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Memory check before inference to avoid VAE Decode using exceeded VRAM.

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Check if free memory is not less than expected before doing actual decoding,
and if it fails, switch to tiled VAE decoding directly.

It seems PyTorch may continue occupying memory until the model is destroyed
after OOM occurs. This commit tries to avoid OOM from happening in the first
place for VAE Decode.

This is for VAE Decode ran with exceeded VRAM from #5737.
This commit is contained in:
William 2024-11-24 18:47:01 +08:00
parent 3d802710e7
commit a3b9b3c1c3
1 changed files with 13 additions and 1 deletions
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12
comfy/sd.py
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@ -348,11 +348,19 @@ class VAE:
return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device) return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)
def decode(self, samples_in): def decode(self, samples_in):
predicted_oom = False
samples = None
out = None
pixel_samples = None pixel_samples = None
try: try:
memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
model_management.load_models_gpu([self.patcher], memory_required=memory_used) model_management.load_models_gpu([self.patcher], memory_required=memory_used)
free_memory = model_management.get_free_memory(self.device) free_memory = model_management.get_free_memory(self.device)
logging.debug(f"Free memory: {free_memory} bytes, predicted memory useage of one batch: {memory_used} bytes")
if free_memory < memory_used:
logging.warning("Warning: Out of memory is predicted for regular VAE decoding, directly switch to tiled VAE decoding.")
predicted_oom = True
raise model_management.OOM_EXCEPTION
batch_number = int(free_memory / memory_used) batch_number = int(free_memory / memory_used)
batch_number = max(1, batch_number) batch_number = max(1, batch_number)
@ -363,6 +371,10 @@ class VAE:
pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device) pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
pixel_samples[x:x+batch_number] = out pixel_samples[x:x+batch_number] = out
except model_management.OOM_EXCEPTION as e: except model_management.OOM_EXCEPTION as e:
samples = None
out = None
pixel_samples = None
if not predicted_oom:
logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
dims = samples_in.ndim - 2 dims = samples_in.ndim - 2
if dims == 1: if dims == 1: