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import numpy as np
import random
from diffusers import StableDiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "hakurei/waifu-diffusion"
torch_dtype = torch.float16 if device == "cuda" else torch.float32
pipe = StableDiffusionPipeline.from_pretrained(
model_repo_id,
torch_dtype=torch_dtype
)
pipe = pipe.to(device)
if device == "cuda":
pipe.enable_xformers_memory_efficient_attention()
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 512 # stable diffusion ideal size
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(device).manual_seed(seed)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt if negative_prompt else None,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
).images[0]
return image, seed
examples = [
"A fantasy portrait of a warrior woman, detailed and dramatic lighting",
"A cute anime character with blue hair and big eyes"
]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
power_device = "GPU" if device == "cuda" else "CPU"
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"# Waifu Diffusion Text-to-Image Generator\nRunning on **{power_device}**")
with gr.Row():
prompt = gr.Textbox(
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.Textbox(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=False,
)
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=64,
value=512,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=64,
value=512,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=15.0,
step=0.1,
value=7.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=20,
)
gr.Examples(examples=examples, inputs=[prompt])
run_button.click(
fn=infer,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
prompt.submit(
fn=infer,
inputs=[
prompt,
negative_prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
demo.queue().launch()
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