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import torch
from safetensors.torch import load_file, save_file
from collections import OrderedDict
meta = OrderedDict()
meta['format'] = "pt"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def reduce_weight(weight, target_size):
weight = weight.to(device, torch.float32)
original_shape = weight.shape
if len(original_shape) == 1:
# For 1D tensors, simply truncate
return weight[:target_size]
if original_shape[0] <= target_size:
return weight
# Reshape the tensor to 2D
flattened = weight.reshape(original_shape[0], -1)
# Perform SVD
U, S, V = torch.svd(flattened)
# Reduce the dimensions
reduced = torch.mm(U[:target_size, :], torch.diag(S)).mm(V.t())
# Reshape back to the original shape with reduced first dimension
new_shape = (target_size,) + original_shape[1:]
return reduced.reshape(new_shape)
def reduce_bias(bias, target_size):
bias = bias.to(device, torch.float32)
return bias[:target_size]
# Load your original state dict
state_dict = load_file(
"/home/jaret/Dev/models/hf/PixArt-Sigma-XL-2-512_MS_t5large_raw/transformer/diffusion_pytorch_model.orig.safetensors")
# Create a new state dict for the reduced model
new_state_dict = {}
for key, value in state_dict.items():
value = value.to(device, torch.float32)
if 'weight' in key or 'scale_shift_table' in key:
if value.shape[0] == 1152:
if len(value.shape) == 4:
orig_shape = value.shape
output_shape = (512, orig_shape[1], orig_shape[2], orig_shape[3]) # reshape to (1152, -1)
# reshape to (1152, -1)
value = value.view(value.shape[0], -1)
value = reduce_weight(value, 512)
value = value.view(output_shape)
else:
# value = reduce_weight(value.t(), 576).t().contiguous()
value = reduce_weight(value, 512)
pass
elif value.shape[0] == 4608:
if len(value.shape) == 4:
orig_shape = value.shape
output_shape = (2048, orig_shape[1], orig_shape[2], orig_shape[3])
value = value.view(value.shape[0], -1)
value = reduce_weight(value, 2048)
value = value.view(output_shape)
else:
value = reduce_weight(value, 2048)
elif value.shape[0] == 6912:
if len(value.shape) == 4:
orig_shape = value.shape
output_shape = (3072, orig_shape[1], orig_shape[2], orig_shape[3])
value = value.view(value.shape[0], -1)
value = reduce_weight(value, 3072)
value = value.view(output_shape)
else:
value = reduce_weight(value, 3072)
if len(value.shape) > 1 and value.shape[
1] == 1152 and 'attn2.to_k.weight' not in key and 'attn2.to_v.weight' not in key:
value = reduce_weight(value.t(), 512).t().contiguous() # Transpose before and after reduction
pass
elif len(value.shape) > 1 and value.shape[1] == 4608:
value = reduce_weight(value.t(), 2048).t().contiguous() # Transpose before and after reduction
pass
elif 'bias' in key:
if value.shape[0] == 1152:
value = reduce_bias(value, 512)
elif value.shape[0] == 4608:
value = reduce_bias(value, 2048)
elif value.shape[0] == 6912:
value = reduce_bias(value, 3072)
new_state_dict[key] = value
# Move all to CPU and convert to float16
for key, value in new_state_dict.items():
new_state_dict[key] = value.cpu().to(torch.float16)
# Save the new state dict
save_file(new_state_dict,
"/home/jaret/Dev/models/hf/PixArt-Sigma-XL-2-512_MS_t5large_raw/transformer/diffusion_pytorch_model.safetensors",
metadata=meta)
print("Done!") |