fix the conversation and run success;
This commit is contained in:
junsong
@@ -226,8 +226,6 @@ class Sana(nn.Module):
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)
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self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
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self.initialize_weights()
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def forward(self, x, timestep, y, mask=None, data_info=None, **kwargs):
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"""
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Forward pass of Sana.
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@@ -16,10 +16,8 @@
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# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
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import math
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import os
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from typing import Optional
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import xformers.ops
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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+2
-497
@@ -14,21 +14,12 @@
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#
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# SPDX-License-Identifier: Apache-2.0
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import math
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import os
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import random
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import re
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import sys
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from collections.abc import Iterable
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from itertools import repeat
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from typing import Union, Tuple
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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import torch.nn.functional as F
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from PIL import Image
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from torch.utils.checkpoint import checkpoint, checkpoint_sequential
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from torchvision import transforms as T
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def _ntuple(n):
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@@ -44,25 +35,6 @@ to_1tuple = _ntuple(1)
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to_2tuple = _ntuple(2)
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def set_grad_checkpoint(model, gc_step=1):
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assert isinstance(model, nn.Module)
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def set_attr(module):
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module.grad_checkpointing = True
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module.grad_checkpointing_step = gc_step
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model.apply(set_attr)
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def set_fp32_attention(model):
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assert isinstance(model, nn.Module)
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def set_attr(module):
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module.fp32_attention = True
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model.apply(set_attr)
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def auto_grad_checkpoint(module, *args, **kwargs):
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if getattr(module, "grad_checkpointing", False):
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if isinstance(module, Iterable):
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@@ -99,478 +71,12 @@ def checkpoint_sequential(functions, step, input, *args, **kwargs):
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input = checkpoint(run_function(start, end, functions), input, preserve_rng_state=preserve)
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return run_function(end + 1, len(functions) - 1, functions)(input)
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def window_partition(x, window_size):
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"""
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Partition into non-overlapping windows with padding if needed.
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Args:
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x (tensor): input tokens with [B, H, W, C].
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window_size (int): window size.
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Returns:
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windows: windows after partition with [B * num_windows, window_size, window_size, C].
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(Hp, Wp): padded height and width before partition
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"""
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B, H, W, C = x.shape
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pad_h = (window_size - H % window_size) % window_size
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pad_w = (window_size - W % window_size) % window_size
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if pad_h > 0 or pad_w > 0:
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x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
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Hp, Wp = H + pad_h, W + pad_w
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x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
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return windows, (Hp, Wp)
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def window_unpartition(windows, window_size, pad_hw, hw):
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"""
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Window unpartition into original sequences and removing padding.
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Args:
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x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
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window_size (int): window size.
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pad_hw (Tuple): padded height and width (Hp, Wp).
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hw (Tuple): original height and width (H, W) before padding.
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Returns:
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x: unpartitioned sequences with [B, H, W, C].
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"""
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Hp, Wp = pad_hw
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H, W = hw
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B = windows.shape[0] // (Hp * Wp // window_size // window_size)
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x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
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if Hp > H or Wp > W:
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x = x[:, :H, :W, :].contiguous()
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return x
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def get_rel_pos(q_size, k_size, rel_pos):
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"""
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Get relative positional embeddings according to the relative positions of
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query and key sizes.
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Args:
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q_size (int): size of query q.
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k_size (int): size of key k.
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rel_pos (Tensor): relative position embeddings (L, C).
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Returns:
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Extracted positional embeddings according to relative positions.
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"""
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max_rel_dist = int(2 * max(q_size, k_size) - 1)
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# Interpolate rel pos if needed.
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if rel_pos.shape[0] != max_rel_dist:
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# Interpolate rel pos.
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rel_pos_resized = F.interpolate(
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rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
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size=max_rel_dist,
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mode="linear",
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)
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rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
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else:
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rel_pos_resized = rel_pos
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# Scale the coords with short length if shapes for q and k are different.
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q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
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k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
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relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
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return rel_pos_resized[relative_coords.long()]
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def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size):
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"""
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Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
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https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
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Args:
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attn (Tensor): attention map.
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q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
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rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
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rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
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q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
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k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
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Returns:
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attn (Tensor): attention map with added relative positional embeddings.
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"""
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q_h, q_w = q_size
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k_h, k_w = k_size
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Rh = get_rel_pos(q_h, k_h, rel_pos_h)
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Rw = get_rel_pos(q_w, k_w, rel_pos_w)
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B, _, dim = q.shape
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r_q = q.reshape(B, q_h, q_w, dim)
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rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
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rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
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attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(
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B, q_h * q_w, k_h * k_w
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)
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return attn
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def mean_flat(tensor):
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return tensor.mean(dim=list(range(1, tensor.ndim)))
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#################################################################################
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# Token Masking and Unmasking #
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#################################################################################
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def get_mask(batch, length, mask_ratio, device, mask_type=None, data_info=None, extra_len=0):
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"""
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Get the binary mask for the input sequence.
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Args:
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- batch: batch size
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- length: sequence length
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- mask_ratio: ratio of tokens to mask
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- data_info: dictionary with info for reconstruction
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return:
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mask_dict with following keys:
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- mask: binary mask, 0 is keep, 1 is remove
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- ids_keep: indices of tokens to keep
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- ids_restore: indices to restore the original order
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"""
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assert mask_type in ["random", "fft", "laplacian", "group"]
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mask = torch.ones([batch, length], device=device)
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len_keep = int(length * (1 - mask_ratio)) - extra_len
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if mask_type == "random" or mask_type == "group":
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noise = torch.rand(batch, length, device=device) # noise in [0, 1]
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ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove
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ids_restore = torch.argsort(ids_shuffle, dim=1)
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# keep the first subset
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ids_keep = ids_shuffle[:, :len_keep]
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ids_removed = ids_shuffle[:, len_keep:]
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elif mask_type in ["fft", "laplacian"]:
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if "strength" in data_info:
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strength = data_info["strength"]
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else:
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N = data_info["N"][0]
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img = data_info["ori_img"]
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# 获取原图的尺寸信息
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_, C, H, W = img.shape
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if mask_type == "fft":
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# 对图片进行reshape，将其变为patch (3, H/N, N, W/N, N)
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reshaped_image = img.reshape((batch, -1, H // N, N, W // N, N))
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fft_image = torch.fft.fftn(reshaped_image, dim=(3, 5))
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# 取绝对值并求和获取频率强度
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strength = torch.sum(torch.abs(fft_image), dim=(1, 3, 5)).reshape(
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(
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batch,
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-1,
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)
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)
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elif type == "laplacian":
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laplacian_kernel = torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype=torch.float32).reshape(
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1, 1, 3, 3
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)
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laplacian_kernel = laplacian_kernel.repeat(C, 1, 1, 1)
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# 对图片进行reshape，将其变为patch (3, H/N, N, W/N, N)
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reshaped_image = img.reshape(-1, C, H // N, N, W // N, N).permute(0, 2, 4, 1, 3, 5).reshape(-1, C, N, N)
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laplacian_response = F.conv2d(reshaped_image, laplacian_kernel, padding=1, groups=C)
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strength = laplacian_response.sum(dim=[1, 2, 3]).reshape(
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(
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batch,
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-1,
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)
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)
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# 对频率强度进行归一化，然后使用torch.multinomial进行采样
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probabilities = strength / (strength.max(dim=1)[0][:, None] + 1e-5)
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ids_shuffle = torch.multinomial(probabilities.clip(1e-5, 1), length, replacement=False)
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ids_keep = ids_shuffle[:, :len_keep]
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ids_restore = torch.argsort(ids_shuffle, dim=1)
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ids_removed = ids_shuffle[:, len_keep:]
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mask[:, :len_keep] = 0
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mask = torch.gather(mask, dim=1, index=ids_restore)
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return {"mask": mask, "ids_keep": ids_keep, "ids_restore": ids_restore, "ids_removed": ids_removed}
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def mask_out_token(x, ids_keep, ids_removed=None):
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"""
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Mask out the tokens specified by ids_keep.
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Args:
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- x: input sequence, [N, L, D]
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- ids_keep: indices of tokens to keep
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return:
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- x_masked: masked sequence
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"""
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N, L, D = x.shape # batch, length, dim
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x_remain = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
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if ids_removed is not None:
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x_masked = torch.gather(x, dim=1, index=ids_removed.unsqueeze(-1).repeat(1, 1, D))
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return x_remain, x_masked
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else:
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return x_remain
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def mask_tokens(x, mask_ratio):
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"""
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Perform per-sample random masking by per-sample shuffling.
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Per-sample shuffling is done by argsort random noise.
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x: [N, L, D], sequence
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"""
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N, L, D = x.shape # batch, length, dim
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len_keep = int(L * (1 - mask_ratio))
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noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
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# sort noise for each sample
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ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove
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ids_restore = torch.argsort(ids_shuffle, dim=1)
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# keep the first subset
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ids_keep = ids_shuffle[:, :len_keep]
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x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
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# generate the binary mask: 0 is keep, 1 is remove
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mask = torch.ones([N, L], device=x.device)
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mask[:, :len_keep] = 0
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mask = torch.gather(mask, dim=1, index=ids_restore)
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return x_masked, mask, ids_restore
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def unmask_tokens(x, ids_restore, mask_token):
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# x: [N, T, D] if extras == 0 (i.e., no cls token) else x: [N, T+1, D]
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mask_tokens = mask_token.repeat(x.shape[0], ids_restore.shape[1] - x.shape[1], 1)
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x = torch.cat([x, mask_tokens], dim=1)
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x = torch.gather(x, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle
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return x
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# Parse 'None' to None and others to float value
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def parse_float_none(s):
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assert isinstance(s, str)
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return None if s == "None" else float(s)
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# ----------------------------------------------------------------------------
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# Parse a comma separated list of numbers or ranges and return a list of ints.
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# Example: '1,2,5-10' returns [1, 2, 5, 6, 7, 8, 9, 10]
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def parse_int_list(s):
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if isinstance(s, list):
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return s
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ranges = []
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range_re = re.compile(r"^(\d+)-(\d+)$")
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for p in s.split(","):
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m = range_re.match(p)
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if m:
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ranges.extend(range(int(m.group(1)), int(m.group(2)) + 1))
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else:
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ranges.append(int(p))
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return ranges
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def init_processes(fn, args):
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"""Initialize the distributed environment."""
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os.environ["MASTER_ADDR"] = args.master_address
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os.environ["MASTER_PORT"] = str(random.randint(2000, 6000))
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print(f'MASTER_ADDR = {os.environ["MASTER_ADDR"]}')
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print(f'MASTER_PORT = {os.environ["MASTER_PORT"]}')
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torch.cuda.set_device(args.local_rank)
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dist.init_process_group(backend="nccl", init_method="env://", rank=args.global_rank, world_size=args.global_size)
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fn(args)
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if args.global_size > 1:
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cleanup()
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def mprint(*args, **kwargs):
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"""
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Print only from rank 0.
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"""
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if dist.get_rank() == 0:
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print(*args, **kwargs)
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def cleanup():
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"""
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End DDP training.
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"""
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dist.barrier()
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mprint("Done!")
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dist.barrier()
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dist.destroy_process_group()
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# ----------------------------------------------------------------------------
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# logging info.
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class Logger:
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"""
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Redirect stderr to stdout, optionally print stdout to a file,
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and optionally force flushing on both stdout and the file.
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"""
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def __init__(self, file_name=None, file_mode="w", should_flush=True):
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self.file = None
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if file_name is not None:
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self.file = open(file_name, file_mode)
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self.should_flush = should_flush
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self.stdout = sys.stdout
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self.stderr = sys.stderr
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|
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sys.stdout = self
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sys.stderr = self
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.close()
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def write(self, text):
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"""Write text to stdout (and a file) and optionally flush."""
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if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
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return
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if self.file is not None:
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self.file.write(text)
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self.stdout.write(text)
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|
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if self.should_flush:
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self.flush()
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def flush(self):
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"""Flush written text to both stdout and a file, if open."""
|
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if self.file is not None:
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self.file.flush()
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self.stdout.flush()
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def close(self):
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"""Flush, close possible files, and remove stdout/stderr mirroring."""
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self.flush()
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|
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# if using multiple loggers, prevent closing in wrong order
|
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if sys.stdout is self:
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sys.stdout = self.stdout
|
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if sys.stderr is self:
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sys.stderr = self.stderr
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|
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if self.file is not None:
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self.file.close()
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|
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class StackedRandomGenerator:
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def __init__(self, device, seeds):
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super().__init__()
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self.generators = [torch.Generator(device).manual_seed(int(seed) % (1 << 32)) for seed in seeds]
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def randn(self, size, **kwargs):
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||||
assert size[0] == len(self.generators)
|
||||
return torch.stack([torch.randn(size[1:], generator=gen, **kwargs) for gen in self.generators])
|
||||
|
||||
def randn_like(self, input):
|
||||
return self.randn(input.shape, dtype=input.dtype, layout=input.layout, device=input.device)
|
||||
|
||||
def randint(self, *args, size, **kwargs):
|
||||
assert size[0] == len(self.generators)
|
||||
return torch.stack([torch.randint(*args, size=size[1:], generator=gen, **kwargs) for gen in self.generators])
|
||||
|
||||
|
||||
def prepare_prompt_ar(prompt, ratios, device="cpu", show=True):
|
||||
# get aspect_ratio or ar
|
||||
aspect_ratios = re.findall(r"--aspect_ratio\s+(\d+:\d+)", prompt)
|
||||
ars = re.findall(r"--ar\s+(\d+:\d+)", prompt)
|
||||
custom_hw = re.findall(r"--hw\s+(\d+:\d+)", prompt)
|
||||
if show:
|
||||
print("aspect_ratios:", aspect_ratios, "ars:", ars, "hws:", custom_hw)
|
||||
prompt_clean = prompt.split("--aspect_ratio")[0].split("--ar")[0].split("--hw")[0]
|
||||
if len(aspect_ratios) + len(ars) + len(custom_hw) == 0 and show:
|
||||
print(
|
||||
"Wrong prompt format. Set to default ar: 1. change your prompt into format '--ar h:w or --hw h:w' for correct generating"
|
||||
)
|
||||
if len(aspect_ratios) != 0:
|
||||
ar = float(aspect_ratios[0].split(":")[0]) / float(aspect_ratios[0].split(":")[1])
|
||||
elif len(ars) != 0:
|
||||
ar = float(ars[0].split(":")[0]) / float(ars[0].split(":")[1])
|
||||
else:
|
||||
ar = 1.0
|
||||
closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - ar))
|
||||
if len(custom_hw) != 0:
|
||||
custom_hw = [float(custom_hw[0].split(":")[0]), float(custom_hw[0].split(":")[1])]
|
||||
else:
|
||||
custom_hw = ratios[closest_ratio]
|
||||
default_hw = ratios[closest_ratio]
|
||||
prompt_show = f"prompt: {prompt_clean.strip()}\nSize: --ar {closest_ratio}, --bin hw {ratios[closest_ratio]}, --custom hw {custom_hw}"
|
||||
return (
|
||||
prompt_clean,
|
||||
prompt_show,
|
||||
torch.tensor(default_hw, device=device)[None],
|
||||
torch.tensor([float(closest_ratio)], device=device)[None],
|
||||
torch.tensor(custom_hw, device=device)[None],
|
||||
)
|
||||
|
||||
|
||||
def resize_and_crop_tensor(samples: torch.Tensor, new_width: int, new_height: int) -> torch.Tensor:
|
||||
orig_height, orig_width = samples.shape[2], samples.shape[3]
|
||||
|
||||
# Check if resizing is needed
|
||||
if orig_height != new_height or orig_width != new_width:
|
||||
ratio = max(new_height / orig_height, new_width / orig_width)
|
||||
resized_width = int(orig_width * ratio)
|
||||
resized_height = int(orig_height * ratio)
|
||||
|
||||
# Resize
|
||||
samples = F.interpolate(samples, size=(resized_height, resized_width), mode="bilinear", align_corners=False)
|
||||
|
||||
# Center Crop
|
||||
start_x = (resized_width - new_width) // 2
|
||||
end_x = start_x + new_width
|
||||
start_y = (resized_height - new_height) // 2
|
||||
end_y = start_y + new_height
|
||||
samples = samples[:, :, start_y:end_y, start_x:end_x]
|
||||
|
||||
return samples
|
||||
|
||||
|
||||
def resize_and_crop_img(img: Image, new_width, new_height):
|
||||
orig_width, orig_height = img.size
|
||||
|
||||
ratio = max(new_width / orig_width, new_height / orig_height)
|
||||
resized_width = int(orig_width * ratio)
|
||||
resized_height = int(orig_height * ratio)
|
||||
|
||||
img = img.resize((resized_width, resized_height), Image.LANCZOS)
|
||||
|
||||
left = (resized_width - new_width) / 2
|
||||
top = (resized_height - new_height) / 2
|
||||
right = (resized_width + new_width) / 2
|
||||
bottom = (resized_height + new_height) / 2
|
||||
|
||||
img = img.crop((left, top, right, bottom))
|
||||
|
||||
return img
|
||||
|
||||
|
||||
def mask_feature(emb, mask):
|
||||
if emb.shape[0] == 1:
|
||||
keep_index = mask.sum().item()
|
||||
return emb[:, :, :keep_index, :], keep_index
|
||||
else:
|
||||
masked_feature = emb * mask[:, None, :, None]
|
||||
return masked_feature, emb.shape[2]
|
||||
|
||||
|
||||
def val2list(x: list or tuple or any, repeat_time=1) -> list: # type: ignore
|
||||
"""Repeat `val` for `repeat_time` times and return the list or val if list/tuple."""
|
||||
if isinstance(x, (list, tuple)):
|
||||
return list(x)
|
||||
return [x for _ in range(repeat_time)]
|
||||
|
||||
|
||||
def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple: # type: ignore
|
||||
"""Return tuple with min_len by repeating element at idx_repeat."""
|
||||
# convert to list first
|
||||
@@ -582,8 +88,7 @@ def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -
|
||||
|
||||
return tuple(x)
|
||||
|
||||
|
||||
def get_same_padding(kernel_size: int or tuple[int, ...]) -> int or tuple[int, ...]:
|
||||
def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[int, Tuple[int, ...]]:
|
||||
if isinstance(kernel_size, tuple):
|
||||
return tuple([get_same_padding(ks) for ks in kernel_size])
|
||||
else:
|
||||
|
||||
Reference in New Issue
Block a user