mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-02-10 13:32:36 +08:00
Merge branch 'comfyanonymous:master' into multiple_workflows
This commit is contained in:
TomoyukiMizuma
GitHub
commit
97b2230801
@ -31,7 +31,7 @@ jobs:
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echo 'import site' >> ./python311._pth
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curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
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./python.exe get-pip.py
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python -m pip wheel torch torchvision torchaudio aiohttp==3.8.4 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu121 -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
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python -m pip wheel torch torchvision torchaudio aiohttp==3.8.5 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu121 -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
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ls ../temp_wheel_dir
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./python.exe -s -m pip install --pre ../temp_wheel_dir/*
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sed -i '1i../ComfyUI' ./python311._pth
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@ -6,8 +6,6 @@ import torch as th
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import torch.nn as nn
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from ..ldm.modules.diffusionmodules.util import (
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conv_nd,
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linear,
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zero_module,
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timestep_embedding,
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)
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@ -15,7 +13,7 @@ from ..ldm.modules.diffusionmodules.util import (
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from ..ldm.modules.attention import SpatialTransformer
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from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
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from ..ldm.util import exists
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import comfy.ops
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class ControlledUnetModel(UNetModel):
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#implemented in the ldm unet
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@ -55,6 +53,8 @@ class ControlNet(nn.Module):
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use_linear_in_transformer=False,
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adm_in_channels=None,
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transformer_depth_middle=None,
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device=None,
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operations=comfy.ops,
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):
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super().__init__()
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assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
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@ -117,9 +117,9 @@ class ControlNet(nn.Module):
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time_embed_dim = model_channels * 4
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
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)
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if self.num_classes is not None:
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@ -132,9 +132,9 @@ class ControlNet(nn.Module):
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assert adm_in_channels is not None
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self.label_emb = nn.Sequential(
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nn.Sequential(
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linear(adm_in_channels, time_embed_dim),
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operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
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)
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)
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else:
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@ -143,28 +143,28 @@ class ControlNet(nn.Module):
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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conv_nd(dims, in_channels, model_channels, 3, padding=1)
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operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
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)
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]
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)
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self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
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self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations)])
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self.input_hint_block = TimestepEmbedSequential(
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conv_nd(dims, hint_channels, 16, 3, padding=1),
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operations.conv_nd(dims, hint_channels, 16, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 16, 16, 3, padding=1),
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operations.conv_nd(dims, 16, 16, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 16, 32, 3, padding=1, stride=2),
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operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2),
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nn.SiLU(),
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conv_nd(dims, 32, 32, 3, padding=1),
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operations.conv_nd(dims, 32, 32, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 32, 96, 3, padding=1, stride=2),
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operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2),
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nn.SiLU(),
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conv_nd(dims, 96, 96, 3, padding=1),
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operations.conv_nd(dims, 96, 96, 3, padding=1),
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nn.SiLU(),
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conv_nd(dims, 96, 256, 3, padding=1, stride=2),
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operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2),
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nn.SiLU(),
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zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
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zero_module(operations.conv_nd(dims, 256, model_channels, 3, padding=1))
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)
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self._feature_size = model_channels
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@ -182,6 +182,7 @@ class ControlNet(nn.Module):
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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operations=operations
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)
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]
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ch = mult * model_channels
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@ -204,11 +205,11 @@ class ControlNet(nn.Module):
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SpatialTransformer(
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ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim,
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disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
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use_checkpoint=use_checkpoint
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use_checkpoint=use_checkpoint, operations=operations
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)
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)
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self.input_blocks.append(TimestepEmbedSequential(*layers))
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self.zero_convs.append(self.make_zero_conv(ch))
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self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
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self._feature_size += ch
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input_block_chans.append(ch)
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if level != len(channel_mult) - 1:
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@ -224,16 +225,17 @@ class ControlNet(nn.Module):
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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down=True,
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operations=operations
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)
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if resblock_updown
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else Downsample(
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ch, conv_resample, dims=dims, out_channels=out_ch
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ch, conv_resample, dims=dims, out_channels=out_ch, operations=operations
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)
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)
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)
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ch = out_ch
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input_block_chans.append(ch)
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self.zero_convs.append(self.make_zero_conv(ch))
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self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
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ds *= 2
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self._feature_size += ch
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@ -253,11 +255,12 @@ class ControlNet(nn.Module):
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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operations=operations
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),
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SpatialTransformer( # always uses a self-attn
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ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
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disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
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use_checkpoint=use_checkpoint
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use_checkpoint=use_checkpoint, operations=operations
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),
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ResBlock(
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ch,
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@ -266,13 +269,14 @@ class ControlNet(nn.Module):
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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operations=operations
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),
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)
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self.middle_block_out = self.make_zero_conv(ch)
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self.middle_block_out = self.make_zero_conv(ch, operations=operations)
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self._feature_size += ch
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def make_zero_conv(self, channels):
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return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
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def make_zero_conv(self, channels, operations=None):
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return TimestepEmbedSequential(zero_module(operations.conv_nd(self.dims, channels, channels, 1, padding=0)))
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def forward(self, x, hint, timesteps, context, y=None, **kwargs):
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t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
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@ -50,18 +50,22 @@ def convert_to_transformers(sd, prefix):
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if "{}proj".format(prefix) in sd_k:
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sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1)
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sd = transformers_convert(sd, prefix, "vision_model.", 32)
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sd = transformers_convert(sd, prefix, "vision_model.", 48)
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return sd
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def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
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if convert_keys:
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sd = convert_to_transformers(sd, prefix)
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if "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
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if "vision_model.encoder.layers.47.layer_norm1.weight" in sd:
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json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json")
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elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
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json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
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else:
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json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
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clip = ClipVisionModel(json_config)
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m, u = clip.load_sd(sd)
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if len(m) > 0:
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print("missing clip vision:", m)
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u = set(u)
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keys = list(sd.keys())
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for k in keys:
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@ -72,4 +76,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
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def load(ckpt_path):
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sd = load_torch_file(ckpt_path)
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return load_clipvision_from_sd(sd)
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if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd:
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return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True)
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else:
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return load_clipvision_from_sd(sd)
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18
comfy/clip_vision_config_g.json
Normal file
18
comfy/clip_vision_config_g.json
Normal file
@ -0,0 +1,18 @@
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{
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"attention_dropout": 0.0,
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"dropout": 0.0,
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"hidden_act": "gelu",
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"hidden_size": 1664,
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"image_size": 224,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"layer_norm_eps": 1e-05,
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"model_type": "clip_vision_model",
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"num_attention_heads": 16,
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"num_channels": 3,
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"num_hidden_layers": 48,
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"patch_size": 14,
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"projection_dim": 1280,
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"torch_dtype": "float32"
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}
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@ -649,7 +649,7 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
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s_in = x.new_ones([x.shape[0]])
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denoised_1, denoised_2 = None, None
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h_1, h_2 = None, None
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h, h_1, h_2 = None, None, None
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for i in trange(len(sigmas) - 1, disable=disable):
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denoised = model(x, sigmas[i] * s_in, **extra_args)
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@ -10,13 +10,14 @@ from .diffusionmodules.util import checkpoint
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from .sub_quadratic_attention import efficient_dot_product_attention
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|
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from comfy import model_management
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import comfy.ops
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|
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if model_management.xformers_enabled():
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import xformers
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import xformers.ops
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|
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from comfy.cli_args import args
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||||
import comfy.ops
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|
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# CrossAttn precision handling
|
||||
if args.dont_upcast_attention:
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||||
print("disabling upcasting of attention")
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@ -52,9 +53,9 @@ 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, device=None):
|
||||
def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
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||||
self.proj = comfy.ops.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
|
||||
self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x):
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x, gate = self.proj(x).chunk(2, dim=-1)
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@ -62,19 +63,19 @@ 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, device=None):
|
||||
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
inner_dim = int(dim * mult)
|
||||
dim_out = default(dim_out, dim)
|
||||
project_in = nn.Sequential(
|
||||
comfy.ops.Linear(dim, inner_dim, dtype=dtype, device=device),
|
||||
operations.Linear(dim, inner_dim, dtype=dtype, device=device),
|
||||
nn.GELU()
|
||||
) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device)
|
||||
) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations)
|
||||
|
||||
self.net = nn.Sequential(
|
||||
project_in,
|
||||
nn.Dropout(dropout),
|
||||
comfy.ops.Linear(inner_dim, dim_out, dtype=dtype, device=device)
|
||||
operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
@ -148,7 +149,7 @@ class SpatialSelfAttention(nn.Module):
|
||||
|
||||
|
||||
class CrossAttentionBirchSan(nn.Module):
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
|
||||
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)
|
||||
@ -156,12 +157,12 @@ class CrossAttentionBirchSan(nn.Module):
|
||||
self.scale = dim_head ** -0.5
|
||||
self.heads = heads
|
||||
|
||||
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_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(
|
||||
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
|
||||
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
@ -245,7 +246,7 @@ class CrossAttentionBirchSan(nn.Module):
|
||||
|
||||
|
||||
class CrossAttentionDoggettx(nn.Module):
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
|
||||
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)
|
||||
@ -253,12 +254,12 @@ class CrossAttentionDoggettx(nn.Module):
|
||||
self.scale = dim_head ** -0.5
|
||||
self.heads = heads
|
||||
|
||||
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_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(
|
||||
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
|
||||
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
@ -343,7 +344,7 @@ class CrossAttentionDoggettx(nn.Module):
|
||||
return self.to_out(r2)
|
||||
|
||||
class CrossAttention(nn.Module):
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
|
||||
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)
|
||||
@ -351,12 +352,12 @@ class CrossAttention(nn.Module):
|
||||
self.scale = dim_head ** -0.5
|
||||
self.heads = heads
|
||||
|
||||
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_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(
|
||||
comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device),
|
||||
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
@ -399,7 +400,7 @@ class CrossAttention(nn.Module):
|
||||
|
||||
class MemoryEfficientCrossAttention(nn.Module):
|
||||
# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None):
|
||||
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__()
|
||||
print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
|
||||
f"{heads} heads.")
|
||||
@ -409,11 +410,11 @@ class MemoryEfficientCrossAttention(nn.Module):
|
||||
self.heads = heads
|
||||
self.dim_head = dim_head
|
||||
|
||||
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_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(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
|
||||
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):
|
||||
@ -450,7 +451,7 @@ class MemoryEfficientCrossAttention(nn.Module):
|
||||
return self.to_out(out)
|
||||
|
||||
class CrossAttentionPytorch(nn.Module):
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None):
|
||||
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)
|
||||
@ -458,11 +459,11 @@ class CrossAttentionPytorch(nn.Module):
|
||||
self.heads = heads
|
||||
self.dim_head = dim_head
|
||||
|
||||
self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_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(comfy.ops.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
|
||||
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):
|
||||
@ -508,14 +509,14 @@ else:
|
||||
|
||||
class BasicTransformerBlock(nn.Module):
|
||||
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
|
||||
disable_self_attn=False, dtype=None, device=None):
|
||||
disable_self_attn=False, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
self.disable_self_attn = disable_self_attn
|
||||
self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
|
||||
context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device) # is a self-attention if not self.disable_self_attn
|
||||
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device)
|
||||
context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn
|
||||
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
|
||||
self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
|
||||
heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device) # is self-attn if context is none
|
||||
heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations) # is self-attn if context is none
|
||||
self.norm1 = nn.LayerNorm(dim, dtype=dtype, device=device)
|
||||
self.norm2 = nn.LayerNorm(dim, dtype=dtype, device=device)
|
||||
self.norm3 = nn.LayerNorm(dim, dtype=dtype, device=device)
|
||||
@ -648,7 +649,7 @@ class SpatialTransformer(nn.Module):
|
||||
def __init__(self, in_channels, n_heads, d_head,
|
||||
depth=1, dropout=0., context_dim=None,
|
||||
disable_self_attn=False, use_linear=False,
|
||||
use_checkpoint=True, dtype=None, device=None):
|
||||
use_checkpoint=True, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
if exists(context_dim) and not isinstance(context_dim, list):
|
||||
context_dim = [context_dim] * depth
|
||||
@ -656,26 +657,26 @@ class SpatialTransformer(nn.Module):
|
||||
inner_dim = n_heads * d_head
|
||||
self.norm = Normalize(in_channels, dtype=dtype, device=device)
|
||||
if not use_linear:
|
||||
self.proj_in = nn.Conv2d(in_channels,
|
||||
self.proj_in = operations.Conv2d(in_channels,
|
||||
inner_dim,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0, dtype=dtype, device=device)
|
||||
else:
|
||||
self.proj_in = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
||||
self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
||||
|
||||
self.transformer_blocks = nn.ModuleList(
|
||||
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
|
||||
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device)
|
||||
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations)
|
||||
for d in range(depth)]
|
||||
)
|
||||
if not use_linear:
|
||||
self.proj_out = nn.Conv2d(inner_dim,in_channels,
|
||||
self.proj_out = operations.Conv2d(inner_dim,in_channels,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0, dtype=dtype, device=device)
|
||||
else:
|
||||
self.proj_out = comfy.ops.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
||||
self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
||||
self.use_linear = use_linear
|
||||
|
||||
def forward(self, x, context=None, transformer_options={}):
|
||||
|
||||
@ -8,8 +8,6 @@ import torch.nn.functional as F
|
||||
|
||||
from .util import (
|
||||
checkpoint,
|
||||
conv_nd,
|
||||
linear,
|
||||
avg_pool_nd,
|
||||
zero_module,
|
||||
normalization,
|
||||
@ -17,7 +15,7 @@ from .util import (
|
||||
)
|
||||
from ..attention import SpatialTransformer
|
||||
from comfy.ldm.util import exists
|
||||
|
||||
import comfy.ops
|
||||
|
||||
class TimestepBlock(nn.Module):
|
||||
"""
|
||||
@ -72,14 +70,14 @@ 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, device=None):
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops):
|
||||
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, device=device)
|
||||
self.conv = operations.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
|
||||
@ -108,7 +106,7 @@ 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, device=None):
|
||||
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels or channels
|
||||
@ -116,7 +114,7 @@ class Downsample(nn.Module):
|
||||
self.dims = dims
|
||||
stride = 2 if dims != 3 else (1, 2, 2)
|
||||
if use_conv:
|
||||
self.op = conv_nd(
|
||||
self.op = operations.conv_nd(
|
||||
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device
|
||||
)
|
||||
else:
|
||||
@ -158,6 +156,7 @@ class ResBlock(TimestepBlock):
|
||||
down=False,
|
||||
dtype=None,
|
||||
device=None,
|
||||
operations=comfy.ops
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
@ -171,7 +170,7 @@ class ResBlock(TimestepBlock):
|
||||
self.in_layers = nn.Sequential(
|
||||
nn.GroupNorm(32, channels, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
|
||||
operations.conv_nd(dims, channels, self.out_channels, 3, padding=1, dtype=dtype, device=device),
|
||||
)
|
||||
|
||||
self.updown = up or down
|
||||
@ -187,7 +186,7 @@ class ResBlock(TimestepBlock):
|
||||
|
||||
self.emb_layers = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
linear(
|
||||
operations.Linear(
|
||||
emb_channels,
|
||||
2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device
|
||||
),
|
||||
@ -197,18 +196,18 @@ class ResBlock(TimestepBlock):
|
||||
nn.SiLU(),
|
||||
nn.Dropout(p=dropout),
|
||||
zero_module(
|
||||
conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
|
||||
operations.conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1, dtype=dtype, device=device)
|
||||
),
|
||||
)
|
||||
|
||||
if self.out_channels == channels:
|
||||
self.skip_connection = nn.Identity()
|
||||
elif use_conv:
|
||||
self.skip_connection = conv_nd(
|
||||
self.skip_connection = operations.conv_nd(
|
||||
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, device=device)
|
||||
self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x, emb):
|
||||
"""
|
||||
@ -317,6 +316,7 @@ class UNetModel(nn.Module):
|
||||
adm_in_channels=None,
|
||||
transformer_depth_middle=None,
|
||||
device=None,
|
||||
operations=comfy.ops,
|
||||
):
|
||||
super().__init__()
|
||||
assert use_spatial_transformer == True, "use_spatial_transformer has to be true"
|
||||
@ -379,9 +379,9 @@ class UNetModel(nn.Module):
|
||||
|
||||
time_embed_dim = model_channels * 4
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
|
||||
operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
)
|
||||
|
||||
if self.num_classes is not None:
|
||||
@ -394,9 +394,9 @@ 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, device=device),
|
||||
operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
|
||||
)
|
||||
)
|
||||
else:
|
||||
@ -405,7 +405,7 @@ class UNetModel(nn.Module):
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
|
||||
operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
|
||||
)
|
||||
]
|
||||
)
|
||||
@ -426,6 +426,7 @@ class UNetModel(nn.Module):
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations,
|
||||
)
|
||||
]
|
||||
ch = mult * model_channels
|
||||
@ -447,7 +448,7 @@ class UNetModel(nn.Module):
|
||||
layers.append(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, device=device
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
@ -468,10 +469,11 @@ class UNetModel(nn.Module):
|
||||
down=True,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
)
|
||||
if resblock_updown
|
||||
else Downsample(
|
||||
ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device
|
||||
ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
)
|
||||
@ -498,11 +500,12 @@ class UNetModel(nn.Module):
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
),
|
||||
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, device=device
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
),
|
||||
ResBlock(
|
||||
ch,
|
||||
@ -513,6 +516,7 @@ class UNetModel(nn.Module):
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
),
|
||||
)
|
||||
self._feature_size += ch
|
||||
@ -532,6 +536,7 @@ class UNetModel(nn.Module):
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
)
|
||||
]
|
||||
ch = model_channels * mult
|
||||
@ -554,7 +559,7 @@ class UNetModel(nn.Module):
|
||||
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, device=device
|
||||
use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
|
||||
)
|
||||
)
|
||||
if level and i == self.num_res_blocks[level]:
|
||||
@ -571,9 +576,10 @@ class UNetModel(nn.Module):
|
||||
up=True,
|
||||
dtype=self.dtype,
|
||||
device=device,
|
||||
operations=operations
|
||||
)
|
||||
if resblock_updown
|
||||
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device)
|
||||
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations)
|
||||
)
|
||||
ds //= 2
|
||||
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
||||
@ -582,12 +588,12 @@ class UNetModel(nn.Module):
|
||||
self.out = nn.Sequential(
|
||||
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, device=device)),
|
||||
zero_module(operations.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, device=device),
|
||||
conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
|
||||
operations.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
|
||||
)
|
||||
|
||||
|
||||
@ -148,13 +148,20 @@ class SDInpaint(BaseModel):
|
||||
super().__init__(model_config, model_type, device=device)
|
||||
self.concat_keys = ("mask", "masked_image")
|
||||
|
||||
def sdxl_pooled(args, noise_augmentor):
|
||||
if "unclip_conditioning" in args:
|
||||
return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor)[:,:1280]
|
||||
else:
|
||||
return args["pooled_output"]
|
||||
|
||||
class SDXLRefiner(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.EPS, device=None):
|
||||
super().__init__(model_config, model_type, device=device)
|
||||
self.embedder = Timestep(256)
|
||||
self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})
|
||||
|
||||
def encode_adm(self, **kwargs):
|
||||
clip_pooled = kwargs["pooled_output"]
|
||||
clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
|
||||
width = kwargs.get("width", 768)
|
||||
height = kwargs.get("height", 768)
|
||||
crop_w = kwargs.get("crop_w", 0)
|
||||
@ -178,9 +185,10 @@ class SDXL(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.EPS, device=None):
|
||||
super().__init__(model_config, model_type, device=device)
|
||||
self.embedder = Timestep(256)
|
||||
self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280})
|
||||
|
||||
def encode_adm(self, **kwargs):
|
||||
clip_pooled = kwargs["pooled_output"]
|
||||
clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor)
|
||||
width = kwargs.get("width", 768)
|
||||
height = kwargs.get("height", 768)
|
||||
crop_w = kwargs.get("crop_w", 0)
|
||||
|
||||
@ -21,6 +21,11 @@ class Conv2d(torch.nn.Conv2d):
|
||||
def reset_parameters(self):
|
||||
return None
|
||||
|
||||
def conv_nd(dims, *args, **kwargs):
|
||||
if dims == 2:
|
||||
return Conv2d(*args, **kwargs)
|
||||
else:
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
@contextmanager
|
||||
def use_comfy_ops(): # Kind of an ugly hack but I can't think of a better way
|
||||
|
||||
@ -53,7 +53,7 @@ def get_models_from_cond(cond, model_type):
|
||||
|
||||
def get_additional_models(positive, negative):
|
||||
"""loads additional models in positive and negative conditioning"""
|
||||
control_nets = get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")
|
||||
control_nets = set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control"))
|
||||
|
||||
control_models = []
|
||||
for m in control_nets:
|
||||
@ -78,7 +78,7 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
|
||||
|
||||
real_model = None
|
||||
models = get_additional_models(positive, negative)
|
||||
comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[2] * noise.shape[3]))
|
||||
comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.shape[0] * noise.shape[2] * noise.shape[3]))
|
||||
real_model = model.model
|
||||
|
||||
noise = noise.to(device)
|
||||
|
||||
@ -478,7 +478,7 @@ def pre_run_control(model, conds):
|
||||
timestep_end = None
|
||||
percent_to_timestep_function = lambda a: model.sigma_to_t(model.t_to_sigma(torch.tensor(a) * 999.0))
|
||||
if 'control' in x[1]:
|
||||
x[1]['control'].pre_run(model.inner_model, percent_to_timestep_function)
|
||||
x[1]['control'].pre_run(model.inner_model.inner_model, percent_to_timestep_function)
|
||||
|
||||
def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
|
||||
cond_cnets = []
|
||||
|
||||
110
comfy/sd.py
110
comfy/sd.py
@ -844,9 +844,119 @@ class ControlNet(ControlBase):
|
||||
out.append(self.control_model_wrapped)
|
||||
return out
|
||||
|
||||
class ControlLoraOps:
|
||||
class Linear(torch.nn.Module):
|
||||
def __init__(self, in_features: int, out_features: int, bias: bool = True,
|
||||
device=None, dtype=None) -> None:
|
||||
factory_kwargs = {'device': device, 'dtype': dtype}
|
||||
super().__init__()
|
||||
self.in_features = in_features
|
||||
self.out_features = out_features
|
||||
self.weight = None
|
||||
self.up = None
|
||||
self.down = None
|
||||
self.bias = None
|
||||
|
||||
def forward(self, input):
|
||||
if self.up is not None:
|
||||
return torch.nn.functional.linear(input, self.weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(self.weight.dtype), self.bias)
|
||||
else:
|
||||
return torch.nn.functional.linear(input, self.weight, self.bias)
|
||||
|
||||
class Conv2d(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size,
|
||||
stride=1,
|
||||
padding=0,
|
||||
dilation=1,
|
||||
groups=1,
|
||||
bias=True,
|
||||
padding_mode='zeros',
|
||||
device=None,
|
||||
dtype=None
|
||||
):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.stride = stride
|
||||
self.padding = padding
|
||||
self.dilation = dilation
|
||||
self.transposed = False
|
||||
self.output_padding = 0
|
||||
self.groups = groups
|
||||
self.padding_mode = padding_mode
|
||||
|
||||
self.weight = None
|
||||
self.bias = None
|
||||
self.up = None
|
||||
self.down = None
|
||||
|
||||
|
||||
def forward(self, input):
|
||||
if self.up is not None:
|
||||
return torch.nn.functional.conv2d(input, self.weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(self.weight.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
|
||||
else:
|
||||
return torch.nn.functional.conv2d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
|
||||
|
||||
def conv_nd(self, dims, *args, **kwargs):
|
||||
if dims == 2:
|
||||
return self.Conv2d(*args, **kwargs)
|
||||
else:
|
||||
raise ValueError(f"unsupported dimensions: {dims}")
|
||||
|
||||
|
||||
class ControlLora(ControlNet):
|
||||
def __init__(self, control_weights, global_average_pooling=False, device=None):
|
||||
ControlBase.__init__(self, device)
|
||||
self.control_weights = control_weights
|
||||
self.global_average_pooling = global_average_pooling
|
||||
|
||||
def pre_run(self, model, percent_to_timestep_function):
|
||||
super().pre_run(model, percent_to_timestep_function)
|
||||
controlnet_config = model.model_config.unet_config.copy()
|
||||
controlnet_config.pop("out_channels")
|
||||
controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1]
|
||||
controlnet_config["operations"] = ControlLoraOps()
|
||||
self.control_model = cldm.ControlNet(**controlnet_config)
|
||||
if model_management.should_use_fp16():
|
||||
self.control_model.half()
|
||||
self.control_model.to(model_management.get_torch_device())
|
||||
diffusion_model = model.diffusion_model
|
||||
sd = diffusion_model.state_dict()
|
||||
cm = self.control_model.state_dict()
|
||||
|
||||
for k in sd:
|
||||
try:
|
||||
set_attr(self.control_model, k, sd[k])
|
||||
except:
|
||||
pass
|
||||
|
||||
for k in self.control_weights:
|
||||
if k not in {"lora_controlnet"}:
|
||||
set_attr(self.control_model, k, self.control_weights[k].to(model_management.get_torch_device()))
|
||||
|
||||
def copy(self):
|
||||
c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
|
||||
self.copy_to(c)
|
||||
return c
|
||||
|
||||
def cleanup(self):
|
||||
del self.control_model
|
||||
self.control_model = None
|
||||
super().cleanup()
|
||||
|
||||
def get_models(self):
|
||||
out = ControlBase.get_models(self)
|
||||
return out
|
||||
|
||||
def load_controlnet(ckpt_path, model=None):
|
||||
controlnet_data = utils.load_torch_file(ckpt_path, safe_load=True)
|
||||
if "lora_controlnet" in controlnet_data:
|
||||
return ControlLora(controlnet_data)
|
||||
|
||||
controlnet_config = None
|
||||
if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
|
||||
|
||||
@ -2,6 +2,7 @@ import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from PIL import Image
|
||||
import math
|
||||
|
||||
import comfy.utils
|
||||
|
||||
@ -209,9 +210,36 @@ class Sharpen:
|
||||
|
||||
return (result,)
|
||||
|
||||
class ImageScaleToTotalPixels:
|
||||
upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic"]
|
||||
crop_methods = ["disabled", "center"]
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
|
||||
"megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}),
|
||||
}}
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "upscale"
|
||||
|
||||
CATEGORY = "image/upscaling"
|
||||
|
||||
def upscale(self, image, upscale_method, megapixels):
|
||||
samples = image.movedim(-1,1)
|
||||
total = int(megapixels * 1024 * 1024)
|
||||
|
||||
scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
|
||||
width = round(samples.shape[3] * scale_by)
|
||||
height = round(samples.shape[2] * scale_by)
|
||||
|
||||
s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
|
||||
s = s.movedim(1,-1)
|
||||
return (s,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"ImageBlend": Blend,
|
||||
"ImageBlur": Blur,
|
||||
"ImageQuantize": Quantize,
|
||||
"ImageSharpen": Sharpen,
|
||||
"ImageScaleToTotalPixels": ImageScaleToTotalPixels,
|
||||
}
|
||||
|
||||
@ -6,6 +6,7 @@
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
|
||||
<link rel="stylesheet" type="text/css" href="./lib/litegraph.css" />
|
||||
<link rel="stylesheet" type="text/css" href="./style.css" />
|
||||
<link rel="stylesheet" type="text/css" href="./user.css" />
|
||||
<script type="text/javascript" src="./lib/litegraph.core.js"></script>
|
||||
<script type="text/javascript" src="./lib/litegraph.extensions.js" defer></script>
|
||||
<script type="module">
|
||||
|
||||
1
web/user.css
Normal file
1
web/user.css
Normal file
@ -0,0 +1 @@
|
||||
/* Put custom styles here */
|
||||
Loading…
Reference in New Issue
Block a user