Update app.py

app.py

@@ -1,26 +1,409 @@
-import random
-import tempfile
-import time
-import gradio as gr
+from huggingface_hub import hf_hub_download
+hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1-base", filename="v2-1_512-ema-pruned.ckpt") 
+
+# Copyright 2022 Lunar Ring. All rights reserved.
+# Written by Johannes Stelzer, email [email protected] 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
-import math
-import re
-
-from gradio import inputs
-from diffusers import (
-    AutoencoderKL,
-    DDIMScheduler,
-    UNet2DConditionModel,
-)
-from modules.model import (
-    CrossAttnProcessor,
-    StableDiffusionPipeline,
-)
-from torchvision import transforms
-from transformers import CLIPTokenizer, CLIPTextModel
+from tqdm.auto import tqdm
 from PIL import Image
-from pathlib import Path
-from safetensors.torch import load_file
-import modules.safe as _
-from modules.lora import LoRANetwork
+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 = True
+        self.transition_can_be_computed = 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_crossfeed_power = self.lb.branch1_crossfeed_power
+        self.branch1_crossfeed_range = self.lb.branch1_crossfeed_range
+        self.branch1_crossfeed_decay = self.lb.branch1_crossfeed_decay
+        self.parental_crossfeed_power = self.lb.parental_crossfeed_power
+        self.parental_crossfeed_range = self.lb.parental_crossfeed_range
+        self.parental_crossfeed_power_decay = self.lb.parental_crossfeed_power_decay
+        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.list_imgs_shown_last = 5*[self.fp_img_empty]
+        self.list_all_segments = []
+        self.dp_session = ""
+        
+        
+    def init_save_dir(self):
+        load_dotenv(find_dotenv(), verbose=False) 
+        self.dp_out = os.getenv("DIR_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):
+        # Dont randomize seed if we are in a multi concat mode. we don't want to change this one otherwise the movie breaks
+        if len(self.list_all_segments) > 0:
+            seed = self.seed1
+        else:
+            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')] #seed
+        self.lb.seed2 = list_ui_elem[list_ui_keys.index('seed2')]
+        
+        self.lb.branch1_crossfeed_power = list_ui_elem[list_ui_keys.index('branch1_crossfeed_power')]
+        self.lb.branch1_crossfeed_range = list_ui_elem[list_ui_keys.index('branch1_crossfeed_range')]
+        self.lb.branch1_crossfeed_decay = list_ui_elem[list_ui_keys.index('branch1_crossfeed_decay')]
+        self.lb.parental_crossfeed_power = list_ui_elem[list_ui_keys.index('parental_crossfeed_power')]
+        self.lb.parental_crossfeed_range = list_ui_elem[list_ui_keys.index('parental_crossfeed_range')]
+        self.lb.parental_crossfeed_power_decay = list_ui_elem[list_ui_keys.index('parental_crossfeed_power_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):
+        if self.fp_img1 is None: # don't do anything
+            return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty]
+        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
+        self.transition_can_be_computed = 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.transition_can_be_computed:
+            list_return = [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty]
+            return list_return
+        
+        list_ui_elem = args
+        self.setup_lb(list_ui_elem)
+        print("STARTING TRANSITION...")
+        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
+        if len(self.list_all_segments) == 0:
+            timestamp_session = get_time('second')
+            self.dp_session = os.path.join(self.dp_out, f"session_{timestamp_session}")
+            os.makedirs(self.dp_session)
+            
+        self.transition_can_be_computed = False
+
+        idx_segment = len(self.list_all_segments) 
+        dp_segment = os.path.join(self.dp_session, f"segment_{str(idx_segment).zfill(3)}")
+            
+        self.list_all_segments.append(dp_segment)
+        self.lb.write_imgs_transition(dp_segment)
+        shutil.copyfile(self.fp_movie, os.path.join(dp_segment, "movie.mp4"))
+        
+        self.lb.swap_forward()
+        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 multi_concat(self):
+        list_fp_movies = []
+        for dp_segment in self.list_all_segments:
+            list_fp_movies.append(os.path.join(dp_segment, "movie.mp4"))
+    
+        # Concatenate movies and save
+        fp_final = os.path.join(self.dp_session, "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   
+
+
+        
+if __name__ == "__main__":    
+    
+    # fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt" 
+    fp_ckpt = "v2-1_512-ema-pruned.ckpt" 
+    bf = BlendingFrontend(StableDiffusionHolder(fp_ckpt)) 
+    # self = BlendingFrontend(None) 
+    
+    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, 200, bf.t_compute_max_allowed, step=1, label='compute budget for transition (seconds)', interactive=True) 
+                duration_video = gr.Slider(1, 100, bf.duration_video, step=0.1, label='result video duration (seconds)', interactive=True) 
+                height = gr.Slider(256, 2048, bf.height, step=128, label='height', interactive=True)
+                width = gr.Slider(256, 2048, bf.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, bf.num_inference_steps, step=1, label='num_inference_steps', interactive=True)
+                        guidance_scale = gr.Slider(1, 25, bf.guidance_scale, step=0.1, label='guidance_scale', interactive=True) 
+                        negative_prompt = gr.Textbox(label="negative prompt")          
+                
+                with gr.Accordion("Seed control: adjust seeds for first and last images", open=True):
+                    with gr.Row():
+                        b_newseed1 = gr.Button("randomize seed 1", variant='secondary')
+                        seed1 = gr.Number(bf.seed1, label="seed 1", interactive=True)
+                        seed2 = gr.Number(bf.seed2, label="seed 2", interactive=True)
+                        b_newseed2 = gr.Button("randomize seed 2", variant='secondary')
+                        
+                with gr.Accordion("Last image crossfeeding.", open=True):
+                    with gr.Row():
+                        branch1_crossfeed_power = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_power, step=0.01, label='branch1 crossfeed power', interactive=True) 
+                        branch1_crossfeed_range = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_range, step=0.01, label='branch1 crossfeed range', interactive=True) 
+                        branch1_crossfeed_decay = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_decay, step=0.01, label='branch1 crossfeed decay', interactive=True) 
+    
+                with gr.Accordion("Transition settings", open=True):
+                    with gr.Row():
+                        parental_crossfeed_power = gr.Slider(0.0, 1.0, bf.parental_crossfeed_power, step=0.01, label='parental crossfeed power', interactive=True) 
+                        parental_crossfeed_range = gr.Slider(0.0, 1.0, bf.parental_crossfeed_range, step=0.01, label='parental crossfeed range', interactive=True) 
+                        parental_crossfeed_power_decay = gr.Slider(0.0, 1.0, bf.parental_crossfeed_power_decay, step=0.01, label='parental crossfeed decay', interactive=True) 
+                    with gr.Row():
+                        depth_strength = gr.Slider(0.01, 0.99, bf.depth_strength, step=0.01, label='depth_strength', interactive=True) 
+                        guidance_scale_mid_damper = gr.Slider(0.01, 2.0, bf.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('append last movie segment (left) to multi movie (right)', variant='primary')
+                
+            
+            # Collect all UI elemts in list to easily pass as inputs in gradio
+            dict_ui_elem = {}
+            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_crossfeed_power"] = branch1_crossfeed_power
+            dict_ui_elem["branch1_crossfeed_range"] = branch1_crossfeed_range
+            dict_ui_elem["branch1_crossfeed_decay"] = branch1_crossfeed_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_crossfeed_range"] = parental_crossfeed_range
+            dict_ui_elem["parental_crossfeed_power"] = parental_crossfeed_power
+            dict_ui_elem["parental_crossfeed_power_decay"] = parental_crossfeed_power_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)
+            bf.list_ui_keys = list_ui_keys
+            
+            b_newseed1.click(bf.randomize_seed1, outputs=seed1)
+            b_newseed2.click(bf.randomize_seed2, outputs=seed2)
+            b_compute1.click(bf.compute_img1, inputs=list_ui_elem, outputs=[img1, img2, img3, img4, img5])
+            b_compute2.click(bf.compute_img2, inputs=list_ui_elem, outputs=[img2, img3, img4, img5])
+            b_compute_transition.click(bf.compute_transition, 
+                                        inputs=list_ui_elem,
+                                        outputs=[img2, img3, img4, vid_single])
+            
+            b_stackforward.click(bf.stack_forward, 
+                          inputs=[prompt2, seed2], 
+                          outputs=[vid_multi, img1, img2, img3, img4, img5, prompt1, seed1, prompt2])
+
+            
+    demo.launch(share=bf.share, inbrowser=True, inline=False)