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Use CPU instead of CUDA

Pallaidium_MacOS
tin2tin 12 months ago committed by GitHub
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1 changed files with 43 additions and 43 deletions
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  1. 86
      __init__.py

86
__init__.py
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@ -2000,7 +2000,7 @@ class SEQUENCER_OT_generate_movie(Operator):
# # pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) # Heavy
# # pipe.enable_vae_slicing()
# else:
# pipe.to("cuda")
# pipe.to('cuda' if torch.cuda.is_available() else 'cpu')
# from diffusers import StableDiffusionXLImg2ImgPipeline
# refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
@ -2016,7 +2016,7 @@ class SEQUENCER_OT_generate_movie(Operator):
# # refiner.enable_vae_tiling()
# # refiner.enable_vae_slicing()
# else:
# refiner.to("cuda")
# refiner.to('cuda' if torch.cuda.is_available() else 'cpu')
if (
movie_model_card == "stabilityai/stable-video-diffusion-img2vid"
or movie_model_card == "stabilityai/stable-video-diffusion-img2vid-xt"
@ -2031,7 +2031,7 @@ class SEQUENCER_OT_generate_movie(Operator):
if low_vram():
refiner.enable_model_cpu_offload()
else:
refiner.to("cuda")
refiner.to('cuda' if torch.cuda.is_available() else 'cpu')
else: # vid2vid / img2vid
if (
movie_model_card == "cerspense/zeroscope_v2_dark_30x448x256"
@ -2063,7 +2063,7 @@ class SEQUENCER_OT_generate_movie(Operator):
# upscale.enable_vae_slicing()
#upscale.unet.enable_forward_chunking(chunk_size=1, dim=1) # heavy:
else:
upscale.to("cuda")
upscale.to('cuda' if torch.cuda.is_available() else 'cpu')
# Models for movie generation
else:
if movie_model_card == "guoyww/animatediff-motion-adapter-v1-5-2":
@ -2095,7 +2095,7 @@ class SEQUENCER_OT_generate_movie(Operator):
pipe.enable_model_cpu_offload()
# pipe.unet.enable_forward_chunking(chunk_size=1, dim=1) # heavy:
else:
upscale.to("cuda")
upscale.to('cuda' if torch.cuda.is_available() else 'cpu')
elif movie_model_card == "VideoCrafter/Image2Video-512":
from diffusers import StableDiffusionPipeline
@ -2114,7 +2114,7 @@ class SEQUENCER_OT_generate_movie(Operator):
pipe.enable_model_cpu_offload()
# pipe.enable_vae_slicing()
else:
pipe.to("cuda")
pipe.to('cuda' if torch.cuda.is_available() else 'cpu')
elif (
movie_model_card == "stabilityai/stable-video-diffusion-img2vid"
or movie_model_card == "stabilityai/stable-video-diffusion-img2vid-xt"
@ -2140,7 +2140,7 @@ class SEQUENCER_OT_generate_movie(Operator):
pipe.enable_model_cpu_offload()
# pipe.enable_vae_slicing()
else:
pipe.to("cuda")
pipe.to('cuda' if torch.cuda.is_available() else 'cpu')
# Model for upscale generated movie
if scene.video_to_video:
if torch.cuda.is_available():
@ -2162,7 +2162,7 @@ class SEQUENCER_OT_generate_movie(Operator):
#upscale.unet.enable_forward_chunking(chunk_size=1, dim=1) # Heavy
# upscale.enable_vae_slicing()
else:
upscale.to("cuda")
upscale.to('cuda' if torch.cuda.is_available() else 'cpu')
if scene.use_freeU and pipe: # Free Lunch
# -------- freeu block registration
print("Process: FreeU")
@ -2511,7 +2511,7 @@ 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)
@ -2536,8 +2536,8 @@ class SEQUENCER_OT_generate_audio(Operator):
pipe.enable_model_cpu_offload()
# pipe.enable_vae_slicing()
else:
pipe.to("cuda")
pipe.to('cuda' if torch.cuda.is_available() else 'cpu')
# Musicgen
elif addon_prefs.audio_model_card == "facebook/musicgen-stereo-small":
from transformers import pipeline
@ -2551,7 +2551,7 @@ class SEQUENCER_OT_generate_audio(Operator):
)
if int(audio_length_in_s * 50) > 1503:
self.report({"INFO"}, "Maximum output duration is 30 sec.")
# Bark
elif addon_prefs.audio_model_card == "bark":
preload_models(
@ -2560,7 +2560,7 @@ class SEQUENCER_OT_generate_audio(Operator):
fine_use_gpu=True,
fine_use_small=True,
)
# Mustango
elif addon_prefs.audio_model_card == "declare-lab/mustango":
import IPython
@ -2573,13 +2573,13 @@ class SEQUENCER_OT_generate_audio(Operator):
model = DiffusionPipeline.from_pretrained(
"declare-lab/mustango"
) # , device="cuda:0", torch_dtype=torch.float16)
# Deadend
else:
print("Audio model not found.")
self.report({"INFO"}, "Audio model not found.")
return {"CANCELLED"}
# Main loop
for i in range(scene.movie_num_batch):
if i > 0:
@ -2630,7 +2630,7 @@ class SEQUENCER_OT_generate_audio(Operator):
# Write the combined audio to a file
write_wav(filename, rate, audio.transpose())
# Musicgen
elif addon_prefs.audio_model_card == "facebook/musicgen-stereo-small":
print("Generate: MusicGen Stereo")
@ -2857,7 +2857,7 @@ class SEQUENCER_OT_generate_image(Operator):
"None of the selected strips are movie, image, text or scene types.",
)
return {"CANCELLED"}
# LOADING MODELS
# models for inpaint
@ -2874,7 +2874,7 @@ class SEQUENCER_OT_generate_image(Operator):
"diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
).to('cuda' if torch.cuda.is_available() else 'cpu')
# Set scheduler
if scene.use_lcm:
@ -2890,8 +2890,8 @@ class SEQUENCER_OT_generate_image(Operator):
# torch.cuda.set_per_process_memory_fraction(0.99)
pipe.enable_model_cpu_offload()
else:
pipe.to("cuda")
pipe.to('cuda' if torch.cuda.is_available() else 'cpu')
# Conversion img2img/vid2img.
elif (
do_convert
@ -2927,8 +2927,8 @@ class SEQUENCER_OT_generate_image(Operator):
# refiner.enable_vae_tiling()
# converter.enable_vae_slicing()
else:
converter.to("cuda")
converter.to('cuda' if torch.cuda.is_available() else 'cpu')
# ControlNet & Illusion
elif (
image_model_card == "lllyasviel/sd-controlnet-canny"
@ -2968,8 +2968,8 @@ class SEQUENCER_OT_generate_image(Operator):
pipe.enable_model_cpu_offload()
# pipe.enable_vae_slicing()
else:
pipe.to("cuda")
pipe.to('cuda' if torch.cuda.is_available() else 'cpu')
# Blip
elif image_model_card == "Salesforce/blipdiffusion":
print("Load: Blip Model")
@ -2982,15 +2982,15 @@ class SEQUENCER_OT_generate_image(Operator):
pipe = BlipDiffusionPipeline.from_pretrained(
"Salesforce/blipdiffusion", torch_dtype=torch.float16
).to("cuda")
).to('cuda' if torch.cuda.is_available() else 'cpu')
else:
from controlnet_aux import CannyDetector
from diffusers.pipelines import BlipDiffusionControlNetPipeline
pipe = BlipDiffusionControlNetPipeline.from_pretrained(
"Salesforce/blipdiffusion-controlnet", torch_dtype=torch.float16
).to("cuda")
).to('cuda' if torch.cuda.is_available() else 'cpu')
# OpenPose
elif image_model_card == "lllyasviel/sd-controlnet-openpose":
print("Load: OpenPose Model")
@ -3034,8 +3034,8 @@ class SEQUENCER_OT_generate_image(Operator):
pipe.enable_model_cpu_offload()
# pipe.enable_vae_slicing()
else:
pipe.to("cuda")
pipe.to('cuda' if torch.cuda.is_available() else 'cpu')
# Scribble
elif image_model_card == "lllyasviel/control_v11p_sd15_scribble":
print("Load: Scribble Model")
@ -3076,8 +3076,8 @@ class SEQUENCER_OT_generate_image(Operator):
# pipe.enable_vae_slicing()
# pipe.enable_forward_chunking(chunk_size=1, dim=1)
else:
pipe.to("cuda")
pipe.to('cuda' if torch.cuda.is_available() else 'cpu')
# Dreamshaper
elif image_model_card == "Lykon/dreamshaper-7":
if do_convert:
@ -3092,7 +3092,7 @@ class SEQUENCER_OT_generate_image(Operator):
) # , custom_pipeline="latent_consistency_txt2img"
pipe.to(torch_device="cuda", torch_dtype=torch.float16)
# Wuerstchen
elif image_model_card == "warp-ai/wuerstchen":
print("Load: Würstchen Model")
@ -3114,8 +3114,8 @@ class SEQUENCER_OT_generate_image(Operator):
if low_vram():
pipe.enable_model_cpu_offload()
else:
pipe.to("cuda")
pipe.to('cuda' if torch.cuda.is_available() else 'cpu')
# DeepFloyd
elif image_model_card == "DeepFloyd/IF-I-M-v1.0":
print("Load: DeepFloyd Model")
@ -3135,8 +3135,8 @@ class SEQUENCER_OT_generate_image(Operator):
if low_vram():
stage_1.enable_model_cpu_offload()
else:
stage_1.to("cuda")
stage_1.to('cuda' if torch.cuda.is_available() else 'cpu')
# stage 2
stage_2 = DiffusionPipeline.from_pretrained(
"DeepFloyd/IF-II-M-v1.0",
@ -3147,8 +3147,8 @@ class SEQUENCER_OT_generate_image(Operator):
if low_vram():
stage_2.enable_model_cpu_offload()
else:
stage_2.to("cuda")
stage_2.to('cuda' if torch.cuda.is_available() else 'cpu')
# stage 3
safety_modules = {
"feature_extractor": stage_1.feature_extractor,
@ -3163,7 +3163,7 @@ class SEQUENCER_OT_generate_image(Operator):
if low_vram():
stage_3.enable_model_cpu_offload()
else:
stage_3.to("cuda")
stage_3.to('cuda' if torch.cuda.is_available() else 'cpu')
# Stable diffusion etc.
else:
@ -3226,7 +3226,7 @@ class SEQUENCER_OT_generate_image(Operator):
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
else:
pipe.to("cuda")
pipe.to('cuda' if torch.cuda.is_available() else 'cpu')
if scene.use_freeU and pipe: # Free Lunch
# -------- freeu block registration
print("Process: FreeU")
@ -3285,7 +3285,7 @@ class SEQUENCER_OT_generate_image(Operator):
# refiner.enable_vae_tiling()
# refiner.enable_vae_slicing()
else:
refiner.to("cuda")
refiner.to('cuda' if torch.cuda.is_available() else 'cpu')
# # Allow longer prompts.
# if image_model_card == "runwayml/stable-diffusion-v1-5":
# if pipe:
@ -3841,7 +3841,7 @@ class SEQUENCER_OT_generate_text(Operator):
)
model = BlipForConditionalGeneration.from_pretrained(
"Salesforce/blip-image-captioning-large", torch_dtype=torch.float16
).to("cuda")
).to('cuda' if torch.cuda.is_available() else 'cpu')
init_image = (
load_first_frame(scene.movie_path)
@ -3852,7 +3852,7 @@ class SEQUENCER_OT_generate_text(Operator):
text = ""
inputs = processor(init_image, text, return_tensors="pt").to(
"cuda", torch.float16
'cuda' if torch.cuda.is_available() else 'cpu', torch.float16
)
out = model.generate(**inputs, max_new_tokens=256)

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