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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 PIL import ImageFilter
from diffusers import ControlNetModel, UniPCMultistepScheduler
# from config import WIDTH, HEIGHT
from palette import ade_palette
from stable_diffusion_controlnet_inpaint_img2img import StableDiffusionControlNetInpaintImg2ImgPipeline
from helpers import flush, postprocess_image_masking, convolution
from pipelines import ControlNetPipeline, SDPipeline, get_inpainting_pipeline, get_controlnet
LOGGING = logging.getLogger(__name__)
@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=50,
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,
num_of_images: int,
resolution:int ) -> List[Image.Image]:
print("make_inpainting", positive_prompt, image, mask_image, negative_prompt, num_of_images, resolution)
"""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()
mask_image = Image.fromarray((mask_image * 255).astype(np.uint8))
mask_image_postproc = convolution(mask_image)
flush()
retList=[]
for x in range(num_of_images):
st.success(f"{pipe.queue_size} images in the queue, can take up to {(pipe.queue_size+1) * 10} seconds")
resp = pipe(image=image,
mask_image=mask_image,
prompt=positive_prompt,
negative_prompt=negative_prompt,
num_inference_steps=50,
height=resolution,
width=resolution,
)
print("RESP !!!!",resp)
generated_image = resp.images[0]
generated_image = postprocess_image_masking(generated_image, image, mask_image_postproc)
retList.append(generated_image)
return retList
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