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| # Copyright 2022 Lunar Ring. All rights reserved. | |
| # Written by Johannes Stelzer, email [email protected] 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 uuid | |
| 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 = uuid.uuid4().hex | |
| 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: | |
| gr.HTML("""<h1>Latent Blending</h1> | |
| <p>Create butter-smooth transitions between prompts, powered by stable diffusion</p> | |
| <p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. | |
| <br/> | |
| <a href="https://huggingface.co/spaces/lunarring/latentblending?duplicate=true"> | |
| <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> | |
| </p>""") | |
| with gr.Row(): | |
| prompt1 = gr.Textbox(label="prompt 1") | |
| prompt2 = gr.Textbox(label="prompt 2") | |
| with gr.Row(): | |
| duration_compute = gr.Slider(10, 25, bf.t_compute_max_allowed, step=1, label='waiting time', interactive=True) | |
| duration_video = gr.Slider(1, 100, bf.duration_video, step=0.1, label='video duration', interactive=True) | |
| height = gr.Slider(256, 1024, bf.height, step=128, label='height', interactive=True) | |
| width = gr.Slider(256, 1024, 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('step1: compute first image', variant='primary') | |
| b_compute2 = gr.Button('step2: compute last image', variant='primary') | |
| b_compute_transition = gr.Button('step3: compute transition', 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 | |
| - waiting time: 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) | |