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simple gradio interface for saving jsons

This commit is contained in:
DGX 2024-02-21 15:22:34 +00:00
parent 02ca854f43
commit 3de2021542
3 changed files with 174 additions and 445 deletions
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1
.gitignore vendored
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@ -7,6 +7,7 @@ __pycache__/
*.so
# Distribution / packaging
*.json
.Python
build/
develop-eggs/

75
examples/multi_trans_json.py Normal file
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@ -0,0 +1,75 @@
import torch
import warnings
from diffusers import AutoPipelineForText2Image
from latentblending.blending_engine import BlendingEngine
from lunar_tools import concatenate_movies
import numpy as np
torch.set_grad_enabled(False)
torch.backends.cudnn.benchmark = False
warnings.filterwarnings('ignore')
import json
# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
# pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
pipe = AutoPipelineForText2Image.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
pipe.to('cuda')
be = BlendingEngine(pipe, do_compile=False)
fp_movie = f'test.mp4'
fp_json = "movie_240221_1520.json"
duration_single_trans = 10
# Load the JSON data from the file
with open(fp_json, 'r') as file:
data = json.load(file)
# Set up width, height, num_inference steps
width = data[0]["width"]
height = data[0]["height"]
num_inference_steps = data[0]["num_inference_steps"]
be.set_dimensions((width, height))
be.set_num_inference_steps(num_inference_steps)
# Initialize lists for prompts, negative prompts, and seeds
list_prompts = []
list_negative_prompts = []
list_seeds = []
# Extract prompts, negative prompts, and seeds from the data
for item in data[1:]: # Skip the first item as it contains settings
list_prompts.append(item["prompt"])
list_negative_prompts.append(item["negative_prompt"])
list_seeds.append(item["seed"])
list_movie_parts = []
for i in range(len(list_prompts) - 1):
# For a multi transition we can save some computation time and recycle the latents
if i == 0:
be.set_prompt1(list_prompts[i])
be.set_negative_prompt(list_negative_prompts[i])
be.set_prompt2(list_prompts[i + 1])
recycle_img1 = False
else:
be.swap_forward()
be.set_negative_prompt(list_negative_prompts[i+1])
be.set_prompt2(list_prompts[i + 1])
recycle_img1 = True
fp_movie_part = f"tmp_part_{str(i).zfill(3)}.mp4"
fixed_seeds = list_seeds[i:i + 2]
# Run latent blending
be.run_transition(
recycle_img1=recycle_img1,
fixed_seeds=fixed_seeds)
# Save movie
be.write_movie_transition(fp_movie_part, duration_single_trans)
list_movie_parts.append(fp_movie_part)
# Finally, concatente the result
concatenate_movies(fp_movie, list_movie_parts)
print(f"DONE! MOVIE SAVED IN {fp_movie}")

527
latentblending/gradio_ui.py
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@ -1,18 +1,3 @@
# 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
@ -20,481 +5,149 @@ 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 uuid
from utils import get_time, add_frames_linear_interp
from huggingface_hub import hf_hub_download
from diffusers import AutoPipelineForText2Image
from latentblending.blending_engine import BlendingEngine
import datetime
warnings.filterwarnings('ignore')
torch.set_grad_enabled(False)
torch.backends.cudnn.benchmark = False
import json
class BlendingFrontend():
def __init__(
self,
sdh,
be,
share=False):
r"""
Gradio Helper Class to collect UI data and start latent blending.
Args:
sdh:
StableDiffusionHolder
be:
Blendingengine
share: bool
Set true to get a shareable gradio link (e.g. for running a remote server)
"""
self.be = be
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
self.prompt = None
self.negative_prompt = None
self.list_seeds = []
self.idx_movie = 0
self.data = []
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 take_image0(self):
return self.take_image(0)
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 take_image1(self):
return self.take_image(1)
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 take_image2(self):
return self.take_image(2)
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 take_image3(self):
return self.take_image(3)
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')]
def take_image(self, id_img):
if self.prompt is None:
print("Cannot take because no prompt was set!")
return [None, None, None, None, ""]
if self.idx_movie == 0:
current_time = datetime.datetime.now()
self.fp_out = "movie_" + current_time.strftime("%y%m%d_%H%M") + ".json"
self.data.append({"settings": "sdxl", "width": bf.be.dh.width_img, "height": self.be.dh.height_img, "num_inference_steps": self.be.dh.num_inference_steps})
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 = uuid.uuid4().hex
print(f"made new user_id: {self.user_id} at {get_time('second')}")
seed = self.list_seeds[id_img]
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)
self.data.append({"iteration": self.idx_movie, "seed": seed, "prompt": self.prompt, "negative_prompt": self.negative_prompt})
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
# Write the data list to a JSON file
with open(self.fp_out, 'w') as f:
json.dump(self.data, f, indent=4)
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]
self.idx_movie += 1
self.prompt = None
return [None, None, None, None, ""]
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_imgs(self, prompt, negative_prompt):
self.prompt = prompt
self.negative_prompt = negative_prompt
self.be.set_prompt1(prompt)
self.be.set_prompt2(prompt)
self.be.set_negative_prompt(negative_prompt)
self.list_seeds = []
self.list_images = []
for i in range(4):
seed = np.random.randint(0, 1000000000)
self.be.seed1 = seed
self.list_seeds.append(seed)
img = self.be.compute_latents1(return_image=True)
self.list_images.append(img)
return self.list_images
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)
width = 786
height = 1024
num_inference_steps = 4
pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
# pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")
be = BlendingEngine(pipe)
be.set_dimensions((width, height))
be.set_num_inference_steps(num_inference_steps)
bf = BlendingFrontend(be)
with gr.Blocks() as demo:
gr.HTML("""<h1>Latent Blending</h1>
<p>Create butter-smooth transitions between prompts, powered by stable diffusion</p>
<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
<br/>
<a href="https://huggingface.co/spaces/lunarring/latentblending?duplicate=true">
<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
</p>""")
with gr.Row():
prompt1 = gr.Textbox(label="prompt 1")
prompt2 = gr.Textbox(label="prompt 2")
with gr.Row():
duration_compute = gr.Slider(10, 25, bf.t_compute_max_allowed, step=1, label='waiting time', interactive=True)
duration_video = gr.Slider(1, 100, bf.duration_video, step=0.1, label='video duration', interactive=True)
height = gr.Slider(256, 1024, bf.height, step=128, label='height', interactive=True)
width = gr.Slider(256, 1024, 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)
prompt = gr.Textbox(label="prompt")
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)
b_compute = gr.Button('compute new images', variant='primary')
with gr.Row():
b_compute1 = gr.Button('step1: compute first image', variant='primary')
b_compute2 = gr.Button('step2: compute last image', variant='primary')
b_compute_transition = gr.Button('step3: compute transition', variant='primary')
with gr.Column():
img0 = gr.Image(label="seed1")
b_take0 = gr.Button('take', variant='primary')
with gr.Column():
img1 = gr.Image(label="seed2")
b_take1 = gr.Button('take', variant='primary')
with gr.Column():
img2 = gr.Image(label="seed3")
b_take2 = gr.Button('take', variant='primary')
with gr.Column():
img3 = gr.Image(label="seed4")
b_take3 = gr.Button('take', 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
- waiting time: 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])
b_compute.click(bf.compute_imgs, inputs=[prompt, negative_prompt], outputs=[img0, img1, img2, img3])
b_take0.click(bf.take_image0, outputs=[img0, img1, img2, img3, prompt])
b_take1.click(bf.take_image1, outputs=[img0, img1, img2, img3, prompt])
b_take2.click(bf.take_image2, outputs=[img0, img1, img2, img3, prompt])
b_take3.click(bf.take_image3, outputs=[img0, img1, img2, img3, prompt])
demo.launch(share=bf.share, inbrowser=True, inline=False)