# Copyright 2022 Lunar Ring. All rights reserved. # Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer # # 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 import torch import numpy as np import warnings import time from tqdm.auto import tqdm from PIL import Image from movie_util import MovieSaver from typing import List, Optional import lpips from utils import interpolate_spherical, interpolate_linear, add_frames_linear_interp, yml_load, yml_save warnings.filterwarnings('ignore') torch.backends.cudnn.benchmark = False torch.set_grad_enabled(False) class LatentBlending(): def __init__( self, dh: 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.dh = dh self.device = self.dh.device self.set_dimensions() 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.tree_latents = [None, None] self.tree_fracts = None self.idx_injection = [] self.tree_status = None self.tree_final_imgs = [] self.text_embedding1 = None self.text_embedding2 = None self.image1_lowres = None self.image2_lowres = None self.negative_prompt = None # Mixing parameters self.branch1_crossfeed_power = 0.0 self.branch1_crossfeed_range = 0.0 self.branch1_crossfeed_decay = 0.0 self.parental_crossfeed_power = 0.3 self.parental_crossfeed_range = 0.6 self.parental_crossfeed_power_decay = 0.9 self.set_guidance_scale(guidance_scale) self.multi_transition_img_first = None self.multi_transition_img_last = None self.dt_unet_step = 0 self.lpips = lpips.LPIPS(net='alex').cuda(self.device) self.set_prompt1("") self.set_prompt2("") self.set_num_inference_steps() self.benchmark_speed() self.set_branching() def benchmark_speed(self): """ Measures the time per diffusion step and for the vae decoding """ text_embeddings = self.dh.get_text_embedding("test") latents_start = self.dh.get_noise(np.random.randint(111111)) # warmup list_latents = self.dh.run_diffusion_sd_xl(text_embeddings=text_embeddings, latents_start=latents_start, return_image=False, idx_start=self.num_inference_steps-1) # bench unet t0 = time.time() list_latents = self.dh.run_diffusion_sd_xl(text_embeddings=text_embeddings, latents_start=latents_start, return_image=False, idx_start=self.num_inference_steps-1) self.dt_unet_step = time.time() - t0 # bench vae t0 = time.time() img = self.dh.latent2image(list_latents[-1]) self.dt_vae = time.time() - t0 def set_dimensions(self, size_output=None): r""" sets the size of the output video. Args: size_output: tuple width x height Note: the size will get automatically adjusted to be divisable by 32. """ self.dh.set_dimensions(size_output) def set_guidance_scale(self, guidance_scale): r""" sets the guidance scale. """ self.guidance_scale_base = guidance_scale self.guidance_scale = guidance_scale self.dh.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.dh.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.dh.guidance_scale = guidance_scale_effective def set_branch1_crossfeed(self, crossfeed_power, crossfeed_range, crossfeed_decay): r""" Sets the crossfeed parameters for the first branch to the last branch. Args: crossfeed_power: float [0,1] Controls the level of cross-feeding between the first and last image branch. crossfeed_range: float [0,1] Sets the duration of active crossfeed during development. crossfeed_decay: float [0,1] Sets decay for branch1_crossfeed_power. Lower values make the decay stronger across the range. """ self.branch1_crossfeed_power = np.clip(crossfeed_power, 0, 1) self.branch1_crossfeed_range = np.clip(crossfeed_range, 0, 1) self.branch1_crossfeed_decay = np.clip(crossfeed_decay, 0, 1) def set_parental_crossfeed(self, crossfeed_power, crossfeed_range, crossfeed_decay): r""" Sets the crossfeed parameters for all transition images (within the first and last branch). Args: crossfeed_power: float [0,1] Controls the level of cross-feeding from the parental branches crossfeed_range: float [0,1] Sets the duration of active crossfeed during development. crossfeed_decay: float [0,1] Sets decay for branch1_crossfeed_power. Lower values make the decay stronger across the range. """ self.parental_crossfeed_power = np.clip(crossfeed_power, 0, 1) self.parental_crossfeed_range = np.clip(crossfeed_range, 0, 1) self.parental_crossfeed_power_decay = np.clip(crossfeed_decay, 0, 1) 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 set_num_inference_steps(self, num_inference_steps=None): if self.dh.is_sdxl_turbo: if num_inference_steps is None: num_inference_steps = 4 else: if num_inference_steps is None: num_inference_steps = 30 self.num_inference_steps = num_inference_steps self.dh.set_num_inference_steps(num_inference_steps) def set_branching(self, depth_strength=None, t_compute_max_allowed=None, nmb_max_branches=None): """ Sets the branching structure of the blending tree. Default arguments depend on pipe! depth_strength: 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. t_compute_max_allowed: Either provide t_compute_max_allowed or nmb_max_branches. The maximum time allowed for computation. Higher values give better results but take longer. nmb_max_branches: int Either provide t_compute_max_allowed or nmb_max_branches. The maximum number of branches to be computed. Higher values give better results. Use this if you want to have controllable results independent of your computer. """ if self.dh.is_sdxl_turbo: assert t_compute_max_allowed is None, "time-based branching not supported for SDXL Turbo" if depth_strength is not None: idx_inject = int(round(self.num_inference_steps*depth_strength)) else: idx_inject = 2 if nmb_max_branches is None: nmb_max_branches = 10 self.list_idx_injection = [idx_inject] self.list_nmb_stems = [nmb_max_branches] else: if depth_strength is None: depth_strength = 0.5 if t_compute_max_allowed is None and nmb_max_branches is None: t_compute_max_allowed = 20 elif t_compute_max_allowed is not None and nmb_max_branches is not None: raise ValueErorr("Either specify t_compute_max_allowed or nmb_max_branches") self.list_idx_injection, self.list_nmb_stems = self.get_time_based_branching(depth_strength, t_compute_max_allowed, nmb_max_branches) def run_transition( self, recycle_img1: Optional[bool] = False, recycle_img2: Optional[bool] = False, fixed_seeds: Optional[List[int]] = None): r""" Function for computing transitions. 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. num_inference_steps: Number of diffusion steps. Higher values will take more compute time. 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. """ # 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' # Random seeds 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] # Compute / Recycle first image if not recycle_img1 or len(self.tree_latents[0]) != self.num_inference_steps: list_latents1 = self.compute_latents1() else: list_latents1 = self.tree_latents[0] # Compute / Recycle first image if not recycle_img2 or len(self.tree_latents[-1]) != self.num_inference_steps: list_latents2 = self.compute_latents2() else: list_latents2 = self.tree_latents[-1] # Reset the tree, injecting the edge latents1/2 we just generated/recycled self.tree_latents = [list_latents1, list_latents2] self.tree_fracts = [0.0, 1.0] self.tree_final_imgs = [self.dh.latent2image((self.tree_latents[0][-1])), self.dh.latent2image((self.tree_latents[-1][-1]))] self.tree_idx_injection = [0, 0] # Set up branching scheme (dependent on provided compute time) if self.dh.is_sdxl_turbo: self.guidance_scale = 0.0 self.parental_crossfeed_power = 1.0 self.parental_crossfeed_power_decay = 1.0 self.parental_crossfeed_range = 1.0 # Run iteratively, starting with the longest trajectory. # Always inserting new branches where they are needed most according to image similarity for s_idx in tqdm(range(len(self.list_idx_injection))): nmb_stems = self.list_nmb_stems[s_idx] idx_injection = self.list_idx_injection[s_idx] for i in range(nmb_stems): fract_mixing, b_parent1, b_parent2 = self.get_mixing_parameters(idx_injection) self.set_guidance_mid_dampening(fract_mixing) list_latents = self.compute_latents_mix(fract_mixing, b_parent1, b_parent2, idx_injection) self.insert_into_tree(fract_mixing, idx_injection, list_latents) # print(f"fract_mixing: {fract_mixing} idx_injection {idx_injection} bp1 {b_parent1} bp2 {b_parent2}") return self.tree_final_imgs def compute_latents1(self, return_image=False): r""" Runs a diffusion trajectory for the first image Args: return_image: bool whether to return an image or the list of latents """ print("starting compute_latents1") list_conditionings = self.get_mixed_conditioning(0) t0 = time.time() latents_start = self.get_noise(self.seed1) list_latents1 = self.run_diffusion( list_conditionings, latents_start=latents_start, idx_start=0) t1 = time.time() self.dt_unet_step = (t1 - t0) / self.num_inference_steps self.tree_latents[0] = list_latents1 if return_image: return self.dh.latent2image(list_latents1[-1]) else: return list_latents1 def compute_latents2(self, return_image=False): r""" Runs a diffusion trajectory for the last image, which may be affected by the first image's trajectory. Args: return_image: bool whether to return an image or the list of latents """ print("starting compute_latents2") list_conditionings = self.get_mixed_conditioning(1) latents_start = self.get_noise(self.seed2) # Influence from branch1 if self.branch1_crossfeed_power > 0.0: # Set up the mixing_coeffs idx_mixing_stop = int(round(self.num_inference_steps * self.branch1_crossfeed_range)) mixing_coeffs = list(np.linspace(self.branch1_crossfeed_power, self.branch1_crossfeed_power * self.branch1_crossfeed_decay, idx_mixing_stop)) mixing_coeffs.extend((self.num_inference_steps - idx_mixing_stop) * [0]) list_latents_mixing = self.tree_latents[0] list_latents2 = self.run_diffusion( list_conditionings, latents_start=latents_start, idx_start=0, list_latents_mixing=list_latents_mixing, mixing_coeffs=mixing_coeffs) else: list_latents2 = self.run_diffusion(list_conditionings, latents_start) self.tree_latents[-1] = list_latents2 if return_image: return self.dh.latent2image(list_latents2[-1]) else: return list_latents2 def compute_latents_mix(self, fract_mixing, b_parent1, b_parent2, idx_injection): r""" Runs a diffusion trajectory, using the latents from the respective parents Args: fract_mixing: float the fraction along the transition axis [0, 1] b_parent1: int index of parent1 to be used b_parent2: int index of parent2 to be used idx_injection: int the index in terms of diffusion steps, where the next insertion will start. """ list_conditionings = self.get_mixed_conditioning(fract_mixing) fract_mixing_parental = (fract_mixing - self.tree_fracts[b_parent1]) / (self.tree_fracts[b_parent2] - self.tree_fracts[b_parent1]) # idx_reversed = self.num_inference_steps - idx_injection list_latents_parental_mix = [] for i in range(self.num_inference_steps): latents_p1 = self.tree_latents[b_parent1][i] latents_p2 = self.tree_latents[b_parent2][i] if latents_p1 is None or latents_p2 is None: latents_parental = None else: latents_parental = interpolate_spherical(latents_p1, latents_p2, fract_mixing_parental) list_latents_parental_mix.append(latents_parental) idx_mixing_stop = int(round(self.num_inference_steps * self.parental_crossfeed_range)) mixing_coeffs = idx_injection * [self.parental_crossfeed_power] nmb_mixing = idx_mixing_stop - idx_injection if nmb_mixing > 0: mixing_coeffs.extend(list(np.linspace(self.parental_crossfeed_power, self.parental_crossfeed_power * self.parental_crossfeed_power_decay, nmb_mixing))) mixing_coeffs.extend((self.num_inference_steps - len(mixing_coeffs)) * [0]) latents_start = list_latents_parental_mix[idx_injection - 1] list_latents = self.run_diffusion( list_conditionings, latents_start=latents_start, idx_start=idx_injection, list_latents_mixing=list_latents_parental_mix, mixing_coeffs=mixing_coeffs) return list_latents def get_time_based_branching(self, depth_strength, t_compute_max_allowed=None, nmb_max_branches=None): r""" Sets up the branching scheme dependent on the time that is granted for compute. The scheme uses an estimation derived from the first image's computation speed. Either provide t_compute_max_allowed or nmb_max_branches Args: depth_strength: 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. t_compute_max_allowed: float The maximum time allowed for computation. Higher values give better results but take longer. Use this if you want to fix your waiting time for the results. nmb_max_branches: int The maximum number of branches to be computed. Higher values give better results. Use this if you want to have controllable results independent of your computer. """ idx_injection_base = int(np.floor(self.num_inference_steps * depth_strength)) steps = int(np.ceil(self.num_inference_steps/10)) list_idx_injection = np.arange(idx_injection_base, self.num_inference_steps, steps) list_nmb_stems = np.ones(len(list_idx_injection), dtype=np.int32) t_compute = 0 if nmb_max_branches is None: assert t_compute_max_allowed is not None, "Either specify t_compute_max_allowed or nmb_max_branches" stop_criterion = "t_compute_max_allowed" elif t_compute_max_allowed is None: assert nmb_max_branches is not None, "Either specify t_compute_max_allowed or nmb_max_branches" stop_criterion = "nmb_max_branches" nmb_max_branches -= 2 # Discounting the outer frames else: raise ValueError("Either specify t_compute_max_allowed or nmb_max_branches") stop_criterion_reached = False is_first_iteration = True while not stop_criterion_reached: list_compute_steps = self.num_inference_steps - list_idx_injection list_compute_steps *= list_nmb_stems t_compute = np.sum(list_compute_steps) * self.dt_unet_step + self.dt_vae * np.sum(list_nmb_stems) t_compute += 2 * (self.num_inference_steps * self.dt_unet_step + self.dt_vae) # outer branches increase_done = False for s_idx in range(len(list_nmb_stems) - 1): if list_nmb_stems[s_idx + 1] / list_nmb_stems[s_idx] >= 1: list_nmb_stems[s_idx] += 1 increase_done = True break if not increase_done: list_nmb_stems[-1] += 1 if stop_criterion == "t_compute_max_allowed" and t_compute > t_compute_max_allowed: stop_criterion_reached = True elif stop_criterion == "nmb_max_branches" and np.sum(list_nmb_stems) >= nmb_max_branches: stop_criterion_reached = True if is_first_iteration: # Need to undersample. list_idx_injection = np.linspace(list_idx_injection[0], list_idx_injection[-1], nmb_max_branches).astype(np.int32) list_nmb_stems = np.ones(len(list_idx_injection), dtype=np.int32) else: is_first_iteration = False # print(f"t_compute {t_compute} list_nmb_stems {list_nmb_stems}") return list_idx_injection, list_nmb_stems def get_mixing_parameters(self, idx_injection): r""" Computes which parental latents should be mixed together to achieve a smooth blend. As metric, we are using lpips image similarity. The insertion takes place where the metric is maximal. Args: idx_injection: int the index in terms of diffusion steps, where the next insertion will start. """ # get_lpips_similarity similarities = self.get_tree_similarities() b_closest1 = np.argmax(similarities) b_closest2 = b_closest1 + 1 fract_closest1 = self.tree_fracts[b_closest1] fract_closest2 = self.tree_fracts[b_closest2] fract_mixing = (fract_closest1 + fract_closest2) / 2 # Ensure that the parents are indeed older b_parent1 = b_closest1 while True: if self.tree_idx_injection[b_parent1] < idx_injection: break else: b_parent1 -= 1 b_parent2 = b_closest2 while True: if self.tree_idx_injection[b_parent2] < idx_injection: break else: b_parent2 += 1 return fract_mixing, b_parent1, b_parent2 def insert_into_tree(self, fract_mixing, idx_injection, list_latents): r""" Inserts all necessary parameters into the trajectory tree. Args: fract_mixing: float the fraction along the transition axis [0, 1] idx_injection: int the index in terms of diffusion steps, where the next insertion will start. list_latents: list list of the latents to be inserted """ b_parent1, b_parent2 = self.get_closest_idx(fract_mixing) idx_tree = b_parent1 + 1 self.tree_latents.insert(idx_tree, list_latents) self.tree_final_imgs.insert(idx_tree, self.dh.latent2image(list_latents[-1])) self.tree_fracts.insert(idx_tree, fract_mixing) self.tree_idx_injection.insert(idx_tree, idx_injection) def get_noise(self, seed): r""" Helper function to get noise given seed. Args: seed: int """ return self.dh.get_noise(seed) @torch.no_grad() def run_diffusion( self, list_conditionings, latents_start: torch.FloatTensor = None, idx_start: int = 0, list_latents_mixing=None, mixing_coeffs=0.0, return_image: Optional[bool] = False): r""" Wrapper function for diffusion runners. Depending on the mode, the correct one will be executed. Args: list_conditionings: list List of all conditionings for the diffusion model. latents_start: 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 list_latents_mixing: torch.FloatTensor List of latents (latent trajectories) that are used for mixing mixing_coeffs: float or list Coefficients, how strong each element of list_latents_mixing will be mixed in. return_image: Optional[bool] Optionally return image directly """ # Ensure correct num_inference_steps in Holder self.dh.set_num_inference_steps(self.num_inference_steps) assert type(list_conditionings) is list, "list_conditionings need to be a list" text_embeddings = list_conditionings[0] return self.dh.run_diffusion_sd_xl( text_embeddings=text_embeddings, latents_start=latents_start, idx_start=idx_start, list_latents_mixing=list_latents_mixing, mixing_coeffs=mixing_coeffs, return_image=return_image) @torch.no_grad() def get_mixed_conditioning(self, fract_mixing): text_embeddings_mix = [] for i in range(len(self.text_embedding1)): if self.text_embedding1[i] is None: mix = None else: mix = interpolate_linear(self.text_embedding1[i], self.text_embedding2[i], fract_mixing) text_embeddings_mix.append(mix) list_conditionings = [text_embeddings_mix] 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.dh.get_text_embedding(prompt) def write_imgs_transition(self, dp_img): r""" Writes the transition images into the folder dp_img. Requires run_transition to be completed. Args: dp_img: str Directory, into which the transition images, yaml file and latents are written. """ imgs_transition = self.tree_final_imgs os.makedirs(dp_img, exist_ok=True) 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")) fp_yml = os.path.join(dp_img, "lowres.yaml") self.save_statedict(fp_yml) def write_movie_transition(self, fp_movie, duration_transition, fps=30): r""" Writes the transition movie to fp_movie, using the given duration and fps.. The missing frames are linearly interpolated. Args: fp_movie: str file pointer to the final movie. duration_transition: float duration of the movie in seonds fps: int fps of the movie """ # Let's get more cheap frames via linear interpolation (duration_transition*fps frames) imgs_transition_ext = add_frames_linear_interp(self.tree_final_imgs, duration_transition, fps) # Save as MP4 if os.path.isfile(fp_movie): os.remove(fp_movie) ms = MovieSaver(fp_movie, fps=fps, shape_hw=[self.dh.height_img, self.dh.width_img]) for img in tqdm(imgs_transition_ext): ms.write_frame(img) ms.finalize() def save_statedict(self, fp_yml): # Dump everything relevant into yaml imgs_transition = self.tree_final_imgs state_dict = self.get_state_dict() state_dict['nmb_images'] = len(imgs_transition) yml_save(fp_yml, state_dict) def get_state_dict(self): state_dict = {} grab_vars = ['prompt1', 'prompt2', 'seed1', 'seed2', 'height', 'width', 'num_inference_steps', 'depth_strength', 'guidance_scale', 'guidance_scale_mid_damper', 'mid_compression_scaler', 'negative_prompt', 'branch1_crossfeed_power', 'branch1_crossfeed_range', 'branch1_crossfeed_decay' 'parental_crossfeed_power', 'parental_crossfeed_range', 'parental_crossfeed_power_decay'] for v in grab_vars: if hasattr(self, v): if v == 'seed1' or v == 'seed2': state_dict[v] = int(getattr(self, v)) elif v == 'guidance_scale': state_dict[v] = float(getattr(self, v)) else: try: state_dict[v] = getattr(self, v) except Exception: pass return state_dict 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.dh.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.dh.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.dh.height = height 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 self.tree_latents[0] = self.tree_latents[-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 = [] def get_lpips_similarity(self, imgA, imgB): r""" Computes the image similarity between two images imgA and imgB. Used to determine the optimal point of insertion to create smooth transitions. High values indicate low similarity. """ tensorA = torch.from_numpy(np.asarray(imgA)).float().cuda(self.device) tensorA = 2 * tensorA / 255.0 - 1 tensorA = tensorA.permute([2, 0, 1]).unsqueeze(0) tensorB = torch.from_numpy(np.asarray(imgB)).float().cuda(self.device) tensorB = 2 * tensorB / 255.0 - 1 tensorB = tensorB.permute([2, 0, 1]).unsqueeze(0) lploss = self.lpips(tensorA, tensorB) lploss = float(lploss[0][0][0][0]) return lploss def get_tree_similarities(self): similarities = [] for i in range(len(self.tree_final_imgs) - 1): similarities.append(self.get_lpips_similarity(self.tree_final_imgs[i], self.tree_final_imgs[i + 1])) return similarities # Auxiliary functions def get_closest_idx( self, fract_mixing: float): r""" Helper function to retrieve the parents for any given mixing. Example: fract_mixing = 0.4 and self.tree_fracts = [0, 0.3, 0.6, 1.0] Will return the two closest values here, i.e. [1, 2] """ pdist = fract_mixing - np.asarray(self.tree_fracts) 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 #%% if __name__ == "__main__": # %% First let us spawn a stable diffusion holder. Uncomment your version of choice. from diffusers_holder import DiffusersHolder from diffusers import DiffusionPipeline from diffusers import AutoencoderTiny pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0" # pretrained_model_name_or_path = "stabilityai/sdxl-turbo" pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16") pipe.to("cuda") # pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16) # pipe.vae = pipe.vae.cuda() dh = DiffusersHolder(pipe) # %% Next let's set up all parameters # size_output = (512, 512) size_output = (1024, 1024) prompt1 = "photo of underwater landscape, fish, und the sea, incredible detail, high resolution" prompt2 = "rendering of an alien planet, strange plants, strange creatures, surreal" negative_prompt = "blurry, ugly, pale" # Optional duration_transition = 12 # In seconds # Spawn latent blending lb = LatentBlending(dh) # lb.dh.set_num_inference_steps(num_inference_steps) lb.set_guidance_scale(0) lb.set_prompt1(prompt1) lb.set_prompt2(prompt2) lb.set_dimensions(size_output) lb.set_negative_prompt(negative_prompt) # Run latent blending lb.run_transition(fixed_seeds=[420, 421]) # Save movie fp_movie = f'test.mp4' lb.write_movie_transition(fp_movie, duration_transition)