701 lines
33 KiB
Python
701 lines
33 KiB
Python
import shutil
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import cv2
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import pandas as pd
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import gc
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import math
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import lpips
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import PIL
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from PIL import Image, ImageOps
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import requests
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import torch
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from torch import nn
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from torch.nn import functional as F
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import torchvision
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import torchvision.transforms as T
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import torchvision.transforms.functional as TF
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from tqdm import tqdm
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from resize_right import resize
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from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
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from datetime import datetime
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import numpy as np
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import random
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from numpy import asarray
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from . import py3d_tools as p3dT
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from . import disco_xform_utils as dxf
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from .CLIP import clip
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import comfy.model_management
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import comfy.utils
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from . import disco_utils
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from .make_cutouts import MakeCutouts, MakeCutoutsDango
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from .midas_model import init_midas_depth_model
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from .settings import DiscoDiffusionSettings
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# Make sure GPU memory doesn't get corrupted from cancelling the run mid-way through, allow a full frame to complete
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stop_on_next_loop = False
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TRANSLATION_SCALE = 1.0/200.0
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def do_3d_step(args: DiscoDiffusionSettings, img_filepath, frame_num, midas_model, midas_transform):
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if args.key_frames:
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translation_x = args.translation_x_series[frame_num]
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translation_y = args.translation_y_series[frame_num]
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translation_z = args.translation_z_series[frame_num]
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rotation_3d_x = args.rotation_3d_x_series[frame_num]
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rotation_3d_y = args.rotation_3d_y_series[frame_num]
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rotation_3d_z = args.rotation_3d_z_series[frame_num]
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print(
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f'translation_x: {translation_x}',
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f'translation_y: {translation_y}',
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f'translation_z: {translation_z}',
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f'rotation_3d_x: {rotation_3d_x}',
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f'rotation_3d_y: {rotation_3d_y}',
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f'rotation_3d_z: {rotation_3d_z}',
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)
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device = comfy.model_management.get_torch_device()
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translate_xyz = [-translation_x*TRANSLATION_SCALE, translation_y *
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TRANSLATION_SCALE, -translation_z*TRANSLATION_SCALE]
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rotate_xyz_degrees = [rotation_3d_x, rotation_3d_y, rotation_3d_z]
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print('translation:', translate_xyz)
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print('rotation:', rotate_xyz_degrees)
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rotate_xyz = [math.radians(rotate_xyz_degrees[0]), math.radians(
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rotate_xyz_degrees[1]), math.radians(rotate_xyz_degrees[2])]
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rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(
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rotate_xyz, device=device), "XYZ").unsqueeze(0)
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print("rot_mat: " + str(rot_mat))
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next_step_pil = dxf.transform_image_3d(img_filepath, midas_model, midas_transform, device,
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rot_mat, translate_xyz, args.near_plane, args.far_plane,
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args.fov, padding_mode=args.padding_mode,
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sampling_mode=args.sampling_mode, midas_weight=args.midas_weight)
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return next_step_pil
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def horiz_symmetry(x):
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[n, c, h, w] = x.size()
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x = torch.concat(
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(x[:, :, :, :w//2], torch.flip(x[:, :, :, :w//2], [-1])), -1)
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print("horizontal symmetry applied")
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return x
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def vert_symmetry(x):
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[n, c, h, w] = x.size()
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x = torch.concat(
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(x[:, :, :h//2, :], torch.flip(x[:, :, :h//2, :], [-2])), -2)
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print("vertical symmetry applied")
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return x
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def id(x):
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return x
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def do_run(diffusion, model, clip_, clip_vision, args: DiscoDiffusionSettings, batchNum):
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seed = args.seed
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print(range(args.start_frame, args.max_frames))
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pbar = comfy.utils.ProgressBar(diffusion.num_timesteps - args.skip_steps)
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if (args.animation_mode == "3D") and (args.midas_weight > 0.0):
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midas_model, midas_transform, midas_net_w, midas_net_h, midas_resize_mode, midas_normalization = init_midas_depth_model(
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args.midas_depth_model)
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for frame_num in range(args.start_frame, args.max_frames):
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if stop_on_next_loop:
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break
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# display.clear_output(wait=True)
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# Print Frame progress if animation mode is on
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# if args.animation_mode != "None":
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# batchBar = tqdm(range(args.max_frames), desc="Frames")
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# batchBar.n = frame_num
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# batchBar.refresh()
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# Inits if not video frames
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if args.animation_mode != "Video Input":
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if args.init_image in ['', 'none', 'None', 'NONE']:
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init_image = None
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else:
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init_image = args.init_image
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init_scale = args.init_scale
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skip_steps = args.skip_steps
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if args.animation_mode == "2D":
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if args.key_frames:
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angle = args.angle_series[frame_num]
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zoom = args.zoom_series[frame_num]
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translation_x = args.translation_x_series[frame_num]
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translation_y = args.translation_y_series[frame_num]
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print(
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f'angle: {angle}',
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f'zoom: {zoom}',
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f'translation_x: {translation_x}',
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f'translation_y: {translation_y}',
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)
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if frame_num > 0:
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seed += 1
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if args.resume_run and frame_num == args.start_frame:
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img_0 = cv2.imread(
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args.batchFolder+f"/{args.batch_name}({batchNum})_{args.start_frame-1:04}.png")
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else:
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img_0 = cv2.imread('prevFrame.png')
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center = (1*img_0.shape[1]//2, 1*img_0.shape[0]//2)
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trans_mat = np.float32(
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[[1, 0, translation_x],
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[0, 1, translation_y]]
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)
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rot_mat = cv2.getRotationMatrix2D(center, angle, zoom)
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trans_mat = np.vstack([trans_mat, [0, 0, 1]])
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rot_mat = np.vstack([rot_mat, [0, 0, 1]])
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transformation_matrix = np.matmul(rot_mat, trans_mat)
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img_0 = cv2.warpPerspective(
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img_0,
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transformation_matrix,
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(img_0.shape[1], img_0.shape[0]),
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borderMode=cv2.BORDER_WRAP
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)
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cv2.imwrite('prevFrameScaled.png', img_0)
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init_image = 'prevFrameScaled.png'
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init_scale = args.frames_scale
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skip_steps = args.calc_frames_skip_steps
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if args.animation_mode == "3D":
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if frame_num > 0:
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seed += 1
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if args.resume_run and frame_num == args.start_frame:
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img_filepath = args.batchFolder + \
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f"/{args.batch_name}({batchNum})_{args.start_frame-1:04}.png"
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if args.turbo_mode and frame_num > args.turbo_preroll:
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shutil.copyfile(img_filepath, 'oldFrameScaled.png')
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else:
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img_filepath = 'prevFrame.png'
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next_step_pil = do_3d_step(
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args, img_filepath, frame_num, midas_model, midas_transform)
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next_step_pil.save('prevFrameScaled.png')
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# Turbo mode - skip some diffusions, use 3d morph for clarity and to save time
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if args.turbo_mode:
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if frame_num == args.turbo_preroll: # start tracking oldframe
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# stash for later blending
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next_step_pil.save('oldFrameScaled.png')
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elif frame_num > args.turbo_preroll:
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# set up 2 warped image sequences, old & new, to blend toward new diff image
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old_frame = do_3d_step(
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args, 'oldFrameScaled.png', frame_num, midas_model, midas_transform)
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old_frame.save('oldFrameScaled.png')
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if frame_num % int(args.turbo_steps) != 0:
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print(
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'turbo skip this frame: skipping clip diffusion steps')
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filename = f'{args.batch_name}({batchNum})_{frame_num:04}.png'
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blend_factor = (
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(frame_num % int(args.turbo_steps))+1)/int(args.turbo_steps)
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print(
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'turbo skip this frame: skipping clip diffusion steps and saving blended frame')
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# this is already updated..
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newWarpedImg = cv2.imread('prevFrameScaled.png')
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oldWarpedImg = cv2.imread('oldFrameScaled.png')
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blendedImage = cv2.addWeighted(
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newWarpedImg, blend_factor, oldWarpedImg, 1-blend_factor, 0.0)
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cv2.imwrite(
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f'{args.batchFolder}/{filename}', blendedImage)
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# save it also as prev_frame to feed next iteration
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next_step_pil.save(f'{img_filepath}')
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if args.vr_mode:
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generate_eye_views(
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TRANSLATION_SCALE, args.batchFolder, filename, frame_num, midas_model, midas_transform)
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continue
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else:
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# if not a skip frame, will run diffusion and need to blend.
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oldWarpedImg = cv2.imread('prevFrameScaled.png')
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# swap in for blending later
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cv2.imwrite(f'oldFrameScaled.png', oldWarpedImg)
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print('clip/diff this frame - generate clip diff image')
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init_image = 'prevFrameScaled.png'
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init_scale = args.frames_scale
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skip_steps = args.calc_frames_skip_steps
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if args.animation_mode == "Video Input":
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init_scale = args.video_init_frames_scale
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skip_steps = args.calc_frames_skip_steps
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if not args.video_init_seed_continuity:
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seed += 1
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if args.video_init_flow_warp:
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if frame_num == 0:
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skip_steps = args.video_init_skip_steps
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init_image = f'{args.videoFramesFolder}/{frame_num+1:04}.jpg'
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if frame_num > 0:
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prev = PIL.Image.open(
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args.batchFolder+f"/{args.batch_name}({batchNum})_{frame_num-1:04}.png")
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frame1_path = f'{args.videoFramesFolder}/{frame_num:04}.jpg'
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frame2 = PIL.Image.open(
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f'{args.videoFramesFolder}/{frame_num+1:04}.jpg')
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flo_path = f"/{args.flo_folder}/{frame1_path.split('/')[-1]}.npy"
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init_image = 'warped.png'
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print(args.video_init_flow_blend)
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weights_path = None
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if args.video_init_check_consistency:
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# TBD
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pass
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import video_input
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video_input.warp(prev, frame2, flo_path, blend=args.video_init_flow_blend,
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weights_path=weights_path).save(init_image)
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else:
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init_image = f'{args.videoFramesFolder}/{frame_num+1:04}.jpg'
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loss_values = []
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if seed is not None:
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np.random.seed(seed)
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random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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target_embeds, weights = [], []
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if args.prompts_series is not None and frame_num >= len(args.prompts_series):
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frame_prompt = args.prompts_series[-1]
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elif args.prompts_series is not None:
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frame_prompt = args.prompts_series[frame_num]
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else:
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frame_prompt = []
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print(args.image_prompts_series)
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if args.image_prompts_series is not None and frame_num >= len(args.image_prompts_series):
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image_prompt = args.image_prompts_series[-1]
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elif args.image_prompts_series is not None:
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image_prompt = args.image_prompts_series[frame_num]
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else:
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image_prompt = []
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device = comfy.model_management.get_torch_device()
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print(f'Frame {frame_num} Prompt: {frame_prompt}')
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clip_models = [clip_] # TODO!!!!!!!!!!!!!!!!!!!!!
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model_stats = []
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for clip_model in clip_models:
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cutn = 16
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model_stat = {"clip_model": None, "target_embeds": [],
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"make_cutouts": None, "weights": []}
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model_stat["clip_model"] = clip_model
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model_stat["clip_vision_model"] = clip_vision
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for prompt in frame_prompt:
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prompt = ", ".join(prompt)
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txt, weight = disco_utils.parse_prompt(prompt)
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# txt = clip_model.encode(prompt).float()
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txt = clip_model.encode_text(clip.tokenize(prompt).to(device)).float()
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if args.fuzzy_prompt:
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for i in range(25):
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model_stat["target_embeds"].append(
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(txt + torch.randn(txt.shape).cuda() * args.rand_mag).clamp(0, 1))
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model_stat["weights"].append(weight)
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else:
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model_stat["target_embeds"].append(txt)
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model_stat["weights"].append(weight)
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if image_prompt:
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model_stat["make_cutouts"] = MakeCutouts(
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clip_model.visual.input_resolution, cutn, skip_augs=args.skip_augs)
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for prompt in image_prompt:
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path, weight = disco_utils.parse_prompt(prompt)
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img = Image.open(disco_utils.fetch(path)).convert('RGB')
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img = TF.resize(
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img, min(args.side_x, args.side_y, *img.size), T.InterpolationMode.LANCZOS)
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batch = model_stat["make_cutouts"](TF.to_tensor(
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img).to(device).unsqueeze(0).mul(2).sub(1))
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embed = clip_vision.encode_image(
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disco_utils.normalize(batch)).float()
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if args.fuzzy_prompt:
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for i in range(25):
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model_stat["target_embeds"].append(
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(embed + torch.randn(embed.shape).cuda() * args.rand_mag).clamp(0, 1))
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weights.extend([weight / cutn] * cutn)
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else:
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model_stat["target_embeds"].append(embed)
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model_stat["weights"].extend([weight / cutn] * cutn)
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model_stat["target_embeds"] = torch.cat(
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model_stat["target_embeds"])
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model_stat["weights"] = torch.tensor(
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model_stat["weights"], device=device)
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if model_stat["weights"].sum().abs() < 1e-3:
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raise RuntimeError('The weights must not sum to 0.')
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model_stat["weights"] /= model_stat["weights"].sum().abs()
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model_stats.append(model_stat)
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init = None
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if init_image is not None:
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init = Image.open(disco_utils.fetch(init_image)).convert('RGB')
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init = init.resize((args.side_x, args.side_y), Image.LANCZOS)
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init = TF.to_tensor(init).to(device).unsqueeze(0).mul(2).sub(1)
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if args.perlin_init:
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init = disco_utils.regen_perlin(args.perlin_mode, args.batch_size)
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cur_t = None
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def cond_fn(x, t, y=None):
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with torch.enable_grad():
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x_is_NaN = False
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x = x.detach().requires_grad_()
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n = x.shape[0]
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if args.MS.use_secondary_model is True:
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alpha = torch.tensor(
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diffusion.sqrt_alphas_cumprod[cur_t], device=device, dtype=torch.float32)
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sigma = torch.tensor(
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diffusion.sqrt_one_minus_alphas_cumprod[cur_t], device=device, dtype=torch.float32)
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cosine_t = disco_utils.alpha_sigma_to_t(alpha, sigma)
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out = args.MS.secondary_model(
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x, cosine_t[None].repeat([n])).pred
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fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]
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x_in = out * fac + x * (1 - fac)
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x_in_grad = torch.zeros_like(x_in)
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else:
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my_t = torch.ones([n], device=device,
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dtype=torch.long) * cur_t
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out = diffusion.p_mean_variance(
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model, x, my_t, clip_denoised=False, model_kwargs={'y': y})
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fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]
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x_in = out['pred_xstart'] * fac + x * (1 - fac)
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x_in_grad = torch.zeros_like(x_in)
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for model_stat in model_stats:
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for i in range(args.cutn_batches):
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# errors on last step without +1, need to find source
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t_int = int(t.item())+1
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# when using SLIP Base model the dimensions need to be hard coded to avoid AttributeError: 'VisionTransformer' object has no attribute 'input_resolution'
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try:
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input_resolution = model_stat["clip_model"].visual.input_resolution
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except:
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input_resolution = 224
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cuts = MakeCutoutsDango(animation_mode=args.animation_mode,
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skip_augs=args.skip_augs,
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cut_size=input_resolution,
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Overview=args.cut_overview[1000-t_int],
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InnerCrop=args.cut_innercut[1000-t_int],
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IC_Size_Pow=args.cut_ic_pow[1000-t_int],
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IC_Grey_P=args.cut_icgray_p[1000-t_int]
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)
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clip_in = disco_utils.normalize(
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cuts(x_in.add(1).div(2)))
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image_embeds = model_stat["clip_vision_model"].encode_image(
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clip_in).float()
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dists = disco_utils.spherical_dist_loss(image_embeds.unsqueeze(
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1), model_stat["target_embeds"].unsqueeze(0))
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dists = dists.view(
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[args.cut_overview[1000-t_int]+args.cut_innercut[1000-t_int], n, -1])
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losses = dists.mul(
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model_stat["weights"]).sum(2).mean(0)
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# log loss, probably shouldn't do per cutn_batch
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loss_values.append(losses.sum().item())
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x_in_grad += torch.autograd.grad(losses.sum() * args.clip_guidance_scale, x_in)[
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0] / args.cutn_batches
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tv_losses = args.tv_loss(x_in)
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if args.MS.use_secondary_model is True:
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range_losses = disco_utils.range_loss(out)
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else:
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range_losses = disco_utils.range_loss(out['pred_xstart'])
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sat_losses = torch.abs(x_in - x_in.clamp(min=-1, max=1)).mean()
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loss = tv_losses.sum() * args.tv_scale + range_losses.sum() * \
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args.range_scale + sat_losses.sum() * args.sat_scale
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if init is not None and init_scale:
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init_losses = args.MS.lpips_model(x_in, init)
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loss = loss + init_losses.sum() * init_scale
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x_in_grad += torch.autograd.grad(loss, x_in)[0]
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if torch.isnan(x_in_grad).any() == False:
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grad = -torch.autograd.grad(x_in, x, x_in_grad)[0]
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else:
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# print("NaN'd")
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x_is_NaN = True
|
|
grad = torch.zeros_like(x)
|
|
if args.clamp_grad and x_is_NaN == False:
|
|
magnitude = grad.square().mean().sqrt()
|
|
# min=-0.02, min=-clamp_max,
|
|
return grad * magnitude.clamp(max=args.clamp_max) / magnitude
|
|
return grad
|
|
|
|
if args.MS.diffusion_sampling_mode == 'ddim':
|
|
sample_fn = diffusion.ddim_sample_loop_progressive
|
|
else:
|
|
sample_fn = diffusion.plms_sample_loop_progressive
|
|
|
|
# image_display = Output()
|
|
for i in range(args.n_batches):
|
|
# if args.animation_mode == 'None':
|
|
# display.clear_output(wait=True)
|
|
# batchBar = tqdm(range(args.n_batches), desc="Batches")
|
|
# batchBar.n = i
|
|
# batchBar.refresh()
|
|
print(f"+++ Batch {i} +++")
|
|
# display.display(image_display)
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
cur_t = diffusion.num_timesteps - skip_steps - 1
|
|
total_steps = cur_t
|
|
|
|
if args.perlin_init:
|
|
init = disco_utils.regen_perlin(
|
|
args.perlin_mode, args.batch_size, True)
|
|
|
|
symmetry_transformation_fn = id
|
|
if args.use_horizontal_symmetry:
|
|
symmetry_transformation_fn = horiz_symmetry
|
|
if args.use_vertical_symmetry:
|
|
symmetry_transformation_fn = vert_symmetry
|
|
|
|
if args.MS.diffusion_sampling_mode == 'ddim':
|
|
samples = sample_fn(
|
|
model,
|
|
(args.batch_size, 3, args.side_y, args.side_x),
|
|
clip_denoised=args.clip_denoised,
|
|
model_kwargs={},
|
|
cond_fn=cond_fn,
|
|
progress=True,
|
|
skip_timesteps=skip_steps,
|
|
init_image=init,
|
|
randomize_class=args.randomize_class,
|
|
eta=args.eta,
|
|
transformation_fn=symmetry_transformation_fn,
|
|
transformation_percent=args.transformation_percent
|
|
)
|
|
else:
|
|
samples = sample_fn(
|
|
model,
|
|
(args.batch_size, 3, args.side_y, args.side_x),
|
|
clip_denoised=args.clip_denoised,
|
|
model_kwargs={},
|
|
cond_fn=cond_fn,
|
|
progress=True,
|
|
skip_timesteps=skip_steps,
|
|
init_image=init,
|
|
randomize_class=args.randomize_class,
|
|
order=2,
|
|
)
|
|
|
|
# with run_display:
|
|
# display.clear_output(wait=True)
|
|
for j, sample in enumerate(samples):
|
|
pbar.update_absolute(j, len(samples))
|
|
cur_t -= 1
|
|
intermediateStep = False
|
|
if args.steps_per_checkpoint is not None:
|
|
if j % args.steps_per_checkpoint == 0 and j > 0:
|
|
intermediateStep = True
|
|
elif j in args.intermediate_saves:
|
|
intermediateStep = True
|
|
# with image_display:
|
|
if j % args.display_rate == 0 or cur_t == -1 or intermediateStep == True:
|
|
for k, image in enumerate(sample['pred_xstart']):
|
|
# tqdm.write(f'Batch {i}, step {j}, output {k}:')
|
|
datetime.now().strftime('%y%m%d-%H%M%S_%f')
|
|
percent = math.ceil(j/total_steps*100)
|
|
if args.n_batches > 0:
|
|
# if intermediates are saved to the subfolder, don't append a step or percentage to the name
|
|
if cur_t == -1 and args.intermediates_in_subfolder is True:
|
|
save_num = f'{frame_num:04}' if args.animation_mode != "None" else i
|
|
filename = f'{args.batch_name}({batchNum})_{save_num}.png'
|
|
else:
|
|
# If we're working with percentages, append it
|
|
if args.steps_per_checkpoint is not None:
|
|
filename = f'{args.batch_name}({batchNum})_{i:04}-{percent:02}%.png'
|
|
# Or else, iIf we're working with specific steps, append those
|
|
else:
|
|
filename = f'{args.batch_name}({batchNum})_{i:04}-{j:03}.png'
|
|
image = TF.to_pil_image(
|
|
image.add(1).div(2).clamp(0, 1))
|
|
if j % args.display_rate == 0 or cur_t == -1:
|
|
image.save('progress.png')
|
|
# display.clear_output(wait=True)
|
|
# display.display(display.Image('progress.png'))
|
|
if args.steps_per_checkpoint is not None:
|
|
if j % args.steps_per_checkpoint == 0 and j > 0:
|
|
if args.intermediates_in_subfolder is True:
|
|
image.save(
|
|
f'{args.partialFolder}/{filename}')
|
|
else:
|
|
image.save(
|
|
f'{args.batchFolder}/{filename}')
|
|
else:
|
|
if j in args.intermediate_saves:
|
|
if args.intermediates_in_subfolder is True:
|
|
image.save(
|
|
f'{args.partialFolder}/{filename}')
|
|
else:
|
|
image.save(
|
|
f'{args.batchFolder}/{filename}')
|
|
if cur_t == -1:
|
|
# if frame_num == 0:
|
|
# save_settings()
|
|
if args.animation_mode != "None":
|
|
image.save('prevFrame.png')
|
|
image.save(f'{args.batchFolder}/{filename}')
|
|
if args.animation_mode == "3D":
|
|
# If turbo, save a blended image
|
|
if args.turbo_mode and frame_num > 0:
|
|
# Mix new image with prevFrameScaled
|
|
blend_factor = (1)/int(args.turbo_steps)
|
|
# This is already updated..
|
|
newFrame = cv2.imread('prevFrame.png')
|
|
prev_frame_warped = cv2.imread(
|
|
'prevFrameScaled.png')
|
|
blendedImage = cv2.addWeighted(
|
|
newFrame, blend_factor, prev_frame_warped, (1-blend_factor), 0.0)
|
|
cv2.imwrite(
|
|
f'{args.batchFolder}/{filename}', blendedImage)
|
|
else:
|
|
image.save(
|
|
f'{args.batchFolder}/{filename}')
|
|
|
|
if args.vr_mode:
|
|
generate_eye_views(
|
|
TRANSLATION_SCALE, args.batchFolder, filename, frame_num, midas_model, midas_transform)
|
|
|
|
# if frame_num != args.max_frames-1:
|
|
# display.clear_output()
|
|
|
|
# plt.plot(np.array(loss_values), 'r')
|
|
|
|
|
|
def generate_eye_views(args, trans_scale, batchFolder, filename, frame_num, midas_model, midas_transform):
|
|
device = comfy.model_management.get_torch_device()
|
|
for i in range(2):
|
|
theta = args.vr_eye_angle * (math.pi/180)
|
|
ray_origin = math.cos(theta) * args.vr_ipd / \
|
|
2 * (-1.0 if i == 0 else 1.0)
|
|
ray_rotation = (theta if i == 0 else -theta)
|
|
translate_xyz = [-(ray_origin)*trans_scale, 0, 0]
|
|
rotate_xyz = [0, (ray_rotation), 0]
|
|
rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(
|
|
rotate_xyz, device=device), "XYZ").unsqueeze(0)
|
|
transformed_image = dxf.transform_image_3d(f'{batchFolder}/{filename}', midas_model, midas_transform, device,
|
|
rot_mat, translate_xyz, args.near_plane, args.far_plane,
|
|
args.fov, padding_mode=args.padding_mode,
|
|
sampling_mode=args.sampling_mode, midas_weight=args.midas_weight, spherical=True)
|
|
eye_file_path = batchFolder + \
|
|
f"/frame_{frame_num:04}" + ('_l' if i == 0 else '_r')+'.png'
|
|
transformed_image.save(eye_file_path)
|
|
|
|
# def save_settings():
|
|
# setting_list = {
|
|
# 'text_prompts': text_prompts,
|
|
# 'image_prompts': image_prompts,
|
|
# 'clip_guidance_scale': clip_guidance_scale,
|
|
# 'tv_scale': tv_scale,
|
|
# 'range_scale': range_scale,
|
|
# 'sat_scale': sat_scale,
|
|
# # 'cutn': cutn,
|
|
# 'cutn_batches': cutn_batches,
|
|
# 'max_frames': max_frames,
|
|
# 'interp_spline': interp_spline,
|
|
# # 'rotation_per_frame': rotation_per_frame,
|
|
# 'init_image': init_image,
|
|
# 'init_scale': init_scale,
|
|
# 'skip_steps': skip_steps,
|
|
# # 'zoom_per_frame': zoom_per_frame,
|
|
# 'frames_scale': frames_scale,
|
|
# 'frames_skip_steps': frames_skip_steps,
|
|
# 'perlin_init': perlin_init,
|
|
# 'perlin_mode': perlin_mode,
|
|
# 'skip_augs': skip_augs,
|
|
# 'randomize_class': randomize_class,
|
|
# 'clip_denoised': clip_denoised,
|
|
# 'clamp_grad': clamp_grad,
|
|
# 'clamp_max': clamp_max,
|
|
# 'seed': seed,
|
|
# 'fuzzy_prompt': fuzzy_prompt,
|
|
# 'rand_mag': rand_mag,
|
|
# 'eta': eta,
|
|
# 'width': width_height[0],
|
|
# 'height': width_height[1],
|
|
# 'diffusion_model': diffusion_model,
|
|
# 'use_secondary_model': use_secondary_model,
|
|
# 'steps': steps,
|
|
# 'diffusion_steps': diffusion_steps,
|
|
# 'diffusion_sampling_mode': diffusion_sampling_mode,
|
|
# 'ViTB32': ViTB32,
|
|
# 'ViTB16': ViTB16,
|
|
# 'ViTL14': ViTL14,
|
|
# 'ViTL14_336px': ViTL14_336px,
|
|
# 'RN101': RN101,
|
|
# 'RN50': RN50,
|
|
# 'RN50x4': RN50x4,
|
|
# 'RN50x16': RN50x16,
|
|
# 'RN50x64': RN50x64,
|
|
# 'ViTB32_laion2b_e16': ViTB32_laion2b_e16,
|
|
# 'ViTB32_laion400m_e31': ViTB32_laion400m_e31,
|
|
# 'ViTB32_laion400m_32': ViTB32_laion400m_32,
|
|
# 'ViTB32quickgelu_laion400m_e31': ViTB32quickgelu_laion400m_e31,
|
|
# 'ViTB32quickgelu_laion400m_e32': ViTB32quickgelu_laion400m_e32,
|
|
# 'ViTB16_laion400m_e31': ViTB16_laion400m_e31,
|
|
# 'ViTB16_laion400m_e32': ViTB16_laion400m_e32,
|
|
# 'RN50_yffcc15m': RN50_yffcc15m,
|
|
# 'RN50_cc12m': RN50_cc12m,
|
|
# 'RN50_quickgelu_yfcc15m': RN50_quickgelu_yfcc15m,
|
|
# 'RN50_quickgelu_cc12m': RN50_quickgelu_cc12m,
|
|
# 'RN101_yfcc15m': RN101_yfcc15m,
|
|
# 'RN101_quickgelu_yfcc15m': RN101_quickgelu_yfcc15m,
|
|
# 'cut_overview': str(cut_overview),
|
|
# 'cut_innercut': str(cut_innercut),
|
|
# 'cut_ic_pow': str(cut_ic_pow),
|
|
# 'cut_icgray_p': str(cut_icgray_p),
|
|
# 'key_frames': key_frames,
|
|
# 'max_frames': max_frames,
|
|
# 'angle': angle,
|
|
# 'zoom': zoom,
|
|
# 'translation_x': translation_x,
|
|
# 'translation_y': translation_y,
|
|
# 'translation_z': translation_z,
|
|
# 'rotation_3d_x': rotation_3d_x,
|
|
# 'rotation_3d_y': rotation_3d_y,
|
|
# 'rotation_3d_z': rotation_3d_z,
|
|
# 'midas_depth_model': midas_depth_model,
|
|
# 'midas_weight': midas_weight,
|
|
# 'near_plane': near_plane,
|
|
# 'far_plane': far_plane,
|
|
# 'fov': fov,
|
|
# 'padding_mode': padding_mode,
|
|
# 'sampling_mode': sampling_mode,
|
|
# 'video_init_path':video_init_path,
|
|
# 'extract_nth_frame':extract_nth_frame,
|
|
# 'video_init_seed_continuity': video_init_seed_continuity,
|
|
# 'turbo_mode':turbo_mode,
|
|
# 'turbo_steps':turbo_steps,
|
|
# 'turbo_preroll':turbo_preroll,
|
|
# 'use_horizontal_symmetry':use_horizontal_symmetry,
|
|
# 'use_vertical_symmetry':use_vertical_symmetry,
|
|
# 'transformation_percent':transformation_percent,
|
|
# #video init settings
|
|
# 'video_init_steps': video_init_steps,
|
|
# 'video_init_clip_guidance_scale': video_init_clip_guidance_scale,
|
|
# 'video_init_tv_scale': video_init_tv_scale,
|
|
# 'video_init_range_scale': video_init_range_scale,
|
|
# 'video_init_sat_scale': video_init_sat_scale,
|
|
# 'video_init_cutn_batches': video_init_cutn_batches,
|
|
# 'video_init_skip_steps': video_init_skip_steps,
|
|
# 'video_init_frames_scale': video_init_frames_scale,
|
|
# 'video_init_frames_skip_steps': video_init_frames_skip_steps,
|
|
# #warp settings
|
|
# 'video_init_flow_warp':video_init_flow_warp,
|
|
# 'video_init_flow_blend':video_init_flow_blend,
|
|
# 'video_init_check_consistency':video_init_check_consistency,
|
|
# 'video_init_blend_mode':video_init_blend_mode
|
|
# }
|
|
# # print('Settings:', setting_list)
|
|
# with open(f"{batchFolder}/{batch_name}({batchNum})_settings.txt", "w+", encoding="utf-8") as f: #save settings
|
|
# json.dump(setting_list, f, ensure_ascii=False, indent=4)
|