to {
        transform: rotate(360deg);
    }
}

#prompt-container{margin-top:-18px;}
#prompt-container .form{border-top-left-radius: 0;border-top-right-radius: 0}
'''

image_blocks = gr.Blocks(css=css, elem_id="total-container")
with image_blocks as demo:
    gr.HTML(read_content("header.html"))
    with gr.Row():
        with gr.Column():
            with gr.Row(elem_id="prompt-container", equal_height=True):
                with gr.Row():
                    prompt = gr.Textbox(placeholder="Your prompt", show_label=False, elem_id="prompt", lines=5)

            with gr.Accordion(label="Advanced Settings", open=False):
                with gr.Row(equal_height=True):
                    guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
                    steps = gr.Number(value=40, minimum=0, maximum=100, step=1, label="steps")
                with gr.Row(equal_height=True):
                    negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt",
                                                 info="what you don't want to see in the image")
                with gr.Row(equal_height=True):
                    models = ['Realistic_V6.0','Realistic_V5.1','Realistic_V5.0','EpicRealism']
                    model = gr.Dropdown(label="Models",choices=models,value="Realistic_V6.0")
                with gr.Row(equal_height=True):
                    schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler",
                                  "DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras",
                                  "DPMSolverMultistepScheduler-Karras-SDE"]
                    scheduler = gr.Dropdown(label="Schedulers", choices=schedulers,
                                            value="DPMSolverMultistepScheduler-Karras")
                with gr.Row(equal_height=True):
                    loras = ['None','add_detail','nayanthara','shobita','surya','vijay','saipallavi']
                    lora = gr.Dropdown(label='Lora', choices=loras, value="None")
                    lora_weight = gr.Number(value=0.5, minimum=0, maximum=1, step=0.01, label="Lora Weights")
            with gr.Row(equal_height=True):
                btn = gr.Button("Generate", elem_id="run_button")

        with gr.Column():
            image_out = gr.Image(label="Output", elem_id="output-img", height=1024, width=512)
    btn.click(fn=predict, inputs=[prompt, negative_prompt, guidance_scale, steps, model,scheduler, lora, lora_weight],
              outputs=[image_out], api_name='run')
    prompt.submit(fn=predict, inputs=[prompt, negative_prompt, guidance_scale, steps, model,scheduler, lora, lora_weight],
                  outputs=[image_out])

image_blocks.queue(max_size=25, api_open=True).launch(show_api=True)