mirror of https://github.com/hpcaitech/ColossalAI
37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
|
import os
|
||
|
|
import sys
|
||
|
|
from copy import deepcopy
|
||
|
|
|
||
|
|
import yaml
|
||
|
|
from datetime import datetime
|
||
|
|
|
||
|
|
from diffusers import StableDiffusionPipeline
|
||
|
|
import torch
|
||
|
|
from ldm.util import instantiate_from_config
|
||
|
|
from main import get_parser
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
with torch.no_grad():
|
||
|
|
yaml_path = "../../train_colossalai.yaml"
|
||
|
|
with open(yaml_path, 'r', encoding='utf-8') as f:
|
||
|
|
config = f.read()
|
||
|
|
base_config = yaml.load(config, Loader=yaml.FullLoader)
|
||
|
|
unet_config = base_config['model']['params']['unet_config']
|
||
|
|
diffusion_model = instantiate_from_config(unet_config).to("cuda:0")
|
||
|
|
|
||
|
|
pipe = StableDiffusionPipeline.from_pretrained(
|
||
|
|
"/data/scratch/diffuser/stable-diffusion-v1-4"
|
||
|
|
).to("cuda:0")
|
||
|
|
dif_model_2 = pipe.unet
|
||
|
|
|
||
|
|
random_input_ = torch.rand((4, 4, 32, 32)).to("cuda:0")
|
||
|
|
random_input_2 = torch.clone(random_input_).to("cuda:0")
|
||
|
|
time_stamp = torch.randint(20, (4,)).to("cuda:0")
|
||
|
|
time_stamp2 = torch.clone(time_stamp).to("cuda:0")
|
||
|
|
context_ = torch.rand((4, 77, 768)).to("cuda:0")
|
||
|
|
context_2 = torch.clone(context_).to("cuda:0")
|
||
|
|
|
||
|
|
out_1 = diffusion_model(random_input_, time_stamp, context_)
|
||
|
|
out_2 = dif_model_2(random_input_2, time_stamp2, context_2)
|
||
|
|
print(out_1.shape)
|
||
|
|
print(out_2['sample'].shape)
|