# 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, sys import torch torch.backends.cudnn.benchmark = False import numpy as np import warnings warnings.filterwarnings('ignore') import warnings import torch from tqdm.auto import tqdm from PIL import Image import torch from movie_util import MovieSaver from typing import Callable, List, Optional, Union from latent_blending import get_time, yml_save, LatentBlending, add_frames_linear_interp, compare_dicts from stable_diffusion_holder import StableDiffusionHolder torch.set_grad_enabled(False) import gradio as gr import copy """ try this: button variant 'primary' for main call-to-action, 'secondary' for a more subdued style gr.Column(scale=1, min_width=600): """ #%% class BlendingFrontend(): def __init__(self, sdh=None): if sdh is None: self.use_debug = True else: self.use_debug = False self.lb = LatentBlending(sdh) self.share = True self.num_inference_steps = 20 self.depth_strength = 0.25 self.seed1 = 42 self.seed2 = 420 self.guidance_scale = 4.0 self.guidance_scale_mid_damper = 0.5 self.mid_compression_scaler = 1.2 self.prompt1 = "" self.prompt2 = "" self.negative_prompt = "" self.list_settings = [] self.state_prev = {} self.state_current = {} self.showing_current = True self.branch1_influence = 0.02 self.imgs_show_last = [] self.imgs_show_current = [] self.nmb_branches_final = 9 self.nmb_imgs_show = 5 # don't change self.fps = 30 self.duration = 10 self.max_size_imgs = 200 # gradio otherwise mega slow if not self.use_debug: self.lb.sdh.num_inference_steps = self.num_inference_steps self.height = self.lb.sdh.height self.width = self.lb.sdh.width else: self.height = 768 self.width = 768 def change_depth_strength(self, value): self.depth_strength = value print(f"changed depth_strength to {value}") def change_num_inference_steps(self, value): self.num_inference_steps = value print(f"changed num_inference_steps to {value}") def change_guidance_scale(self, value): self.guidance_scale = value self.lb.set_guidance_scale(value) print(f"changed guidance_scale to {value}") def change_guidance_scale_mid_damper(self, value): self.guidance_scale_mid_damper = value print(f"changed guidance_scale_mid_damper to {value}") def change_mid_compression_scaler(self, value): self.mid_compression_scaler = value print(f"changed mid_compression_scaler to {value}") def change_branch1_influence(self, value): self.branch1_influence = value print(f"changed branch1_influence to {value}") def change_height(self, value): self.height = value print(f"changed height to {value}") def change_width(self, value): self.width = value print(f"changed width to {value}") def change_nmb_branches_final(self, value): self.nmb_branches_final = value print(f"changed nmb_branches_final to {value}") def change_duration(self, value): self.duration = value print(f"changed duration to {value}") def change_fps(self, value): self.fps = value print(f"changed fps to {value}") def change_prompt1(self, value): self.prompt1 = value # print(f"changed prompt1 to {value}") def change_prompt2(self, value): self.prompt2 = value # print(f"changed prompt2 to {value}") def change_negative_prompt(self, value): self.negative_prompt = value def change_seed1(self, value): self.seed1 = int(value) def change_seed2(self, value): self.seed2 = int(value) def randomize_seed1(self): seed = np.random.randint(0, 10000000) self.change_seed1(seed) print(f"randomize_seed1: new seed = {self.seed1}") return seed def randomize_seed2(self): seed = np.random.randint(0, 10000000) self.change_seed2(seed) print(f"randomize_seed2: new seed = {self.seed2}") return seed def run(self): print("STARTING DIFFUSION!") self.state_prev = self.state_current.copy() self.state_current = self.get_state_dict() # Copy last iteration self.imgs_show_last = copy.deepcopy(self.imgs_show_current) if self.use_debug: list_imgs = [(255*np.random.rand(self.height,self.width,3)).astype(np.uint8) for l in range(5)] list_imgs = [Image.fromarray(l) for l in list_imgs] list_imgs = self.downscale_imgs(list_imgs) self.imgs_show_current = copy.deepcopy(list_imgs) print("DONE! SENDING BACK RESULTS") return list_imgs self.lb.set_width(self.width) self.lb.set_height(self.height) self.lb.autosetup_branching( depth_strength = self.depth_strength, num_inference_steps = self.num_inference_steps, nmb_branches_final = self.nmb_branches_final, nmb_mindist = 3) self.lb.set_prompt1(self.prompt1) self.lb.set_prompt2(self.prompt2) self.lb.set_negative_prompt(self.negative_prompt) self.lb.guidance_scale = self.guidance_scale self.lb.guidance_scale_mid_damper = self.guidance_scale_mid_damper self.lb.mid_compression_scaler = self.mid_compression_scaler self.lb.branch1_influence = self.branch1_influence fixed_seeds = [self.seed1, self.seed2] imgs_transition = self.lb.run_transition(fixed_seeds=fixed_seeds) print(f"DONE DIFFUSION! Resulted in {len(imgs_transition)} images") assert np.mod((self.nmb_branches_final-self.nmb_imgs_show)/4, 1)==0, 'self.nmb_branches_final illegal value!' idx_list = np.linspace(0, self.nmb_branches_final-1, self.nmb_imgs_show).astype(np.int32) list_imgs_preview = [] for j in idx_list: list_imgs_preview.append(Image.fromarray(imgs_transition[j])) # Save as jpgs on disk so we are not sending umcompressed data around timestamp = get_time('second') list_fp_imgs = [] for i in range(len(list_imgs_preview)): fp_img = f"img_preview_{i}_{timestamp}.jpg" list_imgs_preview[i].save(fp_img) list_fp_imgs.append(fp_img) # Save the movie as well imgs_transition_ext = add_frames_linear_interp(imgs_transition, self.duration, self.fps) # Save as movie fp_movie = f"movie_{timestamp}.mp4" if os.path.isfile(fp_movie): os.remove(fp_movie) ms = MovieSaver(fp_movie, fps=self.fps) for img in tqdm(imgs_transition_ext): ms.write_frame(img) ms.finalize() print("DONE SAVING MOVIE! SENDING BACK...") list_return = list_fp_imgs + [fp_movie] return list_return 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'] for v in grab_vars: state_dict[v] = getattr(self, v) return state_dict if __name__ == "__main__": fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt" # fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt" # sdh = 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") negative_prompt = gr.Textbox(label="negative prompt") with gr.Row(): nmb_branches_final = gr.Slider(5, 125, self.nmb_branches_final, step=4, label='nmb trans images', interactive=True) height = gr.Slider(256, 2048, self.height, step=128, label='height', interactive=True) width = gr.Slider(256, 2048, self.width, step=128, label='width', interactive=True) with gr.Row(): num_inference_steps = gr.Slider(5, 100, self.num_inference_steps, step=1, label='num_inference_steps', interactive=True) branch1_influence = gr.Slider(0.0, 1.0, self.branch1_influence, step=0.01, label='branch1_influence', interactive=True) guidance_scale = gr.Slider(1, 25, self.guidance_scale, step=0.1, label='guidance_scale', interactive=True) with gr.Row(): depth_strength = gr.Slider(0.01, 0.99, self.depth_strength, step=0.01, label='depth_strength', interactive=True) duration = gr.Slider(0.1, 30, self.duration, step=0.1, label='video duration', interactive=True) guidance_scale_mid_damper = gr.Slider(0.01, 2.0, self.guidance_scale_mid_damper, step=0.01, label='guidance_scale_mid_damper', interactive=True) with gr.Row(): b_run = gr.Button('COMPUTE!', variant='primary') seed1 = gr.Number(42, label="seed 1", interactive=True) seed2 = gr.Number(420, label="seed 2", interactive=True) with gr.Column(): b_newseed1 = gr.Button("randomize \nseed 1", variant='secondary') b_newseed2 = gr.Button("randomize \nseed 2", variant='secondary') with gr.Row(): img1 = gr.Image(label="1/5") img2 = gr.Image(label="2/5") img3 = gr.Image(label="3/5") img4 = gr.Image(label="4/5") img5 = gr.Image(label="5/5") with gr.Row(): vid = gr.Video() # Bind the on-change methods depth_strength.change(fn=self.change_depth_strength, inputs=depth_strength) num_inference_steps.change(fn=self.change_num_inference_steps, inputs=num_inference_steps) nmb_branches_final.change(fn=self.change_nmb_branches_final, inputs=nmb_branches_final) guidance_scale.change(fn=self.change_guidance_scale, inputs=guidance_scale) guidance_scale_mid_damper.change(fn=self.change_guidance_scale_mid_damper, inputs=guidance_scale_mid_damper) height.change(fn=self.change_height, inputs=height) width.change(fn=self.change_width, inputs=width) prompt1.change(fn=self.change_prompt1, inputs=prompt1) prompt2.change(fn=self.change_prompt2, inputs=prompt2) negative_prompt.change(fn=self.change_negative_prompt, inputs=negative_prompt) seed1.change(fn=self.change_seed1, inputs=seed1) seed2.change(fn=self.change_seed2, inputs=seed2) duration.change(fn=self.change_duration, inputs=duration) branch1_influence.change(fn=self.change_branch1_influence, inputs=branch1_influence) b_newseed1.click(self.randomize_seed1, outputs=seed1) b_newseed2.click(self.randomize_seed2, outputs=seed2) b_run.click(self.run, outputs=[img1, img2, img3, img4, img5, vid]) demo.launch(share=self.share, inbrowser=True, inline=False)