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Attempt to fix CUDA and other errors.

Inpaint_experimental
tin2tin 1 year ago committed by GitHub
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1 changed files with 42 additions and 38 deletions
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  1. 80
      __init__.py

80
__init__.py
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@ -391,7 +391,7 @@ def low_vram():
for i in range(torch.cuda.device_count()): for i in range(torch.cuda.device_count()):
properties = torch.cuda.get_device_properties(i) properties = torch.cuda.get_device_properties(i)
total_vram += properties.total_memory total_vram += properties.total_memory
return (total_vram / (1024**3)) < 6.1 # Y/N under 6.1 GB? return (total_vram / (1024**3)) < 8.1 # Y/N under 6.1 GB?
def import_module(self, module, install_module): def import_module(self, module, install_module):
@ -500,10 +500,10 @@ def install_modules(self):
import_module(self, "sox", "sox") import_module(self, "sox", "sox")
else: else:
import_module(self, "soundfile", "PySoundFile") import_module(self, "soundfile", "PySoundFile")
import_module(self, "diffusers", "diffusers") #import_module(self, "diffusers", "diffusers")
import_module(self, "diffusers", "git+https://github.com/huggingface/diffusers.git@v0.19.3")
# import_module(self, "diffusers", "git+https://github.com/huggingface/diffusers.git") # import_module(self, "diffusers", "git+https://github.com/huggingface/diffusers.git")
import_module(self, "accelerate", "accelerate") import_module(self, "accelerate", "accelerate")
# import_module(self, "diffusers", "git+https://github.com/huggingface/accelerate.git")
import_module(self, "transformers", "transformers") import_module(self, "transformers", "transformers")
# import_module(self, "optimum", "optimum") # import_module(self, "optimum", "optimum")
import_module(self, "sentencepiece", "sentencepiece") import_module(self, "sentencepiece", "sentencepiece")
@ -1006,6 +1006,7 @@ class SEQUENCER_OT_generate_movie(Operator):
import torch import torch
from diffusers import ( from diffusers import (
DiffusionPipeline, DiffusionPipeline,
StableDiffusionXLPipeline,
DPMSolverMultistepScheduler, DPMSolverMultistepScheduler,
TextToVideoSDPipeline, TextToVideoSDPipeline,
VideoToVideoSDPipeline, VideoToVideoSDPipeline,
@ -1065,8 +1066,9 @@ class SEQUENCER_OT_generate_movie(Operator):
import torch import torch
from diffusers import StableDiffusionXLImg2ImgPipeline from diffusers import StableDiffusionXLImg2ImgPipeline
pipe = DiffusionPipeline.from_pretrained( pipe = StableDiffusionXLPipeline.from_pretrained(
image_model_card, "stabilityai/stable-diffusion-xl-base-1.0",
#image_model_card,
torch_dtype=torch.float16, torch_dtype=torch.float16,
variant="fp16", variant="fp16",
) )
@ -1079,9 +1081,9 @@ class SEQUENCER_OT_generate_movie(Operator):
torch.cuda.set_per_process_memory_fraction(0.95) torch.cuda.set_per_process_memory_fraction(0.95)
pipe.enable_model_cpu_offload() pipe.enable_model_cpu_offload()
# pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) # pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.unet.added_cond_kwargs={} #pipe.unet.added_cond_kwargs={}
pipe.enable_vae_slicing() pipe.enable_vae_slicing()
pipe.enable_xformers_memory_efficient_attention() #pipe.enable_xformers_memory_efficient_attention()
else: else:
pipe.to("cuda") pipe.to("cuda")
@ -1101,19 +1103,21 @@ class SEQUENCER_OT_generate_movie(Operator):
torch.cuda.set_per_process_memory_fraction(0.95) torch.cuda.set_per_process_memory_fraction(0.95)
refiner.enable_model_cpu_offload() refiner.enable_model_cpu_offload()
# refiner.unet.enable_forward_chunking(chunk_size=1, dim=1) # refiner.unet.enable_forward_chunking(chunk_size=1, dim=1)
refiner.unet.added_cond_kwargs={} #refiner.unet.added_cond_kwargs={}
refiner.enable_vae_slicing() refiner.enable_vae_slicing()
refiner.enable_xformers_memory_efficient_attention() #refiner.enable_xformers_memory_efficient_attention()
else: else:
refiner.to("cuda") refiner.to("cuda")
else: else:
if movie_model_card == "cerspense/zeroscope_v2_dark_30x448x256" or movie_model_card == "cerspense/zeroscope_v2_576w": # 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" # card = "stabilityai/stable-diffusion-xl-base-1.0"
else: # else:
card = movie_model_card # card = movie_model_card
upscale = VideoToVideoSDPipeline.from_pretrained( upscale = VideoToVideoSDPipeline.from_pretrained(
card, # "cerspense/zeroscope_v2_576w",
"cerspense/zeroscope_v2_XL",
torch_dtype=torch.float16, torch_dtype=torch.float16,
#text_encoder=upscale.text_encoder, #text_encoder=upscale.text_encoder,
#vae=upscale.vae, #vae=upscale.vae,
@ -1123,20 +1127,21 @@ class SEQUENCER_OT_generate_movie(Operator):
upscale.scheduler = DPMSolverMultistepScheduler.from_config(upscale.scheduler.config) upscale.scheduler = DPMSolverMultistepScheduler.from_config(upscale.scheduler.config)
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.unet.added_cond_kwargs={} #upscale.unet.added_cond_kwargs={}
upscale.enable_vae_slicing() upscale.enable_vae_slicing()
upscale.enable_xformers_memory_efficient_attention() #upscale.enable_xformers_memory_efficient_attention()
else: else:
upscale.to("cuda") upscale.to("cuda")
# Models for movie generation # Models for movie generation
else: else:
# Options: https://huggingface.co/docs/diffusers/api/pipelines/text_to_video # Options: https://huggingface.co/docs/diffusers/api/pipelines/text_to_video
pipe = TextToVideoSDPipeline.from_pretrained( #pipe = TextToVideoSDPipeline.from_pretrained(
pipe = DiffusionPipeline.from_pretrained(
movie_model_card, movie_model_card,
torch_dtype=torch.float16, torch_dtype=torch.float16,
# variant="fp16", # variant="fp16",
@ -1149,33 +1154,35 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram: if low_vram:
pipe.enable_model_cpu_offload() pipe.enable_model_cpu_offload()
# pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) # pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.unet.added_cond_kwargs={} #pipe.unet.added_cond_kwargs={}
pipe.enable_vae_slicing() pipe.enable_vae_slicing()
pipe.enable_xformers_memory_efficient_attention() #pipe.enable_xformers_memory_efficient_attention()
else: else:
pipe.to("cuda") pipe.to("cuda")
# Models for upscale generated movie # Model for upscale generated movie
if scene.video_to_video: if scene.video_to_video:
if torch.cuda.is_available(): if torch.cuda.is_available():
torch.cuda.empty_cache() torch.cuda.empty_cache()
# torch.cuda.set_per_process_memory_fraction(0.85) # 6 GB VRAM # torch.cuda.set_per_process_memory_fraction(0.85) # 6 GB VRAM
upscale = VideoToVideoSDPipeline.from_pretrained( # upscale = VideoToVideoSDPipeline.from_pretrained(
upscale = DiffusionPipeline.from_pretrained(
#"cerspense/zeroscope_v2_576w", torch_dtype=torch.float16
"cerspense/zeroscope_v2_XL", 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)
upscale.scheduler = DPMSolverMultistepScheduler.from_config( upscale.scheduler = DPMSolverMultistepScheduler.from_config(
pipe.scheduler.config upscale.scheduler.config
) )
if low_vram: if low_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.unet.added_cond_kwargs={} #upscale.unet.added_cond_kwargs={}
upscale.enable_vae_slicing() upscale.enable_vae_slicing()
upscale.enable_xformers_memory_efficient_attention() #upscale.enable_xformers_memory_efficient_attention()
else: else:
upscale.to("cuda") upscale.to("cuda")
@ -1275,7 +1282,7 @@ class SEQUENCER_OT_generate_movie(Operator):
# Upscale video # Upscale video
if scene.video_to_video: if scene.video_to_video:
video = [ video = [
Image.fromarray(frame).resize((int(x * 2), int(y * 2))) Image.fromarray(frame).resize((closest_divisible_64(int(x * 2)), closest_divisible_64(int(y * 2))))
for frame in video for frame in video
] ]
@ -1313,10 +1320,7 @@ class SEQUENCER_OT_generate_movie(Operator):
print("Upscale Video") print("Upscale Video")
if torch.cuda.is_available(): if torch.cuda.is_available():
torch.cuda.empty_cache() torch.cuda.empty_cache()
video = [ video = [Image.fromarray(frame).resize((closest_divisible_64(x * 2), closest_divisible_64(y * 2))) for frame in video_frames]
Image.fromarray(frame).resize((x * 2, y * 2))
for frame in video_frames
]
video_frames = upscale( video_frames = upscale(
prompt, prompt,
@ -1465,9 +1469,9 @@ class SEQUENCER_OT_generate_audio(Operator):
if low_vram: if low_vram:
pipe.enable_model_cpu_offload() pipe.enable_model_cpu_offload()
# pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) # pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.unet.added_cond_kwargs={} # pipe.unet.added_cond_kwargs={}
pipe.enable_vae_slicing() pipe.enable_vae_slicing()
pipe.enable_xformers_memory_efficient_attention() #pipe.enable_xformers_memory_efficient_attention()
else: else:
pipe.to("cuda") pipe.to("cuda")
@ -1671,9 +1675,9 @@ class SEQUENCER_OT_generate_image(Operator):
torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM torch.cuda.set_per_process_memory_fraction(0.95) # 6 GB VRAM
pipe.enable_model_cpu_offload() pipe.enable_model_cpu_offload()
# pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) # pipe.unet.enable_forward_chunking(chunk_size=1, dim=1)
pipe.unet.added_cond_kwargs={} #pipe.unet.added_cond_kwargs={}
pipe.enable_vae_slicing() pipe.enable_vae_slicing()
pipe.enable_xformers_memory_efficient_attention() #pipe.enable_xformers_memory_efficient_attention()
else: else:
pipe.to("cuda") pipe.to("cuda")
@ -1745,9 +1749,9 @@ class SEQUENCER_OT_generate_image(Operator):
if low_vram: if low_vram:
refiner.enable_model_cpu_offload() refiner.enable_model_cpu_offload()
# refiner.unet.enable_forward_chunking(chunk_size=1, dim=1) # refiner.unet.enable_forward_chunking(chunk_size=1, dim=1)
refiner.unet.added_cond_kwargs={} #refiner.unet.added_cond_kwargs={}
refiner.enable_vae_slicing() refiner.enable_vae_slicing()
refiner.enable_xformers_memory_efficient_attention() #refiner.enable_xformers_memory_efficient_attention()
else: else:
refiner.to("cuda") refiner.to("cuda")

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