Spaces:
Runtime error
Runtime error
File size: 3,720 Bytes
3d4d894 32a644b be0162b 3d4d894 dd0ab9f 3d4d894 41e92f0 3d4d894 b12d9cc 41e92f0 3d4d894 b12d9cc 41e92f0 1c669b7 b12d9cc 41e92f0 3d4d894 41e92f0 dd0ab9f c12df56 3d4d894 41e92f0 b12d9cc c12df56 dd0ab9f 41e92f0 1c669b7 41e92f0 c12df56 dd0ab9f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
"""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 = "") -> 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()
mask_image = Image.fromarray((mask_image * 255).astype(np.uint8))
mask_image_postproc = convolution(mask_image)
flush()
st.success(f"{pipe.queue_size} images in the queue, can take up to {(pipe.queue_size+1) * 10} seconds")
generated_image = pipe(image=image,
mask_image=mask_image,
prompt=positive_prompt,
negative_prompt=negative_prompt,
num_inference_steps=50,
height=HEIGHT,
width=WIDTH,
).images[0]
generated_image = postprocess_image_masking(generated_image, image, mask_image_postproc)
return generated_image
|