File size: 2,549 Bytes
ff1df2a 7d9be07 056a538 f6b6ceb 7fcead6 f6b6ceb 7d9be07 056a538 f6b6ceb aeb71ff f4fec5a aeb71ff 056a538 7d9be07 f6b6ceb 7fcead6 f6b6ceb a5b71c1 0619f29 7d9be07 5cbcb31 0619f29 75d0567 0619f29 5cbcb31 0619f29 70ba535 7d9be07 5cbcb31 a5b71c1 0619f29 7d9be07 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
import gradio as gr
import requests
# Базовые настройки
prompt = gr.Textbox(label="Prompt")
models = gr.Radio(
["Bard", "DALL-E 2", "VQGAN+CLIP", "VQGAN", "CLIP", "InceptionV3", "VQGAN+CLIP-ViT-B16", "VQGAN+CLIP-ViT-B32", "VQGAN+CLIP-ViT-L", "VQGAN+CLIP-ViT-H", "VQGAN+CLIP-ViT-XL"],
label="Model",
)
# Расширенные настройки
negative_prompt = gr.Textbox(label="Negative Prompt")
sampling_method = gr.Radio(
["random", "greedy", "nucleus", "top_k", "top_p"], label="Sampling Method"
)
sampling_steps = gr.Number(
label="Sampling Steps", value=100, min_val=1, max_val=1000
)
cfg_scale = gr.Number(label="CFG Scale", min=0.1, max=10.0, default=1.0)
seed = gr.Number(label="Seed", min=0, max=2**31, default=0)
# Улучшение качества
algorithm = gr.Radio(
["nearest", "bilinear", "bicubic", "lanczos", "cubic", "mitchell", "bicubic_nn", "bicubic_nn_diff", "bicubic_nn_diff_v2"], label="Algorithm"
)
# Функция генерации изображения
def generate_image(prompt, model, negative_prompt, sampling_method, sampling_steps, cfg_scale, seed):
url = "https://api.huggingface.co/models/text-to-image/v1/generate"
data = {
"prompt": prompt,
"model": model,
"negative_prompt": negative_prompt,
"sampling_method": sampling_method,
"sampling_steps": sampling_steps,
"cfg_scale": cfg_scale,
"seed": seed,
}
response = requests.post(url, json=data)
image = response.json()["image"]
return image
# Функция улучшения качества изображения
def improve_quality(image, algorithm):
url = "https://api.huggingface.co/models/text-to-image/v1/improve-quality"
data = {
"image": image,
"algorithm": algorithm,
}
response = requests.post(url, json=data)
image = response.json()["image"]
return image
# Основная функция
def main():
gr.Interface(
generate_image,
inputs=[prompt, models, negative_prompt, sampling_method, sampling_steps, cfg_scale, seed],
outputs=gr.outputs.Image(),
title="Gradio Image Generator",
tabs=[
gr.Tab("Базовые настройки", [prompt, models]),
gr.Tab("Расширенные настройки", [negative_prompt, sampling_method, sampling_steps, cfg_scale, seed]),
gr.Tab("Улучшение качества", [algorithm]),
],
).launch()
if __name__ == "__main__":
main()
|