| import torch |
| import gradio as gr |
| import numpy as np |
| from torch import autocast |
| from PIL import Image |
| from diffusers import StableDiffusionImg2ImgPipeline |
|
|
| |
| device = "cpu" |
| model_id_or_path = "CompVis/stable-diffusion-v1-4" |
| pipe = StableDiffusionImg2ImgPipeline.from_pretrained( |
| model_id_or_path, |
| revision = "fp16", |
| torch_dtype = torch.float32, |
| use_auth_token = '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' |
| ) |
| |
| |
| pipe = pipe.to(device) |
|
|
| def diffuse(x, param): |
| print('in callback') |
| x = Image.fromarray(np.uint8(x)) |
| init_image = x.resize((768, 512)) |
| prompt = 'st petersburg logo' |
| if param == 'Эрмитаж': |
| prompt = "st petersburg logo winter palace image on background hermitage vector style" |
| elif param == 'Казанский собор': |
| prompt = "st petersburg logo kazansky sobor image on background" |
| elif param == 'Мосты': |
| prompt = 'st petersburg logo bridges over neva image on background beutiful high quality' |
|
|
| with autocast("cuda"): |
| images = pipe(prompt=prompt, init_image=init_image, strength=0.7, guidance_scale=7.5).images |
| return [images[0], param] |
|
|
| def flip_image(x, param): |
| return [np.fliplr(x), 'функция приняла на вход ' + param] |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("Слово 'Санкт-Петербург'") |
| with gr.Tab("Санкт-Петербург"): |
| with gr.Row(): |
| image_input = gr.Image() |
| param_input = gr.Radio(["Эрмитаж", "Мосты", "Казанский собор"], label='Что для тебя Санкт-Петербург?') |
| image_output = gr.Image() |
| param_out = gr.Markdown() |
| image_button = gr.Button("GET IMAGE") |
|
|
| image_button.click(diffuse, [image_input, param_input], [image_output, param_out]) |
|
|
| demo.launch() |
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