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Better memory estimation for the SD/Flux VAE on AMD. (#10334)

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comfyanonymous 2025-10-13 19:37:19 -07:00 committed by GitHub
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1 changed files with 7 additions and 2 deletions
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comfy/sd.py
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@ -276,8 +276,13 @@ class VAE:
if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
sd = diffusers_convert.convert_vae_state_dict(sd) sd = diffusers_convert.convert_vae_state_dict(sd)
self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower) if model_management.is_amd():
self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) VAE_KL_MEM_RATIO = 2.73
else:
VAE_KL_MEM_RATIO = 1.0
self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO #These are for AutoencoderKL and need tweaking (should be lower)
self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO
self.downscale_ratio = 8 self.downscale_ratio = 8
self.upscale_ratio = 8 self.upscale_ratio = 8
self.latent_channels = 4 self.latent_channels = 4