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https://github.com/comfyanonymous/ComfyUI.git
synced 2025-09-13 13:05:07 +00:00
Clean up the VAE dtypes code.
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@@ -188,6 +188,12 @@ def is_nvidia():
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return True
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return False
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def is_amd():
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global cpu_state
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if cpu_state == CPUState.GPU:
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if torch.version.hip:
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return True
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return False
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MIN_WEIGHT_MEMORY_RATIO = 0.4
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if is_nvidia():
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@@ -198,27 +204,17 @@ if args.use_pytorch_cross_attention:
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ENABLE_PYTORCH_ATTENTION = True
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XFORMERS_IS_AVAILABLE = False
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VAE_DTYPES = [torch.float32]
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try:
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if is_nvidia():
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if int(torch_version[0]) >= 2:
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if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
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ENABLE_PYTORCH_ATTENTION = True
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if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8:
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VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
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if is_intel_xpu():
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if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
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ENABLE_PYTORCH_ATTENTION = True
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except:
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pass
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if is_intel_xpu():
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VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
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if args.cpu_vae:
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VAE_DTYPES = [torch.float32]
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if ENABLE_PYTORCH_ATTENTION:
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torch.backends.cuda.enable_math_sdp(True)
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torch.backends.cuda.enable_flash_sdp(True)
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@@ -754,7 +750,6 @@ def vae_offload_device():
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return torch.device("cpu")
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def vae_dtype(device=None, allowed_dtypes=[]):
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global VAE_DTYPES
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if args.fp16_vae:
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return torch.float16
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elif args.bf16_vae:
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@@ -763,12 +758,14 @@ def vae_dtype(device=None, allowed_dtypes=[]):
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return torch.float32
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for d in allowed_dtypes:
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if d == torch.float16 and should_use_fp16(device, prioritize_performance=False):
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return d
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if d in VAE_DTYPES:
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if d == torch.float16 and should_use_fp16(device):
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return d
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return VAE_DTYPES[0]
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# NOTE: bfloat16 seems to work on AMD for the VAE but is extremely slow in some cases compared to fp32
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if d == torch.bfloat16 and (not is_amd()) and should_use_bf16(device):
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return d
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return torch.float32
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def get_autocast_device(dev):
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if hasattr(dev, 'type'):
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