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Merge branch 'comfyanonymous:master' into feature/settings

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14
README.md
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@ -87,13 +87,13 @@ Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
Put your VAE in: models/vae
At the time of writing this pytorch has issues with python versions higher than 3.10 so make sure your python/pip versions are 3.10.
### AMD GPUs (Linux only)
AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.4.2```
This is the command to install the nightly with ROCm 5.5 that supports the 7000 series and might have some performance improvements:
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm5.5 -r requirements.txt```
### NVIDIA
@ -178,16 +178,6 @@ To use a textual inversion concepts/embeddings in a text prompt put them in the
```embedding:embedding_filename.pt```
### Fedora
To get python 3.10 on fedora:
```dnf install python3.10```
Then you can:
```python3.10 -m ensurepip```
This will let you use: pip3.10 to install all the dependencies.
## How to increase generation speed?

2
comfy/cli_args.py
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@ -59,12 +59,14 @@ attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", he
parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
vram_group = parser.add_mutually_exclusive_group()
vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
vram_group.add_argument("--normalvram", action="store_true", help="Used to force normal vram use if lowvram gets automatically enabled.")
vram_group.add_argument("--lowvram", action="store_true", help="Split the unet in parts to use less vram.")
vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")

94
comfy/ldm/modules/attention.py
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@ -51,9 +51,9 @@ def init_(tensor):
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
def __init__(self, dim_in, dim_out, dtype=None):
super().__init__()
self.proj = comfy.ops.Linear(dim_in, dim_out * 2)
self.proj = comfy.ops.Linear(dim_in, dim_out * 2, dtype=dtype)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
@ -61,19 +61,19 @@ class GEGLU(nn.Module):
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
comfy.ops.Linear(dim, inner_dim),
comfy.ops.Linear(dim, inner_dim, dtype=dtype),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
) if not glu else GEGLU(dim, inner_dim, dtype=dtype)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
comfy.ops.Linear(inner_dim, dim_out)
comfy.ops.Linear(inner_dim, dim_out, dtype=dtype)
)
def forward(self, x):
@ -89,8 +89,8 @@ def zero_module(module):
return module
def Normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
def Normalize(in_channels, dtype=None):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype)
class SpatialSelfAttention(nn.Module):
@ -147,7 +147,7 @@ class SpatialSelfAttention(nn.Module):
class CrossAttentionBirchSan(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@ -155,12 +155,12 @@ 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)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False)
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_out = nn.Sequential(
comfy.ops.Linear(inner_dim, query_dim),
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype),
nn.Dropout(dropout)
)
@ -244,7 +244,7 @@ class CrossAttentionBirchSan(nn.Module):
class CrossAttentionDoggettx(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@ -252,12 +252,12 @@ 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)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False)
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_out = nn.Sequential(
comfy.ops.Linear(inner_dim, query_dim),
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype),
nn.Dropout(dropout)
)
@ -342,7 +342,7 @@ 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.):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@ -350,12 +350,12 @@ 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)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False)
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_out = nn.Sequential(
comfy.ops.Linear(inner_dim, query_dim),
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype),
nn.Dropout(dropout)
)
@ -398,7 +398,7 @@ 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):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=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.")
@ -408,11 +408,11 @@ class MemoryEfficientCrossAttention(nn.Module):
self.heads = heads
self.dim_head = dim_head
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False)
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_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim), nn.Dropout(dropout))
self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None
def forward(self, x, context=None, value=None, mask=None):
@ -449,7 +449,7 @@ 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.):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
@ -457,11 +457,11 @@ class CrossAttentionPytorch(nn.Module):
self.heads = heads
self.dim_head = dim_head
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False)
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False)
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_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim), nn.Dropout(dropout))
self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None
def forward(self, x, context=None, value=None, mask=None):
@ -507,17 +507,17 @@ 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):
disable_self_attn=False, dtype=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) # is a self-attention if not self.disable_self_attn
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
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)
self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(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)
self.checkpoint = checkpoint
def forward(self, x, context=None, transformer_options={}):
@ -588,34 +588,34 @@ 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):
use_checkpoint=True, dtype=None):
super().__init__()
if exists(context_dim) and not isinstance(context_dim, list):
context_dim = [context_dim]
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = Normalize(in_channels)
self.norm = Normalize(in_channels, dtype=dtype)
if not use_linear:
self.proj_in = nn.Conv2d(in_channels,
inner_dim,
kernel_size=1,
stride=1,
padding=0)
padding=0, dtype=dtype)
else:
self.proj_in = comfy.ops.Linear(in_channels, inner_dim)
self.proj_in = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype)
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)
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype)
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)
padding=0, dtype=dtype)
else:
self.proj_out = comfy.ops.Linear(in_channels, inner_dim)
self.proj_out = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype)
self.use_linear = use_linear
def forward(self, x, context=None, transformer_options={}):

61
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):
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=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)
self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype)
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):
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=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
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype
)
else:
assert self.channels == self.out_channels
@ -208,6 +208,7 @@ class ResBlock(TimestepBlock):
use_checkpoint=False,
up=False,
down=False,
dtype=None
):
super().__init__()
self.channels = channels
@ -219,19 +220,19 @@ class ResBlock(TimestepBlock):
self.use_scale_shift_norm = use_scale_shift_norm
self.in_layers = nn.Sequential(
normalization(channels),
normalization(channels, dtype=dtype),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, 3, padding=1),
conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype),
)
self.updown = up or down
if up:
self.h_upd = Upsample(channels, False, dims)
self.x_upd = Upsample(channels, False, dims)
self.h_upd = Upsample(channels, False, dims, dtype=dtype)
self.x_upd = Upsample(channels, False, dims, dtype=dtype)
elif down:
self.h_upd = Downsample(channels, False, dims)
self.x_upd = Downsample(channels, False, dims)
self.h_upd = Downsample(channels, False, dims, dtype=dtype)
self.x_upd = Downsample(channels, False, dims, dtype=dtype)
else:
self.h_upd = self.x_upd = nn.Identity()
@ -239,15 +240,15 @@ class ResBlock(TimestepBlock):
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
normalization(self.out_channels, dtype=dtype),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype)
),
)
@ -255,10 +256,10 @@ class ResBlock(TimestepBlock):
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(
dims, channels, self.out_channels, 3, padding=1
dims, channels, self.out_channels, 3, padding=1, dtype=dtype
)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1, dtype=dtype)
def forward(self, x, emb):
"""
@ -558,9 +559,9 @@ class UNetModel(nn.Module):
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
linear(model_channels, time_embed_dim, dtype=self.dtype),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
linear(time_embed_dim, time_embed_dim, dtype=self.dtype),
)
if self.num_classes is not None:
@ -573,9 +574,9 @@ 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),
linear(adm_in_channels, time_embed_dim, dtype=self.dtype),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
linear(time_embed_dim, time_embed_dim, dtype=self.dtype),
)
)
else:
@ -584,7 +585,7 @@ class UNetModel(nn.Module):
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1)
conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype)
)
]
)
@ -603,6 +604,7 @@ class UNetModel(nn.Module):
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype
)
]
ch = mult * model_channels
@ -631,7 +633,7 @@ class UNetModel(nn.Module):
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint
use_checkpoint=use_checkpoint, dtype=self.dtype
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
@ -650,10 +652,11 @@ class UNetModel(nn.Module):
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
dtype=self.dtype
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch
ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype
)
)
)
@ -678,6 +681,7 @@ class UNetModel(nn.Module):
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype
),
AttentionBlock(
ch,
@ -688,7 +692,7 @@ class UNetModel(nn.Module):
) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint
use_checkpoint=use_checkpoint, dtype=self.dtype
),
ResBlock(
ch,
@ -697,6 +701,7 @@ class UNetModel(nn.Module):
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype
),
)
self._feature_size += ch
@ -714,6 +719,7 @@ class UNetModel(nn.Module):
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
dtype=self.dtype
)
]
ch = model_channels * mult
@ -742,7 +748,7 @@ class UNetModel(nn.Module):
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint
use_checkpoint=use_checkpoint, dtype=self.dtype
)
)
if level and i == self.num_res_blocks[level]:
@ -757,18 +763,19 @@ class UNetModel(nn.Module):
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
up=True,
dtype=self.dtype
)
if resblock_updown
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
normalization(ch, dtype=self.dtype),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype)),
)
if self.predict_codebook_ids:
self.id_predictor = nn.Sequential(

4
comfy/ldm/modules/diffusionmodules/util.py
View File

@ -206,13 +206,13 @@ def mean_flat(tensor):
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def normalization(channels):
def normalization(channels, dtype=None):
"""
Make a standard normalization layer.
:param channels: number of input channels.
:return: an nn.Module for normalization.
"""
return GroupNorm32(32, channels)
return GroupNorm32(32, channels, dtype=dtype)
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.

8
comfy/model_management.py
View File

@ -151,7 +151,7 @@ if args.lowvram:
lowvram_available = True
elif args.novram:
set_vram_to = VRAMState.NO_VRAM
elif args.highvram:
elif args.highvram or args.gpu_only:
vram_state = VRAMState.HIGH_VRAM
FORCE_FP32 = False
@ -307,6 +307,12 @@ def unload_if_low_vram(model):
return model.cpu()
return model
def text_encoder_device():
if args.gpu_only:
return get_torch_device()
else:
return torch.device("cpu")
def get_autocast_device(dev):
if hasattr(dev, 'type'):
return dev.type

4
comfy/sd.py
View File

@ -467,7 +467,11 @@ class CLIP:
clip = sd1_clip.SD1ClipModel
tokenizer = sd1_clip.SD1Tokenizer
self.device = model_management.text_encoder_device()
params["device"] = self.device
self.cond_stage_model = clip(**(params))
self.cond_stage_model = self.cond_stage_model.to(self.device)
self.tokenizer = tokenizer(embedding_directory=embedding_directory)
self.patcher = ModelPatcher(self.cond_stage_model)
self.layer_idx = None

2
comfy/sd1_clip.py
View File

@ -20,7 +20,7 @@ class ClipTokenWeightEncoder:
output += [z]
if (len(output) == 0):
return self.encode(self.empty_tokens)
return torch.cat(output, dim=-2)
return torch.cat(output, dim=-2).cpu()
class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
"""Uses the CLIP transformer encoder for text (from huggingface)"""

13
server.py
View File

@ -30,6 +30,11 @@ import comfy.model_management
class BinaryEventTypes:
PREVIEW_IMAGE = 1
async def send_socket_catch_exception(function, message):
try:
await function(message)
except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
print("send error:", err)
@web.middleware
async def cache_control(request: web.Request, handler):
@ -489,18 +494,18 @@ class PromptServer():
if sid is None:
for ws in self.sockets.values():
await ws.send_bytes(message)
await send_socket_catch_exception(ws.send_bytes, message)
elif sid in self.sockets:
await self.sockets[sid].send_bytes(message)
await send_socket_catch_exception(self.sockets[sid].send_bytes, message)
async def send_json(self, event, data, sid=None):
message = {"type": event, "data": data}
if sid is None:
for ws in self.sockets.values():
await ws.send_json(message)
await send_socket_catch_exception(ws.send_json, message)
elif sid in self.sockets:
await self.sockets[sid].send_json(message)
await send_socket_catch_exception(self.sockets[sid].send_json, message)
def send_sync(self, event, data, sid=None):
self.loop.call_soon_threadsafe(