xingcheng
/
disco-diffusion
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Adds improved zippy turbo blending

Browse Source
pull/14/head
Adam Letts 3 years ago
parent b350675b76
commit 0e366efcf9
2 changed files with 166 additions and 110 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 138
      Disco_Diffusion.ipynb
  2. 138
      disco.py

138
Disco_Diffusion.ipynb
Unescape View File

@ -392,14 +392,8 @@
" root_path = '.'\n",
"\n",
"import os\n",
"from os import path\n",
"#Simple create paths taken with modifications from Datamosh's Batch VQGAN+CLIP notebook\n",
"def createPath(filepath):\n",
" if path.exists(filepath) == False:\n",
" os.makedirs(filepath)\n",
" print(f'Made {filepath}')\n",
" else:\n",
" print(f'filepath {filepath} exists.')\n",
" os.makedirs(filepath, exist_ok=True)\n",
"\n",
"initDirPath = f'{root_path}/init_images'\n",
"createPath(initDirPath)\n",
@ -432,7 +426,6 @@
"source": [
"#@title ### 1.3 Install and import dependencies\n",
"\n",
"from os.path import exists as path_exists\n",
"import pathlib, shutil\n",
"\n",
"if not is_colab:\n",
@ -476,9 +469,9 @@
" except:\n",
" pass\n",
"\n",
"if not path_exists(f'{model_path}'):\n",
"if not os.path.exists(f'{model_path}'):\n",
" pathlib.Path(model_path).mkdir(parents=True, exist_ok=True)\n",
"if not path_exists(f'{model_path}/dpt_large-midas-2f21e586.pt'):\n",
"if not os.path.exists(f'{model_path}/dpt_large-midas-2f21e586.pt'):\n",
" wget(\"https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt\", model_path)\n",
"\n",
"import sys\n",
@ -575,7 +568,7 @@
"if USE_ADABINS:\n",
" if is_colab:\n",
" gitclone(\"https://github.com/shariqfarooq123/AdaBins.git\")\n",
" if not path_exists(f'{model_path}/AdaBins_nyu.pt'):\n",
" if not os.path.exists(f'{model_path}/AdaBins_nyu.pt'):\n",
" wget(\"https://cloudflare-ipfs.com/ipfs/Qmd2mMnDLWePKmgfS8m6ntAg4nhV5VkUyAydYBp8cWWeB7/AdaBins_nyu.pt\", model_path)\n",
" pathlib.Path(\"pretrained\").mkdir(parents=True, exist_ok=True)\n",
" shutil.copyfile(f\"{model_path}/AdaBins_nyu.pt\", \"pretrained/AdaBins_nyu.pt\")\n",
@ -1003,6 +996,44 @@
"\n",
"stop_on_next_loop = False # Make sure GPU memory doesn't get corrupted from cancelling the run mid-way through, allow a full frame to complete\n",
"\n",
"def do_3d_step(img_filepath, frame_num, midas_model, midas_transform):\n",
" global seed\n",
"\n",
" if args.key_frames:\n",
" translation_x = args.translation_x_series[frame_num]\n",
" translation_y = args.translation_y_series[frame_num]\n",
" translation_z = args.translation_z_series[frame_num]\n",
" rotation_3d_x = args.rotation_3d_x_series[frame_num]\n",
" rotation_3d_y = args.rotation_3d_y_series[frame_num]\n",
" rotation_3d_z = args.rotation_3d_z_series[frame_num]\n",
" print(\n",
" f'translation_x: {translation_x}',\n",
" f'translation_y: {translation_y}',\n",
" f'translation_z: {translation_z}',\n",
" f'rotation_3d_x: {rotation_3d_x}',\n",
" f'rotation_3d_y: {rotation_3d_y}',\n",
" f'rotation_3d_z: {rotation_3d_z}',\n",
" )\n",
"\n",
" if frame_num > 0:\n",
" seed = seed + 1\n",
" if resume_run and frame_num == start_frame:\n",
" img_filepath = batchFolder+f\"/{batch_name}({batchNum})_{start_frame-1:04}.png\"\n",
" else:\n",
" img_filepath = '/content/prevFrame.png' if is_colab else 'prevFrame.png'\n",
" trans_scale = 1.0/200.0\n",
" translate_xyz = [-translation_x*trans_scale, translation_y*trans_scale, -translation_z*trans_scale]\n",
" rotate_xyz = [rotation_3d_x, rotation_3d_y, rotation_3d_z]\n",
" print('translation:',translate_xyz)\n",
" print('rotation:',rotate_xyz)\n",
" rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), \"XYZ\").unsqueeze(0)\n",
" print(\"rot_mat: \" + str(rot_mat))\n",
" next_step_pil = dxf.transform_image_3d(img_filepath, midas_model, midas_transform, DEVICE,\n",
" rot_mat, translate_xyz, args.near_plane, args.far_plane,\n",
" args.fov, padding_mode=args.padding_mode,\n",
" sampling_mode=args.sampling_mode, midas_weight=args.midas_weight)\n",
" return next_step_pil\n",
"\n",
"def do_run():\n",
" seed = args.seed\n",
" print(range(args.start_frame, args.max_frames))\n",
@ -1072,58 +1103,45 @@
" skip_steps = args.calc_frames_skip_steps\n",
"\n",
" if args.animation_mode == \"3D\":\n",
" if args.key_frames:\n",
" angle = args.angle_series[frame_num]\n",
" #zoom = args.zoom_series[frame_num]\n",
" translation_x = args.translation_x_series[frame_num]\n",
" translation_y = args.translation_y_series[frame_num]\n",
" translation_z = args.translation_z_series[frame_num]\n",
" rotation_3d_x = args.rotation_3d_x_series[frame_num]\n",
" rotation_3d_y = args.rotation_3d_y_series[frame_num]\n",
" rotation_3d_z = args.rotation_3d_z_series[frame_num]\n",
" print(\n",
" f'angle: {angle}',\n",
" #f'zoom: {zoom}',\n",
" f'translation_x: {translation_x}',\n",
" f'translation_y: {translation_y}',\n",
" f'translation_z: {translation_z}',\n",
" f'rotation_3d_x: {rotation_3d_x}',\n",
" f'rotation_3d_y: {rotation_3d_y}',\n",
" f'rotation_3d_z: {rotation_3d_z}',\n",
" )\n",
"\n",
" if frame_num > 0:\n",
" if frame_num == 0:\n",
" turbo_blend = False\n",
" else:\n",
" seed = seed + 1 \n",
" if resume_run and frame_num == start_frame:\n",
" img_filepath = batchFolder+f\"/{batch_name}({batchNum})_{start_frame-1:04}.png\"\n",
" if turbo_mode and frame_num > turbo_preroll:\n",
" shutil.copyfile(img_filepath, 'oldFrameScaled.png')\n",
" else:\n",
" img_filepath = '/content/prevFrame.png' if is_colab else 'prevFrame.png'\n",
" trans_scale = 1.0/200.0\n",
" translate_xyz = [-translation_x*trans_scale, translation_y*trans_scale, -translation_z*trans_scale]\n",
" rotate_xyz = [rotation_3d_x, rotation_3d_y, rotation_3d_z]\n",
" print('translation:',translate_xyz)\n",
" print('rotation:',rotate_xyz)\n",
" rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), \"XYZ\").unsqueeze(0)\n",
" print(\"rot_mat: \" + str(rot_mat))\n",
" next_step_pil = dxf.transform_image_3d(img_filepath, midas_model, midas_transform, DEVICE,\n",
" rot_mat, translate_xyz, args.near_plane, args.far_plane,\n",
" args.fov, padding_mode=args.padding_mode,\n",
" sampling_mode=args.sampling_mode, midas_weight=args.midas_weight)\n",
"\n",
" next_step_pil = do_3d_step(img_filepath, frame_num, midas_model, midas_transform)\n",
" next_step_pil.save('prevFrameScaled.png')\n",
"\n",
" ### Turbo mode - skip some diffusions to save time\n",
" turbo_blend = False # default to normal frame saving later\n",
" if turbo_mode and frame_num > 10: #preroll is 10 frames\n",
" if frame_num % int(turbo_steps) != 0:\n",
" ### Turbo mode - skip some diffusions, use 3d morph for clarity and to save time\n",
" turbo_blend = False # default for non-turbo frame saving\n",
" if turbo_mode == True and frame_num == turbo_preroll: #start tracking oldframe\n",
" next_step_pil.save('oldFrameScaled.png')#stash for later blending \n",
" if turbo_mode == True and frame_num > turbo_preroll:\n",
" #set up 2 warped image sequences, old & new, to blend toward new diff image\n",
" old_frame = do_3d_step('oldFrameScaled.png', frame_num, midas_model, midas_transform)\n",
" old_frame.save('oldFrameScaled.png')\n",
" if frame_num % int(turbo_steps) != 0: \n",
" print('turbo skip this frame: skipping clip diffusion steps')\n",
" filename = f'{args.batch_name}({args.batchNum})_{frame_num:04}.png'\n",
" next_step_pil.save(f'{batchFolder}/{filename}') #save it as this frame. done.\n",
" blend_factor = ((frame_num % int(turbo_steps))+1)/int(turbo_steps)\n",
" print('turbo skip this frame: skipping clip diffusion steps and saving blended frame')\n",
" newWarpedImg = cv2.imread('prevFrameScaled.png')#this is already updated..\n",
" oldWarpedImg = cv2.imread('oldFrameScaled.png')\n",
" blendedImage = cv2.addWeighted(newWarpedImg, blend_factor, oldWarpedImg,1-blend_factor, 0.0)\n",
" cv2.imwrite(f'{batchFolder}/{filename}',blendedImage)\n",
" next_step_pil.save(f'{img_filepath}') # save it also as prev_frame to feed next iteration\n",
" turbo_blend = False # default to normal-frame-saving later\n",
" turbo_blend = False\n",
" continue\n",
" else:\n",
" if turbo_frame_blend:\n",
" turbo_blend = True # blend frames for smoothness..\n",
" #if not a skip frame, will run diffusion and need to blend.\n",
" oldWarpedImg = cv2.imread('prevFrameScaled.png')\n",
" cv2.imwrite(f'oldFrameScaled.png',oldWarpedImg)#swap in for blending later \n",
" turbo_blend = True # flag to blend frames after diff generated...\n",
" print('clip/diff this frame - generate clip diff image')\n",
"\n",
" init_image = 'prevFrameScaled.png'\n",
@ -1162,7 +1180,7 @@
" else:\n",
" image_prompt = []\n",
"\n",
" print(f'Frame Prompt: {frame_prompt}')\n",
" print(f'Frame {frame_num} Prompt: {frame_prompt}')\n",
"\n",
" model_stats = []\n",
" for clip_model in clip_models:\n",
@ -1489,6 +1507,7 @@
" 'extract_nth_frame':extract_nth_frame,\n",
" 'turbo_mode':turbo_mode,\n",
" 'turbo_steps':turbo_steps,\n",
" 'turbo_preroll':turbo_preroll,\n",
" 'turbo_frame_blend':turbo_frame_blend,\n",
" }\n",
" # print('Settings:', setting_list)\n",
@ -2542,7 +2561,16 @@
"\n",
"turbo_mode = True #@param {type:\"boolean\"}\n",
"turbo_steps = \"3\" #@param [\"2\",\"3\",\"4\",\"5\",\"6\"] {type:\"string\"}\n",
"turbo_preroll = 10 # frames\n",
"turbo_frame_blend = True #@param {type:\"boolean\"}\n",
"\n",
"#insist turbo be used only w 3d anim.\n",
"if turbo_mode and animation_mode != '3D':\n",
" print('=====')\n",
" print('Turbo mode only available with 3D animations. Disabling Turbo.')\n",
" print('=====')\n",
" turbo_mode = False\n",
"\n",
"#@markdown ---\n",
"\n",
"#@markdown ####**Coherency Settings:**\n",
@ -2978,11 +3006,11 @@
" batchNum = int(run_to_resume)\n",
" if resume_from_frame == 'latest':\n",
" start_frame = len(glob(batchFolder+f\"/{batch_name}({batchNum})_*.png\"))\n",
" if turbo_mode == True and start_frame > 10 and start_frame % int(turbo_steps) != 0:\n",
" if turbo_mode == True and start_frame > turbo_preroll and start_frame % int(turbo_steps) != 0:\n",
" start_frame = start_frame - (start_frame % int(turbo_steps))\n",
" else:\n",
" start_frame = int(resume_from_frame)+1\n",
" if turbo_mode == True and start_frame > 10 and start_frame % int(turbo_steps) != 0:\n",
" if turbo_mode == True and start_frame > turbo_preroll and start_frame % int(turbo_steps) != 0:\n",
" start_frame = start_frame - (start_frame % int(turbo_steps))\n",
" if retain_overwritten_frames is True:\n",
" existing_frames = len(glob(batchFolder+f\"/{batch_name}({batchNum})_*.png\"))\n",
@ -2992,7 +3020,7 @@
"else:\n",
" start_frame = 0\n",
" batchNum = len(glob(batchFolder+\"/*.txt\"))\n",
" while path.isfile(f\"{batchFolder}/{batch_name}({batchNum})_settings.txt\") is True or path.isfile(f\"{batchFolder}/{batch_name}-{batchNum}_settings.txt\") is True:\n",
" while os.path.isfile(f\"{batchFolder}/{batch_name}({batchNum})_settings.txt\") is True or os.path.isfile(f\"{batchFolder}/{batch_name}-{batchNum}_settings.txt\") is True:\n",
" batchNum += 1\n",
"\n",
"print(f'Starting Run: {batch_name}({batchNum}) at frame {start_frame}')\n",

138
disco.py
Unescape View File

@ -326,14 +326,8 @@ else:
root_path = '.'
import os
from os import path
#Simple create paths taken with modifications from Datamosh's Batch VQGAN+CLIP notebook
def createPath(filepath):
if path.exists(filepath) == False:
os.makedirs(filepath)
print(f'Made {filepath}')
else:
print(f'filepath {filepath} exists.')
os.makedirs(filepath, exist_ok=True)
initDirPath = f'{root_path}/init_images'
createPath(initDirPath)
@ -357,7 +351,6 @@ else:
# %%
#@title ### 1.3 Install and import dependencies
from os.path import exists as path_exists
import pathlib, shutil
if not is_colab:
@ -401,9 +394,9 @@ if is_colab:
except:
pass
if not path_exists(f'{model_path}'):
if not os.path.exists(f'{model_path}'):
pathlib.Path(model_path).mkdir(parents=True, exist_ok=True)
if not path_exists(f'{model_path}/dpt_large-midas-2f21e586.pt'):
if not os.path.exists(f'{model_path}/dpt_large-midas-2f21e586.pt'):
wget("https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", model_path)
import sys
@ -500,7 +493,7 @@ warnings.filterwarnings("ignore", category=UserWarning)
if USE_ADABINS:
if is_colab:
gitclone("https://github.com/shariqfarooq123/AdaBins.git")
if not path_exists(f'{model_path}/AdaBins_nyu.pt'):
if not os.path.exists(f'{model_path}/AdaBins_nyu.pt'):
wget("https://cloudflare-ipfs.com/ipfs/Qmd2mMnDLWePKmgfS8m6ntAg4nhV5VkUyAydYBp8cWWeB7/AdaBins_nyu.pt", model_path)
pathlib.Path("pretrained").mkdir(parents=True, exist_ok=True)
shutil.copyfile(f"{model_path}/AdaBins_nyu.pt", "pretrained/AdaBins_nyu.pt")
@ -910,6 +903,44 @@ def range_loss(input):
stop_on_next_loop = False # Make sure GPU memory doesn't get corrupted from cancelling the run mid-way through, allow a full frame to complete
def do_3d_step(img_filepath, frame_num, midas_model, midas_transform):
global seed
if args.key_frames:
translation_x = args.translation_x_series[frame_num]
translation_y = args.translation_y_series[frame_num]
translation_z = args.translation_z_series[frame_num]
rotation_3d_x = args.rotation_3d_x_series[frame_num]
rotation_3d_y = args.rotation_3d_y_series[frame_num]
rotation_3d_z = args.rotation_3d_z_series[frame_num]
print(
f'translation_x: {translation_x}',
f'translation_y: {translation_y}',
f'translation_z: {translation_z}',
f'rotation_3d_x: {rotation_3d_x}',
f'rotation_3d_y: {rotation_3d_y}',
f'rotation_3d_z: {rotation_3d_z}',
)
if frame_num > 0:
seed = seed + 1
if resume_run and frame_num == start_frame:
img_filepath = batchFolder+f"/{batch_name}({batchNum})_{start_frame-1:04}.png"
else:
img_filepath = '/content/prevFrame.png' if is_colab else 'prevFrame.png'
trans_scale = 1.0/200.0
translate_xyz = [-translation_x*trans_scale, translation_y*trans_scale, -translation_z*trans_scale]
rotate_xyz = [rotation_3d_x, rotation_3d_y, rotation_3d_z]
print('translation:',translate_xyz)
print('rotation:',rotate_xyz)
rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), "XYZ").unsqueeze(0)
print("rot_mat: " + str(rot_mat))
next_step_pil = dxf.transform_image_3d(img_filepath, midas_model, midas_transform, DEVICE,
rot_mat, translate_xyz, args.near_plane, args.far_plane,
args.fov, padding_mode=args.padding_mode,
sampling_mode=args.sampling_mode, midas_weight=args.midas_weight)
return next_step_pil
def do_run():
seed = args.seed
print(range(args.start_frame, args.max_frames))
@ -979,58 +1010,45 @@ def do_run():
skip_steps = args.calc_frames_skip_steps
if args.animation_mode == "3D":
if args.key_frames:
angle = args.angle_series[frame_num]
#zoom = args.zoom_series[frame_num]
translation_x = args.translation_x_series[frame_num]
translation_y = args.translation_y_series[frame_num]
translation_z = args.translation_z_series[frame_num]
rotation_3d_x = args.rotation_3d_x_series[frame_num]
rotation_3d_y = args.rotation_3d_y_series[frame_num]
rotation_3d_z = args.rotation_3d_z_series[frame_num]
print(
f'angle: {angle}',
#f'zoom: {zoom}',
f'translation_x: {translation_x}',
f'translation_y: {translation_y}',
f'translation_z: {translation_z}',
f'rotation_3d_x: {rotation_3d_x}',
f'rotation_3d_y: {rotation_3d_y}',
f'rotation_3d_z: {rotation_3d_z}',
)
if frame_num > 0:
if frame_num == 0:
turbo_blend = False
else:
seed = seed + 1
if resume_run and frame_num == start_frame:
img_filepath = batchFolder+f"/{batch_name}({batchNum})_{start_frame-1:04}.png"
if turbo_mode and frame_num > turbo_preroll:
shutil.copyfile(img_filepath, 'oldFrameScaled.png')
else:
img_filepath = '/content/prevFrame.png' if is_colab else 'prevFrame.png'
trans_scale = 1.0/200.0
translate_xyz = [-translation_x*trans_scale, translation_y*trans_scale, -translation_z*trans_scale]
rotate_xyz = [rotation_3d_x, rotation_3d_y, rotation_3d_z]
print('translation:',translate_xyz)
print('rotation:',rotate_xyz)
rot_mat = p3dT.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), "XYZ").unsqueeze(0)
print("rot_mat: " + str(rot_mat))
next_step_pil = dxf.transform_image_3d(img_filepath, midas_model, midas_transform, DEVICE,
rot_mat, translate_xyz, args.near_plane, args.far_plane,
args.fov, padding_mode=args.padding_mode,
sampling_mode=args.sampling_mode, midas_weight=args.midas_weight)
next_step_pil = do_3d_step(img_filepath, frame_num, midas_model, midas_transform)
next_step_pil.save('prevFrameScaled.png')
### Turbo mode - skip some diffusions to save time
turbo_blend = False # default to normal frame saving later
if turbo_mode and frame_num > 10: #preroll is 10 frames
if frame_num % int(turbo_steps) != 0:
### Turbo mode - skip some diffusions, use 3d morph for clarity and to save time
turbo_blend = False # default for non-turbo frame saving
if turbo_mode == True and frame_num == turbo_preroll: #start tracking oldframe
next_step_pil.save('oldFrameScaled.png')#stash for later blending
if turbo_mode == True and frame_num > turbo_preroll:
#set up 2 warped image sequences, old & new, to blend toward new diff image
old_frame = do_3d_step('oldFrameScaled.png', frame_num, midas_model, midas_transform)
old_frame.save('oldFrameScaled.png')
if frame_num % int(turbo_steps) != 0:
print('turbo skip this frame: skipping clip diffusion steps')
filename = f'{args.batch_name}({args.batchNum})_{frame_num:04}.png'
next_step_pil.save(f'{batchFolder}/{filename}') #save it as this frame. done.
blend_factor = ((frame_num % int(turbo_steps))+1)/int(turbo_steps)
print('turbo skip this frame: skipping clip diffusion steps and saving blended frame')
newWarpedImg = cv2.imread('prevFrameScaled.png')#this is already updated..
oldWarpedImg = cv2.imread('oldFrameScaled.png')
blendedImage = cv2.addWeighted(newWarpedImg, blend_factor, oldWarpedImg,1-blend_factor, 0.0)
cv2.imwrite(f'{batchFolder}/{filename}',blendedImage)
next_step_pil.save(f'{img_filepath}') # save it also as prev_frame to feed next iteration
turbo_blend = False # default to normal-frame-saving later
turbo_blend = False
continue
else:
if turbo_frame_blend:
turbo_blend = True # blend frames for smoothness..
#if not a skip frame, will run diffusion and need to blend.
oldWarpedImg = cv2.imread('prevFrameScaled.png')
cv2.imwrite(f'oldFrameScaled.png',oldWarpedImg)#swap in for blending later
turbo_blend = True # flag to blend frames after diff generated...
print('clip/diff this frame - generate clip diff image')
init_image = 'prevFrameScaled.png'
@ -1069,7 +1087,7 @@ def do_run():
else:
image_prompt = []
print(f'Frame Prompt: {frame_prompt}')
print(f'Frame {frame_num} Prompt: {frame_prompt}')
model_stats = []
for clip_model in clip_models:
@ -1396,6 +1414,7 @@ def save_settings():
'extract_nth_frame':extract_nth_frame,
'turbo_mode':turbo_mode,
'turbo_steps':turbo_steps,
'turbo_preroll':turbo_preroll,
'turbo_frame_blend':turbo_frame_blend,
}
# print('Settings:', setting_list)
@ -2398,7 +2417,16 @@ sampling_mode = 'bicubic'#@param {type:"string"}
turbo_mode = True #@param {type:"boolean"}
turbo_steps = "3" #@param ["2","3","4","5","6"] {type:"string"}
turbo_preroll = 10 # frames
turbo_frame_blend = True #@param {type:"boolean"}
#insist turbo be used only w 3d anim.
if turbo_mode and animation_mode != '3D':
print('=====')
print('Turbo mode only available with 3D animations. Disabling Turbo.')
print('=====')
turbo_mode = False
#@markdown ---
#@markdown ####**Coherency Settings:**
@ -2801,11 +2829,11 @@ if resume_run:
batchNum = int(run_to_resume)
if resume_from_frame == 'latest':
start_frame = len(glob(batchFolder+f"/{batch_name}({batchNum})_*.png"))
if turbo_mode == True and start_frame > 10 and start_frame % int(turbo_steps) != 0:
if turbo_mode == True and start_frame > turbo_preroll and start_frame % int(turbo_steps) != 0:
start_frame = start_frame - (start_frame % int(turbo_steps))
else:
start_frame = int(resume_from_frame)+1
if turbo_mode == True and start_frame > 10 and start_frame % int(turbo_steps) != 0:
if turbo_mode == True and start_frame > turbo_preroll and start_frame % int(turbo_steps) != 0:
start_frame = start_frame - (start_frame % int(turbo_steps))
if retain_overwritten_frames is True:
existing_frames = len(glob(batchFolder+f"/{batch_name}({batchNum})_*.png"))
@ -2815,7 +2843,7 @@ if resume_run:
else:
start_frame = 0
batchNum = len(glob(batchFolder+"/*.txt"))
while path.isfile(f"{batchFolder}/{batch_name}({batchNum})_settings.txt") is True or path.isfile(f"{batchFolder}/{batch_name}-{batchNum}_settings.txt") is True:
while os.path.isfile(f"{batchFolder}/{batch_name}({batchNum})_settings.txt") is True or os.path.isfile(f"{batchFolder}/{batch_name}-{batchNum}_settings.txt") is True:
batchNum += 1
print(f'Starting Run: {batch_name}({batchNum}) at frame {start_frame}')

Write Preview
Loading…
Cancel
Save