Ben_Kosytorz
3a67c0979c
- Updated stablediffusion crate path from "../stable-diffusion-burn" to "./crates/stable-diffusion-burn" for proper workspace resolution - Enhanced .gitignore to include generated model files (.mpk, .pt, .bin, .safetensors, .ckpt) and user_data directory - Added Cargo.lock to gitignore with appropriate comment - Reorganized IDE files section in gitignore for better clarity - Added newline at end of file for proper formatting
54 lines
1.1 KiB
Python
54 lines
1.1 KiB
Python
import torch
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import torch.nn as nn
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from torch import Tensor
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import math
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'''import torch
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import torch.nn as nn
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import torch
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norm = torch.nn.LayerNorm(3)
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tensor = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).reshape((2, 3))
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out = norm(tensor)
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print(out)'''
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'''n_channel = 6
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norm = nn.LayerNorm(10)
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height = 10
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width = 10
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n_elements = height * width * n_channel
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t = torch.arange(0, n_elements, dtype=torch.float32).mul_(10.0 / n_elements).sin().reshape(1, n_channel, height, width)
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out = norm(t)
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print(out)'''
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def timestep_embedding(timesteps, dim, max_period=10000):
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half = dim // 2
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freqs = (-math.log(max_period) * torch.arange(half) / half).exp()
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args = timesteps * freqs
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return torch.cat( (args.cos(), args.sin()) ).reshape(1, -1)
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timesteps = Tensor([1, 2, 3]).reshape((3, 1))
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dim = 10
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res = timestep_embedding(timesteps, dim)
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print(res)
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'''n_group = 3
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n_channel = 6
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norm = nn.GroupNorm(n_group, n_channel)
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height = 10
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width = 10
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n_elements = height * width * n_channel
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t = torch.arange(0, n_elements, dtype=torch.float32).mul_(10.0 / n_elements).sin().reshape(1, n_channel, height, width)
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out = norm(t)
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print(out.flatten())''' |