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added latte sampling (#22)

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* added latte sampling

* polish
This commit is contained in:
Frank Lee 2024-03-04 10:43:22 +08:00 committed by GitHub
parent 91275b2b5e
commit 9648d53d4d
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11 changed files with 2587 additions and 162 deletions
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1
.gitignore vendored
View file

@ -164,3 +164,4 @@ cython_debug/
dataset/
runs/
checkpoints/
outputs/

3
open_sora/modeling/__init__.py
View file

@ -1,3 +1,4 @@
from .dit import DiT, DiT_models
from .latte import LatteT2V
__all__ = ["DiT_models", "DiT"]
__all__ = ["DiT_models", "DiT", "LatteT2V"]

3
open_sora/modeling/dit/__init__.py Normal file
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@ -0,0 +1,3 @@
from .dit import DiT, DiT_models
__all__ = ["DiT_models", "DiT"]

102
open_sora/modeling/attn.py → open_sora/modeling/dit/attn.py
View file

@ -37,9 +37,7 @@ class CrossAttention(nn.Module):
):
super().__init__()
self.hidden_size = head_dim * num_heads
cross_attention_dim = (
cross_attention_dim if cross_attention_dim is not None else query_dim
)
cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
self.scale = head_dim**-0.5
self.num_heads = num_heads
@ -50,9 +48,7 @@ class CrossAttention(nn.Module):
self.to_k = nn.Linear(cross_attention_dim, self.hidden_size, bias=bias)
self.to_v = nn.Linear(cross_attention_dim, self.hidden_size, bias=bias)
self.to_out = nn.Sequential(
nn.Linear(self.hidden_size, query_dim), nn.Dropout(dropout)
)
self.to_out = nn.Sequential(nn.Linear(self.hidden_size, query_dim), nn.Dropout(dropout))
def forward(self, hidden_states, context=None, mask=None):
bsz, q_len, _ = hidden_states.shape
@ -66,24 +62,18 @@ class CrossAttention(nn.Module):
# [B, S, H, D]
query = query.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key = key.view(bsz, kv_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
value = value.view(bsz, kv_seq_len, self.num_heads, self.head_dim).transpose(
1, 2
)
value = value.view(bsz, kv_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
if mask is not None:
assert mask.shape == (bsz, 1, q_len, kv_seq_len)
if self.sdpa:
attn_output = F.scaled_dot_product_attention(
query, key, value, attn_mask=mask, scale=self.scale
)
attn_output = F.scaled_dot_product_attention(query, key, value, attn_mask=mask, scale=self.scale)
else:
attn_weights = torch.matmul(query, key.transpose(2, 3)) / self.scale
assert attn_weights.shape == (bsz, self.num_heads, q_len, kv_seq_len)
if mask is not None:
attn_weights = attn_weights + mask
attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
query.dtype
)
attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_output = torch.matmul(attn_weights, value)
assert attn_output.shape == (bsz, self.num_heads, q_len, self.head_dim)
attn_output = attn_output.transpose(1, 2).contiguous()
@ -128,11 +118,7 @@ class SeqParallelCrossAttention(CrossAttention):
sdpa,
)
self.seq_parallel_group = seq_parallel_group
self.seq_parallel_size = (
dist.get_world_size(self.seq_parallel_group)
if seq_parallel_group is not None
else 1
)
self.seq_parallel_size = dist.get_world_size(self.seq_parallel_group) if seq_parallel_group is not None else 1
assert self.num_heads % self.seq_parallel_size == 0
def forward(self, hidden_states, context=None, mask=None):
@ -148,51 +134,35 @@ class SeqParallelCrossAttention(CrossAttention):
num_heads_parallel = self.num_heads // self.seq_parallel_size
hidden_size_parallel = self.hidden_size // self.seq_parallel_size
if self.seq_parallel_size > 1:
query = all_to_all(
query, self.seq_parallel_group, scatter_dim=2, gather_dim=1
)
query = all_to_all(query, self.seq_parallel_group, scatter_dim=2, gather_dim=1)
key = all_to_all(key, self.seq_parallel_group, scatter_dim=2, gather_dim=1)
value = all_to_all(
value, self.seq_parallel_group, scatter_dim=2, gather_dim=1
)
value = all_to_all(value, self.seq_parallel_group, scatter_dim=2, gather_dim=1)
q_len *= self.seq_parallel_size
kv_seq_len *= self.seq_parallel_size
# [B, S, H/P] -> [B, S, N/P, D] -> [B, N/P, S, D]
query = query.view(bsz, q_len, num_heads_parallel, self.head_dim).transpose(
1, 2
)
key = key.view(bsz, kv_seq_len, num_heads_parallel, self.head_dim).transpose(
1, 2
)
value = value.view(
bsz, kv_seq_len, num_heads_parallel, self.head_dim
).transpose(1, 2)
query = query.view(bsz, q_len, num_heads_parallel, self.head_dim).transpose(1, 2)
key = key.view(bsz, kv_seq_len, num_heads_parallel, self.head_dim).transpose(1, 2)
value = value.view(bsz, kv_seq_len, num_heads_parallel, self.head_dim).transpose(1, 2)
if mask is not None:
assert mask.shape == (bsz, 1, q_len, kv_seq_len)
if self.sdpa:
attn_output = F.scaled_dot_product_attention(
query, key, value, attn_mask=mask, scale=self.scale
)
attn_output = F.scaled_dot_product_attention(query, key, value, attn_mask=mask, scale=self.scale)
else:
attn_weights = torch.matmul(query, key.transpose(2, 3)) / self.scale
assert attn_weights.shape == (bsz, num_heads_parallel, q_len, kv_seq_len)
if mask is not None:
attn_weights = attn_weights + mask
attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
query.dtype
)
attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_output = torch.matmul(attn_weights, value)
assert attn_output.shape == (bsz, num_heads_parallel, q_len, self.head_dim)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, hidden_size_parallel)
# [B, S, H/P] -> [B, S/P, H]
if self.seq_parallel_size > 1:
attn_output = all_to_all(
attn_output, self.seq_parallel_group, scatter_dim=1, gather_dim=2
)
attn_output = all_to_all(attn_output, self.seq_parallel_group, scatter_dim=1, gather_dim=2)
attn_output = self.to_out(attn_output)
return attn_output
@ -220,11 +190,7 @@ class FastSeqParallelCrossAttention(SeqParallelCrossAttention):
sdpa,
seq_parallel_group,
)
self.seq_parallel_rank = (
dist.get_rank(self.seq_parallel_group)
if seq_parallel_group is not None
else 0
)
self.seq_parallel_rank = dist.get_rank(self.seq_parallel_group) if seq_parallel_group is not None else 0
self.sequence_parallel_param_slice = slice(
self.hidden_size // self.seq_parallel_size * self.seq_parallel_rank,
self.hidden_size // self.seq_parallel_size * (self.seq_parallel_rank + 1),
@ -239,11 +205,7 @@ class FastSeqParallelCrossAttention(SeqParallelCrossAttention):
self.overlap = overlap
def _get_sliced_params(self, proj_layer: nn.Linear):
bias = bias = (
proj_layer.bias[self.sequence_parallel_param_slice]
if proj_layer.bias is not None
else None
)
bias = bias = proj_layer.bias[self.sequence_parallel_param_slice] if proj_layer.bias is not None else None
return proj_layer.weight[self.sequence_parallel_param_slice], bias
def _proj(self, x: torch.Tensor, proj_layer: nn.Linear):
@ -272,12 +234,8 @@ class FastSeqParallelCrossAttention(SeqParallelCrossAttention):
)
else:
# [B, S/P, H] -> [B, S, H]
hidden_states = gather_forward_split_backward(
hidden_states, 1, self.seq_parallel_group
)
context = gather_forward_split_backward(
context, 1, self.seq_parallel_group
)
hidden_states = gather_forward_split_backward(hidden_states, 1, self.seq_parallel_group)
context = gather_forward_split_backward(context, 1, self.seq_parallel_group)
query = self._proj(hidden_states, self.to_q)
key = self._proj(context, self.to_k)
value = self._proj(context, self.to_v)
@ -293,38 +251,26 @@ class FastSeqParallelCrossAttention(SeqParallelCrossAttention):
kv_seq_len *= self.seq_parallel_size
# [B, S, H/P] -> [B, S, N/P, D] -> [B, N/P, S, D]
query = query.view(bsz, q_len, num_heads_parallel, self.head_dim).transpose(
1, 2
)
key = key.view(bsz, kv_seq_len, num_heads_parallel, self.head_dim).transpose(
1, 2
)
value = value.view(
bsz, kv_seq_len, num_heads_parallel, self.head_dim
).transpose(1, 2)
query = query.view(bsz, q_len, num_heads_parallel, self.head_dim).transpose(1, 2)
key = key.view(bsz, kv_seq_len, num_heads_parallel, self.head_dim).transpose(1, 2)
value = value.view(bsz, kv_seq_len, num_heads_parallel, self.head_dim).transpose(1, 2)
if mask is not None:
assert mask.shape == (bsz, 1, q_len, kv_seq_len)
if self.sdpa:
attn_output = F.scaled_dot_product_attention(
query, key, value, attn_mask=mask, scale=self.scale
)
attn_output = F.scaled_dot_product_attention(query, key, value, attn_mask=mask, scale=self.scale)
else:
attn_weights = torch.matmul(query, key.transpose(2, 3)) / self.scale
assert attn_weights.shape == (bsz, num_heads_parallel, q_len, kv_seq_len)
if mask is not None:
attn_weights = attn_weights + mask
attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
query.dtype
)
attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_output = torch.matmul(attn_weights, value)
assert attn_output.shape == (bsz, num_heads_parallel, q_len, self.head_dim)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, hidden_size_parallel)
# [B, S, H/P] -> [B, S/P, H]
if self.seq_parallel_size > 1:
attn_output = all_to_all(
attn_output, self.seq_parallel_group, scatter_dim=1, gather_dim=2
)
attn_output = all_to_all(attn_output, self.seq_parallel_group, scatter_dim=1, gather_dim=2)
attn_output = self.to_out(attn_output)
return attn_output

110
open_sora/modeling/dit.py → open_sora/modeling/dit/dit.py
View file

@ -21,11 +21,7 @@ from timm.models.vision_transformer import Mlp
from open_sora.utils.comm import gather_seq, split_seq
from .attn import (
CrossAttention,
FastSeqParallelCrossAttention,
SeqParallelCrossAttention,
)
from .attn import CrossAttention, FastSeqParallelCrossAttention, SeqParallelCrossAttention
SUPPORTED_SEQ_PARALLEL_MODES = ["ulysses", "fastseq"]
@ -65,23 +61,17 @@ class TimestepEmbedder(nn.Module):
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
freqs = torch.exp(
-math.log(max_period)
* torch.arange(start=0, end=half, dtype=torch.float32)
/ half
).to(device=t.device)
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
device=t.device
)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat(
[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
)
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(
self.mlp[0].weight.dtype
)
t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(self.mlp[0].weight.dtype)
t_emb = self.mlp(t_freq)
return t_emb
@ -94,9 +84,7 @@ class LabelEmbedder(nn.Module):
def __init__(self, num_classes, hidden_size, dropout_prob):
super().__init__()
use_cfg_embedding = dropout_prob > 0
self.embedding_table = nn.Embedding(
num_classes + use_cfg_embedding, hidden_size
)
self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
self.num_classes = num_classes
self.dropout_prob = dropout_prob
@ -105,9 +93,7 @@ class LabelEmbedder(nn.Module):
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = (
torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
)
drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
else:
drop_ids = force_drop_ids == 1
labels = torch.where(drop_ids, self.num_classes, labels)
@ -134,17 +120,13 @@ class PatchEmbedder(nn.Module):
) -> None:
super().__init__()
self.patch_size = patch_size
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias
)
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
# [B, S, C, P, P] -> [B, S, C*P*P]
x = x.view(*x.shape[:2], -1)
out = F.linear(
x, self.proj.weight.view(self.proj.weight.shape[0], -1), self.proj.bias
)
out = F.linear(x, self.proj.weight.view(self.proj.weight.shape[0], -1), self.proj.bias)
out = self.norm(out)
# [B, S, H]
return out
@ -234,9 +216,7 @@ class DiTBlock(nn.Module):
attn_cls = FastSeqParallelCrossAttention
attn_kwargs["overlap"] = seq_parallel_overlap
else:
raise ValueError(
f"seq_parallel_mode must be one of {SUPPORTED_SEQ_PARALLEL_MODES}"
)
raise ValueError(f"seq_parallel_mode must be one of {SUPPORTED_SEQ_PARALLEL_MODES}")
else:
attn_cls = CrossAttention
self.attn = attn_cls(
@ -264,20 +244,14 @@ class DiTBlock(nn.Module):
act_layer=approx_gelu,
drop=0,
)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
def forward(self, x, attention_mask, t, context=None):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
self.adaLN_modulation(t).chunk(6, dim=1)
)
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(t).chunk(6, dim=1)
x = x + gate_msa.unsqueeze(1) * self.attn(
modulate(self.norm1(x), shift_msa, scale_msa), context, attention_mask
)
x = x + gate_mlp.unsqueeze(1) * self.mlp(
modulate(self.norm2(x), shift_mlp, scale_mlp)
)
x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
@ -289,13 +263,9 @@ class FinalLayer(nn.Module):
def __init__(self, hidden_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(
hidden_size, patch_size * patch_size * out_channels, bias=True
)
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
self.patch_size = patch_size
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)
)
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
def unpatchify(self, x):
b, s, h = x.shape
@ -339,20 +309,10 @@ class DiT(nn.Module):
self.patch_size = patch_size
self.num_heads = num_heads
self.seq_parallel_group = seq_parallel_group
self.seq_parallel_size = (
dist.get_world_size(self.seq_parallel_group)
if seq_parallel_group is not None
else 1
)
self.seq_parallel_rank = (
dist.get_rank(self.seq_parallel_group)
if seq_parallel_group is not None
else 0
)
self.seq_parallel_size = dist.get_world_size(self.seq_parallel_group) if seq_parallel_group is not None else 1
self.seq_parallel_rank = dist.get_rank(self.seq_parallel_group) if seq_parallel_group is not None else 0
self.video_embedder = PatchEmbedder(
patch_size, in_channels, hidden_size, bias=True
)
self.video_embedder = PatchEmbedder(patch_size, in_channels, hidden_size, bias=True)
self.t_embedder = TimestepEmbedder(hidden_size)
self.pos_embed = PositionEmbedding(hidden_size, max_num_embeddings)
self.text_embedder = TextEmbedder(
@ -419,9 +379,7 @@ class DiT(nn.Module):
assert attention_mask.ndim == 4
attention_mask = attention_mask.to(dtype)
inverted_mask = 1.0 - attention_mask
return inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
)
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
return attention_mask
def enable_gradient_checkpointing(self):
@ -444,9 +402,7 @@ class DiT(nn.Module):
text_len = text_latent_states.shape[1]
text_latent_states = self.text_embedder(text_latent_states)
if not self.use_cross_attn:
video_latent_states = torch.cat(
[text_latent_states, video_latent_states], dim=1
)
video_latent_states = torch.cat([text_latent_states, video_latent_states], dim=1)
text_latent_states = None
pos_embed = self.pos_embed(video_latent_states)
video_latent_states = video_latent_states + pos_embed
@ -455,14 +411,10 @@ class DiT(nn.Module):
if self.seq_parallel_group is not None and self.seq_parallel_size > 1:
assert video_latent_states.shape[1] % self.seq_parallel_size == 0
video_latent_states = split_seq(
video_latent_states, self.seq_parallel_size, self.seq_parallel_rank
)
video_latent_states = split_seq(video_latent_states, self.seq_parallel_size, self.seq_parallel_rank)
if text_latent_states is not None:
assert text_latent_states.shape[1] % self.seq_parallel_size == 0
text_latent_states = split_seq(
text_latent_states, self.seq_parallel_size, self.seq_parallel_rank
)
text_latent_states = split_seq(text_latent_states, self.seq_parallel_size, self.seq_parallel_rank)
for block in self.blocks:
if self.grad_checkpointing and self.training:
@ -474,9 +426,7 @@ class DiT(nn.Module):
text_latent_states,
)
else:
video_latent_states = block(
video_latent_states, attention_mask, t, text_latent_states
)
video_latent_states = block(video_latent_states, attention_mask, t, text_latent_states)
if self.seq_parallel_group is not None and self.seq_parallel_size > 1:
video_latent_states = gather_seq(
@ -491,18 +441,14 @@ class DiT(nn.Module):
video_latent_states = self.final_layer(video_latent_states, t)
return video_latent_states
def forward_with_cfg(
self, x, t, text_latent_states, cfg_scale, attention_mask=None
):
def forward_with_cfg(self, x, t, text_latent_states, cfg_scale, attention_mask=None):
"""
Forward pass of DiT, but also batches the unconditional forward pass for classifier-free guidance.
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
half = x[: len(x) // 2]
combined = torch.cat([half, half], dim=0)
model_out = self.forward(
combined, t, text_latent_states, attention_mask=attention_mask
)
model_out = self.forward(combined, t, text_latent_states, attention_mask=attention_mask)
# For exact reproducibility reasons, we apply classifier-free guidance on only
# three channels by default. The standard approach to cfg applies it to all channels.
# This can be done by uncommenting the following line and commenting-out the line following that.
@ -541,9 +487,7 @@ def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate(
[np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0
)
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed

3
open_sora/modeling/latte/__init__.py Normal file
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@ -0,0 +1,3 @@
from .latte_t2v import LatteT2V
__all__ = ["LatteT2V"]

1003
open_sora/modeling/latte/latte_t2v.py Normal file
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File diff suppressed because it is too large Load diff

792
scripts/sample/latte/pipeline_videogen.py Normal file
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@ -0,0 +1,792 @@
# All rights reserved.
# Copyright 2024 Vchitect/Latte
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# copied from https://github.com/Vchitect/Latte/blob/main/sample/pipeline_videogen.py
import html
import inspect
import re
import urllib.parse as ul
from dataclasses import dataclass
from typing import Callable, List, Optional, Tuple, Union
import einops
import torch
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL, Transformer2DModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.schedulers import DPMSolverMultistepScheduler
from diffusers.utils import (
BACKENDS_MAPPING,
BaseOutput,
is_bs4_available,
is_ftfy_available,
logging,
replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor
from transformers import T5EncoderModel, T5Tokenizer
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
if is_bs4_available():
from bs4 import BeautifulSoup
if is_ftfy_available():
import ftfy
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import PixArtAlphaPipeline
>>> # You can replace the checkpoint id with "PixArt-alpha/PixArt-XL-2-512x512" too.
>>> pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16)
>>> # Enable memory optimizations.
>>> pipe.enable_model_cpu_offload()
>>> prompt = "A small cactus with a happy face in the Sahara desert."
>>> image = pipe(prompt).images[0]
```
"""
@dataclass
class VideoPipelineOutput(BaseOutput):
video: torch.Tensor
class VideoGenPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using PixArt-Alpha.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. PixArt-Alpha uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
[t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant.
tokenizer (`T5Tokenizer`):
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
transformer ([`Transformer2DModel`]):
A text conditioned `Transformer2DModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
"""
bad_punct_regex = re.compile(
r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
) # noqa
_optional_components = ["tokenizer", "text_encoder"]
model_cpu_offload_seq = "text_encoder->transformer->vae"
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
vae: AutoencoderKL,
transformer: Transformer2DModel,
scheduler: DPMSolverMultistepScheduler,
):
super().__init__()
self.register_modules(
tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
# Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/utils.py
def mask_text_embeddings(self, emb, mask):
if emb.shape[0] == 1:
keep_index = mask.sum().item()
return emb[:, :, :keep_index, :], keep_index # 1, 120, 4096 -> 1 7 4096
else:
masked_feature = emb * mask[:, None, :, None] # 1 120 4096
return masked_feature, emb.shape[2]
# Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt
def encode_prompt(
self,
prompt: Union[str, List[str]],
do_classifier_free_guidance: bool = True,
negative_prompt: str = "",
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
clean_caption: bool = False,
mask_feature: bool = True,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
negative_prompt (`str` or `List[str]`, *optional*):
The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`
instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). For
PixArt-Alpha, this should be "".
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
num_images_per_prompt (`int`, *optional*, defaults to 1):
number of images that should be generated per prompt
device: (`torch.device`, *optional*):
torch device to place the resulting embeddings on
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. For PixArt-Alpha, it's should be the embeddings of the ""
string.
clean_caption (bool, defaults to `False`):
If `True`, the function will preprocess and clean the provided caption before encoding.
mask_feature: (bool, defaults to `True`):
If `True`, the function will mask the text embeddings.
"""
embeds_initially_provided = prompt_embeds is not None and negative_prompt_embeds is not None
if device is None:
device = self._execution_device
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# See Section 3.1. of the paper.
max_length = 120
if prompt_embeds is None:
prompt = self._text_preprocessing(prompt, clean_caption=clean_caption)
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
text_input_ids, untruncated_ids
):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_length} tokens: {removed_text}"
)
attention_mask = text_inputs.attention_mask.to(device)
prompt_embeds_attention_mask = attention_mask
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds_attention_mask = torch.ones_like(prompt_embeds)
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
elif self.transformer is not None:
dtype = self.transformer.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
prompt_embeds_attention_mask = prompt_embeds_attention_mask.view(bs_embed, -1)
prompt_embeds_attention_mask = prompt_embeds_attention_mask.repeat(num_images_per_prompt, 1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens = [negative_prompt] * batch_size
uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=True,
return_tensors="pt",
)
attention_mask = uncond_input.attention_mask.to(device)
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
else:
negative_prompt_embeds = None
# print(prompt_embeds.shape) # 1 120 4096
# print(negative_prompt_embeds.shape) # 1 120 4096
# Perform additional masking.
if mask_feature and not embeds_initially_provided:
prompt_embeds = prompt_embeds.unsqueeze(1)
masked_prompt_embeds, keep_indices = self.mask_text_embeddings(prompt_embeds, prompt_embeds_attention_mask)
masked_prompt_embeds = masked_prompt_embeds.squeeze(1)
masked_negative_prompt_embeds = (
negative_prompt_embeds[:, :keep_indices, :] if negative_prompt_embeds is not None else None
)
# import torch.nn.functional as F
# padding = (0, 0, 0, 113) # (左, 右, 下, 上)
# masked_prompt_embeds_ = F.pad(masked_prompt_embeds, padding, "constant", 0)
# masked_negative_prompt_embeds_ = F.pad(masked_negative_prompt_embeds, padding, "constant", 0)
# print(masked_prompt_embeds == masked_prompt_embeds_[:, :masked_negative_prompt_embeds.shape[1], ...])
return masked_prompt_embeds, masked_negative_prompt_embeds
# return masked_prompt_embeds_, masked_negative_prompt_embeds_
return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
height,
width,
negative_prompt,
callback_steps,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
if prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warn(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if clean_caption and not is_ftfy_available():
logger.warn(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warn("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
def process(text: str):
if clean_caption:
text = self._clean_caption(text)
text = self._clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text]
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption)
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
#######################################################
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# &quot;
caption = re.sub(r"&quot;?", "", caption)
# &amp
caption = re.sub(r"&amp", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption)
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(caption))
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip()
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(
self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None
):
shape = (
batch_size,
num_channels_latents,
video_length,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
negative_prompt: str = "",
num_inference_steps: int = 20,
timesteps: List[int] = None,
guidance_scale: float = 4.5,
num_images_per_prompt: Optional[int] = 1,
video_length: Optional[int] = None,
height: Optional[int] = None,
width: Optional[int] = None,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
clean_caption: bool = True,
mask_feature: bool = True,
enable_temporal_attentions: bool = True,
enable_vae_temporal_decoder: bool = False,
) -> Union[VideoPipelineOutput, Tuple]:
"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_inference_steps (`int`, *optional*, defaults to 100):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
timesteps are used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 7.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
height (`int`, *optional*, defaults to self.unet.config.sample_size):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size):
The width in pixels of the generated image.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. For PixArt-Alpha this negative prompt should be "". If not
provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt.
mask_feature (`bool` defaults to `True`): If set to `True`, the text embeddings will be masked.
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images
"""
# 1. Check inputs. Raise error if not correct
height = height or self.transformer.config.sample_size * self.vae_scale_factor
width = width or self.transformer.config.sample_size * self.vae_scale_factor
self.check_inputs(prompt, height, width, negative_prompt, callback_steps, prompt_embeds, negative_prompt_embeds)
# 2. Default height and width to transformer
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
do_classifier_free_guidance,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clean_caption=clean_caption,
mask_feature=mask_feature,
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latents.
latent_channels = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
latent_channels,
video_length,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 6.1 Prepare micro-conditions.
added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
if self.transformer.config.sample_size == 128:
resolution = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1)
aspect_ratio = torch.tensor([float(height / width)]).repeat(batch_size * num_images_per_prompt, 1)
resolution = resolution.to(dtype=prompt_embeds.dtype, device=device)
aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device)
added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio}
# 7. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
current_timestep = t
if not torch.is_tensor(current_timestep):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps"
if isinstance(current_timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
elif len(current_timestep.shape) == 0:
current_timestep = current_timestep[None].to(latent_model_input.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
current_timestep = current_timestep.expand(latent_model_input.shape[0])
# predict noise model_output
noise_pred = self.transformer(
latent_model_input,
encoder_hidden_states=prompt_embeds,
timestep=current_timestep,
added_cond_kwargs=added_cond_kwargs,
enable_temporal_attentions=enable_temporal_attentions,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
noise_pred = noise_pred.chunk(2, dim=1)[0]
else:
noise_pred = noise_pred
# compute previous image: x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if not output_type == "latents":
if enable_vae_temporal_decoder:
video = self.decode_latents_with_temporal_decoder(latents)
else:
video = self.decode_latents(latents)
else:
video = latents
return VideoPipelineOutput(video=video)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (video,)
return VideoPipelineOutput(video=video)
def decode_latents(self, latents):
video_length = latents.shape[2]
latents = 1 / self.vae.config.scaling_factor * latents
latents = einops.rearrange(latents, "b c f h w -> (b f) c h w")
video = []
for frame_idx in range(latents.shape[0]):
video.append(self.vae.decode(latents[frame_idx : frame_idx + 1]).sample)
video = torch.cat(video)
video = einops.rearrange(video, "(b f) c h w -> b f h w c", f=video_length)
video = ((video / 2.0 + 0.5).clamp(0, 1) * 255).to(dtype=torch.uint8).cpu().contiguous()
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
return video
def decode_latents_with_temporal_decoder(self, latents):
video_length = latents.shape[2]
latents = 1 / self.vae.config.scaling_factor * latents
latents = einops.rearrange(latents, "b c f h w -> (b f) c h w")
video = []
decode_chunk_size = 14
for frame_idx in range(0, latents.shape[0], decode_chunk_size):
num_frames_in = latents[frame_idx : frame_idx + decode_chunk_size].shape[0]
decode_kwargs = {}
decode_kwargs["num_frames"] = num_frames_in
video.append(self.vae.decode(latents[frame_idx : frame_idx + decode_chunk_size], **decode_kwargs).sample)
video = torch.cat(video)
video = einops.rearrange(video, "(b f) c h w -> b f h w c", f=video_length)
video = ((video / 2.0 + 0.5).clamp(0, 1) * 255).to(dtype=torch.uint8).cpu().contiguous()
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
return video

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#!/usr/bin/env bash
# get args
GPUS=${1:-8}
# get root dir
FOLDER_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
ROOT_DIR=$FOLDER_DIR/../../..
# go to root dir
cd $ROOT_DIR
export PYTHONPATH=$FOLDER_DIR:$PYTHONPATH
python $FOLDER_DIR/sample_t2v.py \
--checkpoint /home/lishenggui/projects/sora/hf-weights/models--maxin-cn--Latte/snapshots/8f0591220fa329f9d917086810b3c0f6544a87c7/t2v.pt \
--model_path /home/lishenggui/projects/sora/hf-weights/models--maxin-cn--Latte/snapshots/8f0591220fa329f9d917086810b3c0f6544a87c7/t2v_required_models/ \
--text_prompt "A dog in astronaut suit and sunglasses floating in space" \
--output_path $ROOT_DIR/outputs/latte

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# All rights reserved.
# Copyright 2024 Vchitect/Latte
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# modified from https://github.com/Vchitect/Latte/blob/main/sample/sample_t2v.py
import argparse
import os
import sys
import torch
from diffusers.models import AutoencoderKL, AutoencoderKLTemporalDecoder
from diffusers.schedulers import (
DDIMScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
KDPM2AncestralDiscreteScheduler,
PNDMScheduler,
)
from diffusers.schedulers.scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from transformers import T5EncoderModel, T5Tokenizer
sys.path.append(os.path.split(sys.path[0])[0])
import imageio
from pipeline_videogen import VideoGenPipeline
from utils import save_video_grid
from download import find_model
from open_sora.modeling import LatteT2V
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, required=True, help="The path to the pretrained model files")
parser.add_argument("--checkpoint", type=str, required=True, help="The path to the t2v.pt file.")
parser.add_argument("--output_path", type=str, required=True, help="The path to save the output")
# generation configs
parser.add_argument(
"--text_prompt", type=str, nargs="+", required=True, help="The text prompt to generate the video."
)
parser.add_argument("--video_length", type=int, default=16, help="The number of frames in the video.")
parser.add_argument("--image_height", type=int, default=256, help="The size of the generated images.")
parser.add_argument("--image_width", type=int, default=256, help="The size of the generated images.")
parser.add_argument("--guidance_scale", type=float, default=7.5, help="The scale of the guidance loss.")
parser.add_argument("--sample_method", type=str, default="PNDM", help="The sampling method to use.")
parser.add_argument("--num_sampling_steps", type=int, default=50, help="The number of sampling steps.")
parser.add_argument(
"--enable_temporal_attentions", action="store_true", default=True, help="Whether to enable temporal attentions."
)
parser.add_argument(
"--enable_vae_temporal_decoder",
action="store_true",
default=True,
help="Whether to enable the VAE temporal decoder.",
)
# Scheduler configs
parser.add_argument("--beta_start", type=float, default=0.0001)
parser.add_argument("--beta_end", type=float, default=0.02)
parser.add_argument("--beta_schedule", type=str, default="linear")
parser.add_argument("--variance_type", type=str, default="learned_range")
args = parser.parse_args()
return args
def main(args):
torch.set_grad_enabled(False)
device = "cuda" if torch.cuda.is_available() else "cpu"
transformer_model = LatteT2V.from_pretrained_2d(
args.model_path, subfolder="transformer", video_length=args.video_length
).to(device, dtype=torch.float16)
state_dict = find_model(args.checkpoint)
transformer_model.load_state_dict(state_dict["model"])
if args.enable_vae_temporal_decoder:
vae = AutoencoderKLTemporalDecoder.from_pretrained(
args.model_path, subfolder="vae_temporal_decoder", torch_dtype=torch.float16
).to(device)
else:
vae = AutoencoderKL.from_pretrained(args.model_path, subfolder="vae", torch_dtype=torch.float16).to(device)
tokenizer = T5Tokenizer.from_pretrained(args.model_path, subfolder="tokenizer")
text_encoder = T5EncoderModel.from_pretrained(
args.model_path, subfolder="text_encoder", torch_dtype=torch.float16
).to(device)
# set eval mode
transformer_model.eval()
vae.eval()
text_encoder.eval()
if args.sample_method == "DDIM":
scheduler = DDIMScheduler.from_pretrained(
args.model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
)
elif args.sample_method == "EulerDiscrete":
scheduler = EulerDiscreteScheduler.from_pretrained(
args.model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
)
elif args.sample_method == "DDPM":
scheduler = DDPMScheduler.from_pretrained(
args.model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
)
elif args.sample_method == "DPMSolverMultistep":
scheduler = DPMSolverMultistepScheduler.from_pretrained(
args.model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
)
elif args.sample_method == "DPMSolverSinglestep":
scheduler = DPMSolverSinglestepScheduler.from_pretrained(
args.model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
)
elif args.sample_method == "PNDM":
scheduler = PNDMScheduler.from_pretrained(
args.model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
)
elif args.sample_method == "HeunDiscrete":
scheduler = HeunDiscreteScheduler.from_pretrained(
args.model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
)
elif args.sample_method == "EulerAncestralDiscrete":
scheduler = EulerAncestralDiscreteScheduler.from_pretrained(
args.model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
)
elif args.sample_method == "DEISMultistep":
scheduler = DEISMultistepScheduler.from_pretrained(
args.model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
)
elif args.sample_method == "KDPM2AncestralDiscrete":
scheduler = KDPM2AncestralDiscreteScheduler.from_pretrained(
args.model_path,
subfolder="scheduler",
beta_start=args.beta_start,
beta_end=args.beta_end,
beta_schedule=args.beta_schedule,
variance_type=args.variance_type,
)
videogen_pipeline = VideoGenPipeline(
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=scheduler, transformer=transformer_model
).to(device)
# videogen_pipeline.enable_xformers_memory_efficient_attention()
if not os.path.exists(args.output_path):
os.makedirs(args.output_path, exist_ok=True)
video_grids = []
for prompt in args.text_prompt:
print("Processing the ({}) prompt".format(prompt))
videos = videogen_pipeline(
prompt,
video_length=args.video_length,
height=args.image_height,
width=args.image_width,
num_inference_steps=args.num_sampling_steps,
guidance_scale=args.guidance_scale,
enable_temporal_attentions=args.enable_temporal_attentions,
num_images_per_prompt=1,
mask_feature=True,
enable_vae_temporal_decoder=args.enable_vae_temporal_decoder,
).video
try:
save_path = os.path.join(args.output_path, prompt.replace(" ", "_") + "_webv-imageio.mp4")
imageio.mimwrite(save_path, videos[0], fps=8, quality=9) # highest quality is 10, lowest is 0
except:
print("Error when saving {}".format(prompt))
video_grids.append(videos)
video_grids = torch.cat(video_grids, dim=0)
video_grids = save_video_grid(video_grids)
# torchvision.io.write_video(args.output_path + '_%04d' % args.run_time + '-.mp4', video_grids, fps=6)
save_path = os.path.join(args.output_path, "grid.mp4")
imageio.mimwrite(save_path, video_grids, fps=8, quality=5)
print("save path {}".format(abspath(args.output_path)))
# save_videos_grid(video, f"./{prompt}.gif")
if __name__ == "__main__":
args = parse_args()
main(args)

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# All rights reserved.
# Copyright 2024 Vchitect/Latte
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# copied from https://github.com/Vchitect/Latte/blob/main/utils.py
import html
import logging
import math
import os
import re
import subprocess
import urllib.parse as ul
from collections import OrderedDict
from typing import Iterable, Union
import torch
import torch.distributed as dist
from diffusers.utils import is_bs4_available, is_ftfy_available
# from torch._six import inf
from torch import inf
from torch.utils.tensorboard import SummaryWriter
if is_bs4_available():
from bs4 import BeautifulSoup
if is_ftfy_available():
import ftfy
_tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]
#################################################################################
# Training Clip Gradients #
#################################################################################
def get_grad_norm(parameters: _tensor_or_tensors, norm_type: float = 2.0) -> torch.Tensor:
r"""
Copy from torch.nn.utils.clip_grad_norm_
Clips gradient norm of an iterable of parameters.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
Args:
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized
max_norm (float or int): max norm of the gradients
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
infinity norm.
error_if_nonfinite (bool): if True, an error is thrown if the total
norm of the gradients from :attr:`parameters` is ``nan``,
``inf``, or ``-inf``. Default: False (will switch to True in the future)
Returns:
Total norm of the parameter gradients (viewed as a single vector).
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
grads = [p.grad for p in parameters if p.grad is not None]
norm_type = float(norm_type)
if len(grads) == 0:
return torch.tensor(0.0)
device = grads[0].device
if norm_type == inf:
norms = [g.detach().abs().max().to(device) for g in grads]
total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
else:
total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
return total_norm
def clip_grad_norm_(
parameters: _tensor_or_tensors,
max_norm: float,
norm_type: float = 2.0,
error_if_nonfinite: bool = False,
clip_grad=True,
) -> torch.Tensor:
r"""
Copy from torch.nn.utils.clip_grad_norm_
Clips gradient norm of an iterable of parameters.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
Args:
parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
single Tensor that will have gradients normalized
max_norm (float or int): max norm of the gradients
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
infinity norm.
error_if_nonfinite (bool): if True, an error is thrown if the total
norm of the gradients from :attr:`parameters` is ``nan``,
``inf``, or ``-inf``. Default: False (will switch to True in the future)
Returns:
Total norm of the parameter gradients (viewed as a single vector).
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
grads = [p.grad for p in parameters if p.grad is not None]
max_norm = float(max_norm)
norm_type = float(norm_type)
if len(grads) == 0:
return torch.tensor(0.0)
device = grads[0].device
if norm_type == inf:
norms = [g.detach().abs().max().to(device) for g in grads]
total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
else:
total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
# print(total_norm)
if clip_grad:
if error_if_nonfinite and torch.logical_or(total_norm.isnan(), total_norm.isinf()):
raise RuntimeError(
f"The total norm of order {norm_type} for gradients from "
"`parameters` is non-finite, so it cannot be clipped. To disable "
"this error and scale the gradients by the non-finite norm anyway, "
"set `error_if_nonfinite=False`"
)
clip_coef = max_norm / (total_norm + 1e-6)
# Note: multiplying by the clamped coef is redundant when the coef is clamped to 1, but doing so
# avoids a `if clip_coef < 1:` conditional which can require a CPU <=> device synchronization
# when the gradients do not reside in CPU memory.
clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
for g in grads:
g.detach().mul_(clip_coef_clamped.to(g.device))
# gradient_cliped = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
# print(gradient_cliped)
return total_norm
def get_experiment_dir(root_dir, args):
# if args.pretrained is not None and 'Latte-XL-2-256x256.pt' not in args.pretrained:
# root_dir += '-WOPRE'
if args.use_compile:
root_dir += "-Compile" # speedup by torch compile
if args.fixed_spatial:
root_dir += "-FixedSpa"
if args.enable_xformers_memory_efficient_attention:
root_dir += "-Xfor"
if args.gradient_checkpointing:
root_dir += "-Gc"
if args.mixed_precision:
root_dir += "-Amp"
if args.image_size == 512:
root_dir += "-512"
return root_dir
#################################################################################
# Training Logger #
#################################################################################
def create_logger(logging_dir):
"""
Create a logger that writes to a log file and stdout.
"""
if dist.get_rank() == 0: # real logger
logging.basicConfig(
level=logging.INFO,
# format='[\033[34m%(asctime)s\033[0m] %(message)s',
format="[%(asctime)s] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")],
)
logger = logging.getLogger(__name__)
else: # dummy logger (does nothing)
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
return logger
def create_tensorboard(tensorboard_dir):
"""
Create a tensorboard that saves losses.
"""
if dist.get_rank() == 0: # real tensorboard
# tensorboard
writer = SummaryWriter(tensorboard_dir)
return writer
def write_tensorboard(writer, *args):
"""
write the loss information to a tensorboard file.
Only for pytorch DDP mode.
"""
if dist.get_rank() == 0: # real tensorboard
writer.add_scalar(args[0], args[1], args[2])
#################################################################################
# EMA Update/ DDP Training Utils #
#################################################################################
@torch.no_grad()
def update_ema(ema_model, model, decay=0.9999):
"""
Step the EMA model towards the current model.
"""
ema_params = OrderedDict(ema_model.named_parameters())
model_params = OrderedDict(model.named_parameters())
for name, param in model_params.items():
# TODO: Consider applying only to params that require_grad to avoid small numerical changes of pos_embed
ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
def requires_grad(model, flag=True):
"""
Set requires_grad flag for all parameters in a model.
"""
for p in model.parameters():
p.requires_grad = flag
def cleanup():
"""
End DDP training.
"""
dist.destroy_process_group()
def setup_distributed(backend="nccl", port=None):
"""Initialize distributed training environment.
support both slurm and torch.distributed.launch
see torch.distributed.init_process_group() for more details
"""
num_gpus = torch.cuda.device_count()
if "SLURM_JOB_ID" in os.environ:
rank = int(os.environ["SLURM_PROCID"])
world_size = int(os.environ["SLURM_NTASKS"])
node_list = os.environ["SLURM_NODELIST"]
addr = subprocess.getoutput(f"scontrol show hostname {node_list} | head -n1")
# specify master port
if port is not None:
os.environ["MASTER_PORT"] = str(port)
elif "MASTER_PORT" not in os.environ:
# os.environ["MASTER_PORT"] = "29566"
os.environ["MASTER_PORT"] = str(29567 + num_gpus)
if "MASTER_ADDR" not in os.environ:
os.environ["MASTER_ADDR"] = addr
os.environ["WORLD_SIZE"] = str(world_size)
os.environ["LOCAL_RANK"] = str(rank % num_gpus)
os.environ["RANK"] = str(rank)
else:
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
# torch.cuda.set_device(rank % num_gpus)
dist.init_process_group(
backend=backend,
world_size=world_size,
rank=rank,
)
#################################################################################
# Testing Utils #
#################################################################################
def save_video_grid(video, nrow=None):
b, t, h, w, c = video.shape
if nrow is None:
nrow = math.ceil(math.sqrt(b))
ncol = math.ceil(b / nrow)
padding = 1
video_grid = torch.zeros((t, (padding + h) * nrow + padding, (padding + w) * ncol + padding, c), dtype=torch.uint8)
print(video_grid.shape)
for i in range(b):
r = i // ncol
c = i % ncol
start_r = (padding + h) * r
start_c = (padding + w) * c
video_grid[:, start_r : start_r + h, start_c : start_c + w] = video[i]
return video_grid
#################################################################################
# MMCV Utils #
#################################################################################
def collect_env():
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import collect_env as collect_base_env
from mmcv.utils import get_git_hash
"""Collect the information of the running environments."""
env_info = collect_base_env()
env_info["MMClassification"] = get_git_hash()[:7]
for name, val in env_info.items():
print(f"{name}: {val}")
print(torch.cuda.get_arch_list())
print(torch.version.cuda)
#################################################################################
# Pixart-alpha Utils #
#################################################################################
bad_punct_regex = re.compile(
r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
)
def text_preprocessing(text, clean_caption=False):
if clean_caption and not is_bs4_available():
clean_caption = False
if clean_caption and not is_ftfy_available():
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
def process(text: str):
if clean_caption:
text = clean_caption(text)
text = clean_caption(text)
else:
text = text.lower().strip()
return text
return [process(t) for t in text]
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def clean_caption(caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
caption = re.sub(
r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
caption = re.sub(
r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa
"",
caption,
) # regex for urls
# html:
caption = BeautifulSoup(caption, features="html.parser").text
# @<nickname>
caption = re.sub(r"@[\w\d]+\b", "", caption)
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
#######################################################
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption = re.sub(r"[`´«»“”¨]", '"', caption)
caption = re.sub(r"[‘’]", "'", caption)
# &quot;
caption = re.sub(r"&quot;?", "", caption)
# &amp
caption = re.sub(r"&amp", "", caption)
# ip adresses:
caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
# article ids:
caption = re.sub(r"\d:\d\d\s+$", "", caption)
# \n
caption = re.sub(r"\\n", " ", caption)
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
#
caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT"""
caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT"""
caption = re.sub(bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT
caption = re.sub(r"\s+\.\s+", r" ", caption) # " . "
# this-is-my-cute-cat / this_is_my_cute_cat
regex2 = re.compile(r"(?:\-|\_)")
if len(re.findall(regex2, caption)) > 3:
caption = re.sub(regex2, " ", caption)
caption = ftfy.fix_text(caption)
caption = html.unescape(html.unescape(caption))
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
caption = re.sub(r"\bpage\s+\d+\b", "", caption)
caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a...
caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
caption = re.sub(r"\b\s+\:\s+", r": ", caption)
caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
caption = re.sub(r"\s+", " ", caption)
caption.strip()
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()