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app.py

@@ -1 +1,492 @@
-import gradio_ui
+# 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
+import torch
+torch.backends.cudnn.benchmark = False
+torch.set_grad_enabled(False)
+import numpy as np
+import warnings
+warnings.filterwarnings('ignore')
+import warnings
+from tqdm.auto import tqdm
+from PIL import Image
+from movie_util import MovieSaver, concatenate_movies
+from latent_blending import LatentBlending
+from stable_diffusion_holder import StableDiffusionHolder
+import gradio as gr
+from dotenv import find_dotenv, load_dotenv
+import shutil
+import random
+from utils import get_time, add_frames_linear_interp
+from huggingface_hub import hf_hub_download
+
+
+class BlendingFrontend():
+    def __init__(
+            self,
+            sdh,
+            share=False):
+        r"""
+        Gradio Helper Class to collect UI data and start latent blending.
+        Args:
+            sdh:
+                StableDiffusionHolder
+            share: bool
+                Set true to get a shareable gradio link (e.g. for running a remote server)
+        """
+        self.share = share
+
+        # UI Defaults
+        self.num_inference_steps = 30
+        self.depth_strength = 0.25
+        self.seed1 = 420
+        self.seed2 = 420
+        self.prompt1 = ""
+        self.prompt2 = ""
+        self.negative_prompt = ""
+        self.fps = 30
+        self.duration_video = 8
+        self.t_compute_max_allowed = 10
+
+        self.lb = LatentBlending(sdh)
+        self.lb.sdh.num_inference_steps = self.num_inference_steps
+        self.init_parameters_from_lb()
+        self.init_save_dir()
+
+        # Vars
+        self.list_fp_imgs_current = []
+        self.recycle_img1 = False
+        self.recycle_img2 = False
+        self.list_all_segments = []
+        self.dp_session = ""
+        self.user_id = None
+
+    def init_parameters_from_lb(self):
+        r"""
+        Automatically init parameters from latentblending instance
+        """
+        self.height = self.lb.sdh.height
+        self.width = self.lb.sdh.width
+        self.guidance_scale = self.lb.guidance_scale
+        self.guidance_scale_mid_damper = self.lb.guidance_scale_mid_damper
+        self.mid_compression_scaler = self.lb.mid_compression_scaler
+        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
+
+    def init_save_dir(self):
+        r"""
+        Initializes the directory where stuff is being saved.
+        You can specify this directory in a ".env" file in your latentblending root, setting
+        DIR_OUT='/path/to/saving'
+        """
+        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)
+        self.save_empty_image()
+
+    def save_empty_image(self):
+        r"""
+        Saves an empty/black dummy image.
+        """
+        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):
+        r"""
+        Randomizes the first seed
+        """
+        seed = np.random.randint(0, 10000000)
+        self.seed1 = int(seed)
+        print(f"randomize_seed1: new seed = {self.seed1}")
+        return seed
+
+    def randomize_seed2(self):
+        r"""
+        Randomizes the second seed
+        """
+        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_vals):
+        r"""
+        Sets all parameters from the UI. Since gradio does not support to pass dictionaries,
+        we have to instead pass keys (list_ui_keys, global) and values (list_ui_vals)
+        """
+        # Collect latent blending variables
+        self.lb.set_width(list_ui_vals[list_ui_keys.index('width')])
+        self.lb.set_height(list_ui_vals[list_ui_keys.index('height')])
+        self.lb.set_prompt1(list_ui_vals[list_ui_keys.index('prompt1')])
+        self.lb.set_prompt2(list_ui_vals[list_ui_keys.index('prompt2')])
+        self.lb.set_negative_prompt(list_ui_vals[list_ui_keys.index('negative_prompt')])
+        self.lb.guidance_scale = list_ui_vals[list_ui_keys.index('guidance_scale')]
+        self.lb.guidance_scale_mid_damper = list_ui_vals[list_ui_keys.index('guidance_scale_mid_damper')]
+        self.t_compute_max_allowed = list_ui_vals[list_ui_keys.index('duration_compute')]
+        self.lb.num_inference_steps = list_ui_vals[list_ui_keys.index('num_inference_steps')]
+        self.lb.sdh.num_inference_steps = list_ui_vals[list_ui_keys.index('num_inference_steps')]
+        self.duration_video = list_ui_vals[list_ui_keys.index('duration_video')]
+        self.lb.seed1 = list_ui_vals[list_ui_keys.index('seed1')]
+        self.lb.seed2 = list_ui_vals[list_ui_keys.index('seed2')]
+        self.lb.branch1_crossfeed_power = list_ui_vals[list_ui_keys.index('branch1_crossfeed_power')]
+        self.lb.branch1_crossfeed_range = list_ui_vals[list_ui_keys.index('branch1_crossfeed_range')]
+        self.lb.branch1_crossfeed_decay = list_ui_vals[list_ui_keys.index('branch1_crossfeed_decay')]
+        self.lb.parental_crossfeed_power = list_ui_vals[list_ui_keys.index('parental_crossfeed_power')]
+        self.lb.parental_crossfeed_range = list_ui_vals[list_ui_keys.index('parental_crossfeed_range')]
+        self.lb.parental_crossfeed_power_decay = list_ui_vals[list_ui_keys.index('parental_crossfeed_power_decay')]
+        self.num_inference_steps = list_ui_vals[list_ui_keys.index('num_inference_steps')]
+        self.depth_strength = list_ui_vals[list_ui_keys.index('depth_strength')]
+
+        if len(list_ui_vals[list_ui_keys.index('user_id')]) > 1:
+            self.user_id = list_ui_vals[list_ui_keys.index('user_id')]
+        else:
+            # generate new user id
+            self.user_id = ''.join((random.choice('ABCDEFGHIJKLMNOPQRSTUVWXYZ') for i in range(8)))
+            print(f"made new user_id: {self.user_id} at {get_time('second')}")
+
+    def save_latents(self, fp_latents, list_latents):
+        r"""
+        Saves a latent trajectory on disk, in npy format.
+        """
+        list_latents_cpu = [l.cpu().numpy() for l in list_latents]
+        np.save(fp_latents, list_latents_cpu)
+
+    def load_latents(self, fp_latents):
+        r"""
+        Loads a latent trajectory from disk, converts to torch tensor.
+        """
+        list_latents_cpu = np.load(fp_latents)
+        list_latents = [torch.from_numpy(l).to(self.lb.device) for l in list_latents_cpu]
+        return list_latents
+
+    def compute_img1(self, *args):
+        r"""
+        Computes the first transition image and returns it for display.
+        Sets all other transition images and last image to empty (as they are obsolete with this operation)
+        """
+        list_ui_vals = args
+        self.setup_lb(list_ui_vals)
+        fp_img1 = os.path.join(self.dp_imgs, f"img1_{self.user_id}")
+        img1 = Image.fromarray(self.lb.compute_latents1(return_image=True))
+        img1.save(fp_img1 + ".jpg")
+        self.save_latents(fp_img1 + ".npy", self.lb.tree_latents[0])
+        self.recycle_img1 = True
+        self.recycle_img2 = False
+        return [fp_img1 + ".jpg", self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.user_id]
+
+    def compute_img2(self, *args):
+        r"""
+        Computes the last transition image and returns it for display.
+        Sets all other transition images to empty (as they are obsolete with this operation)
+        """
+        if not os.path.isfile(os.path.join(self.dp_imgs, f"img1_{self.user_id}.jpg")):  # don't do anything
+            return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.user_id]
+        list_ui_vals = args
+        self.setup_lb(list_ui_vals)
+
+        self.lb.tree_latents[0] = self.load_latents(os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy"))
+        fp_img2 = os.path.join(self.dp_imgs, f"img2_{self.user_id}")
+        img2 = Image.fromarray(self.lb.compute_latents2(return_image=True))
+        img2.save(fp_img2 + '.jpg')
+        self.save_latents(fp_img2 + ".npy", self.lb.tree_latents[-1])
+        self.recycle_img2 = True
+        # fixme save seeds. change filenames?
+        return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, fp_img2 + ".jpg", self.user_id]
+
+    def compute_transition(self, *args):
+        r"""
+        Computes transition images and movie.
+        """
+        list_ui_vals = args
+        self.setup_lb(list_ui_vals)
+        print("STARTING TRANSITION...")
+        fixed_seeds = [self.seed1, self.seed2]
+        # Inject loaded latents (other user interference)
+        self.lb.tree_latents[0] = self.load_latents(os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy"))
+        self.lb.tree_latents[-1] = self.load_latents(os.path.join(self.dp_imgs, f"img2_{self.user_id}.npy"))
+        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 ({get_time('second')}). 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
+        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}_{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 = self.get_fp_video_last()
+        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):
+        r"""
+        Allows to generate multi-segment movies. Sets last image -> first image with all
+        relevant parameters.
+        """
+        # 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)
+
+        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)
+
+        fp_movie_last = self.get_fp_video_last()
+        fp_movie_next = self.get_fp_video_next()
+
+        shutil.copyfile(fp_movie_last, fp_movie_next)
+
+        self.lb.tree_latents[0] = self.load_latents(os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy"))
+        self.lb.tree_latents[-1] = self.load_latents(os.path.join(self.dp_imgs, f"img2_{self.user_id}.npy"))
+        self.lb.swap_forward()
+
+        shutil.copyfile(os.path.join(self.dp_imgs, f"img2_{self.user_id}.npy"), os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy"))
+        fp_multi = self.multi_concat()
+        list_out = [fp_multi]
+
+        list_out.extend([os.path.join(self.dp_imgs, f"img2_{self.user_id}.jpg")])
+        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):
+        r"""
+        Concatentates all stacked segments into one long movie.
+        """
+        list_fp_movies = self.get_fp_video_all()
+        # Concatenate movies and save
+        fp_final = os.path.join(self.dp_session, f"concat_{self.user_id}.mp4")
+        concatenate_movies(fp_final, list_fp_movies)
+        return fp_final
+
+    def get_fp_video_all(self):
+        r"""
+        Collects all stacked movie segments.
+        """
+        list_all = os.listdir(self.dp_movies)
+        str_beg = f"movie_{self.user_id}_"
+        list_user = [l for l in list_all if str_beg in l]
+        list_user.sort()
+        list_user = [os.path.join(self.dp_movies, l) for l in list_user]
+        return list_user
+
+    def get_fp_video_next(self):
+        r"""
+        Gets the filepath of the next movie segment.
+        """
+        list_videos = self.get_fp_video_all()
+        if len(list_videos) == 0:
+            idx_next = 0
+        else:
+            idx_next = len(list_videos)
+        fp_video_next = os.path.join(self.dp_movies, f"movie_{self.user_id}_{str(idx_next).zfill(3)}.mp4")
+        return fp_video_next
+
+    def get_fp_video_last(self):
+        r"""
+        Gets the current video that was saved.
+        """
+        fp_video_last = os.path.join(self.dp_movies, f"last_{self.user_id}.mp4")
+        return fp_video_last
+
+
+if __name__ == "__main__":
+    fp_ckpt = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1-base", filename="v2-1_512-ema-pruned.ckpt")
+    # fp_ckpt = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1", filename="v2-1_768-ema-pruned.ckpt")
+    bf = BlendingFrontend(StableDiffusionHolder(fp_ckpt))
+    # self = BlendingFrontend(None)
+
+    with gr.Blocks() as demo:
+        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', interactive=True)
+            duration_video = gr.Slider(1, 100, bf.duration_video, step=0.1, label='video duration', 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="current single trans")
+            vid_multi = gr.Video(label="concatented multi trans")
+
+        with gr.Row():
+            b_stackforward = gr.Button('append last movie segment (left) to multi movie (right)', variant='primary')
+
+        with gr.Row():
+            gr.Markdown(
+                """
+                # Parameters
+                ## Main
+                - compute budget: set your waiting time for the transition. high values = better quality
+                - video duration: seconds per segment
+                - height/width: in pixels
+
+                ## Diffusion settings
+                - num_inference_steps: number of diffusion steps
+                - guidance_scale: latent blending seems to prefer lower values here
+                - negative prompt: enter negative prompt here, applied for all images
+
+                ## Last image crossfeeding
+                - branch1_crossfeed_power: Controls the level of cross-feeding between the first and last image branch. For preserving structures.
+                - branch1_crossfeed_range: Sets the duration of active crossfeed during development. High values enforce strong structural similarity.
+                - branch1_crossfeed_decay: Sets decay for branch1_crossfeed_power. Lower values make the decay stronger across the range.
+
+                ## Transition settings
+                - parental_crossfeed_power: Similar to branch1_crossfeed_power, however applied for the images withinin the transition.
+                - parental_crossfeed_range: Similar to branch1_crossfeed_range, however applied for the images withinin the transition.
+                - parental_crossfeed_power_decay: Similar to branch1_crossfeed_decay, however applied for the images withinin the transition.
+                - depth_strength: Determines when the blending process will begin in terms of diffusion steps. Low values more inventive but can cause motion.
+                - guidance_scale_mid_damper: Decreases the guidance scale in the middle of a transition.
+                """)
+
+        with gr.Row():
+            user_id = gr.Textbox(label="user id", interactive=False)
+
+        # 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
+        dict_ui_elem["user_id"] = user_id
+
+        # Convert to list, as gradio doesn't seem to accept dicts
+        list_ui_vals = []
+        list_ui_keys = []
+        for k in dict_ui_elem.keys():
+            list_ui_vals.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_vals, outputs=[img1, img2, img3, img4, img5, user_id])
+        b_compute2.click(bf.compute_img2, inputs=list_ui_vals, outputs=[img2, img3, img4, img5, user_id])
+        b_compute_transition.click(bf.compute_transition,
+                                   inputs=list_ui_vals,
+                                   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)