Juan Acevedo entrpn

## forward_of_sdxl_original_unet.py
    def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
        # broadcast timesteps to batch dimension
        timesteps = timesteps.expand(x.shape[0])

        hs = []
        t_emb = get_timestep_embedding(timesteps, self.model_channels)  # , repeat_only=False)
        t_emb = t_emb.to(x.dtype)
        emb = self.time_embed(t_emb)

        assert x.shape[0] == y.shape[0], f"batch size mismatch: {x.shape[0]} != {y.shape[0]}"

## evaluation.ipynb

      
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                jbochi
                / evaluation.ipynb
            
            
              Last active
              June 13, 2024 02:54
            
              
                Recommending GitHub repositories with Google Big Query and implicit library: https://medium.com/@jbochi/recommending-github-repositories-with-google-bigquery-and-the-implicit-library-e6cce666c77
              
          
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## nes.py
"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize
	def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
	# broadcast timesteps to batch dimension
	timesteps = timesteps.expand(x.shape[0])

	hs = []
	t_emb = get_timestep_embedding(timesteps, self.model_channels) # , repeat_only=False)
	t_emb = t_emb.to(x.dtype)
	emb = self.time_embed(t_emb)

	assert x.shape[0] == y.shape[0], f"batch size mismatch: {x.shape[0]} != {y.shape[0]}"
	"""
	A bare bones examples of optimizing a black-box function (f) using
	Natural Evolution Strategies (NES), where the parameter distribution is a
	gaussian of fixed standard deviation.
	"""

	import numpy as np
	np.random.seed(0)

	# the function we want to optimize