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closes #118; fix seglfault in python operator
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import torch | ||
import torch.nn.functional as F | ||
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from diffusers import AutoencoderKL | ||
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from sfast.compilers.stable_diffusion_pipeline_compiler import ( | ||
compile_vae, | ||
CompilationConfig, | ||
) | ||
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device = torch.device("cuda:0") | ||
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SD_2_1_DIFFUSERS_MODEL = "stabilityai/stable-diffusion-2-1" | ||
variant = {"variant": "fp16"} | ||
vae_orig = AutoencoderKL.from_pretrained( | ||
SD_2_1_DIFFUSERS_MODEL, | ||
subfolder="vae", | ||
torch_dtype=torch.float16, | ||
**variant, | ||
) | ||
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vae_orig.to(device) | ||
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sfast_config = CompilationConfig.Default() | ||
sfast_config.enable_xformers = False | ||
sfast_config.enable_triton = True | ||
sfast_config.enable_cuda_graph = False | ||
vae = compile_vae(vae_orig, sfast_config) | ||
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sample_imgs = torch.randn(4, 3, 128, 128, dtype=vae.dtype, device=device) | ||
latents1 = torch.randn(4, 4, 16, 16, dtype=vae.dtype, device=device) | ||
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latents = vae.encode(sample_imgs).latent_dist.sample() | ||
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sample_imgs_dup = sample_imgs.clone().detach().requires_grad_(True) | ||
latents2 = vae_orig.encode(sample_imgs_dup).latent_dist.sample() | ||
print("Test done") |
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