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Add: vid2img

tin2tin-patch-2
tin2tin 1 year ago committed by GitHub
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1 changed files with 45 additions and 20 deletions
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  1. 65
      __init__.py

65
__init__.py
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@ -1,12 +1,12 @@
# https://modelscope.cn/models/damo/text-to-video-synthesis/summary
bl_info = {
"name": "Generative AI",
"name": "Pallaidium - Generative AI",
"author": "tintwotin",
"version": (1, 4),
"version": (1, 5),
"blender": (3, 4, 0),
"location": "Video Sequence Editor > Sidebar > Generative AI",
"description": "Generate media in the VSE",
"description": "AI Generate media in the VSE",
"category": "Sequencer",
}
@ -185,16 +185,16 @@ def style_prompt(prompt):
def closest_divisible_64(num):
# Determine the remainder when num is divided by 64
remainder = (num % 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 remainder <= 32:
if remainder <= 16:
result = num - remainder
return max(result, 192)
# Otherwise, return num + (64 - remainder)
else:
return max(num + (64 - remainder), 192)
return max(num + (32 - remainder), 192)
def find_first_empty_channel(start_frame, end_frame):
@ -607,7 +607,7 @@ def install_modules(self):
]
)
# Modelscope img2vid
# # Modelscope img2vid
# import_module(self, "modelscope", "modelscope==1.8.4")
# #import_module(self, "xformers", "xformers==0.0.20")
# #import_module(self, "torch", "torch==2.0.1")
@ -679,7 +679,7 @@ def input_strips_updated(self, context):
scene = context.scene
input = scene.input_strips
if movie_model_card == "stabilityai/stable-diffusion-xl-base-1.0":
if movie_model_card == "stabilityai/stable-diffusion-xl-base-1.0" and scene.generatorai_typeselect == "video":
scene.input_strips = "input_strips"
if scene.generatorai_typeselect == "video" or scene.generatorai_typeselect == "audio":
scene.inpaint_selected_strip = ""
@ -1230,10 +1230,14 @@ class SEQUENCER_OT_generate_movie(Operator):
# from modelscope.pipelines import pipeline
# from modelscope.outputs import OutputKeys
# from modelscope import snapshot_download
# model_dir = snapshot_download('damo/Image-to-Video', revision='v1.1.0')
# pipe = pipeline(task='image-to-video', model= model_dir, model_revision='v1.1.0')
# #pipe = pipeline(task='image-to-video', model='damo-vilab/MS-Image2Video', model_revision='v1.1.0')
# #pipe = pipeline(task='image-to-video', model='damo/Image-to-Video', model_revision='v1.1.0')
# pipe = pipeline(task='image-to-video', model='C:/Users/45239/.cache/modelscope/hub/damo/Image-to-Video', model_revision='v1.1.0')
#
# # local: pipe = pipeline(task='image-to-video', model='C:/Users/45239/.cache/modelscope/hub/damo/Image-to-Video', model_revision='v1.1.0')
# if low_vram:
# pipe.enable_model_cpu_offload()
@ -1262,8 +1266,8 @@ 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.unet.enable_forward_chunking(chunk_size=1, dim=1) # heavy:
#upscale.enable_vae_tiling()
#upscale.unet.enable_forward_chunking(chunk_size=1, dim=1) # heavy:
upscale.enable_vae_slicing()
else:
upscale.to("cuda")
@ -1301,7 +1305,7 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram:
upscale.enable_model_cpu_offload()
# upscale.unet.enable_forward_chunking(chunk_size=1, dim=1) #Heavy
upscale.unet.enable_forward_chunking(chunk_size=1, dim=1) #Heavy
upscale.enable_vae_slicing()
else:
upscale.to("cuda")
@ -1400,6 +1404,7 @@ class SEQUENCER_OT_generate_movie(Operator):
elif scene.image_path:
print("Process: Image to video")
video = process_image(scene.image_path, int(scene.generate_movie_frames))
video = np.array(video)
# Upscale video
if scene.video_to_video:
@ -1407,6 +1412,11 @@ class SEQUENCER_OT_generate_movie(Operator):
Image.fromarray(frame).resize((closest_divisible_64(int(x * 2)), closest_divisible_64(int(y * 2))))
for frame in video
]
else:
video = [
Image.fromarray(frame).resize((closest_divisible_64(int(x)), closest_divisible_64(int(y))))
for frame in video
]
video_frames = upscale(
prompt,
@ -1830,7 +1840,7 @@ 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 or scene.image_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)
@ -1880,13 +1890,13 @@ class SEQUENCER_OT_generate_image(Operator):
# Models for stable diffusion
elif not image_model_card == "DeepFloyd/IF-I-M-v1.0":
elif not image_model_card == "DeepFloyd/IF-I-M-v1.0" and not scene.image_path and not scene.movie_path:
from diffusers import AutoencoderKL
if image_model_card == "stabilityai/stable-diffusion-xl-base-1.0":
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
#vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained(
image_model_card,
vae=vae,
#vae=vae,
torch_dtype=torch.float16,
variant="fp16",
)
@ -1898,10 +1908,13 @@ class SEQUENCER_OT_generate_image(Operator):
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.watermark = NoWatermark()
if low_vram:
#torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
#pipe.enable_forward_chunking(chunk_size=1, dim=1)
else:
pipe.to("cuda")
@ -1978,7 +1991,7 @@ class SEQUENCER_OT_generate_image(Operator):
if low_vram:
refiner.enable_model_cpu_offload()
refiner.enable_vae_tiling()
#refiner.enable_vae_tiling()
refiner.enable_vae_slicing()
else:
refiner.to("cuda")
@ -2109,9 +2122,18 @@ class SEQUENCER_OT_generate_image(Operator):
image = PIL.Image.fromarray(unmasked_unchanged_image_arr.astype("uint8"))
# Img2img
elif scene.image_path:
print("Process: Image to image")
init_image = load_image(scene.image_path).convert("RGB")
elif scene.image_path or scene.movie_path:
if scene.movie_path:
print("Process: Video to image")
init_image = load_first_frame(scene.movie_path)
init_image = init_image.resize((x, y))
elif scene.image_path:
print("Process: Image to image")
init_image = load_first_frame(scene.image_path)
init_image = init_image.resize((x, y))
#init_image = load_image(scene.image_path).convert("RGB")
image = refiner(
prompt=prompt,
image=init_image,
@ -2235,6 +2257,9 @@ class SEQUENCER_OT_strip_to_generatorAI(Operator):
self.report({"INFO"}, "None of the selected strips are movie, image, or text types.")
return {"CANCELLED"}
if use_strip_data:
print("Use file seed and prompt: Yes")
for count, strip in enumerate(strips):
if strip.type == "TEXT":
if strip.text:

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