406 lines
17 KiB
Python
406 lines
17 KiB
Python
# Copyright 2022 Lunar Ring. All rights reserved.
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# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os, sys
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import torch
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torch.backends.cudnn.benchmark = False
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import numpy as np
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import warnings
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warnings.filterwarnings('ignore')
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import warnings
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import torch
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from tqdm.auto import tqdm
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from PIL import Image
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import torch
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from movie_util import MovieSaver, concatenate_movies
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from typing import Callable, List, Optional, Union
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from latent_blending import get_time, yml_save, LatentBlending, add_frames_linear_interp, compare_dicts
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from stable_diffusion_holder import StableDiffusionHolder
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torch.set_grad_enabled(False)
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import gradio as gr
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import copy
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from dotenv import find_dotenv, load_dotenv
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#%%
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class BlendingFrontend():
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def __init__(self, sdh=None):
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if sdh is None:
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self.use_debug = True
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else:
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self.use_debug = False
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self.lb = LatentBlending(sdh)
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self.share = True
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self.num_inference_steps = 30
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self.depth_strength = 0.25
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self.seed1 = 42
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self.seed2 = 420
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self.guidance_scale = 4.0
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self.guidance_scale_mid_damper = 0.5
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self.mid_compression_scaler = 1.2
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self.prompt1 = ""
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self.prompt2 = ""
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self.negative_prompt = ""
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self.state_current = {}
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self.branch1_influence = 0.3
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self.branch1_max_depth_influence = 0.6
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self.branch1_influence_decay = 0.3
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self.parental_influence = 0.1
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self.parental_max_depth_influence = 1.0
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self.parental_influence_decay = 1.0
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self.nmb_branches_final = 9
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self.nmb_imgs_show = 5 # don't change
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self.fps = 30
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self.duration_video = 10
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self.t_compute_max_allowed = 10
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self.list_fp_imgs_current = []
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self.current_timestamp = None
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self.recycle_img1 = False
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self.recycle_img2 = False
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if not self.use_debug:
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self.lb.sdh.num_inference_steps = self.num_inference_steps
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self.height = self.lb.sdh.height
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self.width = self.lb.sdh.width
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else:
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self.height = 768
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self.width = 768
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self.init_save_dir()
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def init_save_dir(self):
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load_dotenv(find_dotenv(), verbose=False)
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try:
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self.dp_out = os.getenv("dp_out")
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except Exception as e:
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self.dp_out = ""
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# make dummy image
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def save_empty_image(self):
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self.fp_img_empty = os.path.join(self.dp_out, 'empty.jpg')
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Image.fromarray(np.zeros((self.height, self.width, 3), dtype=np.uint8)).save(self.fp_img_empty, quality=5)
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def randomize_seed1(self):
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seed = np.random.randint(0, 10000000)
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self.seed1 = int(seed)
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print(f"randomize_seed1: new seed = {self.seed1}")
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return seed
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def randomize_seed2(self):
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seed = np.random.randint(0, 10000000)
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self.seed2 = int(seed)
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print(f"randomize_seed2: new seed = {self.seed2}")
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return seed
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def setup_lb(self, list_ui_elem):
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# Collect latent blending variables
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self.state_current = self.get_state_dict()
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self.lb.set_width(list_ui_elem[list_ui_keys.index('width')])
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self.lb.set_height(list_ui_elem[list_ui_keys.index('height')])
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self.lb.set_prompt1(list_ui_elem[list_ui_keys.index('prompt1')])
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self.lb.set_prompt2(list_ui_elem[list_ui_keys.index('prompt2')])
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self.lb.set_negative_prompt(list_ui_elem[list_ui_keys.index('negative_prompt')])
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self.lb.guidance_scale = list_ui_elem[list_ui_keys.index('guidance_scale')]
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self.lb.guidance_scale_mid_damper = list_ui_elem[list_ui_keys.index('guidance_scale_mid_damper')]
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self.t_compute_max_allowed = list_ui_elem[list_ui_keys.index('duration_compute')]
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self.lb.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
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self.lb.sdh.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
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self.duration_video = list_ui_elem[list_ui_keys.index('duration_video')]
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self.lb.seed1 = list_ui_elem[list_ui_keys.index('seed1')]
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self.lb.seed2 = list_ui_elem[list_ui_keys.index('seed2')]
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self.lb.branch1_influence = list_ui_elem[list_ui_keys.index('branch1_influence')]
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self.lb.branch1_max_depth_influence = list_ui_elem[list_ui_keys.index('branch1_max_depth_influence')]
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self.lb.branch1_influence_decay = list_ui_elem[list_ui_keys.index('branch1_influence_decay')]
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self.lb.parental_influence = list_ui_elem[list_ui_keys.index('parental_influence')]
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self.lb.parental_max_depth_influence = list_ui_elem[list_ui_keys.index('parental_max_depth_influence')]
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self.lb.parental_influence_decay = list_ui_elem[list_ui_keys.index('parental_influence_decay')]
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def compute_img1(self, *args):
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list_ui_elem = args
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self.setup_lb(list_ui_elem)
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fp_img1 = os.path.join(self.dp_out, f"img1_{get_time('second')}.jpg")
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img1 = Image.fromarray(self.lb.compute_latents1(return_image=True))
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img1.save(fp_img1)
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self.save_empty_image()
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self.recycle_img1 = True
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self.recycle_img2 = False
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return [fp_img1, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty]
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def compute_img2(self, *args):
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list_ui_elem = args
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self.setup_lb(list_ui_elem)
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fp_img2 = os.path.join(self.dp_out, f"img2_{get_time('second')}.jpg")
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img2 = Image.fromarray(self.lb.compute_latents2(return_image=True))
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img2.save(fp_img2)
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self.recycle_img2 = True
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return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, fp_img2]
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def compute_transition(self, *args):
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if not self.recycle_img1:
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print("compute first image before transition")
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return
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if not self.recycle_img2:
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print("compute last image before transition")
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return
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list_ui_elem = args
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self.setup_lb(list_ui_elem)
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print("STARTING DIFFUSION!")
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if self.use_debug:
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list_imgs = [(255*np.random.rand(self.height,self.width,3)).astype(np.uint8) for l in range(5)]
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list_imgs = [Image.fromarray(l) for l in list_imgs]
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print("DONE! SENDING BACK RESULTS")
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return list_imgs
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fixed_seeds = [self.seed1, self.seed2]
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# Run Latent Blending
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imgs_transition = self.lb.run_transition(
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recycle_img1=self.recycle_img1,
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recycle_img2=self.recycle_img2,
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num_inference_steps=self.num_inference_steps,
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depth_strength=self.depth_strength,
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t_compute_max_allowed=self.t_compute_max_allowed,
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fixed_seeds=fixed_seeds
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)
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print(f"Latent Blending pass finished. Resulted in {len(imgs_transition)} images")
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# Subselect three preview images
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idx_img_prev = np.round(np.linspace(0, len(imgs_transition)-1, 5)[1:-1]).astype(np.int32)
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list_imgs_preview = []
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for j in idx_img_prev:
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list_imgs_preview.append(Image.fromarray(imgs_transition[j]))
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# Save the preview imgs as jpgs on disk so we are not sending umcompressed data around
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self.current_timestamp = get_time('second')
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self.list_fp_imgs_current = []
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for i in range(len(list_imgs_preview)):
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fp_img = f"img_preview_{i}_{self.current_timestamp}.jpg"
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list_imgs_preview[i].save(fp_img)
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self.list_fp_imgs_current.append(fp_img)
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# Insert cheap frames for the movie
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imgs_transition_ext = add_frames_linear_interp(imgs_transition, self.duration_video, self.fps)
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# Save as movie
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fp_movie = self.get_fp_movie(self.current_timestamp)
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if os.path.isfile(fp_movie):
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os.remove(fp_movie)
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ms = MovieSaver(fp_movie, fps=self.fps)
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for img in tqdm(imgs_transition_ext):
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ms.write_frame(img)
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ms.finalize()
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print("DONE SAVING MOVIE! SENDING BACK...")
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# Assemble Output, updating the preview images and le movie
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list_return = self.list_fp_imgs_current + [fp_movie]
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return list_return
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def get_fp_movie(self, timestamp, is_stacked=False):
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if not is_stacked:
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fn = f"movie_{timestamp}.mp4"
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else:
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fn = f"movie_stacked_{timestamp}.mp4"
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fp = os.path.join(self.dp_out, fn)
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return fp
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def stack_forward(self, prompt2, seed2):
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# Save preview images, prompts and seeds into dictionary for stacking
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dp_out = os.path.join(self.dp_out, get_time('second'))
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self.lb.write_imgs_transition(dp_out)
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self.lb.swap_forward()
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list_out = [self.list_fp_imgs_current[-1]]
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list_out.extend([self.fp_img_empty]*4)
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list_out.append(prompt2)
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list_out.append(seed2)
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list_out.append("")
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list_out.append(np.random.randint(0, 10000000))
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return list_out
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def stack_movie(self):
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# collect all that are in...
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list_fp_movies = []
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list_fp_movies.append(self.get_fp_movie(timestamp))
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fp_stacked = self.get_fp_movie(get_time('second'), True)
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concatenate_movies(fp_stacked, list_fp_movies)
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return fp_stacked
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def get_state_dict(self):
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state_dict = {}
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grab_vars = ['prompt1', 'prompt2', 'seed1', 'seed2', 'height', 'width',
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'num_inference_steps', 'depth_strength', 'guidance_scale',
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'guidance_scale_mid_damper', 'mid_compression_scaler']
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for v in grab_vars:
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state_dict[v] = getattr(self, v)
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return state_dict
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def get_img_rand():
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return (255*np.random.rand(self.height,self.width,3)).astype(np.uint8)
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def generate_list_output(
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prompt1,
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prompt2,
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seed1,
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seed2,
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list_fp_imgs,
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):
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list_output = []
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list_output.append(prompt1)
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list_output.append(prompt2)
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list_output.append(seed1)
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list_output.append(seed2)
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for fp_img in list_fp_imgs:
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list_output.append(fp_img)
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return list_output
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if __name__ == "__main__":
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# fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
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fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
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sdh = StableDiffusionHolder(fp_ckpt)
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self = BlendingFrontend(sdh) # Yes this is possible in python and yes it is an awesome trick
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# self = BlendingFrontend(None) # Yes this is possible in python and yes it is an awesome trick
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dict_ui_elem = {}
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with gr.Blocks() as demo:
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with gr.Row():
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prompt1 = gr.Textbox(label="prompt 1")
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prompt2 = gr.Textbox(label="prompt 2")
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with gr.Row():
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duration_compute = gr.Slider(5, 45, self.t_compute_max_allowed, step=1, label='compute budget for transition (seconds)', interactive=True)
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duration_video = gr.Slider(0.1, 30, self.duration_video, step=0.1, label='result video duration (seconds)', interactive=True)
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height = gr.Slider(256, 2048, self.height, step=128, label='height', interactive=True)
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width = gr.Slider(256, 2048, self.width, step=128, label='width', interactive=True)
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with gr.Accordion("Advanced Settings (click to expand)", open=False):
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with gr.Accordion("Diffusion settings", open=True):
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with gr.Row():
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num_inference_steps = gr.Slider(5, 100, self.num_inference_steps, step=1, label='num_inference_steps', interactive=True)
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guidance_scale = gr.Slider(1, 25, self.guidance_scale, step=0.1, label='guidance_scale', interactive=True)
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negative_prompt = gr.Textbox(label="negative prompt")
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with gr.Accordion("Seeds control", open=True):
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with gr.Row():
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seed1 = gr.Number(420, label="seed 1", interactive=True)
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b_newseed1 = gr.Button("randomize seed 1", variant='secondary')
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seed2 = gr.Number(420, label="seed 2", interactive=True)
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b_newseed2 = gr.Button("randomize seed 2", variant='secondary')
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with gr.Accordion("Crossfeeding for last image", open=True):
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with gr.Row():
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branch1_influence = gr.Slider(0.0, 1.0, self.branch1_influence, step=0.01, label='crossfeed power', interactive=True)
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branch1_max_depth_influence = gr.Slider(0.0, 1.0, self.branch1_max_depth_influence, step=0.01, label='crossfeed range', interactive=True)
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branch1_influence_decay = gr.Slider(0.0, 1.0, self.branch1_influence_decay, step=0.01, label='crossfeed decay', interactive=True)
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with gr.Accordion("Transition settings", open=True):
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with gr.Row():
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depth_strength = gr.Slider(0.01, 0.99, self.depth_strength, step=0.01, label='depth_strength', interactive=True)
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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)
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parental_influence = gr.Slider(0.0, 1.0, self.parental_influence, step=0.01, label='parental power', interactive=True)
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parental_max_depth_influence = gr.Slider(0.0, 1.0, self.parental_max_depth_influence, step=0.01, label='parental range', interactive=True)
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parental_influence_decay = gr.Slider(0.0, 1.0, self.parental_influence_decay, step=0.01, label='parental decay', interactive=True)
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with gr.Row():
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b_compute1 = gr.Button('compute first image', variant='primary')
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b_compute_transition = gr.Button('compute transition', variant='primary')
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b_compute2 = gr.Button('compute last image', variant='primary')
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with gr.Row():
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img1 = gr.Image(label="1/5")
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img2 = gr.Image(label="2/5")
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img3 = gr.Image(label="3/5")
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img4 = gr.Image(label="4/5")
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img5 = gr.Image(label="5/5")
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with gr.Row():
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vid_transition = gr.Video()
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with gr.Row():
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b_stackforward = gr.Button('multi-movie start next segment (move last image -> first image)')
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# Collect all UI elemts in list to easily pass as inputs
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dict_ui_elem["prompt1"] = prompt1
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dict_ui_elem["negative_prompt"] = negative_prompt
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dict_ui_elem["prompt2"] = prompt2
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dict_ui_elem["duration_compute"] = duration_compute
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dict_ui_elem["duration_video"] = duration_video
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dict_ui_elem["height"] = height
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dict_ui_elem["width"] = width
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dict_ui_elem["depth_strength"] = depth_strength
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dict_ui_elem["branch1_influence"] = branch1_influence
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dict_ui_elem["branch1_max_depth_influence"] = branch1_max_depth_influence
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dict_ui_elem["branch1_influence_decay"] = branch1_influence_decay
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dict_ui_elem["num_inference_steps"] = num_inference_steps
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dict_ui_elem["guidance_scale"] = guidance_scale
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dict_ui_elem["guidance_scale_mid_damper"] = guidance_scale_mid_damper
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dict_ui_elem["seed1"] = seed1
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dict_ui_elem["seed2"] = seed2
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dict_ui_elem["parental_max_depth_influence"] = parental_max_depth_influence
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dict_ui_elem["parental_influence"] = parental_influence
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dict_ui_elem["parental_influence_decay"] = parental_influence_decay
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# Convert to list, as gradio doesn't seem to accept dicts
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list_ui_elem = []
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list_ui_keys = []
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for k in dict_ui_elem.keys():
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list_ui_elem.append(dict_ui_elem[k])
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list_ui_keys.append(k)
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self.list_ui_keys = list_ui_keys
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b_newseed1.click(self.randomize_seed1, outputs=seed1)
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b_newseed2.click(self.randomize_seed2, outputs=seed2)
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b_compute1.click(self.compute_img1, inputs=list_ui_elem, outputs=[img1, img2, img3, img4, img5])
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b_compute2.click(self.compute_img2, inputs=list_ui_elem, outputs=[img2, img3, img4, img5])
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b_compute_transition.click(self.compute_transition,
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inputs=list_ui_elem,
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outputs=[img2, img3, img4, vid_transition])
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b_stackforward.click(self.stack_forward,
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inputs=[prompt2, seed2],
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outputs=[img1, img2, img3, img4, img5, prompt1, seed1, prompt2])
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demo.launch(share=self.share, inbrowser=True, inline=False)
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