storytold/storyteller-ml
2
0
Fork 0
mirror of https://github.com/storytold/storyteller-ml.git synced 2026-10-09 00:09:55 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
feature/sd_workflow
storyteller-ml/animation/rerender-lora/webUI.py
T
salt 0ce2e8ae01 add rerender lora
2023-12-11 12:50:04 -08:00

970 lines
38 KiB
Python
Raw Permalink Blame History

import os
import shutil
from enum import Enum
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import torch.nn.functional as F
import torchvision.transforms as T
from blendmodes.blend import BlendType, blendLayers
from PIL import Image
from pytorch_lightning import seed_everything
from safetensors.torch import load_file
from skimage import exposure
import src.import_util # noqa: F401
from deps.ControlNet.annotator.canny import CannyDetector
from deps.ControlNet.annotator.hed import HEDdetector
from deps.ControlNet.annotator.util import HWC3
from deps.ControlNet.cldm.model import create_model, load_state_dict
from deps.gmflow.gmflow.gmflow import GMFlow
from flow.flow_utils import get_warped_and_mask
from sd_model_cfg import model_dict
from src.config import RerenderConfig
from src.controller import AttentionControl
from src.ddim_v_hacked import DDIMVSampler
from src.freeu import freeu_forward
from src.img_util import find_flat_region, numpy2tensor
from src.video_util import (frame_to_video, get_fps, get_frame_count,
prepare_frames)
inversed_model_dict = dict()
for k, v in model_dict.items():
inversed_model_dict[v] = k
to_tensor = T.PILToTensor()
blur = T.GaussianBlur(kernel_size=(9, 9), sigma=(18, 18))
class ProcessingState(Enum):
NULL = 0
FIRST_IMG = 1
KEY_IMGS = 2
class GlobalState:
def __init__(self):
self.sd_model = None
self.ddim_v_sampler = None
self.detector_type = None
self.detector = None
self.controller = None
self.processing_state = ProcessingState.NULL
flow_model = GMFlow(
feature_channels=128,
num_scales=1,
upsample_factor=8,
num_head=1,
attention_type='swin',
ffn_dim_expansion=4,
num_transformer_layers=6,
).to('cuda')
checkpoint = torch.load('models/gmflow_sintel-0c07dcb3.pth',
map_location=lambda storage, loc: storage)
weights = checkpoint['model'] if 'model' in checkpoint else checkpoint
flow_model.load_state_dict(weights, strict=False)
flow_model.eval()
self.flow_model = flow_model
def update_controller(self, inner_strength, mask_period, cross_period,
ada_period, warp_period, loose_cfattn):
self.controller = AttentionControl(inner_strength,
mask_period,
cross_period,
ada_period,
warp_period,
loose_cfatnn=loose_cfattn)
def update_sd_model(self, sd_model, control_type, freeu_args):
if sd_model == self.sd_model:
return
self.sd_model = sd_model
model = create_model('./deps/ControlNet/models/cldm_v15.yaml').cpu()
if control_type == 'HED':
model.load_state_dict(
load_state_dict('./models/control_sd15_hed.pth',
location='cuda'))
elif control_type == 'canny':
model.load_state_dict(
load_state_dict('./models/control_sd15_canny.pth',
location='cuda'))
model = model.cuda()
sd_model_path = model_dict[sd_model]
if len(sd_model_path) > 0:
model_ext = os.path.splitext(sd_model_path)[1]
if model_ext == '.safetensors':
model.load_state_dict(load_file(sd_model_path), strict=False)
elif model_ext == '.ckpt' or model_ext == '.pth':
model.load_state_dict(torch.load(sd_model_path)['state_dict'],
strict=False)
try:
model.first_stage_model.load_state_dict(torch.load(
'./models/vae-ft-mse-840000-ema-pruned.ckpt')['state_dict'],
strict=False)
except Exception:
print('Warning: We suggest you download the fine-tuned VAE',
'otherwise the generation quality will be degraded')
model.model.diffusion_model.forward = freeu_forward(
model.model.diffusion_model, *freeu_args)
self.ddim_v_sampler = DDIMVSampler(model)
def clear_sd_model(self):
self.sd_model = None
self.ddim_v_sampler = None
torch.cuda.empty_cache()
def update_detector(self, control_type, canny_low=100, canny_high=200):
if self.detector_type == control_type:
return
if control_type == 'HED':
self.detector = HEDdetector()
elif control_type == 'canny':
canny_detector = CannyDetector()
low_threshold = canny_low
high_threshold = canny_high
def apply_canny(x):
return canny_detector(x, low_threshold, high_threshold)
self.detector = apply_canny
global_state = GlobalState()
global_video_path = None
video_frame_count = None
def create_cfg(input_path, prompt, image_resolution, control_strength,
color_preserve, left_crop, right_crop, top_crop, bottom_crop,
control_type, low_threshold, high_threshold, ddim_steps, scale,
seed, sd_model, a_prompt, n_prompt, interval, keyframe_count,
x0_strength, use_constraints, cross_start, cross_end,
style_update_freq, warp_start, warp_end, mask_start, mask_end,
ada_start, ada_end, mask_strength, inner_strength,
smooth_boundary, loose_cfattn, b1, b2, s1, s2):
use_warp = 'shape-aware fusion' in use_constraints
use_mask = 'pixel-aware fusion' in use_constraints
use_ada = 'color-aware AdaIN' in use_constraints
if not use_warp:
warp_start = 1
warp_end = 0
if not use_mask:
mask_start = 1
mask_end = 0
if not use_ada:
ada_start = 1
ada_end = 0
input_name = os.path.split(input_path)[-1].split('.')[0]
frame_count = 2 + keyframe_count * interval
cfg = RerenderConfig()
cfg.create_from_parameters(
input_path,
os.path.join('result', input_name, 'blend.mp4'),
prompt,
a_prompt=a_prompt,
n_prompt=n_prompt,
frame_count=frame_count,
interval=interval,
crop=[left_crop, right_crop, top_crop, bottom_crop],
sd_model=sd_model,
ddim_steps=ddim_steps,
scale=scale,
control_type=control_type,
control_strength=control_strength,
canny_low=low_threshold,
canny_high=high_threshold,
seed=seed,
image_resolution=image_resolution,
x0_strength=x0_strength,
style_update_freq=style_update_freq,
cross_period=(cross_start, cross_end),
warp_period=(warp_start, warp_end),
mask_period=(mask_start, mask_end),
ada_period=(ada_start, ada_end),
mask_strength=mask_strength,
inner_strength=inner_strength,
smooth_boundary=smooth_boundary,
color_preserve=color_preserve,
loose_cfattn=loose_cfattn,
freeu_args=[b1, b2, s1, s2])
return cfg
def cfg_to_input(filename):
cfg = RerenderConfig()
cfg.create_from_path(filename)
keyframe_count = (cfg.frame_count - 2) // cfg.interval
use_constraints = [
'shape-aware fusion', 'pixel-aware fusion', 'color-aware AdaIN'
]
sd_model = inversed_model_dict.get(cfg.sd_model, 'Stable Diffusion 1.5')
args = [
cfg.input_path, cfg.prompt, cfg.image_resolution, cfg.control_strength,
cfg.color_preserve, *cfg.crop, cfg.control_type, cfg.canny_low,
cfg.canny_high, cfg.ddim_steps, cfg.scale, cfg.seed, sd_model,
cfg.a_prompt, cfg.n_prompt, cfg.interval, keyframe_count,
cfg.x0_strength, use_constraints, *cfg.cross_period,
cfg.style_update_freq, *cfg.warp_period, *cfg.mask_period,
*cfg.ada_period, cfg.mask_strength, cfg.inner_strength,
cfg.smooth_boundary, cfg.loose_cfattn, *cfg.freeu_args
]
return args
def setup_color_correction(image):
correction_target = cv2.cvtColor(np.asarray(image.copy()),
cv2.COLOR_RGB2LAB)
return correction_target
def apply_color_correction(correction, original_image):
image = Image.fromarray(
cv2.cvtColor(
exposure.match_histograms(cv2.cvtColor(np.asarray(original_image),
cv2.COLOR_RGB2LAB),
correction,
channel_axis=2),
cv2.COLOR_LAB2RGB).astype('uint8'))
image = blendLayers(image, original_image, BlendType.LUMINOSITY)
return image
@torch.no_grad()
def process(*args):
args_wo_process3 = args[:-2]
first_frame = process1(*args_wo_process3)
keypath = process2(*args_wo_process3)
fullpath = process3(*args)
return first_frame, keypath, fullpath
@torch.no_grad()
def process1(*args):
global global_video_path
cfg = create_cfg(global_video_path, *args)
global global_state
global_state.update_sd_model(cfg.sd_model, cfg.control_type,
cfg.freeu_args)
global_state.update_controller(cfg.inner_strength, cfg.mask_period,
cfg.cross_period, cfg.ada_period,
cfg.warp_period, cfg.loose_cfattn)
global_state.update_detector(cfg.control_type, cfg.canny_low,
cfg.canny_high)
global_state.processing_state = ProcessingState.FIRST_IMG
prepare_frames(cfg.input_path, cfg.input_dir, cfg.image_resolution, cfg.crop, cfg.use_limit_device_resolution)
ddim_v_sampler = global_state.ddim_v_sampler
model = ddim_v_sampler.model
detector = global_state.detector
controller = global_state.controller
model.control_scales = [cfg.control_strength] * 13
num_samples = 1
eta = 0.0
imgs = sorted(os.listdir(cfg.input_dir))
imgs = [os.path.join(cfg.input_dir, img) for img in imgs]
with torch.no_grad():
frame = cv2.imread(imgs[0])
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
img = HWC3(frame)
H, W, C = img.shape
img_ = numpy2tensor(img)
def generate_first_img(img_, strength):
encoder_posterior = model.encode_first_stage(img_.cuda())
x0 = model.get_first_stage_encoding(encoder_posterior).detach()
detected_map = detector(img)
detected_map = HWC3(detected_map)
control = torch.from_numpy(
detected_map.copy()).float().cuda() / 255.0
control = torch.stack([control for _ in range(num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
cond = {
'c_concat': [control],
'c_crossattn': [
model.get_learned_conditioning(
[cfg.prompt + ', ' + cfg.a_prompt] * num_samples)
]
}
un_cond = {
'c_concat': [control],
'c_crossattn':
[model.get_learned_conditioning([cfg.n_prompt] * num_samples)]
}
shape = (4, H // 8, W // 8)
controller.set_task('initfirst')
seed_everything(cfg.seed)
samples, _ = ddim_v_sampler.sample(
cfg.ddim_steps,
num_samples,
shape,
cond,
verbose=False,
eta=eta,
unconditional_guidance_scale=cfg.scale,
unconditional_conditioning=un_cond,
controller=controller,
x0=x0,
strength=strength)
x_samples = model.decode_first_stage(samples)
x_samples_np = (
einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 +
127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
return x_samples, x_samples_np
# When not preserve color, draw a different frame at first and use its
# color to redraw the first frame.
if not cfg.color_preserve:
first_strength = -1
else:
first_strength = 1 - cfg.x0_strength
x_samples, x_samples_np = generate_first_img(img_, first_strength)
if not cfg.color_preserve:
color_corrections = setup_color_correction(
Image.fromarray(x_samples_np[0]))
global_state.color_corrections = color_corrections
img_ = apply_color_correction(color_corrections,
Image.fromarray(img))
img_ = to_tensor(img_).unsqueeze(0)[:, :3] / 127.5 - 1
x_samples, x_samples_np = generate_first_img(
img_, 1 - cfg.x0_strength)
global_state.first_result = x_samples
global_state.first_img = img
Image.fromarray(x_samples_np[0]).save(
os.path.join(cfg.first_dir, 'first.jpg'))
return x_samples_np[0]
@torch.no_grad()
def process2(*args):
global global_state
global global_video_path
if global_state.processing_state != ProcessingState.FIRST_IMG:
raise gr.Error('Please generate the first key image before generating'
' all key images')
cfg = create_cfg(global_video_path, *args)
global_state.update_sd_model(cfg.sd_model, cfg.control_type,
cfg.freeu_args)
global_state.update_detector(cfg.control_type, cfg.canny_low,
cfg.canny_high)
global_state.processing_state = ProcessingState.KEY_IMGS
# reset key dir
shutil.rmtree(cfg.key_dir)
os.makedirs(cfg.key_dir, exist_ok=True)
ddim_v_sampler = global_state.ddim_v_sampler
model = ddim_v_sampler.model
detector = global_state.detector
controller = global_state.controller
flow_model = global_state.flow_model
model.control_scales = [cfg.control_strength] * 13
num_samples = 1
eta = 0.0
firstx0 = True
pixelfusion = cfg.use_mask
imgs = sorted(os.listdir(cfg.input_dir))
imgs = [os.path.join(cfg.input_dir, img) for img in imgs]
first_result = global_state.first_result
first_img = global_state.first_img
pre_result = first_result
pre_img = first_img
for i in range(0, min(len(imgs), cfg.frame_count) - 1, cfg.interval):
cid = i + 1
print(cid)
if cid <= (len(imgs) - 1):
frame = cv2.imread(imgs[cid])
else:
frame = cv2.imread(imgs[len(imgs) - 1])
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
img = HWC3(frame)
H, W, C = img.shape
if cfg.color_preserve or global_state.color_corrections is None:
img_ = numpy2tensor(img)
else:
img_ = apply_color_correction(global_state.color_corrections,
Image.fromarray(img))
img_ = to_tensor(img_).unsqueeze(0)[:, :3] / 127.5 - 1
encoder_posterior = model.encode_first_stage(img_.cuda())
x0 = model.get_first_stage_encoding(encoder_posterior).detach()
detected_map = detector(img)
detected_map = HWC3(detected_map)
control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
control = torch.stack([control for _ in range(num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
cond = {
'c_concat': [control],
'c_crossattn': [
model.get_learned_conditioning(
[cfg.prompt + ', ' + cfg.a_prompt] * num_samples)
]
}
un_cond = {
'c_concat': [control],
'c_crossattn':
[model.get_learned_conditioning([cfg.n_prompt] * num_samples)]
}
shape = (4, H // 8, W // 8)
cond['c_concat'] = [control]
un_cond['c_concat'] = [control]
image1 = torch.from_numpy(pre_img).permute(2, 0, 1).float()
image2 = torch.from_numpy(img).permute(2, 0, 1).float()
warped_pre, bwd_occ_pre, bwd_flow_pre = get_warped_and_mask(
flow_model, image1, image2, pre_result, False)
blend_mask_pre = blur(
F.max_pool2d(bwd_occ_pre, kernel_size=9, stride=1, padding=4))
blend_mask_pre = torch.clamp(blend_mask_pre + bwd_occ_pre, 0, 1)
image1 = torch.from_numpy(first_img).permute(2, 0, 1).float()
warped_0, bwd_occ_0, bwd_flow_0 = get_warped_and_mask(
flow_model, image1, image2, first_result, False)
blend_mask_0 = blur(
F.max_pool2d(bwd_occ_0, kernel_size=9, stride=1, padding=4))
blend_mask_0 = torch.clamp(blend_mask_0 + bwd_occ_0, 0, 1)
if firstx0:
mask = 1 - F.max_pool2d(blend_mask_0, kernel_size=8)
controller.set_warp(
F.interpolate(bwd_flow_0 / 8.0,
scale_factor=1. / 8,
mode='bilinear'), mask)
else:
mask = 1 - F.max_pool2d(blend_mask_pre, kernel_size=8)
controller.set_warp(
F.interpolate(bwd_flow_pre / 8.0,
scale_factor=1. / 8,
mode='bilinear'), mask)
controller.set_task('keepx0, keepstyle')
seed_everything(cfg.seed)
samples, intermediates = ddim_v_sampler.sample(
cfg.ddim_steps,
num_samples,
shape,
cond,
verbose=False,
eta=eta,
unconditional_guidance_scale=cfg.scale,
unconditional_conditioning=un_cond,
controller=controller,
x0=x0,
strength=1 - cfg.x0_strength)
direct_result = model.decode_first_stage(samples)
if not pixelfusion:
pre_result = direct_result
pre_img = img
viz = (
einops.rearrange(direct_result, 'b c h w -> b h w c') * 127.5 +
127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
else:
blend_results = (1 - blend_mask_pre
) * warped_pre + blend_mask_pre * direct_result
blend_results = (
1 - blend_mask_0) * warped_0 + blend_mask_0 * blend_results
bwd_occ = 1 - torch.clamp(1 - bwd_occ_pre + 1 - bwd_occ_0, 0, 1)
blend_mask = blur(
F.max_pool2d(bwd_occ, kernel_size=9, stride=1, padding=4))
blend_mask = 1 - torch.clamp(blend_mask + bwd_occ, 0, 1)
encoder_posterior = model.encode_first_stage(blend_results)
xtrg = model.get_first_stage_encoding(
encoder_posterior).detach() # * mask
blend_results_rec = model.decode_first_stage(xtrg)
encoder_posterior = model.encode_first_stage(blend_results_rec)
xtrg_rec = model.get_first_stage_encoding(
encoder_posterior).detach()
xtrg_ = (xtrg + 1 * (xtrg - xtrg_rec)) # * mask
blend_results_rec_new = model.decode_first_stage(xtrg_)
tmp = (abs(blend_results_rec_new - blend_results).mean(
dim=1, keepdims=True) > 0.25).float()
mask_x = F.max_pool2d((F.interpolate(
tmp, scale_factor=1 / 8., mode='bilinear') > 0).float(),
kernel_size=3,
stride=1,
padding=1)
mask = (1 - F.max_pool2d(1 - blend_mask, kernel_size=8)
) # * (1-mask_x)
if cfg.smooth_boundary:
noise_rescale = find_flat_region(mask)
else:
noise_rescale = torch.ones_like(mask)
masks = []
for j in range(cfg.ddim_steps):
if j <= cfg.ddim_steps * cfg.mask_period[
0] or j >= cfg.ddim_steps * cfg.mask_period[1]:
masks += [None]
else:
masks += [mask * cfg.mask_strength]
# mask 3
# xtrg = ((1-mask_x) *
# (xtrg + xtrg - xtrg_rec) + mask_x * samples) * mask
# mask 2
# xtrg = (xtrg + 1 * (xtrg - xtrg_rec)) * mask
xtrg = (xtrg + (1 - mask_x) * (xtrg - xtrg_rec)) * mask # mask 1
tasks = 'keepstyle, keepx0'
if not firstx0:
tasks += ', updatex0'
if i % cfg.style_update_freq == 0:
tasks += ', updatestyle'
controller.set_task(tasks, 1.0)
seed_everything(cfg.seed)
samples, _ = ddim_v_sampler.sample(
cfg.ddim_steps,
num_samples,
shape,
cond,
verbose=False,
eta=eta,
unconditional_guidance_scale=cfg.scale,
unconditional_conditioning=un_cond,
controller=controller,
x0=x0,
strength=1 - cfg.x0_strength,
xtrg=xtrg,
mask=masks,
noise_rescale=noise_rescale)
x_samples = model.decode_first_stage(samples)
pre_result = x_samples
pre_img = img
viz = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 +
127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
Image.fromarray(viz[0]).save(
os.path.join(cfg.key_dir, f'{cid:04d}.png'))
key_video_path = os.path.join(cfg.work_dir, 'key.mp4')
fps = get_fps(cfg.input_path)
fps //= cfg.interval
frame_to_video(key_video_path, cfg.key_dir, fps, False)
return key_video_path
@torch.no_grad()
def process3(*args):
max_process = args[-2]
use_poisson = args[-1]
args = args[:-2]
global global_video_path
global global_state
if global_state.processing_state != ProcessingState.KEY_IMGS:
raise gr.Error('Please generate key images before propagation')
global_state.clear_sd_model()
cfg = create_cfg(global_video_path, *args)
# reset blend dir
blend_dir = os.path.join(cfg.work_dir, 'blend')
if os.path.exists(blend_dir):
shutil.rmtree(blend_dir)
os.makedirs(blend_dir, exist_ok=True)
video_base_dir = cfg.work_dir
o_video = cfg.output_path
fps = get_fps(cfg.input_path)
end_frame = cfg.frame_count - 1
interval = cfg.interval
key_dir = os.path.split(cfg.key_dir)[-1]
o_video_cmd = f'--output {o_video}'
ps = '-ps' if use_poisson else ''
cmd = (f'python video_blend.py {video_base_dir} --beg 1 --end {end_frame} '
f'--itv {interval} --key {key_dir} {o_video_cmd} --fps {fps} '
f'--n_proc {max_process} {ps}')
print(cmd)
os.system(cmd)
return o_video
block = gr.Blocks().queue()
with block:
with gr.Row():
gr.Markdown('## Rerender A Video')
with gr.Row():
with gr.Column():
input_path = gr.Video(label='Input Video',
source='upload',
format='mp4',
visible=True)
prompt = gr.Textbox(label='Prompt')
seed = gr.Slider(label='Seed',
minimum=0,
maximum=2147483647,
step=1,
value=0,
randomize=True)
run_button = gr.Button(value='Run All')
with gr.Row():
run_button1 = gr.Button(value='Run 1st Key Frame')
run_button2 = gr.Button(value='Run Key Frames')
run_button3 = gr.Button(value='Run Propagation')
with gr.Accordion('Advanced options for the 1st frame translation',
open=False):
image_resolution = gr.Slider(label='Frame resolution',
minimum=256,
maximum=768,
value=512,
step=64)
control_strength = gr.Slider(label='ControlNet strength',
minimum=0.0,
maximum=2.0,
value=1.0,
step=0.01)
x0_strength = gr.Slider(
label='Denoising strength',
minimum=0.00,
maximum=1.05,
value=0.75,
step=0.05,
info=('0: fully recover the input.'
'1.05: fully rerender the input.'))
color_preserve = gr.Checkbox(
label='Preserve color',
value=True,
info='Keep the color of the input video')
with gr.Row():
left_crop = gr.Slider(label='Left crop length',
minimum=0,
maximum=512,
value=0,
step=1)
right_crop = gr.Slider(label='Right crop length',
minimum=0,
maximum=512,
value=0,
step=1)
with gr.Row():
top_crop = gr.Slider(label='Top crop length',
minimum=0,
maximum=512,
value=0,
step=1)
bottom_crop = gr.Slider(label='Bottom crop length',
minimum=0,
maximum=512,
value=0,
step=1)
with gr.Row():
control_type = gr.Dropdown(['HED', 'canny'],
label='Control type',
value='HED')
low_threshold = gr.Slider(label='Canny low threshold',
minimum=1,
maximum=255,
value=100,
step=1)
high_threshold = gr.Slider(label='Canny high threshold',
minimum=1,
maximum=255,
value=200,
step=1)
ddim_steps = gr.Slider(label='Steps',
minimum=20,
maximum=100,
value=20,
step=20)
scale = gr.Slider(label='CFG scale',
minimum=0.1,
maximum=30.0,
value=7.5,
step=0.1)
sd_model_list = list(model_dict.keys())
sd_model = gr.Dropdown(sd_model_list,
label='Base model',
value='Stable Diffusion 1.5')
a_prompt = gr.Textbox(label='Added prompt',
value='best quality, extremely detailed')
n_prompt = gr.Textbox(
label='Negative prompt',
value=('longbody, lowres, bad anatomy, bad hands, '
'missing fingers, extra digit, fewer digits, '
'cropped, worst quality, low quality'))
with gr.Row():
b1 = gr.Slider(label='FreeU first-stage backbone factor',
minimum=1,
maximum=1.6,
value=1,
step=0.01,
info='FreeU to enhance texture and color')
b2 = gr.Slider(label='FreeU second-stage backbone factor',
minimum=1,
maximum=1.6,
value=1,
step=0.01)
with gr.Row():
s1 = gr.Slider(label='FreeU first-stage skip factor',
minimum=0,
maximum=1,
value=1,
step=0.01)
s2 = gr.Slider(label='FreeU second-stage skip factor',
minimum=0,
maximum=1,
value=1,
step=0.01)
with gr.Accordion('Advanced options for the key fame translation',
open=False):
interval = gr.Slider(
label='Key frame frequency (K)',
minimum=1,
maximum=1,
value=1,
step=1,
info='Uniformly sample the key frames every K frames')
keyframe_count = gr.Slider(label='Number of key frames',
minimum=1,
maximum=1,
value=1,
step=1)
use_constraints = gr.CheckboxGroup(
[
'shape-aware fusion', 'pixel-aware fusion',
'color-aware AdaIN'
],
label='Select the cross-frame contraints to be used',
value=[
'shape-aware fusion', 'pixel-aware fusion',
'color-aware AdaIN'
]),
with gr.Row():
cross_start = gr.Slider(
label='Cross-frame attention start',
minimum=0,
maximum=1,
value=0,
step=0.05)
cross_end = gr.Slider(label='Cross-frame attention end',
minimum=0,
maximum=1,
value=1,
step=0.05)
style_update_freq = gr.Slider(
label='Cross-frame attention update frequency',
minimum=1,
maximum=100,
value=1,
step=1,
info=('Update the key and value for '
'cross-frame attention every N key frames'))
loose_cfattn = gr.Checkbox(
label='Loose Cross-frame attention',
value=True,
info='Select to make output better match the input video')
with gr.Row():
warp_start = gr.Slider(label='Shape-aware fusion start',
minimum=0,
maximum=1,
value=0,
step=0.05)
warp_end = gr.Slider(label='Shape-aware fusion end',
minimum=0,
maximum=1,
value=0.1,
step=0.05)
with gr.Row():
mask_start = gr.Slider(label='Pixel-aware fusion start',
minimum=0,
maximum=1,
value=0.5,
step=0.05)
mask_end = gr.Slider(label='Pixel-aware fusion end',
minimum=0,
maximum=1,
value=0.8,
step=0.05)
with gr.Row():
ada_start = gr.Slider(label='Color-aware AdaIN start',
minimum=0,
maximum=1,
value=0.8,
step=0.05)
ada_end = gr.Slider(label='Color-aware AdaIN end',
minimum=0,
maximum=1,
value=1,
step=0.05)
mask_strength = gr.Slider(label='Pixel-aware fusion strength',
minimum=0,
maximum=1,
value=0.5,
step=0.01)
inner_strength = gr.Slider(
label='Pixel-aware fusion detail level',
minimum=0.5,
maximum=1,
value=0.9,
step=0.01,
info='Use a low value to prevent artifacts')
smooth_boundary = gr.Checkbox(
label='Smooth fusion boundary',
value=True,
info='Select to prevent artifacts at boundary')
with gr.Accordion(
'Advanced options for the full video translation',
open=False):
use_poisson = gr.Checkbox(
label='Gradient blending',
value=True,
info=('Blend the output video in gradient, to reduce'
' ghosting artifacts (but may increase flickers)'))
max_process = gr.Slider(label='Number of parallel processes',
minimum=1,
maximum=16,
value=4,
step=1)
with gr.Accordion('Example configs', open=True):
config_dir = 'config'
config_list = [
'real2sculpture.json', 'van_gogh_man.json', 'woman.json'
]
args_list = []
for config in config_list:
try:
config_path = os.path.join(config_dir, config)
args = cfg_to_input(config_path)
args_list.append(args)
except FileNotFoundError:
# The video file does not exist, skipped
pass
ips = [
prompt, image_resolution, control_strength, color_preserve,
left_crop, right_crop, top_crop, bottom_crop, control_type,
low_threshold, high_threshold, ddim_steps, scale, seed,
sd_model, a_prompt, n_prompt, interval, keyframe_count,
x0_strength, use_constraints[0], cross_start, cross_end,
style_update_freq, warp_start, warp_end, mask_start,
mask_end, ada_start, ada_end, mask_strength,
inner_strength, smooth_boundary, loose_cfattn, b1, b2, s1,
s2
]
gr.Examples(
examples=args_list,
inputs=[input_path, *ips],
)
with gr.Column():
result_image = gr.Image(label='Output first frame',
type='numpy',
interactive=False)
result_keyframe = gr.Video(label='Output key frame video',
format='mp4',
interactive=False)
result_video = gr.Video(label='Output full video',
format='mp4',
interactive=False)
def input_uploaded(path):
frame_count = get_frame_count(path)
if frame_count <= 2:
raise gr.Error('The input video is too short!'
'Please input another video.')
default_interval = min(10, frame_count - 2)
max_keyframe = (frame_count - 2) // default_interval
global video_frame_count
video_frame_count = frame_count
global global_video_path
global_video_path = path
return gr.Slider.update(value=default_interval,
maximum=max_keyframe), gr.Slider.update(
value=max_keyframe, maximum=max_keyframe)
def input_changed(path):
frame_count = get_frame_count(path)
if frame_count <= 2:
return gr.Slider.update(maximum=1), gr.Slider.update(maximum=1)
default_interval = min(10, frame_count - 2)
max_keyframe = (frame_count - 2) // default_interval
global video_frame_count
video_frame_count = frame_count
global global_video_path
global_video_path = path
return gr.Slider.update(maximum=max_keyframe), \
gr.Slider.update(maximum=max_keyframe)
def interval_changed(interval):
global video_frame_count
if video_frame_count is None:
return gr.Slider.update()
max_keyframe = (video_frame_count - 2) // interval
return gr.Slider.update(value=max_keyframe, maximum=max_keyframe)
input_path.change(input_changed, input_path, [interval, keyframe_count])
input_path.upload(input_uploaded, input_path, [interval, keyframe_count])
interval.change(interval_changed, interval, keyframe_count)
ips_process3 = [*ips, max_process, use_poisson]
run_button.click(fn=process,
inputs=ips_process3,
outputs=[result_image, result_keyframe, result_video])
run_button1.click(fn=process1, inputs=ips, outputs=[result_image])
run_button2.click(fn=process2, inputs=ips, outputs=[result_keyframe])
run_button3.click(fn=process3, inputs=ips_process3, outputs=[result_video])
block.launch(server_name='localhost')
Reference in New Issue View Git Blame Copy Permalink