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Running
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A10G
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#!/usr/bin/env python
import random
import gradio as gr
import numpy as np
import PIL.Image
import torch
from diffusers import DDPMScheduler, StableDiffusionXLAdapterPipeline, T2IAdapter
DESCRIPTION = "# T2I-Adapter-SDXL Sketch"
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
adapter = T2IAdapter.from_pretrained(
"Adapter/t2iadapter",
subfolder="sketch_sdxl_1.0",
torch_dtype=torch.float16,
adapter_type="full_adapter_xl",
)
scheduler = DDPMScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
model_id,
adapter=adapter,
safety_checker=None,
torch_dtype=torch.float16,
variant="fp16",
scheduler=scheduler,
)
pipe.to(device)
else:
pipe = None
MAX_SEED = np.iinfo(np.int32).max
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
def run(
image: PIL.Image.Image,
prompt: str,
negative_prompt: str,
num_steps=50,
guidance_scale=7.5,
seed=0,
) -> PIL.Image.Image:
# Convert the input image, which is a boolean image, to a grayscale image whose value is 0 or 255.
image = image.convert("L")
generator = torch.Generator(device=device).manual_seed(seed)
out = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=image,
num_inference_steps=num_steps,
generator=generator,
guidance_scale=guidance_scale,
).images[0]
return out
with gr.Blocks() as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
image = gr.Image(
source="canvas",
tool="sketch",
type="pil",
image_mode="1",
invert_colors=True,
shape=(1024, 1024),
brush_radius=20,
height=600,
)
prompt = gr.Textbox(label="Prompt")
run_button = gr.Button("Run")
with gr.Accordion("Advanced options", open=False):
negative_prompt = gr.Textbox(
label="Negative prompt", value="extra digit, fewer digits, cropped, worst quality, low quality"
)
num_steps = gr.Slider(
label="Number of steps",
minimum=1,
maximum=100,
step=1,
value=50,
)
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.1,
maximum=30.0,
step=0.1,
value=7.5,
)
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.Column():
result = gr.Image(label="Result", height=600)
inputs = [
image,
prompt,
negative_prompt,
num_steps,
guidance_scale,
seed,
]
prompt.submit(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=run,
inputs=inputs,
outputs=result,
api_name=False,
)
run_button.click(
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=run,
inputs=inputs,
outputs=result,
api_name="run",
)
if __name__ == "__main__":
demo.queue(max_size=20).launch()
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