Spaces:
Running
Running
File size: 2,712 Bytes
8ad30ba 783bf66 8ad30ba 98e3f08 8ad30ba 783bf66 8ad30ba 783bf66 831a077 783bf66 |
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 71 72 73 74 75 76 77 78 79 80 81 82 |
# pip install transformers gradio scipy ftfy "ipywidgets>=7,<8" datasets diffusers
import random
import gradio as gr
import torch
from torch import autocast
from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline
model_id = "hakurei/waifu-diffusion"
device = "cuda" if torch.cuda.is_available() else "cpu"
def __def_helper():
StableDiffusionPipeline.__call__()
pipe = StableDiffusionPipeline.from_pretrained(model_id,
resume_download=True, # 模型文件断点续传
torch_dtype=torch.float16,
revision='fp16')
pipe = pipe.to(device)
def infer(prompt, width, height, nums, steps, guidance_scale, seed):
print(prompt, width, height, nums, steps, guidance_scale, seed)
if prompt is not None and prompt != "":
if seed is None or seed == '' or seed == -1:
seed = int(random.randrange(4294967294))
with autocast("cuda"):
generator = torch.Generator("cuda").manual_seed(seed)
images = pipe([prompt] * nums,
height=height,
width=width,
num_inference_steps=steps,
generator=generator,
guidance_scale=guidance_scale
)["sample"]
return images
description = """
prompt 素材：[https://lexica.art](https://lexica.art) \n
seed：为空会使用随机seed
"""
# with block as demo:
def run():
_app = gr.Interface(
fn=infer,
title="Waifu Diffusion",
description=description,
inputs=[
gr.Textbox(label="输入 prompt"),
gr.Slider(512, 1024, 512, step=64, label="width"),
gr.Slider(512, 1024, 512, step=64, label="height"),
gr.Slider(1, 4, 1, step=1, label="Number of Images"),
gr.Slider(10, 150, step=1, value=50,
label="num_inference_steps:\n"
"去噪步骤的数量。更多的去噪步骤通常会导致更高质量的图像，但会降低推理速度。"),
gr.Slider(0, 20, 7.5, step=0.5,
label="guidance_scale:\n" +
"较高的引导比例鼓励生成与文本“提示”密切相关的图像，通常以降低图像质量为代价"),
gr.Textbox(label="随机 send",
placeholder="Random Seed",
lines=1),
],
outputs=[
gr.Gallery(label="Generated images")
])
return _app
app = run()
app.launch(debug=True)
|