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import os
import sys
import numpy
import torch
import rembg
import threading
import urllib.request
from PIL import Image
import streamlit as st
import huggingface_hub
img_example_counter = 0
iret_base = 'resources/examples'
iret = [
dict(rimageinput=os.path.join(iret_base, x), dispi=os.path.join(iret_base, x))
for x in sorted(os.listdir(iret_base))
]
class SAMAPI:
predictor = None
@staticmethod
@st.cache_resource
def get_instance(sam_checkpoint=None):
if SAMAPI.predictor is None:
if sam_checkpoint is None:
sam_checkpoint = "tmp/sam_vit_h_4b8939.pth"
if not os.path.exists(sam_checkpoint):
os.makedirs('tmp', exist_ok=True)
urllib.request.urlretrieve(
"https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
sam_checkpoint
)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model_type = "default"
from segment_anything import sam_model_registry, SamPredictor
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)
predictor = SamPredictor(sam)
SAMAPI.predictor = predictor
return SAMAPI.predictor
@staticmethod
def segment_api(rgb, mask=None, bbox=None, sam_checkpoint=None):
"""
Parameters
----------
rgb : np.ndarray h,w,3 uint8
mask: np.ndarray h,w bool
Returns
-------
"""
np = numpy
predictor = SAMAPI.get_instance(sam_checkpoint)
predictor.set_image(rgb)
if mask is None and bbox is None:
box_input = None
else:
# mask to bbox
if bbox is None:
y1, y2, x1, x2 = np.nonzero(mask)[0].min(), np.nonzero(mask)[0].max(), np.nonzero(mask)[1].min(), \
np.nonzero(mask)[1].max()
else:
x1, y1, x2, y2 = bbox
box_input = np.array([[x1, y1, x2, y2]])
masks, scores, logits = predictor.predict(
box=box_input,
multimask_output=True,
return_logits=False,
)
mask = masks[-1]
return mask
def image_examples(samples, ncols, return_key=None, example_text="Examples"):
global img_example_counter
trigger = False
with st.expander(example_text, True):
for i in range(len(samples) // ncols):
cols = st.columns(ncols)
for j in range(ncols):
idx = i * ncols + j
if idx >= len(samples):
continue
entry = samples[idx]
with cols[j]:
st.image(entry['dispi'])
img_example_counter += 1
with st.columns(5)[2]:
this_trigger = st.button('\+', key='imgexuse%d' % img_example_counter)
trigger = trigger or this_trigger
if this_trigger:
trigger = entry[return_key]
return trigger
def segment_img(img: Image):
output = rembg.remove(img)
mask = numpy.array(output)[:, :, 3] > 0
sam_mask = SAMAPI.segment_api(numpy.array(img)[:, :, :3], mask)
segmented_img = Image.new("RGBA", img.size, (0, 0, 0, 0))
segmented_img.paste(img, mask=Image.fromarray(sam_mask))
return segmented_img
def segment_6imgs(zero123pp_imgs):
imgs = [zero123pp_imgs.crop([0, 0, 320, 320]),
zero123pp_imgs.crop([320, 0, 640, 320]),
zero123pp_imgs.crop([0, 320, 320, 640]),
zero123pp_imgs.crop([320, 320, 640, 640]),
zero123pp_imgs.crop([0, 640, 320, 960]),
zero123pp_imgs.crop([320, 640, 640, 960])]
segmented_imgs = []
for i, img in enumerate(imgs):
output = rembg.remove(img)
mask = numpy.array(output)[:, :, 3]
mask = SAMAPI.segment_api(numpy.array(img)[:, :, :3], mask)
data = numpy.array(img)[:,:,:3]
data[mask == 0] = [255, 255, 255]
segmented_imgs.append(data)
result = numpy.concatenate([
numpy.concatenate([segmented_imgs[0], segmented_imgs[1]], axis=1),
numpy.concatenate([segmented_imgs[2], segmented_imgs[3]], axis=1),
numpy.concatenate([segmented_imgs[4], segmented_imgs[5]], axis=1)
])
return Image.fromarray(result)
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
@st.cache_data
def check_dependencies():
reqs = []
try:
import diffusers
except ImportError:
import traceback
traceback.print_exc()
print("Error: `diffusers` not found.", file=sys.stderr)
reqs.append("diffusers==0.20.2")
else:
if not diffusers.__version__.startswith("0.20"):
print(
f"Warning: You are using an unsupported version of diffusers ({diffusers.__version__}), which may lead to performance issues.",
file=sys.stderr
)
print("Recommended version is `diffusers==0.20.2`.", file=sys.stderr)
try:
import transformers
except ImportError:
import traceback
traceback.print_exc()
print("Error: `transformers` not found.", file=sys.stderr)
reqs.append("transformers==4.29.2")
if torch.__version__ < '2.0':
try:
import xformers
except ImportError:
print("Warning: You are using PyTorch 1.x without a working `xformers` installation.", file=sys.stderr)
print("You may see a significant memory overhead when running the model.", file=sys.stderr)
if len(reqs):
print(f"Info: Fix all dependency errors with `pip install {' '.join(reqs)}`.")
@st.cache_resource
def load_zero123plus_pipeline():
if 'HF_TOKEN' in os.environ:
huggingface_hub.login(os.environ['HF_TOKEN'])
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
pipeline = DiffusionPipeline.from_pretrained(
"sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline",
torch_dtype=torch.float16
)
# Feel free to tune the scheduler
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
pipeline.scheduler.config, timestep_spacing='trailing'
)
if torch.cuda.is_available():
pipeline.to('cuda:0')
sys.main_lock = threading.Lock()
return pipeline
check_dependencies()
pipeline = load_zero123plus_pipeline()
SAMAPI.get_instance()
torch.set_grad_enabled(False)
st.title("Zero123++ Demo")
# st.caption("For faster inference without waiting in queue, you may clone the space and run it yourself.")
prog = st.progress(0.0, "Idle")
pic = st.file_uploader("Upload an Image", key='imageinput', type=['png', 'jpg', 'webp'])
left, right = st.columns(2)
with left:
rem_input_bg = st.checkbox("Remove Input Background")
with right:
rem_output_bg = st.checkbox("Remove Output Background")
num_inference_steps = st.slider("Number of Inference Steps", 15, 100, 75)
st.caption("Diffusion Steps. For general real or synthetic objects, around 28 is enough. For objects with delicate details such as faces (either realistic or illustration), you may need 75 or more steps.")
cfg_scale = st.slider("Classifier Free Guidance Scale", 1.0, 10.0, 4.0)
seed = st.text_input("Seed", "42")
submit = False
if st.button("Submit"):
submit = True
results_container = st.container()
sample_got = image_examples(iret, 4, 'rimageinput')
if sample_got:
pic = sample_got
with results_container:
if sample_got or submit:
prog.progress(0.03, "Waiting in Queue...")
with sys.main_lock:
seed = int(seed)
torch.manual_seed(seed)
img = Image.open(pic)
if max(img.size) > 1280:
w, h = img.size
w = round(1280 / max(img.size) * w)
h = round(1280 / max(img.size) * h)
img = img.resize((w, h))
left, right = st.columns(2)
with left:
st.image(img)
st.caption("Input Image")
prog.progress(0.1, "Preparing Inputs")
if rem_input_bg:
with right:
img = segment_img(img)
st.image(img)
st.caption("Input (Background Removed)")
img = expand2square(img, (127, 127, 127, 0))
pipeline.set_progress_bar_config(disable=True)
result = pipeline(
img,
num_inference_steps=num_inference_steps,
guidance_scale=cfg_scale,
generator=torch.Generator(pipeline.device).manual_seed(seed),
callback=lambda i, t, latents: prog.progress(0.1 + 0.8 * i / num_inference_steps, "Diffusion Step %d" % i)
).images[0]
prog.progress(0.9, "Post Processing")
left, right = st.columns(2)
with left:
st.image(result)
st.caption("Result")
if rem_output_bg:
result = segment_6imgs(result)
with right:
st.image(result)
st.caption("Result (Background Removed)")
prog.progress(1.0, "Idle")
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