# Copyright 2022 Lunar Ring. All rights reserved. # # 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. import os, sys dp_git = "/home/lugo/git/" sys.path.append('util') # sys.path.append('../stablediffusion/ldm') import torch torch.backends.cudnn.benchmark = False import numpy as np import warnings warnings.filterwarnings('ignore') import time import subprocess import warnings import torch from tqdm.auto import tqdm from PIL import Image # import matplotlib.pyplot as plt import torch from movie_util import MovieSaver import datetime from typing import Callable, List, Optional, Union import inspect from threading import Thread torch.set_grad_enabled(False) from omegaconf import OmegaConf from torch import autocast from contextlib import nullcontext from ldm.models.diffusion.ddim import DDIMSampler from ldm.util import instantiate_from_config from ldm.models.diffusion.ddpm import LatentUpscaleDiffusion, LatentInpaintDiffusion from stable_diffusion_holder import StableDiffusionHolder import yaml #%% class LatentBlending(): def __init__( self, sdh: None, guidance_scale: float = 4, guidance_scale_mid_damper: float = 0.5, mid_compression_scaler: float = 1.2, ): r""" Initializes the latent blending class. Args: guidance_scale: float Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. guidance_scale_mid_damper: float = 0.5 Reduces the guidance scale towards the middle of the transition. A value of 0.5 would decrease the guidance_scale towards the middle linearly by 0.5. mid_compression_scaler: float = 2.0 Increases the sampling density in the middle (where most changes happen). Higher value imply more values in the middle. However the inflection point can occur outside the middle, thus high values can give rough transitions. Values around 2 should be fine. """ assert guidance_scale_mid_damper>0 and guidance_scale_mid_damper<=1.0, f"guidance_scale_mid_damper neees to be in interval (0,1], you provided {guidance_scale_mid_damper}" self.sdh = sdh self.device = self.sdh.device self.width = self.sdh.width self.height = self.sdh.height self.guidance_scale_mid_damper = guidance_scale_mid_damper self.mid_compression_scaler = mid_compression_scaler self.seed1 = 0 self.seed2 = 0 # Initialize vars self.prompt1 = "" self.prompt2 = "" self.negative_prompt = "" self.tree_latents = None self.tree_fracts = None self.tree_status = None self.tree_final_imgs = [] self.list_nmb_branches_prev = [] self.list_injection_idx_prev = [] self.text_embedding1 = None self.text_embedding2 = None self.image1_lowres = None self.image2_lowres = None self.stop_diffusion = False self.negative_prompt = None self.num_inference_steps = self.sdh.num_inference_steps self.noise_level_upscaling = 20 self.list_injection_idx = None self.list_nmb_branches = None self.branch2_independence = False self.set_guidance_scale(guidance_scale) self.init_mode() def init_mode(self): r""" Sets the operational mode. Currently supported are standard, inpainting and x4 upscaling. """ if isinstance(self.sdh.model, LatentUpscaleDiffusion): self.mode = 'upscale' elif isinstance(self.sdh.model, LatentInpaintDiffusion): self.sdh.image_source = None self.sdh.mask_image = None self.mode = 'inpaint' else: self.mode = 'standard' def set_guidance_scale(self, guidance_scale): r""" sets the guidance scale. """ self.guidance_scale_base = guidance_scale self.guidance_scale = guidance_scale self.sdh.guidance_scale = guidance_scale def set_negative_prompt(self, negative_prompt): r"""Set the negative prompt. Currenty only one negative prompt is supported """ self.negative_prompt = negative_prompt self.sdh.set_negative_prompt(negative_prompt) def set_guidance_mid_dampening(self, fract_mixing): r""" Tunes the guidance scale down as a linear function of fract_mixing, towards 0.5 the minimum will be reached. """ mid_factor = 1 - np.abs(fract_mixing - 0.5)/ 0.5 max_guidance_reduction = self.guidance_scale_base * (1-self.guidance_scale_mid_damper) - 1 guidance_scale_effective = self.guidance_scale_base - max_guidance_reduction*mid_factor self.guidance_scale = guidance_scale_effective self.sdh.guidance_scale = guidance_scale_effective def set_prompt1(self, prompt: str): r""" Sets the first prompt (for the first keyframe) including text embeddings. Args: prompt: str ABC trending on artstation painted by Greg Rutkowski """ prompt = prompt.replace("_", " ") self.prompt1 = prompt self.text_embedding1 = self.get_text_embeddings(self.prompt1) def set_prompt2(self, prompt: str): r""" Sets the second prompt (for the second keyframe) including text embeddings. Args: prompt: str XYZ trending on artstation painted by Greg Rutkowski """ prompt = prompt.replace("_", " ") self.prompt2 = prompt self.text_embedding2 = self.get_text_embeddings(self.prompt2) def set_image1(self, image: Image): r""" Sets the first image (keyframe), relevant for the upscaling model transitions. Args: image: Image """ self.image1_lowres = image def set_image2(self, image: Image): r""" Sets the second image (keyframe), relevant for the upscaling model transitions. Args: image: Image """ self.image2_lowres = image def load_branching_profile( self, quality: str = 'medium', depth_strength: float = 0.65, nmb_frames: int = 100, nmb_mindist: int = 3, ): r""" Helper function to set up the branching structure automatically. Args: quality: str Determines how many diffusion steps are being made + how many branches in total. Tradeoff between quality and speed of computation. Choose: lowest, low, medium, high, ultra depth_strength: float = 0.65, Determines how deep the first injection will happen. Deeper injections will cause (unwanted) formation of new structures, more shallow values will go into alpha-blendy land. nmb_frames: int = 360, total number of frames nmb_mindist: int = 3 minimum distance in terms of diffusion iteratinos between subsequent injections """ if quality == 'lowest': num_inference_steps = 12 nmb_branches_final = 5 elif quality == 'low': num_inference_steps = 15 nmb_branches_final = nmb_frames//16 elif quality == 'medium': num_inference_steps = 30 nmb_branches_final = nmb_frames//8 elif quality == 'high': num_inference_steps = 60 nmb_branches_final = nmb_frames//4 elif quality == 'ultra': num_inference_steps = 100 nmb_branches_final = nmb_frames//2 elif quality == 'upscaling_step1': num_inference_steps = 40 nmb_branches_final = 12 elif quality == 'upscaling_step2': num_inference_steps = 100 nmb_branches_final = 6 else: raise ValueError(f"quality = '{quality}' not supported") self.autosetup_branching(depth_strength, num_inference_steps, nmb_branches_final) def autosetup_branching( self, depth_strength: float = 0.65, num_inference_steps: int = 30, nmb_branches_final: int = 20, nmb_mindist: int = 3, ): r""" Automatically sets up the branching schedule. Args: depth_strength: float = 0.65, Determines how deep the first injection will happen. Deeper injections will cause (unwanted) formation of new structures, more shallow values will go into alpha-blendy land. num_inference_steps: int Number of diffusion steps. Higher values will take more compute time. nmb_branches_final (int): The number of diffusion-generated images at the end of the inference. nmb_mindist (int): The minimum number of diffusion steps between two injections. """ idx_injection_first = int(np.round(num_inference_steps*depth_strength)) idx_injection_last = num_inference_steps - nmb_mindist nmb_injections = int(np.floor(num_inference_steps/5)) - 1 list_injection_idx = [0] list_injection_idx.extend(np.linspace(idx_injection_first, idx_injection_last, nmb_injections).astype(int)) list_nmb_branches = np.round(np.logspace(np.log10(2), np.log10(nmb_branches_final), nmb_injections+1)).astype(int) # Cleanup. There should be at least nmb_mindist diffusion steps between each injection and list_nmb_branches increases list_nmb_branches_clean = [list_nmb_branches[0]] list_injection_idx_clean = [list_injection_idx[0]] for idx_injection, nmb_branches in zip(list_injection_idx[1:], list_nmb_branches[1:]): if idx_injection - list_injection_idx_clean[-1] >= nmb_mindist and nmb_branches > list_nmb_branches_clean[-1]: list_nmb_branches_clean.append(nmb_branches) list_injection_idx_clean.append(idx_injection) list_nmb_branches_clean[-1] = nmb_branches_final list_injection_idx_clean = [int(l) for l in list_injection_idx_clean] list_nmb_branches_clean = [int(l) for l in list_nmb_branches_clean] list_injection_idx = list_injection_idx_clean list_nmb_branches = list_nmb_branches_clean list_nmb_branches = list_nmb_branches list_injection_idx = list_injection_idx print(f"autosetup_branching: num_inference_steps: {num_inference_steps} list_nmb_branches: {list_nmb_branches} list_injection_idx: {list_injection_idx}") self.setup_branching(num_inference_steps, list_nmb_branches=list_nmb_branches, list_injection_idx=list_injection_idx) def setup_branching(self, num_inference_steps: int =30, list_nmb_branches: List[int] = None, list_injection_strength: List[float] = None, list_injection_idx: List[int] = None, ): r""" Sets the branching structure for making transitions. num_inference_steps: int Number of diffusion steps. Larger values will take more compute time. list_nmb_branches: List[int]: list of the number of branches for each injection. list_injection_strength: List[float]: list of injection strengths within interval [0, 1), values need to be increasing. Alternatively you can direclty specify the list_injection_idx. list_injection_idx: List[int]: list of injection strengths within interval [0, 1), values need to be increasing. Alternatively you can specify the list_injection_strength. """ # Assert assert not((list_injection_strength is not None) and (list_injection_idx is not None)), "suppyl either list_injection_strength or list_injection_idx" if list_injection_strength is None: assert list_injection_idx is not None, "Supply either list_injection_idx or list_injection_strength" assert isinstance(list_injection_idx[0], int) or isinstance(list_injection_idx[0], np.int) , "Need to supply integers for list_injection_idx" if list_injection_idx is None: assert list_injection_strength is not None, "Supply either list_injection_idx or list_injection_strength" # Create the injection indexes list_injection_idx = [int(round(x*num_inference_steps)) for x in list_injection_strength] assert min(np.diff(list_injection_idx)) > 0, 'Injection idx needs to be increasing' if min(np.diff(list_injection_idx)) < 2: print("Warning: your injection spacing is very tight. consider increasing the distances") assert isinstance(list_injection_strength[1], np.floating) or isinstance(list_injection_strength[1], float), "Need to supply floats for list_injection_strength" # we are checking element 1 in list_injection_strength because "0" is an int... [0, 0.5] assert max(list_injection_idx) < num_inference_steps, "Decrease the injection index or strength" assert len(list_injection_idx) == len(list_nmb_branches), "Need to have same length" assert max(list_injection_idx) < num_inference_steps,"Injection index cannot happen after last diffusion step! Decrease list_injection_idx or list_injection_strength[-1]" # Auto inits list_injection_idx_ext = list_injection_idx[:] list_injection_idx_ext.append(num_inference_steps) # If injection at depth 0 not specified, we will start out with 2 branches if list_injection_idx_ext[0] != 0: list_injection_idx_ext.insert(0,0) list_nmb_branches.insert(0,2) assert list_nmb_branches[0] == 2, "Need to start with 2 branches. set list_nmb_branches[0]=2" # Set attributes self.num_inference_steps = num_inference_steps self.sdh.num_inference_steps = num_inference_steps self.list_nmb_branches = list_nmb_branches self.list_injection_idx = list_injection_idx self.list_injection_idx_ext = list_injection_idx_ext self.init_tree_struct() def init_tree_struct(self): r""" Initializes tree variables for holding latents etc. """ self.tree_latents = [] self.tree_fracts = [] self.tree_status = [] self.tree_final_imgs_timing = [0]*self.list_nmb_branches[-1] nmb_blocks_time = len(self.list_injection_idx_ext)-1 for t_block in range(nmb_blocks_time): nmb_branches = self.list_nmb_branches[t_block] list_fract_mixing_current = get_spacing(nmb_branches, self.mid_compression_scaler) self.tree_fracts.append(list_fract_mixing_current) self.tree_latents.append([None]*nmb_branches) self.tree_status.append(['untouched']*nmb_branches) def run_transition( self, recycle_img1: Optional[bool] = False, recycle_img2: Optional[bool] = False, fixed_seeds: Optional[List[int]] = None, premature_stop: Optional[int] = np.inf, ): r""" Returns a list of transition images using spherical latent blending. Args: recycle_img1: Optional[bool]: Don't recompute the latents for the first keyframe (purely prompt1). Saves compute. recycle_img2: Optional[bool]: Don't recompute the latents for the second keyframe (purely prompt2). Saves compute. fixed_seeds: Optional[List[int)]: You can supply two seeds that are used for the first and second keyframe (prompt1 and prompt2). Otherwise random seeds will be taken. premature_stop: Optional[int]: Stop the computation after premature_stop frames have been computed in the transition """ # Sanity checks first assert self.text_embedding1 is not None, 'Set the first text embedding with .set_prompt1(...) before' assert self.text_embedding2 is not None, 'Set the second text embedding with .set_prompt2(...) before' assert self.list_injection_idx is not None, 'Set the branching structure before, by calling autosetup_branching or setup_branching' if fixed_seeds is not None: if fixed_seeds == 'randomize': fixed_seeds = list(np.random.randint(0, 1000000, 2).astype(np.int32)) else: assert len(fixed_seeds)==2, "Supply a list with len = 2" self.seed1 = fixed_seeds[0] self.seed2 = fixed_seeds[1] # Process interruption variable self.stop_diffusion = False # Ensure correct num_inference_steps in holder self.sdh.num_inference_steps = self.num_inference_steps # Make a backup for future reference self.list_nmb_branches_prev = self.list_nmb_branches[:] self.list_injection_idx_prev = self.list_injection_idx[:] # Pre-define entire branching tree structures self.tree_final_imgs = [None]*self.list_nmb_branches[-1] nmb_blocks_time = len(self.list_injection_idx_ext)-1 if not recycle_img1 and not recycle_img2: self.init_tree_struct() else: self.tree_final_imgs = [None]*self.list_nmb_branches[-1] for t_block in range(nmb_blocks_time): nmb_branches = self.list_nmb_branches[t_block] for idx_branch in range(nmb_branches): self.tree_status[t_block][idx_branch] = 'untouched' if recycle_img1: self.tree_status[t_block][0] = 'computed' self.tree_final_imgs[0] = self.sdh.latent2image(self.tree_latents[-1][0][-1]) self.tree_final_imgs_timing[0] = 0 if recycle_img2: self.tree_status[t_block][-1] = 'computed' self.tree_final_imgs[-1] = self.sdh.latent2image(self.tree_latents[-1][-1][-1]) self.tree_final_imgs_timing[-1] = 0 # setup compute order: goal: try to get last branch computed asap. # first compute the right keyframe. needs to be there in any case list_compute = [] list_local_stem = [] for t_block in range(nmb_blocks_time - 1, -1, -1): if self.tree_status[t_block][0] == 'untouched': self.tree_status[t_block][0] = 'prefetched' list_local_stem.append([t_block, 0]) list_compute.extend(list_local_stem[::-1]) # setup compute order: start from last leafs (the final transition images) and work way down. what parents do they need? for idx_leaf in range(1, self.list_nmb_branches[-1]): list_local_stem = [] t_block = nmb_blocks_time - 1 t_block_prev = t_block - 1 self.tree_status[t_block][idx_leaf] = 'prefetched' list_local_stem.append([t_block, idx_leaf]) idx_leaf_deep = idx_leaf for t_block in range(nmb_blocks_time-1, 0, -1): t_block_prev = t_block - 1 fract_mixing = self.tree_fracts[t_block][idx_leaf_deep] list_fract_mixing_prev = self.tree_fracts[t_block_prev] b_parent1, b_parent2 = get_closest_idx(fract_mixing, list_fract_mixing_prev) assert self.tree_status[t_block_prev][b_parent1] != 'untouched', 'Branch destruction??? This should never happen!' if self.tree_status[t_block_prev][b_parent2] == 'untouched': self.tree_status[t_block_prev][b_parent2] = 'prefetched' list_local_stem.append([t_block_prev, b_parent2]) idx_leaf_deep = b_parent2 list_compute.extend(list_local_stem[::-1]) # Diffusion computations start here time_start = time.time() for t_block, idx_branch in tqdm(list_compute, desc="computing transition", smoothing=0.01): if self.stop_diffusion: print("run_transition: process interrupted") return self.tree_final_imgs if idx_branch > premature_stop: print(f"run_transition: premature_stop criterion reached. returning tree with {premature_stop} branches") return self.tree_final_imgs # print(f"computing t_block {t_block} idx_branch {idx_branch}") idx_stop = self.list_injection_idx_ext[t_block+1] fract_mixing = self.tree_fracts[t_block][idx_branch] list_conditionings = self.get_mixed_conditioning(fract_mixing) self.set_guidance_mid_dampening(fract_mixing) # print(f"fract_mixing {fract_mixing} guid {self.sdh.guidance_scale}") if t_block == 0: if fixed_seeds is not None: if idx_branch == 0: self.set_seed(fixed_seeds[0]) elif idx_branch == self.list_nmb_branches[0] -1: self.set_seed(fixed_seeds[1]) # Inject latents from first branch for very first block if not self.branch2_independence and idx_branch==1: list_latents = self.tree_latents[0][0] else: list_latents = self.run_diffusion(list_conditionings, idx_stop=idx_stop) else: # find parents latents b_parent1, b_parent2 = get_closest_idx(fract_mixing, self.tree_fracts[t_block-1]) latents1 = self.tree_latents[t_block-1][b_parent1][-1] if fract_mixing == 0: latents2 = latents1 else: latents2 = self.tree_latents[t_block-1][b_parent2][-1] idx_start = self.list_injection_idx_ext[t_block] fract_mixing_parental = (fract_mixing - self.tree_fracts[t_block-1][b_parent1]) / (self.tree_fracts[t_block-1][b_parent2] - self.tree_fracts[t_block-1][b_parent1]) latents_for_injection = interpolate_spherical(latents1, latents2, fract_mixing_parental) list_latents = self.run_diffusion(list_conditionings, latents_for_injection, idx_start=idx_start, idx_stop=idx_stop) self.tree_latents[t_block][idx_branch] = list_latents self.tree_status[t_block][idx_branch] = 'computed' # Convert latents to image directly for the last t_block if t_block == nmb_blocks_time-1: self.tree_final_imgs[idx_branch] = self.sdh.latent2image(list_latents[-1]) self.tree_final_imgs_timing[idx_branch] = time.time() - time_start return self.tree_final_imgs def run_multi_transition( self, fp_movie: str, list_prompts: List[str], list_seeds: List[int] = None, fps: float = 24, duration_single_trans: float = 15, ): r""" Runs multiple transitions and stitches them together. You can supply the seeds for each prompt. Args: fp_movie: file path for movie saving list_prompts: List[float]: list of the prompts. There will be a transition starting from the first to the last. list_seeds: List[int] = None: Random Seeds for each prompt. fps: float: frames per second duration_single_trans: float: The duration of a single transition prompt[i] -> prompt[i+1]. The duration of your movie will be duration_single_trans * len(list_prompts) """ if list_seeds is None: list_seeds = list(np.random.randint(0, 10e10, len(list_prompts))) assert len(list_prompts) == len(list_seeds), "Supply the same number of prompts and seeds" ms = MovieSaver(fp_movie, fps=fps) for i in range(len(list_prompts)-1): print(f"Starting movie segment {i+1}/{len(list_prompts)-1}") if i==0: self.set_prompt1(list_prompts[i]) self.set_prompt2(list_prompts[i+1]) recycle_img1 = False else: self.swap_forward() self.set_prompt2(list_prompts[i+1]) recycle_img1 = True local_seeds = [list_seeds[i], list_seeds[i+1]] list_imgs = self.run_transition(recycle_img1=recycle_img1, fixed_seeds=local_seeds) list_imgs_interp = add_frames_linear_interp(list_imgs, fps, duration_single_trans) # Save movie frame for img in list_imgs_interp: ms.write_frame(img) ms.finalize() print("run_multi_transition: All completed.") @torch.no_grad() def run_diffusion( self, list_conditionings, latents_for_injection: torch.FloatTensor = None, idx_start: int = -1, idx_stop: int = -1, return_image: Optional[bool] = False ): r""" Wrapper function for run_diffusion_standard and run_diffusion_inpaint. Depending on the mode, the correct one will be executed. Args: list_conditionings: List of all conditionings for the diffusion model. latents_for_injection: torch.FloatTensor Latents that are used for injection idx_start: int Index of the diffusion process start and where the latents_for_injection are injected idx_stop: int Index of the diffusion process end. return_image: Optional[bool] Optionally return image directly """ # Ensure correct num_inference_steps in Holder self.sdh.num_inference_steps = self.num_inference_steps assert type(list_conditionings) is list, "list_conditionings need to be a list" if self.mode == 'standard': text_embeddings = list_conditionings[0] return self.sdh.run_diffusion_standard(text_embeddings, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image) elif self.mode == 'inpaint': text_embeddings = list_conditionings[0] assert self.sdh.image_source is not None, "image_source is None. Please run init_inpainting first." assert self.sdh.mask_image is not None, "image_source is None. Please run init_inpainting first." return self.sdh.run_diffusion_inpaint(text_embeddings, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image) elif self.mode == 'upscale': cond = list_conditionings[0] uc_full = list_conditionings[1] return self.sdh.run_diffusion_upscaling(cond, uc_full, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image) def run_upscaling_step1( self, dp_img: str, depth_strength: float = 0.65, num_inference_steps: int = 30, nmb_branches_final: int = 10, fixed_seeds: Optional[List[int]] = None, ): r""" Initializes inpainting with a source and maks image. Args: dp_img: Path to directory where the low-res images and yaml will be saved to. This directory cannot exist and will be created here. quality: str Determines how many diffusion steps are being made + how many branches in total. We suggest to leave it with upscaling_step1 which has 10 final branches. depth_strength: float = 0.65, Determines how deep the first injection will happen. Deeper injections will cause (unwanted) formation of new structures, more shallow values will go into alpha-blendy land. fixed_seeds: Optional[List[int)]: You can supply two seeds that are used for the first and second keyframe (prompt1 and prompt2). Otherwise random seeds will be taken. """ assert self.text_embedding1 is not None, 'run set_prompt1(yourprompt1) first' assert self.text_embedding2 is not None, 'run set_prompt2(yourprompt2) first' assert not os.path.isdir(dp_img), f"directory already exists: {dp_img}" if fixed_seeds is None: fixed_seeds = list(np.random.randint(0, 1000000, 2).astype(np.int32)) # Run latent blending self.autosetup_branching(depth_strength, num_inference_steps, nmb_branches_final) imgs_transition = self.run_transition(fixed_seeds=fixed_seeds) self.write_imgs_transition(dp_img, imgs_transition) print(f"run_upscaling_step1: completed! {dp_img}") def run_upscaling_step2( self, dp_img: str, depth_strength: float = 0.65, num_inference_steps: int = 30, nmb_branches_final: int = 10, fixed_seeds: Optional[List[int]] = None, ): fp_yml = os.path.join(dp_img, "lowres.yaml") fp_movie = os.path.join(dp_img, "movie.mp4") fps = 24 ms = MovieSaver(fp_movie, fps=fps) assert os.path.isfile(fp_yml), "lowres.yaml does not exist. did you forget run_upscaling_step1?" dict_stuff = yml_load(fp_yml) # load lowres images nmb_images_lowres = dict_stuff['nmb_images'] prompt1 = dict_stuff['prompt1'] prompt2 = dict_stuff['prompt2'] imgs_lowres = [] for i in range(nmb_images_lowres): fp_img_lowres = os.path.join(dp_img, f"lowres_img_{str(i).zfill(4)}.jpg") assert os.path.isfile(fp_img_lowres), f"{fp_img_lowres} does not exist. did you forget run_upscaling_step1?" imgs_lowres.append(Image.open(fp_img_lowres)) # set up upscaling text_embeddingA = self.sdh.get_text_embedding(prompt1) text_embeddingB = self.sdh.get_text_embedding(prompt2) self.autosetup_branching(depth_strength, num_inference_steps, nmb_branches_final) duration_single_trans = 3 list_fract_mixing = np.linspace(0, 1, nmb_images_lowres-1) for i in range(nmb_images_lowres-1): print(f"Starting movie segment {i+1}/{nmb_images_lowres-1}") self.text_embedding1 = interpolate_linear(text_embeddingA, text_embeddingB, list_fract_mixing[i]) self.text_embedding2 = interpolate_linear(text_embeddingA, text_embeddingB, 1-list_fract_mixing[i]) if i==0: recycle_img1 = False else: self.swap_forward() recycle_img1 = True self.set_image1(imgs_lowres[i]) self.set_image2(imgs_lowres[i+1]) list_imgs = self.run_transition(recycle_img1=recycle_img1) list_imgs_interp = add_frames_linear_interp(list_imgs, fps, duration_single_trans) # Save movie frame for img in list_imgs_interp: ms.write_frame(img) ms.finalize() def init_inpainting( self, image_source: Union[Image.Image, np.ndarray] = None, mask_image: Union[Image.Image, np.ndarray] = None, init_empty: Optional[bool] = False, ): r""" Initializes inpainting with a source and maks image. Args: image_source: Union[Image.Image, np.ndarray] Source image onto which the mask will be applied. mask_image: Union[Image.Image, np.ndarray] Mask image, value = 0 will stay untouched, value = 255 subjet to diffusion init_empty: Optional[bool]: Initialize inpainting with an empty image and mask, effectively disabling inpainting, useful for generating a first image for transitions using diffusion. """ self.init_mode() self.sdh.init_inpainting(image_source, mask_image, init_empty) @torch.no_grad() def get_mixed_conditioning(self, fract_mixing): if self.mode == 'standard': text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing) list_conditionings = [text_embeddings_mix] elif self.mode == 'inpaint': text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing) list_conditionings = [text_embeddings_mix] elif self.mode == 'upscale': text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing) cond, uc_full = self.sdh.get_cond_upscaling(self.image1_lowres, text_embeddings_mix, self.noise_level_upscaling) condB, uc_fullB = self.sdh.get_cond_upscaling(self.image2_lowres, text_embeddings_mix, self.noise_level_upscaling) cond['c_concat'][0] = interpolate_spherical(cond['c_concat'][0], condB['c_concat'][0], fract_mixing) uc_full['c_concat'][0] = interpolate_spherical(uc_full['c_concat'][0], uc_fullB['c_concat'][0], fract_mixing) list_conditionings = [cond, uc_full] else: raise ValueError(f"mix_conditioning: unknown mode {self.mode}") return list_conditionings @torch.no_grad() def get_text_embeddings( self, prompt: str ): r""" Computes the text embeddings provided a string with a prompts. Adapted from stable diffusion repo Args: prompt: str ABC trending on artstation painted by Old Greg. """ return self.sdh.get_text_embedding(prompt) def write_imgs_transition(self, dp_img, imgs_transition): r""" Writes the transition images into the folder dp_img. """ os.makedirs(dp_img) for i, img in enumerate(imgs_transition): img_leaf = Image.fromarray(img) img_leaf.save(os.path.join(dp_img, f"lowres_img_{str(i).zfill(4)}.jpg")) # Dump everything relevant into yaml dict_stuff = {} dict_stuff['prompt1'] = self.prompt1 dict_stuff['prompt2'] = self.prompt2 dict_stuff['seed1'] = int(self.seed1) dict_stuff['seed2'] = int(self.seed2) dict_stuff['num_inference_steps'] = self.num_inference_steps dict_stuff['height'] = self.sdh.height dict_stuff['width'] = self.sdh.width dict_stuff['nmb_images'] = len(imgs_transition) yml_save(os.path.join(dp_img, "lowres.yaml"), dict_stuff) def randomize_seed(self): r""" Set a random seed for a fresh start. """ seed = np.random.randint(999999999) self.set_seed(seed) def set_seed(self, seed: int): r""" Set a the seed for a fresh start. """ self.seed = seed self.sdh.seed = seed def set_width(self, width): r""" Set the width of the resulting image. """ assert np.mod(width, 64) == 0, "set_width: value needs to be divisible by 64" self.width = width self.sdh.width = width def set_height(self, height): r""" Set the height of the resulting image. """ assert np.mod(height, 64) == 0, "set_height: value needs to be divisible by 64" self.height = height self.sdh.height = height def inject_latents(self, list_latents, inject_img1=True, inject_img2=False): r""" Injects list of latents into tree structure. """ assert inject_img1 != inject_img2, "Either inject into img1 or img2" assert self.tree_latents is not None, "You need to setup the branching beforehand, run autosetup_branching() or setup_branching() before" for t_block in range(len(self.list_injection_idx)): if inject_img1: self.tree_latents[t_block][0] = list_latents[self.list_injection_idx_ext[t_block]:self.list_injection_idx_ext[t_block+1]] if inject_img2: self.tree_latents[t_block][-1] = list_latents[self.list_injection_idx_ext[t_block]:self.list_injection_idx_ext[t_block+1]] def swap_forward(self): r""" Moves over keyframe two -> keyframe one. Useful for making a sequence of transitions as in run_multi_transition() """ # Move over all latents for t_block in range(len(self.tree_latents)): self.tree_latents[t_block][0] = self.tree_latents[t_block][-1] # Move over prompts and text embeddings self.prompt1 = self.prompt2 self.text_embedding1 = self.text_embedding2 # Final cleanup for extra sanity self.tree_final_imgs = [] # Auxiliary functions def get_closest_idx( fract_mixing: float, list_fract_mixing_prev: List[float], ): r""" Helper function to retrieve the parents for any given mixing. Example: fract_mixing = 0.4 and list_fract_mixing_prev = [0, 0.3, 0.6, 1.0] Will return the two closest values from list_fract_mixing_prev, i.e. [1, 2] """ pdist = fract_mixing - np.asarray(list_fract_mixing_prev) pdist_pos = pdist.copy() pdist_pos[pdist_pos<0] = np.inf b_parent1 = np.argmin(pdist_pos) pdist_neg = -pdist.copy() pdist_neg[pdist_neg<=0] = np.inf b_parent2= np.argmin(pdist_neg) if b_parent1 > b_parent2: tmp = b_parent2 b_parent2 = b_parent1 b_parent1 = tmp return b_parent1, b_parent2 @torch.no_grad() def interpolate_spherical(p0, p1, fract_mixing: float): r""" Helper function to correctly mix two random variables using spherical interpolation. See https://en.wikipedia.org/wiki/Slerp The function will always cast up to float64 for sake of extra 4. Args: p0: First tensor for interpolation p1: Second tensor for interpolation fract_mixing: float Mixing coefficient of interval [0, 1]. 0 will return in p0 1 will return in p1 0.x will return a mix between both preserving angular velocity. """ if p0.dtype == torch.float16: recast_to = 'fp16' else: recast_to = 'fp32' p0 = p0.double() p1 = p1.double() norm = torch.linalg.norm(p0) * torch.linalg.norm(p1) epsilon = 1e-7 dot = torch.sum(p0 * p1) / norm dot = dot.clamp(-1+epsilon, 1-epsilon) theta_0 = torch.arccos(dot) sin_theta_0 = torch.sin(theta_0) theta_t = theta_0 * fract_mixing s0 = torch.sin(theta_0 - theta_t) / sin_theta_0 s1 = torch.sin(theta_t) / sin_theta_0 interp = p0*s0 + p1*s1 if recast_to == 'fp16': interp = interp.half() elif recast_to == 'fp32': interp = interp.float() return interp def interpolate_linear(p0, p1, fract_mixing): r""" Helper function to mix two variables using standard linear interpolation. Args: p0: First tensor / np.ndarray for interpolation p1: Second tensor / np.ndarray for interpolation fract_mixing: float Mixing coefficient of interval [0, 1]. 0 will return in p0 1 will return in p1 0.x will return a linear mix between both. """ reconvert_uint8 = False if type(p0) is np.ndarray and p0.dtype == 'uint8': reconvert_uint8 = True p0 = p0.astype(np.float64) if type(p1) is np.ndarray and p1.dtype == 'uint8': reconvert_uint8 = True p1 = p1.astype(np.float64) interp = (1-fract_mixing) * p0 + fract_mixing * p1 if reconvert_uint8: interp = np.clip(interp, 0, 255).astype(np.uint8) return interp def add_frames_linear_interp( list_imgs: List[np.ndarray], fps_target: Union[float, int] = None, duration_target: Union[float, int] = None, nmb_frames_target: int=None, ): r""" Helper function to cheaply increase the number of frames given a list of images, by virtue of standard linear interpolation. The number of inserted frames will be automatically adjusted so that the total of number of frames can be fixed precisely, using a random shuffling technique. The function allows 1:1 comparisons between transitions as videos. Args: list_imgs: List[np.ndarray) List of images, between each image new frames will be inserted via linear interpolation. fps_target: OptionA: specify here the desired frames per second. duration_target: OptionA: specify here the desired duration of the transition in seconds. nmb_frames_target: OptionB: directly fix the total number of frames of the output. """ # Sanity if nmb_frames_target is not None and fps_target is not None: raise ValueError("You cannot specify both fps_target and nmb_frames_target") if fps_target is None: assert nmb_frames_target is not None, "Either specify nmb_frames_target or nmb_frames_target" if nmb_frames_target is None: assert fps_target is not None, "Either specify duration_target and fps_target OR nmb_frames_target" assert duration_target is not None, "Either specify duration_target and fps_target OR nmb_frames_target" nmb_frames_target = fps_target*duration_target # Get number of frames that are missing nmb_frames_diff = len(list_imgs)-1 nmb_frames_missing = nmb_frames_target - nmb_frames_diff - 1 if nmb_frames_missing < 1: return list_imgs list_imgs_float = [img.astype(np.float32) for img in list_imgs] # Distribute missing frames, append nmb_frames_to_insert(i) frames for each frame mean_nmb_frames_insert = nmb_frames_missing/nmb_frames_diff constfact = np.floor(mean_nmb_frames_insert) remainder_x = 1-(mean_nmb_frames_insert - constfact) nmb_iter = 0 while True: nmb_frames_to_insert = np.random.rand(nmb_frames_diff) nmb_frames_to_insert[nmb_frames_to_insert<=remainder_x] = 0 nmb_frames_to_insert[nmb_frames_to_insert>remainder_x] = 1 nmb_frames_to_insert += constfact if np.sum(nmb_frames_to_insert) == nmb_frames_missing: break nmb_iter += 1 if nmb_iter > 100000: print("add_frames_linear_interp: issue with inserting the right number of frames") break nmb_frames_to_insert = nmb_frames_to_insert.astype(np.int32) list_imgs_interp = [] for i in range(len(list_imgs_float)-1):#, desc="STAGE linear interp"): img0 = list_imgs_float[i] img1 = list_imgs_float[i+1] list_imgs_interp.append(img0.astype(np.uint8)) list_fracts_linblend = np.linspace(0, 1, nmb_frames_to_insert[i]+2)[1:-1] for fract_linblend in list_fracts_linblend: img_blend = interpolate_linear(img0, img1, fract_linblend).astype(np.uint8) list_imgs_interp.append(img_blend.astype(np.uint8)) if i==len(list_imgs_float)-2: list_imgs_interp.append(img1.astype(np.uint8)) return list_imgs_interp def get_spacing(nmb_points: int, scaling: float): """ Helper function for getting nonlinear spacing between 0 and 1, symmetric around 0.5 Args: nmb_points: int Number of points between [0, 1] scaling: float Higher values will return higher sampling density around 0.5 """ if scaling < 1.7: return np.linspace(0, 1, nmb_points) nmb_points_per_side = nmb_points//2 + 1 if np.mod(nmb_points, 2) != 0: # uneven case left_side = np.abs(np.linspace(1, 0, nmb_points_per_side)**scaling / 2 - 0.5) right_side = 1-left_side[::-1][1:] else: left_side = np.abs(np.linspace(1, 0, nmb_points_per_side)**scaling / 2 - 0.5)[0:-1] right_side = 1-left_side[::-1] all_fracts = np.hstack([left_side, right_side]) return all_fracts def get_time(resolution=None): """ Helper function returning an nicely formatted time string, e.g. 221117_1620 """ if resolution==None: resolution="second" if resolution == "day": t = time.strftime('%y%m%d', time.localtime()) elif resolution == "minute": t = time.strftime('%y%m%d_%H%M', time.localtime()) elif resolution == "second": t = time.strftime('%y%m%d_%H%M%S', time.localtime()) elif resolution == "millisecond": t = time.strftime('%y%m%d_%H%M%S', time.localtime()) t += "_" t += str("{:03d}".format(int(int(datetime.utcnow().strftime('%f'))/1000))) else: raise ValueError("bad resolution provided: %s" %resolution) return t def yml_load(fp_yml, print_fields=False): """ Helper function for loading yaml files """ with open(fp_yml) as f: data = yaml.load(f, Loader=yaml.loader.SafeLoader) dict_data = dict(data) print("load: loaded {}".format(fp_yml)) return dict_data def yml_save(fp_yml, dict_stuff): """ Helper function for saving yaml files """ with open(fp_yml, 'w') as f: data = yaml.dump(dict_stuff, f, sort_keys=False, default_flow_style=False) print("yml_save: saved {}".format(fp_yml)) #%% le main if __name__ == "__main__": # xxxx #%% First let us spawn a stable diffusion holder device = "cuda" fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt" fp_config = 'configs/v2-inference-v.yaml' sdh = StableDiffusionHolder(fp_ckpt, fp_config, device) #%% Next let's set up all parameters quality = 'medium' depth_strength = 0.65 # Specifies how deep (in terms of diffusion iterations the first branching happens) fixed_seeds = [69731932, 504430820] prompt1 = "photo of a beautiful cherry forest covered in white flowers, ambient light, very detailed, magic" prompt2 = "photo of an golden statue with a funny hat, surrounded by ferns and vines, grainy analog photograph, mystical ambience, incredible detail" duration_transition = 12 # In seconds fps = 30 # Spawn latent blending self = LatentBlending(sdh) self.load_branching_profile(quality=quality, depth_strength=0.3) self.set_prompt1(prompt1) self.set_prompt2(prompt2) # Run latent blending imgs_transition = self.run_transition(fixed_seeds=fixed_seeds)