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Improved img2img, img2vid and vid2vid

Inpaint_experimental
tin2tin 1 year ago committed by GitHub
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1 changed files with 13 additions and 12 deletions
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  1. 25
      __init__.py

25
__init__.py
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@ -313,12 +313,12 @@ def process_image(image_path, frames_nr):
import cv2 import cv2
img = cv2.imread(image_path) img = cv2.imread(image_path)
# Create a temporary folder for storing frames # Create a temporary folder for storing frames
temp_image_folder = "temp_images" temp_image_folder = "temp_images"
if not os.path.exists(temp_image_folder): if not os.path.exists(temp_image_folder):
os.makedirs(temp_image_folder) os.makedirs(temp_image_folder)
# Add zoom motion to the image and save frames # Add zoom motion to the image and save frames
zoom_factor = 1.01 zoom_factor = 1.01
for i in range(frames_nr): for i in range(frames_nr):
@ -979,7 +979,7 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram: if low_vram:
torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM
upscale.enable_model_cpu_offload() upscale.enable_model_cpu_offload()
# upscale.unet.enable_forward_chunking(chunk_size=1, dim=1) # upscale.unet.enable_forward_chunking(chunk_size=1, dim=1)
upscale.enable_vae_slicing() upscale.enable_vae_slicing()
upscale.enable_xformers_memory_efficient_attention() upscale.enable_xformers_memory_efficient_attention()
@ -988,7 +988,8 @@ class SEQUENCER_OT_generate_movie(Operator):
else: else:
print("\nMov2mov processing:") print("\nMov2mov processing:")
upscale = VideoToVideoSDPipeline.from_pretrained( upscale = VideoToVideoSDPipeline.from_pretrained(
"cerspense/zeroscope_v2_XL", torch_dtype=torch.float16 movie_model_card, torch_dtype=torch.float16
#"cerspense/zeroscope_v2_XL", torch_dtype=torch.float16
) )
# upscale = VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16) # upscale = VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
@ -997,7 +998,7 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram: if low_vram:
torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM
upscale.enable_model_cpu_offload() upscale.enable_model_cpu_offload()
upscale.unet.enable_forward_chunking(chunk_size=1, dim=1) upscale.unet.enable_forward_chunking(chunk_size=1, dim=1)
upscale.enable_vae_slicing() upscale.enable_vae_slicing()
upscale.enable_xformers_memory_efficient_attention() upscale.enable_xformers_memory_efficient_attention()
@ -1105,7 +1106,7 @@ class SEQUENCER_OT_generate_movie(Operator):
frames = process_video(input_video_path, output_video_path) frames = process_video(input_video_path, output_video_path)
elif scene.image_path: elif scene.image_path:
print(scene.image_path) print(scene.image_path)
frames = process_image(scene.image_path, int(scene.generate_movie_frames)) frames = process_image(scene.image_path, int(scene.generate_movie_frames))
video_frames = [] video_frames = []
# Iterate through the frames # Iterate through the frames
@ -1134,7 +1135,7 @@ class SEQUENCER_OT_generate_movie(Operator):
video = load_video_as_np_array(video_path) video = load_video_as_np_array(video_path)
elif scene.image_path: elif scene.image_path:
print(scene.image_path) print(scene.image_path)
frames = process_image(scene.image_path, int(scene.generate_movie_frames)) frames = process_image(scene.image_path, int(scene.generate_movie_frames))
video = np.array(frames) video = np.array(frames)
if scene.video_to_video: if scene.video_to_video:
@ -1505,7 +1506,7 @@ class SEQUENCER_OT_generate_image(Operator):
image_model_card = addon_prefs.image_model_card image_model_card = addon_prefs.image_model_card
# Model for generate # Model for generate
# DeepFloyd # DeepFloyd
if image_model_card == "DeepFloyd/IF-I-M-v1.0": if image_model_card == "DeepFloyd/IF-I-M-v1.0":
from huggingface_hub.commands.user import login from huggingface_hub.commands.user import login
@ -1783,7 +1784,7 @@ class SEQUENCER_OT_strip_to_generatorAI(Operator):
if not strips: if not strips:
self.report({"INFO"}, "Select strips for batch processing.") self.report({"INFO"}, "Select strips for batch processing.")
return {"CANCELLED"} return {"CANCELLED"}
for strip in strips: for strip in strips:
if strip.type == "TEXT": if strip.type == "TEXT":
if strip.text: if strip.text:
@ -1810,18 +1811,18 @@ class SEQUENCER_OT_strip_to_generatorAI(Operator):
strip_prompt = (strip_prompt.replace(str(file_seed)+"_", "")) strip_prompt = (strip_prompt.replace(str(file_seed)+"_", ""))
context.scene.movie_use_random = False context.scene.movie_use_random = False
context.scene.movie_num_seed = file_seed context.scene.movie_num_seed = file_seed
print("Processing: " + strip_prompt + ", " + prompt) print("Processing: " + strip_prompt + ", " + prompt)
print("Seed: "+str(file_seed)) print("Seed: "+str(file_seed))
scene.generate_movie_prompt = strip_prompt + ", " + prompt scene.generate_movie_prompt = strip_prompt + ", " + prompt
scene.frame_current = strip.frame_final_start scene.frame_current = strip.frame_final_start
if type == "movie": if type == "movie":
sequencer.generate_movie() sequencer.generate_movie()
if type == "audio": if type == "audio":
sequencer.generate_audio() sequencer.generate_audio()
if type == "image": if type == "image":
sequencer.generate_image() sequencer.generate_image()
context.scene.generate_movie_prompt = prompt context.scene.generate_movie_prompt = prompt
context.scene.movie_use_random = use_random context.scene.movie_use_random = use_random
context.scene.movie_num_seed = seed context.scene.movie_num_seed = seed

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