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nodes.py
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nodes.py
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import numpy as np
import cv2
from PIL import Image, ImageFilter
import uuid
from scipy.interpolate import interp1d, PchipInterpolator
import torchvision
from .utils import *
import os
import folder_paths
import json
from .DragNUWA_net import Net, args
import os.path
import sys
import torch.nn.functional as F
def readFlow(fn):
""" Read .flo file in Middlebury format"""
# Code adapted from:
# http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy
# WARNING: this will work on little-endian architectures (eg Intel x86) only!
# print 'fn = %s'%(fn)
with open(fn, 'rb') as f:
magic = np.fromfile(f, np.float32, count=1)
if 202021.25 != magic:
print('Magic number incorrect. Invalid .flo file')
return None
else:
w = np.fromfile(f, np.int32, count=1)
h = np.fromfile(f, np.int32, count=1)
# print 'Reading %d x %d flo file\n' % (w, h)
data = np.fromfile(f, np.float32, count=2 * int(w) * int(h))
# Reshape testdata into 3D array (columns, rows, bands)
# The reshape here is for visualization, the original code is (w,h,2)
return np.resize(data, (int(h), int(w), 2))
def load_flo_files(folder_path):
#Load flow files
flo_files = [file for file in os.listdir(folder_path) if file.endswith('.flo')]
flo_data = []
for file_name in flo_files:
file_path = os.path.join(folder_path, file_name)
flow_data = readFlow(file_path)
if flow_data is not None:
flo_data.append(flow_data)
return flo_data
def interpolate_trajectory(points, n_points):
x = [point[0] for point in points]
y = [point[1] for point in points]
t = np.linspace(0, 1, len(points))
# fx = interp1d(t, x, kind='cubic')
# fy = interp1d(t, y, kind='cubic')
fx = PchipInterpolator(t, x)
fy = PchipInterpolator(t, y)
new_t = np.linspace(0, 1, n_points)
new_x = fx(new_t)
new_y = fy(new_t)
new_points = list(zip(new_x, new_y))
return new_points
def visualize_drag_v2(background_image_path, splited_tracks, width, height):
trajectory_maps = []
background_image = Image.open(background_image_path).convert('RGBA')
background_image = background_image.resize((width, height))
w, h = background_image.size
transparent_background = np.array(background_image)
transparent_background[:, :, -1] = 128
transparent_background = Image.fromarray(transparent_background)
# Create a transparent layer with the same size as the background image
transparent_layer = np.zeros((h, w, 4))
for splited_track in splited_tracks:
if len(splited_track) > 1:
splited_track = interpolate_trajectory(splited_track, 16)
splited_track = splited_track[:16]
for i in range(len(splited_track)-1):
start_point = (int(splited_track[i][0]), int(splited_track[i][1]))
end_point = (int(splited_track[i+1][0]), int(splited_track[i+1][1]))
vx = end_point[0] - start_point[0]
vy = end_point[1] - start_point[1]
arrow_length = np.sqrt(vx**2 + vy**2)
if i == len(splited_track)-2:
cv2.arrowedLine(transparent_layer, start_point, end_point, (255, 0, 0, 192), 2, tipLength=8 / arrow_length)
else:
cv2.line(transparent_layer, start_point, end_point, (255, 0, 0, 192), 2)
else:
cv2.circle(transparent_layer, (int(splited_track[0][0]), int(splited_track[0][1])), 5, (255, 0, 0, 192), -1)
transparent_layer = Image.fromarray(transparent_layer.astype(np.uint8))
trajectory_map = Image.alpha_composite(transparent_background, transparent_layer)
trajectory_maps.append(trajectory_map)
return trajectory_maps, transparent_layer
class Drag:
def __init__(self, device, model_path, cfg_path, height, width, model_length):
self.device = device
#cf = import_filename(cfg_path)
#Net, args = cf.Net, cf.args
drag_nuwa_net = Net(args)
#state_dict = file2data(model_path, map_location='cpu')
adaptively_load_state_dict(drag_nuwa_net, model_path)
drag_nuwa_net.eval()
drag_nuwa_net.to(device)
# drag_nuwa_net.half()
self.drag_nuwa_net = drag_nuwa_net
self.height = height
self.width = width
_, model_step, _ = split_filename(model_path)
self.ouput_prefix = f'{model_step}_{width}X{height}'
self.model_length = model_length
@torch.no_grad()
def forward_sample(self, input_drag, input_first_frame, motion_bucket_id, outputs=dict()):
device = self.device
b, l, h, w, c = input_drag.size()
drag = self.drag_nuwa_net.apply_gaussian_filter_on_drag(input_drag)
drag = torch.cat([torch.zeros_like(drag[:, 0]).unsqueeze(1), drag], dim=1) # pad the first frame with zero flow
drag = rearrange(drag, 'b l h w c -> b l c h w')
input_conditioner = dict()
input_conditioner['cond_frames_without_noise'] = input_first_frame
input_conditioner['cond_frames'] = (input_first_frame + 0.02 * torch.randn_like(input_first_frame))
input_conditioner['motion_bucket_id'] = torch.tensor([motion_bucket_id]).to(drag.device).repeat(b * (l+1))
input_conditioner['fps_id'] = torch.tensor([self.drag_nuwa_net.args.fps]).to(drag.device).repeat(b * (l+1))
input_conditioner['cond_aug'] = torch.tensor([0.02]).to(drag.device).repeat(b * (l+1))
input_conditioner_uc = {}
for key in input_conditioner.keys():
if key not in input_conditioner_uc and isinstance(input_conditioner[key], torch.Tensor):
input_conditioner_uc[key] = input_conditioner[key].clone()
c, uc = self.drag_nuwa_net.conditioner.get_unconditional_conditioning(
input_conditioner,
batch_uc=input_conditioner_uc,
force_uc_zero_embeddings=[
"cond_frames",
"cond_frames_without_noise",
],
)
for k in ["crossattn", "concat"]:
uc[k] = repeat(uc[k], "b ... -> b t ...", t=self.drag_nuwa_net.num_frames)
uc[k] = rearrange(uc[k], "b t ... -> (b t) ...")
c[k] = repeat(c[k], "b ... -> b t ...", t=self.drag_nuwa_net.num_frames)
c[k] = rearrange(c[k], "b t ... -> (b t) ...")
H, W = input_conditioner['cond_frames_without_noise'].shape[2:]
shape = (self.drag_nuwa_net.num_frames, 4, H // 8, W // 8)
randn = torch.randn(shape).to(self.device)
additional_model_inputs = {}
additional_model_inputs["image_only_indicator"] = torch.zeros(
2, self.drag_nuwa_net.num_frames
).to(self.device)
additional_model_inputs["num_video_frames"] = self.drag_nuwa_net.num_frames
additional_model_inputs["flow"] = drag.repeat(2, 1, 1, 1, 1) # c and uc
def denoiser(input, sigma, c):
return self.drag_nuwa_net.denoiser(self.drag_nuwa_net.model, input, sigma, c, **additional_model_inputs)
samples_z = self.drag_nuwa_net.sampler(denoiser, randn, cond=c, uc=uc)
samples = self.drag_nuwa_net.decode_first_stage(samples_z)
outputs['logits_imgs'] = rearrange(samples, '(b l) c h w -> b l c h w', b=b)
return outputs
def run(self, first_frame, tracking_points, inference_batch_size, motion_bucket_id):
#original_width, original_height=576, 320
input_all_points = tracking_points
resized_all_points = [tuple([tuple([int(e1[0]*1), int(e1[1]*1)]) for e1 in e]) for e in input_all_points]
input_drag = torch.zeros(self.model_length - 1, self.height, self.width, 2) ##input points
for splited_track in resized_all_points:
if len(splited_track) == 1: # stationary point
displacement_point = tuple([splited_track[0][0] + 1, splited_track[0][1] + 1])
splited_track = tuple([splited_track[0], displacement_point])
# interpolate the track
splited_track = interpolate_trajectory(splited_track, self.model_length)
splited_track = splited_track[:self.model_length]
if len(splited_track) < self.model_length:
splited_track = splited_track + [splited_track[-1]] * (self.model_length -len(splited_track))
for i in range(self.model_length - 1):
start_point = splited_track[i]
end_point = splited_track[i+1]
input_drag[i][int(start_point[1])][int(start_point[0])][0] = end_point[0] - start_point[0]
input_drag[i][int(start_point[1])][int(start_point[0])][1] = end_point[1] - start_point[1]
image_pil = first_frame.resize((self.width, self.height), Image.BILINEAR).convert('RGB')
#visualized_drag, _ = visualize_drag_v2(first_frame_path, resized_all_points, self.width, self.height)
first_frames_transform = transforms.Compose([
lambda x: Image.fromarray(x),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
outputs = None
ouput_video_list = []
num_inference = 1
for i in tqdm(range(num_inference)):
if not outputs:
first_frames = pil2arr(first_frame)
first_frames = repeat(first_frames_transform(first_frames), 'c h w -> b c h w', b=inference_batch_size).to(self.device)
else:
first_frames = outputs['logits_imgs'][:, -1]
outputs = self.forward_sample(
repeat(input_drag[i*(self.model_length - 1):(i+1)*(self.model_length - 1)], 'l h w c -> b l h w c', b=inference_batch_size).to(self.device),
first_frames,
motion_bucket_id)
ouput_video_list.append(outputs['logits_imgs'])
ouput_tensor = [ouput_video_list[0][0]]
for i in range(inference_batch_size):
for j in range(num_inference - 1):
ouput_tensor.append(ouput_video_list[j+1][i][1:])
ouput_tensor=torch.cat(ouput_tensor, dim=0)
data=[transforms.ToPILImage('RGB')(utils.make_grid(e.to(torch.float32).cpu(), normalize=True, value_range=(-1, 1))) for e in ouput_tensor]
data = [torch.unsqueeze(torch.tensor(np.array(image).astype(np.float32) / 255.0), 0) for image in data]
return torch.cat(tuple(data), dim=0).unsqueeze(0)
def load_motionbrush_from_tracking_points(self, tracking_points):
#original_width, original_height=576, 320
tracking_points=json.loads(tracking_points)
input_all_points = tracking_points
resized_all_points = [tuple([tuple([int(e1[0]*1), int(e1[1]*1)]) for e1 in e]) for e in input_all_points]
motionbrush = torch.zeros(self.model_length - 1, self.height, self.width, 2) ##input points
for splited_track in resized_all_points:
if len(splited_track) == 1: # stationary point
displacement_point = tuple([splited_track[0][0] + 1, splited_track[0][1] + 1])
splited_track = tuple([splited_track[0], displacement_point])
# interpolate the track
splited_track = interpolate_trajectory(splited_track, self.model_length)
splited_track = splited_track[:self.model_length]
if len(splited_track) < self.model_length:
splited_track = splited_track + [splited_track[-1]] * (self.model_length -len(splited_track))
for i in range(self.model_length - 1):
start_point = splited_track[i]
end_point = splited_track[i+1]
motionbrush[i][int(start_point[1])][int(start_point[0])][0] = end_point[0] - start_point[0]
motionbrush[i][int(start_point[1])][int(start_point[0])][1] = end_point[1] - start_point[1]
return motionbrush
def load_motionbrush_from_optical_flow_directory(self, flow_path):
flos = load_flo_files(flow_path)
motionbrush = torch.zeros(self.model_length - 1, self.height, self.width, 2) ##input points
for i in range(self.model_length - 1):
motionbrush[i] = F.interpolate(torch.from_numpy(flos[i]).unsqueeze(0).permute(0, 3, 1, 2).float() , size=(self.height,self.width), mode='bilinear', align_corners=True).squeeze().permute(1, 2, 0)
return motionbrush
def load_motionbrush_from_optical_flow(self, optical_flow):
motionbrush = torch.zeros(self.model_length - 1, self.height, self.width, 2)
for i in range(self.model_length - 1):
motionbrush[i] = F.interpolate(optical_flow[i].unsqueeze(0).permute(0, 3, 1, 2).float() , size=(self.height,self.width), mode='bilinear', align_corners=True).squeeze().permute(1, 2, 0)
return motionbrush
def run_2(self, first_frame, tracking_points, inference_batch_size, motion_bucket_id, use_optical, flow_path):
#original_width, original_height=576, 320
input_all_points = tracking_points
resized_all_points = [tuple([tuple([int(e1[0]*1), int(e1[1]*1)]) for e1 in e]) for e in input_all_points]
input_drag = torch.zeros(self.model_length - 1, self.height, self.width, 2) ##input points
for splited_track in resized_all_points:
if len(splited_track) == 1: # stationary point
displacement_point = tuple([splited_track[0][0] + 1, splited_track[0][1] + 1])
splited_track = tuple([splited_track[0], displacement_point])
# interpolate the track
splited_track = interpolate_trajectory(splited_track, self.model_length)
splited_track = splited_track[:self.model_length]
if len(splited_track) < self.model_length:
splited_track = splited_track + [splited_track[-1]] * (self.model_length -len(splited_track))
for i in range(self.model_length - 1):
start_point = splited_track[i]
end_point = splited_track[i+1]
input_drag[i][int(start_point[1])][int(start_point[0])][0] = end_point[0] - start_point[0]
input_drag[i][int(start_point[1])][int(start_point[0])][1] = end_point[1] - start_point[1]
#Load optical flow
if use_optical :
flos = load_flo_files(flow_path)
for i in range(self.model_length-1):
input_drag[i] = torch.from_numpy(flos[i])
image_pil = first_frame.resize((self.width, self.height), Image.BILINEAR).convert('RGB')
#visualized_drag, _ = visualize_drag_v2(first_frame_path, resized_all_points, self.width, self.height)
first_frames_transform = transforms.Compose([
lambda x: Image.fromarray(x),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
outputs = None
ouput_video_list = []
num_inference = 1
for i in tqdm(range(num_inference)):
if not outputs:
first_frames = pil2arr(first_frame)
first_frames = repeat(first_frames_transform(first_frames), 'c h w -> b c h w', b=inference_batch_size).to(self.device)
else:
first_frames = outputs['logits_imgs'][:, -1]
outputs = self.forward_sample(
repeat(input_drag[i*(self.model_length - 1):(i+1)*(self.model_length - 1)], 'l h w c -> b l h w c', b=inference_batch_size).to(self.device),
first_frames,
motion_bucket_id)
ouput_video_list.append(outputs['logits_imgs'])
ouput_tensor = [ouput_video_list[0][0]]
for i in range(inference_batch_size):
for j in range(num_inference - 1):
ouput_tensor.append(ouput_video_list[j+1][i][1:])
ouput_tensor=torch.cat(ouput_tensor, dim=0)
data=[transforms.ToPILImage('RGB')(utils.make_grid(e.to(torch.float32).cpu(), normalize=True, value_range=(-1, 1))) for e in ouput_tensor]
data = [torch.unsqueeze(torch.tensor(np.array(image).astype(np.float32) / 255.0), 0) for image in data]
return torch.cat(tuple(data), dim=0).unsqueeze(0)
def run_brush(self, first_frame, motion_brush, inference_batch_size, motion_bucket_id):
#original_width, original_height=576, 320
input_drag=motion_brush
print(input_drag.shape)
image_pil = first_frame.resize((self.width, self.height), Image.BILINEAR).convert('RGB')
#visualized_drag, _ = visualize_drag_v2(first_frame_path, resized_all_points, self.width, self.height)
first_frames_transform = transforms.Compose([
lambda x: Image.fromarray(x),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
outputs = None
ouput_video_list = []
num_inference = 1
for i in tqdm(range(num_inference)):
if not outputs:
first_frames = pil2arr(first_frame)
first_frames = repeat(first_frames_transform(first_frames), 'c h w -> b c h w', b=inference_batch_size).to(self.device)
else:
first_frames = outputs['logits_imgs'][:, -1]
outputs = self.forward_sample(
repeat(input_drag[i*(self.model_length - 1):(i+1)*(self.model_length - 1)], 'l h w c -> b l h w c', b=inference_batch_size).to(self.device),
first_frames,
motion_bucket_id)
ouput_video_list.append(outputs['logits_imgs'])
ouput_tensor = [ouput_video_list[0][0]]
for i in range(inference_batch_size):
for j in range(num_inference - 1):
ouput_tensor.append(ouput_video_list[j+1][i][1:])
ouput_tensor=torch.cat(ouput_tensor, dim=0)
data=[transforms.ToPILImage('RGB')(utils.make_grid(e.to(torch.float32).cpu(), normalize=True, value_range=(-1, 1))) for e in ouput_tensor]
data = [torch.unsqueeze(torch.tensor(np.array(image).astype(np.float32) / 255.0), 0) for image in data]
return torch.cat(tuple(data), dim=0).unsqueeze(0)
class LoadCheckPointDragNUWA:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"default": "drag_nuwa_svd.pth"}),
"dimension": (["576x320","512x512","320x576"], {"default": "576x320"}),
"model_length": ("INT", {"default": 14}),
}
}
RETURN_TYPES = ("DragNUWA",)
RETURN_NAMES = ("model",)
FUNCTION = "load_dragnuwa"
CATEGORY = "DragNUWA"
def load_dragnuwa(self, ckpt_name, dimension, model_length):
width=int(dimension.split('x')[0])
height=int(dimension.split('x')[1])
comfy_path = os.path.dirname(folder_paths.__file__)
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
current_path = os.path.abspath(os.path.dirname(__file__))
sys.path.append(current_path)
DragNUWA_net = Drag("cuda:0", ckpt_path, f'{comfy_path}/custom_nodes/ComfyUI-DragNUWA/DragNUWA_net.py', height, width, model_length)
return (DragNUWA_net,)
class InstantCameraMotionBrush:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model_length": ("INT", {"default": 14}),
"width": ("INT", {"default": 576}),
"height": ("INT", {"default": 320}),
"action": (["left","right","up","down","zoomin","zoomout"], {"default":"left"}),
"speed": ("FLOAT", {"default": 1}),
}
}
RETURN_TYPES = ("MotionBrush",)
FUNCTION = "run_inference"
CATEGORY = "DragNUWA"
def run_inference(self, model_length, width, height, action, speed):
#speed=speed/255
motion_brush = torch.zeros(model_length-1, height, width, 2)
xmotionbrush=motion_brush[:,:,:,:1]
ymotionbrush=motion_brush[:,:,:,1:]
xcount=10
ycount=10
if width<xcount:
xcount=width
if height<ycount:
ycount=height
xratio=(width)/xcount
yratio=(height)/ycount
model_width=width
model_height=height
box=[0,0,width,height]
tracking_points=[]
if action=="zoomin":
for j in range(ycount-1):
for k in range(xcount-1):
item=[]
for i in range(model_length - 1):
xi=int(k*xratio)+i*speed/(width/2)*(k*xratio-width/2)
yi=int(j*yratio)+i*speed/(height/2)*(j*yratio-height/2)
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
elif action=="zoomout":
for j in range(ycount-1):
for k in range(xcount-1):
item=[]
for i in range(model_length - 1):
xi=int(k*xratio)+i*-speed/(width/2)*(k*xratio-width/2)
yi=int(j*yratio)+i*-speed/(height/2)*(j*yratio-height/2)
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
else:
if action=="left":
for j in range(ycount-1):
for k in range(xcount-1):
item=[]
for i in range(model_length - 1):
xi=int(k*xratio)+i*-speed
yi=int(j*yratio)
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
elif action=="right":
for j in range(ycount-1):
for k in range(xcount-1):
item=[]
for i in range(model_length - 1):
xi=int(k*xratio)+i*speed
yi=int(j*yratio)
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
elif action=="up":
for j in range(ycount-1):
for k in range(xcount-1):
item=[]
for i in range(model_length - 1):
xi=int(k*xratio)
yi=int(j*yratio)+i*-speed
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
elif action=="down":
for j in range(ycount-1):
for k in range(xcount-1):
item=[]
for i in range(model_length - 1):
xi=int(k*xratio)
yi=int(j*yratio)+i*speed
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
#print(tracking_points)
motion_brush=load_motionbrush_from_tracking_points_without_model(model_length,model_width,model_height,tracking_points)
return (motion_brush,)
def load_motionbrush_from_tracking_points_without_model(model_length, width, height, tracking_points):
#original_width, original_height=576, 320
input_all_points = tracking_points
resized_all_points = [tuple([tuple([int(e1[0]*1), int(e1[1]*1)]) for e1 in e]) for e in input_all_points]
motionbrush = torch.zeros(model_length- 1, height, width, 2) ##input points
for splited_track in resized_all_points:
if len(splited_track) == 1: # stationary point
displacement_point = tuple([splited_track[0][0] + 1, splited_track[0][1] + 1])
splited_track = tuple([splited_track[0], displacement_point])
# interpolate the track
splited_track = interpolate_trajectory(splited_track, model_length)
splited_track = splited_track[:model_length]
if len(splited_track) < model_length:
splited_track = splited_track + [splited_track[-1]] * (model_length -len(splited_track))
for i in range(model_length - 1):
start_point = splited_track[i]
end_point = splited_track[i+1]
motionbrush[i][int(start_point[1])][int(start_point[0])][0] = end_point[0] - start_point[0]
motionbrush[i][int(start_point[1])][int(start_point[0])][1] = end_point[1] - start_point[1]
return motionbrush
class InstantObjectMotionBrush:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model_length": ("INT", {"default": 14}),
"brush_mask": ("MASK",),
"action": (["left","right","up","down","zoomin","zoomout"], {"default":"left"}),
"speed": ("FLOAT", {"default": 5}),
}
}
RETURN_TYPES = ("MotionBrush",)
FUNCTION = "run_inference"
CATEGORY = "DragNUWA"
def run_inference(self, model_length, brush_mask, action, speed):
model_width=brush_mask.shape[2]
model_height=brush_mask.shape[1]
from torchvision.ops import masks_to_boxes
boxes = masks_to_boxes(brush_mask)
box=boxes[0].int().tolist()
print(f'model_width{model_width}model_height{model_height}box{box}')
xcount=10
ycount=10
if box[2]-box[0]<xcount:
xcount=box[2]-box[0]
if box[3]-box[1]<ycount:
ycount=box[3]-box[1]
xratio=(box[2]-box[0])/xcount
yratio=(box[3]-box[1])/ycount
tracking_points=[]
if action=="zoomin":
for j in range(ycount-1):
for k in range(xcount-1):
if not bool(brush_mask[0][box[1]+int(j*yratio)][box[0]+int(k*xratio)]):
continue
item=[]
for i in range(model_length - 1):
width=box[2]-box[0]
height=box[3]-box[1]
xi=box[0]+int(k*xratio)+i*speed/(width/2)*(k*xratio-width/2)
yi=box[1]+int(j*yratio)+i*speed/(height/2)*(j*yratio-height/2)
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
elif action=="zoomout":
for j in range(ycount-1):
for k in range(xcount-1):
if not bool(brush_mask[0][box[1]+int(j*yratio)][box[0]+int(k*xratio)]):
continue
item=[]
for i in range(model_length - 1):
width=box[2]-box[0]
height=box[3]-box[1]
xi=box[0]+int(k*xratio)+i*-speed/(width/2)*(k*xratio-width/2)
yi=box[1]+int(j*yratio)+i*-speed/(height/2)*(j*yratio-height/2)
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
else:
if action=="left":
for j in range(ycount-1):
for k in range(xcount-1):
if not bool(brush_mask[0][box[1]+int(j*yratio)][box[0]+int(k*xratio)]):
continue
item=[]
for i in range(model_length - 1):
width=box[2]-box[0]
height=box[3]-box[1]
xi=box[0]+int(k*xratio)+i*-speed
yi=box[1]+int(j*yratio)
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
elif action=="right":
for j in range(ycount-1):
for k in range(xcount-1):
if not bool(brush_mask[0][box[1]+int(j*yratio)][box[0]+int(k*xratio)]):
continue
item=[]
for i in range(model_length - 1):
width=box[2]-box[0]
height=box[3]-box[1]
xi=box[0]+int(k*xratio)+i*speed
yi=box[1]+int(j*yratio)
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
elif action=="up":
for j in range(ycount-1):
for k in range(xcount-1):
if not bool(brush_mask[0][box[1]+int(j*yratio)][box[0]+int(k*xratio)]):
continue
item=[]
for i in range(model_length - 1):
width=box[2]-box[0]
height=box[3]-box[1]
xi=box[0]+int(k*xratio)
yi=box[1]+int(j*yratio)+i*-speed
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
elif action=="down":
for j in range(ycount-1):
for k in range(xcount-1):
if not bool(brush_mask[0][box[1]+int(j*yratio)][box[0]+int(k*xratio)]):
continue
item=[]
for i in range(model_length - 1):
width=box[2]-box[0]
height=box[3]-box[1]
xi=box[0]+int(k*xratio)
yi=box[1]+int(j*yratio)+i*speed
if xi>model_width-1:
xi=model_width-1
if yi>model_height-1:
yi=model_height-1
if xi<0:
xi=0
if yi<0:
yi=0
item.append([xi,yi])
tracking_points.append(item)
#print(tracking_points)
motion_brush=load_motionbrush_from_tracking_points_without_model(model_length,model_width,model_height,tracking_points)
return (motion_brush,)
class LoadMotionBrushFromTrackingPoints:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("DragNUWA",),
"tracking_points": ("STRING", {"multiline": True, "default":"[[[25,25],[128,128]]]"}),
}
}
RETURN_TYPES = ("MotionBrush",)
FUNCTION = "run_inference"
CATEGORY = "DragNUWA"
def run_inference(self, model, tracking_points):
return (model.load_motionbrush_from_tracking_points(tracking_points),)
class LoadMotionBrushFromTrackingPointsWithoutModel:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model_length": ("INT", {"default": 14}),
"width": ("INT", {"default": 36}),
"height": ("INT", {"default": 20}),
"tracking_points": ("STRING", {"multiline": True, "default":"[[[1,1],[2,2]]]"}),
}
}
RETURN_TYPES = ("MotionBrush",)
FUNCTION = "run_inference"
CATEGORY = "DragNUWA"
def run_inference(self, model_length, width, height, tracking_points):
tracking_points=json.loads(tracking_points)
motionbrush=load_motionbrush_from_tracking_points_without_model(model_length, width, height, tracking_points)
return (motionbrush,)
class LoadMotionBrushFromOpticalFlowDirectory:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("DragNUWA",),
"optical_flow_directory": ("STRING", {"default": "X://path/to/optical_flow", "vhs_path_extensions": []}),
}
}
RETURN_TYPES = ("MotionBrush",)
FUNCTION = "run_inference"
CATEGORY = "DragNUWA"
def run_inference(self, model, optical_flow_directory):
return (model.load_motionbrush_from_optical_flow_directory(optical_flow_directory),)
class LoadMotionBrushFromOpticalFlow:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("DragNUWA",),
"optical_flow": ("OPTICAL_FLOW",),
}
}
RETURN_TYPES = ("MotionBrush",)
FUNCTION = "run_inference"
CATEGORY = "DragNUWA"
def run_inference(self, model, optical_flow):
return (model.load_motionbrush_from_optical_flow(optical_flow),)
class DragNUWARunMotionBrush:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("DragNUWA",),
"image": ("IMAGE",),
"motion_brush": ("MotionBrush",),
"inference_batch_size": ("INT", {"default": 1, "min": 1, "max": 1}),
"motion_bucket_id": ("INT", {"default": 4, "min": 1, "max": 100}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "run_inference"
CATEGORY = "DragNUWA"
def run_inference(self, model, image, motion_brush, inference_batch_size, motion_bucket_id):
image = 255.0 * image[0].cpu().numpy()
image_pil = Image.fromarray(np.clip(image, 0, 255).astype(np.uint8))
raw_w, raw_h = image_pil.size
resize_ratio = max(model.width/raw_w, model.height/raw_h)
image_pil = image_pil.resize((int(raw_w * resize_ratio), int(raw_h * resize_ratio)), Image.BILINEAR)
image_pil = transforms.CenterCrop((model.height, model.width))(image_pil.convert('RGB'))
#return model.run(image_pil, tracking_points, inference_batch_size, motion_bucket_id)
return model.run_brush(image_pil, motion_brush, inference_batch_size, motion_bucket_id)
class DragNUWARun:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("DragNUWA",),
"image": ("IMAGE",),
"tracking_points": ("STRING", {"multiline": True, "default":"[[[25,25],[128,128]]]"}),
"inference_batch_size": ("INT", {"default": 1, "min": 1, "max": 1}),
"motion_bucket_id": ("INT", {"default": 4, "min": 1, "max": 100}),
"use_optical_flow": ("BOOLEAN", {"default": False}),
"directory": ("STRING", {"default": "X://path/to/optical_flow", "vhs_path_extensions": []}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "run_inference"
CATEGORY = "DragNUWA"
def run_inference(self, model, image, tracking_points, inference_batch_size, motion_bucket_id, use_optical_flow, directory):
image = 255.0 * image[0].cpu().numpy()
image_pil = Image.fromarray(np.clip(image, 0, 255).astype(np.uint8))
raw_w, raw_h = image_pil.size
resize_ratio = max(model.width/raw_w, model.height/raw_h)
image_pil = image_pil.resize((int(raw_w * resize_ratio), int(raw_h * resize_ratio)), Image.BILINEAR)
image_pil = transforms.CenterCrop((model.height, model.width))(image_pil.convert('RGB'))
tracking_points=json.loads(tracking_points)
#return model.run(image_pil, tracking_points, inference_batch_size, motion_bucket_id)
return model.run_2(image_pil, tracking_points, inference_batch_size, motion_bucket_id, use_optical_flow, directory)
class LoadPoseKeyPoints:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"file_name": (os.listdir(folder_paths.output_directory), {"default": "PoseKeypoint_00001.json"}),
}
}
RETURN_TYPES = ("POSE_KEYPOINT",)
FUNCTION = "run"
CATEGORY = "DragNUWA"
def run(self, file_name):
path = os.path.join(folder_paths.output_directory, file_name)
with open(path) as fr:
return (json.load(fr),)
class SplitTrackingPoints:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"pose_kps": ("POSE_KEYPOINT",),
"split_index": ("INT",{"default":0}),
"height": ("INT", {"default": 320}),
"width": ("INT", {"default": 576}),
},
"optional": {
"last_pose_kps": ("POSE_KEYPOINT",{"default":None}),
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("tracking_points",)
FUNCTION = "split_tracking_points"
OUTPUT_NODE = True
CATEGORY = "DragNUWA"
def split_tracking_points(self, pose_kps, split_index, height, width, last_pose_kps=None):
if split_index!=0:
if last_pose_kps is not None:
pose_kps[split_index*14]=last_pose_kps[0]
trajs=[]
for ipose in range(int(len(pose_kps[split_index*14]["people"][0]["pose_keypoints_2d"])/3)):
traj=[]
for itracking in range(14):
people=pose_kps[split_index*14+itracking]["people"]
if people[0]["pose_keypoints_2d"][ipose*3+2]==1.0:
x=people[0]["pose_keypoints_2d"][ipose*3]
y=people[0]["pose_keypoints_2d"][ipose*3+1]
if x<=width and y<=height:
traj.append([x,y])
else:
break
else:
if len(traj)>0:
traj.append(traj[len(traj)-1])
else:
break
if len(traj)>0:
trajs.append(traj)
return (json.dumps(trajs),)
class GetFirstImage:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE", ),
},
}
RETURN_TYPES = ("IMAGE", )
FUNCTION = "run"
OUTPUT_NODE = False
CATEGORY = "DragNUWA"
def run(self, images: torch.Tensor):
shape = images.shape
len_first_dim = shape[0]
selected_indexes=f"0"
selected_index: list[int] = []
total_indexes: list[int] = list(range(len_first_dim))
for s in selected_indexes.strip().split(','):
try:
if ":" in s:
_li = s.strip().split(':', maxsplit=1)
_start = _li[0]
_end = _li[1]
if _start and _end:
selected_index.extend(
total_indexes[int(_start):int(_end)]
)
elif _start:
selected_index.extend(
total_indexes[int(_start):]
)
elif _end:
selected_index.extend(
total_indexes[:int(_end)]
)
else:
x: int = int(s.strip())
if x < len_first_dim:
selected_index.append(x)
except:
pass
return (images[selected_index, :, :, :], )
class GetLastImage:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"images": ("IMAGE", ),