Update app.py
Browse files
app.py
CHANGED
@@ -5,157 +5,78 @@ import torch
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from diffusers import DiffusionPipeline
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from datasets import load_dataset
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# Configurações do dispositivo e modelo
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_repo_id = "stabilityai/sdxl-turbo" # Substitua pelo modelo desejado
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pipe = pipe.to(device)
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# Definições de parâmetros gerais
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 752
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# Carregando o dataset do Hugging Face
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dataset = load_dataset("LEIDIA/Data_Womleimg")
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"A woman wearing a full blue leather catsuit",
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"A woman in
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"A legs woman in
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"A woman in long red leather jacket, red leather shorts and
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"A legs woman in cream color leather pants",
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"A woman in purple leather leggings with
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]
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width=MAX_IMAGE_SIZE,
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).images[0]
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return image
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# Função completa para inferência com mais parâmetros
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def advanced_infer(
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prompt,
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negative_prompt,
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seed,
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randomize_seed,
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width,
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height,
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guidance_scale,
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num_inference_steps,
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):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator().manual_seed(seed)
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image = pipe(
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prompt=prompt,
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guidance_scale=
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width=
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height=height,
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generator=generator,
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).images[0]
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return image, seed
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# Interface Gradio
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with gr.Blocks() as demo:
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with gr.Column(elem_id="col-container"):
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gr.Markdown("## Text-to-Image Optimized for CPU")
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with gr.Row():
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label="Prompt",
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show_label=False,
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max_lines=1,
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placeholder="Enter your prompt",
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container=False,
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)
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maximum=7,
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step=1,
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value=15,
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)
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# Botão para gerar a imagem
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generate_button = gr.Button("Generate")
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result = gr.Image(label="Generated Image")
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# Clique no botão para gerar a imagem
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generate_button.click(
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inputs=
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outputs=result,
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)
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# Configurações avançadas
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with gr.Accordion("Advanced Settings", open=False):
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negative_prompt = gr.Textbox(
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label="Negative prompt",
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max_lines=1,
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placeholder="Enter a negative prompt",
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=64,
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maximum=MAX_IMAGE_SIZE,
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step=8,
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value=512,
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)
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height = gr.Slider(
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label="Height",
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minimum=64,
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maximum=MAX_IMAGE_SIZE,
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step=8,
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value=512,
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)
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guidance_scale = gr.Slider(
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label="Guidance scale",
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minimum=0.0,
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maximum=20.0,
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step=0.5,
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value=1.0,
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)
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# Exemplos de prompts
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gr.Examples(
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examples=[
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"A woman wearing leather pants",
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"A woman in a red leather jacket",
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],
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inputs=[prompt],
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)
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if __name__ == "__main__":
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demo.launch()
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from diffusers import DiffusionPipeline
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from datasets import load_dataset
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# Configurações do dispositivo para uso apenas da CPU
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device = "cpu"
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model_repo_id = "stabilityai/sdxl-turbo" # Continuando com o modelo especificado
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# Carregar o pipeline configurado para CPU
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pipe = DiffusionPipeline.from_pretrained(model_repo_id)
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pipe = pipe.to(device)
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# Carregando o dataset do Hugging Face
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dataset = load_dataset("LEIDIA/Data_Womleimg")
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# Parâmetros para carregar o dataset personalizado
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dataset_descriptions = [
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"A woman wearing a full blue leather catsuit",
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"A woman in black leather pants",
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"A legs woman in tight high blue leather boots",
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"A woman in a long red leather jacket, red leather shorts, and tight high red leather boots",
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"A legs woman in cream color leather pants",
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"A woman in purple leather leggings with tight high black leather boots",
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"A woman in black leather top and a long black leather skirt",
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"A blonde woman with long curly hair wearing a yellow mini tight leather skirt",
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"A thin Asian woman wearing a thigh-long black leather dress",
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"Simple high brown leather boots",
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"A beautiful brunette woman wearing leather clothes",
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"A beautiful brunette woman in a sleeveless black dress seated at a bar holding a glass of champagne, with a cozy and elegant atmosphere in the background.",
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"A curly blonde woman wearing a bold red leather jacket paired with black leather tight pants and red high-heeled leather boots, creating a modern and edgy vibe.",
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"An ebony woman standing outdoors against a backdrop of rolling hills and a cloudy sky, wearing a striking outfit of a red leather shirt, black leather mini corset, red plaid skirt, and knee-high red lace-up leather boots.",
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"A blonde curly woman wearing a fitted, shiny blue leather outfit including a jacket and pants with metallic buttons, paired with knee-high boots, in a neutral-colored room.",
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"A girl in a black leather outfit with a heart-shaped cutout top, high-waisted leggings, and a purple cape, giving a superhero vibe.",
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"A girl in a sleek black leather cropped top with a zip closure and high-waisted bottom, paired with long black gloves and pink hair styled in a ponytail, creating a bold and fashion-forward look.",
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"A girl wearing a form-fitting black leather top, with long pink hair cascading down, creating a striking contrast in a neutral background."
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]
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# Definir parâmetros padrão para geração rápida
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DEFAULT_PROMPT = "A beautiful brunette woman wearing a leather outfit"
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DEFAULT_INFERENCE_STEPS = 6
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IMAGE_WIDTH = 512
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IMAGE_HEIGHT = 512
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GUIDANCE_SCALE = 1.5
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# Função simples para gerar imagem
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def infer_simple(prompt):
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image = pipe(
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prompt=prompt,
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num_inference_steps=DEFAULT_INFERENCE_STEPS,
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guidance_scale=GUIDANCE_SCALE,
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height=IMAGE_HEIGHT,
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width=IMAGE_WIDTH,
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).images[0]
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return image
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# Interface Gradio
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with gr.Blocks() as demo:
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with gr.Row():
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gr.Markdown("## Text-to-Image Wom Test - Quick CPU Version")
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prompt = gr.Textbox(
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label="Prompt",
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value=DEFAULT_PROMPT,
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placeholder="Describe the image you want to generate",
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)
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generate_button = gr.Button("Generate")
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result = gr.Image(label="Generated Image")
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generate_button.click(
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fn=infer_simple,
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inputs=prompt,
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outputs=result,
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)
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if __name__ == "__main__":
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demo.launch()
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