app.py

import gradio as gr
import numpy as np
import random

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
from diffusers import StableDiffusionPipeline
from diffusers import OnnxRuntimeModel
import torch



device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "runwayml/stable-diffusion-v1-5"  # Replace to the model you would like to use

pipe = OnnxRuntimeModel.from_pretrained("model_path", provider="CPUExecutionProvider")
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch.float16)
pipe.enable_attention_slicing()  # Divide o cálculo de atenção para melhorar o desempenho em dispositivos com menos memória

if torch.cuda.is_available():
    torch_dtype = torch.float16
else: 
    torch_dtype = torch.float32

pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 752


from datasets import load_dataset, Dataset

dataset = load_dataset("LEIDIA/Data_Womleimg")  # Exemplo do seu dataset no Hugging Face

# Adicionar descrições ao dataset
descriptions = [
    "A woman wearing a full blue leather catsuit",
    "A woman in a black leather pants",
    "A legs woman in tigh high blue leather boots",
    "A woman in long red leather jacket, red leather shorts and a tigh high red leather boots",
    "A legs woman in cream color leather pants",
    "A woman in purple leather leggings with tigh high black leather boots",
    "A woman in black leather top and a long black leather skirt ",
    "A blonde long hair curly woman using a yellow mini tight leather skirt",
    "A thin asian woman using a thigh long black leather dress",
    "A simple high brown leather boots",
    "A beautiful face brunette woman using a leather clothes",
    "A beautiful brunette woman wearing a sleeveless black dress, seated at a bar, She is holding a glass champagne, The background is softly lit, with warm lighting and blurred bottles on shelves, creating a cozy and elegant atmosphere.",
    "A curly blonde woman is wearing a bold and stylish outfit red leather jacket paired with black leather tight pants and red high-heeled leather boots, The outfit has a modern and edgy vibe, with a focus on vibrant colors and sleek materials.",
    "Ebony woman standing outdoors against a backdrop of rolling hills and a cloudy sky, wearing a striking outfit consisting of a red leather shirt, a black leather mini corset, and a red plaid skirt with a long panel on one side,also wearing knee-high red lace-up leather boots, Their hair is voluminous and styled in natural curls, The setting appears to be a grassy landscape.",
    "Blonde curly woman is wearing a fitted, shiny blue outfit made of what appears to be leather or vinyl clothes, The ensemble includes a jacket and pants with metallic buttons and a belt at the waist, They are also wearing knee-high boots with lacing details, Their hair is styled in voluminous, curly blonde locks. The setting is a simple, neutral-colored room with a concrete or stone-like wall and floor.",
    "The girl is wearing a black leather outfit include top,legging and sleeves,consisting of a fitted top with a heart-shaped cutout and high-waisted pants, The ensemble includes a purple cape and the individual has long purple hair,The style is reminiscent of superhero or fantasy attire, emphasizing a bold and sleek look.",
    "The girl dressed in a sleek, black leather outfit. The attire includes a cropped top with a zip closure and a high-waisted bottom, both designed to accentuate the figure's silhouette, The individual has long, pink hair styled in a ponytail, and is wearing long black gloves that reach the upper arms. The background appears to be softly lit, enhancing the glossy texture of the leather,The overall look is bold and fashion-forward, with a striking color contrast between the black and pink elements.",
    "a girl wearing a form-fitting black leather top that highlights their physique, The top has a high neckline and is sleeveless, emphasizing the shoulders and arms, The individual has long, pink hair cascading down, adding a striking contrast to the outfit. The background is a light, neutral color, which helps to accentuate the subject, The overall aesthetic is bold and fashion-forward."
    # Adicione uma descrição para cada imagem no dataset
]


def infer(prompt, num_inference_steps):
    image = pipe(
        prompt=prompt,
        num_inference_steps=num_inference_steps,
        height=MAX_IMAGE_SIZE,
        width=MAX_IMAGE_SIZE,
    ).images[0]
    return image


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    return image, seed


examples = [
    " A Woman using Leather Pants "
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

# Interface Gradio
with gr.Blocks() as demo:
    with gr.Column(elem_id="col-container"):
         gr.Markdown("## Text-to-Image Optimized for CPU")
        
            with gr.Row():
               prompt = gr.Textbox(
                   label="Prompt",
                   show_label=False,
                   max_lines=1,
                   placeholder="Enter your prompt",
                   container=False,
            )
            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False) 


        with gr.Row():
             generate_button = gr.Button("Generate")
             result = gr.Image(label="Generated Image")
             generate_button.click(infer, inputs=[prompt, num_inference_steps], outputs=result)

    demo.launch()

            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=False,
            )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=0,
                    maximum=MAX_IMAGE_SIZE,
                    step=8,
                    value=600,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=0,
                    maximum=MAX_IMAGE_SIZE,
                    step=8,
                    value=752,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.1,
                    value=0.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=10,
                    step=1,
                    value=15,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch()