mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-02-10 05:22:34 +08:00
Merge branch 'comfyanonymous:master' into refactor/execution
This commit is contained in:
Dr.Lt.Data
GitHub
commit
f86755f66e
@ -94,7 +94,7 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins
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```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/rocm5.4.2```
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This is the command to install the nightly with ROCm 5.6 that supports the 7000 series and might have some performance improvements:
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```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm5.6 -r requirements.txt```
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```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm5.6```
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### NVIDIA
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@ -84,6 +84,8 @@ parser.add_argument("--dont-print-server", action="store_true", help="Don't prin
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parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
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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).")
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parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
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args = parser.parse_args()
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if args.windows_standalone_build:
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@ -3,7 +3,6 @@ import math
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from scipy import integrate
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import torch
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from torch import nn
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from torchdiffeq import odeint
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import torchsde
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from tqdm.auto import trange, tqdm
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@ -287,30 +286,6 @@ def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, o
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return x
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@torch.no_grad()
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def log_likelihood(model, x, sigma_min, sigma_max, extra_args=None, atol=1e-4, rtol=1e-4):
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extra_args = {} if extra_args is None else extra_args
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s_in = x.new_ones([x.shape[0]])
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v = torch.randint_like(x, 2) * 2 - 1
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fevals = 0
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def ode_fn(sigma, x):
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nonlocal fevals
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with torch.enable_grad():
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x = x[0].detach().requires_grad_()
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denoised = model(x, sigma * s_in, **extra_args)
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d = to_d(x, sigma, denoised)
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fevals += 1
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grad = torch.autograd.grad((d * v).sum(), x)[0]
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d_ll = (v * grad).flatten(1).sum(1)
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return d.detach(), d_ll
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x_min = x, x.new_zeros([x.shape[0]])
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t = x.new_tensor([sigma_min, sigma_max])
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sol = odeint(ode_fn, x_min, t, atol=atol, rtol=rtol, method='dopri5')
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latent, delta_ll = sol[0][-1], sol[1][-1]
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ll_prior = torch.distributions.Normal(0, sigma_max).log_prob(latent).flatten(1).sum(1)
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return ll_prior + delta_ll, {'fevals': fevals}
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class PIDStepSizeController:
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"""A PID controller for ODE adaptive step size control."""
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def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8):
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@ -52,9 +52,9 @@ def init_(tensor):
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# feedforward
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class GEGLU(nn.Module):
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def __init__(self, dim_in, dim_out, dtype=None):
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def __init__(self, dim_in, dim_out, dtype=None, device=None):
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super().__init__()
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self.proj = comfy.ops.Linear(dim_in, dim_out * 2, dtype=dtype)
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self.proj = comfy.ops.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
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def forward(self, x):
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x, gate = self.proj(x).chunk(2, dim=-1)
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@ -62,19 +62,19 @@ class GEGLU(nn.Module):
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class FeedForward(nn.Module):
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def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None):
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def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = default(dim_out, dim)
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project_in = nn.Sequential(
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comfy.ops.Linear(dim, inner_dim, dtype=dtype),
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comfy.ops.Linear(dim, inner_dim, dtype=dtype, device=device),
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nn.GELU()
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) if not glu else GEGLU(dim, inner_dim, dtype=dtype)
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) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device)
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self.net = nn.Sequential(
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project_in,
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nn.Dropout(dropout),
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comfy.ops.Linear(inner_dim, dim_out, dtype=dtype)
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comfy.ops.Linear(inner_dim, dim_out, dtype=dtype, device=device)
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)
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def forward(self, x):
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@ -90,8 +90,8 @@ def zero_module(module):
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return module
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def Normalize(in_channels, dtype=None):
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return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype)
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def Normalize(in_channels, dtype=None, device=None):
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return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
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class SpatialSelfAttention(nn.Module):
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@ -148,7 +148,7 @@ class SpatialSelfAttention(nn.Module):
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class CrossAttentionBirchSan(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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@ -156,12 +156,12 @@ class CrossAttentionBirchSan(nn.Module):
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self.scale = dim_head ** -0.5
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self.heads = heads
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_out = nn.Sequential(
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comfy.ops.Linear(inner_dim, query_dim, dtype=dtype),
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comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
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nn.Dropout(dropout)
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)
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@ -245,7 +245,7 @@ class CrossAttentionBirchSan(nn.Module):
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class CrossAttentionDoggettx(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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@ -253,12 +253,12 @@ class CrossAttentionDoggettx(nn.Module):
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self.scale = dim_head ** -0.5
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self.heads = heads
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_out = nn.Sequential(
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comfy.ops.Linear(inner_dim, query_dim, dtype=dtype),
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comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
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nn.Dropout(dropout)
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)
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@ -343,7 +343,7 @@ class CrossAttentionDoggettx(nn.Module):
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return self.to_out(r2)
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class CrossAttention(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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@ -351,12 +351,12 @@ class CrossAttention(nn.Module):
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self.scale = dim_head ** -0.5
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self.heads = heads
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_out = nn.Sequential(
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comfy.ops.Linear(inner_dim, query_dim, dtype=dtype),
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comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
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nn.Dropout(dropout)
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)
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@ -399,7 +399,7 @@ class CrossAttention(nn.Module):
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class MemoryEfficientCrossAttention(nn.Module):
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# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None):
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super().__init__()
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print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
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f"{heads} heads.")
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@ -409,11 +409,11 @@ class MemoryEfficientCrossAttention(nn.Module):
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self.heads = heads
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self.dim_head = dim_head
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype), nn.Dropout(dropout))
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self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
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self.attention_op: Optional[Any] = None
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def forward(self, x, context=None, value=None, mask=None):
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@ -450,7 +450,7 @@ class MemoryEfficientCrossAttention(nn.Module):
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return self.to_out(out)
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class CrossAttentionPytorch(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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@ -458,11 +458,11 @@ class CrossAttentionPytorch(nn.Module):
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self.heads = heads
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self.dim_head = dim_head
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype)
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self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype), nn.Dropout(dropout))
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self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
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self.attention_op: Optional[Any] = None
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def forward(self, x, context=None, value=None, mask=None):
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@ -508,17 +508,17 @@ else:
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class BasicTransformerBlock(nn.Module):
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def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
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disable_self_attn=False, dtype=None):
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disable_self_attn=False, dtype=None, device=None):
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super().__init__()
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self.disable_self_attn = disable_self_attn
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self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
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context_dim=context_dim if self.disable_self_attn else None, dtype=dtype) # is a self-attention if not self.disable_self_attn
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self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype)
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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
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self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device)
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self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
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heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype) # is self-attn if context is none
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self.norm1 = nn.LayerNorm(dim, dtype=dtype)
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self.norm2 = nn.LayerNorm(dim, dtype=dtype)
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self.norm3 = nn.LayerNorm(dim, dtype=dtype)
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heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device) # is self-attn if context is none
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self.norm1 = nn.LayerNorm(dim, dtype=dtype, device=device)
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self.norm2 = nn.LayerNorm(dim, dtype=dtype, device=device)
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self.norm3 = nn.LayerNorm(dim, dtype=dtype, device=device)
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self.checkpoint = checkpoint
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self.n_heads = n_heads
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self.d_head = d_head
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@ -648,34 +648,34 @@ class SpatialTransformer(nn.Module):
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def __init__(self, in_channels, n_heads, d_head,
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depth=1, dropout=0., context_dim=None,
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disable_self_attn=False, use_linear=False,
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use_checkpoint=True, dtype=None):
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use_checkpoint=True, dtype=None, device=None):
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super().__init__()
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if exists(context_dim) and not isinstance(context_dim, list):
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context_dim = [context_dim] * depth
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self.in_channels = in_channels
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inner_dim = n_heads * d_head
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self.norm = Normalize(in_channels, dtype=dtype)
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self.norm = Normalize(in_channels, dtype=dtype, device=device)
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if not use_linear:
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self.proj_in = nn.Conv2d(in_channels,
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inner_dim,
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kernel_size=1,
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stride=1,
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padding=0, dtype=dtype)
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padding=0, dtype=dtype, device=device)
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else:
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self.proj_in = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype)
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self.proj_in = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device)
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self.transformer_blocks = nn.ModuleList(
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[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
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disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype)
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disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device)
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for d in range(depth)]
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)
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if not use_linear:
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self.proj_out = nn.Conv2d(inner_dim,in_channels,
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kernel_size=1,
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stride=1,
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padding=0, dtype=dtype)
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padding=0, dtype=dtype, device=device)
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else:
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self.proj_out = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype)
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self.proj_out = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device)
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self.use_linear = use_linear
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def forward(self, x, context=None, transformer_options={}):
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@ -8,6 +8,7 @@ from typing import Optional, Any
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from ..attention import MemoryEfficientCrossAttention
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from comfy import model_management
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import comfy.ops
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if model_management.xformers_enabled_vae():
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import xformers
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@ -48,7 +49,7 @@ class Upsample(nn.Module):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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self.conv = torch.nn.Conv2d(in_channels,
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self.conv = comfy.ops.Conv2d(in_channels,
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in_channels,
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kernel_size=3,
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stride=1,
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@ -67,7 +68,7 @@ class Downsample(nn.Module):
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self.with_conv = with_conv
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if self.with_conv:
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# no asymmetric padding in torch conv, must do it ourselves
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self.conv = torch.nn.Conv2d(in_channels,
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self.conv = comfy.ops.Conv2d(in_channels,
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in_channels,
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kernel_size=3,
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stride=2,
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@ -95,30 +96,30 @@ class ResnetBlock(nn.Module):
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self.swish = torch.nn.SiLU(inplace=True)
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self.norm1 = Normalize(in_channels)
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self.conv1 = torch.nn.Conv2d(in_channels,
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||||
self.conv1 = comfy.ops.Conv2d(in_channels,
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||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
if temb_channels > 0:
|
||||
self.temb_proj = torch.nn.Linear(temb_channels,
|
||||
self.temb_proj = comfy.ops.Linear(temb_channels,
|
||||
out_channels)
|
||||
self.norm2 = Normalize(out_channels)
|
||||
self.dropout = torch.nn.Dropout(dropout, inplace=True)
|
||||
self.conv2 = torch.nn.Conv2d(out_channels,
|
||||
self.conv2 = comfy.ops.Conv2d(out_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
if self.in_channels != self.out_channels:
|
||||
if self.use_conv_shortcut:
|
||||
self.conv_shortcut = torch.nn.Conv2d(in_channels,
|
||||
self.conv_shortcut = comfy.ops.Conv2d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
else:
|
||||
self.nin_shortcut = torch.nn.Conv2d(in_channels,
|
||||
self.nin_shortcut = comfy.ops.Conv2d(in_channels,
|
||||
out_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
@ -188,22 +189,22 @@ class AttnBlock(nn.Module):
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = Normalize(in_channels)
|
||||
self.q = torch.nn.Conv2d(in_channels,
|
||||
self.q = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.k = torch.nn.Conv2d(in_channels,
|
||||
self.k = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.v = torch.nn.Conv2d(in_channels,
|
||||
self.v = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.proj_out = torch.nn.Conv2d(in_channels,
|
||||
self.proj_out = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
@ -243,22 +244,22 @@ class MemoryEfficientAttnBlock(nn.Module):
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = Normalize(in_channels)
|
||||
self.q = torch.nn.Conv2d(in_channels,
|
||||
self.q = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.k = torch.nn.Conv2d(in_channels,
|
||||
self.k = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.v = torch.nn.Conv2d(in_channels,
|
||||
self.v = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.proj_out = torch.nn.Conv2d(in_channels,
|
||||
self.proj_out = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
@ -302,22 +303,22 @@ class MemoryEfficientAttnBlockPytorch(nn.Module):
|
||||
self.in_channels = in_channels
|
||||
|
||||
self.norm = Normalize(in_channels)
|
||||
self.q = torch.nn.Conv2d(in_channels,
|
||||
self.q = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.k = torch.nn.Conv2d(in_channels,
|
||||
self.k = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.v = torch.nn.Conv2d(in_channels,
|
||||
self.v = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0)
|
||||
self.proj_out = torch.nn.Conv2d(in_channels,
|
||||
self.proj_out = comfy.ops.Conv2d(in_channels,
|
||||
in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
@ -399,14 +400,14 @@ class Model(nn.Module):
|
||||
# timestep embedding
|
||||
self.temb = nn.Module()
|
||||
self.temb.dense = nn.ModuleList([
|
||||
torch.nn.Linear(self.ch,
|
||||
comfy.ops.Linear(self.ch,
|
||||
self.temb_ch),
|
||||
torch.nn.Linear(self.temb_ch,
|
||||
comfy.ops.Linear(self.temb_ch,
|
||||
self.temb_ch),
|
||||
])
|
||||
|
||||
# downsampling
|
||||
self.conv_in = torch.nn.Conv2d(in_channels,
|
||||
self.conv_in = comfy.ops.Conv2d(in_channels,
|
||||
self.ch,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
@ -475,7 +476,7 @@ class Model(nn.Module):
|
||||
|
||||
# end
|
||||
self.norm_out = Normalize(block_in)
|
||||
self.conv_out = torch.nn.Conv2d(block_in,
|
||||
self.conv_out = comfy.ops.Conv2d(block_in,
|
||||
out_ch,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
@ -548,7 +549,7 @@ class Encoder(nn.Module):
|
||||
self.in_channels = in_channels
|
||||
|
||||
# downsampling
|
||||
self.conv_in = torch.nn.Conv2d(in_channels,
|
||||
self.conv_in = comfy.ops.Conv2d(in_channels,
|
||||
self.ch,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
@ -593,7 +594,7 @@ class Encoder(nn.Module):
|
||||
|
||||
# end
|
||||
self.norm_out = Normalize(block_in)
|
||||
self.conv_out = torch.nn.Conv2d(block_in,
|
||||
self.conv_out = comfy.ops.Conv2d(block_in,
|
||||
2*z_channels if double_z else z_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
@ -653,7 +654,7 @@ class Decoder(nn.Module):
|
||||
self.z_shape, np.prod(self.z_shape)))
|
||||
|
||||
# z to block_in
|
||||
self.conv_in = torch.nn.Conv2d(z_channels,
|
||||
self.conv_in = comfy.ops.Conv2d(z_channels,
|
||||
block_in,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
@ -695,7 +696,7 @@ class Decoder(nn.Module):
|
||||
|
||||
# end
|
||||
self.norm_out = Normalize(block_in)
|
||||
self.conv_out = torch.nn.Conv2d(block_in,
|
||||
self.conv_out = comfy.ops.Conv2d(block_in,
|
||||
out_ch,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
|
||||
@ -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
|
||||
)
|
||||
|
||||
|
||||
@ -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):
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -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 = {}
|
||||
|
||||
@ -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
|
||||
|
||||
@ -6,6 +6,8 @@ import folder_paths
|
||||
import json
|
||||
import os
|
||||
|
||||
from comfy.cli_args import args
|
||||
|
||||
class ModelMergeSimple:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -101,8 +103,7 @@ class CheckpointSave:
|
||||
if prompt is not None:
|
||||
prompt_info = json.dumps(prompt)
|
||||
|
||||
metadata = {"prompt": prompt_info}
|
||||
|
||||
metadata = {}
|
||||
|
||||
enable_modelspec = True
|
||||
if isinstance(model.model, comfy.model_base.SDXL):
|
||||
@ -127,9 +128,11 @@ class CheckpointSave:
|
||||
elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION:
|
||||
metadata["modelspec.predict_key"] = "v"
|
||||
|
||||
if extra_pnginfo is not None:
|
||||
for x in extra_pnginfo:
|
||||
metadata[x] = json.dumps(extra_pnginfo[x])
|
||||
if not args.disable_metadata:
|
||||
metadata["prompt"] = prompt_info
|
||||
if extra_pnginfo is not None:
|
||||
for x in extra_pnginfo:
|
||||
metadata[x] = json.dumps(extra_pnginfo[x])
|
||||
|
||||
output_checkpoint = f"{filename}_{counter:05}_.safetensors"
|
||||
output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
|
||||
|
||||
26
nodes.py
26
nodes.py
@ -26,6 +26,8 @@ import comfy.utils
|
||||
import comfy.clip_vision
|
||||
|
||||
import comfy.model_management
|
||||
from comfy.cli_args import args
|
||||
|
||||
import importlib
|
||||
|
||||
import folder_paths
|
||||
@ -352,10 +354,12 @@ class SaveLatent:
|
||||
if prompt is not None:
|
||||
prompt_info = json.dumps(prompt)
|
||||
|
||||
metadata = {"prompt": prompt_info}
|
||||
if extra_pnginfo is not None:
|
||||
for x in extra_pnginfo:
|
||||
metadata[x] = json.dumps(extra_pnginfo[x])
|
||||
metadata = None
|
||||
if not args.disable_metadata:
|
||||
metadata = {"prompt": prompt_info}
|
||||
if extra_pnginfo is not None:
|
||||
for x in extra_pnginfo:
|
||||
metadata[x] = json.dumps(extra_pnginfo[x])
|
||||
|
||||
file = f"{filename}_{counter:05}_.latent"
|
||||
file = os.path.join(full_output_folder, file)
|
||||
@ -1214,12 +1218,14 @@ class SaveImage:
|
||||
for image in images:
|
||||
i = 255. * image.cpu().numpy()
|
||||
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
|
||||
metadata = PngInfo()
|
||||
if prompt is not None:
|
||||
metadata.add_text("prompt", json.dumps(prompt))
|
||||
if extra_pnginfo is not None:
|
||||
for x in extra_pnginfo:
|
||||
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
|
||||
metadata = None
|
||||
if not args.disable_metadata:
|
||||
metadata = PngInfo()
|
||||
if prompt is not None:
|
||||
metadata.add_text("prompt", json.dumps(prompt))
|
||||
if extra_pnginfo is not None:
|
||||
for x in extra_pnginfo:
|
||||
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
|
||||
|
||||
file = f"{filename}_{counter:05}_.png"
|
||||
img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
|
||||
|
||||
@ -1,5 +1,4 @@
|
||||
torch
|
||||
torchdiffeq
|
||||
torchsde
|
||||
einops
|
||||
transformers>=4.25.1
|
||||
|
||||
Loading…
Reference in New Issue
Block a user