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Merge branch 'comfyanonymous:master' into master

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patientx 2025-01-16 13:59:32 +03:00 committed by GitHub
commit ed13b68e4f
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3 changed files with 6 additions and 6 deletions
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4
comfy/ldm/cosmos/vae.py
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@ -89,8 +89,8 @@ class CausalContinuousVideoTokenizer(nn.Module):
self.distribution = IdentityDistribution() # ContinuousFormulation[formulation_name].value()
num_parameters = sum(param.numel() for param in self.parameters())
logging.info(f"model={self.name}, num_parameters={num_parameters:,}")
logging.info(
logging.debug(f"model={self.name}, num_parameters={num_parameters:,}")
logging.debug(
f"z_channels={z_channels}, latent_channels={self.latent_channels}."
)

4
comfy/sd.py
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@ -388,8 +388,8 @@ class VAE:
ddconfig = {'z_channels': 16, 'latent_channels': self.latent_channels, 'z_factor': 1, 'resolution': 1024, 'in_channels': 3, 'out_channels': 3, 'channels': 128, 'channels_mult': [2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [32], 'dropout': 0.0, 'patch_size': 4, 'num_groups': 1, 'temporal_compression': 8, 'spacial_compression': 8}
self.first_stage_model = comfy.ldm.cosmos.vae.CausalContinuousVideoTokenizer(**ddconfig)
#TODO: these values are a bit off because this is not a standard VAE
self.memory_used_decode = lambda shape, dtype: (220 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
self.memory_used_encode = lambda shape, dtype: (500 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
self.memory_used_decode = lambda shape, dtype: (50 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
self.working_dtypes = [torch.bfloat16, torch.float32]
else:
logging.warning("WARNING: No VAE weights detected, VAE not initalized.")

4
comfy/supported_models.py
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@ -788,7 +788,7 @@ class HunyuanVideo(supported_models_base.BASE):
unet_extra_config = {}
latent_format = latent_formats.HunyuanVideo
memory_usage_factor = 2.0 #TODO
memory_usage_factor = 1.7 #TODO
supported_inference_dtypes = [torch.bfloat16, torch.float32]
@ -839,7 +839,7 @@ class CosmosT2V(supported_models_base.BASE):
unet_extra_config = {}
latent_format = latent_formats.Cosmos1CV8x8x8
memory_usage_factor = 2.4 #TODO
memory_usage_factor = 1.6 #TODO
supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] #TODO