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FlowStateLatentChooser.py
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FlowStateLatentChooser.py
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# Project: FlowState Latent Chooser
# Description: Select from input/imported images to create a new batch of latent images, or select an empty latent.
# Version: 1.0.0
# Author: Johnathan Chivington
# Contact: johnathan@flowstateengineering.com | youtube.com/@flowstateeng
##
# SYSTEM STATUS
##
print(f' - Loaded Latent Chooser node.')
##
# FS IMPORTS
##
from .FS_Types import *
from .FS_Constants import *
from .FS_Assets import *
##
# OUTSIDE IMPORTS
##
import torch
import numpy as np
import os, sys
import node_helpers
import folder_paths
from PIL import Image, ImageOps, ImageSequence
from comfy import model_management
##
# NODES
##
class FlowStateLatentChooser:
CATEGORY = 'FlowState/latent'
DESCRIPTION = 'Create a new batch of latent images to be denoised via sampling.'
FUNCTION = 'create_latent'
RETURN_TYPES = LATENT_CHOOSER
RETURN_NAMES = ('latent', 'image', 'width', 'height', )
OUTPUT_TOOLTIPS = (
'The latent image batch.',
'The image batch.',
'Image width.',
'Image height.',
)
@classmethod
def __init__(self):
self.device = model_management.intermediate_device()
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
return {
'required': {
'model_type': FS_MODEL_TYPE_LIST,
'width': IMG_WIDTH,
'height': IMG_HEIGHT,
'latent_type': (['empty_latent', 'input_img', 'imported_img'],),
'image': (sorted(files), {'image_upload': True}),
'vae': VAE_IN
},
'optional': {
'pixels': IMAGE
}
}
@classmethod
def generate(self, width, height, selected_model, batch_size=1):
latent_channels = 16 if selected_model == 'FLUX' else 4
latent = torch.zeros([batch_size, latent_channels, height // 8, width // 8], device=self.device)
return latent
@classmethod
def VALIDATE_INPUTS(s, image, vae=None):
if not folder_paths.exists_annotated_filepath(image):
return 'Invalid image file: {}'.format(image)
return True
@classmethod
def load_and_encode(self, image, vae):
image_path = folder_paths.get_annotated_filepath(image)
img = node_helpers.pillow(Image.open, image_path)
output_images = []
w, h = None, None
excluded_formats = ['MPO']
for i in ImageSequence.Iterator(img):
i = node_helpers.pillow(ImageOps.exif_transpose, i)
if i.mode == 'I':
i = i.point(lambda i: i * (1 / 255))
image = i.convert('RGB')
if len(output_images) == 0:
w = image.size[0]
h = image.size[1]
if image.size[0] != w or image.size[1] != h:
continue
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((64,64), dtype=torch.float32, device='cpu')
output_images.append(image)
if len(output_images) > 1 and img.format not in excluded_formats:
output_image = torch.cat(output_images, dim=0)
else:
output_image = output_images[0]
encoded = vae.encode(output_image[:,:,:,:3])
return encoded, output_image
def create_latent(self, model_type, latent_type, image, vae, width, height, pixels=None):
print(f'\n\n\nFlowState Latent Chooser - {model_type}')
selected_model = model_type if isinstance(model_type, str) else model_type[0]
if latent_type == 'empty_latent':
print(f' - Preparing empty latent.')
latent = self.generate(width, height, selected_model)
return ({'samples':latent}, None, width, height, )
elif latent_type == 'input_img':
print(f' - Preparing latent from input image.')
latent = vae.encode(pixels[:,:,:,:3])
img_width = pixels.shape[2]
img_height = pixels.shape[1]
return ({'samples':latent}, pixels, img_width, img_height, )
else:
print(f' - Preparing latent from imported image.')
latent, loaded_img = self.load_and_encode(image, vae)
img_width = loaded_img.shape[2]
img_height = loaded_img.shape[1]
return ({'samples':latent}, loaded_img, img_width, img_height, )