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"""This file contains methods for inference and image generation."""
import logging
from typing import List, Tuple, Dict
import streamlit as st
import torch
import gc
import time
import numpy as np
from PIL import Image
from time import perf_counter
from contextlib import contextmanager
from scipy.signal import fftconvolve
from PIL import ImageFilter
from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
from diffusers import ControlNetModel, UniPCMultistepScheduler
from diffusers import StableDiffusionInpaintPipeline
from config import WIDTH, HEIGHT
from palette import ade_palette
from stable_diffusion_controlnet_inpaint_img2img import StableDiffusionControlNetInpaintImg2ImgPipeline
LOGGING = logging.getLogger(__name__)
def flush():
gc.collect()
torch.cuda.empty_cache()
class ControlNetPipeline:
def __init__(self):
self.in_use = False
self.controlnet = ControlNetModel.from_pretrained(
"BertChristiaens/controlnet-seg-room", torch_dtype=torch.float16)
self.pipe = StableDiffusionControlNetInpaintImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
controlnet=self.controlnet,
safety_checker=None,
torch_dtype=torch.float16
)
self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
self.pipe.enable_xformers_memory_efficient_attention()
self.pipe = self.pipe.to("cuda")
self.waiting_queue = []
self.count = 0
@property
def queue_size(self):
return len(self.waiting_queue)
def __call__(self, **kwargs):
self.count += 1
number = self.count
self.waiting_queue.append(number)
# wait until the next number in the queue is the current number
while self.waiting_queue[0] != number:
print(f"Wait for your turn {number} in queue {self.waiting_queue}")
time.sleep(0.5)
pass
# it's your turn, so remove the number from the queue
# and call the function
print("It's the turn of", self.count)
results = self.pipe(**kwargs)
self.waiting_queue.pop(0)
flush()
return results
class SDPipeline:
def __init__(self):
self.pipe = StableDiffusionInpaintPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-inpainting",
torch_dtype=torch.float16,
safety_checker=None,
)
self.pipe.enable_xformers_memory_efficient_attention()
self.pipe = self.pipe.to("cuda")
self.waiting_queue = []
self.count = 0
@property
def queue_size(self):
return len(self.waiting_queue)
def __call__(self, **kwargs):
self.count += 1
number = self.count
self.waiting_queue.append(number)
# wait until the next number in the queue is the current number
while self.waiting_queue[0] != number:
print(f"Wait for your turn {number} in queue {self.waiting_queue}")
time.sleep(0.5)
pass
# it's your turn, so remove the number from the queue
# and call the function
print("It's the turn of", self.count)
results = self.pipe(**kwargs)
self.waiting_queue.pop(0)
flush()
return results
def convolution(mask: Image.Image, size=9) -> Image:
"""Method to blur the mask
Args:
mask (Image): masking image
size (int, optional): size of the blur. Defaults to 9.
Returns:
Image: blurred mask
"""
mask = np.array(mask.convert("L"))
conv = np.ones((size, size)) / size**2
mask_blended = fftconvolve(mask, conv, 'same')
mask_blended = mask_blended.astype(np.uint8).copy()
border = size
# replace borders with original values
mask_blended[:border, :] = mask[:border, :]
mask_blended[-border:, :] = mask[-border:, :]
mask_blended[:, :border] = mask[:, :border]
mask_blended[:, -border:] = mask[:, -border:]
return Image.fromarray(mask_blended).convert("L")
def postprocess_image_masking(inpainted: Image, image: Image, mask: Image) -> Image:
"""Method to postprocess the inpainted image
Args:
inpainted (Image): inpainted image
image (Image): original image
mask (Image): mask
Returns:
Image: inpainted image
"""
final_inpainted = Image.composite(inpainted.convert("RGBA"), image.convert("RGBA"), mask)
return final_inpainted.convert("RGB")
@st.experimental_singleton(max_entries=5)
def get_controlnet() -> ControlNetModel:
"""Method to load the controlnet model
Returns:
ControlNetModel: controlnet model
"""
pipe = ControlNetPipeline()
return pipe
@st.experimental_singleton(max_entries=5)
def get_segmentation_pipeline() -> Tuple[AutoImageProcessor, UperNetForSemanticSegmentation]:
"""Method to load the segmentation pipeline
Returns:
Tuple[AutoImageProcessor, UperNetForSemanticSegmentation]: segmentation pipeline
"""
image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
image_segmentor = UperNetForSemanticSegmentation.from_pretrained(
"openmmlab/upernet-convnext-small")
return image_processor, image_segmentor
@st.experimental_singleton(max_entries=5)
def get_inpainting_pipeline() -> StableDiffusionInpaintPipeline:
"""Method to load the inpainting pipeline
Returns:
StableDiffusionInpaintPipeline: inpainting pipeline
"""
pipe = SDPipeline()
return pipe
@torch.inference_mode()
def make_image_controlnet(image: np.ndarray,
mask_image: np.ndarray,
controlnet_conditioning_image: np.ndarray,
positive_prompt: str, negative_prompt: str,
seed: int = 2356132) -> List[Image.Image]:
"""Method to make image using controlnet
Args:
image (np.ndarray): input image
mask_image (np.ndarray): mask image
controlnet_conditioning_image (np.ndarray): conditioning image
positive_prompt (str): positive prompt string
negative_prompt (str): negative prompt string
seed (int, optional): seed. Defaults to 2356132.
Returns:
List[Image.Image]: list of generated images
"""
pipe = get_controlnet()
flush()
image = Image.fromarray(image).convert("RGB")
controlnet_conditioning_image = Image.fromarray(controlnet_conditioning_image).convert("RGB").filter(ImageFilter.GaussianBlur(radius = 9))
mask_image = Image.fromarray((mask_image * 255).astype(np.uint8)).convert("RGB")
mask_image_postproc = convolution(mask_image)
st.success(f"{pipe.queue_size} images in the queue, can take up to {(pipe.queue_size)+1 * 10} seconds")
generated_image = pipe(
prompt=positive_prompt,
negative_prompt=negative_prompt,
num_inference_steps=20,
strength=[1.00, ],
guidance_scale=[7.0],
generator=[torch.Generator(device="cuda").manual_seed(seed)],
image=image,
mask_image=mask_image,
controlnet_conditioning_image=controlnet_conditioning_image,
).images[0]
generated_image = postprocess_image_masking(generated_image, image, mask_image_postproc)
return generated_image
@torch.inference_mode()
def make_inpainting(positive_prompt: str,
image: Image,
mask_image: np.ndarray,
negative_prompt: str = "") -> List[Image.Image]:
"""Method to make inpainting
Args:
positive_prompt (str): positive prompt string
image (Image): input image
mask_image (np.ndarray): mask image
negative_prompt (str, optional): negative prompt string. Defaults to "".
Returns:
List[Image.Image]: list of generated images
"""
pipe = get_inpainting_pipeline()
flush()
image_ = pipe(image=image,
mask_image=Image.fromarray((mask_image * 255).astype(np.uint8)),
prompt=positive_prompt,
negative_prompt=negative_prompt,
num_inference_steps=20,
height=HEIGHT,
width=WIDTH,
**common_parameters
).images[0]
return image_
@torch.inference_mode()
@torch.autocast('cuda')
def segment_image(image: Image) -> Image:
"""Method to segment image
Args:
image (Image): input image
Returns:
Image: segmented image
"""
image_processor, image_segmentor = get_segmentation_pipeline()
pixel_values = image_processor(image, return_tensors="pt").pixel_values
with torch.no_grad():
outputs = image_segmentor(pixel_values)
seg = image_processor.post_process_semantic_segmentation(
outputs, target_sizes=[image.size[::-1]])[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
palette = np.array(ade_palette())
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
color_seg = color_seg.astype(np.uint8)
seg_image = Image.fromarray(color_seg).convert('RGB')
return seg_image |