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Memory check before inference to avoid VAE Decode using exceeded VRAM. #5745

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Memory check before inference to avoid VAE Decode using exceeded VRAM. #5745

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14 changes: 13 additions & 1 deletion comfy/sd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -348,11 +348,19 @@ def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048):
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):
predicted_oom = False
samples = None
out = None
pixel_samples = None
try:
memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
model_management.load_models_gpu([self.patcher], memory_required=memory_used)
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 = max(1, batch_number)

Expand All @@ -363,7 +371,11 @@ def decode(self, samples_in):
pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
pixel_samples[x:x+batch_number] = out
except model_management.OOM_EXCEPTION as e:
logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
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.")
dims = samples_in.ndim - 2
if dims == 1:
pixel_samples = self.decode_tiled_1d(samples_in)
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