401 lines
16 KiB
Python
401 lines
16 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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#%%
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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 = 20
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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.list_settings = []
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self.state_current = {}
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self.showing_current = True
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self.branch1_influence = 0.02
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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 = 10
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self.dict_multi_trans = {}
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self.dict_multi_trans_include = {}
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self.multi_trans_currently_shown = []
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self.list_fp_imgs_current = []
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self.current_timestamp = None
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self.nmb_trans_stack = 8
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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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# make dummy image
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self.fp_img_empty = '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 change_depth_strength(self, value):
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self.depth_strength = value
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print(f"changed depth_strength to {value}")
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def change_num_inference_steps(self, value):
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self.num_inference_steps = value
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print(f"changed num_inference_steps to {value}")
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def change_guidance_scale(self, value):
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self.guidance_scale = value
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self.lb.set_guidance_scale(value)
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print(f"changed guidance_scale to {value}")
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def change_guidance_scale_mid_damper(self, value):
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self.guidance_scale_mid_damper = value
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print(f"changed guidance_scale_mid_damper to {value}")
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def change_mid_compression_scaler(self, value):
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self.mid_compression_scaler = value
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print(f"changed mid_compression_scaler to {value}")
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def change_branch1_influence(self, value):
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self.branch1_influence = value
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print(f"changed branch1_influence to {value}")
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def change_height(self, value):
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self.height = value
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print(f"changed height to {value}")
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def change_width(self, value):
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self.width = value
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print(f"changed width to {value}")
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def change_nmb_branches_final(self, value):
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self.nmb_branches_final = value
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print(f"changed nmb_branches_final to {value}")
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def change_duration(self, value):
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self.duration = value
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print(f"changed duration to {value}")
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def change_fps(self, value):
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self.fps = value
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print(f"changed fps to {value}")
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def change_negative_prompt(self, value):
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self.negative_prompt = value
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def change_seed1(self, value):
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self.seed1 = int(value)
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def change_seed2(self, value):
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self.seed2 = int(value)
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def randomize_seed1(self):
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seed = np.random.randint(0, 10000000)
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self.change_seed1(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.change_seed2(seed)
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print(f"randomize_seed2: new seed = {self.seed2}")
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return seed
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def compute_transition(self, prompt1, prompt2):
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self.prompt1 = prompt1
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self.prompt2 = prompt2
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print("STARTING DIFFUSION!")
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self.state_current = self.get_state_dict()
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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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# Collect latent blending variables
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self.lb.set_width(self.width)
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self.lb.set_height(self.height)
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self.lb.autosetup_branching(
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depth_strength = self.depth_strength,
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num_inference_steps = self.num_inference_steps,
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nmb_branches_final = self.nmb_branches_final,
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nmb_mindist = 3)
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self.lb.set_prompt1(self.prompt1)
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self.lb.set_prompt2(self.prompt2)
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self.lb.set_negative_prompt(self.negative_prompt)
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self.lb.guidance_scale = self.guidance_scale
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self.lb.guidance_scale_mid_damper = self.guidance_scale_mid_damper
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self.lb.mid_compression_scaler = self.mid_compression_scaler
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self.lb.branch1_influence = self.branch1_influence
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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(fixed_seeds=fixed_seeds)
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print(f"Latent Blending pass finished. Resulted in {len(imgs_transition)} images")
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# Subselect the preview images (hard fixed to self.nmb_imgs_show=5)
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assert np.mod((self.nmb_branches_final-self.nmb_imgs_show)/4, 1)==0, 'self.nmb_branches_final illegal value!'
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idx_list = np.linspace(0, self.nmb_branches_final-1, self.nmb_imgs_show).astype(np.int32)
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list_imgs_preview = []
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for j in idx_list:
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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, 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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return f"movie_{timestamp}.mp4"
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else:
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return f"movie_stacked_{timestamp}.mp4"
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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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self.dict_multi_trans[self.current_timestamp] = generate_list_output(self.prompt1, self.prompt2, self.seed1, self.seed2, self.list_fp_imgs_current)
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self.dict_multi_trans_include[self.current_timestamp] = True
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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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list_out_multi_tab = self.update_trans_stacks()
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list_out.extend(list_out_multi_tab)
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# self.nmb_trans_stack = len(self.dict_multi_trans_include)
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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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for timestamp in self.multi_trans_currently_shown:
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if timestamp is not None:
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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 update_trans_stacks(self):
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print("Updating transition stack...")
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self.multi_trans_currently_shown = []
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list_output = []
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# Figure out which transitions should be shown
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for timestamp in self.dict_multi_trans_include.keys():
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if len(self.multi_trans_currently_shown) >= self.nmb_trans_stack:
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continue
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if self.dict_multi_trans_include[timestamp]:
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last_timestamp_vals = self.dict_multi_trans[timestamp]
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list_output.extend(self.dict_multi_trans[timestamp])
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self.multi_trans_currently_shown.append(timestamp)
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print(f"including timestamp: {timestamp}")
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# Fill with empty images if below nmb_trans_stack
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nmb_empty_missing = self.nmb_trans_stack - len(self.multi_trans_currently_shown)
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for i in range(nmb_empty_missing):
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list_output.extend([gr.update(visible=False)]*len(last_timestamp_vals))
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self.multi_trans_currently_shown.append(None)
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return list_output
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def remove_trans(self, idx_row):
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idx_row = int(idx_row)
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# do removal...
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if idx_row < len(self.multi_trans_currently_shown):
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timestamp = self.multi_trans_currently_shown[idx_row]
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if timestamp in self.dict_multi_trans_include.keys():
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self.dict_multi_trans_include[timestamp] = False
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print(f"remove_trans called: {timestamp}")
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else:
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print(f"remove_trans called: idx_row too large {idx_row}")
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return self.update_trans_stacks()
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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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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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negative_prompt = gr.Textbox(label="negative prompt")
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with gr.Row():
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nmb_branches_final = gr.Slider(5, 125, self.nmb_branches_final, step=4, label='nmb trans images', 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.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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branch1_influence = gr.Slider(0.0, 1.0, self.branch1_influence, step=0.01, label='branch1_influence', 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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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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duration = gr.Slider(0.1, 30, self.duration, step=0.1, label='video duration', 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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with gr.Row():
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seed1 = gr.Number(42, 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.Row():
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b_compute_transition = gr.Button('compute transition', 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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# Bind the on-change methods
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depth_strength.change(fn=self.change_depth_strength, inputs=depth_strength)
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num_inference_steps.change(fn=self.change_num_inference_steps, inputs=num_inference_steps)
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nmb_branches_final.change(fn=self.change_nmb_branches_final, inputs=nmb_branches_final)
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guidance_scale.change(fn=self.change_guidance_scale, inputs=guidance_scale)
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guidance_scale_mid_damper.change(fn=self.change_guidance_scale_mid_damper, inputs=guidance_scale_mid_damper)
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height.change(fn=self.change_height, inputs=height)
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width.change(fn=self.change_width, inputs=width)
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negative_prompt.change(fn=self.change_negative_prompt, inputs=negative_prompt)
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seed1.change(fn=self.change_seed1, inputs=seed1)
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seed2.change(fn=self.change_seed2, inputs=seed2)
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duration.change(fn=self.change_duration, inputs=duration)
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branch1_influence.change(fn=self.change_branch1_influence, inputs=branch1_influence)
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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_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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b_compute_transition.click(self.compute_transition,
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inputs=[prompt1, prompt2],
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outputs=[img1, img2, img3, img4, img5, vid_transition])
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demo.launch(share=self.share, inbrowser=True, inline=False)
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