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from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
import gradio as gr
import torch
from PIL import Image
import time
import psutil
import random
# from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
start_time = time.time()
current_steps = 15
pipe = DiffusionPipeline.from_pretrained("timbrooks/instruct-pix2pix", torch_dtype=torch.float16, safety_checker=None)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
if torch.cuda.is_available():
pipe = pipe.to("cuda")
def error_str(error, title="Error"):
return (
f"""#### {title}
{error}"""
if error
else ""
)
def inference(
prompt,
text_guidance_scale,
image_guidance_scale,
image,
steps,
neg_prompt="",
width=512,
height=512,
seed=0,
):
print(psutil.virtual_memory()) # print memory usage
if seed == 0:
seed = random.randint(0, 2147483647)
generator = torch.Generator("cuda").manual_seed(seed)
try:
ratio = min(height / image.height, width / image.width)
image = image.resize((int(image.width * ratio), int(image.height * ratio)), Image.LANCZOS)
result = pipe(
prompt,
negative_prompt=neg_prompt,
image=image,
num_inference_steps=int(steps),
image_guidance_scale=image_guidance_scale,
guidance_scale=text_guidance_scale,
generator=generator,
)
# return replace_nsfw_images(result)
return result.images, f"Done. Seed: {seed}"
except Exception as e:
return None, error_str(e)
def replace_nsfw_images(results):
for i in range(len(results.images)):
if results.nsfw_content_detected[i]:
results.images[i] = Image.open("nsfw.png")
return results.images
with gr.Blocks(css="style.css") as demo:
gr.HTML(
f"""
<div class="finetuned-diffusion-div">
<div>
<h1>Instruct-Pix2Pix Diffusion</h1>
</div>
<p>
Demo for Instruct-Pix2Pix Diffusion: https://github.com/timothybrooks/instruct-pix2pix
</p>
<p>
Running on <b>{device}</b>
</p>
<p>You can also duplicate this space and upgrade to gpu by going to settings:<br>
<a style="display:inline-block" href="https://huggingface.co/spaces/patrickvonplaten/finetuned_diffusion?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
</div>
"""
)
with gr.Row():
with gr.Column(scale=55):
with gr.Group():
with gr.Box(visible=False) as custom_model_group:
gr.HTML(
"<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>"
)
with gr.Row():
prompt = gr.Textbox(
label="Prompt",
show_label=False,
max_lines=2,
placeholder="Enter prompt.",
).style(container=False)
generate = gr.Button(value="Generate").style(
rounded=(False, True, True, False)
)
# image_out = gr.Image(height=512)
gallery = gr.Gallery(
label="Generated images", show_label=False, elem_id="gallery"
).style(grid=[2], height="auto")
state_info = gr.Textbox(label="State", show_label=False, max_lines=2).style(
container=False
)
error_output = gr.Markdown()
with gr.Column(scale=45):
with gr.Tab("Options"):
with gr.Group():
neg_prompt = gr.Textbox(
label="Negative prompt",
placeholder="What to exclude from the image",
)
n_images = gr.Slider(
label="Images", value=1, minimum=1, maximum=4, step=1
)
with gr.Row():
steps = gr.Slider(
label="Steps",
value=current_steps,
minimum=2,
maximum=75,
step=1,
)
with gr.Row():
width = gr.Slider(
label="Width", value=512, minimum=64, maximum=1024, step=8
)
height = gr.Slider(
label="Height", value=512, minimum=64, maximum=1024, step=8
)
seed = gr.Slider(
0, 2147483647, label="Seed (0 = random)", value=0, step=1
)
with gr.Group():
image = gr.Image(
label="Image", height=256, tool="editor", type="pil"
)
text_guidance_scale = gr.Slider(
label="Text Guidance Scale", minimum=1.0, value=5.5, maximum=15, step=0.1
)
image_guidance_scale = gr.Slider(
label="Image Guidance Scale",
minimum=1.0,
maximum=15,
step=0.1,
value=1.5,
)
inputs = [
prompt,
text_guidance_scale,
image_guidance_scale,
image,
steps,
neg_prompt,
width,
height,
seed,
]
outputs = [gallery, error_output]
prompt.submit(inference, inputs=inputs, outputs=outputs)
generate.click(inference, inputs=inputs, outputs=outputs)
ex = gr.Examples(
[
["turn him into a cyborg", 7.5, 1.2, "./statue.jpg", 20]
],
inputs=[prompt, text_guidance_scale, image_guidance_scale, image, steps],
outputs=outputs,
fn=inference,
cache_examples=True,
)
print(f"Space built in {time.time() - start_time:.2f} seconds")
demo.queue(concurrency_count=1)
demo.launch()
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