Spaces:
Running
on
Zero
Running
on
Zero
import spaces | |
import gradio as gr | |
import numpy as np | |
import PIL.Image | |
from PIL import Image | |
import random | |
from diffusers import ControlNetModel, StableDiffusionXLPipeline, AutoencoderKL | |
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler | |
import cv2 | |
import torch | |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
pipe = StableDiffusionXLPipeline.from_pretrained( | |
"votepurchase/waiREALMIX_v70", | |
torch_dtype=torch.float16, | |
) | |
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) | |
pipe.to(device) | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = 1216 | |
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps): | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
generator = torch.Generator().manual_seed(seed) | |
output_image = pipe( | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
guidance_scale=guidance_scale, | |
num_inference_steps=num_inference_steps, | |
width=width, | |
height=height, | |
generator=generator | |
).images[0] | |
return output_image | |
css = """ | |
#col-container { | |
margin: 0 auto; | |
max-width: 520px; | |
} | |
""" | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
with gr.Row(): | |
prompt = gr.Text( | |
label="Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt", | |
container=False, | |
) | |
run_button = gr.Button("Run", scale=0) | |
result = gr.Image(label="Result", show_label=False) | |
with gr.Accordion("Advanced Settings", open=False): | |
negative_prompt = gr.Text( | |
label="Negative prompt", | |
max_lines=1, | |
placeholder="Enter a negative prompt", | |
value="nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn" | |
) | |
seed = gr.Slider( | |
label="Seed", | |
minimum=0, | |
maximum=MAX_SEED, | |
step=1, | |
value=0, | |
) | |
randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
with gr.Row(): | |
width = gr.Slider( | |
label="Width", | |
minimum=256, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024,#832, | |
) | |
height = gr.Slider( | |
label="Height", | |
minimum=256, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024,#1216, | |
) | |
with gr.Row(): | |
guidance_scale = gr.Slider( | |
label="Guidance scale", | |
minimum=0.0, | |
maximum=20.0, | |
step=0.1, | |
value=7, | |
) | |
num_inference_steps = gr.Slider( | |
label="Number of inference steps", | |
minimum=1, | |
maximum=28, | |
step=1, | |
value=28, | |
) | |
run_button.click(#lambda x: None, inputs=None, outputs=result).then( | |
fn=infer, | |
inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
outputs=[result] | |
) | |
demo.queue().launch() |