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Initialize the unet directly on the target device.

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comfyanonymous 2023-07-29 14:51:56 -04:00
parent ad5866b02b
commit 4b957a0010
6 changed files with 110 additions and 103 deletions
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94
comfy/ldm/modules/attention.py
View File

@ -52,9 +52,9 @@ def init_(tensor):
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out, dtype=None):
def __init__(self, dim_in, dim_out, dtype=None, device=None):
super().__init__()
self.proj = comfy.ops.Linear(dim_in, dim_out * 2, dtype=dtype)
self.proj = comfy.ops.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
@ -62,19 +62,19 @@ class GEGLU(nn.Module):
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
comfy.ops.Linear(dim, inner_dim, dtype=dtype),
comfy.ops.Linear(dim, inner_dim, dtype=dtype, device=device),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim, dtype=dtype)
) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
comfy.ops.Linear(inner_dim, dim_out, dtype=dtype)
comfy.ops.Linear(inner_dim, dim_out, dtype=dtype, device=device)
)
def forward(self, x):
@ -90,8 +90,8 @@ def zero_module(module):
return module
def Normalize(in_channels, dtype=None):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype)
def Normalize(in_channels, dtype=None, device=None):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
class SpatialSelfAttention(nn.Module):
@ -148,7 +148,7 @@ class SpatialSelfAttention(nn.Module):
class CrossAttentionBirchSan(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@ -156,12 +156,12 @@ class CrossAttentionBirchSan(nn.Module):
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype),
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
@ -245,7 +245,7 @@ class CrossAttentionBirchSan(nn.Module):
class CrossAttentionDoggettx(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@ -253,12 +253,12 @@ class CrossAttentionDoggettx(nn.Module):
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype),
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
@ -343,7 +343,7 @@ class CrossAttentionDoggettx(nn.Module):
return self.to_out(r2)
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@ -351,12 +351,12 @@ class CrossAttention(nn.Module):
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype),
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
@ -399,7 +399,7 @@ class CrossAttention(nn.Module):
class MemoryEfficientCrossAttention(nn.Module):
# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None):
super().__init__()
print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
f"{heads} heads.")
@ -409,11 +409,11 @@ class MemoryEfficientCrossAttention(nn.Module):
self.heads = heads
self.dim_head = dim_head
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype), nn.Dropout(dropout))
self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None
def forward(self, x, context=None, value=None, mask=None):
@ -450,7 +450,7 @@ class MemoryEfficientCrossAttention(nn.Module):
return self.to_out(out)
class CrossAttentionPytorch(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@ -458,11 +458,11 @@ class CrossAttentionPytorch(nn.Module):
self.heads = heads
self.dim_head = dim_head
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype), nn.Dropout(dropout))
self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None
def forward(self, x, context=None, value=None, mask=None):
@ -508,17 +508,17 @@ else:
class BasicTransformerBlock(nn.Module):
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
disable_self_attn=False, dtype=None):
disable_self_attn=False, dtype=None, device=None):
super().__init__()
self.disable_self_attn = disable_self_attn
self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
context_dim=context_dim if self.disable_self_attn else None, dtype=dtype) # is a self-attention if not self.disable_self_attn
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype)
context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device) # is a self-attention if not self.disable_self_attn
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device)
self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim, dtype=dtype)
self.norm2 = nn.LayerNorm(dim, dtype=dtype)
self.norm3 = nn.LayerNorm(dim, dtype=dtype)
heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim, dtype=dtype, device=device)
self.norm2 = nn.LayerNorm(dim, dtype=dtype, device=device)
self.norm3 = nn.LayerNorm(dim, dtype=dtype, device=device)
self.checkpoint = checkpoint
self.n_heads = n_heads
self.d_head = d_head
@ -648,34 +648,34 @@ class SpatialTransformer(nn.Module):
def __init__(self, in_channels, n_heads, d_head,
depth=1, dropout=0., context_dim=None,
disable_self_attn=False, use_linear=False,
use_checkpoint=True, dtype=None):
use_checkpoint=True, dtype=None, device=None):
super().__init__()
if exists(context_dim) and not isinstance(context_dim, list):
context_dim = [context_dim] * depth
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = Normalize(in_channels, dtype=dtype)
self.norm = Normalize(in_channels, dtype=dtype, device=device)
if not use_linear:
self.proj_in = nn.Conv2d(in_channels,
inner_dim,
kernel_size=1,
stride=1,
padding=0, dtype=dtype)
padding=0, dtype=dtype, device=device)
else:
self.proj_in = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype)
self.proj_in = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device)
self.transformer_blocks = nn.ModuleList(
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype)
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device)
for d in range(depth)]
)
if not use_linear:
self.proj_out = nn.Conv2d(inner_dim,in_channels,
kernel_size=1,
stride=1,
padding=0, dtype=dtype)
padding=0, dtype=dtype, device=device)
else:
self.proj_out = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype)
self.proj_out = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device)
self.use_linear = use_linear
def forward(self, x, context=None, transformer_options={}):

80
comfy/ldm/modules/diffusionmodules/openaimodel.py
View File

@ -111,14 +111,14 @@ class Upsample(nn.Module):
upsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None):
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
if use_conv:
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype)
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device)
def forward(self, x, output_shape=None):
assert x.shape[1] == self.channels
@ -160,7 +160,7 @@ class Downsample(nn.Module):
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None):
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
@ -169,7 +169,7 @@ class Downsample(nn.Module):
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device
)
else:
assert self.channels == self.out_channels
@ -208,7 +208,8 @@ class ResBlock(TimestepBlock):
use_checkpoint=False,
up=False,
down=False,
dtype=None
dtype=None,
device=None,
):
super().__init__()
self.channels = channels
@ -220,19 +221,19 @@ class ResBlock(TimestepBlock):
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(
nn.GroupNorm(32, channels, dtype=dtype),
nn.GroupNorm(32, channels, dtype=dtype, device=device),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype),
conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
)
self.updown = up or down
if up:
self.h_upd = Upsample(channels, False, dims, dtype=dtype)
self.x_upd = Upsample(channels, False, dims, dtype=dtype)
self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device)
elif down:
self.h_upd = Downsample(channels, False, dims, dtype=dtype)
self.x_upd = Downsample(channels, False, dims, dtype=dtype)
self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device)
else:
self.h_upd = self.x_upd = nn.Identity()
@ -240,15 +241,15 @@ class ResBlock(TimestepBlock):
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype
2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device
),
)
self.out_layers = nn.Sequential(
nn.GroupNorm(32, self.out_channels, dtype=dtype),
nn.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype)
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
),
)
@ -256,10 +257,10 @@ class ResBlock(TimestepBlock):
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(
dims, channels, self.out_channels, 3, padding=1, dtype=dtype
dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device
)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1, dtype=dtype)
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
def forward(self, x, emb):
"""
@ -503,6 +504,7 @@ class UNetModel(nn.Module):
use_linear_in_transformer=False,
adm_in_channels=None,
transformer_depth_middle=None,
device=None,
):
super().__init__()
if use_spatial_transformer:
@ -564,9 +566,9 @@ class UNetModel(nn.Module):
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim, dtype=self.dtype),
linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim, dtype=self.dtype),
linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
)
if self.num_classes is not None:
@ -579,9 +581,9 @@ class UNetModel(nn.Module):
assert adm_in_channels is not None
self.label_emb = nn.Sequential(
nn.Sequential(
linear(adm_in_channels, time_embed_dim, dtype=self.dtype),
linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim, dtype=self.dtype),
linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
)
)
else:
@ -590,7 +592,7 @@ class UNetModel(nn.Module):
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype)
conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
)
]
)
@ -609,7 +611,8 @@ class UNetModel(nn.Module):
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype
dtype=self.dtype,
device=device,
)
]
ch = mult * model_channels
@ -638,7 +641,7 @@ class UNetModel(nn.Module):
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint, dtype=self.dtype
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
@ -657,11 +660,12 @@ class UNetModel(nn.Module):
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
dtype=self.dtype
dtype=self.dtype,
device=device,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype
ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device
)
)
)
@ -686,7 +690,8 @@ class UNetModel(nn.Module):
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype
dtype=self.dtype,
device=device,
),
AttentionBlock(
ch,
@ -697,7 +702,7 @@ class UNetModel(nn.Module):
) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint, dtype=self.dtype
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device
),
ResBlock(
ch,
@ -706,7 +711,8 @@ class UNetModel(nn.Module):
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype
dtype=self.dtype,
device=device,
),
)
self._feature_size += ch
@ -724,7 +730,8 @@ class UNetModel(nn.Module):
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype
dtype=self.dtype,
device=device,
)
]
ch = model_channels * mult
@ -753,7 +760,7 @@ class UNetModel(nn.Module):
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint, dtype=self.dtype
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device
)
)
if level and i == self.num_res_blocks[level]:
@ -768,24 +775,25 @@ class UNetModel(nn.Module):
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
up=True,
dtype=self.dtype
dtype=self.dtype,
device=device,
)
if resblock_updown
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype)
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
nn.GroupNorm(32, ch, dtype=self.dtype),
nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype)),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
)
if self.predict_codebook_ids:
self.id_predictor = nn.Sequential(
nn.GroupNorm(32, ch, dtype=self.dtype),
conv_nd(dims, model_channels, n_embed, 1),
nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
#nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
)

20
comfy/model_base.py
View File

@ -12,14 +12,14 @@ class ModelType(Enum):
V_PREDICTION = 2
class BaseModel(torch.nn.Module):
def __init__(self, model_config, model_type=ModelType.EPS):
def __init__(self, model_config, model_type=ModelType.EPS, device=None):
super().__init__()
unet_config = model_config.unet_config
self.latent_format = model_config.latent_format
self.model_config = model_config
self.register_schedule(given_betas=None, beta_schedule="linear", timesteps=1000, linear_start=0.00085, linear_end=0.012, cosine_s=8e-3)
self.diffusion_model = UNetModel(**unet_config)
self.diffusion_model = UNetModel(**unet_config, device=device)
self.model_type = model_type
self.adm_channels = unet_config.get("adm_in_channels", None)
if self.adm_channels is None:
@ -107,8 +107,8 @@ class BaseModel(torch.nn.Module):
class SD21UNCLIP(BaseModel):
def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION):
super().__init__(model_config, model_type)
def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None):
super().__init__(model_config, model_type, device=device)
self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config)
def encode_adm(self, **kwargs):
@ -143,13 +143,13 @@ class SD21UNCLIP(BaseModel):
return adm_out
class SDInpaint(BaseModel):
def __init__(self, model_config, model_type=ModelType.EPS):
super().__init__(model_config, model_type)
def __init__(self, model_config, model_type=ModelType.EPS, device=None):
super().__init__(model_config, model_type, device=device)
self.concat_keys = ("mask", "masked_image")
class SDXLRefiner(BaseModel):
def __init__(self, model_config, model_type=ModelType.EPS):
super().__init__(model_config, model_type)
def __init__(self, model_config, model_type=ModelType.EPS, device=None):
super().__init__(model_config, model_type, device=device)
self.embedder = Timestep(256)
def encode_adm(self, **kwargs):
@ -174,8 +174,8 @@ class SDXLRefiner(BaseModel):
return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
class SDXL(BaseModel):
def __init__(self, model_config, model_type=ModelType.EPS):
super().__init__(model_config, model_type)
def __init__(self, model_config, model_type=ModelType.EPS, device=None):
super().__init__(model_config, model_type, device=device)
self.embedder = Timestep(256)
def encode_adm(self, **kwargs):

3
comfy/sd.py
View File

@ -1169,8 +1169,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
offload_device = model_management.unet_offload_device()
model = model_config.get_model(sd, "model.diffusion_model.")
model = model.to(offload_device)
model = model_config.get_model(sd, "model.diffusion_model.", device=offload_device)
model.load_model_weights(sd, "model.diffusion_model.")
if output_vae:

8
comfy/supported_models.py
View File

@ -109,8 +109,8 @@ class SDXLRefiner(supported_models_base.BASE):
latent_format = latent_formats.SDXL
def get_model(self, state_dict, prefix=""):
return model_base.SDXLRefiner(self)
def get_model(self, state_dict, prefix="", device=None):
return model_base.SDXLRefiner(self, device=device)
def process_clip_state_dict(self, state_dict):
keys_to_replace = {}
@ -152,8 +152,8 @@ class SDXL(supported_models_base.BASE):
else:
return model_base.ModelType.EPS
def get_model(self, state_dict, prefix=""):
return model_base.SDXL(self, model_type=self.model_type(state_dict, prefix))
def get_model(self, state_dict, prefix="", device=None):
return model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device)
def process_clip_state_dict(self, state_dict):
keys_to_replace = {}

8
comfy/supported_models_base.py
View File

@ -53,13 +53,13 @@ class BASE:
for x in self.unet_extra_config:
self.unet_config[x] = self.unet_extra_config[x]
def get_model(self, state_dict, prefix=""):
def get_model(self, state_dict, prefix="", device=None):
if self.inpaint_model():
return model_base.SDInpaint(self, model_type=self.model_type(state_dict, prefix))
return model_base.SDInpaint(self, model_type=self.model_type(state_dict, prefix), device=device)
elif self.noise_aug_config is not None:
return model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix))
return model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device)
else:
return model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix))
return model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device)
def process_clip_state_dict(self, state_dict):
return state_dict