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183
comfy/ldm/chroma/layers.py
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183
comfy/ldm/chroma/layers.py
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@ -0,0 +1,183 @@
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import torch
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from torch import Tensor, nn
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from .math import attention
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from comfy.ldm.flux.layers import (
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MLPEmbedder,
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RMSNorm,
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QKNorm,
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SelfAttention,
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ModulationOut,
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)
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class ChromaModulationOut(ModulationOut):
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@classmethod
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def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut:
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return cls(
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shift=tensor[:, offset : offset + 1, :],
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scale=tensor[:, offset + 1 : offset + 2, :],
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gate=tensor[:, offset + 2 : offset + 3, :],
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)
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class Approximator(nn.Module):
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def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None):
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super().__init__()
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self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
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self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
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self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
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self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device)
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@property
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def device(self):
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# Get the device of the module (assumes all parameters are on the same device)
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return next(self.parameters()).device
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def forward(self, x: Tensor) -> Tensor:
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x = self.in_proj(x)
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for layer, norms in zip(self.layers, self.norms):
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x = x + layer(norms(x))
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x = self.out_proj(x)
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return x
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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, flipped_img_txt=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_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_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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self.flipped_img_txt = flipped_img_txt
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def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None):
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(img_mod1, img_mod2), (txt_mod1, txt_mod2) = 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 = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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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 = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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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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attn = attention(torch.cat((txt_q, img_q), dim=2),
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torch.cat((txt_k, img_k), dim=2),
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torch.cat((txt_v, img_v), dim=2),
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pe=pe, mask=attn_mask)
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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_mod1.gate * self.txt_attn.proj(txt_attn)
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txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
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if txt.dtype == torch.float16:
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txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
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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,
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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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def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
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mod = 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 = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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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, mask=attn_mask)
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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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x += mod.gate * output
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if x.dtype == torch.float16:
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x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
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return x
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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, out_channels, bias=True, dtype=dtype, device=device)
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def forward(self, x: Tensor, vec: Tensor) -> Tensor:
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shift, scale = vec
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shift = shift.squeeze(1)
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scale = scale.squeeze(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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44
comfy/ldm/chroma/math.py
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44
comfy/ldm/chroma/math.py
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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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import comfy.model_management
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def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
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q_shape = q.shape
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k_shape = k.shape
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q = q.float().reshape(*q.shape[:-1], -1, 1, 2)
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k = k.float().reshape(*k.shape[:-1], -1, 1, 2)
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q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v)
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k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v)
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heads = q.shape[1]
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x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask)
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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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if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled():
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device = torch.device("cpu")
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else:
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device = pos.device
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scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
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omega = 1.0 / (theta**scale)
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out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), 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.to(dtype=torch.float32, device=pos.device)
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def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: 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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271
comfy/ldm/chroma/model.py
Normal file
271
comfy/ldm/chroma/model.py
Normal file
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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 einops import rearrange, repeat
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import comfy.ldm.common_dit
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from comfy.ldm.flux.layers import (
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EmbedND,
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timestep_embedding,
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)
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from .layers import (
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DoubleStreamBlock,
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LastLayer,
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SingleStreamBlock,
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Approximator,
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ChromaModulationOut,
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)
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@dataclass
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class ChromaParams:
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in_channels: int
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out_channels: 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
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theta: int
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patch_size: int
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qkv_bias: bool
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in_dim: int
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out_dim: int
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hidden_dim: int
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n_layers: int
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class Chroma(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, final_layer=True, 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 = ChromaParams(**kwargs)
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self.params = params
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self.patch_size = params.patch_size
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self.in_channels = params.in_channels
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self.out_channels = params.out_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.in_dim = params.in_dim
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self.out_dim = params.out_dim
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self.hidden_dim = params.hidden_dim
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self.n_layers = params.n_layers
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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.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
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# set as nn identity for now, will overwrite it later.
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self.distilled_guidance_layer = Approximator(
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in_dim=self.in_dim,
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hidden_dim=self.hidden_dim,
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out_dim=self.out_dim,
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||||
n_layers=self.n_layers,
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||||
dtype=dtype, device=device, operations=operations
|
||||
)
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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,
|
||||
mlp_ratio=params.mlp_ratio,
|
||||
qkv_bias=params.qkv_bias,
|
||||
dtype=dtype, device=device, operations=operations
|
||||
)
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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)
|
||||
for _ in range(params.depth_single_blocks)
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||||
]
|
||||
)
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||||
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||||
if final_layer:
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||||
self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
|
||||
|
||||
self.skip_mmdit = []
|
||||
self.skip_dit = []
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||||
self.lite = False
|
||||
|
||||
def get_modulations(self, tensor: torch.Tensor, block_type: str, *, idx: int = 0):
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# This function slices up the modulations tensor which has the following layout:
|
||||
# single : num_single_blocks * 3 elements
|
||||
# double_img : num_double_blocks * 6 elements
|
||||
# double_txt : num_double_blocks * 6 elements
|
||||
# final : 2 elements
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||||
if block_type == "final":
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||||
return (tensor[:, -2:-1, :], tensor[:, -1:, :])
|
||||
single_block_count = self.params.depth_single_blocks
|
||||
double_block_count = self.params.depth
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||||
offset = 3 * idx
|
||||
if block_type == "single":
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return ChromaModulationOut.from_offset(tensor, offset)
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||||
# Double block modulations are 6 elements so we double 3 * idx.
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||||
offset *= 2
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||||
if block_type in {"double_img", "double_txt"}:
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||||
# Advance past the single block modulations.
|
||||
offset += 3 * single_block_count
|
||||
if block_type == "double_txt":
|
||||
# Advance past the double block img modulations.
|
||||
offset += 6 * double_block_count
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||||
return (
|
||||
ChromaModulationOut.from_offset(tensor, offset),
|
||||
ChromaModulationOut.from_offset(tensor, offset + 3),
|
||||
)
|
||||
raise ValueError("Bad block_type")
|
||||
|
||||
|
||||
def forward_orig(
|
||||
self,
|
||||
img: Tensor,
|
||||
img_ids: Tensor,
|
||||
txt: Tensor,
|
||||
txt_ids: Tensor,
|
||||
timesteps: Tensor,
|
||||
guidance: Tensor = None,
|
||||
control = None,
|
||||
transformer_options={},
|
||||
attn_mask: Tensor = None,
|
||||
) -> Tensor:
|
||||
patches_replace = transformer_options.get("patches_replace", {})
|
||||
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)
|
||||
|
||||
# distilled vector guidance
|
||||
mod_index_length = 344
|
||||
distill_timestep = timestep_embedding(timesteps.detach().clone(), 16).to(img.device, img.dtype)
|
||||
# guidance = guidance *
|
||||
distil_guidance = timestep_embedding(guidance.detach().clone(), 16).to(img.device, img.dtype)
|
||||
|
||||
# get all modulation index
|
||||
modulation_index = timestep_embedding(torch.arange(mod_index_length), 32).to(img.device, img.dtype)
|
||||
# we need to broadcast the modulation index here so each batch has all of the index
|
||||
modulation_index = modulation_index.unsqueeze(0).repeat(img.shape[0], 1, 1).to(img.device, img.dtype)
|
||||
# and we need to broadcast timestep and guidance along too
|
||||
timestep_guidance = torch.cat([distill_timestep, distil_guidance], dim=1).unsqueeze(1).repeat(1, mod_index_length, 1).to(img.dtype).to(img.device, img.dtype)
|
||||
# then and only then we could concatenate it together
|
||||
input_vec = torch.cat([timestep_guidance, modulation_index], dim=-1).to(img.device, img.dtype)
|
||||
|
||||
mod_vectors = self.distilled_guidance_layer(input_vec)
|
||||
|
||||
txt = self.txt_in(txt)
|
||||
|
||||
ids = torch.cat((txt_ids, img_ids), dim=1)
|
||||
pe = self.pe_embedder(ids)
|
||||
|
||||
blocks_replace = patches_replace.get("dit", {})
|
||||
for i, block in enumerate(self.double_blocks):
|
||||
if i not in self.skip_mmdit:
|
||||
double_mod = (
|
||||
self.get_modulations(mod_vectors, "double_img", idx=i),
|
||||
self.get_modulations(mod_vectors, "double_txt", idx=i),
|
||||
)
|
||||
if ("double_block", i) in blocks_replace:
|
||||
def block_wrap(args):
|
||||
out = {}
|
||||
out["img"], out["txt"] = block(img=args["img"],
|
||||
txt=args["txt"],
|
||||
vec=args["vec"],
|
||||
pe=args["pe"],
|
||||
attn_mask=args.get("attn_mask"))
|
||||
return out
|
||||
|
||||
out = blocks_replace[("double_block", i)]({"img": img,
|
||||
"txt": txt,
|
||||
"vec": double_mod,
|
||||
"pe": pe,
|
||||
"attn_mask": attn_mask},
|
||||
{"original_block": block_wrap})
|
||||
txt = out["txt"]
|
||||
img = out["img"]
|
||||
else:
|
||||
img, txt = block(img=img,
|
||||
txt=txt,
|
||||
vec=double_mod,
|
||||
pe=pe,
|
||||
attn_mask=attn_mask)
|
||||
|
||||
if control is not None: # Controlnet
|
||||
control_i = control.get("input")
|
||||
if i < len(control_i):
|
||||
add = control_i[i]
|
||||
if add is not None:
|
||||
img += add
|
||||
|
||||
img = torch.cat((txt, img), 1)
|
||||
|
||||
for i, block in enumerate(self.single_blocks):
|
||||
if i not in self.skip_dit:
|
||||
single_mod = self.get_modulations(mod_vectors, "single", idx=i)
|
||||
if ("single_block", i) in blocks_replace:
|
||||
def block_wrap(args):
|
||||
out = {}
|
||||
out["img"] = block(args["img"],
|
||||
vec=args["vec"],
|
||||
pe=args["pe"],
|
||||
attn_mask=args.get("attn_mask"))
|
||||
return out
|
||||
|
||||
out = blocks_replace[("single_block", i)]({"img": img,
|
||||
"vec": single_mod,
|
||||
"pe": pe,
|
||||
"attn_mask": attn_mask},
|
||||
{"original_block": block_wrap})
|
||||
img = out["img"]
|
||||
else:
|
||||
img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask)
|
||||
|
||||
if control is not None: # Controlnet
|
||||
control_o = control.get("output")
|
||||
if i < len(control_o):
|
||||
add = control_o[i]
|
||||
if add is not None:
|
||||
img[:, txt.shape[1] :, ...] += add
|
||||
|
||||
img = img[:, txt.shape[1] :, ...]
|
||||
final_mod = self.get_modulations(mod_vectors, "final")
|
||||
img = self.final_layer(img, vec=final_mod) # (N, T, patch_size ** 2 * out_channels)
|
||||
return img
|
||||
|
||||
def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
|
||||
bs, c, h, w = x.shape
|
||||
patch_size = 2
|
||||
x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
|
||||
|
||||
img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
|
||||
|
||||
h_len = ((h + (patch_size // 2)) // patch_size)
|
||||
w_len = ((w + (patch_size // 2)) // patch_size)
|
||||
img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
|
||||
img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
|
||||
img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
|
||||
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, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
|
||||
return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]
|
||||
@ -378,7 +378,7 @@ def model_lora_keys_unet(model, key_map={}):
|
||||
key_lora = k[len("diffusion_model."):-len(".weight")]
|
||||
key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format
|
||||
|
||||
if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux
|
||||
if isinstance(model, comfy.model_base.Flux) or isinstance(model, comfy.model_base.Chroma): #Diffusers lora Flux or a diffusers lora Chroma
|
||||
diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
|
||||
for k in diffusers_keys:
|
||||
if k.endswith(".weight"):
|
||||
|
||||
@ -37,6 +37,7 @@ import comfy.ldm.cosmos.model
|
||||
import comfy.ldm.lumina.model
|
||||
import comfy.ldm.wan.model
|
||||
import comfy.ldm.hunyuan3d.model
|
||||
import comfy.ldm.chroma.model
|
||||
|
||||
import comfy.model_management
|
||||
import comfy.patcher_extension
|
||||
@ -1056,3 +1057,64 @@ class Hunyuan3Dv2(BaseModel):
|
||||
if guidance is not None:
|
||||
out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
|
||||
return out
|
||||
|
||||
class Chroma(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma.model.Chroma)
|
||||
|
||||
def concat_cond(self, **kwargs):
|
||||
try:
|
||||
#Handle Flux control loras dynamically changing the img_in weight.
|
||||
num_channels = self.diffusion_model.img_in.weight.shape[1]
|
||||
except:
|
||||
#Some cases like tensorrt might not have the weights accessible
|
||||
num_channels = self.model_config.unet_config["in_channels"]
|
||||
|
||||
out_channels = self.model_config.unet_config["out_channels"]
|
||||
|
||||
if num_channels <= out_channels:
|
||||
return None
|
||||
|
||||
image = kwargs.get("concat_latent_image", None)
|
||||
noise = kwargs.get("noise", None)
|
||||
device = kwargs["device"]
|
||||
|
||||
if image is None:
|
||||
image = torch.zeros_like(noise)
|
||||
|
||||
image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
|
||||
image = utils.resize_to_batch_size(image, noise.shape[0])
|
||||
image = self.process_latent_in(image)
|
||||
if num_channels <= out_channels * 2:
|
||||
return image
|
||||
|
||||
#inpaint model
|
||||
mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
|
||||
if mask is None:
|
||||
mask = torch.ones_like(noise)[:, :1]
|
||||
|
||||
mask = torch.mean(mask, dim=1, keepdim=True)
|
||||
mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, "bilinear", "center")
|
||||
mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8)
|
||||
mask = utils.resize_to_batch_size(mask, noise.shape[0])
|
||||
return torch.cat((image, mask), dim=1)
|
||||
|
||||
|
||||
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)
|
||||
# upscale the attention mask, since now we
|
||||
attention_mask = kwargs.get("attention_mask", None)
|
||||
if attention_mask is not None:
|
||||
shape = kwargs["noise"].shape
|
||||
mask_ref_size = kwargs["attention_mask_img_shape"]
|
||||
# the model will pad to the patch size, and then divide
|
||||
# essentially dividing and rounding up
|
||||
(h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size))
|
||||
attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok))
|
||||
out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
|
||||
guidance = 0.0
|
||||
out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor((guidance,)))
|
||||
return out
|
||||
|
||||
@ -154,6 +154,32 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
|
||||
dit_config["guidance_embed"] = len(guidance_keys) > 0
|
||||
return dit_config
|
||||
|
||||
if '{}distilled_guidance_layer.0.norms.0.scale'.format(key_prefix) in state_dict_keys or '{}distilled_guidance_layer.norms.0.scale'.format(key_prefix) in state_dict_keys: #Chroma
|
||||
dit_config = {}
|
||||
dit_config["image_model"] = "chroma"
|
||||
dit_config["depth"] = 48
|
||||
dit_config["in_channels"] = 64
|
||||
patch_size = 2
|
||||
dit_config["patch_size"] = patch_size
|
||||
in_key = "{}img_in.weight".format(key_prefix)
|
||||
if in_key in state_dict_keys:
|
||||
dit_config["in_channels"] = state_dict[in_key].shape[1]
|
||||
dit_config["out_channels"] = 64
|
||||
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"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
|
||||
dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
|
||||
dit_config["axes_dim"] = [16, 56, 56]
|
||||
dit_config["theta"] = 10000
|
||||
dit_config["qkv_bias"] = True
|
||||
dit_config["in_dim"] = 64
|
||||
dit_config["out_dim"] = 3072
|
||||
dit_config["hidden_dim"] = 5120
|
||||
dit_config["n_layers"] = 5
|
||||
return dit_config
|
||||
|
||||
if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and '{}img_in.weight'.format(key_prefix) in state_dict_keys: #Flux
|
||||
dit_config = {}
|
||||
dit_config["image_model"] = "flux"
|
||||
|
||||
@ -41,6 +41,7 @@ import comfy.text_encoders.hunyuan_video
|
||||
import comfy.text_encoders.cosmos
|
||||
import comfy.text_encoders.lumina2
|
||||
import comfy.text_encoders.wan
|
||||
import comfy.text_encoders.chroma
|
||||
|
||||
import comfy.model_patcher
|
||||
import comfy.lora
|
||||
@ -702,6 +703,7 @@ class CLIPType(Enum):
|
||||
COSMOS = 11
|
||||
LUMINA2 = 12
|
||||
WAN = 13
|
||||
CHROMA = 14
|
||||
|
||||
|
||||
def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
|
||||
@ -810,6 +812,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
|
||||
clip_target.clip = comfy.text_encoders.wan.te(**t5xxl_detect(clip_data))
|
||||
clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer
|
||||
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
|
||||
elif clip_type == CLIPType.CHROMA:
|
||||
clip_target.clip = comfy.text_encoders.chroma.chroma_te(**t5xxl_detect(clip_data))
|
||||
clip_target.tokenizer = comfy.text_encoders.chroma.ChromaT5Tokenizer
|
||||
else: #CLIPType.MOCHI
|
||||
clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
|
||||
clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer
|
||||
|
||||
@ -17,6 +17,7 @@ import comfy.text_encoders.hunyuan_video
|
||||
import comfy.text_encoders.cosmos
|
||||
import comfy.text_encoders.lumina2
|
||||
import comfy.text_encoders.wan
|
||||
import comfy.text_encoders.chroma
|
||||
|
||||
from . import supported_models_base
|
||||
from . import latent_formats
|
||||
@ -1025,6 +1026,34 @@ class Hunyuan3Dv2mini(Hunyuan3Dv2):
|
||||
|
||||
latent_format = latent_formats.Hunyuan3Dv2mini
|
||||
|
||||
models = [LotusD, 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, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, Hunyuan3Dv2mini, Hunyuan3Dv2]
|
||||
class Chroma(supported_models_base.BASE):
|
||||
unet_config = {
|
||||
"image_model": "chroma",
|
||||
}
|
||||
|
||||
unet_extra_config = {
|
||||
}
|
||||
|
||||
sampling_settings = {
|
||||
"multiplier": 1.0,
|
||||
}
|
||||
|
||||
latent_format = comfy.latent_formats.Flux
|
||||
|
||||
memory_usage_factor = 3.2
|
||||
|
||||
supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
|
||||
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
out = model_base.Chroma(self, device=device)
|
||||
return out
|
||||
|
||||
def clip_target(self, state_dict={}):
|
||||
pref = self.text_encoder_key_prefix[0]
|
||||
t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
|
||||
return supported_models_base.ClipTarget(comfy.text_encoders.chroma.ChromaTokenizer, comfy.text_encoders.chroma.chroma_te(**t5_detect))
|
||||
|
||||
models = [LotusD, 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, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, Hunyuan3Dv2mini, Hunyuan3Dv2, Chroma]
|
||||
|
||||
models += [SVD_img2vid]
|
||||
|
||||
43
comfy/text_encoders/chroma.py
Normal file
43
comfy/text_encoders/chroma.py
Normal file
@ -0,0 +1,43 @@
|
||||
from comfy import sd1_clip
|
||||
import comfy.text_encoders.t5
|
||||
import os
|
||||
from transformers import T5TokenizerFast
|
||||
|
||||
|
||||
class T5XXLModel(sd1_clip.SDClipModel):
|
||||
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}):
|
||||
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
|
||||
t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None)
|
||||
if t5xxl_scaled_fp8 is not None:
|
||||
model_options = model_options.copy()
|
||||
model_options["scaled_fp8"] = t5xxl_scaled_fp8
|
||||
|
||||
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, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
|
||||
|
||||
|
||||
class ChromaT5XXL(sd1_clip.SD1ClipModel):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
|
||||
|
||||
|
||||
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, embedding_directory=embedding_directory, 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=1)
|
||||
|
||||
|
||||
class ChromaT5Tokenizer(sd1_clip.SD1Tokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
||||
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
|
||||
|
||||
|
||||
def chroma_te(dtype_t5=None, t5xxl_scaled_fp8=None):
|
||||
class ChromaTEModel_(ChromaT5XXL):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}):
|
||||
if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
|
||||
model_options = model_options.copy()
|
||||
model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
|
||||
if dtype is None:
|
||||
dtype = dtype_t5
|
||||
super().__init__(device=device, dtype=dtype, model_options=model_options)
|
||||
return ChromaTEModel_
|
||||
4
nodes.py
4
nodes.py
@ -917,7 +917,7 @@ class CLIPLoader:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
|
||||
"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan"], ),
|
||||
"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "chroma"], ),
|
||||
},
|
||||
"optional": {
|
||||
"device": (["default", "cpu"], {"advanced": True}),
|
||||
@ -948,6 +948,8 @@ class CLIPLoader:
|
||||
clip_type = comfy.sd.CLIPType.LUMINA2
|
||||
elif type == "wan":
|
||||
clip_type = comfy.sd.CLIPType.WAN
|
||||
elif type == "chroma":
|
||||
clip_type = comfy.sd.CLIPType.CHROMA
|
||||
else:
|
||||
clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user