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Update __init__.py

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tin2tin 1 year ago committed by GitHub
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1 changed files with 27 additions and 24 deletions
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  1. 51
      __init__.py

51
__init__.py
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@ -183,16 +183,16 @@ def style_prompt(prompt):
return return_array
def closest_divisible_64(num):
def closest_divisible_32(num):
# Determine the remainder when num is divided by 64
remainder = (num % 32)
# If the remainder is less than or equal to 32, return num - remainder,
# but ensure the result is not less than 64
# If the remainder is less than or equal to 16, return num - remainder,
# but ensure the result is not less than 192
if remainder <= 16:
result = num - remainder
return max(result, 192)
# Otherwise, return num + (64 - remainder)
# Otherwise, return num + (32 - remainder)
else:
return max(num + (32 - remainder), 192)
@ -333,8 +333,8 @@ def process_frames(frame_folder_path, target_width):
target_height = int((target_width / frame_width) * frame_height)
# Ensure width and height are divisible by 64
target_width = closest_divisible_64(target_width)
target_height = closest_divisible_64(target_height)
target_width = closest_divisible_32(target_width)
target_height = closest_divisible_32(target_height)
img = img.resize((target_width, target_height), Image.ANTIALIAS)
img = img.convert("RGB")
@ -692,7 +692,6 @@ def output_strips_updated(self, context):
scene = context.scene
type = scene.generatorai_typeselect
input = scene.input_strips
print(type)
if type == "movie" or type == "audio":
scene.inpaint_selected_strip = ""
@ -1163,8 +1162,8 @@ class SEQUENCER_OT_generate_movie(Operator):
negative_prompt = scene.generate_movie_negative_prompt +", "+ style_prompt(scene.generate_movie_prompt)[1] +", nsfw nude nudity"
movie_x = scene.generate_movie_x
movie_y = scene.generate_movie_y
x = scene.generate_movie_x = closest_divisible_64(movie_x)
y = scene.generate_movie_y = closest_divisible_64(movie_y)
x = scene.generate_movie_x = closest_divisible_32(movie_x)
y = scene.generate_movie_y = closest_divisible_32(movie_y)
duration = scene.generate_movie_frames
movie_num_inference_steps = scene.movie_num_inference_steps
movie_num_guidance = scene.movie_num_guidance
@ -1205,7 +1204,7 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram:
pipe.enable_model_cpu_offload()
#pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) # Heavy
pipe.enable_vae_slicing()
#pipe.enable_vae_slicing()
else:
pipe.to("cuda")
@ -1221,8 +1220,8 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram:
refiner.enable_model_cpu_offload()
refiner.enable_vae_tiling()
refiner.enable_vae_slicing()
#refiner.enable_vae_tiling()
#refiner.enable_vae_slicing()
else:
refiner.to("cuda")
@ -1266,7 +1265,7 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram:
#torch.cuda.set_per_process_memory_fraction(0.98)
upscale.enable_model_cpu_offload()
#upscale.enable_vae_tiling()
upscale.enable_vae_tiling()
#upscale.unet.enable_forward_chunking(chunk_size=1, dim=1) # heavy:
upscale.enable_vae_slicing()
else:
@ -1339,7 +1338,7 @@ class SEQUENCER_OT_generate_movie(Operator):
seed = (
seed
if not context.scene.movie_use_random
else random.randint(0, 999999)
else random.randint(-2147483647, 2147483647)
)
print("Seed: "+str(seed))
context.scene.movie_num_seed = seed
@ -1409,12 +1408,12 @@ class SEQUENCER_OT_generate_movie(Operator):
# Upscale video
if scene.video_to_video:
video = [
Image.fromarray(frame).resize((closest_divisible_64(int(x * 2)), closest_divisible_64(int(y * 2))))
Image.fromarray(frame).resize((closest_divisible_32(int(x * 2)), closest_divisible_32(int(y * 2))))
for frame in video
]
else:
video = [
Image.fromarray(frame).resize((closest_divisible_64(int(x)), closest_divisible_64(int(y))))
Image.fromarray(frame).resize((closest_divisible_32(int(x)), closest_divisible_32(int(y))))
for frame in video
]
@ -1441,7 +1440,7 @@ class SEQUENCER_OT_generate_movie(Operator):
# Upscale video
# if scene.video_to_video:
# video = [
# Image.fromarray(frame).resize((closest_divisible_64(int(x * 2)), closest_divisible_64(int(y * 2))))
# Image.fromarray(frame).resize((closest_divisible_32(int(x * 2)), closest_divisible_32(int(y * 2))))
# for frame in video
# ]
@ -1481,7 +1480,7 @@ class SEQUENCER_OT_generate_movie(Operator):
print("Upscale: Video")
if torch.cuda.is_available():
torch.cuda.empty_cache()
video = [Image.fromarray(frame).resize((closest_divisible_64(x * 2), closest_divisible_64(y * 2))) for frame in video_frames]
video = [Image.fromarray(frame).resize((closest_divisible_32(x * 2), closest_divisible_32(y * 2))) for frame in video_frames]
video_frames = upscale(
prompt,
@ -1829,8 +1828,8 @@ class SEQUENCER_OT_generate_image(Operator):
negative_prompt = scene.generate_movie_negative_prompt +", "+ style_prompt(scene.generate_movie_prompt)[1] +", nsfw, nude, nudity,"
image_x = scene.generate_movie_x
image_y = scene.generate_movie_y
x = scene.generate_movie_x = closest_divisible_64(image_x)
y = scene.generate_movie_y = closest_divisible_64(image_y)
x = scene.generate_movie_x = closest_divisible_32(image_x)
y = scene.generate_movie_y = closest_divisible_32(image_y)
duration = scene.generate_movie_frames
image_num_inference_steps = scene.movie_num_inference_steps
image_num_guidance = scene.movie_num_guidance
@ -1840,7 +1839,7 @@ class SEQUENCER_OT_generate_image(Operator):
addon_prefs = preferences.addons[__name__].preferences
image_model_card = addon_prefs.image_model_card
do_inpaint = input == "input_strips" and scene.inpaint_selected_strip and type == "image"
do_refine = scene.refine_sd #and (scene.image_path or scene.movie_path) # or image_model_card == "stabilityai/stable-diffusion-xl-base-1.0") #and not do_inpaint
do_refine = scene.refine_sd or scene.image_path or scene.movie_path # or image_model_card == "stabilityai/stable-diffusion-xl-base-1.0") #and not do_inpaint
# LOADING MODELS
print("Model: " + image_model_card)
@ -2021,7 +2020,7 @@ class SEQUENCER_OT_generate_image(Operator):
seed = (
seed
if not context.scene.movie_use_random
else random.randint(0, 999999)
else random.randint(-2147483647, 2147483647)
)
print("Seed: "+str(seed))
context.scene.movie_num_seed = seed
@ -2227,6 +2226,10 @@ class SEQUENCER_OT_strip_to_generatorAI(Operator):
return context.scene and context.scene.sequence_editor
def execute(self, context):
bpy.types.Scene.movie_path = ""
bpy.types.Scene.image_path = ""
preferences = context.preferences
addon_prefs = preferences.addons[__name__].preferences
play_sound = addon_prefs.playsound
@ -2453,7 +2456,7 @@ def register():
bpy.types.Scene.movie_num_seed = bpy.props.IntProperty(
name="movie_num_seed",
default=1,
min=1,
min=-2147483647,
max=2147483647,
)
@ -2563,7 +2566,7 @@ def register():
name="image_power",
default=0.50,
min=0.05,
max=0.95,
max=0.82,
)
styles_array = load_styles(os.path.dirname(os.path.abspath(__file__))+"/styles.json")

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