diff --git a/.gitignore b/.gitignore
index 5e629be..de29b3f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,6 +7,7 @@ __pycache__/
*.so
# Distribution / packaging
+*.json
.Python
build/
develop-eggs/
diff --git a/examples/multi_trans_json.py b/examples/multi_trans_json.py
new file mode 100644
index 0000000..fafaffb
--- /dev/null
+++ b/examples/multi_trans_json.py
@@ -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}")
\ No newline at end of file
diff --git a/latentblending/gradio_ui.py b/latentblending/gradio_ui.py
index 106cf54..2b6d491 100644
--- a/latentblending/gradio_ui.py
+++ b/latentblending/gradio_ui.py
@@ -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 take_image1(self):
+ return self.take_image(1)
+
+ def take_image2(self):
+ return self.take_image(2)
+
+ def take_image3(self):
+ return self.take_image(3)
+
- 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_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})
- 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)
+ seed = self.list_seeds[id_img]
+
+ self.data.append({"iteration": self.idx_movie, "seed": seed, "prompt": self.prompt, "negative_prompt": self.negative_prompt})
- 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
+ # Write the data list to a JSON file
+ with open(self.fp_out, 'w') as f:
+ json.dump(self.data, f, indent=4)
- 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
+ self.idx_movie += 1
+ self.prompt = None
+ return [None, None, None, None, ""]
- 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 = uuid.uuid4().hex
- 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
+ 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
+
+
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("""Latent Blending
-Create butter-smooth transitions between prompts, powered by stable diffusion
-For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-
-
-
-
""")
with gr.Row():
- prompt1 = gr.Textbox(label="prompt 1")
- prompt2 = gr.Textbox(label="prompt 2")
+ prompt = gr.Textbox(label="prompt")
+ negative_prompt = gr.Textbox(label="negative prompt")
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)
- 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)