|
import gradio as gr |
|
|
|
import os |
|
|
|
from PIL import Image |
|
import random |
|
|
|
import torch |
|
from diffusers import StableDiffusionPipeline, AutoencoderKL |
|
|
|
def gen_seed(): |
|
random_data = os.urandom(3) |
|
seed = int.from_bytes(random_data, byteorder="big") |
|
return seed |
|
|
|
repo = "IDKiro/sdxs-512-0.9" |
|
weight_type = torch.float32 |
|
|
|
|
|
pipe = StableDiffusionPipeline.from_pretrained(repo, torch_dtype=weight_type) |
|
|
|
|
|
|
|
|
|
|
|
|
|
prompt = "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour" |
|
|
|
def sdxs_run(prompt, steps, guidance, seed): |
|
|
|
image = pipe( |
|
prompt, |
|
num_inference_steps=steps, |
|
guidance_scale=guidance, |
|
generator=torch.Generator(device="cpu").manual_seed(seed) |
|
).images[0] |
|
return image |
|
|
|
|
|
|
|
def update_seed(rand, seed): |
|
if rand: |
|
return gen_seed() |
|
else: |
|
return seed |
|
|
|
desc = """# SDXS CPU Test Space |
|
Just a quick test. Model is `sdxs-512-0.9` for txt2img. |
|
""" |
|
|
|
with gr.Blocks() as demo: |
|
gr.Markdown(desc) |
|
with gr.Group(): |
|
with gr.Row(): |
|
img = gr.Image(label='SDXS Generated Image') |
|
with gr.Row(): |
|
prompt = gr.Textbox(label='Enter your prompt (English)', scale=8, value="portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour") |
|
with gr.Row(): |
|
with gr.Accordion("More options", open=False): |
|
steps = gr.Slider(label="Number of steps", value=1, minimum=1, maximum=20, step=1) |
|
guidance = gr.Slider(label="Guidance", value=0, minimum=0, maximum=2, step=0.1) |
|
seed = gr.Slider(label="Seed", minimum=20, maximum=100000000, step=1, randomize=True) |
|
rand = gr.Checkbox(label="Randomize Seed After Generation?", value=True) |
|
with gr.Row(): |
|
submit = gr.Button(scale=1, variant='primary') |
|
|
|
submit.click(fn=sdxs_run, inputs=[prompt, steps, guidance, seed], outputs=img).then(fn=update_seed, inputs=[rand, seed], outputs=seed) |
|
|
|
demo.queue(max_size=20).launch() |
|
|
