first
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space-nuko
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#!/usr/bin/env python
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from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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+276
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import pathlib, shutil, os, sys
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from dataclasses import dataclass
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from functools import partial
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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 io
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import math
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import lpips
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from PIL import Image, ImageOps
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import requests
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from glob import glob
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import json
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from types import SimpleNamespace
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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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import hashlib
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from functools import partial
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from numpy import asarray
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import time
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import warnings
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import os
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import comfy.model_management
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from . import disco_utils
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from .settings import DiscoDiffusionSettings
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from .do_run import do_run
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# %%
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# !! {"metadata":{
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# !! "id": "DoTheRun"
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# !! }}
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#@title Do the Run!
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#@markdown `n_batches` ignored with animation modes.
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def diffuse(clip, args: DiscoDiffusionSettings, batchNum):
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args.display_rate = 20 #@param{type: 'number'}
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args.n_batches = 50 #@param{type: 'number'}
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if args.animation_mode == 'Video Input':
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args.steps = args.video_init_steps
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#Update Model Settings
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timestep_respacing = f'ddim{args.steps}'
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diffusion_steps = (1000//args.steps)*args.steps if args.steps < 1000 else args.steps
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args.MS.model_config.update({
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'timestep_respacing': timestep_respacing,
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'diffusion_steps': diffusion_steps,
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})
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args.batch_size = 1
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def move_files(start_num, end_num, old_folder, new_folder):
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for i in range(start_num, end_num):
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old_file = old_folder + f'/{args.batch_name}({batchNum})_{i:04}.png'
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new_file = new_folder + f'/{args.batch_name}({batchNum})_{i:04}.png'
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os.rename(old_file, new_file)
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#@markdown ---
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args.resume_run = False #@param{type: 'boolean'}
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run_to_resume = 'latest' #@param{type: 'string'}
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resume_from_frame = 'latest' #@param{type: 'string'}
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retain_overwritten_frames = False #@param{type: 'boolean'}
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if retain_overwritten_frames:
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retainFolder = f'{args.batchFolder}/retained'
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os.makedirs(retainFolder, exist_ok=True)
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skip_step_ratio = int(args.frames_skip_steps.rstrip("%")) / 100
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args.calc_frames_skip_steps = math.floor(args.steps * skip_step_ratio)
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if args.animation_mode == 'Video Input':
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frames = sorted(glob(args.in_path+'/*.*'));
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if len(frames)==0:
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sys.exit("ERROR: 0 frames found.\nPlease check your video input path and rerun the video settings cell.")
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flows = glob(args.flo_folder+'/*.*')
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if (len(flows)==0) and args.video_init_flow_warp:
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sys.exit("ERROR: 0 flow files found.\nPlease rerun the flow generation cell.")
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if args.steps <= args.calc_frames_skip_steps:
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sys.exit("ERROR: You can't skip more steps than your total steps")
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if args.resume_run:
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if run_to_resume == 'latest':
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try:
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batchNum
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except:
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batchNum = len(glob(f"{args.batchFolder}/{args.batch_name}(*)_settings.txt"))-1
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else:
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batchNum = int(run_to_resume)
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if resume_from_frame == 'latest':
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start_frame = len(glob(args.batchFolder+f"/{args.batch_name}({batchNum})_*.png"))
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if args.animation_mode != '3D' and args.turbo_mode == True and start_frame > args.turbo_preroll and start_frame % int(args.turbo_steps) != 0:
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start_frame = start_frame - (start_frame % int(args.turbo_steps))
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else:
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start_frame = int(resume_from_frame)+1
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if args.animation_mode != '3D' and args.turbo_mode == True and start_frame > args.turbo_preroll and start_frame % int(args.turbo_steps) != 0:
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start_frame = start_frame - (start_frame % int(args.turbo_steps))
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if retain_overwritten_frames is True:
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existing_frames = len(glob(args.batchFolder+f"/{args.batch_name}({batchNum})_*.png"))
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frames_to_save = existing_frames - start_frame
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print(f'Moving {frames_to_save} frames to the Retained folder')
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move_files(start_frame, existing_frames, args.batchFolder, retainFolder)
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else:
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start_frame = 0
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batchNum = len(glob(args.batchFolder+"/*.txt"))
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while os.path.isfile(f"{args.batchFolder}/{args.batch_name}({batchNum})_settings.txt") or os.path.isfile(f"{args.batchFolder}/{args.batch_name}-{batchNum}_settings.txt"):
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batchNum += 1
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print(f'Starting Run: {args.batch_name}({batchNum}) at frame {start_frame}')
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if args.set_seed == 'random_seed':
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random.seed()
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seed = random.randint(0, 2**32)
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# print(f'Using seed: {seed}')
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else:
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seed = int(args.set_seed)
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args.n_batches = args.n_batches if args.animation_mode == 'None' else 1
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args.max_frames = args.max_frames if args.animation_mode == 'None' else 1
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args.start_frame = start_frame
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args.seed = seed
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args.prompts_series = disco_utils.split_prompts(args.text_prompts, args.max_frames) if args.text_prompts else None,
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args.image_prompts_series = disco_utils.split_prompts(args.image_prompts, args.max_frames) if args.image_prompts else None,
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# args = {
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# 'batchNum': batchNum,
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# 'prompts_series':split_prompts(text_prompts) if text_prompts else None,
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# 'image_prompts_series':split_prompts(image_prompts) if image_prompts else None,
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# 'seed': seed,
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# 'display_rate':display_rate,
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# 'n_batches':n_batches if animation_mode == 'None' else 1,
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# 'batch_size':batch_size,
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# 'batch_name': batch_name,
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# 'steps': steps,
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# 'diffusion_sampling_mode': diffusion_sampling_mode,
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# 'width_height': width_height,
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# 'clip_guidance_scale': clip_guidance_scale,
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# 'tv_scale': tv_scale,
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# 'range_scale': range_scale,
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# 'sat_scale': sat_scale,
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# 'cutn_batches': cutn_batches,
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# 'init_image': init_image,
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# 'init_scale': init_scale,
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# 'skip_steps': skip_steps,
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# 'side_x': side_x,
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# 'side_y': side_y,
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# 'timestep_respacing': timestep_respacing,
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# 'diffusion_steps': diffusion_steps,
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# 'animation_mode': animation_mode,
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# 'video_init_path': video_init_path,
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# 'extract_nth_frame': extract_nth_frame,
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# 'video_init_seed_continuity': video_init_seed_continuity,
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# 'key_frames': key_frames,
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# 'max_frames': max_frames if animation_mode != "None" else 1,
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# 'interp_spline': interp_spline,
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# 'start_frame': start_frame,
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# 'angle': angle,
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# 'zoom': zoom,
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# 'translation_x': translation_x,
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# 'translation_y': translation_y,
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# 'translation_z': translation_z,
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# 'rotation_3d_x': rotation_3d_x,
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# 'rotation_3d_y': rotation_3d_y,
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# 'rotation_3d_z': rotation_3d_z,
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# 'midas_depth_model': midas_depth_model,
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# 'midas_weight': midas_weight,
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# 'near_plane': near_plane,
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# 'far_plane': far_plane,
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# 'fov': fov,
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# 'padding_mode': padding_mode,
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# 'sampling_mode': sampling_mode,
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# 'angle_series':angle_series,
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# 'zoom_series':zoom_series,
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# 'translation_x_series':translation_x_series,
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# 'translation_y_series':translation_y_series,
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# 'translation_z_series':translation_z_series,
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# 'rotation_3d_x_series':rotation_3d_x_series,
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# 'rotation_3d_y_series':rotation_3d_y_series,
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# 'rotation_3d_z_series':rotation_3d_z_series,
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# 'frames_scale': frames_scale,
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# 'skip_step_ratio': skip_step_ratio,
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# 'calc_frames_skip_steps': calc_frames_skip_steps,
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# 'text_prompts': text_prompts,
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# 'image_prompts': image_prompts,
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# 'cut_overview': eval(cut_overview),
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# 'cut_innercut': eval(cut_innercut),
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# 'cut_ic_pow': eval(cut_ic_pow),
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# 'cut_icgray_p': eval(cut_icgray_p),
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# 'intermediate_saves': intermediate_saves,
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# 'intermediates_in_subfolder': intermediates_in_subfolder,
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# 'steps_per_checkpoint': steps_per_checkpoint,
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# 'perlin_init': perlin_init,
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# 'perlin_mode': perlin_mode,
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# 'set_seed': set_seed,
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# 'eta': eta,
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# 'clamp_grad': clamp_grad,
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# 'clamp_max': clamp_max,
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# 'skip_augs': skip_augs,
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# 'randomize_class': randomize_class,
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# 'clip_denoised': clip_denoised,
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# 'fuzzy_prompt': fuzzy_prompt,
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# 'rand_mag': rand_mag,
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# 'turbo_mode':turbo_mode,
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# 'turbo_steps':turbo_steps,
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# 'turbo_preroll':turbo_preroll,
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# 'use_vertical_symmetry': use_vertical_symmetry,
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# 'use_horizontal_symmetry': use_horizontal_symmetry,
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# 'transformation_percent': transformation_percent,
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# #video init settings
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# 'video_init_steps': video_init_steps,
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# 'video_init_clip_guidance_scale': video_init_clip_guidance_scale,
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# 'video_init_tv_scale': video_init_tv_scale,
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# 'video_init_range_scale': video_init_range_scale,
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# 'video_init_sat_scale': video_init_sat_scale,
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# 'video_init_cutn_batches': video_init_cutn_batches,
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# 'video_init_skip_steps': video_init_skip_steps,
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# 'video_init_frames_scale': video_init_frames_scale,
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# 'video_init_frames_skip_steps': video_init_frames_skip_steps,
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# #warp settings
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# 'video_init_flow_warp':video_init_flow_warp,
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# 'video_init_flow_blend':video_init_flow_blend,
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# 'video_init_check_consistency':video_init_check_consistency,
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# 'video_init_blend_mode':video_init_blend_mode
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# }
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# if animation_mode == 'Video Input':
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# # This isn't great in terms of what will get saved to the settings.. but it should work.
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# args['steps'] = args['video_init_steps']
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# args['clip_guidance_scale'] = args['video_init_clip_guidance_scale']
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# args['tv_scale'] = args['video_init_tv_scale']
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# args['range_scale'] = args['video_init_range_scale']
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# args['sat_scale'] = args['video_init_sat_scale']
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# args['cutn_batches'] = args['video_init_cutn_batches']
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# args['skip_steps'] = args['video_init_skip_steps']
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# args['frames_scale'] = args['video_init_frames_scale']
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# args['frames_skip_steps'] = args['video_init_frames_skip_steps']
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# args = SimpleNamespace(**args)
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device = comfy.model_management.get_torch_device()
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print('Prepping model...')
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model, diffusion = create_model_and_diffusion(**args.MS.model_config)
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if args.MS.diffusion_model == 'custom':
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model.load_state_dict(torch.load(args.MS.custom_path, map_location='cpu'))
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else:
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model.load_state_dict(torch.load(f'{args.MS.model_path}/{args.MS.get_model_filename(args.MS.diffusion_model)}', map_location='cpu'))
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model.requires_grad_(False).eval().to(device)
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for name, param in model.named_parameters():
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if 'qkv' in name or 'norm' in name or 'proj' in name:
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param.requires_grad_()
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if args.MS.model_config['use_fp16']:
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model.convert_to_fp16()
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gc.collect()
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torch.cuda.empty_cache()
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try:
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do_run(diffusion, model, clip, args, batchNum)
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except KeyboardInterrupt:
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pass
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finally:
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print('Seed used:', seed)
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gc.collect()
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torch.cuda.empty_cache()
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@@ -0,0 +1,36 @@
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PROJECT_DIR = os.path.abspath(os.getcwd())
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USE_ADABINS = False
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import pathlib, shutil, os, sys
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from dataclasses import dataclass
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from functools import partial
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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 io
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import math
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import lpips
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from PIL import Image, ImageOps
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import requests
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from glob import glob
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import json
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from types import SimpleNamespace
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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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import hashlib
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from functools import partial
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from numpy import asarray
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import time
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import warnings
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warnings.filterwarnings("ignore", category=UserWarning)
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+201
@@ -0,0 +1,201 @@
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#!/usr/bin/env python
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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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import torchvision.transforms as T
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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 io
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import math
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import lpips
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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.transforms.functional as TF
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import subprocess
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from importlib import util as importlibutil
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import numpy as np
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import comfy.model_management
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def split_prompts(prompts, max_frames):
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prompt_series = pd.Series([np.nan for a in range(max_frames)])
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for i, prompt in prompts.items():
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prompt_series[i] = prompt
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# prompt_series = prompt_series.astype(str)
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prompt_series = prompt_series.ffill().bfill()
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return prompt_series
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# https://gist.github.com/adefossez/0646dbe9ed4005480a2407c62aac8869
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def interp(t):
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return 3 * t**2 - 2 * t ** 3
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def perlin(width, height, scale=10, device=None):
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gx, gy = torch.randn(2, width + 1, height + 1, 1, 1, device=device)
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xs = torch.linspace(0, 1, scale + 1)[:-1, None].to(device)
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ys = torch.linspace(0, 1, scale + 1)[None, :-1].to(device)
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wx = 1 - interp(xs)
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wy = 1 - interp(ys)
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dots = 0
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dots += wx * wy * (gx[:-1, :-1] * xs + gy[:-1, :-1] * ys)
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dots += (1 - wx) * wy * (-gx[1:, :-1] * (1 - xs) + gy[1:, :-1] * ys)
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dots += wx * (1 - wy) * (gx[:-1, 1:] * xs - gy[:-1, 1:] * (1 - ys))
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dots += (1 - wx) * (1 - wy) * (-gx[1:, 1:] * (1 - xs) - gy[1:, 1:] * (1 - ys))
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return dots.permute(0, 2, 1, 3).contiguous().view(width * scale, height * scale)
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def perlin_ms(octaves, width, height, grayscale, device=None):
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if not device:
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device = comfy.model_management.get_torch_device()
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out_array = [0.5] if grayscale else [0.5, 0.5, 0.5]
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# out_array = [0.0] if grayscale else [0.0, 0.0, 0.0]
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for i in range(1 if grayscale else 3):
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scale = 2 ** len(octaves)
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oct_width = width
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oct_height = height
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for oct in octaves:
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p = perlin(oct_width, oct_height, scale, device)
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out_array[i] += p * oct
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scale //= 2
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oct_width *= 2
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oct_height *= 2
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return torch.cat(out_array)
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def create_perlin_noise(side_x, side_y, octaves=[1, 1, 1, 1], width=2, height=2, grayscale=True):
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out = perlin_ms(octaves, width, height, grayscale)
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if grayscale:
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out = TF.resize(size=(side_y, side_x), img=out.unsqueeze(0))
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out = TF.to_pil_image(out.clamp(0, 1)).convert('RGB')
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else:
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out = out.reshape(-1, 3, out.shape[0]//3, out.shape[1])
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out = TF.resize(size=(side_y, side_x), img=out)
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out = TF.to_pil_image(out.clamp(0, 1).squeeze())
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out = ImageOps.autocontrast(out)
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return out
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def regen_perlin(perlin_mode, batch_size, expand=False):
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if perlin_mode == 'color':
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init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
|
||||
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, False)
|
||||
elif perlin_mode == 'gray':
|
||||
init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, True)
|
||||
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
|
||||
else:
|
||||
init = create_perlin_noise([1.5**-i*0.5 for i in range(12)], 1, 1, False)
|
||||
init2 = create_perlin_noise([1.5**-i*0.5 for i in range(8)], 4, 4, True)
|
||||
|
||||
device = comfy.model_management.get_torch_device()
|
||||
init = TF.to_tensor(init).add(TF.to_tensor(init2)).div(2).to(device).unsqueeze(0).mul(2).sub(1)
|
||||
del init2
|
||||
if expand:
|
||||
return init.expand(batch_size, -1, -1, -1)
|
||||
return init
|
||||
|
||||
def fetch(url_or_path):
|
||||
if str(url_or_path).startswith('http://') or str(url_or_path).startswith('https://'):
|
||||
r = requests.get(url_or_path)
|
||||
r.raise_for_status()
|
||||
fd = io.BytesIO()
|
||||
fd.write(r.content)
|
||||
fd.seek(0)
|
||||
return fd
|
||||
return open(url_or_path, 'rb')
|
||||
|
||||
def read_image_workaround(path):
|
||||
"""OpenCV reads images as BGR, Pillow saves them as RGB. Work around
|
||||
this incompatibility to avoid colour inversions."""
|
||||
im_tmp = cv2.imread(path)
|
||||
return cv2.cvtColor(im_tmp, cv2.COLOR_BGR2RGB)
|
||||
|
||||
def parse_prompt(prompt):
|
||||
if prompt.startswith('http://') or prompt.startswith('https://'):
|
||||
vals = prompt.rsplit(':', 2)
|
||||
vals = [vals[0] + ':' + vals[1], *vals[2:]]
|
||||
else:
|
||||
vals = prompt.rsplit(':', 1)
|
||||
vals = vals + ['', '1'][len(vals):]
|
||||
return vals[0], float(vals[1])
|
||||
|
||||
def sinc(x):
|
||||
return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))
|
||||
|
||||
def lanczos(x, a):
|
||||
cond = torch.logical_and(-a < x, x < a)
|
||||
out = torch.where(cond, sinc(x) * sinc(x/a), x.new_zeros([]))
|
||||
return out / out.sum()
|
||||
|
||||
def ramp(ratio, width):
|
||||
n = math.ceil(width / ratio + 1)
|
||||
out = torch.empty([n])
|
||||
cur = 0
|
||||
for i in range(out.shape[0]):
|
||||
out[i] = cur
|
||||
cur += ratio
|
||||
return torch.cat([-out[1:].flip([0]), out])[1:-1]
|
||||
|
||||
def resample(input, size, align_corners=True):
|
||||
n, c, h, w = input.shape
|
||||
dh, dw = size
|
||||
|
||||
input = input.reshape([n * c, 1, h, w])
|
||||
|
||||
if dh < h:
|
||||
kernel_h = lanczos(ramp(dh / h, 2), 2).to(input.device, input.dtype)
|
||||
pad_h = (kernel_h.shape[0] - 1) // 2
|
||||
input = F.pad(input, (0, 0, pad_h, pad_h), 'reflect')
|
||||
input = F.conv2d(input, kernel_h[None, None, :, None])
|
||||
|
||||
if dw < w:
|
||||
kernel_w = lanczos(ramp(dw / w, 2), 2).to(input.device, input.dtype)
|
||||
pad_w = (kernel_w.shape[0] - 1) // 2
|
||||
input = F.pad(input, (pad_w, pad_w, 0, 0), 'reflect')
|
||||
input = F.conv2d(input, kernel_w[None, None, None, :])
|
||||
|
||||
input = input.reshape([n, c, h, w])
|
||||
return F.interpolate(input, size, mode='bicubic', align_corners=align_corners)
|
||||
|
||||
def spherical_dist_loss(x, y):
|
||||
x = F.normalize(x, dim=-1)
|
||||
y = F.normalize(y, dim=-1)
|
||||
return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
|
||||
|
||||
def tv_loss(input):
|
||||
"""L2 total variation loss, as in Mahendran et al."""
|
||||
input = F.pad(input, (0, 1, 0, 1), 'replicate')
|
||||
x_diff = input[..., :-1, 1:] - input[..., :-1, :-1]
|
||||
y_diff = input[..., 1:, :-1] - input[..., :-1, :-1]
|
||||
return (x_diff**2 + y_diff**2).mean([1, 2, 3])
|
||||
|
||||
|
||||
def range_loss(input):
|
||||
return (input - input.clamp(-1, 1)).pow(2).mean([1, 2, 3])
|
||||
|
||||
|
||||
normalize = T.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
|
||||
|
||||
|
||||
def module_exists(module_name):
|
||||
return importlibutil.find_spec(module_name)
|
||||
|
||||
def gitclone(url, targetdir=None):
|
||||
if targetdir:
|
||||
res = subprocess.run(['git', 'clone', url, targetdir], stdout=subprocess.PIPE).stdout.decode('utf-8')
|
||||
else:
|
||||
res = subprocess.run(['git', 'clone', url], stdout=subprocess.PIPE).stdout.decode('utf-8')
|
||||
print(res)
|
||||
|
||||
def pipi(modulestr):
|
||||
res = subprocess.run(['pip', 'install', modulestr], stdout=subprocess.PIPE).stdout.decode('utf-8')
|
||||
print(res)
|
||||
|
||||
def pipie(modulestr):
|
||||
res = subprocess.run(['git', 'install', '-e', modulestr], stdout=subprocess.PIPE).stdout.decode('utf-8')
|
||||
print(res)
|
||||
|
||||
def wget(url, outputdir):
|
||||
res = subprocess.run(['wget', url, '-P', f'{outputdir}'], stdout=subprocess.PIPE).stdout.decode('utf-8')
|
||||
print(res)
|
||||
@@ -0,0 +1,150 @@
|
||||
import torch
|
||||
import torchvision
|
||||
from . import py3d_tools as p3d
|
||||
import midas_utils
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import sys, math
|
||||
import cv2
|
||||
import pandas as pd
|
||||
import gc
|
||||
import math
|
||||
import lpips
|
||||
from PIL import Image, ImageOps
|
||||
import requests
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
import torchvision
|
||||
import torchvision.transforms as T
|
||||
import torchvision.transforms.functional as TF
|
||||
from tqdm import tqdm
|
||||
from resize_right import resize
|
||||
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
|
||||
import numpy as np
|
||||
from numpy import asarray
|
||||
|
||||
MAX_ADABINS_AREA = 500000
|
||||
MIN_ADABINS_AREA = 448*448
|
||||
|
||||
@torch.no_grad()
|
||||
def transform_image_3d(img_filepath, midas_model, midas_transform, device, rot_mat=torch.eye(3).unsqueeze(0), translate=(0.,0.,-0.04), near=2000, far=20000, fov_deg=60, padding_mode='border', sampling_mode='bicubic', midas_weight = 0.3,spherical=False):
|
||||
img_pil = Image.open(open(img_filepath, 'rb')).convert('RGB')
|
||||
w, h = img_pil.size
|
||||
image_tensor = torchvision.transforms.functional.to_tensor(img_pil).to(device)
|
||||
|
||||
use_adabins = midas_weight < 1.0
|
||||
|
||||
if use_adabins:
|
||||
try:
|
||||
from infer import InferenceHelper
|
||||
except:
|
||||
print("disco_xform_utils.py failed to import InferenceHelper. Please ensure that AdaBins directory is in the path (i.e. via sys.path.append('./AdaBins') or other means).")
|
||||
sys.exit()
|
||||
|
||||
# AdaBins
|
||||
"""
|
||||
predictions using nyu dataset
|
||||
"""
|
||||
print("Running AdaBins depth estimation implementation...")
|
||||
infer_helper = InferenceHelper(dataset='nyu', device=device)
|
||||
|
||||
image_pil_area = w*h
|
||||
if image_pil_area > MAX_ADABINS_AREA:
|
||||
scale = math.sqrt(MAX_ADABINS_AREA) / math.sqrt(image_pil_area)
|
||||
depth_input = img_pil.resize((int(w*scale), int(h*scale)), Image.LANCZOS) # LANCZOS is supposed to be good for downsampling.
|
||||
elif image_pil_area < MIN_ADABINS_AREA:
|
||||
scale = math.sqrt(MIN_ADABINS_AREA) / math.sqrt(image_pil_area)
|
||||
depth_input = img_pil.resize((int(w*scale), int(h*scale)), Image.BICUBIC)
|
||||
else:
|
||||
depth_input = img_pil
|
||||
try:
|
||||
_, adabins_depth = infer_helper.predict_pil(depth_input)
|
||||
if image_pil_area != MAX_ADABINS_AREA:
|
||||
adabins_depth = torchvision.transforms.functional.resize(torch.from_numpy(adabins_depth), image_tensor.shape[-2:], interpolation=torchvision.transforms.functional.InterpolationMode.BICUBIC).squeeze().to(device)
|
||||
else:
|
||||
adabins_depth = torch.from_numpy(adabins_depth).squeeze().to(device)
|
||||
adabins_depth_np = adabins_depth.cpu().numpy()
|
||||
except:
|
||||
pass
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# MiDaS
|
||||
img_midas = midas_utils.read_image(img_filepath)
|
||||
img_midas_input = midas_transform({"image": img_midas})["image"]
|
||||
midas_optimize = True
|
||||
|
||||
# MiDaS depth estimation implementation
|
||||
print("Running MiDaS depth estimation implementation...")
|
||||
sample = torch.from_numpy(img_midas_input).float().to(device).unsqueeze(0)
|
||||
if midas_optimize==True and device == torch.device("cuda"):
|
||||
sample = sample.to(memory_format=torch.channels_last)
|
||||
sample = sample.half()
|
||||
prediction_torch = midas_model.forward(sample)
|
||||
prediction_torch = torch.nn.functional.interpolate(
|
||||
prediction_torch.unsqueeze(1),
|
||||
size=img_midas.shape[:2],
|
||||
mode="bicubic",
|
||||
align_corners=False,
|
||||
).squeeze()
|
||||
prediction_np = prediction_torch.clone().cpu().numpy()
|
||||
|
||||
print("Finished depth estimation.")
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# MiDaS makes the near values greater, and the far values lesser. Let's reverse that and try to align with AdaBins a bit better.
|
||||
prediction_np = np.subtract(50.0, prediction_np)
|
||||
prediction_np = prediction_np / 19.0
|
||||
|
||||
if use_adabins:
|
||||
adabins_weight = 1.0 - midas_weight
|
||||
depth_map = prediction_np*midas_weight + adabins_depth_np*adabins_weight
|
||||
else:
|
||||
depth_map = prediction_np
|
||||
|
||||
depth_map = np.expand_dims(depth_map, axis=0)
|
||||
depth_tensor = torch.from_numpy(depth_map).squeeze().to(device)
|
||||
|
||||
pixel_aspect = 1.0 # really.. the aspect of an individual pixel! (so usually 1.0)
|
||||
persp_cam_old = p3d.FoVPerspectiveCameras(near, far, pixel_aspect, fov=fov_deg, degrees=True, device=device)
|
||||
persp_cam_new = p3d.FoVPerspectiveCameras(near, far, pixel_aspect, fov=fov_deg, degrees=True, R=rot_mat, T=torch.tensor([translate]), device=device)
|
||||
|
||||
# range of [-1,1] is important to torch grid_sample's padding handling
|
||||
y,x = torch.meshgrid(torch.linspace(-1.,1.,h,dtype=torch.float32,device=device),torch.linspace(-1.,1.,w,dtype=torch.float32,device=device))
|
||||
z = torch.as_tensor(depth_tensor, dtype=torch.float32, device=device)
|
||||
xyz_old_world = torch.stack((x.flatten(), y.flatten(), z.flatten()), dim=1)
|
||||
|
||||
# Transform the points using pytorch3d. With current functionality, this is overkill and prevents it from working on Windows.
|
||||
# If you want it to run on Windows (without pytorch3d), then the transforms (and/or perspective if that's separate) can be done pretty easily without it.
|
||||
xyz_old_cam_xy = persp_cam_old.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]
|
||||
xyz_new_cam_xy = persp_cam_new.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]
|
||||
|
||||
offset_xy = xyz_new_cam_xy - xyz_old_cam_xy
|
||||
# affine_grid theta param expects a batch of 2D mats. Each is 2x3 to do rotation+translation.
|
||||
identity_2d_batch = torch.tensor([[1.,0.,0.],[0.,1.,0.]], device=device).unsqueeze(0)
|
||||
# coords_2d will have shape (N,H,W,2).. which is also what grid_sample needs.
|
||||
coords_2d = torch.nn.functional.affine_grid(identity_2d_batch, [1,1,h,w], align_corners=False)
|
||||
offset_coords_2d = coords_2d - torch.reshape(offset_xy, (h,w,2)).unsqueeze(0)
|
||||
|
||||
if spherical:
|
||||
spherical_grid = get_spherical_projection(h, w, torch.tensor([0,0], device=device), -0.4,device=device)#align_corners=False
|
||||
stage_image = torch.nn.functional.grid_sample(image_tensor.add(1/512 - 0.0001).unsqueeze(0), offset_coords_2d, mode=sampling_mode, padding_mode=padding_mode, align_corners=True)
|
||||
new_image = torch.nn.functional.grid_sample(stage_image, spherical_grid,align_corners=True) #, mode=sampling_mode, padding_mode=padding_mode, align_corners=False)
|
||||
else:
|
||||
new_image = torch.nn.functional.grid_sample(image_tensor.add(1/512 - 0.0001).unsqueeze(0), offset_coords_2d, mode=sampling_mode, padding_mode=padding_mode, align_corners=False)
|
||||
|
||||
img_pil = torchvision.transforms.ToPILImage()(new_image.squeeze().clamp(0,1.))
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
return img_pil
|
||||
|
||||
def get_spherical_projection(H, W, center, magnitude,device):
|
||||
xx, yy = torch.linspace(-1, 1, W,dtype=torch.float32,device=device), torch.linspace(-1, 1, H,dtype=torch.float32,device=device)
|
||||
gridy, gridx = torch.meshgrid(yy, xx)
|
||||
grid = torch.stack([gridx, gridy], dim=-1)
|
||||
d = center - grid
|
||||
d_sum = torch.sqrt((d**2).sum(axis=-1))
|
||||
grid += d * d_sum.unsqueeze(-1) * magnitude
|
||||
return grid.unsqueeze(0)
|
||||
@@ -0,0 +1,691 @@
|
||||
import shutil
|
||||
import cv2
|
||||
import pandas as pd
|
||||
import gc
|
||||
import math
|
||||
import lpips
|
||||
import PIL
|
||||
from PIL import Image, ImageOps
|
||||
import requests
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
import torchvision
|
||||
import torchvision.transforms as T
|
||||
import torchvision.transforms.functional as TF
|
||||
from tqdm import tqdm
|
||||
from resize_right import resize
|
||||
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
|
||||
from datetime import datetime
|
||||
import numpy as np
|
||||
import random
|
||||
from numpy import asarray
|
||||
from . import py3d_tools as p3dT
|
||||
from . import disco_xform_utils as dxf
|
||||
|
||||
import comfy.model_management
|
||||
|
||||
from . import disco_utils
|
||||
from .make_cutouts import MakeCutouts, MakeCutoutsDango
|
||||
from .midas_model import init_midas_depth_model
|
||||
from .settings import DiscoDiffusionSettings
|
||||
|
||||
# Make sure GPU memory doesn't get corrupted from cancelling the run mid-way through, allow a full frame to complete
|
||||
stop_on_next_loop = False
|
||||
TRANSLATION_SCALE = 1.0/200.0
|
||||
|
||||
def do_3d_step(args: DiscoDiffusionSettings, img_filepath, frame_num, midas_model, midas_transform):
|
||||
if args.key_frames:
|
||||
translation_x = args.translation_x_series[frame_num]
|
||||
translation_y = args.translation_y_series[frame_num]
|
||||
translation_z = args.translation_z_series[frame_num]
|
||||
rotation_3d_x = args.rotation_3d_x_series[frame_num]
|
||||
rotation_3d_y = args.rotation_3d_y_series[frame_num]
|
||||
rotation_3d_z = args.rotation_3d_z_series[frame_num]
|
||||
print(
|
||||
f'translation_x: {translation_x}',
|
||||
f'translation_y: {translation_y}',
|
||||
f'translation_z: {translation_z}',
|
||||
f'rotation_3d_x: {rotation_3d_x}',
|
||||
f'rotation_3d_y: {rotation_3d_y}',
|
||||
f'rotation_3d_z: {rotation_3d_z}',
|
||||
)
|
||||
|
||||
device = comfy.model_management.get_torch_device()
|
||||
|
||||
translate_xyz = [-translation_x*TRANSLATION_SCALE, translation_y *
|
||||
TRANSLATION_SCALE, -translation_z*TRANSLATION_SCALE]
|
||||
rotate_xyz_degrees = [rotation_3d_x, rotation_3d_y, rotation_3d_z]
|
||||
print('translation:', translate_xyz)
|
||||
print('rotation:', rotate_xyz_degrees)
|
||||
rotate_xyz = [math.radians(rotate_xyz_degrees[0]), math.radians(
|
||||
rotate_xyz_degrees[1]), math.radians(rotate_xyz_degrees[2])]
|
||||
rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(
|
||||
rotate_xyz, device=device), "XYZ").unsqueeze(0)
|
||||
print("rot_mat: " + str(rot_mat))
|
||||
next_step_pil = dxf.transform_image_3d(img_filepath, 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)
|
||||
return next_step_pil
|
||||
|
||||
|
||||
def horiz_symmetry(x):
|
||||
[n, c, h, w] = x.size()
|
||||
x = torch.concat(
|
||||
(x[:, :, :, :w//2], torch.flip(x[:, :, :, :w//2], [-1])), -1)
|
||||
print("horizontal symmetry applied")
|
||||
return x
|
||||
|
||||
|
||||
def vert_symmetry(x):
|
||||
[n, c, h, w] = x.size()
|
||||
x = torch.concat(
|
||||
(x[:, :, :h//2, :], torch.flip(x[:, :, :h//2, :], [-2])), -2)
|
||||
print("vertical symmetry applied")
|
||||
return x
|
||||
|
||||
|
||||
def id(x):
|
||||
return x
|
||||
|
||||
|
||||
def do_run(diffusion, model, clip, args: DiscoDiffusionSettings, batchNum):
|
||||
seed = args.seed
|
||||
print(range(args.start_frame, args.max_frames))
|
||||
|
||||
if (args.animation_mode == "3D") and (args.midas_weight > 0.0):
|
||||
midas_model, midas_transform, midas_net_w, midas_net_h, midas_resize_mode, midas_normalization = init_midas_depth_model(
|
||||
args.midas_depth_model)
|
||||
for frame_num in range(args.start_frame, args.max_frames):
|
||||
if stop_on_next_loop:
|
||||
break
|
||||
|
||||
# display.clear_output(wait=True)
|
||||
|
||||
# Print Frame progress if animation mode is on
|
||||
if args.animation_mode != "None":
|
||||
batchBar = tqdm(range(args.max_frames), desc="Frames")
|
||||
batchBar.n = frame_num
|
||||
batchBar.refresh()
|
||||
|
||||
# Inits if not video frames
|
||||
if args.animation_mode != "Video Input":
|
||||
if args.init_image in ['', 'none', 'None', 'NONE']:
|
||||
init_image = None
|
||||
else:
|
||||
init_image = args.init_image
|
||||
init_scale = args.init_scale
|
||||
skip_steps = args.skip_steps
|
||||
|
||||
if args.animation_mode == "2D":
|
||||
if args.key_frames:
|
||||
angle = args.angle_series[frame_num]
|
||||
zoom = args.zoom_series[frame_num]
|
||||
translation_x = args.translation_x_series[frame_num]
|
||||
translation_y = args.translation_y_series[frame_num]
|
||||
print(
|
||||
f'angle: {angle}',
|
||||
f'zoom: {zoom}',
|
||||
f'translation_x: {translation_x}',
|
||||
f'translation_y: {translation_y}',
|
||||
)
|
||||
|
||||
if frame_num > 0:
|
||||
seed += 1
|
||||
if args.resume_run and frame_num == args.start_frame:
|
||||
img_0 = cv2.imread(
|
||||
args.batchFolder+f"/{args.batch_name}({batchNum})_{args.start_frame-1:04}.png")
|
||||
else:
|
||||
img_0 = cv2.imread('prevFrame.png')
|
||||
center = (1*img_0.shape[1]//2, 1*img_0.shape[0]//2)
|
||||
trans_mat = np.float32(
|
||||
[[1, 0, translation_x],
|
||||
[0, 1, translation_y]]
|
||||
)
|
||||
rot_mat = cv2.getRotationMatrix2D(center, angle, zoom)
|
||||
trans_mat = np.vstack([trans_mat, [0, 0, 1]])
|
||||
rot_mat = np.vstack([rot_mat, [0, 0, 1]])
|
||||
transformation_matrix = np.matmul(rot_mat, trans_mat)
|
||||
img_0 = cv2.warpPerspective(
|
||||
img_0,
|
||||
transformation_matrix,
|
||||
(img_0.shape[1], img_0.shape[0]),
|
||||
borderMode=cv2.BORDER_WRAP
|
||||
)
|
||||
|
||||
cv2.imwrite('prevFrameScaled.png', img_0)
|
||||
init_image = 'prevFrameScaled.png'
|
||||
init_scale = args.frames_scale
|
||||
skip_steps = args.calc_frames_skip_steps
|
||||
|
||||
if args.animation_mode == "3D":
|
||||
if frame_num > 0:
|
||||
seed += 1
|
||||
if args.resume_run and frame_num == args.start_frame:
|
||||
img_filepath = args.batchFolder + \
|
||||
f"/{args.batch_name}({batchNum})_{args.start_frame-1:04}.png"
|
||||
if args.turbo_mode and frame_num > args.turbo_preroll:
|
||||
shutil.copyfile(img_filepath, 'oldFrameScaled.png')
|
||||
else:
|
||||
img_filepath = 'prevFrame.png'
|
||||
|
||||
next_step_pil = do_3d_step(
|
||||
args, img_filepath, frame_num, midas_model, midas_transform)
|
||||
next_step_pil.save('prevFrameScaled.png')
|
||||
|
||||
# Turbo mode - skip some diffusions, use 3d morph for clarity and to save time
|
||||
if args.turbo_mode:
|
||||
if frame_num == args.turbo_preroll: # start tracking oldframe
|
||||
# stash for later blending
|
||||
next_step_pil.save('oldFrameScaled.png')
|
||||
elif frame_num > args.turbo_preroll:
|
||||
# set up 2 warped image sequences, old & new, to blend toward new diff image
|
||||
old_frame = do_3d_step(
|
||||
args, 'oldFrameScaled.png', frame_num, midas_model, midas_transform)
|
||||
old_frame.save('oldFrameScaled.png')
|
||||
if frame_num % int(args.turbo_steps) != 0:
|
||||
print(
|
||||
'turbo skip this frame: skipping clip diffusion steps')
|
||||
filename = f'{args.batch_name}({batchNum})_{frame_num:04}.png'
|
||||
blend_factor = (
|
||||
(frame_num % int(args.turbo_steps))+1)/int(args.turbo_steps)
|
||||
print(
|
||||
'turbo skip this frame: skipping clip diffusion steps and saving blended frame')
|
||||
# this is already updated..
|
||||
newWarpedImg = cv2.imread('prevFrameScaled.png')
|
||||
oldWarpedImg = cv2.imread('oldFrameScaled.png')
|
||||
blendedImage = cv2.addWeighted(
|
||||
newWarpedImg, blend_factor, oldWarpedImg, 1-blend_factor, 0.0)
|
||||
cv2.imwrite(
|
||||
f'{args.batchFolder}/{filename}', blendedImage)
|
||||
# save it also as prev_frame to feed next iteration
|
||||
next_step_pil.save(f'{img_filepath}')
|
||||
if args.vr_mode:
|
||||
generate_eye_views(
|
||||
TRANSLATION_SCALE, args.batchFolder, filename, frame_num, midas_model, midas_transform)
|
||||
continue
|
||||
else:
|
||||
# if not a skip frame, will run diffusion and need to blend.
|
||||
oldWarpedImg = cv2.imread('prevFrameScaled.png')
|
||||
# swap in for blending later
|
||||
cv2.imwrite(f'oldFrameScaled.png', oldWarpedImg)
|
||||
print('clip/diff this frame - generate clip diff image')
|
||||
|
||||
init_image = 'prevFrameScaled.png'
|
||||
init_scale = args.frames_scale
|
||||
skip_steps = args.calc_frames_skip_steps
|
||||
|
||||
if args.animation_mode == "Video Input":
|
||||
init_scale = args.video_init_frames_scale
|
||||
skip_steps = args.calc_frames_skip_steps
|
||||
if not args.video_init_seed_continuity:
|
||||
seed += 1
|
||||
if args.video_init_flow_warp:
|
||||
if frame_num == 0:
|
||||
skip_steps = args.video_init_skip_steps
|
||||
init_image = f'{args.videoFramesFolder}/{frame_num+1:04}.jpg'
|
||||
if frame_num > 0:
|
||||
prev = PIL.Image.open(
|
||||
args.batchFolder+f"/{args.batch_name}({batchNum})_{frame_num-1:04}.png")
|
||||
|
||||
frame1_path = f'{args.videoFramesFolder}/{frame_num:04}.jpg'
|
||||
frame2 = PIL.Image.open(
|
||||
f'{args.videoFramesFolder}/{frame_num+1:04}.jpg')
|
||||
flo_path = f"/{args.flo_folder}/{frame1_path.split('/')[-1]}.npy"
|
||||
|
||||
init_image = 'warped.png'
|
||||
print(args.video_init_flow_blend)
|
||||
weights_path = None
|
||||
if args.video_init_check_consistency:
|
||||
# TBD
|
||||
pass
|
||||
|
||||
import video_input
|
||||
video_input.warp(prev, frame2, flo_path, blend=args.video_init_flow_blend,
|
||||
weights_path=weights_path).save(init_image)
|
||||
|
||||
else:
|
||||
init_image = f'{args.videoFramesFolder}/{frame_num+1:04}.jpg'
|
||||
|
||||
loss_values = []
|
||||
|
||||
if seed is not None:
|
||||
np.random.seed(seed)
|
||||
random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
torch.backends.cudnn.deterministic = True
|
||||
|
||||
target_embeds, weights = [], []
|
||||
|
||||
if args.prompts_series is not None and frame_num >= len(args.prompts_series):
|
||||
frame_prompt = args.prompts_series[-1]
|
||||
elif args.prompts_series is not None:
|
||||
frame_prompt = args.prompts_series[frame_num]
|
||||
else:
|
||||
frame_prompt = []
|
||||
|
||||
print(args.image_prompts_series)
|
||||
if args.image_prompts_series is not None and frame_num >= len(args.image_prompts_series):
|
||||
image_prompt = args.image_prompts_series[-1]
|
||||
elif args.image_prompts_series is not None:
|
||||
image_prompt = args.image_prompts_series[frame_num]
|
||||
else:
|
||||
image_prompt = []
|
||||
|
||||
device = comfy.model_management.get_torch_device()
|
||||
|
||||
print(f'Frame {frame_num} Prompt: {frame_prompt}')
|
||||
|
||||
clip_models = [clip] # TODO!!!!!!!!!!!!!!!!!!!!!
|
||||
|
||||
model_stats = []
|
||||
for clip_model in clip_models:
|
||||
cutn = 16
|
||||
model_stat = {"clip_model": None, "target_embeds": [],
|
||||
"make_cutouts": None, "weights": []}
|
||||
model_stat["clip_model"] = clip_model
|
||||
|
||||
for prompt in frame_prompt:
|
||||
txt, weight = disco_utils.parse_prompt(prompt)
|
||||
txt = clip_model.encode(prompt).float()
|
||||
|
||||
if args.fuzzy_prompt:
|
||||
for i in range(25):
|
||||
model_stat["target_embeds"].append(
|
||||
(txt + torch.randn(txt.shape).cuda() * args.rand_mag).clamp(0, 1))
|
||||
model_stat["weights"].append(weight)
|
||||
else:
|
||||
model_stat["target_embeds"].append(txt)
|
||||
model_stat["weights"].append(weight)
|
||||
|
||||
if image_prompt:
|
||||
model_stat["make_cutouts"] = MakeCutouts(
|
||||
clip_model.visual.input_resolution, cutn, skip_augs=args.skip_augs)
|
||||
for prompt in image_prompt:
|
||||
path, weight = disco_utils.parse_prompt(prompt)
|
||||
img = Image.open(disco_utils.fetch(path)).convert('RGB')
|
||||
img = TF.resize(
|
||||
img, min(args.side_x, args.side_y, *img.size), T.InterpolationMode.LANCZOS)
|
||||
batch = model_stat["make_cutouts"](TF.to_tensor(
|
||||
img).to(device).unsqueeze(0).mul(2).sub(1))
|
||||
embed = clip_model.encode_image(
|
||||
disco_utils.normalize(batch)).float()
|
||||
if args.fuzzy_prompt:
|
||||
for i in range(25):
|
||||
model_stat["target_embeds"].append(
|
||||
(embed + torch.randn(embed.shape).cuda() * args.rand_mag).clamp(0, 1))
|
||||
weights.extend([weight / cutn] * cutn)
|
||||
else:
|
||||
model_stat["target_embeds"].append(embed)
|
||||
model_stat["weights"].extend([weight / cutn] * cutn)
|
||||
|
||||
model_stat["target_embeds"] = torch.cat(
|
||||
model_stat["target_embeds"])
|
||||
model_stat["weights"] = torch.tensor(
|
||||
model_stat["weights"], device=device)
|
||||
if model_stat["weights"].sum().abs() < 1e-3:
|
||||
raise RuntimeError('The weights must not sum to 0.')
|
||||
model_stat["weights"] /= model_stat["weights"].sum().abs()
|
||||
model_stats.append(model_stat)
|
||||
|
||||
init = None
|
||||
if init_image is not None:
|
||||
init = Image.open(disco_utils.fetch(init_image)).convert('RGB')
|
||||
init = init.resize((args.side_x, args.side_y), Image.LANCZOS)
|
||||
init = TF.to_tensor(init).to(device).unsqueeze(0).mul(2).sub(1)
|
||||
|
||||
if args.perlin_init:
|
||||
init = disco_utils.regen_perlin()
|
||||
|
||||
cur_t = None
|
||||
|
||||
def cond_fn(x, t, y=None):
|
||||
with torch.enable_grad():
|
||||
x_is_NaN = False
|
||||
x = x.detach().requires_grad_()
|
||||
n = x.shape[0]
|
||||
if args.MS.use_secondary_model is True:
|
||||
alpha = torch.tensor(
|
||||
diffusion.sqrt_alphas_cumprod[cur_t], device=device, dtype=torch.float32)
|
||||
sigma = torch.tensor(
|
||||
diffusion.sqrt_one_minus_alphas_cumprod[cur_t], device=device, dtype=torch.float32)
|
||||
cosine_t = disco_utils.alpha_sigma_to_t(alpha, sigma)
|
||||
out = args.MS.secondary_model(
|
||||
x, cosine_t[None].repeat([n])).pred
|
||||
fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]
|
||||
x_in = out * fac + x * (1 - fac)
|
||||
x_in_grad = torch.zeros_like(x_in)
|
||||
else:
|
||||
my_t = torch.ones([n], device=device,
|
||||
dtype=torch.long) * cur_t
|
||||
out = diffusion.p_mean_variance(
|
||||
model, x, my_t, clip_denoised=False, model_kwargs={'y': y})
|
||||
fac = diffusion.sqrt_one_minus_alphas_cumprod[cur_t]
|
||||
x_in = out['pred_xstart'] * fac + x * (1 - fac)
|
||||
x_in_grad = torch.zeros_like(x_in)
|
||||
for model_stat in model_stats:
|
||||
for i in range(args.cutn_batches):
|
||||
# errors on last step without +1, need to find source
|
||||
t_int = int(t.item())+1
|
||||
# when using SLIP Base model the dimensions need to be hard coded to avoid AttributeError: 'VisionTransformer' object has no attribute 'input_resolution'
|
||||
try:
|
||||
input_resolution = model_stat["clip_model"].visual.input_resolution
|
||||
except:
|
||||
input_resolution = 224
|
||||
|
||||
cuts = MakeCutoutsDango(animation_mode=args.animation_mode,
|
||||
skip_augs=args.skip_augs,
|
||||
cut_size=input_resolution,
|
||||
Overview=args.cut_overview[1000-t_int],
|
||||
InnerCrop=args.cut_innercut[1000-t_int],
|
||||
IC_Size_Pow=args.cut_ic_pow[1000-t_int],
|
||||
IC_Grey_P=args.cut_icgray_p[1000-t_int]
|
||||
)
|
||||
clip_in = disco_utils.normalize(
|
||||
cuts(x_in.add(1).div(2)))
|
||||
image_embeds = model_stat["clip_model"].encode_image(
|
||||
clip_in).float()
|
||||
dists = disco_utils.spherical_dist_loss(image_embeds.unsqueeze(
|
||||
1), model_stat["target_embeds"].unsqueeze(0))
|
||||
dists = dists.view(
|
||||
[args.cut_overview[1000-t_int]+args.cut_innercut[1000-t_int], n, -1])
|
||||
losses = dists.mul(
|
||||
model_stat["weights"]).sum(2).mean(0)
|
||||
# log loss, probably shouldn't do per cutn_batch
|
||||
loss_values.append(losses.sum().item())
|
||||
x_in_grad += torch.autograd.grad(losses.sum() * args.clip_guidance_scale, x_in)[
|
||||
0] / args.cutn_batches
|
||||
tv_losses = args.tv_loss(x_in)
|
||||
if args.MS.use_secondary_model is True:
|
||||
range_losses = disco_utils.range_loss(out)
|
||||
else:
|
||||
range_losses = disco_utils.range_loss(out['pred_xstart'])
|
||||
sat_losses = torch.abs(x_in - x_in.clamp(min=-1, max=1)).mean()
|
||||
loss = tv_losses.sum() * args.tv_scale + range_losses.sum() * \
|
||||
args.range_scale + sat_losses.sum() * args.sat_scale
|
||||
if init is not None and init_scale:
|
||||
init_losses = args.MS.lpips_model(x_in, init)
|
||||
loss = loss + init_losses.sum() * init_scale
|
||||
x_in_grad += torch.autograd.grad(loss, x_in)[0]
|
||||
if torch.isnan(x_in_grad).any() == False:
|
||||
grad = -torch.autograd.grad(x_in, x, x_in_grad)[0]
|
||||
else:
|
||||
# print("NaN'd")
|
||||
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.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('')
|
||||
# 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)
|
||||
|
||||
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.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):
|
||||
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)
|
||||
+165
@@ -0,0 +1,165 @@
|
||||
import cv2
|
||||
import pandas as pd
|
||||
import gc
|
||||
import lpips
|
||||
from PIL import Image, ImageOps
|
||||
import requests
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
import torchvision
|
||||
import torchvision.transforms as T
|
||||
import torchvision.transforms.functional as TF
|
||||
from tqdm import tqdm
|
||||
from resize_right import resize
|
||||
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
|
||||
import numpy as np
|
||||
from numpy import asarray
|
||||
|
||||
from .settings import DiscoDiffusionSettings
|
||||
from . import disco_utils
|
||||
|
||||
|
||||
class MakeCutouts(nn.Module):
|
||||
def __init__(self, cut_size, cutn, skip_augs=False):
|
||||
super().__init__()
|
||||
self.cut_size = cut_size
|
||||
self.cutn = cutn
|
||||
self.skip_augs = skip_augs
|
||||
self.augs = T.Compose([
|
||||
T.RandomHorizontalFlip(p=0.5),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.RandomAffine(degrees=15, translate=(0.1, 0.1)),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.RandomPerspective(distortion_scale=0.4, p=0.7),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.RandomGrayscale(p=0.15),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
# T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
|
||||
])
|
||||
|
||||
def forward(self, input):
|
||||
input = T.Pad(input.shape[2]//4, fill=0)(input)
|
||||
sideY, sideX = input.shape[2:4]
|
||||
max_size = min(sideX, sideY)
|
||||
|
||||
cutouts = []
|
||||
for ch in range(self.cutn):
|
||||
if ch > self.cutn - self.cutn//4:
|
||||
cutout = input.clone()
|
||||
else:
|
||||
size = int(max_size * torch.zeros(1,).normal_(mean=.8, std=.3).clip(float(self.cut_size/max_size), 1.))
|
||||
offsetx = torch.randint(0, abs(sideX - size + 1), ())
|
||||
offsety = torch.randint(0, abs(sideY - size + 1), ())
|
||||
cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
|
||||
|
||||
if not self.skip_augs:
|
||||
cutout = self.augs(cutout)
|
||||
cutouts.append(disco_utils.resample(cutout, (self.cut_size, self.cut_size)))
|
||||
del cutout
|
||||
|
||||
cutouts = torch.cat(cutouts, dim=0)
|
||||
return cutouts
|
||||
|
||||
cutout_debug = False
|
||||
padargs = {}
|
||||
|
||||
class MakeCutoutsDango(nn.Module):
|
||||
def __init__(self,
|
||||
animation_mode: str,
|
||||
skip_augs,
|
||||
cut_size, Overview=4,
|
||||
InnerCrop = 0, IC_Size_Pow=0.5, IC_Grey_P = 0.2
|
||||
):
|
||||
super().__init__()
|
||||
self.cut_size = cut_size
|
||||
self.skip_augs = skip_augs
|
||||
self.Overview = Overview
|
||||
self.InnerCrop = InnerCrop
|
||||
self.IC_Size_Pow = IC_Size_Pow
|
||||
self.IC_Grey_P = IC_Grey_P
|
||||
if animation_mode == 'None':
|
||||
self.augs = T.Compose([
|
||||
T.RandomHorizontalFlip(p=0.5),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.RandomAffine(degrees=10, translate=(0.05, 0.05), interpolation = T.InterpolationMode.BILINEAR),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.RandomGrayscale(p=0.1),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
|
||||
])
|
||||
elif animation_mode == 'Video Input':
|
||||
self.augs = T.Compose([
|
||||
T.RandomHorizontalFlip(p=0.5),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.RandomAffine(degrees=15, translate=(0.1, 0.1)),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.RandomPerspective(distortion_scale=0.4, p=0.7),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.RandomGrayscale(p=0.15),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
# T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
|
||||
])
|
||||
elif animation_mode == '2D' or animation_mode == '3D':
|
||||
self.augs = T.Compose([
|
||||
T.RandomHorizontalFlip(p=0.4),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.RandomAffine(degrees=10, translate=(0.05, 0.05), interpolation = T.InterpolationMode.BILINEAR),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.RandomGrayscale(p=0.1),
|
||||
T.Lambda(lambda x: x + torch.randn_like(x) * 0.01),
|
||||
T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.3),
|
||||
])
|
||||
|
||||
|
||||
def forward(self, input):
|
||||
cutouts = []
|
||||
gray = T.Grayscale(3)
|
||||
sideY, sideX = input.shape[2:4]
|
||||
max_size = min(sideX, sideY)
|
||||
min_size = min(sideX, sideY, self.cut_size)
|
||||
max(sideX, sideY)
|
||||
output_shape = [1,3,self.cut_size,self.cut_size]
|
||||
[1,3,self.cut_size+2,self.cut_size+2]
|
||||
pad_input = F.pad(input,((sideY-max_size)//2,(sideY-max_size)//2,(sideX-max_size)//2,(sideX-max_size)//2), **padargs)
|
||||
cutout = resize(pad_input, out_shape=output_shape)
|
||||
|
||||
if self.Overview>0:
|
||||
if self.Overview<=4:
|
||||
if self.Overview>=1:
|
||||
cutouts.append(cutout)
|
||||
if self.Overview>=2:
|
||||
cutouts.append(gray(cutout))
|
||||
if self.Overview>=3:
|
||||
cutouts.append(TF.hflip(cutout))
|
||||
if self.Overview==4:
|
||||
cutouts.append(gray(TF.hflip(cutout)))
|
||||
else:
|
||||
cutout = resize(pad_input, out_shape=output_shape)
|
||||
for _ in range(self.Overview):
|
||||
cutouts.append(cutout)
|
||||
|
||||
if cutout_debug:
|
||||
# if is_colab:
|
||||
# TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save("/content/cutout_overview0.jpg",quality=99)
|
||||
TF.to_pil_image(cutouts[0].clamp(0, 1).squeeze(0)).save("cutout_overview0.jpg",quality=99)
|
||||
|
||||
|
||||
if self.InnerCrop >0:
|
||||
for i in range(self.InnerCrop):
|
||||
size = int(torch.rand([])**self.IC_Size_Pow * (max_size - min_size) + min_size)
|
||||
offsetx = torch.randint(0, sideX - size + 1, ())
|
||||
offsety = torch.randint(0, sideY - size + 1, ())
|
||||
cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
|
||||
if i <= int(self.IC_Grey_P * self.InnerCrop):
|
||||
cutout = gray(cutout)
|
||||
cutout = resize(cutout, out_shape=output_shape)
|
||||
cutouts.append(cutout)
|
||||
if cutout_debug:
|
||||
# if is_colab:
|
||||
# TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save("/content/cutout_InnerCrop.jpg",quality=99)
|
||||
# else:
|
||||
TF.to_pil_image(cutouts[-1].clamp(0, 1).squeeze(0)).save("cutout_InnerCrop.jpg",quality=99)
|
||||
cutouts = torch.cat(cutouts)
|
||||
if self.skip_augs is not True: cutouts=self.augs(cutouts)
|
||||
return cutouts
|
||||
+122
@@ -0,0 +1,122 @@
|
||||
import cv2
|
||||
import pandas as pd
|
||||
import gc
|
||||
import torch
|
||||
import lpips
|
||||
from PIL import Image, ImageOps
|
||||
import requests
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
import torchvision
|
||||
import torchvision.transforms as T
|
||||
import torchvision.transforms.functional as TF
|
||||
from tqdm import tqdm
|
||||
from resize_right import resize
|
||||
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
|
||||
import numpy as np
|
||||
from numpy import asarray
|
||||
|
||||
from midas.dpt_depth import DPTDepthModel
|
||||
from midas.midas_net import MidasNet
|
||||
from midas.midas_net_custom import MidasNet_small
|
||||
from midas.transforms import Resize, NormalizeImage, PrepareForNet
|
||||
|
||||
import comfy.model_management
|
||||
|
||||
|
||||
default_models = {}
|
||||
|
||||
|
||||
def init_midas_depth_model(midas_model_type="dpt_large", optimize=True):
|
||||
global default_models
|
||||
|
||||
midas_model = None
|
||||
net_w = None
|
||||
net_h = None
|
||||
resize_mode = None
|
||||
normalization = None
|
||||
|
||||
print(f"Initializing MiDaS '{midas_model_type}' depth model...")
|
||||
# load network
|
||||
midas_model_path = default_models[midas_model_type]
|
||||
assert False # TODO
|
||||
|
||||
if midas_model_type == "dpt_large": # DPT-Large
|
||||
midas_model = DPTDepthModel(
|
||||
path=midas_model_path,
|
||||
backbone="vitl16_384",
|
||||
non_negative=True,
|
||||
)
|
||||
net_w, net_h = 384, 384
|
||||
resize_mode = "minimal"
|
||||
normalization = NormalizeImage(
|
||||
mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
|
||||
elif midas_model_type == "dpt_hybrid": # DPT-Hybrid
|
||||
midas_model = DPTDepthModel(
|
||||
path=midas_model_path,
|
||||
backbone="vitb_rn50_384",
|
||||
non_negative=True,
|
||||
)
|
||||
net_w, net_h = 384, 384
|
||||
resize_mode = "minimal"
|
||||
normalization = NormalizeImage(
|
||||
mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
|
||||
elif midas_model_type == "dpt_hybrid_nyu": # DPT-Hybrid-NYU
|
||||
midas_model = DPTDepthModel(
|
||||
path=midas_model_path,
|
||||
backbone="vitb_rn50_384",
|
||||
non_negative=True,
|
||||
)
|
||||
net_w, net_h = 384, 384
|
||||
resize_mode = "minimal"
|
||||
normalization = NormalizeImage(
|
||||
mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
|
||||
elif midas_model_type == "midas_v21":
|
||||
midas_model = MidasNet(midas_model_path, non_negative=True)
|
||||
net_w, net_h = 384, 384
|
||||
resize_mode = "upper_bound"
|
||||
normalization = NormalizeImage(
|
||||
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
|
||||
)
|
||||
elif midas_model_type == "midas_v21_small":
|
||||
midas_model = MidasNet_small(midas_model_path, features=64, backbone="efficientnet_lite3",
|
||||
exportable=True, non_negative=True, blocks={'expand': True})
|
||||
net_w, net_h = 256, 256
|
||||
resize_mode = "upper_bound"
|
||||
normalization = NormalizeImage(
|
||||
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
|
||||
)
|
||||
else:
|
||||
print(f"midas_model_type '{midas_model_type}' not implemented")
|
||||
assert False
|
||||
|
||||
midas_transform = T.Compose(
|
||||
[
|
||||
Resize(
|
||||
net_w,
|
||||
net_h,
|
||||
resize_target=None,
|
||||
keep_aspect_ratio=True,
|
||||
ensure_multiple_of=32,
|
||||
resize_method=resize_mode,
|
||||
image_interpolation_method=cv2.INTER_CUBIC,
|
||||
),
|
||||
normalization,
|
||||
PrepareForNet(),
|
||||
]
|
||||
)
|
||||
|
||||
midas_model.eval()
|
||||
|
||||
device = comfy.model_management.get_torch_device()
|
||||
|
||||
if optimize is True:
|
||||
if device == torch.device("cuda"):
|
||||
midas_model = midas_model.to(memory_format=torch.channels_last)
|
||||
midas_model = midas_model.half()
|
||||
|
||||
midas_model.to(device)
|
||||
|
||||
print(f"MiDaS '{midas_model_type}' depth model initialized.")
|
||||
return midas_model, midas_transform, net_w, net_h, resize_mode, normalization
|
||||
@@ -0,0 +1,239 @@
|
||||
from urllib.parse import urlparse
|
||||
import os
|
||||
import hashlib
|
||||
import lpips
|
||||
import torchvision.transforms as T
|
||||
import os
|
||||
import cv2
|
||||
import pandas as pd
|
||||
import gc
|
||||
import lpips
|
||||
from PIL import Image, ImageOps
|
||||
import requests
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
import torchvision
|
||||
import torchvision.transforms as T
|
||||
import torchvision.transforms.functional as TF
|
||||
from tqdm import tqdm
|
||||
from resize_right import resize
|
||||
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
|
||||
import numpy as np
|
||||
import hashlib
|
||||
from numpy import asarray
|
||||
|
||||
from .secondary_diffusion_model import SecondaryDiffusionImageNet2
|
||||
from . import disco_utils
|
||||
|
||||
import comfy.model_management
|
||||
|
||||
diff_model_map = {
|
||||
'256x256_diffusion_uncond': { 'downloaded': False, 'sha': 'a37c32fffd316cd494cf3f35b339936debdc1576dad13fe57c42399a5dbc78b1', 'uri_list': ['https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt', 'https://www.dropbox.com/s/9tqnqo930mpnpcn/256x256_diffusion_uncond.pt'] },
|
||||
'512x512_diffusion_uncond_finetune_008100': { 'downloaded': False, 'sha': '9c111ab89e214862b76e1fa6a1b3f1d329b1a88281885943d2cdbe357ad57648', 'uri_list': ['https://huggingface.co/lowlevelware/512x512_diffusion_unconditional_ImageNet/resolve/main/512x512_diffusion_uncond_finetune_008100.pt', 'https://the-eye.eu/public/AI/models/512x512_diffusion_unconditional_ImageNet/512x512_diffusion_uncond_finetune_008100.pt'] },
|
||||
'portrait_generator_v001': { 'downloaded': False, 'sha': 'b7e8c747af880d4480b6707006f1ace000b058dd0eac5bb13558ba3752d9b5b9', 'uri_list': ['https://huggingface.co/felipe3dartist/portrait_generator_v001/resolve/main/portrait_generator_v001_ema_0.9999_1MM.pt'] },
|
||||
'pixelartdiffusion_expanded': { 'downloaded': False, 'sha': 'a73b40556634034bf43b5a716b531b46fb1ab890634d854f5bcbbef56838739a', 'uri_list': ['https://huggingface.co/KaliYuga/PADexpanded/resolve/main/PADexpanded.pt'] },
|
||||
'pixel_art_diffusion_hard_256': { 'downloaded': False, 'sha': 'be4a9de943ec06eef32c65a1008c60ad017723a4d35dc13169c66bb322234161', 'uri_list': ['https://huggingface.co/KaliYuga/pixel_art_diffusion_hard_256/resolve/main/pixel_art_diffusion_hard_256.pt'] },
|
||||
'pixel_art_diffusion_soft_256': { 'downloaded': False, 'sha': 'd321590e46b679bf6def1f1914b47c89e762c76f19ab3e3392c8ca07c791039c', 'uri_list': ['https://huggingface.co/KaliYuga/pixel_art_diffusion_soft_256/resolve/main/pixel_art_diffusion_soft_256.pt'] },
|
||||
'pixelartdiffusion4k': { 'downloaded': False, 'sha': 'a1ba4f13f6dabb72b1064f15d8ae504d98d6192ad343572cc416deda7cccac30', 'uri_list': ['https://huggingface.co/KaliYuga/pixelartdiffusion4k/resolve/main/pixelartdiffusion4k.pt'] },
|
||||
'watercolordiffusion_2': { 'downloaded': False, 'sha': '49c281b6092c61c49b0f1f8da93af9b94be7e0c20c71e662e2aa26fee0e4b1a9', 'uri_list': ['https://huggingface.co/KaliYuga/watercolordiffusion_2/resolve/main/watercolordiffusion_2.pt'] },
|
||||
'watercolordiffusion': { 'downloaded': False, 'sha': 'a3e6522f0c8f278f90788298d66383b11ac763dd5e0d62f8252c962c23950bd6', 'uri_list': ['https://huggingface.co/KaliYuga/watercolordiffusion/resolve/main/watercolordiffusion.pt'] },
|
||||
'PulpSciFiDiffusion': { 'downloaded': False, 'sha': 'b79e62613b9f50b8a3173e5f61f0320c7dbb16efad42a92ec94d014f6e17337f', 'uri_list': ['https://huggingface.co/KaliYuga/PulpSciFiDiffusion/resolve/main/PulpSciFiDiffusion.pt'] },
|
||||
'secondary': { 'downloaded': False, 'sha': '983e3de6f95c88c81b2ca7ebb2c217933be1973b1ff058776b970f901584613a', 'uri_list': ['https://huggingface.co/spaces/huggi/secondary_model_imagenet_2.pth/resolve/main/secondary_model_imagenet_2.pth', 'https://the-eye.eu/public/AI/models/v-diffusion/secondary_model_imagenet_2.pth', 'https://ipfs.pollinations.ai/ipfs/bafybeibaawhhk7fhyhvmm7x24zwwkeuocuizbqbcg5nqx64jq42j75rdiy/secondary_model_imagenet_2.pth'] },
|
||||
}
|
||||
|
||||
class ModelSettings:
|
||||
def __init__(self):
|
||||
self.root_path = os.getcwd()
|
||||
self.model_path = f'{self.root_path}/models'
|
||||
#@markdown ####**Models Settings (note: For pixel art, the best is pixelartdiffusion_expanded):**
|
||||
self.diffusion_model = "512x512_diffusion_uncond_finetune_008100" #@param ["256x256_diffusion_uncond", "512x512_diffusion_uncond_finetune_008100", "portrait_generator_v001", "pixelartdiffusion_expanded", "pixel_art_diffusion_hard_256", "pixel_art_diffusion_soft_256", "pixelartdiffusion4k", "watercolordiffusion_2", "watercolordiffusion", "PulpSciFiDiffusion", "custom"]
|
||||
|
||||
self.use_secondary_model = True #@param {type: 'boolean'}
|
||||
self.diffusion_sampling_mode = 'ddim' #@param ['plms','ddim']
|
||||
#@markdown #####**Custom model:**
|
||||
self.custom_path = '/content/drive/MyDrive/deep_learning/ddpm/ema_0.9999_058000.pt'#@param {type: 'string'}
|
||||
|
||||
#@markdown #####**CLIP settings:**
|
||||
self.use_checkpoint = True #@param {type: 'boolean'}
|
||||
self.ViTB32 = True #@param{type:"boolean"}
|
||||
self.ViTB16 = True #@param{type:"boolean"}
|
||||
self.ViTL14 = False #@param{type:"boolean"}
|
||||
self.ViTL14_336px = False #@param{type:"boolean"}
|
||||
self.RN101 = False #@param{type:"boolean"}
|
||||
self.RN50 = True #@param{type:"boolean"}
|
||||
self.RN50x4 = False #@param{type:"boolean"}
|
||||
self.RN50x16 = False #@param{type:"boolean"}
|
||||
self.RN50x64 = False #@param{type:"boolean"}
|
||||
|
||||
#@markdown #####**OpenCLIP settings:**
|
||||
self.ViTB32_laion2b_e16 = False #@param{type:"boolean"}
|
||||
self.ViTB32_laion400m_e31 = False #@param{type:"boolean"}
|
||||
self.ViTB32_laion400m_32 = False #@param{type:"boolean"}
|
||||
self.ViTB32quickgelu_laion400m_e31 = False #@param{type:"boolean"}
|
||||
self.ViTB32quickgelu_laion400m_e32 = False #@param{type:"boolean"}
|
||||
self.ViTB16_laion400m_e31 = False #@param{type:"boolean"}
|
||||
self.ViTB16_laion400m_e32 = False #@param{type:"boolean"}
|
||||
self.RN50_yffcc15m = False #@param{type:"boolean"}
|
||||
self.RN50_cc12m = False #@param{type:"boolean"}
|
||||
self.RN50_quickgelu_yfcc15m = False #@param{type:"boolean"}
|
||||
self.RN50_quickgelu_cc12m = False #@param{type:"boolean"}
|
||||
self.RN101_yfcc15m = False #@param{type:"boolean"}
|
||||
self.RN101_quickgelu_yfcc15m = False #@param{type:"boolean"}
|
||||
|
||||
#@markdown If you're having issues with model downloads, check this to compare SHA's:
|
||||
self.check_model_SHA = False #@param{type:"boolean"}
|
||||
|
||||
self.kaliyuga_pixel_art_model_names = ['pixelartdiffusion_expanded', 'pixel_art_diffusion_hard_256', 'pixel_art_diffusion_soft_256', 'pixelartdiffusion4k', 'PulpSciFiDiffusion']
|
||||
self.kaliyuga_watercolor_model_names = ['watercolordiffusion', 'watercolordiffusion_2']
|
||||
self.kaliyuga_pulpscifi_model_names = ['PulpSciFiDiffusion']
|
||||
self.diffusion_models_256x256_list = ['256x256_diffusion_uncond'] + self.kaliyuga_pixel_art_model_names + self.kaliyuga_watercolor_model_names + self.kaliyuga_pulpscifi_model_names
|
||||
|
||||
|
||||
def get_model_filename(self, diffusion_model_name):
|
||||
model_uri = diff_model_map[diffusion_model_name]['uri_list'][0]
|
||||
model_filename = os.path.basename(urlparse(model_uri).path)
|
||||
return model_filename
|
||||
|
||||
def download_model(self, diffusion_model_name, uri_index=0):
|
||||
if diffusion_model_name != 'custom':
|
||||
model_filename = self.get_model_filename(diffusion_model_name)
|
||||
model_local_path = os.path.join(self.model_path, model_filename)
|
||||
if os.path.exists(model_local_path) and self.check_model_SHA:
|
||||
print(f'Checking {diffusion_model_name} File')
|
||||
with open(model_local_path, "rb") as f:
|
||||
bytes = f.read()
|
||||
hash = hashlib.sha256(bytes).hexdigest()
|
||||
if hash == diff_model_map[diffusion_model_name]['sha']:
|
||||
print(f'{diffusion_model_name} SHA matches')
|
||||
diff_model_map[diffusion_model_name]['downloaded'] = True
|
||||
else:
|
||||
print(f"{diffusion_model_name} SHA doesn't match. Will redownload it.")
|
||||
elif os.path.exists(model_local_path) and not self.check_model_SHA or diff_model_map[diffusion_model_name]['downloaded']:
|
||||
print(f'{diffusion_model_name} already downloaded. If the file is corrupt, enable check_model_SHA.')
|
||||
diff_model_map[diffusion_model_name]['downloaded'] = True
|
||||
|
||||
if not diff_model_map[diffusion_model_name]['downloaded']:
|
||||
for model_uri in diff_model_map[diffusion_model_name]['uri_list']:
|
||||
disco_utils.wget(model_uri, self.model_path)
|
||||
if os.path.exists(model_local_path):
|
||||
diff_model_map[diffusion_model_name]['downloaded'] = True
|
||||
return
|
||||
else:
|
||||
print(f'{diffusion_model_name} model download from {model_uri} failed. Will try any fallback uri.')
|
||||
print(f'{diffusion_model_name} download failed.')
|
||||
|
||||
def setup(self, S):
|
||||
# Download the diffusion model(s)
|
||||
self.download_model(self.diffusion_model)
|
||||
if self.use_secondary_model:
|
||||
self.download_model('secondary')
|
||||
|
||||
self.model_config = model_and_diffusion_defaults()
|
||||
if self.diffusion_model == '512x512_diffusion_uncond_finetune_008100':
|
||||
self.model_config.update({
|
||||
'attention_resolutions': '32, 16, 8',
|
||||
'class_cond': False,
|
||||
'diffusion_steps': 1000, #No need to edit this, it is taken care of later.
|
||||
'rescale_timesteps': True,
|
||||
'timestep_respacing': 250, #No need to edit this, it is taken care of later.
|
||||
'image_size': 512,
|
||||
'learn_sigma': True,
|
||||
'noise_schedule': 'linear',
|
||||
'num_channels': 256,
|
||||
'num_head_channels': 64,
|
||||
'num_res_blocks': 2,
|
||||
'resblock_updown': True,
|
||||
'use_checkpoint': self.use_checkpoint,
|
||||
'use_fp16': not S.useCPU,
|
||||
'use_scale_shift_norm': True,
|
||||
})
|
||||
elif self.diffusion_model == '256x256_diffusion_uncond':
|
||||
self.model_config.update({
|
||||
'attention_resolutions': '32, 16, 8',
|
||||
'class_cond': False,
|
||||
'diffusion_steps': 1000, #No need to edit this, it is taken care of later.
|
||||
'rescale_timesteps': True,
|
||||
'timestep_respacing': 250, #No need to edit this, it is taken care of later.
|
||||
'image_size': 256,
|
||||
'learn_sigma': True,
|
||||
'noise_schedule': 'linear',
|
||||
'num_channels': 256,
|
||||
'num_head_channels': 64,
|
||||
'num_res_blocks': 2,
|
||||
'resblock_updown': True,
|
||||
'use_checkpoint': self.use_checkpoint,
|
||||
'use_fp16': not S.useCPU,
|
||||
'use_scale_shift_norm': True,
|
||||
})
|
||||
elif self.diffusion_model == 'portrait_generator_v001':
|
||||
self.model_config.update({
|
||||
'attention_resolutions': '32, 16, 8',
|
||||
'class_cond': False,
|
||||
'diffusion_steps': 1000,
|
||||
'rescale_timesteps': True,
|
||||
'image_size': 512,
|
||||
'learn_sigma': True,
|
||||
'noise_schedule': 'linear',
|
||||
'num_channels': 128,
|
||||
'num_heads': 4,
|
||||
'num_res_blocks': 2,
|
||||
'resblock_updown': True,
|
||||
'use_checkpoint': self.use_checkpoint,
|
||||
'use_fp16': True,
|
||||
'use_scale_shift_norm': True,
|
||||
})
|
||||
else: # E.g. A model finetuned by KaliYuga
|
||||
self.model_config.update({
|
||||
'attention_resolutions': '16',
|
||||
'class_cond': False,
|
||||
'diffusion_steps': 1000,
|
||||
'rescale_timesteps': True,
|
||||
'timestep_respacing': 'ddim100',
|
||||
'image_size': 256,
|
||||
'learn_sigma': True,
|
||||
'noise_schedule': 'linear',
|
||||
'num_channels': 128,
|
||||
'num_heads': 1,
|
||||
'num_res_blocks': 2,
|
||||
'use_checkpoint': self.use_checkpoint,
|
||||
'use_fp16': True,
|
||||
'use_scale_shift_norm': False,
|
||||
})
|
||||
|
||||
self.model_default = self.model_config['image_size']
|
||||
|
||||
device = comfy.model_management.get_torch_device()
|
||||
|
||||
if self.use_secondary_model:
|
||||
self.secondary_model = SecondaryDiffusionImageNet2()
|
||||
self.secondary_model.load_state_dict(torch.load(f'{self.model_path}/secondary_model_imagenet_2.pth', map_location='cpu'))
|
||||
self.secondary_model.eval().requires_grad_(False).to(device)
|
||||
|
||||
self.clip_models = []
|
||||
#if self.ViTB32: clip_models.append(clip.load('ViT-B/32', jit=False)[0].eval().requires_grad_(False).to(device))
|
||||
#if self.ViTB16: clip_models.append(clip.load('ViT-B/16', jit=False)[0].eval().requires_grad_(False).to(device))
|
||||
#if self.ViTL14: clip_models.append(clip.load('ViT-L/14', jit=False)[0].eval().requires_grad_(False).to(device))
|
||||
#if self.ViTL14_336px: clip_models.append(clip.load('ViT-L/14@336px', jit=False)[0].eval().requires_grad_(False).to(device))
|
||||
#if self.RN50: clip_models.append(clip.load('RN50', jit=False)[0].eval().requires_grad_(False).to(device))
|
||||
#if self.RN50x4: clip_models.append(clip.load('RN50x4', jit=False)[0].eval().requires_grad_(False).to(device))
|
||||
#if self.RN50x16: clip_models.append(clip.load('RN50x16', jit=False)[0].eval().requires_grad_(False).to(device))
|
||||
#if self.RN50x64: clip_models.append(clip.load('RN50x64', jit=False)[0].eval().requires_grad_(False).to(device))
|
||||
#if self.RN101: clip_models.append(clip.load('RN101', jit=False)[0].eval().requires_grad_(False).to(device))
|
||||
#if self.ViTB32_laion2b_e16: clip_models.append(open_clip.create_model('ViT-B-32', pretrained='laion2b_e16').eval().requires_grad_(False).to(device))
|
||||
#if self.ViTB32_laion400m_e31: clip_models.append(open_clip.create_model('ViT-B-32', pretrained='laion400m_e31').eval().requires_grad_(False).to(device))
|
||||
#if self.ViTB32_laion400m_32: clip_models.append(open_clip.create_model('ViT-B-32', pretrained='laion400m_e32').eval().requires_grad_(False).to(device))
|
||||
#if self.ViTB32quickgelu_laion400m_e31: clip_models.append(open_clip.create_model('ViT-B-32-quickgelu', pretrained='laion400m_e31').eval().requires_grad_(False).to(device))
|
||||
#if self.ViTB32quickgelu_laion400m_e32: clip_models.append(open_clip.create_model('ViT-B-32-quickgelu', pretrained='laion400m_e32').eval().requires_grad_(False).to(device))
|
||||
#if self.ViTB16_laion400m_e31: clip_models.append(open_clip.create_model('ViT-B-16', pretrained='laion400m_e31').eval().requires_grad_(False).to(device))
|
||||
#if self.ViTB16_laion400m_e32: clip_models.append(open_clip.create_model('ViT-B-16', pretrained='laion400m_e32').eval().requires_grad_(False).to(device))
|
||||
#if self.RN50_yffcc15m: clip_models.append(open_clip.create_model('RN50', pretrained='yfcc15m').eval().requires_grad_(False).to(device))
|
||||
#if self.RN50_cc12m: clip_models.append(open_clip.create_model('RN50', pretrained='cc12m').eval().requires_grad_(False).to(device))
|
||||
#if self.RN50_quickgelu_yfcc15m: clip_models.append(open_clip.create_model('RN50-quickgelu', pretrained='yfcc15m').eval().requires_grad_(False).to(device))
|
||||
#if self.RN50_quickgelu_cc12m: clip_models.append(open_clip.create_model('RN50-quickgelu', pretrained='cc12m').eval().requires_grad_(False).to(device))
|
||||
#if self.RN101_yfcc15m: clip_models.append(open_clip.create_model('RN101', pretrained='yfcc15m').eval().requires_grad_(False).to(device))
|
||||
#if self.RN101_quickgelu_yfcc15m: clip_models.append(open_clip.create_model('RN101-quickgelu', pretrained='yfcc15m').eval().requires_grad_(False).to(device))
|
||||
|
||||
self.lpips_model = lpips.LPIPS(net='vgg').to(device)
|
||||
|
||||
S.MS = self
|
||||
@@ -0,0 +1,49 @@
|
||||
import os.path
|
||||
|
||||
NODE_FILE = os.path.abspath(__file__)
|
||||
DISCO_DIFFUSION_ROOT = os.path.dirname(NODE_FILE)
|
||||
|
||||
import sys
|
||||
sys.path.append(os.path.join(DISCO_DIFFUSION_ROOT, "MiDaS"))
|
||||
sys.path.append(os.path.join(DISCO_DIFFUSION_ROOT, "ResizeRight"))
|
||||
sys.path.append(os.path.join(DISCO_DIFFUSION_ROOT, "guided-diffusion"))
|
||||
sys.path.append(os.path.join(DISCO_DIFFUSION_ROOT, "RAFT/core"))
|
||||
|
||||
|
||||
from .settings import DiscoDiffusionSettings
|
||||
from .model_settings import ModelSettings
|
||||
from .diffuse import diffuse
|
||||
|
||||
|
||||
class DiscoDiffusion:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {"text": ("STRING", {"multiline": True}),
|
||||
"clip": ("CLIP", ),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
}}
|
||||
RETURN_TYPES = ()
|
||||
FUNCTION = "generate"
|
||||
|
||||
CATEGORY = "sampling"
|
||||
|
||||
OUTPUT_NODE = True
|
||||
|
||||
def __init__(self):
|
||||
self.settings = DiscoDiffusionSettings()
|
||||
self.model_settings = ModelSettings()
|
||||
self.settings.setup(self.model_settings)
|
||||
self.model_settings.setup(self.settings)
|
||||
|
||||
def generate(self, text, clip, seed):
|
||||
diffuse(clip, self.settings, 0)
|
||||
return { "ui": { "images": {} } }
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"ComfyUI_DiscoDiffusion": DiscoDiffusion,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"ComfyUI_DiscoDiffusion": "Disco Diffusion",
|
||||
}
|
||||
+1799
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,10 @@
|
||||
lpips
|
||||
datetime
|
||||
guided-diffusion@git+https://github.com/kostarion/guided-diffusion
|
||||
imageio
|
||||
imageio-ffmpeg==0.4.4
|
||||
lpips
|
||||
datetime
|
||||
pandas
|
||||
opencv-python
|
||||
regex
|
||||
@@ -0,0 +1,193 @@
|
||||
from dataclasses import dataclass
|
||||
from functools import partial
|
||||
import cv2
|
||||
import pandas as pd
|
||||
import gc
|
||||
import math
|
||||
import lpips
|
||||
from PIL import Image, ImageOps
|
||||
import requests
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
import torchvision
|
||||
import torchvision.transforms as T
|
||||
import torchvision.transforms.functional as TF
|
||||
from tqdm import tqdm
|
||||
from resize_right import resize
|
||||
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
|
||||
import numpy as np
|
||||
from functools import partial
|
||||
from numpy import asarray
|
||||
|
||||
|
||||
def append_dims(x, n):
|
||||
return x[(Ellipsis, *(None,) * (n - x.ndim))]
|
||||
|
||||
|
||||
def alpha_sigma_to_t(alpha, sigma):
|
||||
return torch.atan2(sigma, alpha) * 2 / math.pi
|
||||
|
||||
|
||||
def expand_to_planes(x, shape):
|
||||
return append_dims(x, len(shape)).repeat([1, 1, *shape[2:]])
|
||||
|
||||
|
||||
def t_to_alpha_sigma(t):
|
||||
return torch.cos(t * math.pi / 2), torch.sin(t * math.pi / 2)
|
||||
|
||||
|
||||
@dataclass
|
||||
class DiffusionOutput:
|
||||
v: torch.Tensor
|
||||
pred: torch.Tensor
|
||||
eps: torch.Tensor
|
||||
|
||||
|
||||
class ConvBlock(nn.Sequential):
|
||||
def __init__(self, c_in, c_out):
|
||||
super().__init__(
|
||||
nn.Conv2d(c_in, c_out, 3, padding=1),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
|
||||
|
||||
class SkipBlock(nn.Module):
|
||||
def __init__(self, main, skip=None):
|
||||
super().__init__()
|
||||
self.main = nn.Sequential(*main)
|
||||
self.skip = skip if skip else nn.Identity()
|
||||
|
||||
def forward(self, input):
|
||||
return torch.cat([self.main(input), self.skip(input)], dim=1)
|
||||
|
||||
|
||||
class FourierFeatures(nn.Module):
|
||||
def __init__(self, in_features, out_features, std=1.):
|
||||
super().__init__()
|
||||
assert out_features % 2 == 0
|
||||
self.weight = nn.Parameter(torch.randn(
|
||||
[out_features // 2, in_features]) * std)
|
||||
|
||||
def forward(self, input):
|
||||
f = 2 * math.pi * input @ self.weight.T
|
||||
return torch.cat([f.cos(), f.sin()], dim=-1)
|
||||
|
||||
|
||||
class SecondaryDiffusionImageNet(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
c = 64 # The base channel count
|
||||
|
||||
self.timestep_embed = FourierFeatures(1, 16)
|
||||
|
||||
self.net = nn.Sequential(
|
||||
ConvBlock(3 + 16, c),
|
||||
ConvBlock(c, c),
|
||||
SkipBlock([
|
||||
nn.AvgPool2d(2),
|
||||
ConvBlock(c, c * 2),
|
||||
ConvBlock(c * 2, c * 2),
|
||||
SkipBlock([
|
||||
nn.AvgPool2d(2),
|
||||
ConvBlock(c * 2, c * 4),
|
||||
ConvBlock(c * 4, c * 4),
|
||||
SkipBlock([
|
||||
nn.AvgPool2d(2),
|
||||
ConvBlock(c * 4, c * 8),
|
||||
ConvBlock(c * 8, c * 4),
|
||||
nn.Upsample(scale_factor=2, mode='bilinear',
|
||||
align_corners=False),
|
||||
]),
|
||||
ConvBlock(c * 8, c * 4),
|
||||
ConvBlock(c * 4, c * 2),
|
||||
nn.Upsample(scale_factor=2, mode='bilinear',
|
||||
align_corners=False),
|
||||
]),
|
||||
ConvBlock(c * 4, c * 2),
|
||||
ConvBlock(c * 2, c),
|
||||
nn.Upsample(scale_factor=2, mode='bilinear',
|
||||
align_corners=False),
|
||||
]),
|
||||
ConvBlock(c * 2, c),
|
||||
nn.Conv2d(c, 3, 3, padding=1),
|
||||
)
|
||||
|
||||
def forward(self, input, t):
|
||||
timestep_embed = expand_to_planes(
|
||||
self.timestep_embed(t[:, None]), input.shape)
|
||||
v = self.net(torch.cat([input, timestep_embed], dim=1))
|
||||
alphas, sigmas = map(
|
||||
partial(append_dims, n=v.ndim), t_to_alpha_sigma(t))
|
||||
pred = input * alphas - v * sigmas
|
||||
eps = input * sigmas + v * alphas
|
||||
return DiffusionOutput(v, pred, eps)
|
||||
|
||||
|
||||
class SecondaryDiffusionImageNet2(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
c = 64 # The base channel count
|
||||
cs = [c, c * 2, c * 2, c * 4, c * 4, c * 8]
|
||||
|
||||
self.timestep_embed = FourierFeatures(1, 16)
|
||||
self.down = nn.AvgPool2d(2)
|
||||
self.up = nn.Upsample(
|
||||
scale_factor=2, mode='bilinear', align_corners=False)
|
||||
|
||||
self.net = nn.Sequential(
|
||||
ConvBlock(3 + 16, cs[0]),
|
||||
ConvBlock(cs[0], cs[0]),
|
||||
SkipBlock([
|
||||
self.down,
|
||||
ConvBlock(cs[0], cs[1]),
|
||||
ConvBlock(cs[1], cs[1]),
|
||||
SkipBlock([
|
||||
self.down,
|
||||
ConvBlock(cs[1], cs[2]),
|
||||
ConvBlock(cs[2], cs[2]),
|
||||
SkipBlock([
|
||||
self.down,
|
||||
ConvBlock(cs[2], cs[3]),
|
||||
ConvBlock(cs[3], cs[3]),
|
||||
SkipBlock([
|
||||
self.down,
|
||||
ConvBlock(cs[3], cs[4]),
|
||||
ConvBlock(cs[4], cs[4]),
|
||||
SkipBlock([
|
||||
self.down,
|
||||
ConvBlock(cs[4], cs[5]),
|
||||
ConvBlock(cs[5], cs[5]),
|
||||
ConvBlock(cs[5], cs[5]),
|
||||
ConvBlock(cs[5], cs[4]),
|
||||
self.up,
|
||||
]),
|
||||
ConvBlock(cs[4] * 2, cs[4]),
|
||||
ConvBlock(cs[4], cs[3]),
|
||||
self.up,
|
||||
]),
|
||||
ConvBlock(cs[3] * 2, cs[3]),
|
||||
ConvBlock(cs[3], cs[2]),
|
||||
self.up,
|
||||
]),
|
||||
ConvBlock(cs[2] * 2, cs[2]),
|
||||
ConvBlock(cs[2], cs[1]),
|
||||
self.up,
|
||||
]),
|
||||
ConvBlock(cs[1] * 2, cs[1]),
|
||||
ConvBlock(cs[1], cs[0]),
|
||||
self.up,
|
||||
]),
|
||||
ConvBlock(cs[0] * 2, cs[0]),
|
||||
nn.Conv2d(cs[0], 3, 3, padding=1),
|
||||
)
|
||||
|
||||
def forward(self, input, t):
|
||||
timestep_embed = expand_to_planes(
|
||||
self.timestep_embed(t[:, None]), input.shape)
|
||||
v = self.net(torch.cat([input, timestep_embed], dim=1))
|
||||
alphas, sigmas = map(
|
||||
partial(append_dims, n=v.ndim), t_to_alpha_sigma(t))
|
||||
pred = input * alphas - v * sigmas
|
||||
eps = input * sigmas + v * alphas
|
||||
return DiffusionOutput(v, pred, eps)
|
||||
+611
@@ -0,0 +1,611 @@
|
||||
from dataclasses import dataclass
|
||||
import pathlib
|
||||
import os
|
||||
import pathlib
|
||||
import os
|
||||
from dataclasses import dataclass
|
||||
import cv2
|
||||
import pandas as pd
|
||||
import gc
|
||||
import math
|
||||
import lpips
|
||||
from PIL import Image, ImageOps
|
||||
import requests
|
||||
from glob import glob
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
import torchvision
|
||||
import torchvision.transforms as T
|
||||
import torchvision.transforms.functional as TF
|
||||
from tqdm import tqdm
|
||||
from resize_right import resize
|
||||
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
|
||||
import numpy as np
|
||||
from numpy import asarray
|
||||
import subprocess
|
||||
|
||||
from .video_input import setup_raft
|
||||
from .video_input import setup_video_input_mode
|
||||
from .video_input import generate_optical_flow
|
||||
from .model_settings import ModelSettings
|
||||
|
||||
|
||||
@dataclass
|
||||
class DiscoDiffusionSettings:
|
||||
def __init__(self):
|
||||
self.root_path = os.getcwd()
|
||||
self.initDirPath = f'{self.root_path}/init_images'
|
||||
os.makedirs(self.initDirPath, exist_ok=True)
|
||||
self.outDirPath = f'{self.root_path}/images_out'
|
||||
os.makedirs(self.outDirPath, exist_ok=True)
|
||||
|
||||
self.useCPU = False
|
||||
|
||||
# %%
|
||||
# !! {"metadata":{
|
||||
# !! "id": "BasicSettings"
|
||||
# !! }}
|
||||
# @markdown ####**Basic Settings:**
|
||||
self.batch_name = 'TimeToDisco' # @param{type: 'string'}
|
||||
# @param [25,50,100,150,250,500,1000]{type: 'raw', allow-input: true}
|
||||
self.steps = 250
|
||||
self.width_height_for_512x512_models = [
|
||||
1280, 768] # @param{type: 'raw'}
|
||||
self.clip_guidance_scale = 5000 # @param{type: 'number'}
|
||||
self.tv_scale = 0 # @param{type: 'number'}
|
||||
self.range_scale = 150 # @param{type: 'number'}
|
||||
self.sat_scale = 0 # @param{type: 'number'}
|
||||
self.cutn_batches = 4 # @param{type: 'number'}
|
||||
self.skip_augs = False # @param{type: 'boolean'}
|
||||
|
||||
# @markdown ####**Image dimensions to be used for 256x256 models (e.g. pixelart models):**
|
||||
self.width_height_for_256x256_models = [
|
||||
512, 448] # @param{type: 'raw'}
|
||||
|
||||
# @markdown ####**Video Init Basic Settings:**
|
||||
# @param [25,50,100,150,250,500,1000]{type: 'raw', allow-input: true}
|
||||
self.video_init_steps = 100
|
||||
self.video_init_clip_guidance_scale = 1000 # @param{type: 'number'}
|
||||
self.video_init_tv_scale = 0.1 # @param{type: 'number'}
|
||||
self.video_init_range_scale = 150 # @param{type: 'number'}
|
||||
self.video_init_sat_scale = 300 # @param{type: 'number'}
|
||||
self.video_init_cutn_batches = 4 # @param{type: 'number'}
|
||||
self.video_init_skip_steps = 50 # @param{type: 'integer'}
|
||||
|
||||
# @markdown ---
|
||||
|
||||
# @markdown ####**Init Image Settings:**
|
||||
self.init_image = None # @param{type: 'string'}
|
||||
self.init_scale = 1000 # @param{type: 'integer'}
|
||||
self.skip_steps = 10 # @param{type: 'integer'}
|
||||
# @markdown *Make sure you set skip_steps to ~50% of your steps if you want to use an init image.*
|
||||
|
||||
# Make folder for batch
|
||||
self.batchFolder = f'{self.outDirPath}/{self.batch_name}'
|
||||
os.makedirs(self.batchFolder, exist_ok=True)
|
||||
|
||||
# @markdown ####**Animation Mode:**
|
||||
# @param ['None', '2D', '3D', 'Video Input'] {type:'string'}
|
||||
self.animation_mode = 'None'
|
||||
# @markdown *For animation, you probably want to turn `cutn_batches` to 1 to make it quicker.*
|
||||
|
||||
self.video_init_path = "init.mp4" # @param {type: 'string'}
|
||||
self.extract_nth_frame = 2 # @param {type: 'number'}
|
||||
# @param {type: 'boolean'}
|
||||
self.persistent_frame_output_in_batch_folder = True
|
||||
self.video_init_seed_continuity = False # @param {type: 'boolean'}
|
||||
# @markdown #####**Video Optical Flow Settings:**
|
||||
self.video_init_flow_warp = True # @param {type: 'boolean'}
|
||||
# Call optical flow from video frames and warp prev frame with flow
|
||||
# @param {type: 'number'} #0 - take next frame, 1 - take prev warped frame
|
||||
self.video_init_flow_blend = 0.999
|
||||
self.video_init_check_consistency = False # Insert param here when ready
|
||||
# @param ['None', 'linear', 'optical flow']
|
||||
self.video_init_blend_mode = "optical flow"
|
||||
|
||||
# @markdown ---
|
||||
|
||||
# @markdown ####**2D Animation Settings:**
|
||||
# @markdown `zoom` is a multiplier of dimensions, 1 is no zoom.
|
||||
# @markdown All rotations are provided in degrees.
|
||||
|
||||
self.key_frames = True # @param {type:"boolean"}
|
||||
self.max_frames = 10000 # @param {type:"number"}
|
||||
|
||||
# Do not change, currently will not look good. param ['Linear','Quadratic','Cubic']{type:"string"}
|
||||
self.interp_spline = 'Linear'
|
||||
self.angle = "0:(0)" # @param {type:"string"}
|
||||
self.zoom = "0: (1), 10: (1.05)" # @param {type:"string"}
|
||||
self.translation_x = "0: (0)" # @param {type:"string"}
|
||||
self.translation_y = "0: (0)" # @param {type:"string"}
|
||||
self.translation_z = "0: (10.0)" # @param {type:"string"}
|
||||
self.rotation_3d_x = "0: (0)" # @param {type:"string"}
|
||||
self.rotation_3d_y = "0: (0)" # @param {type:"string"}
|
||||
self.rotation_3d_z = "0: (0)" # @param {type:"string"}
|
||||
self.midas_depth_model = "dpt_large" # @param {type:"string"}
|
||||
self.midas_weight = 0.3 # @param {type:"number"}
|
||||
self.near_plane = 200 # @param {type:"number"}
|
||||
self.far_plane = 10000 # @param {type:"number"}
|
||||
self.fov = 40 # @param {type:"number"}
|
||||
self.padding_mode = 'border' # @param {type:"string"}
|
||||
self.sampling_mode = 'bicubic' # @param {type:"string"}
|
||||
|
||||
# ======= TURBO MODE
|
||||
# @markdown ---
|
||||
# @markdown ####**Turbo Mode (3D anim only):**
|
||||
# @markdown (Starts after frame 10,) skips diffusion steps and just uses depth map to warp images for skipped frames.
|
||||
# @markdown Speeds up rendering by 2x-4x, and may improve image coherence between frames.
|
||||
# @markdown For different settings tuned for Turbo Mode, refer to the original Disco-Turbo Github: https://github.com/zippy731/disco-diffusion-turbo
|
||||
|
||||
self.turbo_mode = False # @param {type:"boolean"}
|
||||
self.turbo_steps = "3" # @param ["2","3","4","5","6"] {type:"string"}
|
||||
self.turbo_preroll = 10 # frames
|
||||
|
||||
# insist turbo be used only w 3d anim.
|
||||
if self.turbo_mode and self.animation_mode != '3D':
|
||||
print('=====')
|
||||
print('Turbo mode only available with 3D animations. Disabling Turbo.')
|
||||
print('=====')
|
||||
self.turbo_mode = False
|
||||
|
||||
# @markdown ---
|
||||
|
||||
# @markdown ####**Coherency Settings:**
|
||||
# @markdown `frame_scale` tries to guide the new frame to looking like the old one. A good default is 1500.
|
||||
self.frames_scale = 1500 # @param{type: 'integer'}
|
||||
# @markdown `frame_skip_steps` will blur the previous frame - higher values will flicker less but struggle to add enough new detail to zoom into.
|
||||
# @param ['40%', '50%', '60%', '70%', '80%'] {type: 'string'}
|
||||
self.frames_skip_steps = '60%'
|
||||
|
||||
# @markdown ####**Video Init Coherency Settings:**
|
||||
# @markdown `frame_scale` tries to guide the new frame to looking like the old one. A good default is 1500.
|
||||
self.video_init_frames_scale = 15000 # @param{type: 'integer'}
|
||||
# @markdown `frame_skip_steps` will blur the previous frame - higher values will flicker less but struggle to add enough new detail to zoom into.
|
||||
# @param ['40%', '50%', '60%', '70%', '80%'] {type: 'string'}
|
||||
self.video_init_frames_skip_steps = '70%'
|
||||
|
||||
# ======= VR MODE
|
||||
# @markdown ---
|
||||
# @markdown ####**VR Mode (3D anim only):**
|
||||
# @markdown Enables stereo rendering of left/right eye views (supporting Turbo) which use a different (fish-eye) camera projection matrix.
|
||||
# @markdown Note the images you're prompting will work better if they have some inherent wide-angle aspect
|
||||
# @markdown The generated images will need to be combined into left/right videos. These can then be stitched into the VR180 format.
|
||||
# @markdown Google made the VR180 Creator tool but subsequently stopped supporting it. It's available for download in a few places including https://www.patrickgrunwald.de/vr180-creator-download
|
||||
# @markdown The tool is not only good for stitching (videos and photos) but also for adding the correct metadata into existing videos, which is needed for services like YouTube to identify the format correctly.
|
||||
# @markdown Watching YouTube VR videos isn't necessarily the easiest depending on your headset. For instance Oculus have a dedicated media studio and store which makes the files easier to access on a Quest https://creator.oculus.com/manage/mediastudio/
|
||||
# @markdown
|
||||
# @markdown The command to get ffmpeg to concat your frames for each eye is in the form: `ffmpeg -framerate 15 -i frame_%4d_l.png l.mp4` (repeat for r)
|
||||
|
||||
self.vr_mode = False # @param {type:"boolean"}
|
||||
# @markdown `vr_eye_angle` is the y-axis rotation of the eyes towards the center
|
||||
self.vr_eye_angle = 0.5 # @param{type:"number"}
|
||||
# @markdown interpupillary distance (between the eyes)
|
||||
self.vr_ipd = 5.0 # @param{type:"number"}
|
||||
|
||||
# %%
|
||||
# !! {"metadata":{
|
||||
# !! "id": "ExtraSetTop"
|
||||
# !! }}
|
||||
# """
|
||||
# ### Extra Settings
|
||||
# Partial Saves, Advanced Settings, Cutn Scheduling
|
||||
# """
|
||||
|
||||
# %%
|
||||
# !! {"metadata":{
|
||||
# !! "id": "ExtraSettings"
|
||||
# !! }}
|
||||
# @markdown ####**Saving:**
|
||||
|
||||
self.intermediate_saves = 0 # @param{type: 'raw'}
|
||||
self.intermediates_in_subfolder = True # @param{type: 'boolean'}
|
||||
# @markdown Intermediate steps will save a copy at your specified intervals. You can either format it as a single integer or a list of specific steps
|
||||
|
||||
# @markdown A value of `2` will save a copy at 33% and 66%. 0 will save none.
|
||||
|
||||
# @markdown A value of `[5, 9, 34, 45]` will save at steps 5, 9, 34, and 45. (Make sure to include the brackets)
|
||||
|
||||
# @markdown ---
|
||||
|
||||
# @markdown ####**Advanced Settings:**
|
||||
# @markdown *There are a few extra advanced settings available if you double click this cell.*
|
||||
|
||||
# @markdown *Perlin init will replace your init, so uncheck if using one.*
|
||||
|
||||
self.perlin_init = False # @param{type: 'boolean'}
|
||||
self.perlin_mode = 'mixed' # @param ['mixed', 'color', 'gray']
|
||||
self.set_seed = 'random_seed' # @param{type: 'string'}
|
||||
self.eta = 0.8 # @param{type: 'number'}
|
||||
self.clamp_grad = True # @param{type: 'boolean'}
|
||||
self.clamp_max = 0.05 # @param{type: 'number'}
|
||||
|
||||
# EXTRA ADVANCED SETTINGS:
|
||||
self.randomize_class = True
|
||||
self.clip_denoised = False
|
||||
self.fuzzy_prompt = False
|
||||
self.rand_mag = 0.05
|
||||
|
||||
# @markdown ---
|
||||
|
||||
# @markdown ####**Cutn Scheduling:**
|
||||
# @markdown Format: `[40]*400+[20]*600` = 40 cuts for the first 400 /1000 steps, then 20 for the last 600/1000
|
||||
|
||||
# @markdown cut_overview and cut_innercut are cumulative for total cutn on any given step. Overview cuts see the entire image and are good for early structure, innercuts are your standard cutn.
|
||||
|
||||
self.cut_overview = "[12]*400+[4]*600" # @param {type: 'string'}
|
||||
self.cut_innercut = "[4]*400+[12]*600" # @param {type: 'string'}
|
||||
self.cut_ic_pow = "[1]*1000" # @param {type: 'string'}
|
||||
self.cut_icgray_p = "[0.2]*400+[0]*600" # @param {type: 'string'}
|
||||
|
||||
# @markdown KaliYuga model settings. Refer to [cut_ic_pow](https://ezcharts.miraheze.org/wiki/Category:Cut_ic_pow) as a guide. Values between 1 and 100 all work.
|
||||
# @param {type: 'string'}
|
||||
self.pad_or_pulp_cut_overview = "[15]*100+[15]*100+[12]*100+[12]*100+[6]*100+[4]*100+[2]*200+[0]*200"
|
||||
# @param {type: 'string'}
|
||||
self.pad_or_pulp_cut_innercut = "[1]*100+[1]*100+[4]*100+[4]*100+[8]*100+[8]*100+[10]*200+[10]*200"
|
||||
# @param {type: 'string'}
|
||||
self.pad_or_pulp_cut_ic_pow = "[12]*300+[12]*100+[12]*50+[12]*50+[10]*100+[10]*100+[10]*300"
|
||||
# @param {type: 'string'}
|
||||
self.pad_or_pulp_cut_icgray_p = "[0.87]*100+[0.78]*50+[0.73]*50+[0.64]*60+[0.56]*40+[0.50]*50+[0.33]*100+[0.19]*150+[0]*400"
|
||||
|
||||
# @param {type: 'string'}
|
||||
self.watercolor_cut_overview = "[14]*200+[12]*200+[4]*400+[0]*200"
|
||||
# @param {type: 'string'}
|
||||
self.watercolor_cut_innercut = "[2]*200+[4]*200+[12]*400+[12]*200"
|
||||
# @param {type: 'string'}
|
||||
self.watercolor_cut_ic_pow = "[12]*300+[12]*100+[12]*50+[12]*50+[10]*100+[10]*100+[10]*300"
|
||||
# @param {type: 'string'}
|
||||
self.watercolor_cut_icgray_p = "[0.7]*100+[0.6]*100+[0.45]*100+[0.3]*100+[0]*600"
|
||||
|
||||
# @markdown ---
|
||||
|
||||
# @markdown ####**Transformation Settings:**
|
||||
self.use_vertical_symmetry = False # @param {type:"boolean"}
|
||||
self.use_horizontal_symmetry = False # @param {type:"boolean"}
|
||||
self.transformation_percent = [0.09] # @param
|
||||
|
||||
# %%
|
||||
# !! {"metadata":{
|
||||
# !! "id": "PromptsTop"
|
||||
# !! }}
|
||||
"""
|
||||
### Prompts
|
||||
`animation_mode: None` will only use the first set. `animation_mode: 2D / Video` will run through them per the set frames and hold on the last one.
|
||||
"""
|
||||
|
||||
# %%
|
||||
# !! {"metadata":{
|
||||
# !! "id": "Prompts"
|
||||
# !! }}
|
||||
# Note: If using a pixelart diffusion model, try adding "#pixelart" to the end of the prompt for a stronger effect. It'll tend to work a lot better!
|
||||
self.text_prompts = {
|
||||
0: ["A beautiful painting of a singular lighthouse, shining its light across a tumultuous sea of blood by greg rutkowski and thomas kinkade, Trending on artstation.", "yellow color scheme"]
|
||||
# 100: ["This set of prompts start at frame 100", "This prompt has weight five:5"],
|
||||
}
|
||||
|
||||
self.image_prompts = {
|
||||
# 0:['ImagePromptsWorkButArentVeryGood.png:2',],
|
||||
}
|
||||
|
||||
def setup(self, MS: ModelSettings):
|
||||
self.MS = MS
|
||||
|
||||
if type(self.intermediate_saves) is not list:
|
||||
if self.intermediate_saves:
|
||||
self.steps_per_checkpoint = math.floor(
|
||||
(self.steps - self.skip_steps - 1) // (self.intermediate_saves+1))
|
||||
self.steps_per_checkpoint = self.steps_per_checkpoint if self.steps_per_checkpoint > 0 else 1
|
||||
print(f'Will save every {self.steps_per_checkpoint} steps')
|
||||
else:
|
||||
self.steps_per_checkpoint = self.steps+10
|
||||
else:
|
||||
self.steps_per_checkpoint = None
|
||||
|
||||
if self.intermediate_saves and self.intermediates_in_subfolder is True:
|
||||
self.partialFolder = f'{self.batchFolder}/partials'
|
||||
os.makedirs(self.partialFolder, exist_ok=True)
|
||||
|
||||
self.width_height = self.width_height_for_256x256_models if MS.diffusion_model in MS.diffusion_models_256x256_list else self.width_height_for_512x512_models
|
||||
|
||||
# Get corrected sizes
|
||||
self.side_x = (self.width_height[0]//64)*64
|
||||
self.side_y = (self.width_height[1]//64)*64
|
||||
if self.side_x != self.width_height[0] or self.side_y != self.width_height[1]:
|
||||
print(
|
||||
f'Changing output size to {self.side_x}x{self.side_y}. Dimensions must by multiples of 64.')
|
||||
|
||||
if (MS.diffusion_model in MS.kaliyuga_pixel_art_model_names) or (MS.diffusion_model in MS.kaliyuga_pulpscifi_model_names):
|
||||
self.cut_overview = self.pad_or_pulp_cut_overview
|
||||
self.cut_innercut = self.pad_or_pulp_cut_innercut
|
||||
self.cut_ic_pow = self.pad_or_pulp_cut_ic_pow
|
||||
self.cut_icgray_p = self.pad_or_pulp_cut_icgray_p
|
||||
elif MS.diffusion_model in MS.kaliyuga_watercolor_model_names:
|
||||
self.cut_overview = self.watercolor_cut_overview
|
||||
self.cut_innercut = self.watercolor_cut_innercut
|
||||
self.cut_ic_pow = self.watercolor_cut_ic_pow
|
||||
self.cut_icgray_p = self.watercolor_cut_icgray_p
|
||||
|
||||
# insist VR be used only w 3d anim.
|
||||
if self.vr_mode and self.animation_mode != '3D':
|
||||
print('=====')
|
||||
print('VR mode only available with 3D animations. Disabling VR.')
|
||||
print('=====')
|
||||
self.vr_mode = False
|
||||
|
||||
if self.key_frames:
|
||||
try:
|
||||
self.angle_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.angle))
|
||||
except RuntimeError as e:
|
||||
print(
|
||||
"WARNING: You have selected to use key frames, but you have not "
|
||||
"formatted `angle` correctly for key frames.\n"
|
||||
"Attempting to interpret `angle` as "
|
||||
f'"0: ({self.angle})"\n'
|
||||
"Please read the instructions to find out how to use key frames "
|
||||
"correctly.\n"
|
||||
)
|
||||
self.angle = f"0: ({self.angle})"
|
||||
self.angle_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.angle))
|
||||
|
||||
try:
|
||||
self.zoom_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.zoom))
|
||||
except RuntimeError as e:
|
||||
print(
|
||||
"WARNING: You have selected to use key frames, but you have not "
|
||||
"formatted `zoom` correctly for key frames.\n"
|
||||
"Attempting to interpret `zoom` as "
|
||||
f'"0: ({self.zoom})"\n'
|
||||
"Please read the instructions to find out how to use key frames "
|
||||
"correctly.\n"
|
||||
)
|
||||
self.zoom = f"0: ({self.zoom})"
|
||||
self.zoom_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.zoom))
|
||||
|
||||
try:
|
||||
self.translation_x_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.translation_x))
|
||||
except RuntimeError as e:
|
||||
print(
|
||||
"WARNING: You have selected to use key frames, but you have not "
|
||||
"formatted `translation_x` correctly for key frames.\n"
|
||||
"Attempting to interpret `translation_x` as "
|
||||
f'"0: ({self.translation_x})"\n'
|
||||
"Please read the instructions to find out how to use key frames "
|
||||
"correctly.\n"
|
||||
)
|
||||
self.translation_x = f"0: ({self.translation_x})"
|
||||
self.translation_x_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.translation_x))
|
||||
|
||||
try:
|
||||
self.translation_y_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.translation_y))
|
||||
except RuntimeError as e:
|
||||
print(
|
||||
"WARNING: You have selected to use key frames, but you have not "
|
||||
"formatted `translation_y` correctly for key frames.\n"
|
||||
"Attempting to interpret `translation_y` as "
|
||||
f'"0: ({self.translation_y})"\n'
|
||||
"Please read the instructions to find out how to use key frames "
|
||||
"correctly.\n"
|
||||
)
|
||||
self.translation_y = f"0: ({self.translation_y})"
|
||||
self.translation_y_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.translation_y))
|
||||
|
||||
try:
|
||||
get_inbetweens(self.max_frames, self.interp_spline,
|
||||
parse_key_frames(self.translation_z))
|
||||
except RuntimeError as e:
|
||||
print(
|
||||
"WARNING: You have selected to use key frames, but you have not "
|
||||
"formatted `translation_z` correctly for key frames.\n"
|
||||
"Attempting to interpret `translation_z` as "
|
||||
f'"0: ({self.translation_z})"\n'
|
||||
"Please read the instructions to find out how to use key frames "
|
||||
"correctly.\n"
|
||||
)
|
||||
self.translation_z = f"0: ({self.translation_z})"
|
||||
self.translation_z_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.translation_z))
|
||||
|
||||
try:
|
||||
self.rotation_3d_x_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.rotation_3d_x))
|
||||
except RuntimeError as e:
|
||||
print(
|
||||
"WARNING: You have selected to use key frames, but you have not "
|
||||
"formatted `rotation_3d_x` correctly for key frames.\n"
|
||||
"Attempting to interpret `rotation_3d_x` as "
|
||||
f'"0: ({self.rotation_3d_x})"\n'
|
||||
"Please read the instructions to find out how to use key frames "
|
||||
"correctly.\n"
|
||||
)
|
||||
self.rotation_3d_x = f"0: ({self.rotation_3d_x})"
|
||||
self.rotation_3d_x_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.rotation_3d_x))
|
||||
|
||||
try:
|
||||
self.rotation_3d_y_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.rotation_3d_y))
|
||||
except RuntimeError as e:
|
||||
print(
|
||||
"WARNING: You have selected to use key frames, but you have not "
|
||||
"formatted `rotation_3d_y` correctly for key frames.\n"
|
||||
"Attempting to interpret `rotation_3d_y` as "
|
||||
f'"0: ({self.rotation_3d_y})"\n'
|
||||
"Please read the instructions to find out how to use key frames "
|
||||
"correctly.\n"
|
||||
)
|
||||
self.rotation_3d_y = f"0: ({self.rotation_3d_y})"
|
||||
self.rotation_3d_y_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.rotation_3d_y))
|
||||
|
||||
try:
|
||||
self.rotation_3d_z_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.rotation_3d_z))
|
||||
except RuntimeError as e:
|
||||
print(
|
||||
"WARNING: You have selected to use key frames, but you have not "
|
||||
"formatted `rotation_3d_z` correctly for key frames.\n"
|
||||
"Attempting to interpret `rotation_3d_z` as "
|
||||
f'"0: ({self.rotation_3d_z})"\n'
|
||||
"Please read the instructions to find out how to use key frames "
|
||||
"correctly.\n"
|
||||
)
|
||||
self.rotation_3d_z = f"0: ({self.rotation_3d_z})"
|
||||
self.rotation_3d_z_series = get_inbetweens(
|
||||
self.max_frames, self.interp_spline, parse_key_frames(self.rotation_3d_z))
|
||||
|
||||
else:
|
||||
self.angle = float(self.angle)
|
||||
self.zoom = float(self.zoom)
|
||||
self.translation_x = float(self.translation_x)
|
||||
self.translation_y = float(self.translation_y)
|
||||
self.translation_z = float(self.translation_z)
|
||||
self.rotation_3d_x = float(self.rotation_3d_x)
|
||||
self.rotation_3d_y = float(self.rotation_3d_y)
|
||||
self.rotation_3d_z = float(self.rotation_3d_z)
|
||||
|
||||
if self.animation_mode == 'Video Input':
|
||||
self.max_frames = len(glob(f'{self.videoFramesFolder}/*.jpg'))
|
||||
|
||||
# Call optical flow from video frames and warp prev frame with flow
|
||||
if self.persistent_frame_output_in_batch_folder: # suggested by Chris the Wizard#8082 at discord
|
||||
self.videoFramesFolder = f'{self.batchFolder}/videoFrames'
|
||||
else:
|
||||
self.videoFramesFolder = f'/content/videoFrames'
|
||||
os.makedirs(self.videoFramesFolder, exist_ok=True)
|
||||
print(
|
||||
f"Exporting Video Frames (1 every {self.extract_nth_frame})...")
|
||||
try:
|
||||
for f in pathlib.Path(f'{self.videoFramesFolder}').glob('*.jpg'):
|
||||
f.unlink()
|
||||
except Exception as err:
|
||||
print(err)
|
||||
vf = f'select=not(mod(n\,{self.extract_nth_frame}))'
|
||||
if os.path.exists(self.video_init_path):
|
||||
subprocess.run(['ffmpeg', '-i', f'{self.video_init_path}', '-vf', f'{vf}', '-vsync', 'vfr', '-q:v', '2', '-loglevel',
|
||||
'error', '-stats', f'{self.videoFramesFolder}/%04d.jpg'], stdout=subprocess.PIPE).stdout.decode('utf-8')
|
||||
else:
|
||||
print(
|
||||
f'\nWARNING!\n\nVideo not found: {self.video_init_path}.\nPlease check your video path.\n')
|
||||
#!ffmpeg -i {video_init_path} -vf {vf} -vsync vfr -q:v 2 -loglevel error -stats {videoFramesFolder}/%04d.jpg
|
||||
|
||||
setup_raft()
|
||||
setup_video_input_mode(self)
|
||||
generate_optical_flow(self)
|
||||
|
||||
|
||||
def parse_key_frames(string, prompt_parser=None):
|
||||
"""Given a string representing frame numbers paired with parameter values at that frame,
|
||||
return a dictionary with the frame numbers as keys and the parameter values as the values.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
string: string
|
||||
Frame numbers paired with parameter values at that frame number, in the format
|
||||
'framenumber1: (parametervalues1), framenumber2: (parametervalues2), ...'
|
||||
prompt_parser: function or None, optional
|
||||
If provided, prompt_parser will be applied to each string of parameter values.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
Frame numbers as keys, parameter values at that frame number as values
|
||||
|
||||
Raises
|
||||
------
|
||||
RuntimeError
|
||||
If the input string does not match the expected format.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> parse_key_frames("10:(Apple: 1| Orange: 0), 20: (Apple: 0| Orange: 1| Peach: 1)")
|
||||
{10: 'Apple: 1| Orange: 0', 20: 'Apple: 0| Orange: 1| Peach: 1'}
|
||||
|
||||
>>> parse_key_frames("10:(Apple: 1| Orange: 0), 20: (Apple: 0| Orange: 1| Peach: 1)", prompt_parser=lambda x: x.lower()))
|
||||
{10: 'apple: 1| orange: 0', 20: 'apple: 0| orange: 1| peach: 1'}
|
||||
"""
|
||||
import re
|
||||
pattern = r'((?P<frame>[0-9]+):[\s]*[\(](?P<param>[\S\s]*?)[\)])'
|
||||
frames = dict()
|
||||
for match_object in re.finditer(pattern, string):
|
||||
frame = int(match_object.groupdict()['frame'])
|
||||
param = match_object.groupdict()['param']
|
||||
if prompt_parser:
|
||||
frames[frame] = prompt_parser(param)
|
||||
else:
|
||||
frames[frame] = param
|
||||
|
||||
if frames == {} and len(string) != 0:
|
||||
raise RuntimeError('Key Frame string not correctly formatted')
|
||||
return frames
|
||||
|
||||
|
||||
def get_inbetweens(max_frames, interp_method, key_frames, integer=False):
|
||||
"""Given a dict with frame numbers as keys and a parameter value as values,
|
||||
return a pandas Series containing the value of the parameter at every frame from 0 to max_frames.
|
||||
Any values not provided in the input dict are calculated by linear interpolation between
|
||||
the values of the previous and next provided frames. If there is no previous provided frame, then
|
||||
the value is equal to the value of the next provided frame, or if there is no next provided frame,
|
||||
then the value is equal to the value of the previous provided frame. If no frames are provided,
|
||||
all frame values are NaN.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
key_frames: dict
|
||||
A dict with integer frame numbers as keys and numerical values of a particular parameter as values.
|
||||
integer: Bool, optional
|
||||
If True, the values of the output series are converted to integers.
|
||||
Otherwise, the values are floats.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.Series
|
||||
A Series with length max_frames representing the parameter values for each frame.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> max_frames = 5
|
||||
>>> get_inbetweens({1: 5, 3: 6})
|
||||
0 5.0
|
||||
1 5.0
|
||||
2 5.5
|
||||
3 6.0
|
||||
4 6.0
|
||||
dtype: float64
|
||||
|
||||
>>> get_inbetweens({1: 5, 3: 6}, integer=True)
|
||||
0 5
|
||||
1 5
|
||||
2 5
|
||||
3 6
|
||||
4 6
|
||||
dtype: int64
|
||||
"""
|
||||
key_frame_series = pd.Series([np.nan for a in range(max_frames)])
|
||||
|
||||
for i, value in key_frames.items():
|
||||
key_frame_series[i] = value
|
||||
key_frame_series = key_frame_series.astype(float)
|
||||
|
||||
if interp_method == 'Cubic' and len(key_frames.items()) <= 3:
|
||||
interp_method = 'Quadratic'
|
||||
|
||||
if interp_method == 'Quadratic' and len(key_frames.items()) <= 2:
|
||||
interp_method = 'Linear'
|
||||
|
||||
key_frame_series[0] = key_frame_series[key_frame_series.first_valid_index()]
|
||||
key_frame_series[max_frames -
|
||||
1] = key_frame_series[key_frame_series.last_valid_index()]
|
||||
# key_frame_series = key_frame_series.interpolate(method=intrp_method,order=1, limit_direction='both')
|
||||
key_frame_series = key_frame_series.interpolate(
|
||||
method=interp_method.lower(), limit_direction='both')
|
||||
if integer:
|
||||
return key_frame_series.astype(int)
|
||||
return key_frame_series
|
||||
@@ -0,0 +1,208 @@
|
||||
import shutil
|
||||
import sys
|
||||
import cv2
|
||||
import pandas as pd
|
||||
import gc
|
||||
import lpips
|
||||
from PIL import Image, ImageOps
|
||||
import requests
|
||||
from glob import glob
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
import torchvision
|
||||
import torchvision.transforms as T
|
||||
import torchvision.transforms.functional as TF
|
||||
from tqdm import tqdm
|
||||
from resize_right import resize
|
||||
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
|
||||
import numpy as np
|
||||
from numpy import asarray
|
||||
import warnings
|
||||
import PIL
|
||||
from tqdm import trange
|
||||
import os
|
||||
|
||||
from .settings import DiscoDiffusionSettings
|
||||
|
||||
skip_video_for_run_all = False # @param {type: 'boolean'}
|
||||
|
||||
# @title ### **Create video**
|
||||
# @markdown Video file will save in the same folder as your images.
|
||||
|
||||
|
||||
def create_video(batchNum, args: DiscoDiffusionSettings):
|
||||
if args.animation_mode == 'Video Input':
|
||||
frames = sorted(glob(args.in_path+'/*.*'))
|
||||
if len(frames) == 0:
|
||||
sys.exit(
|
||||
"ERROR: 0 frames found.\nPlease check your video input path and rerun the video settings cell.")
|
||||
flows = glob(args.flo_folder+'/*.*')
|
||||
if (len(flows) == 0) and args.video_init_flow_warp:
|
||||
sys.exit(
|
||||
"ERROR: 0 flow files found.\nPlease rerun the flow generation cell.")
|
||||
|
||||
blend = 0.5 # @param {type: 'number'}
|
||||
args.video_init_check_consistency = False # @param {type: 'boolean'}
|
||||
if skip_video_for_run_all == True:
|
||||
print(
|
||||
'Skipping video creation, uncheck skip_video_for_run_all if you want to run it')
|
||||
|
||||
else:
|
||||
# import subprocess in case this cell is run without the above cells
|
||||
import subprocess
|
||||
|
||||
latest_run = batchNum
|
||||
|
||||
folder = args.batch_name # @param
|
||||
run = latest_run # @param
|
||||
final_frame = 'final_frame'
|
||||
|
||||
# @param {type:"number"} This is the frame where the video will start
|
||||
init_frame = 1
|
||||
# @param {type:"number"} You can change i to the number of the last frame you want to generate. It will raise an error if that number of frames does not exist.
|
||||
last_frame = final_frame
|
||||
fps = 12 # @param {type:"number"}
|
||||
# view_video_in_cell = True #@param {type: 'boolean'}
|
||||
|
||||
frames = []
|
||||
# tqdm.write('Generating video...')
|
||||
|
||||
if last_frame == 'final_frame':
|
||||
last_frame = len(glob(args.batchFolder+f"/{folder}({run})_*.png"))
|
||||
print(f'Total frames: {last_frame}')
|
||||
|
||||
image_path = f"{args.outDirPath}/{folder}/{folder}({run})_%04d.png"
|
||||
filepath = f"{args.outDirPath}/{folder}/{folder}({run}).mp4"
|
||||
|
||||
if (args.video_init_blend_mode == 'optical flow') and (args.animation_mode == 'Video Input'):
|
||||
image_path = f"{args.outDirPath}/{folder}/flow/{folder}({run})_%04d.png"
|
||||
filepath = f"{args.outDirPath}/{folder}/{folder}({run})_flow.mp4"
|
||||
if last_frame == 'final_frame':
|
||||
last_frame = len(
|
||||
glob(args.batchFolder+f"/flow/{folder}({run})_*.png"))
|
||||
flo_out = args.batchFolder+f"/flow"
|
||||
args.createPath(flo_out)
|
||||
frames_in = sorted(
|
||||
glob(args.batchFolder+f"/{folder}({run})_*.png"))
|
||||
shutil.copy(frames_in[0], flo_out)
|
||||
for i in trange(init_frame, min(len(frames_in), last_frame)):
|
||||
frame1_path = frames_in[i-1]
|
||||
frame2_path = frames_in[i]
|
||||
|
||||
frame1 = PIL.Image.open(frame1_path)
|
||||
frame2 = PIL.Image.open(frame2_path)
|
||||
frame1_stem = f"{(int(frame1_path.split('/')[-1].split('_')[-1][:-4])+1):04}.jpg"
|
||||
flo_path = f"/{args.flo_folder}/{frame1_stem}.npy"
|
||||
weights_path = None
|
||||
if args.video_init_check_consistency:
|
||||
# TBD
|
||||
pass
|
||||
import video_input
|
||||
video_input.warp(frame1, frame2, flo_path, blend=blend, weights_path=weights_path).save(
|
||||
args.batchFolder+f"/flow/{folder}({run})_{i:04}.png")
|
||||
if args.video_init_blend_mode == 'linear':
|
||||
image_path = f"{args.outDirPath}/{folder}/blend/{folder}({run})_%04d.png"
|
||||
filepath = f"{args.outDirPath}/{folder}/{folder}({run})_blend.mp4"
|
||||
if last_frame == 'final_frame':
|
||||
last_frame = len(
|
||||
glob(args.batchFolder+f"/blend/{folder}({run})_*.png"))
|
||||
blend_out = args.batchFolder+f"/blend"
|
||||
os.makedirs(blend_out, exist_ok=True)
|
||||
frames_in = glob(args.batchFolder+f"/{folder}({run})_*.png")
|
||||
shutil.copy(frames_in[0], blend_out)
|
||||
for i in trange(1, len(frames_in)):
|
||||
frame1_path = frames_in[i-1]
|
||||
frame2_path = frames_in[i]
|
||||
|
||||
frame1 = PIL.Image.open(frame1_path)
|
||||
frame2 = PIL.Image.open(frame2_path)
|
||||
|
||||
frame = PIL.Image.fromarray((np.array(frame1)*(1-blend) + np.array(frame2)*(
|
||||
blend)).astype('uint8')).save(args.batchFolder+f"/blend/{folder}({run})_{i:04}.png")
|
||||
|
||||
cmd = [
|
||||
'ffmpeg',
|
||||
'-y',
|
||||
'-vcodec',
|
||||
'png',
|
||||
'-r',
|
||||
str(fps),
|
||||
'-start_number',
|
||||
str(init_frame),
|
||||
'-i',
|
||||
image_path,
|
||||
'-frames:v',
|
||||
str(last_frame+1),
|
||||
'-c:v',
|
||||
'libx264',
|
||||
'-vf',
|
||||
f'fps={fps}',
|
||||
'-pix_fmt',
|
||||
'yuv420p',
|
||||
'-crf',
|
||||
'17',
|
||||
'-preset',
|
||||
'veryslow',
|
||||
filepath
|
||||
]
|
||||
|
||||
process = subprocess.Popen(
|
||||
cmd, cwd=f'{args.batchFolder}', stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
||||
stdout, stderr = process.communicate()
|
||||
if process.returncode != 0:
|
||||
print(stderr)
|
||||
raise RuntimeError(stderr)
|
||||
else:
|
||||
print("The video is ready and saved to the images folder")
|
||||
|
||||
# if view_video_in_cell:
|
||||
# mp4 = open(filepath,'rb').read()
|
||||
# data_url = "data:video/mp4;base64," + b64encode(mp4).decode()
|
||||
# display.HTML(f'<video width=400 controls><source src="{data_url}" type="video/mp4"></video>')
|
||||
|
||||
# %%
|
||||
# !! {"main_metadata":{
|
||||
# !! "anaconda-cloud": {},
|
||||
# !! "accelerator": "GPU",
|
||||
# !! "colab": {
|
||||
# !! "collapsed_sections": [
|
||||
# !! "CreditsChTop",
|
||||
# !! "TutorialTop",
|
||||
# !! "CheckGPU",
|
||||
# !! "InstallDeps",
|
||||
# !! "DefMidasFns",
|
||||
# !! "DefFns",
|
||||
# !! "DefSecModel",
|
||||
# !! "DefSuperRes",
|
||||
# !! "AnimSetTop",
|
||||
# !! "ExtraSetTop",
|
||||
# !! "InstallRAFT",
|
||||
# !! "CustModel",
|
||||
# !! "FlowFns1",
|
||||
# !! "FlowFns2"
|
||||
# !! ],
|
||||
# !! "machine_shape": "hm",
|
||||
# !! "name": "Disco Diffusion v5.61 [Now with portrait_generator_v001]",
|
||||
# !! "private_outputs": true,
|
||||
# !! "provenance": [],
|
||||
# !! "include_colab_link": true
|
||||
# !! },
|
||||
# !! "kernelspec": {
|
||||
# !! "display_name": "Python 3",
|
||||
# !! "language": "python",
|
||||
# !! "name": "python3"
|
||||
# !! },
|
||||
# !! "language_info": {
|
||||
# !! "codemirror_mode": {
|
||||
# !! "name": "ipython",
|
||||
# !! "version": 3
|
||||
# !! },
|
||||
# !! "file_extension": ".py",
|
||||
# !! "mimetype": "text/x-python",
|
||||
# !! "name": "python",
|
||||
# !! "nbconvert_exporter": "python",
|
||||
# !! "pygments_lexer": "ipython3",
|
||||
# !! "version": "3.6.1"
|
||||
# !! }
|
||||
# !! }}
|
||||
+229
@@ -0,0 +1,229 @@
|
||||
import PIL
|
||||
import argparse
|
||||
from PIL import Image
|
||||
import pathlib
|
||||
import os
|
||||
import cv2
|
||||
import pandas as pd
|
||||
import gc
|
||||
import subprocess
|
||||
import lpips
|
||||
from PIL import Image, ImageOps
|
||||
import requests
|
||||
from glob import glob
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
import torchvision
|
||||
import torchvision.transforms as T
|
||||
import torchvision.transforms.functional as TF
|
||||
from tqdm import tqdm
|
||||
from resize_right import resize
|
||||
from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults
|
||||
import numpy as np
|
||||
from numpy import asarray
|
||||
from raft import RAFT
|
||||
from raftutils.utils import InputPadder
|
||||
from raftutils import flow_viz
|
||||
|
||||
|
||||
from folder_paths import models_dir
|
||||
|
||||
|
||||
# %%
|
||||
# !! {"metadata":{
|
||||
# !! "id": "InstallRAFT"
|
||||
# !! }}
|
||||
# @title Install RAFT for Video input animation mode only
|
||||
# @markdown Run once per session. Doesn't download again if model path exists.
|
||||
# @markdown Use force download to reload raft models if needed
|
||||
force_download = False # @param {type:'boolean'}
|
||||
|
||||
|
||||
def setup_raft():
|
||||
pass
|
||||
|
||||
# @title Define optical flow functions for Video input animation mode only
|
||||
def setup_video_input_mode(S):
|
||||
S.in_path = S.videoFramesFolder
|
||||
# f'{in_path}/out_flo_fwd'
|
||||
# f'{models_dir}/RAFT/core'
|
||||
|
||||
|
||||
args2 = argparse.Namespace()
|
||||
args2.small = False
|
||||
args2.mixed_precision = True
|
||||
|
||||
|
||||
TAG_CHAR = np.array([202021.25], np.float32)
|
||||
|
||||
|
||||
def writeFlow(filename, uv, v=None):
|
||||
"""
|
||||
https://github.com/NVIDIA/flownet2-pytorch/blob/master/utils/flow_utils.py
|
||||
Copyright 2017 NVIDIA CORPORATION
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
|
||||
Write optical flow to file.
|
||||
|
||||
If v is None, uv is assumed to contain both u and v channels,
|
||||
stacked in depth.
|
||||
Original code by Deqing Sun, adapted from Daniel Scharstein.
|
||||
"""
|
||||
nBands = 2
|
||||
|
||||
if v is None:
|
||||
assert (uv.ndim == 3)
|
||||
assert (uv.shape[2] == 2)
|
||||
u = uv[:, :, 0]
|
||||
v = uv[:, :, 1]
|
||||
else:
|
||||
u = uv
|
||||
|
||||
assert (u.shape == v.shape)
|
||||
height, width = u.shape
|
||||
f = open(filename, 'wb')
|
||||
# write the header
|
||||
f.write(TAG_CHAR)
|
||||
np.array(width).astype(np.int32).tofile(f)
|
||||
np.array(height).astype(np.int32).tofile(f)
|
||||
# arrange into matrix form
|
||||
tmp = np.zeros((height, width*nBands))
|
||||
tmp[:, np.arange(width)*2] = u
|
||||
tmp[:, np.arange(width)*2 + 1] = v
|
||||
tmp.astype(np.float32).tofile(f)
|
||||
f.close()
|
||||
|
||||
|
||||
def load_img(img, size):
|
||||
img = Image.open(img).convert('RGB').resize(size)
|
||||
return torch.from_numpy(np.array(img)).permute(2, 0, 1).float()[None, ...].cuda()
|
||||
|
||||
|
||||
def get_flow(frame1, frame2, model, iters=20):
|
||||
padder = InputPadder(frame1.shape)
|
||||
frame1, frame2 = padder.pad(frame1, frame2)
|
||||
_, flow12 = model(frame1, frame2, iters=iters, test_mode=True)
|
||||
flow12 = flow12[0].permute(1, 2, 0).detach().cpu().numpy()
|
||||
|
||||
return flow12
|
||||
|
||||
|
||||
def warp_flow(img, flow):
|
||||
h, w = flow.shape[:2]
|
||||
flow = flow.copy()
|
||||
flow[:, :, 0] += np.arange(w)
|
||||
flow[:, :, 1] += np.arange(h)[:, np.newaxis]
|
||||
res = cv2.remap(img, flow, None, cv2.INTER_LINEAR)
|
||||
return res
|
||||
|
||||
|
||||
def makeEven(_x):
|
||||
return _x if (_x % 2 == 0) else _x+1
|
||||
|
||||
|
||||
def fit(img, maxsize=512):
|
||||
maxdim = max(*img.size)
|
||||
if maxdim > maxsize:
|
||||
# if True:
|
||||
ratio = maxsize/maxdim
|
||||
x, y = img.size
|
||||
size = (makeEven(int(x*ratio)), makeEven(int(y*ratio)))
|
||||
img = img.resize(size)
|
||||
return img
|
||||
|
||||
|
||||
def warp(frame1, frame2, flo_path, blend=0.5, weights_path=None):
|
||||
flow21 = np.load(flo_path)
|
||||
frame1pil = np.array(frame1.convert('RGB').resize(
|
||||
(flow21.shape[1], flow21.shape[0])))
|
||||
frame1_warped21 = warp_flow(frame1pil, flow21)
|
||||
# frame2pil = frame1pil
|
||||
frame2pil = np.array(frame2.convert('RGB').resize(
|
||||
(flow21.shape[1], flow21.shape[0])))
|
||||
|
||||
if weights_path:
|
||||
# TBD
|
||||
pass
|
||||
else:
|
||||
blended_w = frame2pil*(1-blend) + frame1_warped21*(blend)
|
||||
|
||||
return PIL.Image.fromarray(blended_w.astype('uint8'))
|
||||
|
||||
# in_path = videoFramesFolder
|
||||
# f'{in_path}/out_flo_fwd'
|
||||
|
||||
# in_path+'/temp_flo'
|
||||
# in_path+'/out_flo_fwd'
|
||||
# TBD flow backwards!
|
||||
|
||||
# os.chdir(models_dir)
|
||||
|
||||
# @title Generate optical flow and consistency maps
|
||||
# @markdown Run once per init video
|
||||
|
||||
|
||||
def generate_optical_flow(S):
|
||||
force_flow_generation = False # @param {type:'boolean'}
|
||||
in_path = S.videoFramesFolder
|
||||
flo_folder = f'{in_path}/out_flo_fwd'
|
||||
|
||||
if not S.video_init_flow_warp:
|
||||
print('video_init_flow_warp not set, skipping')
|
||||
|
||||
if (S.animation_mode == 'Video Input') and (S.video_init_flow_warp):
|
||||
flows = glob(flo_folder+'/*.*')
|
||||
if (len(flows) > 0) and not force_flow_generation:
|
||||
print(
|
||||
f'Skipping flow generation:\nFound {len(flows)} existing flow files in current working folder: {flo_folder}.\nIf you wish to generate new flow files, check force_flow_generation and run this cell again.')
|
||||
|
||||
if (len(flows) == 0) or force_flow_generation:
|
||||
frames = sorted(glob(in_path+'/*.*'))
|
||||
if len(frames) < 2:
|
||||
print(
|
||||
f'WARNING!\nCannot create flow maps: Found {len(frames)} frames extracted from your video input.\nPlease check your video path.')
|
||||
if len(frames) >= 2:
|
||||
|
||||
raft_model = torch.nn.DataParallel(RAFT(args2))
|
||||
raft_model.load_state_dict(torch.load(
|
||||
f'{S.root_path}/RAFT/models/raft-things.pth'))
|
||||
raft_model = raft_model.module.cuda().eval()
|
||||
|
||||
for f in pathlib.Path(f'{S.flo_fwd_folder}').glob('*.*'):
|
||||
f.unlink()
|
||||
|
||||
temp_flo = in_path+'/temp_flo'
|
||||
flo_fwd_folder = in_path+'/out_flo_fwd'
|
||||
|
||||
os.makedirs(flo_fwd_folder, exist_ok=True)
|
||||
os.makedirs(temp_flo, exist_ok=True)
|
||||
|
||||
# TBD Call out to a consistency checker?
|
||||
|
||||
for frame1, frame2 in tqdm(zip(frames[:-1], frames[1:]), total=len(frames)-1):
|
||||
|
||||
out_flow21_fn = f"{flo_fwd_folder}/{frame1.split('/')[-1]}"
|
||||
|
||||
frame1 = load_img(frame1, S.width_height)
|
||||
frame2 = load_img(frame2, S.width_height)
|
||||
|
||||
flow21 = get_flow(frame2, frame1, raft_model)
|
||||
np.save(out_flow21_fn, flow21)
|
||||
|
||||
if S.video_init_check_consistency:
|
||||
# TBD
|
||||
pass
|
||||
|
||||
del raft_model
|
||||
gc.collect()
|
||||
Reference in New Issue
Block a user