From efed92ed90686e00f5e4dd3209c7444d02515e12 Mon Sep 17 00:00:00 2001 From: Johannes Stelzer Date: Sun, 8 Jan 2023 10:33:45 +0100 Subject: [PATCH] first gradio version --- gradio_ui.py | 338 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 338 insertions(+) create mode 100644 gradio_ui.py diff --git a/gradio_ui.py b/gradio_ui.py new file mode 100644 index 0000000..0382d15 --- /dev/null +++ b/gradio_ui.py @@ -0,0 +1,338 @@ +# 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 +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 +from stable_diffusion_holder import StableDiffusionHolder +torch.set_grad_enabled(False) +import gradio as gr +import copy + + + +""" +experiment with slider as output -> does it change in the browser? +guidance scale has no effect +get a movie as result +seed bug: also shows changes from before +mid compression scaler can destroy tree +""" + + +#%% + +def compare_dicts(a, b): + """ + Compares two dictionaries a and b and returns a dictionary c, with all + keys,values that have shared keys in a and b but same values in a and b. + The values of a and b are stacked together in the output. + Example: + a = {}; a['bobo'] = 4 + b = {}; b['bobo'] = 5 + c = dict_compare(a,b) + c = {"bobo",[4,5]} + """ + c = {} + for key in a.keys(): + if key in b.keys(): + val_a = a[key] + val_b = b[key] + if val_a != val_b: + c[key] = [val_a, val_b] + return c + +class BlendingFrontend(): + def __init__(self): + self.use_debug = False + self.share = True + self.height = 512 + self.width = 512 + self.num_inference_steps = 30 + 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.dp_base = '/home/lugo/latentblending' + self.list_settings = [] + self.state_prev = {} + self.state_current = {} + self.showing_current = True + self.imgs_show_last = [] + self.imgs_show_current = [] + if not self.use_debug: + self.init_diffusion() + + def init_diffusion(self): + fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt" + fp_config = 'configs/v2-inference.yaml' + + # 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, height=self.height , width=self.width, num_inference_steps=self.num_inference_steps) + self.lb = LatentBlending(sdh) + self.use_debug = False + + 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_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_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_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, x): + 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(200,200,3)).astype(np.uint8) for l in range(5)] + self.imgs_show_current = copy.deepcopy(list_imgs) + return list_imgs + # FIXME TODO ASSERTS + self.lb.sdh.height = self.height + self.lb.sdh.width = self.width + + # list_nmb_branches = [2, 6, 15] + # list_injection_strength = [0.0, self.depth_strength, 0.9] + + # self.lb.setup_branching( + # num_inference_steps = self.num_inference_steps, + # list_nmb_branches = list_nmb_branches, + # list_injection_strength = list_injection_strength + # ) + + self.lb.autosetup_branching( + depth_strength = self.depth_strength, + num_inference_steps = self.num_inference_steps, + nmb_branches_final = 13, + nmb_mindist = 2) + + self.lb.set_prompt1(self.prompt1) + self.lb.set_prompt2(self.prompt2) + + 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 + + fixed_seeds = [self.seed1, self.seed2] + imgs_transition = self.lb.run_transition(fixed_seeds=fixed_seeds) + imgs_transition = [Image.fromarray(l) for l in imgs_transition] + print(f"DONE DIFFUSION! Resulted in {len(imgs_transition)} images") + nmb_imgs_show = 5 + idx_list = np.arange(0, nmb_imgs_show).astype(np.int32)*3 + list_imgs = [] + for j in idx_list: + list_imgs.append(imgs_transition[j]) + + self.imgs_show_current = copy.deepcopy(list_imgs) + + return list_imgs + + + + def save(self): + if self.lb.tree_final_imgs[0] is None: + return + print("save is called!") + dp_img = os.path.join(self.dp_base, get_time("second")) + imgs_transition = self.lb.tree_final_imgs + self.lb.write_imgs_transition(dp_img, imgs_transition) + + fps = 20 + # Let's get more cheap frames via linear interpolation (duration_transition*fps frames) + imgs_transition_ext = add_frames_linear_interp(imgs_transition, 5, fps) + + # Save as MP4 + fp_movie = os.path.join(dp_img, "movie_lowres.mp4") + if os.path.isfile(fp_movie): + os.remove(fp_movie) + ms = MovieSaver(fp_movie, fps=fps) + for img in tqdm(imgs_transition_ext): + ms.write_frame(img) + ms.finalize() + return fp_movie + + + + 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 + + + def compare_last(self): + if len(self.state_prev) == 0 or len(self.state_current) == 0: + return "" + + if self.showing_current: + # inject the last images that were shown and return str of changes + str_fill = "showing last version: " + list_return = self.imgs_show_last + idx = 0 + verb = 'was' + self.showing_current = False + + elif not self.showing_current: + # inject the current images and show no string + str_fill = "showing current version: " + verb = 'is' + idx = 1 + list_return = self.imgs_show_current + self.showing_current = True + + dict_diff = compare_dicts(self.state_prev, self.state_current) + for key in dict_diff: + str_fill += f"{key} {verb} {dict_diff[key][idx]}, " + str_fill = str_fill[:-2] + list_return.extend([str_fill]) + return list_return + +self = BlendingFrontend() + + +with gr.Blocks() as demo: + + with gr.Row(): + text1 = gr.Textbox(label="prompt 1") + text2 = gr.Textbox(label="prompt 2") + + with gr.Row(): + depth_strength = gr.Slider(0.01, 0.99, self.depth_strength, step=0.01, label='depth_strength', interactive=True) + guidance_scale = gr.Slider(1, 25, self.guidance_scale, step=0.1, label='guidance_scale', 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) + mid_compression_scaler = gr.Slider(1.0, 2.0, self.mid_compression_scaler, step=0.01, label='mid_compression_scaler', interactive=True) + + with gr.Row(): + num_inference_steps = gr.Slider(5, 100, self.num_inference_steps, step=1, label='num_inference_steps', 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(): + b_newseed1 = gr.Button("rand seed 1") + seed1 = gr.Number(42, label="seed 1", interactive=True) + b_newseed2 = gr.Button("rand seed 2") + seed2 = gr.Number(420, label="seed 2", interactive=True) + b_compare = gr.Button("compare") + b_save = gr.Button('save!') + + with gr.Row(): + b_run = gr.Button('run preview!') + + 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(): + compare_text = gr.Textbox(label="") + + 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) + + 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) + mid_compression_scaler.change(fn=self.change_mid_compression_scaler, inputs=mid_compression_scaler) + + height.change(fn=self.change_height, inputs=height) + width.change(fn=self.change_width, inputs=width) + text1.change(fn=self.change_prompt1, inputs=text1) + text2.change(fn=self.change_prompt2, inputs=text2) + seed1.change(fn=self.change_seed1, inputs=seed1) + seed2.change(fn=self.change_seed2, inputs=seed2) + + b_newseed1.click(self.randomize_seed1, outputs=seed1) + b_newseed2.click(self.randomize_seed2, outputs=seed2) + b_compare.click(self.compare_last, outputs=[img1, img2, img3, img4, img5, compare_text]) + b_run.click(self.run, outputs=[img1, img2, img3, img4, img5]) + b_save.click(self.save, outputs=vid) + +demo.launch(share=self.share)