AuraFlow model implementation.
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comfyanonymous
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#AuraFlow MMDiT
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#Originally written by the AuraFlow Authors
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from comfy.ldm.modules.attention import optimized_attention
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def modulate(x, shift, scale):
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return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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def find_multiple(n: int, k: int) -> int:
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if n % k == 0:
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return n
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return n + k - (n % k)
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class MLP(nn.Module):
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def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None:
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super().__init__()
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if hidden_dim is None:
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hidden_dim = 4 * dim
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n_hidden = int(2 * hidden_dim / 3)
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n_hidden = find_multiple(n_hidden, 256)
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self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
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self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
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self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
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x = self.c_proj(x)
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return x
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class MultiHeadLayerNorm(nn.Module):
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def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None):
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# Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78
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super().__init__()
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self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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mean = hidden_states.mean(-1, keepdim=True)
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variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
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hidden_states = (hidden_states - mean) * torch.rsqrt(
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variance + self.variance_epsilon
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)
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hidden_states = self.weight.to(torch.float32) * hidden_states
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return hidden_states.to(input_dtype)
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class SingleAttention(nn.Module):
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def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
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super().__init__()
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self.n_heads = n_heads
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self.head_dim = dim // n_heads
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# this is for cond
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self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.q_norm1 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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self.k_norm1 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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#@torch.compile()
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def forward(self, c):
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bsz, seqlen1, _ = c.shape
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q, k, v = self.w1q(c), self.w1k(c), self.w1v(c)
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q = q.view(bsz, seqlen1, self.n_heads, self.head_dim)
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k = k.view(bsz, seqlen1, self.n_heads, self.head_dim)
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v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)
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q, k = self.q_norm1(q), self.k_norm1(k)
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output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
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c = self.w1o(output)
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return c
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class DoubleAttention(nn.Module):
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def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
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super().__init__()
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self.n_heads = n_heads
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self.head_dim = dim // n_heads
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# this is for cond
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self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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# this is for x
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self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
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self.q_norm1 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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self.k_norm1 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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self.q_norm2 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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self.k_norm2 = (
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MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
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if mh_qknorm
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else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
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)
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#@torch.compile()
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def forward(self, c, x):
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bsz, seqlen1, _ = c.shape
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bsz, seqlen2, _ = x.shape
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seqlen = seqlen1 + seqlen2
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cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c)
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cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim)
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ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim)
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cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim)
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cq, ck = self.q_norm1(cq), self.k_norm1(ck)
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xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x)
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xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim)
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xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim)
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xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim)
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xq, xk = self.q_norm2(xq), self.k_norm2(xk)
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# concat all
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q, k, v = (
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torch.cat([cq, xq], dim=1),
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torch.cat([ck, xk], dim=1),
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torch.cat([cv, xv], dim=1),
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)
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output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
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c, x = output.split([seqlen1, seqlen2], dim=1)
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c = self.w1o(c)
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x = self.w2o(x)
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return c, x
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class MMDiTBlock(nn.Module):
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def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None):
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super().__init__()
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self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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if not is_last:
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self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
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self.modC = nn.Sequential(
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nn.SiLU(),
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operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
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)
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else:
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self.modC = nn.Sequential(
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nn.SiLU(),
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operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
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)
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self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
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self.modX = nn.Sequential(
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nn.SiLU(),
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operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
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)
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self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
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self.is_last = is_last
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#@torch.compile()
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def forward(self, c, x, global_cond, **kwargs):
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cres, xres = c, x
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cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = (
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self.modC(global_cond).chunk(6, dim=1)
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)
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c = modulate(self.normC1(c), cshift_msa, cscale_msa)
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# xpath
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xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = (
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self.modX(global_cond).chunk(6, dim=1)
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)
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x = modulate(self.normX1(x), xshift_msa, xscale_msa)
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# attention
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c, x = self.attn(c, x)
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c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
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c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp))
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c = cres + c
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x = self.normX2(xres + xgate_msa.unsqueeze(1) * x)
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x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp))
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x = xres + x
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return c, x
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class DiTBlock(nn.Module):
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# like MMDiTBlock, but it only has X
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def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None):
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super().__init__()
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self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
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self.modCX = nn.Sequential(
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nn.SiLU(),
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operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
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)
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self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
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self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
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#@torch.compile()
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def forward(self, cx, global_cond, **kwargs):
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cxres = cx
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(
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global_cond
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).chunk(6, dim=1)
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cx = modulate(self.norm1(cx), shift_msa, scale_msa)
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cx = self.attn(cx)
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cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
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mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
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cx = gate_mlp.unsqueeze(1) * mlpout
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cx = cxres + cx
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return cx
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class TimestepEmbedder(nn.Module):
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def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
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super().__init__()
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self.mlp = nn.Sequential(
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operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device),
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nn.SiLU(),
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operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device),
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)
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self.frequency_embedding_size = frequency_embedding_size
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@staticmethod
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def timestep_embedding(t, dim, max_period=10000):
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half = dim // 2
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freqs = 1000 * torch.exp(
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-math.log(max_period) * torch.arange(start=0, end=half) / half
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).to(t.device)
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args = t[:, None] * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat(
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[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
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)
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return embedding
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#@torch.compile()
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def forward(self, t, dtype):
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t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
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t_emb = self.mlp(t_freq)
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return t_emb
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class MMDiT(nn.Module):
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def __init__(
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self,
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in_channels=4,
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out_channels=4,
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patch_size=2,
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dim=3072,
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n_layers=36,
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n_double_layers=4,
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n_heads=12,
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global_conddim=3072,
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cond_seq_dim=2048,
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max_seq=32 * 32,
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device=None,
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dtype=None,
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operations=None,
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):
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super().__init__()
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self.dtype = dtype
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self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations)
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self.cond_seq_linear = operations.Linear(
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cond_seq_dim, dim, bias=False, dtype=dtype, device=device
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) # linear for something like text sequence.
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self.init_x_linear = operations.Linear(
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patch_size * patch_size * in_channels, dim, dtype=dtype, device=device
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) # init linear for patchified image.
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self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device))
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self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device))
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self.double_layers = nn.ModuleList([])
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self.single_layers = nn.ModuleList([])
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for idx in range(n_double_layers):
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self.double_layers.append(
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MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations)
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)
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for idx in range(n_double_layers, n_layers):
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self.single_layers.append(
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DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations)
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)
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self.final_linear = operations.Linear(
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dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device
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)
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self.modF = nn.Sequential(
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nn.SiLU(),
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operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
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)
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self.out_channels = out_channels
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self.patch_size = patch_size
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self.n_double_layers = n_double_layers
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self.n_layers = n_layers
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self.h_max = round(max_seq**0.5)
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self.w_max = round(max_seq**0.5)
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@torch.no_grad()
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def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)):
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# extend pe
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pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]]
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pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1)
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# now we need to extend this to target_dim. for this we will use interpolation.
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# we will use torch.nn.functional.interpolate
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pe_as_2d = F.interpolate(
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pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear"
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)
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pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)
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self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()
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self.h_max, self.w_max = target_dim
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print("PE extended to", target_dim)
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def pe_selection_index_based_on_dim(self, h, w):
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h_p, w_p = h // self.patch_size, w // self.patch_size
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original_pe_indexes = torch.arange(self.positional_encoding.shape[1])
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original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max)
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starth = self.h_max // 2 - h_p // 2
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endh =starth + h_p
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startw = self.w_max // 2 - w_p // 2
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endw = startw + w_p
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original_pe_indexes = original_pe_indexes[
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starth:endh, startw:endw
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]
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return original_pe_indexes.flatten()
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|
||||
def unpatchify(self, x, h, w):
|
||||
c = self.out_channels
|
||||
p = self.patch_size
|
||||
|
||||
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
|
||||
x = torch.einsum("nhwpqc->nchpwq", x)
|
||||
imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
|
||||
return imgs
|
||||
|
||||
def patchify(self, x):
|
||||
B, C, H, W = x.size()
|
||||
pad_h = (self.patch_size - H % self.patch_size) % self.patch_size
|
||||
pad_w = (self.patch_size - W % self.patch_size) % self.patch_size
|
||||
|
||||
x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect')
|
||||
x = x.view(
|
||||
B,
|
||||
C,
|
||||
(H + 1) // self.patch_size,
|
||||
self.patch_size,
|
||||
(W + 1) // self.patch_size,
|
||||
self.patch_size,
|
||||
)
|
||||
x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
|
||||
return x
|
||||
|
||||
def apply_pos_embeds(self, x, h, w):
|
||||
h = (h + 1) // self.patch_size
|
||||
w = (w + 1) // self.patch_size
|
||||
max_dim = max(h, w)
|
||||
|
||||
cur_dim = self.h_max
|
||||
pos_encoding = self.positional_encoding.reshape(1, cur_dim, cur_dim, -1).to(device=x.device, dtype=x.dtype)
|
||||
|
||||
if max_dim > cur_dim:
|
||||
pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1)
|
||||
cur_dim = max_dim
|
||||
|
||||
from_h = (cur_dim - h) // 2
|
||||
from_w = (cur_dim - w) // 2
|
||||
pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w]
|
||||
return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
|
||||
|
||||
def forward(self, x, timestep, context, **kwargs):
|
||||
# patchify x, add PE
|
||||
b, c, h, w = x.shape
|
||||
|
||||
# pe_indexes = self.pe_selection_index_based_on_dim(h, w)
|
||||
# print(pe_indexes, pe_indexes.shape)
|
||||
|
||||
x = self.init_x_linear(self.patchify(x)) # B, T_x, D
|
||||
x = self.apply_pos_embeds(x, h, w)
|
||||
# x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype)
|
||||
# x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype)
|
||||
|
||||
# process conditions for MMDiT Blocks
|
||||
c_seq = context # B, T_c, D_c
|
||||
t = timestep
|
||||
|
||||
c = self.cond_seq_linear(c_seq) # B, T_c, D
|
||||
c = torch.cat([self.register_tokens.to(device=c.device, dtype=c.dtype).repeat(c.size(0), 1, 1), c], dim=1)
|
||||
|
||||
global_cond = self.t_embedder(t, x.dtype) # B, D
|
||||
|
||||
if len(self.double_layers) > 0:
|
||||
for layer in self.double_layers:
|
||||
c, x = layer(c, x, global_cond, **kwargs)
|
||||
|
||||
if len(self.single_layers) > 0:
|
||||
c_len = c.size(1)
|
||||
cx = torch.cat([c, x], dim=1)
|
||||
for layer in self.single_layers:
|
||||
cx = layer(cx, global_cond, **kwargs)
|
||||
|
||||
x = cx[:, c_len:]
|
||||
|
||||
fshift, fscale = self.modF(global_cond).chunk(2, dim=1)
|
||||
|
||||
x = modulate(x, fshift, fscale)
|
||||
x = self.final_linear(x)
|
||||
x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w]
|
||||
return x
|
||||
|
|
@ -6,6 +6,7 @@ from comfy.ldm.cascade.stage_b import StageB
|
|||
from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
|
||||
from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
|
||||
from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
|
||||
import comfy.ldm.aura.mmdit
|
||||
import comfy.ldm.audio.dit
|
||||
import comfy.ldm.audio.embedders
|
||||
import comfy.model_management
|
||||
|
|
@ -598,6 +599,17 @@ class SD3(BaseModel):
|
|||
area = input_shape[0] * input_shape[2] * input_shape[3]
|
||||
return (area * 0.3) * (1024 * 1024)
|
||||
|
||||
class AuraFlow(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.aura.mmdit.MMDiT)
|
||||
|
||||
def extra_conds(self, **kwargs):
|
||||
out = super().extra_conds(**kwargs)
|
||||
cross_attn = kwargs.get("cross_attn", None)
|
||||
if cross_attn is not None:
|
||||
out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
|
||||
return out
|
||||
|
||||
|
||||
class StableAudio1(BaseModel):
|
||||
def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None):
|
||||
|
|
|
|||
|
|
@ -105,6 +105,12 @@ def detect_unet_config(state_dict, key_prefix):
|
|||
unet_config["audio_model"] = "dit1.0"
|
||||
return unet_config
|
||||
|
||||
if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit
|
||||
unet_config = {}
|
||||
unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1]
|
||||
unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1]
|
||||
return unet_config
|
||||
|
||||
if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
|
||||
return None
|
||||
|
||||
|
|
@ -253,6 +259,8 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
|
|||
def unet_prefix_from_state_dict(state_dict):
|
||||
if "model.model.postprocess_conv.weight" in state_dict: #audio models
|
||||
unet_key_prefix = "model.model."
|
||||
elif "model.double_layers.0.attn.w1q.weight" in state_dict: #aura flow
|
||||
unet_key_prefix = "model."
|
||||
else:
|
||||
unet_key_prefix = "model.diffusion_model."
|
||||
return unet_key_prefix
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ from . import sd2_clip
|
|||
from . import sdxl_clip
|
||||
from . import sd3_clip
|
||||
from . import sa_t5
|
||||
import comfy.text_encoders.aura_t5
|
||||
|
||||
import comfy.model_patcher
|
||||
import comfy.lora
|
||||
|
|
@ -415,6 +416,9 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
|
|||
if weight.shape[-1] == 4096:
|
||||
clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
|
||||
clip_target.tokenizer = sd3_clip.SD3Tokenizer
|
||||
elif weight.shape[-1] == 2048:
|
||||
clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
|
||||
clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
|
||||
elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
|
||||
clip_target.clip = sa_t5.SAT5Model
|
||||
clip_target.tokenizer = sa_t5.SAT5Tokenizer
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ from . import sd2_clip
|
|||
from . import sdxl_clip
|
||||
from . import sd3_clip
|
||||
from . import sa_t5
|
||||
import comfy.text_encoders.aura_t5
|
||||
|
||||
from . import supported_models_base
|
||||
from . import latent_formats
|
||||
|
|
@ -556,7 +557,28 @@ class StableAudio(supported_models_base.BASE):
|
|||
def clip_target(self, state_dict={}):
|
||||
return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model)
|
||||
|
||||
class AuraFlow(supported_models_base.BASE):
|
||||
unet_config = {
|
||||
"cond_seq_dim": 2048,
|
||||
}
|
||||
|
||||
models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio]
|
||||
sampling_settings = {
|
||||
"multiplier": 1.0,
|
||||
}
|
||||
|
||||
unet_extra_config = {}
|
||||
latent_format = latent_formats.SDXL
|
||||
|
||||
vae_key_prefix = ["vae."]
|
||||
text_encoder_key_prefix = ["text_encoders."]
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
out = model_base.AuraFlow(self, device=device)
|
||||
return out
|
||||
|
||||
def clip_target(self, state_dict={}):
|
||||
return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model)
|
||||
|
||||
models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow]
|
||||
|
||||
models += [SVD_img2vid]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,22 @@
|
|||
from comfy import sd1_clip
|
||||
from transformers import LlamaTokenizerFast
|
||||
import comfy.t5
|
||||
import os
|
||||
|
||||
class PT5XlModel(sd1_clip.SDClipModel):
|
||||
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
|
||||
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_config_xl.json")
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.t5.T5, enable_attention_masks=True, zero_out_masked=True)
|
||||
|
||||
class PT5XlTokenizer(sd1_clip.SDTokenizer):
|
||||
def __init__(self, embedding_directory=None):
|
||||
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer")
|
||||
super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=LlamaTokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1)
|
||||
|
||||
class AuraT5Tokenizer(sd1_clip.SD1Tokenizer):
|
||||
def __init__(self, embedding_directory=None):
|
||||
super().__init__(embedding_directory=embedding_directory, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer)
|
||||
|
||||
class AuraT5Model(sd1_clip.SD1ClipModel):
|
||||
def __init__(self, device="cpu", dtype=None, **kwargs):
|
||||
super().__init__(device=device, dtype=dtype, name="pile_t5xl", clip_model=PT5XlModel, **kwargs)
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
{
|
||||
"d_ff": 5120,
|
||||
"d_kv": 64,
|
||||
"d_model": 2048,
|
||||
"decoder_start_token_id": 0,
|
||||
"dropout_rate": 0.1,
|
||||
"eos_token_id": 2,
|
||||
"dense_act_fn": "gelu_pytorch_tanh",
|
||||
"initializer_factor": 1.0,
|
||||
"is_encoder_decoder": true,
|
||||
"is_gated_act": true,
|
||||
"layer_norm_epsilon": 1e-06,
|
||||
"model_type": "umt5",
|
||||
"num_decoder_layers": 24,
|
||||
"num_heads": 32,
|
||||
"num_layers": 24,
|
||||
"output_past": true,
|
||||
"pad_token_id": 1,
|
||||
"relative_attention_num_buckets": 32,
|
||||
"tie_word_embeddings": false,
|
||||
"vocab_size": 32128
|
||||
}
|
||||
|
|
@ -0,0 +1,102 @@
|
|||
{
|
||||
"<extra_id_0>": 32099,
|
||||
"<extra_id_10>": 32089,
|
||||
"<extra_id_11>": 32088,
|
||||
"<extra_id_12>": 32087,
|
||||
"<extra_id_13>": 32086,
|
||||
"<extra_id_14>": 32085,
|
||||
"<extra_id_15>": 32084,
|
||||
"<extra_id_16>": 32083,
|
||||
"<extra_id_17>": 32082,
|
||||
"<extra_id_18>": 32081,
|
||||
"<extra_id_19>": 32080,
|
||||
"<extra_id_1>": 32098,
|
||||
"<extra_id_20>": 32079,
|
||||
"<extra_id_21>": 32078,
|
||||
"<extra_id_22>": 32077,
|
||||
"<extra_id_23>": 32076,
|
||||
"<extra_id_24>": 32075,
|
||||
"<extra_id_25>": 32074,
|
||||
"<extra_id_26>": 32073,
|
||||
"<extra_id_27>": 32072,
|
||||
"<extra_id_28>": 32071,
|
||||
"<extra_id_29>": 32070,
|
||||
"<extra_id_2>": 32097,
|
||||
"<extra_id_30>": 32069,
|
||||
"<extra_id_31>": 32068,
|
||||
"<extra_id_32>": 32067,
|
||||
"<extra_id_33>": 32066,
|
||||
"<extra_id_34>": 32065,
|
||||
"<extra_id_35>": 32064,
|
||||
"<extra_id_36>": 32063,
|
||||
"<extra_id_37>": 32062,
|
||||
"<extra_id_38>": 32061,
|
||||
"<extra_id_39>": 32060,
|
||||
"<extra_id_3>": 32096,
|
||||
"<extra_id_40>": 32059,
|
||||
"<extra_id_41>": 32058,
|
||||
"<extra_id_42>": 32057,
|
||||
"<extra_id_43>": 32056,
|
||||
"<extra_id_44>": 32055,
|
||||
"<extra_id_45>": 32054,
|
||||
"<extra_id_46>": 32053,
|
||||
"<extra_id_47>": 32052,
|
||||
"<extra_id_48>": 32051,
|
||||
"<extra_id_49>": 32050,
|
||||
"<extra_id_4>": 32095,
|
||||
"<extra_id_50>": 32049,
|
||||
"<extra_id_51>": 32048,
|
||||
"<extra_id_52>": 32047,
|
||||
"<extra_id_53>": 32046,
|
||||
"<extra_id_54>": 32045,
|
||||
"<extra_id_55>": 32044,
|
||||
"<extra_id_56>": 32043,
|
||||
"<extra_id_57>": 32042,
|
||||
"<extra_id_58>": 32041,
|
||||
"<extra_id_59>": 32040,
|
||||
"<extra_id_5>": 32094,
|
||||
"<extra_id_60>": 32039,
|
||||
"<extra_id_61>": 32038,
|
||||
"<extra_id_62>": 32037,
|
||||
"<extra_id_63>": 32036,
|
||||
"<extra_id_64>": 32035,
|
||||
"<extra_id_65>": 32034,
|
||||
"<extra_id_66>": 32033,
|
||||
"<extra_id_67>": 32032,
|
||||
"<extra_id_68>": 32031,
|
||||
"<extra_id_69>": 32030,
|
||||
"<extra_id_6>": 32093,
|
||||
"<extra_id_70>": 32029,
|
||||
"<extra_id_71>": 32028,
|
||||
"<extra_id_72>": 32027,
|
||||
"<extra_id_73>": 32026,
|
||||
"<extra_id_74>": 32025,
|
||||
"<extra_id_75>": 32024,
|
||||
"<extra_id_76>": 32023,
|
||||
"<extra_id_77>": 32022,
|
||||
"<extra_id_78>": 32021,
|
||||
"<extra_id_79>": 32020,
|
||||
"<extra_id_7>": 32092,
|
||||
"<extra_id_80>": 32019,
|
||||
"<extra_id_81>": 32018,
|
||||
"<extra_id_82>": 32017,
|
||||
"<extra_id_83>": 32016,
|
||||
"<extra_id_84>": 32015,
|
||||
"<extra_id_85>": 32014,
|
||||
"<extra_id_86>": 32013,
|
||||
"<extra_id_87>": 32012,
|
||||
"<extra_id_88>": 32011,
|
||||
"<extra_id_89>": 32010,
|
||||
"<extra_id_8>": 32091,
|
||||
"<extra_id_90>": 32009,
|
||||
"<extra_id_91>": 32008,
|
||||
"<extra_id_92>": 32007,
|
||||
"<extra_id_93>": 32006,
|
||||
"<extra_id_94>": 32005,
|
||||
"<extra_id_95>": 32004,
|
||||
"<extra_id_96>": 32003,
|
||||
"<extra_id_97>": 32002,
|
||||
"<extra_id_98>": 32001,
|
||||
"<extra_id_99>": 32000,
|
||||
"<extra_id_9>": 32090
|
||||
}
|
||||
|
|
@ -0,0 +1,125 @@
|
|||
{
|
||||
"additional_special_tokens": [
|
||||
"<extra_id_99>",
|
||||
"<extra_id_98>",
|
||||
"<extra_id_97>",
|
||||
"<extra_id_96>",
|
||||
"<extra_id_95>",
|
||||
"<extra_id_94>",
|
||||
"<extra_id_93>",
|
||||
"<extra_id_92>",
|
||||
"<extra_id_91>",
|
||||
"<extra_id_90>",
|
||||
"<extra_id_89>",
|
||||
"<extra_id_88>",
|
||||
"<extra_id_87>",
|
||||
"<extra_id_86>",
|
||||
"<extra_id_85>",
|
||||
"<extra_id_84>",
|
||||
"<extra_id_83>",
|
||||
"<extra_id_82>",
|
||||
"<extra_id_81>",
|
||||
"<extra_id_80>",
|
||||
"<extra_id_79>",
|
||||
"<extra_id_78>",
|
||||
"<extra_id_77>",
|
||||
"<extra_id_76>",
|
||||
"<extra_id_75>",
|
||||
"<extra_id_74>",
|
||||
"<extra_id_73>",
|
||||
"<extra_id_72>",
|
||||
"<extra_id_71>",
|
||||
"<extra_id_70>",
|
||||
"<extra_id_69>",
|
||||
"<extra_id_68>",
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||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32054": {
|
||||
"content": "<extra_id_45>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32055": {
|
||||
"content": "<extra_id_44>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32056": {
|
||||
"content": "<extra_id_43>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32057": {
|
||||
"content": "<extra_id_42>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32058": {
|
||||
"content": "<extra_id_41>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32059": {
|
||||
"content": "<extra_id_40>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32060": {
|
||||
"content": "<extra_id_39>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32061": {
|
||||
"content": "<extra_id_38>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32062": {
|
||||
"content": "<extra_id_37>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32063": {
|
||||
"content": "<extra_id_36>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32064": {
|
||||
"content": "<extra_id_35>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32065": {
|
||||
"content": "<extra_id_34>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32066": {
|
||||
"content": "<extra_id_33>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32067": {
|
||||
"content": "<extra_id_32>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32068": {
|
||||
"content": "<extra_id_31>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32069": {
|
||||
"content": "<extra_id_30>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32070": {
|
||||
"content": "<extra_id_29>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32071": {
|
||||
"content": "<extra_id_28>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32072": {
|
||||
"content": "<extra_id_27>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32073": {
|
||||
"content": "<extra_id_26>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32074": {
|
||||
"content": "<extra_id_25>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32075": {
|
||||
"content": "<extra_id_24>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32076": {
|
||||
"content": "<extra_id_23>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32077": {
|
||||
"content": "<extra_id_22>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32078": {
|
||||
"content": "<extra_id_21>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32079": {
|
||||
"content": "<extra_id_20>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32080": {
|
||||
"content": "<extra_id_19>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32081": {
|
||||
"content": "<extra_id_18>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32082": {
|
||||
"content": "<extra_id_17>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32083": {
|
||||
"content": "<extra_id_16>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32084": {
|
||||
"content": "<extra_id_15>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32085": {
|
||||
"content": "<extra_id_14>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32086": {
|
||||
"content": "<extra_id_13>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32087": {
|
||||
"content": "<extra_id_12>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32088": {
|
||||
"content": "<extra_id_11>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32089": {
|
||||
"content": "<extra_id_10>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32090": {
|
||||
"content": "<extra_id_9>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32091": {
|
||||
"content": "<extra_id_8>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32092": {
|
||||
"content": "<extra_id_7>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32093": {
|
||||
"content": "<extra_id_6>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32094": {
|
||||
"content": "<extra_id_5>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32095": {
|
||||
"content": "<extra_id_4>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32096": {
|
||||
"content": "<extra_id_3>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32097": {
|
||||
"content": "<extra_id_2>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32098": {
|
||||
"content": "<extra_id_1>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32099": {
|
||||
"content": "<extra_id_0>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<extra_id_99>",
|
||||
"<extra_id_98>",
|
||||
"<extra_id_97>",
|
||||
"<extra_id_96>",
|
||||
"<extra_id_95>",
|
||||
"<extra_id_94>",
|
||||
"<extra_id_93>",
|
||||
"<extra_id_92>",
|
||||
"<extra_id_91>",
|
||||
"<extra_id_90>",
|
||||
"<extra_id_89>",
|
||||
"<extra_id_88>",
|
||||
"<extra_id_87>",
|
||||
"<extra_id_86>",
|
||||
"<extra_id_85>",
|
||||
"<extra_id_84>",
|
||||
"<extra_id_83>",
|
||||
"<extra_id_82>",
|
||||
"<extra_id_81>",
|
||||
"<extra_id_80>",
|
||||
"<extra_id_79>",
|
||||
"<extra_id_78>",
|
||||
"<extra_id_77>",
|
||||
"<extra_id_76>",
|
||||
"<extra_id_75>",
|
||||
"<extra_id_74>",
|
||||
"<extra_id_73>",
|
||||
"<extra_id_72>",
|
||||
"<extra_id_71>",
|
||||
"<extra_id_70>",
|
||||
"<extra_id_69>",
|
||||
"<extra_id_68>",
|
||||
"<extra_id_67>",
|
||||
"<extra_id_66>",
|
||||
"<extra_id_65>",
|
||||
"<extra_id_64>",
|
||||
"<extra_id_63>",
|
||||
"<extra_id_62>",
|
||||
"<extra_id_61>",
|
||||
"<extra_id_60>",
|
||||
"<extra_id_59>",
|
||||
"<extra_id_58>",
|
||||
"<extra_id_57>",
|
||||
"<extra_id_56>",
|
||||
"<extra_id_55>",
|
||||
"<extra_id_54>",
|
||||
"<extra_id_53>",
|
||||
"<extra_id_52>",
|
||||
"<extra_id_51>",
|
||||
"<extra_id_50>",
|
||||
"<extra_id_49>",
|
||||
"<extra_id_48>",
|
||||
"<extra_id_47>",
|
||||
"<extra_id_46>",
|
||||
"<extra_id_45>",
|
||||
"<extra_id_44>",
|
||||
"<extra_id_43>",
|
||||
"<extra_id_42>",
|
||||
"<extra_id_41>",
|
||||
"<extra_id_40>",
|
||||
"<extra_id_39>",
|
||||
"<extra_id_38>",
|
||||
"<extra_id_37>",
|
||||
"<extra_id_36>",
|
||||
"<extra_id_35>",
|
||||
"<extra_id_34>",
|
||||
"<extra_id_33>",
|
||||
"<extra_id_32>",
|
||||
"<extra_id_31>",
|
||||
"<extra_id_30>",
|
||||
"<extra_id_29>",
|
||||
"<extra_id_28>",
|
||||
"<extra_id_27>",
|
||||
"<extra_id_26>",
|
||||
"<extra_id_25>",
|
||||
"<extra_id_24>",
|
||||
"<extra_id_23>",
|
||||
"<extra_id_22>",
|
||||
"<extra_id_21>",
|
||||
"<extra_id_20>",
|
||||
"<extra_id_19>",
|
||||
"<extra_id_18>",
|
||||
"<extra_id_17>",
|
||||
"<extra_id_16>",
|
||||
"<extra_id_15>",
|
||||
"<extra_id_14>",
|
||||
"<extra_id_13>",
|
||||
"<extra_id_12>",
|
||||
"<extra_id_11>",
|
||||
"<extra_id_10>",
|
||||
"<extra_id_9>",
|
||||
"<extra_id_8>",
|
||||
"<extra_id_7>",
|
||||
"<extra_id_6>",
|
||||
"<extra_id_5>",
|
||||
"<extra_id_4>",
|
||||
"<extra_id_3>",
|
||||
"<extra_id_2>",
|
||||
"<extra_id_1>",
|
||||
"<extra_id_0>"
|
||||
],
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": false,
|
||||
"model_max_length": 512,
|
||||
"pad_token": null,
|
||||
"padding_side": "right",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
|
|
@ -3,7 +3,8 @@ torchsde
|
|||
torchvision
|
||||
torchaudio
|
||||
einops
|
||||
transformers>=4.25.1
|
||||
transformers>=4.28.1
|
||||
tokenizers>=0.13.3
|
||||
safetensors>=0.4.2
|
||||
aiohttp
|
||||
pyyaml
|
||||
|
|
|
|||
Loading…
Reference in New Issue