Update app.py

app.py

@@ -1,404 +1,26 @@
-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 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 random
+import tempfile
+import time
+import gradio as gr
 import numpy as np
-import warnings
-warnings.filterwarnings('ignore')
-import warnings
 import torch
-from tqdm.auto import tqdm
+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 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
-
-
-#%%
-
-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)
+from pathlib import Path
+from safetensors.torch import load_file
+import modules.safe as _
+from modules.lora import LoRANetwork