Basic Flux Schnell and Flux Dev model implementation.
This commit is contained in:
comfyanonymous
parent
7ad574bffd
commit
1589b58d3e
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@ -139,3 +139,14 @@ class SD3(LatentFormat):
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class StableAudio1(LatentFormat):
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latent_channels = 64
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class Flux(SD3):
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def __init__(self):
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self.scale_factor = 0.3611
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self.shift_factor = 0.1159
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def process_in(self, latent):
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return (latent - self.shift_factor) * self.scale_factor
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def process_out(self, latent):
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return (latent / self.scale_factor) + self.shift_factor
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@ -0,0 +1,257 @@
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import math
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from dataclasses import dataclass
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import torch
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from einops import rearrange
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from torch import Tensor, nn
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from .math import attention, rope
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import comfy.ops
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class EmbedND(nn.Module):
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def __init__(self, dim: int, theta: int, axes_dim: list[int]):
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super().__init__()
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self.dim = dim
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self.theta = theta
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self.axes_dim = axes_dim
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def forward(self, ids: Tensor) -> Tensor:
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n_axes = ids.shape[-1]
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emb = torch.cat(
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[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
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dim=-3,
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)
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return emb.unsqueeze(1)
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def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
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"""
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Create sinusoidal timestep embeddings.
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:param t: a 1-D Tensor of N indices, one per batch element.
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These may be fractional.
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an (N, D) Tensor of positional embeddings.
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"""
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t = time_factor * t
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half = dim // 2
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freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
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t.device
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)
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args = t[:, None].float() * 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([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
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if torch.is_floating_point(t):
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embedding = embedding.to(t)
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return embedding
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class MLPEmbedder(nn.Module):
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def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None):
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super().__init__()
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self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
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self.silu = nn.SiLU()
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self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device)
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def forward(self, x: Tensor) -> Tensor:
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return self.out_layer(self.silu(self.in_layer(x)))
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class RMSNorm(torch.nn.Module):
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def __init__(self, dim: int, dtype=None, device=None, operations=None):
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super().__init__()
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self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device))
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def forward(self, x: Tensor):
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x_dtype = x.dtype
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x = x.float()
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rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
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return (x * rrms).to(dtype=x_dtype) * comfy.ops.cast_to(self.scale, dtype=x_dtype, device=x.device)
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class QKNorm(torch.nn.Module):
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def __init__(self, dim: int, dtype=None, device=None, operations=None):
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super().__init__()
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self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
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self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
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def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
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q = self.query_norm(q)
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k = self.key_norm(k)
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return q.to(v), k.to(v)
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class SelfAttention(nn.Module):
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def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
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self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
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self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
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def forward(self, x: Tensor, pe: Tensor) -> Tensor:
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qkv = self.qkv(x)
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q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
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q, k = self.norm(q, k, v)
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x = attention(q, k, v, pe=pe)
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x = self.proj(x)
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return x
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@dataclass
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class ModulationOut:
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shift: Tensor
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scale: Tensor
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gate: Tensor
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class Modulation(nn.Module):
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def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None):
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super().__init__()
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self.is_double = double
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self.multiplier = 6 if double else 3
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self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device)
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def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
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out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
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return (
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ModulationOut(*out[:3]),
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ModulationOut(*out[3:]) if self.is_double else None,
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)
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class DoubleStreamBlock(nn.Module):
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def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, dtype=None, device=None, operations=None):
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super().__init__()
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mlp_hidden_dim = int(hidden_size * mlp_ratio)
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self.num_heads = num_heads
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self.hidden_size = hidden_size
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self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
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self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
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self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.img_mlp = nn.Sequential(
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operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
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nn.GELU(approximate="tanh"),
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operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
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)
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self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
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self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
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self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.txt_mlp = nn.Sequential(
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operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
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nn.GELU(approximate="tanh"),
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operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
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)
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def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
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img_mod1, img_mod2 = self.img_mod(vec)
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txt_mod1, txt_mod2 = self.txt_mod(vec)
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# prepare image for attention
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img_modulated = self.img_norm1(img)
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img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
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img_qkv = self.img_attn.qkv(img_modulated)
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img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
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img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
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# prepare txt for attention
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txt_modulated = self.txt_norm1(txt)
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txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
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txt_qkv = self.txt_attn.qkv(txt_modulated)
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txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
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txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
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# run actual attention
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q = torch.cat((txt_q, img_q), dim=2)
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k = torch.cat((txt_k, img_k), dim=2)
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v = torch.cat((txt_v, img_v), dim=2)
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attn = attention(q, k, v, pe=pe)
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txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
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# calculate the img bloks
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img = img + img_mod1.gate * self.img_attn.proj(img_attn)
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img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
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# calculate the txt bloks
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txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
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txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
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return img, txt
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class SingleStreamBlock(nn.Module):
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"""
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A DiT block with parallel linear layers as described in
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https://arxiv.org/abs/2302.05442 and adapted modulation interface.
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"""
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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mlp_ratio: float = 4.0,
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qk_scale: float | None = None,
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dtype=None,
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device=None,
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operations=None
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):
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super().__init__()
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self.hidden_dim = hidden_size
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self.num_heads = num_heads
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head_dim = hidden_size // num_heads
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self.scale = qk_scale or head_dim**-0.5
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self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
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# qkv and mlp_in
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self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
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# proj and mlp_out
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self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
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self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
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self.hidden_size = hidden_size
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self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.mlp_act = nn.GELU(approximate="tanh")
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self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
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def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
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mod, _ = self.modulation(vec)
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x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
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qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
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q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
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q, k = self.norm(q, k, v)
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# compute attention
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attn = attention(q, k, v, pe=pe)
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# compute activation in mlp stream, cat again and run second linear layer
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output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
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return x + mod.gate * output
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class LastLayer(nn.Module):
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def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
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super().__init__()
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self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
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self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
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def forward(self, x: Tensor, vec: Tensor) -> Tensor:
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shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
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x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
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x = self.linear(x)
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return x
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@ -0,0 +1,29 @@
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import torch
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from einops import rearrange
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from torch import Tensor
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from comfy.ldm.modules.attention import optimized_attention
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def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
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q, k = apply_rope(q, k, pe)
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heads = q.shape[1]
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x = optimized_attention(q, k, v, heads, skip_reshape=True)
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return x
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def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
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assert dim % 2 == 0
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scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
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omega = 1.0 / (theta**scale)
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out = torch.einsum("...n,d->...nd", pos, omega)
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out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
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out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
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return out.float()
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def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
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xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
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xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
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xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
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xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
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return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
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@ -0,0 +1,136 @@
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#Original code can be found on: https://github.com/black-forest-labs/flux
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from dataclasses import dataclass
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import torch
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from torch import Tensor, nn
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from .layers import (
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DoubleStreamBlock,
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EmbedND,
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LastLayer,
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MLPEmbedder,
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SingleStreamBlock,
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timestep_embedding,
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)
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from einops import rearrange, repeat
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@dataclass
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class FluxParams:
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in_channels: int
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vec_in_dim: int
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context_in_dim: int
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hidden_size: int
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mlp_ratio: float
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num_heads: int
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depth: int
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depth_single_blocks: int
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axes_dim: list[int]
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theta: int
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qkv_bias: bool
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guidance_embed: bool
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class Flux(nn.Module):
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"""
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Transformer model for flow matching on sequences.
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"""
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def __init__(self, image_model=None, dtype=None, device=None, operations=None, **kwargs):
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super().__init__()
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self.dtype = dtype
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params = FluxParams(**kwargs)
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self.params = params
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self.in_channels = params.in_channels
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self.out_channels = self.in_channels
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if params.hidden_size % params.num_heads != 0:
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raise ValueError(
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f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
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)
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pe_dim = params.hidden_size // params.num_heads
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if sum(params.axes_dim) != pe_dim:
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raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
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self.hidden_size = params.hidden_size
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self.num_heads = params.num_heads
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self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
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self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
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self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
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self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
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self.guidance_in = (
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MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
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)
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self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
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self.double_blocks = nn.ModuleList(
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[
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DoubleStreamBlock(
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self.hidden_size,
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self.num_heads,
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mlp_ratio=params.mlp_ratio,
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qkv_bias=params.qkv_bias,
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dtype=dtype, device=device, operations=operations
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)
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for _ in range(params.depth)
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]
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)
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self.single_blocks = nn.ModuleList(
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[
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SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
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for _ in range(params.depth_single_blocks)
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]
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)
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self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
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||||
def forward_orig(
|
||||
self,
|
||||
img: Tensor,
|
||||
img_ids: Tensor,
|
||||
txt: Tensor,
|
||||
txt_ids: Tensor,
|
||||
timesteps: Tensor,
|
||||
y: Tensor,
|
||||
guidance: Tensor | None = None,
|
||||
) -> Tensor:
|
||||
if img.ndim != 3 or txt.ndim != 3:
|
||||
raise ValueError("Input img and txt tensors must have 3 dimensions.")
|
||||
|
||||
# running on sequences img
|
||||
img = self.img_in(img)
|
||||
vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))
|
||||
if self.params.guidance_embed:
|
||||
if guidance is None:
|
||||
raise ValueError("Didn't get guidance strength for guidance distilled model.")
|
||||
vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
|
||||
|
||||
vec = vec + self.vector_in(y)
|
||||
txt = self.txt_in(txt)
|
||||
|
||||
ids = torch.cat((txt_ids, img_ids), dim=1)
|
||||
pe = self.pe_embedder(ids)
|
||||
|
||||
for block in self.double_blocks:
|
||||
img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
|
||||
|
||||
img = torch.cat((txt, img), 1)
|
||||
for block in self.single_blocks:
|
||||
img = block(img, vec=vec, pe=pe)
|
||||
img = img[:, txt.shape[1] :, ...]
|
||||
|
||||
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
|
||||
return img
|
||||
|
||||
def forward(self, x, timestep, context, y, guidance, **kwargs):
|
||||
bs, c, h, w = x.shape
|
||||
img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
|
||||
|
||||
img_ids = torch.zeros((h // 2, w // 2, 3), device=x.device, dtype=x.dtype)
|
||||
img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=x.device, dtype=x.dtype)[:, None]
|
||||
img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=x.device, dtype=x.dtype)[None, :]
|
||||
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
|
||||
|
||||
txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
|
||||
out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance)
|
||||
return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h // 2, w=w // 2, ph=2, pw=2)
|
||||
|
|
@ -10,6 +10,8 @@ import comfy.ldm.aura.mmdit
|
|||
import comfy.ldm.hydit.models
|
||||
import comfy.ldm.audio.dit
|
||||
import comfy.ldm.audio.embedders
|
||||
import comfy.ldm.flux.model
|
||||
|
||||
import comfy.model_management
|
||||
import comfy.conds
|
||||
import comfy.ops
|
||||
|
|
@ -26,6 +28,7 @@ class ModelType(Enum):
|
|||
EDM = 5
|
||||
FLOW = 6
|
||||
V_PREDICTION_CONTINUOUS = 7
|
||||
FLUX = 8
|
||||
|
||||
|
||||
from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
|
||||
|
|
@ -53,6 +56,9 @@ def model_sampling(model_config, model_type):
|
|||
elif model_type == ModelType.V_PREDICTION_CONTINUOUS:
|
||||
c = V_PREDICTION
|
||||
s = ModelSamplingContinuousV
|
||||
elif model_type == ModelType.FLUX:
|
||||
c = comfy.model_sampling.CONST
|
||||
s = comfy.model_sampling.ModelSamplingFlux
|
||||
|
||||
class ModelSampling(s, c):
|
||||
pass
|
||||
|
|
@ -681,3 +687,18 @@ class HunyuanDiT(BaseModel):
|
|||
|
||||
out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
|
||||
return out
|
||||
|
||||
class Flux(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux)
|
||||
|
||||
def encode_adm(self, **kwargs):
|
||||
return kwargs["pooled_output"]
|
||||
|
||||
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)
|
||||
out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
|
||||
return out
|
||||
|
|
|
|||
|
|
@ -128,6 +128,23 @@ def detect_unet_config(state_dict, key_prefix):
|
|||
unet_config["image_model"] = "hydit1"
|
||||
return unet_config
|
||||
|
||||
if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys: #Flux
|
||||
dit_config = {}
|
||||
dit_config["image_model"] = "flux"
|
||||
dit_config["in_channels"] = 64
|
||||
dit_config["vec_in_dim"] = 768
|
||||
dit_config["context_in_dim"] = 4096
|
||||
dit_config["hidden_size"] = 3072
|
||||
dit_config["mlp_ratio"] = 4.0
|
||||
dit_config["num_heads"] = 24
|
||||
dit_config["depth"] = 19
|
||||
dit_config["depth_single_blocks"] = 38
|
||||
dit_config["axes_dim"] = [16, 56, 56]
|
||||
dit_config["theta"] = 10000
|
||||
dit_config["qkv_bias"] = True
|
||||
dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
|
||||
return dit_config
|
||||
|
||||
if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
|
||||
return None
|
||||
|
||||
|
|
|
|||
|
|
@ -272,3 +272,43 @@ class StableCascadeSampling(ModelSamplingDiscrete):
|
|||
|
||||
percent = 1.0 - percent
|
||||
return self.sigma(torch.tensor(percent))
|
||||
|
||||
|
||||
def flux_time_shift(mu: float, sigma: float, t):
|
||||
return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
|
||||
|
||||
class ModelSamplingFlux(torch.nn.Module):
|
||||
def __init__(self, model_config=None):
|
||||
super().__init__()
|
||||
if model_config is not None:
|
||||
sampling_settings = model_config.sampling_settings
|
||||
else:
|
||||
sampling_settings = {}
|
||||
|
||||
self.set_parameters(shift=sampling_settings.get("shift", 1.15))
|
||||
|
||||
def set_parameters(self, shift=1.15, timesteps=10000):
|
||||
self.shift = shift
|
||||
ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps))
|
||||
self.register_buffer('sigmas', ts)
|
||||
|
||||
@property
|
||||
def sigma_min(self):
|
||||
return self.sigmas[0]
|
||||
|
||||
@property
|
||||
def sigma_max(self):
|
||||
return self.sigmas[-1]
|
||||
|
||||
def timestep(self, sigma):
|
||||
return sigma
|
||||
|
||||
def sigma(self, timestep):
|
||||
return flux_time_shift(self.shift, 1.0, timestep)
|
||||
|
||||
def percent_to_sigma(self, percent):
|
||||
if percent <= 0.0:
|
||||
return 1.0
|
||||
if percent >= 1.0:
|
||||
return 0.0
|
||||
return 1.0 - percent
|
||||
|
|
|
|||
|
|
@ -23,6 +23,7 @@ import comfy.text_encoders.sd3_clip
|
|||
import comfy.text_encoders.sa_t5
|
||||
import comfy.text_encoders.aura_t5
|
||||
import comfy.text_encoders.hydit
|
||||
import comfy.text_encoders.flux
|
||||
|
||||
import comfy.model_patcher
|
||||
import comfy.lora
|
||||
|
|
@ -387,6 +388,7 @@ class CLIPType(Enum):
|
|||
SD3 = 3
|
||||
STABLE_AUDIO = 4
|
||||
HUNYUAN_DIT = 5
|
||||
FLUX = 6
|
||||
|
||||
def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
|
||||
clip_data = []
|
||||
|
|
@ -438,6 +440,9 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
|
|||
elif clip_type == CLIPType.HUNYUAN_DIT:
|
||||
clip_target.clip = comfy.text_encoders.hydit.HyditModel
|
||||
clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
|
||||
elif clip_type == CLIPType.FLUX:
|
||||
clip_target.clip = comfy.text_encoders.flux.FluxClipModel
|
||||
clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
|
||||
else:
|
||||
clip_target.clip = sdxl_clip.SDXLClipModel
|
||||
clip_target.tokenizer = sdxl_clip.SDXLTokenizer
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ import comfy.text_encoders.sd3_clip
|
|||
import comfy.text_encoders.sa_t5
|
||||
import comfy.text_encoders.aura_t5
|
||||
import comfy.text_encoders.hydit
|
||||
import comfy.text_encoders.flux
|
||||
|
||||
from . import supported_models_base
|
||||
from . import latent_formats
|
||||
|
|
@ -619,7 +620,45 @@ class HunyuanDiT1(HunyuanDiT):
|
|||
"linear_end" : 0.03,
|
||||
}
|
||||
|
||||
class Flux(supported_models_base.BASE):
|
||||
unet_config = {
|
||||
"image_model": "flux",
|
||||
"guidance_embed": True,
|
||||
}
|
||||
|
||||
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, HunyuanDiT, HunyuanDiT1]
|
||||
sampling_settings = {
|
||||
}
|
||||
|
||||
unet_extra_config = {}
|
||||
latent_format = latent_formats.Flux
|
||||
supported_inference_dtypes = [torch.bfloat16, torch.float32]
|
||||
|
||||
vae_key_prefix = ["vae."]
|
||||
text_encoder_key_prefix = ["text_encoders."]
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
out = model_base.Flux(self, device=device)
|
||||
return out
|
||||
|
||||
def clip_target(self, state_dict={}):
|
||||
return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.FluxClipModel)
|
||||
|
||||
class FluxSchnell(Flux):
|
||||
unet_config = {
|
||||
"image_model": "flux",
|
||||
"guidance_embed": False,
|
||||
}
|
||||
|
||||
sampling_settings = {
|
||||
"multiplier": 1.0,
|
||||
"shift": 1.0,
|
||||
}
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device)
|
||||
return out
|
||||
|
||||
|
||||
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, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell]
|
||||
|
||||
models += [SVD_img2vid]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,64 @@
|
|||
from comfy import sd1_clip
|
||||
import comfy.text_encoders.t5
|
||||
from transformers import T5TokenizerFast
|
||||
import torch
|
||||
import os
|
||||
|
||||
class T5XXLModel(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_config_xxl.json")
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5)
|
||||
|
||||
class T5XXLTokenizer(sd1_clip.SDTokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
||||
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
|
||||
super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
|
||||
|
||||
|
||||
class FluxTokenizer:
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
||||
self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
|
||||
self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
|
||||
|
||||
def tokenize_with_weights(self, text:str, return_word_ids=False):
|
||||
out = {}
|
||||
out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
|
||||
out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
|
||||
return out
|
||||
|
||||
def untokenize(self, token_weight_pair):
|
||||
return self.clip_g.untokenize(token_weight_pair)
|
||||
|
||||
def state_dict(self):
|
||||
return {}
|
||||
|
||||
|
||||
class FluxClipModel(torch.nn.Module):
|
||||
def __init__(self, device="cpu", dtype=None):
|
||||
super().__init__()
|
||||
self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False)
|
||||
self.t5xxl = T5XXLModel(device=device, dtype=dtype)
|
||||
self.dtypes = set([dtype])
|
||||
|
||||
def set_clip_options(self, options):
|
||||
self.clip_l.set_clip_options(options)
|
||||
self.t5xxl.set_clip_options(options)
|
||||
|
||||
def reset_clip_options(self):
|
||||
self.clip_l.reset_clip_options()
|
||||
self.t5xxl.reset_clip_options()
|
||||
|
||||
def encode_token_weights(self, token_weight_pairs):
|
||||
token_weight_pairs_l = token_weight_pairs["l"]
|
||||
token_weight_pars_t5 = token_weight_pairs["t5xxl"]
|
||||
|
||||
t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
|
||||
l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
|
||||
return t5_out, l_pooled
|
||||
|
||||
def load_sd(self, sd):
|
||||
if "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
|
||||
return self.clip_l.load_sd(sd)
|
||||
else:
|
||||
return self.t5xxl.load_sd(sd)
|
||||
|
||||
|
|
@ -3,7 +3,7 @@ import time
|
|||
import logging
|
||||
from typing import Set, List, Dict, Tuple
|
||||
|
||||
supported_pt_extensions: Set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl'])
|
||||
supported_pt_extensions: Set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'])
|
||||
|
||||
SupportedFileExtensionsType = Set[str]
|
||||
ScanPathType = List[str]
|
||||
|
|
|
|||
4
nodes.py
4
nodes.py
|
|
@ -859,7 +859,7 @@ class DualCLIPLoader:
|
|||
def INPUT_TYPES(s):
|
||||
return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ),
|
||||
"clip_name2": (folder_paths.get_filename_list("clip"), ),
|
||||
"type": (["sdxl", "sd3"], ),
|
||||
"type": (["sdxl", "sd3", "flux"], ),
|
||||
}}
|
||||
RETURN_TYPES = ("CLIP",)
|
||||
FUNCTION = "load_clip"
|
||||
|
|
@ -873,6 +873,8 @@ class DualCLIPLoader:
|
|||
clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
|
||||
elif type == "sd3":
|
||||
clip_type = comfy.sd.CLIPType.SD3
|
||||
elif type == "flux":
|
||||
clip_type = comfy.sd.CLIPType.FLUX
|
||||
|
||||
clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
|
||||
return (clip,)
|
||||
|
|
|
|||
Loading…
Reference in New Issue