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Merge remote-tracking branch 'origin/master' into group-nodes

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pythongosssss 2023-10-16 12:49:00 +01:00
commit 323c3107c8
20 changed files with 304 additions and 432 deletions
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1
README.md
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@ -46,6 +46,7 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
| Ctrl + S | Save workflow |
| Ctrl + O | Load workflow |
| Ctrl + A | Select all nodes |
| Alt + C | Collapse/uncollapse selected nodes |
| Ctrl + M | Mute/unmute selected nodes |
| Ctrl + B | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) |
| Delete/Backspace | Delete selected nodes |

6
comfy/cldm/cldm.py
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@ -34,8 +34,7 @@ class ControlNet(nn.Module):
dims=2,
num_classes=None,
use_checkpoint=False,
use_fp16=False,
use_bf16=False,
dtype=torch.float32,
num_heads=-1,
num_head_channels=-1,
num_heads_upsample=-1,
@ -108,8 +107,7 @@ class ControlNet(nn.Module):
self.conv_resample = conv_resample
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.dtype = th.bfloat16 if use_bf16 else self.dtype
self.dtype = dtype
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample

2
comfy/cli_args.py
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@ -53,6 +53,8 @@ fp_group = parser.add_mutually_exclusive_group()
fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
parser.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.")
fpvae_group = parser.add_mutually_exclusive_group()
fpvae_group.add_argument("--fp16-vae", action="store_true", help="Run the VAE in fp16, might cause black images.")
fpvae_group.add_argument("--fp32-vae", action="store_true", help="Run the VAE in full precision fp32.")

11
comfy/controlnet.py
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@ -292,8 +292,8 @@ def load_controlnet(ckpt_path, model=None):
controlnet_config = None
if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
use_fp16 = comfy.model_management.should_use_fp16()
controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16)
unet_dtype = comfy.model_management.unet_dtype()
controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, unet_dtype)
diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config)
diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
@ -353,8 +353,8 @@ def load_controlnet(ckpt_path, model=None):
return net
if controlnet_config is None:
use_fp16 = comfy.model_management.should_use_fp16()
controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, use_fp16, True).unet_config
unet_dtype = comfy.model_management.unet_dtype()
controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config
controlnet_config.pop("out_channels")
controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
@ -383,8 +383,7 @@ def load_controlnet(ckpt_path, model=None):
missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
print(missing, unexpected)
if use_fp16:
control_model = control_model.half()
control_model = control_model.to(unet_dtype)
global_average_pooling = False
filename = os.path.splitext(ckpt_path)[0]

538
comfy/ldm/modules/attention.py
View File

@ -94,253 +94,220 @@ def zero_module(module):
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)
def attention_basic(q, k, v, heads, mask=None):
h = heads
scale = (q.shape[-1] // heads) ** -0.5
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
class SpatialSelfAttention(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
# force cast to fp32 to avoid overflowing
if _ATTN_PRECISION =="fp32":
with torch.autocast(enabled=False, device_type = 'cuda'):
q, k = q.float(), k.float()
sim = einsum('b i d, b j d -> b i j', q, k) * scale
else:
sim = einsum('b i d, b j d -> b i j', q, k) * scale
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
del q, k
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
if exists(mask):
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
# compute attention
b,c,h,w = q.shape
q = rearrange(q, 'b c h w -> b (h w) c')
k = rearrange(k, 'b c h w -> b c (h w)')
w_ = torch.einsum('bij,bjk->bik', q, k)
# attention, what we cannot get enough of
sim = sim.softmax(dim=-1)
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = rearrange(v, 'b c h w -> b c (h w)')
w_ = rearrange(w_, 'b i j -> b j i')
h_ = torch.einsum('bij,bjk->bik', v, w_)
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
h_ = self.proj_out(h_)
return x+h_
out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
return out
class CrossAttentionBirchSan(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
def attention_sub_quad(query, key, value, heads, mask=None):
scale = (query.shape[-1] // heads) ** -0.5
query = query.unflatten(-1, (heads, -1)).transpose(1,2).flatten(end_dim=1)
key_t = key.transpose(1,2).unflatten(1, (heads, -1)).flatten(end_dim=1)
del key
value = value.unflatten(-1, (heads, -1)).transpose(1,2).flatten(end_dim=1)
self.scale = dim_head ** -0.5
self.heads = heads
dtype = query.dtype
upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
if upcast_attention:
bytes_per_token = torch.finfo(torch.float32).bits//8
else:
bytes_per_token = torch.finfo(query.dtype).bits//8
batch_x_heads, q_tokens, _ = query.shape
_, _, k_tokens = key_t.shape
qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
self.to_out = nn.Sequential(
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD
def forward(self, x, context=None, value=None, mask=None):
h = self.heads
kv_chunk_size_min = None
query = self.to_q(x)
context = default(context, x)
key = self.to_k(context)
if value is not None:
value = self.to_v(value)
else:
value = self.to_v(context)
#not sure at all about the math here
#TODO: tweak this
if mem_free_total > 8192 * 1024 * 1024 * 1.3:
query_chunk_size_x = 1024 * 4
elif mem_free_total > 4096 * 1024 * 1024 * 1.3:
query_chunk_size_x = 1024 * 2
else:
query_chunk_size_x = 1024
kv_chunk_size_min_x = None
kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 2.0) // 1024) * 1024
if kv_chunk_size_x < 1024:
kv_chunk_size_x = None
del context, x
if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
# the big matmul fits into our memory limit; do everything in 1 chunk,
# i.e. send it down the unchunked fast-path
query_chunk_size = q_tokens
kv_chunk_size = k_tokens
else:
query_chunk_size = query_chunk_size_x
kv_chunk_size = kv_chunk_size_x
kv_chunk_size_min = kv_chunk_size_min_x
query = query.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
key_t = key.transpose(1,2).unflatten(1, (self.heads, -1)).flatten(end_dim=1)
del key
value = value.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
hidden_states = efficient_dot_product_attention(
query,
key_t,
value,
query_chunk_size=query_chunk_size,
kv_chunk_size=kv_chunk_size,
kv_chunk_size_min=kv_chunk_size_min,
use_checkpoint=False,
upcast_attention=upcast_attention,
)
dtype = query.dtype
upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
if upcast_attention:
bytes_per_token = torch.finfo(torch.float32).bits//8
else:
bytes_per_token = torch.finfo(query.dtype).bits//8
batch_x_heads, q_tokens, _ = query.shape
_, _, k_tokens = key_t.shape
qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
hidden_states = hidden_states.to(dtype)
mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
return hidden_states
chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD
def attention_split(q, k, v, heads, mask=None):
scale = (q.shape[-1] // heads) ** -0.5
h = heads
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
kv_chunk_size_min = None
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
#not sure at all about the math here
#TODO: tweak this
if mem_free_total > 8192 * 1024 * 1024 * 1.3:
query_chunk_size_x = 1024 * 4
elif mem_free_total > 4096 * 1024 * 1024 * 1.3:
query_chunk_size_x = 1024 * 2
else:
query_chunk_size_x = 1024
kv_chunk_size_min_x = None
kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 2.0) // 1024) * 1024
if kv_chunk_size_x < 1024:
kv_chunk_size_x = None
mem_free_total = model_management.get_free_memory(q.device)
if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
# the big matmul fits into our memory limit; do everything in 1 chunk,
# i.e. send it down the unchunked fast-path
query_chunk_size = q_tokens
kv_chunk_size = k_tokens
else:
query_chunk_size = query_chunk_size_x
kv_chunk_size = kv_chunk_size_x
kv_chunk_size_min = kv_chunk_size_min_x
hidden_states = efficient_dot_product_attention(
query,
key_t,
value,
query_chunk_size=query_chunk_size,
kv_chunk_size=kv_chunk_size,
kv_chunk_size_min=kv_chunk_size_min,
use_checkpoint=self.training,
upcast_attention=upcast_attention,
)
hidden_states = hidden_states.to(dtype)
hidden_states = hidden_states.unflatten(0, (-1, self.heads)).transpose(1,2).flatten(start_dim=2)
out_proj, dropout = self.to_out
hidden_states = out_proj(hidden_states)
hidden_states = dropout(hidden_states)
return hidden_states
gb = 1024 ** 3
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
modifier = 3 if q.element_size() == 2 else 2.5
mem_required = tensor_size * modifier
steps = 1
class CrossAttentionDoggettx(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
self.scale = dim_head ** -0.5
self.heads = heads
if steps > 64:
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
def forward(self, x, context=None, value=None, mask=None):
h = self.heads
q_in = self.to_q(x)
context = default(context, x)
k_in = self.to_k(context)
if value is not None:
v_in = self.to_v(value)
del value
else:
v_in = self.to_v(context)
del context, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
del q_in, k_in, v_in
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
mem_free_total = model_management.get_free_memory(q.device)
gb = 1024 ** 3
tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
modifier = 3 if q.element_size() == 2 else 2.5
mem_required = tensor_size * modifier
steps = 1
if mem_required > mem_free_total:
steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
if steps > 64:
max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
# print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
first_op_done = False
cleared_cache = False
while True:
try:
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
if _ATTN_PRECISION =="fp32":
with torch.autocast(enabled=False, device_type = 'cuda'):
s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * self.scale
else:
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
first_op_done = True
s2 = s1.softmax(dim=-1).to(v.dtype)
del s1
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
del s2
break
except model_management.OOM_EXCEPTION as e:
if first_op_done == False:
model_management.soft_empty_cache(True)
if cleared_cache == False:
cleared_cache = True
print("out of memory error, emptying cache and trying again")
continue
steps *= 2
if steps > 64:
raise e
print("out of memory error, increasing steps and trying again", steps)
# print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
first_op_done = False
cleared_cache = False
while True:
try:
slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
if _ATTN_PRECISION =="fp32":
with torch.autocast(enabled=False, device_type = 'cuda'):
s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale
else:
s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale
first_op_done = True
s2 = s1.softmax(dim=-1).to(v.dtype)
del s1
r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
del s2
break
except model_management.OOM_EXCEPTION as e:
if first_op_done == False:
model_management.soft_empty_cache(True)
if cleared_cache == False:
cleared_cache = True
print("out of memory error, emptying cache and trying again")
continue
steps *= 2
if steps > 64:
raise e
print("out of memory error, increasing steps and trying again", steps)
else:
raise e
del q, k, v
del q, k, v
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
return r2
return self.to_out(r2)
def attention_xformers(q, k, v, heads, mask=None):
b, _, _ = q.shape
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, t.shape[1], heads, -1)
.permute(0, 2, 1, 3)
.reshape(b * heads, t.shape[1], -1)
.contiguous(),
(q, k, v),
)
# actually compute the attention, what we cannot get enough of
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
if exists(mask):
raise NotImplementedError
out = (
out.unsqueeze(0)
.reshape(b, heads, out.shape[1], -1)
.permute(0, 2, 1, 3)
.reshape(b, out.shape[1], -1)
)
return out
def attention_pytorch(q, k, v, heads, mask=None):
b, _, dim_head = q.shape
dim_head //= heads
q, k, v = map(
lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
(q, k, v),
)
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
if exists(mask):
raise NotImplementedError
out = (
out.transpose(1, 2).reshape(b, -1, heads * dim_head)
)
return out
optimized_attention = attention_basic
if model_management.xformers_enabled():
print("Using xformers cross attention")
optimized_attention = attention_xformers
elif model_management.pytorch_attention_enabled():
print("Using pytorch cross attention")
optimized_attention = attention_pytorch
else:
if args.use_split_cross_attention:
print("Using split optimization for cross attention")
optimized_attention = attention_split
else:
print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
optimized_attention = attention_sub_quad
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
@ -348,62 +315,6 @@ class CrossAttention(nn.Module):
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
nn.Dropout(dropout)
)
def forward(self, x, context=None, value=None, mask=None):
h = self.heads
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
if value is not None:
v = self.to_v(value)
del value
else:
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
# force cast to fp32 to avoid overflowing
if _ATTN_PRECISION =="fp32":
with torch.autocast(enabled=False, device_type = 'cuda'):
q, k = q.float(), k.float()
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
else:
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
del q, k
if exists(mask):
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
sim = sim.softmax(dim=-1)
out = einsum('b i j, b j d -> b i d', sim, v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
return self.to_out(out)
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, device=None, operations=comfy.ops):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.heads = heads
self.dim_head = dim_head
@ -412,7 +323,6 @@ class MemoryEfficientCrossAttention(nn.Module):
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(operations.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):
q = self.to_q(x)
@ -424,85 +334,9 @@ class MemoryEfficientCrossAttention(nn.Module):
else:
v = self.to_v(context)
b, _, _ = q.shape
q, k, v = map(
lambda t: t.unsqueeze(3)
.reshape(b, t.shape[1], self.heads, self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b * self.heads, t.shape[1], self.dim_head)
.contiguous(),
(q, k, v),
)
# actually compute the attention, what we cannot get enough of
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
if exists(mask):
raise NotImplementedError
out = (
out.unsqueeze(0)
.reshape(b, self.heads, out.shape[1], self.dim_head)
.permute(0, 2, 1, 3)
.reshape(b, out.shape[1], self.heads * self.dim_head)
)
out = optimized_attention(q, k, v, self.heads, mask)
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, device=None, operations=comfy.ops):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.heads = heads
self.dim_head = dim_head
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.to_out = nn.Sequential(operations.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):
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
if value is not None:
v = self.to_v(value)
del value
else:
v = self.to_v(context)
b, _, _ = q.shape
q, k, v = map(
lambda t: t.view(b, -1, self.heads, self.dim_head).transpose(1, 2),
(q, k, v),
)
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
if exists(mask):
raise NotImplementedError
out = (
out.transpose(1, 2).reshape(b, -1, self.heads * self.dim_head)
)
return self.to_out(out)
if model_management.xformers_enabled():
print("Using xformers cross attention")
CrossAttention = MemoryEfficientCrossAttention
elif model_management.pytorch_attention_enabled():
print("Using pytorch cross attention")
CrossAttention = CrossAttentionPytorch
else:
if args.use_split_cross_attention:
print("Using split optimization for cross attention")
CrossAttention = CrossAttentionDoggettx
else:
print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
CrossAttention = CrossAttentionBirchSan
class BasicTransformerBlock(nn.Module):
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,

15
comfy/ldm/modules/diffusionmodules/model.py
View File

@ -6,7 +6,6 @@ import numpy as np
from einops import rearrange
from typing import Optional, Any
from ..attention import MemoryEfficientCrossAttention
from comfy import model_management
import comfy.ops
@ -352,20 +351,11 @@ class MemoryEfficientAttnBlockPytorch(nn.Module):
out = self.proj_out(out)
return x+out
class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
def forward(self, x, context=None, mask=None):
b, c, h, w = x.shape
x = rearrange(x, 'b c h w -> b (h w) c')
out = super().forward(x, context=context, mask=mask)
out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
return x + out
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
if model_management.xformers_enabled_vae() and attn_type == "vanilla":
attn_type = "vanilla-xformers"
if model_management.pytorch_attention_enabled() and attn_type == "vanilla":
elif model_management.pytorch_attention_enabled() and attn_type == "vanilla":
attn_type = "vanilla-pytorch"
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
if attn_type == "vanilla":
@ -376,9 +366,6 @@ def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
return MemoryEfficientAttnBlock(in_channels)
elif attn_type == "vanilla-pytorch":
return MemoryEfficientAttnBlockPytorch(in_channels)
elif type == "memory-efficient-cross-attn":
attn_kwargs["query_dim"] = in_channels
return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
elif attn_type == "none":
return nn.Identity(in_channels)
else:

6
comfy/ldm/modules/diffusionmodules/openaimodel.py
View File

@ -296,8 +296,7 @@ class UNetModel(nn.Module):
dims=2,
num_classes=None,
use_checkpoint=False,
use_fp16=False,
use_bf16=False,
dtype=th.float32,
num_heads=-1,
num_head_channels=-1,
num_heads_upsample=-1,
@ -370,8 +369,7 @@ class UNetModel(nn.Module):
self.conv_resample = conv_resample
self.num_classes = num_classes
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.dtype = th.bfloat16 if use_bf16 else self.dtype
self.dtype = dtype
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample

32
comfy/model_detection.py
View File

@ -14,7 +14,7 @@ def count_blocks(state_dict_keys, prefix_string):
count += 1
return count
def detect_unet_config(state_dict, key_prefix, use_fp16):
def detect_unet_config(state_dict, key_prefix, dtype):
state_dict_keys = list(state_dict.keys())
unet_config = {
@ -32,7 +32,7 @@ def detect_unet_config(state_dict, key_prefix, use_fp16):
else:
unet_config["adm_in_channels"] = None
unet_config["use_fp16"] = use_fp16
unet_config["dtype"] = dtype
model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]
in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]
@ -116,15 +116,15 @@ def model_config_from_unet_config(unet_config):
print("no match", unet_config)
return None
def model_config_from_unet(state_dict, unet_key_prefix, use_fp16, use_base_if_no_match=False):
unet_config = detect_unet_config(state_dict, unet_key_prefix, use_fp16)
def model_config_from_unet(state_dict, unet_key_prefix, dtype, use_base_if_no_match=False):
unet_config = detect_unet_config(state_dict, unet_key_prefix, dtype)
model_config = model_config_from_unet_config(unet_config)
if model_config is None and use_base_if_no_match:
return comfy.supported_models_base.BASE(unet_config)
else:
return model_config
def unet_config_from_diffusers_unet(state_dict, use_fp16):
def unet_config_from_diffusers_unet(state_dict, dtype):
match = {}
attention_resolutions = []
@ -147,47 +147,47 @@ def unet_config_from_diffusers_unet(state_dict, use_fp16):
match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4],
'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64}
SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2560, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 384,
'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384,
'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 4, 4, 0], 'channel_mult': [1, 2, 4, 4],
'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280, "num_head_channels": 64}
SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'adm_in_channels': None, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64}
SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2048, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64}
SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 1536, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024}
SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'adm_in_channels': None, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, "num_heads": 8}
SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': 2, 'attention_resolutions': [4], 'transformer_depth': [0, 0, 1], 'channel_mult': [1, 2, 4],
'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64}
SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': 2, 'attention_resolutions': [], 'transformer_depth': [0, 0, 0], 'channel_mult': [1, 2, 4],
'transformer_depth_middle': 0, 'use_linear_in_transformer': True, "num_head_channels": 64, 'context_dim': 1}
SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 9, 'model_channels': 320,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,
'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4],
'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64}
@ -203,8 +203,8 @@ def unet_config_from_diffusers_unet(state_dict, use_fp16):
return unet_config
return None
def model_config_from_diffusers_unet(state_dict, use_fp16):
unet_config = unet_config_from_diffusers_unet(state_dict, use_fp16)
def model_config_from_diffusers_unet(state_dict, dtype):
unet_config = unet_config_from_diffusers_unet(state_dict, dtype)
if unet_config is not None:
return model_config_from_unet_config(unet_config)
return None

21
comfy/model_management.py
View File

@ -154,14 +154,18 @@ def is_nvidia():
return True
return False
ENABLE_PYTORCH_ATTENTION = args.use_pytorch_cross_attention
ENABLE_PYTORCH_ATTENTION = False
if args.use_pytorch_cross_attention:
ENABLE_PYTORCH_ATTENTION = True
XFORMERS_IS_AVAILABLE = False
VAE_DTYPE = torch.float32
try:
if is_nvidia():
torch_version = torch.version.__version__
if int(torch_version[0]) >= 2:
if ENABLE_PYTORCH_ATTENTION == False and XFORMERS_IS_AVAILABLE == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
ENABLE_PYTORCH_ATTENTION = True
if torch.cuda.is_bf16_supported():
VAE_DTYPE = torch.bfloat16
@ -186,7 +190,6 @@ if ENABLE_PYTORCH_ATTENTION:
torch.backends.cuda.enable_math_sdp(True)
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_mem_efficient_sdp(True)
XFORMERS_IS_AVAILABLE = False
if args.lowvram:
set_vram_to = VRAMState.LOW_VRAM
@ -354,6 +357,8 @@ def load_models_gpu(models, memory_required=0):
current_loaded_models.insert(0, current_loaded_models.pop(index))
models_already_loaded.append(loaded_model)
else:
if hasattr(x, "model"):
print(f"Requested to load {x.model.__class__.__name__}")
models_to_load.append(loaded_model)
if len(models_to_load) == 0:
@ -363,7 +368,7 @@ def load_models_gpu(models, memory_required=0):
free_memory(extra_mem, d, models_already_loaded)
return
print("loading new")
print(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
total_memory_required = {}
for loaded_model in models_to_load:
@ -405,7 +410,6 @@ def load_model_gpu(model):
def cleanup_models():
to_delete = []
for i in range(len(current_loaded_models)):
print(sys.getrefcount(current_loaded_models[i].model))
if sys.getrefcount(current_loaded_models[i].model) <= 2:
to_delete = [i] + to_delete
@ -444,6 +448,13 @@ def unet_inital_load_device(parameters, dtype):
else:
return cpu_dev
def unet_dtype(device=None, model_params=0):
if args.bf16_unet:
return torch.bfloat16
if should_use_fp16(device=device, model_params=model_params):
return torch.float16
return torch.float32
def text_encoder_offload_device():
if args.gpu_only:
return get_torch_device()

20
comfy/sd.py
View File

@ -327,7 +327,9 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
if "params" in model_config_params["unet_config"]:
unet_config = model_config_params["unet_config"]["params"]
if "use_fp16" in unet_config:
fp16 = unet_config["use_fp16"]
fp16 = unet_config.pop("use_fp16")
if fp16:
unet_config["dtype"] = torch.float16
noise_aug_config = None
if "noise_aug_config" in model_config_params:
@ -405,12 +407,12 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
clip_target = None
parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.")
fp16 = model_management.should_use_fp16(model_params=parameters)
unet_dtype = model_management.unet_dtype(model_params=parameters)
class WeightsLoader(torch.nn.Module):
pass
model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", fp16)
model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype)
if model_config is None:
raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
@ -418,12 +420,8 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
if output_clipvision:
clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
dtype = torch.float32
if fp16:
dtype = torch.float16
if output_model:
inital_load_device = model_management.unet_inital_load_device(parameters, dtype)
inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
offload_device = model_management.unet_offload_device()
model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device)
model.load_model_weights(sd, "model.diffusion_model.")
@ -458,15 +456,15 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
def load_unet(unet_path): #load unet in diffusers format
sd = comfy.utils.load_torch_file(unet_path)
parameters = comfy.utils.calculate_parameters(sd)
fp16 = model_management.should_use_fp16(model_params=parameters)
unet_dtype = model_management.unet_dtype(model_params=parameters)
if "input_blocks.0.0.weight" in sd: #ldm
model_config = model_detection.model_config_from_unet(sd, "", fp16)
model_config = model_detection.model_config_from_unet(sd, "", unet_dtype)
if model_config is None:
raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
new_sd = sd
else: #diffusers
model_config = model_detection.model_config_from_diffusers_unet(sd, fp16)
model_config = model_detection.model_config_from_diffusers_unet(sd, unet_dtype)
if model_config is None:
print("ERROR UNSUPPORTED UNET", unet_path)
return None

4
comfy/utils.py
View File

@ -408,6 +408,10 @@ def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_am
output[b:b+1] = out/out_div
return output
PROGRESS_BAR_ENABLED = True
def set_progress_bar_enabled(enabled):
global PROGRESS_BAR_ENABLED
PROGRESS_BAR_ENABLED = enabled
PROGRESS_BAR_HOOK = None
def set_progress_bar_global_hook(function):

3
comfy_extras/nodes_custom_sampler.py
View File

@ -3,6 +3,7 @@ import comfy.sample
from comfy.k_diffusion import sampling as k_diffusion_sampling
import latent_preview
import torch
import comfy.utils
class BasicScheduler:
@ -219,7 +220,7 @@ class SamplerCustom:
x0_output = {}
callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
disable_pbar = False
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
out = latent.copy()

12
comfy_extras/nodes_mask.py
View File

@ -240,8 +240,8 @@ class MaskComposite:
right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))
visible_width, visible_height = (right - left, bottom - top,)
source_portion = source[:visible_height, :visible_width]
destination_portion = destination[top:bottom, left:right]
source_portion = source[:, :visible_height, :visible_width]
destination_portion = destination[:, top:bottom, left:right]
if operation == "multiply":
output[:, top:bottom, left:right] = destination_portion * source_portion
@ -282,10 +282,10 @@ class FeatherMask:
def feather(self, mask, left, top, right, bottom):
output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone()
left = min(left, output.shape[1])
right = min(right, output.shape[1])
top = min(top, output.shape[0])
bottom = min(bottom, output.shape[0])
left = min(left, output.shape[-1])
right = min(right, output.shape[-1])
top = min(top, output.shape[-2])
bottom = min(bottom, output.shape[-2])
for x in range(left):
feather_rate = (x + 1.0) / left

19
execution.py
View File

@ -2,6 +2,7 @@ import os
import sys
import copy
import json
import logging
import threading
import heapq
import traceback
@ -156,7 +157,7 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute
if server.client_id is not None:
server.send_sync("executed", { "node": unique_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
except comfy.model_management.InterruptProcessingException as iex:
print("Processing interrupted")
logging.info("Processing interrupted")
# skip formatting inputs/outputs
error_details = {
@ -177,8 +178,8 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute
for node_id, node_outputs in outputs.items():
output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs]
print("!!! Exception during processing !!!")
print(traceback.format_exc())
logging.error("!!! Exception during processing !!!")
logging.error(traceback.format_exc())
error_details = {
"node_id": unique_id,
@ -636,11 +637,11 @@ def validate_prompt(prompt):
if valid is True:
good_outputs.add(o)
else:
print(f"Failed to validate prompt for output {o}:")
logging.error(f"Failed to validate prompt for output {o}:")
if len(reasons) > 0:
print("* (prompt):")
logging.error("* (prompt):")
for reason in reasons:
print(f" - {reason['message']}: {reason['details']}")
logging.error(f" - {reason['message']}: {reason['details']}")
errors += [(o, reasons)]
for node_id, result in validated.items():
valid = result[0]
@ -656,11 +657,11 @@ def validate_prompt(prompt):
"dependent_outputs": [],
"class_type": class_type
}
print(f"* {class_type} {node_id}:")
logging.error(f"* {class_type} {node_id}:")
for reason in reasons:
print(f" - {reason['message']}: {reason['details']}")
logging.error(f" - {reason['message']}: {reason['details']}")
node_errors[node_id]["dependent_outputs"].append(o)
print("Output will be ignored")
logging.error("Output will be ignored")
if len(good_outputs) == 0:
errors_list = []

16
extra_model_paths.yaml.example
View File

@ -1,5 +1,6 @@
#Rename this to extra_model_paths.yaml and ComfyUI will load it
#config for a1111 ui
#all you have to do is change the base_path to where yours is installed
a111:
@ -19,6 +20,21 @@ a111:
hypernetworks: models/hypernetworks
controlnet: models/ControlNet
#config for comfyui
#your base path should be either an existing comfy install or a central folder where you store all of your models, loras, etc.
#comfyui:
# base_path: path/to/comfyui/
# checkpoints: models/checkpoints/
# clip: models/clip/
# clip_vision: models/clip_vision/
# configs: models/configs/
# controlnet: models/controlnet/
# embeddings: models/embeddings/
# loras: models/loras/
# upscale_models: models/upscale_models/
# vae: models/vae/
#other_ui:
# base_path: path/to/ui
# checkpoints: models/checkpoints

4
folder_paths.py
View File

@ -29,6 +29,8 @@ folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes
folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})
output_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "output")
temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp")
input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
@ -144,7 +146,7 @@ def recursive_search(directory, excluded_dir_names=None):
return result, dirs
def filter_files_extensions(files, extensions):
return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files)))
return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files)))

2
nodes.py
View File

@ -1202,7 +1202,7 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
noise_mask = latent["noise_mask"]
callback = latent_preview.prepare_callback(model, steps)
disable_pbar = False
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)

9
web/extensions/core/groupOptions.js
View File

@ -38,6 +38,15 @@ app.registerExtension({
}
}
options.push({
content: "Select Nodes",
callback: () => {
this.selectNodes(nodesInGroup);
this.graph.change();
this.canvas.focus();
}
});
// Modes
// 0: Always
// 1: On Event

12
web/scripts/app.js
View File

@ -928,6 +928,16 @@ export class ComfyApp {
block_default = true;
}
// Alt + C collapse/uncollapse
if (e.key === 'c' && e.altKey) {
if (this.selected_nodes) {
for (var i in this.selected_nodes) {
this.selected_nodes[i].collapse()
}
}
block_default = true;
}
// Ctrl+C Copy
if ((e.key === 'c') && (e.metaKey || e.ctrlKey)) {
// Trigger onCopy
@ -1607,7 +1617,7 @@ export class ComfyApp {
all_inputs = all_inputs.concat(Object.keys(parent.inputs))
for (let parent_input in all_inputs) {
parent_input = all_inputs[parent_input];
if (parent.inputs[parent_input].type === node.inputs[i].type) {
if (parent.inputs[parent_input]?.type === node.inputs[i].type) {
link = parent.getInputLink(parent_input);
if (link) {
parent = parent.getInputNode(parent_input);

3
web/scripts/ui.js
View File

@ -809,7 +809,8 @@ export class ComfyUI {
if (
this.lastQueueSize != 0 &&
status.exec_info.queue_remaining == 0 &&
document.getElementById("autoQueueCheckbox").checked
document.getElementById("autoQueueCheckbox").checked &&
! app.lastExecutionError
) {
app.queuePrompt(0, this.batchCount);
}