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import gradio as gr
import numpy as np
import random
# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
#model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use
model_repo_id = "CompVis/stable-diffusion-v1-4"
model_dropdown = ['stabilityai/sdxl-turbo', 'CompVis/stable-diffusion-v1-4' ]
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
prompt,
negative_prompt,
randomize_seed,
width,
height,
model_repo_id=model_repo_id,
seed=42,
guidance_scale=7,
num_inference_steps=20,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
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 image, seed
examples = [
"A young lady in a Russian embroidered kaftan is sitting on a beautiful carved veranda, holding a cup to her mouth and drinking tea from the cup. With her other hand, the girl holds a saucer. The cup and saucer are painted with gzhel. Next to the girl on the table stands a samovar, and steam can be seen above it.",
"Puss in Boots wearing a sombrero crosses the Grand Canyon on a tightrope with a guitar.",
"A cat is playing a song called "About the Cat" on an accordion by the sea at sunset. The sun is quickly setting behind the horizon, and the light is fading.",
"A cat walks through the grass on the streets of an abandoned city. The camera view is always focused on the cat's face.",
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice",
]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Text-to-Image SemaSci Template")
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, variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
# model_repo_id = gr.Text(
# label="Model Id",
# max_lines=1,
# placeholder="Choose model",
# visible=True,
# value=model_repo_id,
# )
model_repo_id = gr.Dropdown(
label="Model Id",
choices=model_dropdown,
info="Choose model",
visible=True,
allow_custom_value=True,
value=model_repo_id,
)
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=True,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=42,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024, # Replace with defaults that work for your model
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024, # Replace with defaults that work for your model
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=7.0, # Replace with defaults that work for your model
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=20, # Replace with defaults that work for your model
)
gr.Examples(examples=examples, inputs=[prompt])
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
negative_prompt,
randomize_seed,
width,
height,
model_repo_id,
seed,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch()
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