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main.py
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main.py
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import argparse
import time
import torch
from torchvision.utils import save_image
from DeepCache import DeepCacheSDHelper
def set_random_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_type", type=str, default = "sd1.5")
parser.add_argument("--prompt", type=str, default='a photo of an astronaut on a moon')
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--cache_interval", type=int, default=3)
parser.add_argument("--cache_branch_id", type=int, default=0)
args = parser.parse_args()
if args.model_type.lower() == 'sdxl':
from diffusers import StableDiffusionXLPipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
'stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16, variant="fp16", use_safetensors=True
).to("cuda:0")
elif args.model_type.lower() == 'sd1.5':
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16
).to("cuda:0")
elif args.model_type.lower() == 'sd2.1':
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1-base', torch_dtype=torch.float16
).to("cuda:0")
elif args.model_type.lower() == 'svd':
from diffusers import StableVideoDiffusionPipeline
pipe = StableVideoDiffusionPipeline.from_pretrained(
"stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
)
pipe.enable_model_cpu_offload()
elif args.model_type.lower() == 'sd-inpaint':
from diffusers import StableDiffusionInpaintPipeline
pipe = StableDiffusionInpaintPipeline.from_pretrained(
'runwayml/stable-diffusion-inpainting', torch_dtype=torch.float16
).to("cuda:0")
elif args.model_type.lower() == 'sdxl-inpaint':
from diffusers import StableDiffusionXLInpaintPipeline
pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
'diffusers/stable-diffusion-xl-1.0-inpainting-0.1', torch_dtype=torch.float16
).to("cuda:0")
elif args.model_type.lower() == 'sd-img2img':
from diffusers import StableDiffusionImg2ImgPipeline
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
)
pipe.enable_model_cpu_offload()
else:
raise NotImplementedError
prompt = args.prompt
seed = args.seed
if args.model_type.lower() == 'svd':
import time
from diffusers.utils import load_image, export_to_video
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true")
print("Running Original Pipeline...")
set_random_seed(42)
start_time = time.time()
frames = pipe(
image,
decode_chunk_size=8,
).frames[0]
origin_time = time.time() - start_time
export_to_video(frames, "{}_origin.mp4".format('rocket'), fps=7)
print("Enable DeepCache...")
helper = DeepCacheSDHelper(pipe=pipe)
helper.set_params(
cache_interval=args.cache_interval,
cache_branch_id=args.cache_branch_id,
)
helper.enable()
print("Running Pipeline with DeepCache...")
set_random_seed(42)
start_time = time.time()
frames = pipe(
image,
decode_chunk_size=8,
).frames[0]
deepcache_time = time.time() - start_time
export_to_video(frames, "{}_deepcache.mp4".format('rocket'), fps=7)
helper.disable()
elif 'inpaint' in args.model_type.lower():
from diffusers.utils import load_image
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
image = load_image(img_url)
mask_image = load_image(mask_url)
prompt = "a tiger sitting on a park bench"
# warmup
_ = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0]
set_random_seed(seed)
start_time = time.time()
image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0]
origin_time = time.time() - start_time
image.save("inpaint_origin.png")
print("Enable DeepCache...")
helper = DeepCacheSDHelper(pipe=pipe)
helper.set_params(
cache_interval=args.cache_interval,
cache_branch_id=args.cache_branch_id,
)
helper.enable()
print("Running Pipeline with DeepCache...")
start_time = time.time()
set_random_seed(seed)
deepcache_image= pipe(
prompt=prompt,image=image, mask_image=mask_image
).images[0]
deepcache_time = time.time() - start_time
deepcache_image.save("inpaint_deepcache.png")
elif args.model_type.lower() == 'sd-img2img':
from diffusers.utils import make_image_grid, load_image
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png"
init_image = load_image(url)
init_image.save("img2img_init.png")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
# Warmup
image = pipe(prompt, image=init_image).images[0]
set_random_seed(seed)
start_time = time.time()
image = pipe(prompt, image=init_image).images[0]
origin_time = time.time() - start_time
image.save("img2img_ori.png")
print("Enable DeepCache...")
helper = DeepCacheSDHelper(pipe=pipe)
start_time = time.time()
helper.set_params(
cache_interval=args.cache_interval,
cache_branch_id=args.cache_branch_id,
)
helper.enable()
print("Running Pipeline with DeepCache...")
set_random_seed(seed)
start_time = time.time()
deepcache_img = pipe(prompt, image=init_image).images[0]
deepcache_time = time.time() - start_time
deepcache_img.save("img2img_deepcache.png")
else:
import time
print("Warmup GPU...")
for _ in range(1):
set_random_seed(seed)
_ = pipe(prompt)
print("Running Original Pipeline...")
set_random_seed(seed)
start_time = time.time()
pipeline_output = pipe(
prompt,
output_type='pt'
).images[0]
origin_time = time.time() - start_time
save_image([pipeline_output], 'text2img_origin.png')
print("Enable DeepCache...")
helper = DeepCacheSDHelper(pipe=pipe)
start_time = time.time()
helper.set_params(
cache_interval=args.cache_interval,
cache_branch_id=args.cache_branch_id,
)
helper.enable()
print("Running Pipeline with DeepCache...")
set_random_seed(seed)
deepcache_pipeline_output = pipe(
prompt,
output_type='pt'
).images[0]
deepcache_time = time.time() - start_time
save_image([deepcache_pipeline_output], 'text2img_deepcache.png')
helper.disable()
print("Done! Original Pipeline: {:.2f} seconds, DeepCache: {:.2f} seconds. Speedup Ratio = {:.2f}".format(origin_time, deepcache_time, origin_time/deepcache_time))