app.py

import os

import gradio as gr
from transformers import pipeline

# from diffusers import StableDiffusionPipeline
# import torch


sd_description = "文字生成图片"
sd_examples = [["小猫"], ["cat"], ["dog"]]
sd_demo = gr.Interface.load("models/runwayml/stable-diffusion-v1-5", title='文字生成图片', examples=sd_examples)


pipe = pipeline("image-classification")
examples = [[os.path.join(os.path.dirname(__file__), "lion.jpg")], [os.path.join(os.path.dirname(__file__), "cat.jpeg")]]
app = gr.Interface.from_pipeline(pipe, examples=examples, title='图片识别')


# model_id = "dreamlike-art/dreamlike-photoreal-2.0"
# pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
# pipe_v1 = pipe.to("cpu")


# def generate_image_v1(prompt):
#     return pipe_v1(prompt).images[0]

# examples = [["落日"], ["沙滩"]]
# app_v1 = gr.Interface(fn=generate_image_v1, inputs="text", outputs="image", examples=examples)


demo = gr.TabbedInterface([sd_demo, app], ["文字生成图片", "图片识别"])

demo.queue(concurrency_count=2)
demo.launch()