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import gradio as gr
import numpy as np
import random
from diffusers import SemanticStableDiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "runwayml/stable-diffusion-v1-5" # Replace to the model you would like to use
torch_dtype = torch.float32
pipe = SemanticStableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float32)
pipe = pipe.to(device)
out = pipe(
prompt="a photo of the face of a woman",
num_images_per_prompt=1,
guidance_scale=7,
editing_prompt=[
"smiling, smile", # Concepts to apply
"glasses, wearing glasses",
"curls, wavy hair, curly hair",
"beard, full beard, mustache",
],
reverse_editing_direction=[False, False, False, False], # Direction of guidance i.e. increase all concepts
edit_warmup_steps=[10, 10, 10, 10], # Warmup period for each concept
edit_guidance_scale=[4, 5, 5, 5.4], # Guidance scale for each concept
edit_threshold=[
0.99,
0.975,
0.925,
0.96,
], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance
edit_mom_beta=0.6, # Momentum beta
edit_weights=[1, 1, 1, 1, 1], # Weights of the individual concepts against each other
)
examples = [
"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 Gradio 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):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=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=0.0, # 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=2, # 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,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result],
)
if __name__ == "__main__":
demo.launch()
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