latentblending/gradio_ui.py

# 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, concatenate_movies
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
from dotenv import find_dotenv, load_dotenv
import shutil

"""
never hit compute trans -> multi movie add fail

"""


#%%

class BlendingFrontend():
    def __init__(self, sdh=None):
        self.num_inference_steps = 30
        if sdh is None:
            self.use_debug = True
            self.height = 768
            self.width = 768
        else:
            self.use_debug = False
            self.lb = LatentBlending(sdh)
            self.lb.sdh.num_inference_steps = self.num_inference_steps
            self.height = self.lb.sdh.height
            self.width = self.lb.sdh.width
        
        self.init_save_dir()
        self.save_empty_image()
        self.share = False
        self.depth_strength = 0.25
        self.seed1 = 420
        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.state_current = {}
        self.branch1_influence = 0.3
        self.branch1_max_depth_influence = 0.6
        self.branch1_influence_decay = 0.3
        self.parental_influence = 0.1
        self.parental_max_depth_influence = 1.0
        self.parental_influence_decay = 1.0    
        self.nmb_branches_final = 9
        self.nmb_imgs_show = 5 # don't change
        self.fps = 30
        self.duration_video = 10
        self.t_compute_max_allowed = 10
        self.list_fp_imgs_current = []
        self.current_timestamp = None
        self.recycle_img1 = False
        self.recycle_img2 = False
        self.fp_img1 = None
        self.fp_img2 = None
        self.multi_idx_current = -1
        self.multi_list_concat = []
        self.list_imgs_shown_last = 5*[self.fp_img_empty]
        self.nmb_trans_stack = 6
        
        
        
    def init_save_dir(self):
        load_dotenv(find_dotenv(), verbose=False) 
        self.dp_out = os.getenv("dp_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)
        
        
        
        # make dummy image
    def save_empty_image(self):
        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):
        seed = np.random.randint(0, 10000000)
        self.seed1 = int(seed)
        print(f"randomize_seed1: new seed = {self.seed1}")
        return seed
        
    def randomize_seed2(self):
        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_elem):
        # Collect latent blending variables
        self.state_current = self.get_state_dict()
        self.lb.set_width(list_ui_elem[list_ui_keys.index('width')])
        self.lb.set_height(list_ui_elem[list_ui_keys.index('height')])
        self.lb.set_prompt1(list_ui_elem[list_ui_keys.index('prompt1')])
        self.lb.set_prompt2(list_ui_elem[list_ui_keys.index('prompt2')])
        self.lb.set_negative_prompt(list_ui_elem[list_ui_keys.index('negative_prompt')])
        self.lb.guidance_scale = list_ui_elem[list_ui_keys.index('guidance_scale')]
        self.lb.guidance_scale_mid_damper = list_ui_elem[list_ui_keys.index('guidance_scale_mid_damper')]
        self.t_compute_max_allowed = list_ui_elem[list_ui_keys.index('duration_compute')]
        self.lb.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
        self.lb.sdh.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
        self.duration_video = list_ui_elem[list_ui_keys.index('duration_video')]
        self.lb.seed1 = list_ui_elem[list_ui_keys.index('seed1')]
        self.lb.seed2 = list_ui_elem[list_ui_keys.index('seed2')]
        
        self.lb.branch1_influence = list_ui_elem[list_ui_keys.index('branch1_influence')]
        self.lb.branch1_max_depth_influence = list_ui_elem[list_ui_keys.index('branch1_max_depth_influence')]
        self.lb.branch1_influence_decay = list_ui_elem[list_ui_keys.index('branch1_influence_decay')]
        self.lb.parental_influence = list_ui_elem[list_ui_keys.index('parental_influence')]
        self.lb.parental_max_depth_influence = list_ui_elem[list_ui_keys.index('parental_max_depth_influence')]
        self.lb.parental_influence_decay = list_ui_elem[list_ui_keys.index('parental_influence_decay')]
        self.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
        self.depth_strength = list_ui_elem[list_ui_keys.index('depth_strength')]
        
    
    def compute_img1(self, *args):
        list_ui_elem = args
        self.setup_lb(list_ui_elem)
        self.fp_img1 = os.path.join(self.dp_imgs, f"img1_{get_time('second')}.jpg")
        img1 = Image.fromarray(self.lb.compute_latents1(return_image=True))
        img1.save(self.fp_img1)
        self.recycle_img1 = True
        self.recycle_img2 = False
        return [self.fp_img1, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty]
    
    def compute_img2(self, *args):
        list_ui_elem = args
        self.setup_lb(list_ui_elem)
        self.fp_img2 = os.path.join(self.dp_imgs, f"img2_{get_time('second')}.jpg")
        img2 = Image.fromarray(self.lb.compute_latents2(return_image=True))
        img2.save(self.fp_img2)
        self.recycle_img2 = True
        return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img2]
        
    def compute_transition(self, *args):
        
        if not self.recycle_img1:
            print("compute first image before transition")
            return
        if not self.recycle_img2:
            print("compute last image before transition")
            return
        
        
        list_ui_elem = args
        self.setup_lb(list_ui_elem)
        print("STARTING DIFFUSION!")
        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]
            print("DONE! SENDING BACK RESULTS")
            return list_imgs
        
        fixed_seeds = [self.seed1, self.seed2]
        
        # Run Latent Blending
        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. 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
        self.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}_{self.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 = os.path.join(self.dp_movies, f"movie_{self.current_timestamp}.mp4") 
        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):
        # Save preview images, prompts and seeds into dictionary for stacking
        # self.list_imgs_shown_last = self.get_multi_trans_imgs_preview(f"lowres_{self.current_timestamp}")[0:5]
        timestamp_section = get_time('second')
        self.lb.write_imgs_transition(os.path.join(self.dp_out, f"lowres_{timestamp_section}"))
        self.lb.write_imgs_transition(os.path.join(self.dp_out, "lowres_current"))
        shutil.copyfile(self.fp_movie, os.path.join(self.dp_out, f"lowres_{timestamp_section}", "movie.mp4"))
        
        self.lb.swap_forward()
        self.multi_append()
        fp_multi = self.multi_concat()
        list_out = [fp_multi]
        list_out.extend([self.fp_img2])
        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 get_list_all_stacked(self):
        list_all = os.listdir(os.path.join(self.dp_out))
        list_all = [l for l in list_all if l[:8]=="lowres_2"]
        list_all.sort()
        return list_all

       
    def multi_append(self):
        list_all = self.get_list_all_stacked()
        dn = list_all[self.multi_idx_current]
        self.multi_list_concat.append(dn)
        list_short = [dn[7:] for dn in self.multi_list_concat]
        str_out = "\n".join(list_short)
        return str_out
       
    def multi_reset(self):
        self.multi_list_concat = []
        str_out = ""
        return str_out
       
    def multi_concat(self):
        # Make new output directory
        dp_multi = os.path.join(self.dp_out, f"multi_{get_time('second')}")       
        os.makedirs(dp_multi, exist_ok=False)
        
        # Copy all low-res folders (prepending multi001_xxxx), however leave out the movie.mp4
        # also collect all movie.mp4
        list_fp_movies = []
        for i, dn in enumerate(self.multi_list_concat):
            dp_source = os.path.join(self.dp_out, dn)
            dp_sequence = os.path.join(dp_multi, f"{str(i).zfill(3)}_{dn}")
            os.makedirs(dp_sequence, exist_ok=False)
            list_source = os.listdir(dp_source)
            list_source = [l for l in list_source if not l.endswith(".mp4")]
            for fn in list_source:
                shutil.copyfile(os.path.join(dp_source, fn), os.path.join(dp_sequence, fn))
            list_fp_movies.append(os.path.join(dp_source, "movie.mp4"))
            
        # Concatenate movies and save
        fp_final = os.path.join(dp_multi, "movie.mp4")
        concatenate_movies(fp_final, list_fp_movies)
        return fp_final

    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 get_img_rand():
    return (255*np.random.rand(self.height,self.width,3)).astype(np.uint8)

def generate_list_output(
        prompt1,
        prompt2,
        seed1,
        seed2,
        list_fp_imgs,
        ):
    list_output = []
    list_output.append(prompt1)
    list_output.append(prompt2)
    list_output.append(seed1)
    list_output.append(seed2)
    for fp_img in list_fp_imgs:
        list_output.append(fp_img)
    
    return list_output


        
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" 
    self = BlendingFrontend(StableDiffusionHolder(fp_ckpt)) # Yes this is possible in python and yes it is an awesome trick
    # self = BlendingFrontend(None) # Yes this is possible in python and yes it is an awesome trick
    
    dict_ui_elem = {}
    
    with gr.Blocks() as demo:
        with gr.Tab("Single Transition"):
            with gr.Row():
                prompt1 = gr.Textbox(label="prompt 1")
                prompt2 = gr.Textbox(label="prompt 2")
            
            with gr.Row():
                duration_compute = gr.Slider(5, 45, self.t_compute_max_allowed, step=1, label='compute budget for transition (seconds)', interactive=True) 
                duration_video = gr.Slider(0.1, 30, self.duration_video, step=0.1, label='result video duration (seconds)', 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.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, self.num_inference_steps, step=1, label='num_inference_steps', interactive=True)
                        guidance_scale = gr.Slider(1, 25, self.guidance_scale, step=0.1, label='guidance_scale', interactive=True) 
                        negative_prompt = gr.Textbox(label="negative prompt")          
                
                with gr.Accordion("Seeds control", open=True):
                    with gr.Row():
                        b_newseed1 = gr.Button("randomize seed 1", variant='secondary')
                        seed1 = gr.Number(self.seed1, label="seed 1", interactive=True)
                        seed2 = gr.Number(self.seed2, label="seed 2", interactive=True)
                        b_newseed2 = gr.Button("randomize seed 2", variant='secondary')
                        
                with gr.Accordion("Crossfeeding for last image", open=True):
                    with gr.Row():
                        branch1_influence = gr.Slider(0.0, 1.0, self.branch1_influence, step=0.01, label='crossfeed power', interactive=True) 
                        branch1_max_depth_influence = gr.Slider(0.0, 1.0, self.branch1_max_depth_influence, step=0.01, label='crossfeed range', interactive=True) 
                        branch1_influence_decay = gr.Slider(0.0, 1.0, self.branch1_influence_decay, step=0.01, label='crossfeed decay', interactive=True) 
    
                with gr.Accordion("Transition settings", open=True):
                    with gr.Row():
                        parental_influence = gr.Slider(0.0, 1.0, self.parental_influence, step=0.01, label='parental power', interactive=True) 
                        parental_max_depth_influence = gr.Slider(0.0, 1.0, self.parental_max_depth_influence, step=0.01, label='parental range', interactive=True) 
                        parental_influence_decay = gr.Slider(0.0, 1.0, self.parental_influence_decay, step=0.01, label='parental decay', interactive=True) 
                    with gr.Row():
                        depth_strength = gr.Slider(0.01, 0.99, self.depth_strength, step=0.01, label='depth_strength', 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_compute1 = gr.Button('compute first image', variant='primary')
                b_compute_transition = gr.Button('compute transition', variant='primary')
                b_compute2 = gr.Button('compute last image', 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="single trans")
                vid_multi = gr.Video(label="multi trans")
                
            with gr.Row():
                # b_restart = gr.Button("RESTART EVERYTHING")
                b_stackforward = gr.Button('multi-movie start next segment (move last image -> first image)', variant='primary')
                
            
            # Collect all UI elemts in list to easily pass as inputs
            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_influence"] = branch1_influence
            dict_ui_elem["branch1_max_depth_influence"] = branch1_max_depth_influence
            dict_ui_elem["branch1_influence_decay"] = branch1_influence_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_max_depth_influence"] = parental_max_depth_influence
            dict_ui_elem["parental_influence"] = parental_influence
            dict_ui_elem["parental_influence_decay"] = parental_influence_decay
            
            # Convert to list, as gradio doesn't seem to accept dicts
            list_ui_elem = []
            list_ui_keys = []
            for k in dict_ui_elem.keys():
                list_ui_elem.append(dict_ui_elem[k])
                list_ui_keys.append(k)
            self.list_ui_keys = list_ui_keys
            
            b_newseed1.click(self.randomize_seed1, outputs=seed1)
            b_newseed2.click(self.randomize_seed2, outputs=seed2)
            b_compute1.click(self.compute_img1, inputs=list_ui_elem, outputs=[img1, img2, img3, img4, img5])
            b_compute2.click(self.compute_img2, inputs=list_ui_elem, outputs=[img2, img3, img4, img5])
            b_compute_transition.click(self.compute_transition, 
                                        inputs=list_ui_elem,
                                        outputs=[img2, img3, img4, vid_single])
            
            b_stackforward.click(self.stack_forward, 
                          inputs=[prompt2, seed2], 
                          outputs=[vid_multi, img1, img2, img3, img4, img5, prompt1, seed1, prompt2])

            # b_restart.click(self.multi_reset)
            
            
    demo.launch(share=self.share, inbrowser=True, inline=False)