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Closer memory usage factors for PID (#14123)
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@ -1214,7 +1214,7 @@ class PixelDiTT2I(supported_models_base.BASE):
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}
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latent_format = latent_formats.PixelDiTPixel
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memory_usage_factor = 0.18
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memory_usage_factor = 0.04
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supported_inference_dtypes = [torch.bfloat16, torch.float32]
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vae_key_prefix = ["vae."]
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@ -1263,7 +1263,7 @@ class PiD(PixelDiTT2I):
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"shift": 1.5, # close approximation of the original distill 4 steps [0.999, 0.866, 0.634, 0.342, 0]
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}
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memory_usage_factor = 0.07
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memory_usage_factor = 0.04
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def get_model(self, state_dict, prefix="", device=None):
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return model_base.PiD(self, device=device)
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