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Expose batch input in panel 

Expose Strip Power in Panel. 
Add files to date marked folders. 
Additional tweaks.
Inpaint_experimental
tin2tin 1 year ago committed by GitHub
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1 changed files with 164 additions and 80 deletions
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  1. 244
      __init__.py

244
__init__.py
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@ -25,6 +25,8 @@ import sys, os, aud, re
import string
from os.path import dirname, realpath, isdir, join, basename
import shutil
from datetime import date
os_platform = platform.system() # 'Linux', 'Darwin', 'Java', 'Windows'
@ -242,7 +244,7 @@ def load_video_as_np_array(video_path):
# return value
def process_frames(frame_folder_path):
def process_frames(frame_folder_path, target_width):
from PIL import Image
import cv2
@ -259,7 +261,7 @@ def process_frames(frame_folder_path):
# Process the image (resize and convert to RGB)
frame_width, frame_height = img.size
target_width = 1024
#target_width = 512
target_height = int((target_width / frame_width) * frame_height)
# Ensure width and height are divisible by 64
@ -279,9 +281,10 @@ def process_video(input_video_path, output_video_path):
import shutil
# Create a temporary folder for storing frames
temp_image_folder = "temp_images"
temp_image_folder = clean_path(dirname(realpath(__file__)) + "/temp_images")
if not os.path.exists(temp_image_folder):
os.makedirs(temp_image_folder)
# Open the video file using OpenCV
cap = cv2.VideoCapture(input_video_path)
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
@ -298,7 +301,7 @@ def process_video(input_video_path, output_video_path):
cap.release()
# Process frames using the separate function
processed_frames = process_frames(temp_image_folder)
processed_frames = process_frames(temp_image_folder, 512)
# print("Temp folder: "+temp_image_folder)
# Clean up: Delete the temporary image folder
@ -314,7 +317,7 @@ def process_image(image_path, frames_nr):
img = cv2.imread(image_path)
# Create a temporary folder for storing frames
temp_image_folder = "temp_images"
temp_image_folder = clean_path(dirname(realpath(__file__)) + "/temp_images")
if not os.path.exists(temp_image_folder):
os.makedirs(temp_image_folder)
@ -327,7 +330,7 @@ def process_image(image_path, frames_nr):
zoom_factor += 0.1
# Process frames using the separate function
processed_frames = process_frames(temp_image_folder)
processed_frames = process_frames(temp_image_folder, 1024)
# Clean up: Delete the temporary image folder
shutil.rmtree(temp_image_folder)
@ -351,6 +354,7 @@ def import_module(self, module, install_module):
set_system_console_topmost(True)
module = str(module)
try:
exec("import " + module)
except ModuleNotFoundError:
@ -371,15 +375,18 @@ def import_module(self, module, install_module):
"--user",
]
)
try:
exec("import " + module)
except ModuleNotFoundError:
return False
return True
def install_modules(self):
app_path = site.USER_SITE
if app_path not in sys.path:
sys.path.append(app_path)
pybin = sys.executable
@ -450,12 +457,14 @@ def install_modules(self):
import_module(self, "soundfile", "PySoundFile")
import_module(self, "diffusers", "diffusers")
# import_module(self, "diffusers", "git+https://github.com/huggingface/diffusers.git")
# import_module(self, "diffusers", "git+https://github.com/huggingface/accelerate.git")
import_module(self, "accelerate", "accelerate")
# import_module(self, "diffusers", "git+https://github.com/huggingface/accelerate.git")
import_module(self, "transformers", "transformers")
# import_module(self, "optimum", "optimum")
import_module(self, "sentencepiece", "sentencepiece")
import_module(self, "safetensors", "safetensors")
# import_module(self, "cv2", "opencv_python")
import_module(self, "cv2", "opencv_python")
import_module(self, "PIL", "pillow")
import_module(self, "scipy", "scipy")
import_module(self, "IPython", "IPython")
import_module(self, "bark", "git+https://github.com/suno-ai/bark.git")
@ -464,7 +473,6 @@ def install_modules(self):
import_module(self, "imwatermark", "invisible-watermark>=0.2.0")
# import_module(self, "triton", "C://Users//45239//Downloads//triton-2.0.0-cp310-cp310-win_amd64.whl")
# import_module(self, "audiocraft", "git+https://github.com/facebookresearch/audiocraft.git")
# import_module(self, "PIL", "pillow")
# subprocess.check_call([pybin,"-m","pip","install","force-reinstall","no-deps","pre xformers"])
subprocess.check_call([pybin, "-m", "pip", "install", "numpy", "--upgrade"])
if os_platform == "Windows":
@ -824,8 +832,7 @@ class SEQEUNCER_PT_generate_ai(Panel): # UI
layout.use_property_decorate = False
scene = context.scene
type = scene.generatorai_typeselect
col = layout.column()
col.prop(context.scene, "generatorai_typeselect", text="")
input = scene.input_strips
layout = self.layout
col = layout.column(align=True)
@ -843,15 +850,20 @@ class SEQEUNCER_PT_generate_ai(Panel): # UI
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
if type == "movie" or type == "image":
col = layout.column(align=True)
col.prop(context.scene, "generate_movie_x", text="X")
col.prop(context.scene, "generate_movie_y", text="Y")
col = layout.column(align=True)
if type == "movie" or type == "image":
col.prop(context.scene, "generate_movie_frames", text="Frames")
if type == "audio" and audio_model_card != "bark":
col.prop(context.scene, "audio_length_in_f", text="Frames")
if type == "audio" and audio_model_card == "bark":
col = layout.column(align=True)
col.prop(context.scene, "speakers", text="Speaker")
@ -866,7 +878,6 @@ class SEQEUNCER_PT_generate_ai(Panel): # UI
sub_row.prop(context.scene, "movie_num_seed", text="Seed")
row.prop(context.scene, "movie_use_random", text="", icon="QUESTION")
sub_row.active = not context.scene.movie_use_random
col.prop(context.scene, "movie_num_batch", text="Batch Count")
if type == "movie" and (
movie_model_card == "cerspense/zeroscope_v2_dark_30x448x256"
@ -877,6 +888,7 @@ class SEQEUNCER_PT_generate_ai(Panel): # UI
sub_col = col.row()
sub_col.prop(context.scene, "denoising_strength", text="Denoising")
sub_col.active = context.scene.video_to_video
if type == "image" and (
image_model_card == "stabilityai/stable-diffusion-xl-base-1.0"
):
@ -885,17 +897,34 @@ class SEQEUNCER_PT_generate_ai(Panel): # UI
sub_col = col.row()
sub_col.prop(context.scene, "denoising_strength", text="Denoising")
sub_col.active = context.scene.refine_sd
row = layout.row(align=True)
row.scale_y = 1.1
if type == "movie":
if movie_model_card == "stabilityai/stable-diffusion-xl-base-1.0":
row.operator("sequencer.text_to_generator", text="Generate from Strips")
else:
row.operator("sequencer.generate_movie", text="Generate")
if type == "image":
row.operator("sequencer.generate_image", text="Generate")
if type == "audio":
row.operator("sequencer.generate_audio", text="Generate")
col = layout.column()
col.prop(context.scene, "input_strips", text="Input")
if input =="input_strips":
col.prop(context.scene, "image_power", text="Strip Power")
col = layout.column()
col.prop(context.scene, "generatorai_typeselect", text="Output")
col.prop(context.scene, "movie_num_batch", text="Batch Count")
if input =="input_strips":
row = layout.row(align=True)
row.scale_y = 1.1
row.operator("sequencer.text_to_generator", text="Generate")
else:
row = layout.row(align=True)
row.scale_y = 1.1
if type == "movie":
if movie_model_card == "stabilityai/stable-diffusion-xl-base-1.0":
row.operator("sequencer.text_to_generator", text="Generate from Strips")
else:
row.operator("sequencer.generate_movie", text="Generate")
if type == "image":
row.operator("sequencer.generate_image", text="Generate")
if type == "audio":
row.operator("sequencer.generate_audio", text="Generate")
class SEQUENCER_OT_generate_movie(Operator):
@ -908,9 +937,11 @@ class SEQUENCER_OT_generate_movie(Operator):
def execute(self, context):
scene = context.scene
if not scene.generate_movie_prompt:
self.report({"INFO"}, "Text prompt in the Generative AI tab is empty!")
return {"CANCELLED"}
try:
import torch
from diffusers import (
@ -929,6 +960,7 @@ class SEQUENCER_OT_generate_movie(Operator):
"In the add-on preferences, install dependencies.",
)
return {"CANCELLED"}
show_system_console(True)
set_system_console_topmost(True)
@ -936,9 +968,11 @@ class SEQUENCER_OT_generate_movie(Operator):
if not seq_editor:
scene.sequence_editor_create()
# clear the VRAM
if torch.cuda.is_available():
torch.cuda.empty_cache()
current_frame = scene.frame_current
prompt = scene.generate_movie_prompt
negative_prompt = scene.generate_movie_negative_prompt + " nsfw nude nudity"
@ -959,13 +993,13 @@ class SEQUENCER_OT_generate_movie(Operator):
if torch.cuda.is_available():
torch.cuda.empty_cache()
# LOADING MODULES
# Models for refine imported movie
if scene.movie_path or scene.image_path:
if movie_model_card == "stabilityai/stable-diffusion-xl-base-1.0":
print("\nImg2img processing:")
import torch
from diffusers import StableDiffusionXLImg2ImgPipeline
@ -982,6 +1016,7 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram:
pipe.enable_model_cpu_offload()
# pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.unet.added_cond_kwargs={}
pipe.enable_vae_slicing()
pipe.enable_xformers_memory_efficient_attention()
else:
@ -1002,13 +1037,13 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram:
refiner.enable_model_cpu_offload()
# refiner.unet.enable_forward_chunking(chunk_size=1, dim=1)
refiner.unet.added_cond_kwargs={}
refiner.enable_vae_slicing()
refiner.enable_xformers_memory_efficient_attention()
else:
refiner.to("cuda")
else:
print("\nMov2mov processing:")
if movie_model_card == "cerspense/zeroscope_v2_dark_30x448x256" or movie_model_card == "cerspense/zeroscope_v2_576w":
card = "stabilityai/stable-diffusion-xl-base-1.0"
else:
@ -1028,6 +1063,7 @@ class SEQUENCER_OT_generate_movie(Operator):
upscale.enable_model_cpu_offload()
# upscale.unet.enable_forward_chunking(chunk_size=1, dim=1)
upscale.unet.added_cond_kwargs={}
upscale.enable_vae_slicing()
upscale.enable_xformers_memory_efficient_attention()
else:
@ -1049,6 +1085,7 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram:
pipe.enable_model_cpu_offload()
# pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.unet.added_cond_kwargs={}
pipe.enable_vae_slicing()
pipe.enable_xformers_memory_efficient_attention()
else:
@ -1062,9 +1099,11 @@ class SEQUENCER_OT_generate_movie(Operator):
if torch.cuda.is_available():
torch.cuda.empty_cache()
# torch.cuda.set_per_process_memory_fraction(0.85) # 6 GB VRAM
upscale = VideoToVideoSDPipeline.from_pretrained(
"cerspense/zeroscope_v2_XL", torch_dtype=torch.float16
)
# upscale = VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
upscale.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config
@ -1073,6 +1112,7 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram:
upscale.enable_model_cpu_offload()
# upscale.unet.enable_forward_chunking(chunk_size=1, dim=1)
upscale.unet.added_cond_kwargs={}
upscale.enable_vae_slicing()
upscale.enable_xformers_memory_efficient_attention()
else:
@ -1084,6 +1124,7 @@ class SEQUENCER_OT_generate_movie(Operator):
for i in range(scene.movie_num_batch):
if torch.cuda.is_available():
torch.cuda.empty_cache()
if i > 0:
empty_channel = scene.sequence_editor.active_strip.channel
start_frame = (
@ -1099,6 +1140,7 @@ class SEQUENCER_OT_generate_movie(Operator):
(scene.movie_num_batch * duration) + scene.frame_current,
)
start_frame = scene.frame_current
# generate video
seed = context.scene.movie_num_seed
seed = (
@ -1146,7 +1188,7 @@ class SEQUENCER_OT_generate_movie(Operator):
prompt,
negative_prompt=negative_prompt,
num_inference_steps=movie_num_inference_steps,
#strength=0.6,
strength=1.00 - scene.image_power,
denoising_start=0.7,
denoising_end=0.90,
guidance_scale=movie_num_guidance,
@ -1163,14 +1205,21 @@ class SEQUENCER_OT_generate_movie(Operator):
# vid2vid
else:
if scene.movie_path:
video = load_video_as_np_array(video_path)
# print("\nVid2vid processing:")
# print(video_path)
elif scene.image_path:
print(scene.image_path)
# print("\nImg2vid processing:")
# print(scene.image_path)
video = process_image(scene.image_path, int(scene.generate_movie_frames))
video = np.array(video)
if scene.video_to_video:
if scene.video_to_video and scene.video_to_video and (
movie_model_card == "cerspense/zeroscope_v2_dark_30x448x256"
or movie_model_card == "cerspense/zeroscope_v2_576w"
):
video = [
Image.fromarray(frame).resize((int(x * 2), int(y * 2)))
for frame in video
@ -1179,7 +1228,7 @@ class SEQUENCER_OT_generate_movie(Operator):
video_frames = upscale(
prompt,
video=video,
#strength=0.8, #Enable this to only convert the shapes
strength=1.00 - scene.image_power,
negative_prompt=negative_prompt,
num_inference_steps=movie_num_inference_steps,
guidance_scale=movie_num_guidance,
@ -1188,6 +1237,7 @@ class SEQUENCER_OT_generate_movie(Operator):
# Generation of movie
else:
print("\nGenerating video:")
video_frames = pipe(
prompt,
negative_prompt=negative_prompt,
@ -1209,6 +1259,7 @@ class SEQUENCER_OT_generate_movie(Operator):
movie_model_card == "cerspense/zeroscope_v2_dark_30x448x256"
or movie_model_card == "cerspense/zeroscope_v2_576w"
):
print("\nUpscale video:")
if torch.cuda.is_available():
torch.cuda.empty_cache()
video = [
@ -1229,7 +1280,7 @@ class SEQUENCER_OT_generate_movie(Operator):
# Move to folder
src_path = export_to_video(video_frames)
dst_path = clean_path(
dirname(realpath(__file__)) + "/" + os.path.basename(src_path)
dirname(realpath(__file__)) + "/"+ str(date.today()) + "/" + os.path.basename(src_path)
)
shutil.move(src_path, dst_path)
@ -1237,6 +1288,7 @@ class SEQUENCER_OT_generate_movie(Operator):
if not os.path.isfile(dst_path):
print("No resulting file found.")
return {"CANCELLED"}
for window in bpy.context.window_manager.windows:
screen = window.screen
for area in screen.areas:
@ -1259,16 +1311,19 @@ class SEQUENCER_OT_generate_movie(Operator):
strip.use_proxy = True
strip.name = str(seed) + "_" + prompt
bpy.ops.sequencer.rebuild_proxy()
if i > 0:
scene.frame_current = (
scene.sequence_editor.active_strip.frame_final_start
)
# Redraw UI to display the new strip. Remove this if Blender crashes: https://docs.blender.org/api/current/info_gotcha.html#can-i-redraw-during-script-execution
bpy.ops.wm.redraw_timer(type="DRAW_WIN_SWAP", iterations=1)
break
# clear the VRAM
if torch.cuda.is_available():
torch.cuda.empty_cache()
bpy.types.Scene.movie_path = ""
bpy.ops.renderreminder.play_notification()
scene.frame_current = current_frame
@ -1286,11 +1341,14 @@ class SEQUENCER_OT_generate_audio(Operator):
def execute(self, context):
scene = context.scene
if not scene.generate_movie_prompt:
self.report({"INFO"}, "Text prompt in the Generative AI tab is empty!")
return {"CANCELLED"}
if not scene.sequence_editor:
scene.sequence_editor_create()
preferences = context.preferences
addon_prefs = preferences.addons[__name__].preferences
@ -1314,11 +1372,13 @@ class SEQUENCER_OT_generate_audio(Operator):
from IPython.display import Audio
from scipy.io.wavfile import write as write_wav
import xformers
if addon_prefs.audio_model_card == "facebook/audiogen-medium":
import torchaudio
from audiocraft.models import AudioGen
from audiocraft.data.audio import audio_write
from scipy.io.wavfile import write as write_wav
if addon_prefs.audio_model_card == "bark":
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import numpy as np
@ -1329,6 +1389,7 @@ class SEQUENCER_OT_generate_audio(Operator):
from bark.api import semantic_to_waveform
from bark import generate_audio, SAMPLE_RATE
from scipy.io.wavfile import write as write_wav
except ModuleNotFoundError:
print("Dependencies needs to be installed in the add-on preferences.")
self.report(
@ -1336,12 +1397,14 @@ class SEQUENCER_OT_generate_audio(Operator):
"Dependencies needs to be installed in the add-on preferences.",
)
return {"CANCELLED"}
show_system_console(True)
set_system_console_topmost(True)
# clear the VRAM
if torch.cuda.is_available():
torch.cuda.empty_cache()
if addon_prefs.audio_model_card == "cvssp/audioldm-s-full-v2":
repo_id = addon_prefs.audio_model_card
pipe = AudioLDMPipeline.from_pretrained(
@ -1351,10 +1414,12 @@ class SEQUENCER_OT_generate_audio(Operator):
if low_vram:
pipe.enable_model_cpu_offload()
# pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.unet.added_cond_kwargs={}
pipe.enable_vae_slicing()
pipe.enable_xformers_memory_efficient_attention()
else:
pipe.to("cuda")
elif addon_prefs.audio_model_card == "facebook/audiogen-medium":
pipe = AudioGen.get_pretrained("facebook/audiogen-medium")
pipe = pipe.to("cuda")
@ -1366,8 +1431,9 @@ class SEQUENCER_OT_generate_audio(Operator):
fine_use_gpu=True,
fine_use_small=True,
)
for i in range(scene.movie_num_batch):
# wm.progress_update(i)
if i > 0:
empty_channel = scene.sequence_editor.active_strip.channel
start_frame = (
@ -1383,6 +1449,7 @@ class SEQUENCER_OT_generate_audio(Operator):
100000000000000000000,
)
start_frame = scene.frame_current
if addon_prefs.audio_model_card == "bark":
rate = 24000
GEN_TEMP = 0.6
@ -1414,7 +1481,7 @@ class SEQUENCER_OT_generate_audio(Operator):
pieces
) # Audio(np.concatenate(pieces), rate=rate)
filename = clean_path(
dirname(realpath(__file__)) + "/" + prompt + ".wav"
dirname(realpath(__file__)) + "/"+ str(date.today()) + "/" + prompt + ".wav"
)
# Write the combined audio to a file
@ -1452,7 +1519,7 @@ class SEQUENCER_OT_generate_audio(Operator):
rate = 16000
filename = clean_path(
dirname(realpath(__file__)) + "/" + prompt + ".wav"
dirname(realpath(__file__)) + "/"+ str(date.today()) + "/" + prompt + ".wav"
)
write_wav(filename, rate, audio.transpose()) # .transpose()
filepath = filename
@ -1538,10 +1605,33 @@ class SEQUENCER_OT_generate_image(Operator):
addon_prefs = preferences.addons[__name__].preferences
image_model_card = addon_prefs.image_model_card
# Model for generate
# LOADING MMODELS
# Models for stable diffusion
if not image_model_card == "DeepFloyd/IF-I-M-v1.0":
pipe = DiffusionPipeline.from_pretrained(
image_model_card,
torch_dtype=torch.float16,
variant="fp16",
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config
)
if low_vram:
torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM
pipe.enable_model_cpu_offload()
# pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.unet.added_cond_kwargs={}
pipe.enable_vae_slicing()
pipe.enable_xformers_memory_efficient_attention()
else:
pipe.to("cuda")
# DeepFloyd
if image_model_card == "DeepFloyd/IF-I-M-v1.0":
elif image_model_card == "DeepFloyd/IF-I-M-v1.0":
from huggingface_hub.commands.user import login
result = login(token=addon_prefs.hugginface_token)
@ -1591,33 +1681,13 @@ class SEQUENCER_OT_generate_image(Operator):
stage_3.enable_xformers_memory_efficient_attention()
else:
stage_3.to("cuda")
else: # model for stable diffusion
pipe = DiffusionPipeline.from_pretrained(
image_model_card,
torch_dtype=torch.float16,
variant="fp16",
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config
)
if low_vram:
pipe.enable_model_cpu_offload()
# pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.enable_vae_slicing()
pipe.enable_xformers_memory_efficient_attention()
else:
pipe.to("cuda")
# Add refiner model if chosen.
if (
scene.refine_sd
and image_model_card == "stabilityai/stable-diffusion-xl-base-1.0"
) or scene.image_path:
if (scene.refine_sd and image_model_card == "stabilityai/stable-diffusion-xl-base-1.0") or scene.image_path:
from diffusers import StableDiffusionXLImg2ImgPipeline
refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
#"stabilityai/stable-diffusion-xl-base-1.0",
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=pipe.text_encoder_2,
vae=pipe.vae,
@ -1629,6 +1699,7 @@ class SEQUENCER_OT_generate_image(Operator):
if low_vram:
refiner.enable_model_cpu_offload()
# refiner.unet.enable_forward_chunking(chunk_size=1, dim=1)
refiner.unet.added_cond_kwargs={}
refiner.enable_vae_slicing()
refiner.enable_xformers_memory_efficient_attention()
else:
@ -1636,7 +1707,7 @@ class SEQUENCER_OT_generate_image(Operator):
# Main Generate Loop:
for i in range(scene.movie_num_batch):
# wm.progress_update(i)
if i > 0:
empty_channel = scene.sequence_editor.active_strip.channel
start_frame = (
@ -1709,12 +1780,10 @@ class SEQUENCER_OT_generate_image(Operator):
image = refiner(
prompt=prompt,
image=init_image,
strength=0.8,
strength=1.00 - scene.image_power,
negative_prompt=negative_prompt,
num_inference_steps=image_num_inference_steps,
guidance_scale=image_num_guidance,
# height=y,
# width=x,
generator=generator,
).images[0]
# generate
@ -1734,7 +1803,6 @@ class SEQUENCER_OT_generate_image(Operator):
and image_model_card == "stabilityai/stable-diffusion-xl-base-1.0"
#and not scene.image_path
):
# n_steps = 50
image = refiner(
prompt,
negative_prompt=negative_prompt,
@ -1748,7 +1816,7 @@ class SEQUENCER_OT_generate_image(Operator):
filename = clean_filename(
str(seed) + "_" + context.scene.generate_movie_prompt
)
out_path = clean_path(dirname(realpath(__file__)) + "/" + filename + ".png")
out_path = clean_path(dirname(realpath(__file__)) + "/"+ str(date.today()) +"/" + filename + ".png")
image.save(out_path)
# Add strip
@ -1781,7 +1849,6 @@ class SEQUENCER_OT_generate_image(Operator):
torch.cuda.empty_cache()
bpy.ops.renderreminder.play_notification()
# wm.progress_end()
scene.frame_current = current_frame
# clear the VRAM
@ -1821,10 +1888,10 @@ class SEQUENCER_OT_strip_to_generatorAI(Operator):
self.report({"INFO"}, "Select strips for batch processing.")
return {"CANCELLED"}
for strip in strips:
for count, strip in enumerate(strips):
if strip.type == "TEXT":
if strip.text:
print("Processing: " + strip.text + ", " + prompt)
print("\n" + str(count+1) + "/"+ str(len(strips)) + " Processing: " + strip.text + ", " + prompt)
scene.generate_movie_prompt = strip.text + ", " + prompt
scene.frame_current = strip.frame_final_start
if type == "movie":
@ -1850,7 +1917,7 @@ class SEQUENCER_OT_strip_to_generatorAI(Operator):
context.scene.movie_use_random = False
context.scene.movie_num_seed = file_seed
print("Processing: " + strip_prompt + ", " + prompt)
print("\n" + str(count+1) + "/"+ str(len(strips)) + " Processing: " + strip_prompt + ", " + prompt)
scene.generate_movie_prompt = strip_prompt + ", " + prompt
scene.frame_current = strip.frame_final_start
@ -1883,7 +1950,7 @@ class SEQUENCER_OT_strip_to_generatorAI(Operator):
context.scene.movie_use_random = False
context.scene.movie_num_seed = file_seed
print("Processing: " + strip_prompt + ", " + prompt)
print("\n" + str(count+1) + "/"+ str(len(strips)) + " Processing: " + strip_prompt + ", " + prompt)
scene.generate_movie_prompt = strip_prompt + ", " + prompt
scene.generate_movie_prompt = prompt
scene.frame_current = strip.frame_final_start
@ -1913,14 +1980,14 @@ class SEQUENCER_OT_strip_to_generatorAI(Operator):
return {"FINISHED"}
def panel_text_to_generatorAI(self, context):
layout = self.layout
layout.separator()
layout.operator(
"sequencer.text_to_generator",
text="Generative AI",
icon="SHADERFX",
)
#def panel_text_to_generatorAI(self, context):
# layout = self.layout
# layout.separator()
# layout.operator(
# "sequencer.text_to_generator",
# text="Generative AI",
# icon="SHADERFX",
# )
classes = (
@ -1997,7 +2064,7 @@ def register():
default=1,
)
# The seed number.
# The guidance number.
bpy.types.Scene.movie_num_guidance = bpy.props.FloatProperty(
name="movie_num_guidance",
default=15.0,
@ -2088,9 +2155,26 @@ def register():
bpy.types.Scene.image_path = bpy.props.StringProperty(name="image_path", default="")
bpy.types.Scene.image_path = ""
bpy.types.Scene.input_strips = bpy.props.EnumProperty(
name="Sound",
items=[
("generate", "Generate (No Input)", "Generate(No Input)"),
("input_strips", "Strips", "Selected Strips"),
],
default="generate",
)
bpy.types.Scene.image_power = bpy.props.FloatProperty(
name="image_power",
default=0.95,
min=0.05,
max=0.95,
)
for cls in classes:
bpy.utils.register_class(cls)
bpy.types.SEQUENCER_MT_add.append(panel_text_to_generatorAI)
#bpy.types.SEQUENCER_MT_add.append(panel_text_to_generatorAI)
def unregister():
@ -2112,7 +2196,7 @@ def unregister():
del bpy.types.Scene.denoising_strength
del bpy.types.Scene.video_to_video
bpy.types.SEQUENCER_MT_add.remove(panel_text_to_generatorAI)
#bpy.types.SEQUENCER_MT_add.remove(panel_text_to_generatorAI)
if __name__ == "__main__":

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