# 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 torch.backends.cudnn.benchmark = False torch.set_grad_enabled(False) import numpy as np import warnings warnings.filterwarnings('ignore') import warnings from tqdm.auto import tqdm from PIL import Image from movie_util import MovieSaver, concatenate_movies from latent_blending import LatentBlending from stable_diffusion_holder import StableDiffusionHolder import gradio as gr from dotenv import find_dotenv, load_dotenv import shutil import random from utils import get_time, add_frames_linear_interp from huggingface_hub import hf_hub_download class BlendingFrontend(): def __init__( self, sdh, share=False): r""" Gradio Helper Class to collect UI data and start latent blending. Args: sdh: StableDiffusionHolder share: bool Set true to get a shareable gradio link (e.g. for running a remote server) """ self.share = share # UI Defaults self.num_inference_steps = 30 self.depth_strength = 0.25 self.seed1 = 420 self.seed2 = 420 self.prompt1 = "" self.prompt2 = "" self.negative_prompt = "" self.fps = 30 self.duration_video = 8 self.t_compute_max_allowed = 10 self.lb = LatentBlending(sdh) self.lb.sdh.num_inference_steps = self.num_inference_steps self.init_parameters_from_lb() self.init_save_dir() # Vars self.list_fp_imgs_current = [] self.recycle_img1 = False self.recycle_img2 = False self.list_all_segments = [] self.dp_session = "" self.user_id = None def init_parameters_from_lb(self): r""" Automatically init parameters from latentblending instance """ self.height = self.lb.sdh.height self.width = self.lb.sdh.width self.guidance_scale = self.lb.guidance_scale self.guidance_scale_mid_damper = self.lb.guidance_scale_mid_damper self.mid_compression_scaler = self.lb.mid_compression_scaler self.branch1_crossfeed_power = self.lb.branch1_crossfeed_power self.branch1_crossfeed_range = self.lb.branch1_crossfeed_range self.branch1_crossfeed_decay = self.lb.branch1_crossfeed_decay self.parental_crossfeed_power = self.lb.parental_crossfeed_power self.parental_crossfeed_range = self.lb.parental_crossfeed_range self.parental_crossfeed_power_decay = self.lb.parental_crossfeed_power_decay def init_save_dir(self): r""" Initializes the directory where stuff is being saved. You can specify this directory in a ".env" file in your latentblending root, setting DIR_OUT='/path/to/saving' """ load_dotenv(find_dotenv(), verbose=False) self.dp_out = os.getenv("DIR_OUT") if self.dp_out is None: self.dp_out = "" self.dp_imgs = os.path.join(self.dp_out, "imgs") os.makedirs(self.dp_imgs, exist_ok=True) self.dp_movies = os.path.join(self.dp_out, "movies") os.makedirs(self.dp_movies, exist_ok=True) self.save_empty_image() def save_empty_image(self): r""" Saves an empty/black dummy image. """ self.fp_img_empty = os.path.join(self.dp_imgs, 'empty.jpg') Image.fromarray(np.zeros((self.height, self.width, 3), dtype=np.uint8)).save(self.fp_img_empty, quality=5) def randomize_seed1(self): r""" Randomizes the first seed """ seed = np.random.randint(0, 10000000) self.seed1 = int(seed) print(f"randomize_seed1: new seed = {self.seed1}") return seed def randomize_seed2(self): r""" Randomizes the second seed """ seed = np.random.randint(0, 10000000) self.seed2 = int(seed) print(f"randomize_seed2: new seed = {self.seed2}") return seed def setup_lb(self, list_ui_vals): r""" Sets all parameters from the UI. Since gradio does not support to pass dictionaries, we have to instead pass keys (list_ui_keys, global) and values (list_ui_vals) """ # Collect latent blending variables self.lb.set_width(list_ui_vals[list_ui_keys.index('width')]) self.lb.set_height(list_ui_vals[list_ui_keys.index('height')]) self.lb.set_prompt1(list_ui_vals[list_ui_keys.index('prompt1')]) self.lb.set_prompt2(list_ui_vals[list_ui_keys.index('prompt2')]) self.lb.set_negative_prompt(list_ui_vals[list_ui_keys.index('negative_prompt')]) self.lb.guidance_scale = list_ui_vals[list_ui_keys.index('guidance_scale')] self.lb.guidance_scale_mid_damper = list_ui_vals[list_ui_keys.index('guidance_scale_mid_damper')] self.t_compute_max_allowed = list_ui_vals[list_ui_keys.index('duration_compute')] self.lb.num_inference_steps = list_ui_vals[list_ui_keys.index('num_inference_steps')] self.lb.sdh.num_inference_steps = list_ui_vals[list_ui_keys.index('num_inference_steps')] self.duration_video = list_ui_vals[list_ui_keys.index('duration_video')] self.lb.seed1 = list_ui_vals[list_ui_keys.index('seed1')] self.lb.seed2 = list_ui_vals[list_ui_keys.index('seed2')] self.lb.branch1_crossfeed_power = list_ui_vals[list_ui_keys.index('branch1_crossfeed_power')] self.lb.branch1_crossfeed_range = list_ui_vals[list_ui_keys.index('branch1_crossfeed_range')] self.lb.branch1_crossfeed_decay = list_ui_vals[list_ui_keys.index('branch1_crossfeed_decay')] self.lb.parental_crossfeed_power = list_ui_vals[list_ui_keys.index('parental_crossfeed_power')] self.lb.parental_crossfeed_range = list_ui_vals[list_ui_keys.index('parental_crossfeed_range')] self.lb.parental_crossfeed_power_decay = list_ui_vals[list_ui_keys.index('parental_crossfeed_power_decay')] self.num_inference_steps = list_ui_vals[list_ui_keys.index('num_inference_steps')] self.depth_strength = list_ui_vals[list_ui_keys.index('depth_strength')] if len(list_ui_vals[list_ui_keys.index('user_id')]) > 1: self.user_id = list_ui_vals[list_ui_keys.index('user_id')] else: # generate new user id self.user_id = ''.join((random.choice('ABCDEFGHIJKLMNOPQRSTUVWXYZ') for i in range(8))) print(f"made new user_id: {self.user_id} at {get_time('second')}") def save_latents(self, fp_latents, list_latents): r""" Saves a latent trajectory on disk, in npy format. """ list_latents_cpu = [l.cpu().numpy() for l in list_latents] np.save(fp_latents, list_latents_cpu) def load_latents(self, fp_latents): r""" Loads a latent trajectory from disk, converts to torch tensor. """ list_latents_cpu = np.load(fp_latents) list_latents = [torch.from_numpy(l).to(self.lb.device) for l in list_latents_cpu] return list_latents def compute_img1(self, *args): r""" Computes the first transition image and returns it for display. Sets all other transition images and last image to empty (as they are obsolete with this operation) """ list_ui_vals = args self.setup_lb(list_ui_vals) fp_img1 = os.path.join(self.dp_imgs, f"img1_{self.user_id}") img1 = Image.fromarray(self.lb.compute_latents1(return_image=True)) img1.save(fp_img1 + ".jpg") self.save_latents(fp_img1 + ".npy", self.lb.tree_latents[0]) self.recycle_img1 = True self.recycle_img2 = False return [fp_img1 + ".jpg", self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.user_id] def compute_img2(self, *args): r""" Computes the last transition image and returns it for display. Sets all other transition images to empty (as they are obsolete with this operation) """ if not os.path.isfile(os.path.join(self.dp_imgs, f"img1_{self.user_id}.jpg")): # don't do anything return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.user_id] list_ui_vals = args self.setup_lb(list_ui_vals) self.lb.tree_latents[0] = self.load_latents(os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy")) fp_img2 = os.path.join(self.dp_imgs, f"img2_{self.user_id}") img2 = Image.fromarray(self.lb.compute_latents2(return_image=True)) img2.save(fp_img2 + '.jpg') self.save_latents(fp_img2 + ".npy", self.lb.tree_latents[-1]) self.recycle_img2 = True # fixme save seeds. change filenames? return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, fp_img2 + ".jpg", self.user_id] def compute_transition(self, *args): r""" Computes transition images and movie. """ list_ui_vals = args self.setup_lb(list_ui_vals) print("STARTING TRANSITION...") fixed_seeds = [self.seed1, self.seed2] # Inject loaded latents (other user interference) self.lb.tree_latents[0] = self.load_latents(os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy")) self.lb.tree_latents[-1] = self.load_latents(os.path.join(self.dp_imgs, f"img2_{self.user_id}.npy")) imgs_transition = self.lb.run_transition( recycle_img1=self.recycle_img1, recycle_img2=self.recycle_img2, num_inference_steps=self.num_inference_steps, depth_strength=self.depth_strength, t_compute_max_allowed=self.t_compute_max_allowed, fixed_seeds=fixed_seeds) print(f"Latent Blending pass finished ({get_time('second')}). Resulted in {len(imgs_transition)} images") # Subselect three preview images idx_img_prev = np.round(np.linspace(0, len(imgs_transition) - 1, 5)[1:-1]).astype(np.int32) list_imgs_preview = [] for j in idx_img_prev: list_imgs_preview.append(Image.fromarray(imgs_transition[j])) # Save the preview imgs as jpgs on disk so we are not sending umcompressed data around current_timestamp = get_time('second') self.list_fp_imgs_current = [] for i in range(len(list_imgs_preview)): fp_img = os.path.join(self.dp_imgs, f"img_preview_{i}_{current_timestamp}.jpg") list_imgs_preview[i].save(fp_img) self.list_fp_imgs_current.append(fp_img) # Insert cheap frames for the movie imgs_transition_ext = add_frames_linear_interp(imgs_transition, self.duration_video, self.fps) # Save as movie self.fp_movie = self.get_fp_video_last() if os.path.isfile(self.fp_movie): os.remove(self.fp_movie) ms = MovieSaver(self.fp_movie, fps=self.fps) for img in tqdm(imgs_transition_ext): ms.write_frame(img) ms.finalize() print("DONE SAVING MOVIE! SENDING BACK...") # Assemble Output, updating the preview images and le movie list_return = self.list_fp_imgs_current + [self.fp_movie] return list_return def stack_forward(self, prompt2, seed2): r""" Allows to generate multi-segment movies. Sets last image -> first image with all relevant parameters. """ # Save preview images, prompts and seeds into dictionary for stacking if len(self.list_all_segments) == 0: timestamp_session = get_time('second') self.dp_session = os.path.join(self.dp_out, f"session_{timestamp_session}") os.makedirs(self.dp_session) idx_segment = len(self.list_all_segments) dp_segment = os.path.join(self.dp_session, f"segment_{str(idx_segment).zfill(3)}") self.list_all_segments.append(dp_segment) self.lb.write_imgs_transition(dp_segment) fp_movie_last = self.get_fp_video_last() fp_movie_next = self.get_fp_video_next() shutil.copyfile(fp_movie_last, fp_movie_next) self.lb.tree_latents[0] = self.load_latents(os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy")) self.lb.tree_latents[-1] = self.load_latents(os.path.join(self.dp_imgs, f"img2_{self.user_id}.npy")) self.lb.swap_forward() shutil.copyfile(os.path.join(self.dp_imgs, f"img2_{self.user_id}.npy"), os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy")) fp_multi = self.multi_concat() list_out = [fp_multi] list_out.extend([os.path.join(self.dp_imgs, f"img2_{self.user_id}.jpg")]) list_out.extend([self.fp_img_empty] * 4) list_out.append(gr.update(interactive=False, value=prompt2)) list_out.append(gr.update(interactive=False, value=seed2)) list_out.append("") list_out.append(np.random.randint(0, 10000000)) print(f"stack_forward: fp_multi {fp_multi}") return list_out def multi_concat(self): r""" Concatentates all stacked segments into one long movie. """ list_fp_movies = self.get_fp_video_all() # Concatenate movies and save fp_final = os.path.join(self.dp_session, f"concat_{self.user_id}.mp4") concatenate_movies(fp_final, list_fp_movies) return fp_final def get_fp_video_all(self): r""" Collects all stacked movie segments. """ list_all = os.listdir(self.dp_movies) str_beg = f"movie_{self.user_id}_" list_user = [l for l in list_all if str_beg in l] list_user.sort() list_user = [os.path.join(self.dp_movies, l) for l in list_user] return list_user def get_fp_video_next(self): r""" Gets the filepath of the next movie segment. """ list_videos = self.get_fp_video_all() if len(list_videos) == 0: idx_next = 0 else: idx_next = len(list_videos) fp_video_next = os.path.join(self.dp_movies, f"movie_{self.user_id}_{str(idx_next).zfill(3)}.mp4") return fp_video_next def get_fp_video_last(self): r""" Gets the current video that was saved. """ fp_video_last = os.path.join(self.dp_movies, f"last_{self.user_id}.mp4") return fp_video_last if __name__ == "__main__": fp_ckpt = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1-base", filename="v2-1_512-ema-pruned.ckpt") # fp_ckpt = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1", filename="v2-1_768-ema-pruned.ckpt") bf = BlendingFrontend(StableDiffusionHolder(fp_ckpt)) # self = BlendingFrontend(None) with gr.Blocks() as demo: with gr.Row(): prompt1 = gr.Textbox(label="prompt 1") prompt2 = gr.Textbox(label="prompt 2") with gr.Row(): duration_compute = gr.Slider(5, 200, bf.t_compute_max_allowed, step=1, label='compute budget', interactive=True) duration_video = gr.Slider(1, 100, bf.duration_video, step=0.1, label='video duration', interactive=True) height = gr.Slider(256, 2048, bf.height, step=128, label='height', interactive=True) width = gr.Slider(256, 2048, bf.width, step=128, label='width', interactive=True) with gr.Accordion("Advanced Settings (click to expand)", open=False): with gr.Accordion("Diffusion settings", open=True): with gr.Row(): num_inference_steps = gr.Slider(5, 100, bf.num_inference_steps, step=1, label='num_inference_steps', interactive=True) guidance_scale = gr.Slider(1, 25, bf.guidance_scale, step=0.1, label='guidance_scale', interactive=True) negative_prompt = gr.Textbox(label="negative prompt") with gr.Accordion("Seed control: adjust seeds for first and last images", open=True): with gr.Row(): b_newseed1 = gr.Button("randomize seed 1", variant='secondary') seed1 = gr.Number(bf.seed1, label="seed 1", interactive=True) seed2 = gr.Number(bf.seed2, label="seed 2", interactive=True) b_newseed2 = gr.Button("randomize seed 2", variant='secondary') with gr.Accordion("Last image crossfeeding.", open=True): with gr.Row(): branch1_crossfeed_power = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_power, step=0.01, label='branch1 crossfeed power', interactive=True) branch1_crossfeed_range = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_range, step=0.01, label='branch1 crossfeed range', interactive=True) branch1_crossfeed_decay = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_decay, step=0.01, label='branch1 crossfeed decay', interactive=True) with gr.Accordion("Transition settings", open=True): with gr.Row(): parental_crossfeed_power = gr.Slider(0.0, 1.0, bf.parental_crossfeed_power, step=0.01, label='parental crossfeed power', interactive=True) parental_crossfeed_range = gr.Slider(0.0, 1.0, bf.parental_crossfeed_range, step=0.01, label='parental crossfeed range', interactive=True) parental_crossfeed_power_decay = gr.Slider(0.0, 1.0, bf.parental_crossfeed_power_decay, step=0.01, label='parental crossfeed decay', interactive=True) with gr.Row(): depth_strength = gr.Slider(0.01, 0.99, bf.depth_strength, step=0.01, label='depth_strength', interactive=True) guidance_scale_mid_damper = gr.Slider(0.01, 2.0, bf.guidance_scale_mid_damper, step=0.01, label='guidance_scale_mid_damper', interactive=True) with gr.Row(): b_compute1 = gr.Button('compute first image', variant='primary') b_compute_transition = gr.Button('compute transition', variant='primary') b_compute2 = gr.Button('compute last image', variant='primary') with gr.Row(): img1 = gr.Image(label="1/5") img2 = gr.Image(label="2/5", show_progress=False) img3 = gr.Image(label="3/5", show_progress=False) img4 = gr.Image(label="4/5", show_progress=False) img5 = gr.Image(label="5/5") with gr.Row(): vid_single = gr.Video(label="current single trans") vid_multi = gr.Video(label="concatented multi trans") with gr.Row(): b_stackforward = gr.Button('append last movie segment (left) to multi movie (right)', variant='primary') with gr.Row(): gr.Markdown( """ # Parameters ## Main - compute budget: set your waiting time for the transition. high values = better quality - video duration: seconds per segment - height/width: in pixels ## Diffusion settings - num_inference_steps: number of diffusion steps - guidance_scale: latent blending seems to prefer lower values here - negative prompt: enter negative prompt here, applied for all images ## Last image crossfeeding - branch1_crossfeed_power: Controls the level of cross-feeding between the first and last image branch. For preserving structures. - branch1_crossfeed_range: Sets the duration of active crossfeed during development. High values enforce strong structural similarity. - branch1_crossfeed_decay: Sets decay for branch1_crossfeed_power. Lower values make the decay stronger across the range. ## Transition settings - parental_crossfeed_power: Similar to branch1_crossfeed_power, however applied for the images withinin the transition. - parental_crossfeed_range: Similar to branch1_crossfeed_range, however applied for the images withinin the transition. - parental_crossfeed_power_decay: Similar to branch1_crossfeed_decay, however applied for the images withinin the transition. - depth_strength: Determines when the blending process will begin in terms of diffusion steps. Low values more inventive but can cause motion. - guidance_scale_mid_damper: Decreases the guidance scale in the middle of a transition. """) with gr.Row(): user_id = gr.Textbox(label="user id", interactive=False) # Collect all UI elemts in list to easily pass as inputs in gradio dict_ui_elem = {} dict_ui_elem["prompt1"] = prompt1 dict_ui_elem["negative_prompt"] = negative_prompt dict_ui_elem["prompt2"] = prompt2 dict_ui_elem["duration_compute"] = duration_compute dict_ui_elem["duration_video"] = duration_video dict_ui_elem["height"] = height dict_ui_elem["width"] = width dict_ui_elem["depth_strength"] = depth_strength dict_ui_elem["branch1_crossfeed_power"] = branch1_crossfeed_power dict_ui_elem["branch1_crossfeed_range"] = branch1_crossfeed_range dict_ui_elem["branch1_crossfeed_decay"] = branch1_crossfeed_decay dict_ui_elem["num_inference_steps"] = num_inference_steps dict_ui_elem["guidance_scale"] = guidance_scale dict_ui_elem["guidance_scale_mid_damper"] = guidance_scale_mid_damper dict_ui_elem["seed1"] = seed1 dict_ui_elem["seed2"] = seed2 dict_ui_elem["parental_crossfeed_range"] = parental_crossfeed_range dict_ui_elem["parental_crossfeed_power"] = parental_crossfeed_power dict_ui_elem["parental_crossfeed_power_decay"] = parental_crossfeed_power_decay dict_ui_elem["user_id"] = user_id # Convert to list, as gradio doesn't seem to accept dicts list_ui_vals = [] list_ui_keys = [] for k in dict_ui_elem.keys(): list_ui_vals.append(dict_ui_elem[k]) list_ui_keys.append(k) bf.list_ui_keys = list_ui_keys b_newseed1.click(bf.randomize_seed1, outputs=seed1) b_newseed2.click(bf.randomize_seed2, outputs=seed2) b_compute1.click(bf.compute_img1, inputs=list_ui_vals, outputs=[img1, img2, img3, img4, img5, user_id]) b_compute2.click(bf.compute_img2, inputs=list_ui_vals, outputs=[img2, img3, img4, img5, user_id]) b_compute_transition.click(bf.compute_transition, inputs=list_ui_vals, outputs=[img2, img3, img4, vid_single]) b_stackforward.click(bf.stack_forward, inputs=[prompt2, seed2], outputs=[vid_multi, img1, img2, img3, img4, img5, prompt1, seed1, prompt2]) demo.launch(share=bf.share, inbrowser=True, inline=False)