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import gradio as gr
import torch
from PIL import Image
import numpy as np
from diffusers import StableDiffusionDepth2ImgPipeline
from pathlib import Path
dept2img = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth",
torch_dtype=torch.float16,
).to("cuda")
def pad_image(input_image):
pad_w, pad_h = np.max(((2, 2), np.ceil(
np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
im_padded = Image.fromarray(
np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
w, h = im_padded.size
if w == h:
return im_padded
elif w > h:
new_image = Image.new(im_padded.mode, (w, w), (0, 0, 0))
new_image.paste(im_padded, (0, (w - h) // 2))
return new_image
else:
new_image = Image.new(im_padded.mode, (h, h), (0, 0, 0))
new_image.paste(im_padded, ((h - w) // 2, 0))
return new_image
def predict(input_image, prompt, negative_prompt, steps, num_samples, scale, seed, strength, depth_image=None):
depth = None
if depth_image is not None:
depth_image = pad_image(depth_image)
depth = np.array(depth_image.convert("L"))
depth = depth.astype(np.float32) / 255.0
depth = depth[None, None]
depth = torch.from_numpy(depth)
init_image = input_image.convert("RGB")
image = pad_image(init_image) # resize to integer multiple of 32
image = image.resize((512, 512))
result = dept2img(
image=image,
prompt=prompt,
negative_prompt=negative_prompt,
depth_image=depth,
seed=seed,
strength=strength,
num_inference_steps=steps,
guidance_scale=scale,
num_images_per_prompt=num_samples,
)
return result['images']
block = gr.Blocks().queue()
with block:
with gr.Row():
with gr.Column():
gr.Markdown("## Stable Diffusion 2 Depth2Img")
gr.HTML("<p><a href='https://huggingface.co/spaces/radames/stable-diffusion-depth2img?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>")
with gr.Row():
with gr.Column():
input_image = gr.Image(source='upload', type="pil")
depth_image = gr.Image(
source='upload', type="pil", label="Depth image Optional", value=None)
prompt = gr.Textbox(label="Prompt")
negative_prompt = gr.Textbox(label="Negative Pompt")
run_button = gr.Button(label="Run")
with gr.Accordion("Advanced options", open=False):
num_samples = gr.Slider(
label="Images", minimum=1, maximum=4, value=1, step=1)
steps = gr.Slider(label="Steps", minimum=1,
maximum=50, value=50, step=1)
scale = gr.Slider(
label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1
)
strength = gr.Slider(
label="Strength", minimum=0.0, maximum=1.0, value=0.9, step=0.01
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=2147483647,
step=1,
randomize=True,
)
with gr.Column():
gallery = gr.Gallery(label="Generated images", show_label=False).style(
grid=[2], height="auto")
gr.Examples(
examples=[
["./examples/baby.jpg", "high definition photo of a baby astronaut space walking at the international space station with earth seeing from above in the background",
"", 50, 4, 9.0, 123123123, 0.8, None],
["./examples/gol.jpg", "professional photo of a Elmo jumping between two high rises, beautiful colorful city landscape in the background",
"", 50, 4, 9.0, 1734133747, 0.9, None],
["./examples/bag.jpg", "a photo of a bag of cookies in the bathroom", "low light, dark, blurry", 50, 4, 9.0, 1734133747, 0.9, "./examples/depth.jpg"],
["./examples/smile_face.jpg", "a hand holding a very spherical orange", "low light, dark, blurry", 50, 4, 6.0, 961736534, 0.5, "./examples/smile_depth.jpg"]
],
inputs=[input_image, prompt, negative_prompt, steps,
num_samples, scale, seed, strength, depth_image],
outputs=[gallery],
fn=predict,
cache_examples=True,
)
run_button.click(fn=predict, inputs=[input_image, prompt, negative_prompt,
steps, num_samples, scale, seed, strength, depth_image], outputs=[gallery])
block.launch(show_api=False) |