merge with upstream

.github/workflows/windows_release_nightly_pytorch.yml

@@ -31,7 +31,7 @@ jobs:
             echo 'import site' >> ./python311._pth
             curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
             ./python.exe get-pip.py
-            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
+            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
             ls ../temp_wheel_dir
             ./python.exe -s -m pip install --pre ../temp_wheel_dir/*
             sed -i '1i../ComfyUI' ./python311._pth

comfy/cldm/cldm.py

@@ -6,8 +6,6 @@ import torch as th
 import torch.nn as nn
 
 from ..ldm.modules.diffusionmodules.util import (
-    conv_nd,
-    linear,
     zero_module,
     timestep_embedding,
 )
@@ -15,7 +13,7 @@ from ..ldm.modules.diffusionmodules.util import (
 from ..ldm.modules.attention import SpatialTransformer
 from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
 from ..ldm.util import exists
-
+import comfy.ops
 
 class ControlledUnetModel(UNetModel):
     #implemented in the ldm unet
@@ -55,6 +53,8 @@ class ControlNet(nn.Module):
         use_linear_in_transformer=False,
         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"
@@ -117,9 +117,9 @@ class ControlNet(nn.Module):
 
         time_embed_dim = model_channels * 4
         self.time_embed = nn.Sequential(
-            linear(model_channels, time_embed_dim),
+            operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device),
             nn.SiLU(),
-            linear(time_embed_dim, time_embed_dim),
+            operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
         )
 
         if self.num_classes is not None:
@@ -132,9 +132,9 @@ class ControlNet(nn.Module):
                 assert adm_in_channels is not None
                 self.label_emb = nn.Sequential(
                     nn.Sequential(
-                        linear(adm_in_channels, time_embed_dim),
+                        operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device),
                         nn.SiLU(),
-                        linear(time_embed_dim, time_embed_dim),
+                        operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device),
                     )
                 )
             else:
@@ -143,28 +143,28 @@ class ControlNet(nn.Module):
         self.input_blocks = nn.ModuleList(
             [
                 TimestepEmbedSequential(
-                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                    operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device)
                 )
             ]
         )
-        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
+        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations)])
 
         self.input_hint_block = TimestepEmbedSequential(
-                    conv_nd(dims, hint_channels, 16, 3, padding=1),
+                    operations.conv_nd(dims, hint_channels, 16, 3, padding=1),
                     nn.SiLU(),
-                    conv_nd(dims, 16, 16, 3, padding=1),
+                    operations.conv_nd(dims, 16, 16, 3, padding=1),
                     nn.SiLU(),
-                    conv_nd(dims, 16, 32, 3, padding=1, stride=2),
+                    operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2),
                     nn.SiLU(),
-                    conv_nd(dims, 32, 32, 3, padding=1),
+                    operations.conv_nd(dims, 32, 32, 3, padding=1),
                     nn.SiLU(),
-                    conv_nd(dims, 32, 96, 3, padding=1, stride=2),
+                    operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2),
                     nn.SiLU(),
-                    conv_nd(dims, 96, 96, 3, padding=1),
+                    operations.conv_nd(dims, 96, 96, 3, padding=1),
                     nn.SiLU(),
-                    conv_nd(dims, 96, 256, 3, padding=1, stride=2),
+                    operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2),
                     nn.SiLU(),
-                    zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
+                    zero_module(operations.conv_nd(dims, 256, model_channels, 3, padding=1))
         )
 
         self._feature_size = model_channels
@@ -182,6 +182,7 @@ class ControlNet(nn.Module):
                         dims=dims,
                         use_checkpoint=use_checkpoint,
                         use_scale_shift_norm=use_scale_shift_norm,
+                        operations=operations
                     )
                 ]
                 ch = mult * model_channels
@@ -204,11 +205,11 @@ class ControlNet(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
+                                use_checkpoint=use_checkpoint, operations=operations
                             )
                         )
                 self.input_blocks.append(TimestepEmbedSequential(*layers))
-                self.zero_convs.append(self.make_zero_conv(ch))
+                self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
                 self._feature_size += ch
                 input_block_chans.append(ch)
             if level != len(channel_mult) - 1:
@@ -224,16 +225,17 @@ class ControlNet(nn.Module):
                             use_checkpoint=use_checkpoint,
                             use_scale_shift_norm=use_scale_shift_norm,
                             down=True,
+                            operations=operations
                         )
                         if resblock_updown
                         else Downsample(
-                            ch, conv_resample, dims=dims, out_channels=out_ch
+                            ch, conv_resample, dims=dims, out_channels=out_ch, operations=operations
                         )
                     )
                 )
                 ch = out_ch
                 input_block_chans.append(ch)
-                self.zero_convs.append(self.make_zero_conv(ch))
+                self.zero_convs.append(self.make_zero_conv(ch, operations=operations))
                 ds *= 2
                 self._feature_size += ch
 
@@ -253,11 +255,12 @@ class ControlNet(nn.Module):
                 dims=dims,
                 use_checkpoint=use_checkpoint,
                 use_scale_shift_norm=use_scale_shift_norm,
+                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
+                            use_checkpoint=use_checkpoint, operations=operations
                         ),
             ResBlock(
                 ch,
@@ -266,16 +269,17 @@ class ControlNet(nn.Module):
                 dims=dims,
                 use_checkpoint=use_checkpoint,
                 use_scale_shift_norm=use_scale_shift_norm,
+                operations=operations
             ),
         )
-        self.middle_block_out = self.make_zero_conv(ch)
+        self.middle_block_out = self.make_zero_conv(ch, operations=operations)
         self._feature_size += ch
 
-    def make_zero_conv(self, channels):
+    def make_zero_conv(self, channels, operations=None):
-        return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
+        return TimestepEmbedSequential(zero_module(operations.conv_nd(self.dims, channels, channels, 1, padding=0)))
 
     def forward(self, x, hint, timesteps, context, y=None, **kwargs):
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
         emb = self.time_embed(t_emb)
 
         guided_hint = self.input_hint_block(hint, emb, context)
@@ -283,9 +287,6 @@ class ControlNet(nn.Module):
         outs = []
 
         hs = []
-        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-        emb = self.time_embed(t_emb)
-
         if self.num_classes is not None:
             assert y.shape[0] == x.shape[0]
             emb = emb + self.label_emb(y)

comfy/cli_args.py

@@ -58,6 +58,8 @@ fpvae_group.add_argument("--bf16-vae", action="store_true", help="Run the VAE in
 
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
+parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize when loading models with Intel GPUs.")
+
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"
     Auto = "auto"
@@ -82,6 +84,9 @@ vram_group.add_argument("--novram", action="store_true", help="When lowvram isn'
 vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
 
 
+parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.")
+
+
 parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
 parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")
 parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")

comfy/clip_vision.py

@@ -25,6 +25,7 @@ class ClipVisionModel():
 
     def encode_image(self, image):
         img = torch.clip((255. * image), 0, 255).round().int()
+        img = list(map(lambda a: a, img))
         inputs = self.processor(images=img, return_tensors="pt")
         outputs = self.model(**inputs)
         return outputs
@@ -49,18 +50,22 @@ def convert_to_transformers(sd, prefix):
         if "{}proj".format(prefix) in sd_k:
             sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1)
 
-        sd = transformers_convert(sd, prefix, "vision_model.", 32)
+        sd = transformers_convert(sd, prefix, "vision_model.", 48)
     return sd
 
 def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     if convert_keys:
         sd = convert_to_transformers(sd, prefix)
-    if "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
+    if "vision_model.encoder.layers.47.layer_norm1.weight" in sd:
+        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json")
+    elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
         json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
     else:
         json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
     clip = ClipVisionModel(json_config)
     m, u = clip.load_sd(sd)
+    if len(m) > 0:
+        print("missing clip vision:", m)
     u = set(u)
     keys = list(sd.keys())
     for k in keys:
@@ -71,4 +76,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
 
 def load(ckpt_path):
     sd = load_torch_file(ckpt_path)
-    return load_clipvision_from_sd(sd)
+    if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd:
+        return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True)
+    else:
+        return load_clipvision_from_sd(sd)

comfy/clip_vision_config_g.json

@@ -0,0 +1,18 @@
+{
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_size": 1664,
+  "image_size": 224,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 48,
+  "patch_size": 14,
+  "projection_dim": 1280,
+  "torch_dtype": "float32"
+}

comfy/cmd/execution.py

@@ -409,6 +409,7 @@ class PromptExecutor:
                     d = self.outputs_ui.pop(x)
                     del d
 
+            comfy.model_management.cleanup_models()
             if self.server.client_id is not None:
                 self.server.send_sync("execution_cached", {"nodes": list(current_outputs), "prompt_id": prompt_id},
                                       self.server.client_id)

comfy/cmd/server.py

@@ -4,6 +4,7 @@ import glob
 import struct
 import sys
 import shutil
+from urllib.parse import quote
 
 from PIL import Image, ImageOps
 from io import BytesIO
@@ -80,6 +81,8 @@ class PromptServer():
 
         mimetypes.init()
         mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8'
+
+        self.supports = ["custom_nodes_from_web"]
         self.prompt_queue = None
         self.loop = loop
         self.messages = asyncio.Queue()
@@ -140,9 +143,17 @@ class PromptServer():
 
         @routes.get("/extensions")
         async def get_extensions(request):
-            files = glob.glob(os.path.join(self.web_root, 'extensions/**/*.js'), recursive=True)
+            files = glob.glob(os.path.join(
-            return web.json_response(
+                self.web_root, 'extensions/**/*.js'), recursive=True)
-                list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files)))
+
+            extensions = list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files))
+
+            for name, dir in nodes.EXTENSION_WEB_DIRS.items():
+                files = glob.glob(os.path.join(dir, '**/*.js'), recursive=True)
+                extensions.extend(list(map(lambda f: "/extensions/" + quote(
+                    name) + "/" + os.path.relpath(f, dir).replace("\\", "/"), files)))
+
+            return web.json_response(extensions)
 
         def get_dir_by_type(dir_type=None):
             type_dir = ""
@@ -638,6 +649,12 @@ class PromptServer():
 
     def add_routes(self):
         self.app.add_routes(self.routes)
+
+        for name, dir in nodes.EXTENSION_WEB_DIRS.items():
+            self.app.add_routes([
+                web.static('/extensions/' + urllib.parse.quote(name), dir, follow_symlinks=True),
+            ])
+
         self.app.add_routes([
             web.static('/', self.web_root, follow_symlinks=True),
         ])

comfy/gligen.py

@@ -244,30 +244,15 @@ class Gligen(nn.Module):
         self.position_net = position_net
         self.key_dim = key_dim
         self.max_objs = 30
-        self.lowvram = False
+        self.current_device = torch.device("cpu")
 
     def _set_position(self, boxes, masks, positive_embeddings):
-        if self.lowvram == True:
-            self.position_net.to(boxes.device)
-
         objs = self.position_net(boxes, masks, positive_embeddings)
-
+        def func(x, extra_options):
-        if self.lowvram == True:
+            key = extra_options["transformer_index"]
-            self.position_net.cpu()
+            module = self.module_list[key]
-            def func_lowvram(x, extra_options):
+            return module(x, objs)
-                key = extra_options["transformer_index"]
+        return func
-                module = self.module_list[key]
-                module.to(x.device)
-                r = module(x, objs)
-                module.cpu()
-                return r
-            return func_lowvram
-        else:
-            def func(x, extra_options):
-                key = extra_options["transformer_index"]
-                module = self.module_list[key]
-                return module(x, objs)
-            return func
 
     def set_position(self, latent_image_shape, position_params, device):
         batch, c, h, w = latent_image_shape
@@ -312,14 +297,6 @@ class Gligen(nn.Module):
             masks.to(device),
             conds.to(device))
 
-    def set_lowvram(self, value=True):
-        self.lowvram = value
-
-    def cleanup(self):
-        self.lowvram = False
-
-    def get_models(self):
-        return [self]
 
 def load_gligen(sd):
     sd_k = sd.keys()

comfy/k_diffusion/sampling.py

@@ -649,7 +649,7 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
     s_in = x.new_ones([x.shape[0]])
 
     denoised_1, denoised_2 = None, None
-    h_1, h_2 = None, None
+    h, h_1, h_2 = None, None, None
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)

comfy/ldm/modules/attention.py

@@ -10,13 +10,14 @@ from .diffusionmodules.util import checkpoint
 from .sub_quadratic_attention import efficient_dot_product_attention
 
 from comfy import model_management
-import comfy.ops
 
 if model_management.xformers_enabled():
     import xformers
     import xformers.ops
 
 from comfy.cli_args import args
+import comfy.ops
+
 # CrossAttn precision handling
 if args.dont_upcast_attention:
     print("disabling upcasting of attention")
@@ -52,9 +53,9 @@ def init_(tensor):
 
 # feedforward
 class GEGLU(nn.Module):
-    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__()
-        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):
         x, gate = self.proj(x).chunk(2, dim=-1)
@@ -62,19 +63,19 @@ class GEGLU(nn.Module):
 
 
 class FeedForward(nn.Module):
-    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_q = operations.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_k = operations.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_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_q = operations.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_k = operations.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_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_q = operations.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_k = operations.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_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_q = operations.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_k = operations.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_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_q = operations.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_k = operations.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_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
+                              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)
+        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={}):

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -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
         )
 

comfy/model_base.py

@@ -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)

comfy/model_detection.py

@@ -121,9 +121,20 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_fp16):
     return model_config_from_unet_config(unet_config)
 
 
-def model_config_from_diffusers_unet(state_dict, use_fp16):
+def unet_config_from_diffusers_unet(state_dict, use_fp16):
     match = {}
-    match["context_dim"] = state_dict["down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k.weight"].shape[1]
+    attention_resolutions = []
+
+    attn_res = 1
+    for i in range(5):
+        k = "down_blocks.{}.attentions.1.transformer_blocks.0.attn2.to_k.weight".format(i)
+        if k in state_dict:
+            match["context_dim"] = state_dict[k].shape[1]
+            attention_resolutions.append(attn_res)
+        attn_res *= 2
+
+    match["attention_resolutions"] = attention_resolutions
+
     match["model_channels"] = state_dict["conv_in.weight"].shape[0]
     match["in_channels"] = state_dict["conv_in.weight"].shape[1]
     match["adm_in_channels"] = None
@@ -135,22 +146,22 @@ def model_config_from_diffusers_unet(state_dict, use_fp16):
     SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
             'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
             'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 2, 10], 'channel_mult': [1, 2, 4],
-            'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048}
+            'transformer_depth_middle': 10, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64}
 
     SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
                     'num_classes': 'sequential', 'adm_in_channels': 2560, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 384,
                     'num_res_blocks': 2, 'attention_resolutions': [2, 4], 'transformer_depth': [0, 4, 4, 0], 'channel_mult': [1, 2, 4, 4],
-                    'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280}
+                    'transformer_depth_middle': 4, 'use_linear_in_transformer': True, 'context_dim': 1280, "num_head_channels": 64}
 
     SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
             'adm_in_channels': None, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
             'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
-            'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024}
+            'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64}
 
     SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
                     'num_classes': 'sequential', 'adm_in_channels': 2048, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
                     'num_res_blocks': 2, 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
-                    'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024}
+                    'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, "num_head_channels": 64}
 
     SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
                     'num_classes': 'sequential', 'adm_in_channels': 1536, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
@@ -160,9 +171,20 @@ def model_config_from_diffusers_unet(state_dict, use_fp16):
     SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
             'adm_in_channels': None, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': 2,
             'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4],
-            'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768}
+            'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, "num_heads": 8}
 
-    supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl]
+    SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+            'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
+            'num_res_blocks': 2, 'attention_resolutions': [4], 'transformer_depth': [0, 0, 1], 'channel_mult': [1, 2, 4],
+            'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 2048, "num_head_channels": 64}
+
+    SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+            'num_classes': 'sequential', 'adm_in_channels': 2816, 'use_fp16': use_fp16, 'in_channels': 4, 'model_channels': 320,
+            'num_res_blocks': 2, 'attention_resolutions': [], 'transformer_depth': [0, 0, 0], 'channel_mult': [1, 2, 4],
+            'transformer_depth_middle': 0, 'use_linear_in_transformer': True, "num_head_channels": 64, 'context_dim': 1}
+
+
+    supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet]
 
     for unet_config in supported_models:
         matches = True
@@ -171,5 +193,11 @@ def model_config_from_diffusers_unet(state_dict, use_fp16):
                 matches = False
                 break
         if matches:
-            return model_config_from_unet_config(unet_config)
+            return unet_config
+    return None
+
+def model_config_from_diffusers_unet(state_dict, use_fp16):
+    unet_config = unet_config_from_diffusers_unet(state_dict, use_fp16)
+    if unet_config is not None:
+        return model_config_from_unet_config(unet_config)
     return None

comfy/model_management.py

@@ -2,6 +2,7 @@ import psutil
 from enum import Enum
 from comfy.cli_args import args
 import torch
+import sys
 
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
@@ -87,8 +88,10 @@ def get_total_memory(dev=None, torch_total_too=False):
             mem_total = 1024 * 1024 * 1024 #TODO
             mem_total_torch = mem_total
         elif xpu_available:
+            stats = torch.xpu.memory_stats(dev)
+            mem_reserved = stats['reserved_bytes.all.current']
             mem_total = torch.xpu.get_device_properties(dev).total_memory
-            mem_total_torch = mem_total
+            mem_total_torch = mem_reserved
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_reserved = stats['reserved_bytes.all.current']
@@ -201,8 +204,13 @@ if cpu_state == CPUState.MPS:
 
 print(f"Set vram state to: {vram_state.name}")
 
+DISABLE_SMART_MEMORY = args.disable_smart_memory
+
+if DISABLE_SMART_MEMORY:
+    print("Disabling smart memory management")
 
 def get_torch_device_name(device):
+    global xpu_available
     if hasattr(device, 'type'):
         if device.type == "cuda":
             try:
@@ -212,6 +220,8 @@ def get_torch_device_name(device):
             return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend)
         else:
             return "{}".format(device.type)
+    elif xpu_available:
+        return "{} {}".format(device, torch.xpu.get_device_name(device))
     else:
         return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
 
@@ -221,132 +231,168 @@ except:
     print("Could not pick default device.")
 
 
-current_loaded_model = None
+current_loaded_models = []
-current_gpu_controlnets = []
 
-model_accelerated = False
+class LoadedModel:
+    def __init__(self, model):
+        self.model = model
+        self.model_accelerated = False
+        self.device = model.load_device
 
+    def model_memory(self):
+        return self.model.model_size()
 
-def unload_model():
+    def model_memory_required(self, device):
-    global current_loaded_model
+        if device == self.model.current_device:
-    global model_accelerated
+            return 0
-    global current_gpu_controlnets
+        else:
-    global vram_state
+            return self.model_memory()
 
-    if current_loaded_model is not None:
+    def model_load(self, lowvram_model_memory=0):
-        if model_accelerated:
+        global xpu_available
-            accelerate.hooks.remove_hook_from_submodules(current_loaded_model.model)
+        patch_model_to = None
-            model_accelerated = False
+        if lowvram_model_memory == 0:
+            patch_model_to = self.device
 
-        current_loaded_model.unpatch_model()
+        self.model.model_patches_to(self.device)
-        current_loaded_model.model.to(current_loaded_model.offload_device)
+        self.model.model_patches_to(self.model.model_dtype())
-        current_loaded_model.model_patches_to(current_loaded_model.offload_device)
-        current_loaded_model = None
-        if vram_state != VRAMState.HIGH_VRAM:
-            soft_empty_cache()
 
-    if vram_state != VRAMState.HIGH_VRAM:
+        try:
-        if len(current_gpu_controlnets) > 0:
+            self.real_model = self.model.patch_model(device_to=patch_model_to) #TODO: do something with loras and offloading to CPU
-            for n in current_gpu_controlnets:
+        except Exception as e:
-                n.cpu()
+            self.model.unpatch_model(self.model.offload_device)
-            current_gpu_controlnets = []
+            self.model_unload()
+            raise e
+
+        if lowvram_model_memory > 0:
+            print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024))
+            device_map = accelerate.infer_auto_device_map(self.real_model, max_memory={0: "{}MiB".format(lowvram_model_memory // (1024 * 1024)), "cpu": "16GiB"})
+            accelerate.dispatch_model(self.real_model, device_map=device_map, main_device=self.device)
+            self.model_accelerated = True
+
+        if xpu_available and not args.disable_ipex_optimize:
+            self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True)
+
+        return self.real_model
+
+    def model_unload(self):
+        if self.model_accelerated:
+            accelerate.hooks.remove_hook_from_submodules(self.real_model)
+            self.model_accelerated = False
+
+        self.model.unpatch_model(self.model.offload_device)
+        self.model.model_patches_to(self.model.offload_device)
+
+    def __eq__(self, other):
+        return self.model is other.model
 
 def minimum_inference_memory():
-    return (768 * 1024 * 1024)
+    return (1024 * 1024 * 1024)
+
+def unload_model_clones(model):
+    to_unload = []
+    for i in range(len(current_loaded_models)):
+        if model.is_clone(current_loaded_models[i].model):
+            to_unload = [i] + to_unload
+
+    for i in to_unload:
+        print("unload clone", i)
+        current_loaded_models.pop(i).model_unload()
+
+def free_memory(memory_required, device, keep_loaded=[]):
+    unloaded_model = False
+    for i in range(len(current_loaded_models) -1, -1, -1):
+        if DISABLE_SMART_MEMORY:
+            current_free_mem = 0
+        else:
+            current_free_mem = get_free_memory(device)
+        if current_free_mem > memory_required:
+            break
+        shift_model = current_loaded_models[i]
+        if shift_model.device == device:
+            if shift_model not in keep_loaded:
+                current_loaded_models.pop(i).model_unload()
+                unloaded_model = True
+
+    if unloaded_model:
+        soft_empty_cache()
+
+
+def load_models_gpu(models, memory_required=0):
+    global vram_state
+
+    inference_memory = minimum_inference_memory()
+    extra_mem = max(inference_memory, memory_required)
+
+    models_to_load = []
+    models_already_loaded = []
+    for x in models:
+        loaded_model = LoadedModel(x)
+
+        if loaded_model in current_loaded_models:
+            index = current_loaded_models.index(loaded_model)
+            current_loaded_models.insert(0, current_loaded_models.pop(index))
+            models_already_loaded.append(loaded_model)
+        else:
+            models_to_load.append(loaded_model)
+
+    if len(models_to_load) == 0:
+        devs = set(map(lambda a: a.device, models_already_loaded))
+        for d in devs:
+            if d != torch.device("cpu"):
+                free_memory(extra_mem, d, models_already_loaded)
+        return
+
+    print("loading new")
+
+    total_memory_required = {}
+    for loaded_model in models_to_load:
+        unload_model_clones(loaded_model.model)
+        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
+
+    for device in total_memory_required:
+        if device != torch.device("cpu"):
+            free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded)
+
+    for loaded_model in models_to_load:
+        model = loaded_model.model
+        torch_dev = model.load_device
+        if is_device_cpu(torch_dev):
+            vram_set_state = VRAMState.DISABLED
+        else:
+            vram_set_state = vram_state
+        lowvram_model_memory = 0
+        if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
+            model_size = loaded_model.model_memory_required(torch_dev)
+            current_free_mem = get_free_memory(torch_dev)
+            lowvram_model_memory = int(max(256 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
+            if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary
+                vram_set_state = VRAMState.LOW_VRAM
+            else:
+                lowvram_model_memory = 0
+
+        if vram_set_state == VRAMState.NO_VRAM:
+            lowvram_model_memory = 256 * 1024 * 1024
+
+        cur_loaded_model = loaded_model.model_load(lowvram_model_memory)
+        current_loaded_models.insert(0, loaded_model)
+    return
+
 
 def load_model_gpu(model):
-    global current_loaded_model
+    return load_models_gpu([model])
-    global vram_state
-    global model_accelerated
 
-    if model is current_loaded_model:
+def cleanup_models():
-        return
+    to_delete = []
-    unload_model()
+    for i in range(len(current_loaded_models)):
+        print(sys.getrefcount(current_loaded_models[i].model))
+        if sys.getrefcount(current_loaded_models[i].model) <= 2:
+            to_delete = [i] + to_delete
 
-    torch_dev = model.load_device
+    for i in to_delete:
-    model.model_patches_to(torch_dev)
+        x = current_loaded_models.pop(i)
-    model.model_patches_to(model.model_dtype())
+        x.model_unload()
-    current_loaded_model = model
+        del x
-
-    if is_device_cpu(torch_dev):
-        vram_set_state = VRAMState.DISABLED
-    else:
-        vram_set_state = vram_state
-
-    if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
-        model_size = model.model_size()
-        current_free_mem = get_free_memory(torch_dev)
-        lowvram_model_memory = int(max(256 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
-        if model_size > (current_free_mem - minimum_inference_memory()): #only switch to lowvram if really necessary
-            vram_set_state = VRAMState.LOW_VRAM
-
-    real_model = model.model
-    patch_model_to = None
-    if vram_set_state == VRAMState.DISABLED:
-        pass
-    elif vram_set_state == VRAMState.NORMAL_VRAM or vram_set_state == VRAMState.HIGH_VRAM or vram_set_state == VRAMState.SHARED:
-        model_accelerated = False
-        patch_model_to = torch_dev
-
-    try:
-        real_model = model.patch_model(device_to=patch_model_to)
-    except Exception as e:
-        model.unpatch_model()
-        unload_model()
-        raise e
-
-    if patch_model_to is not None:
-        real_model.to(torch_dev)
-
-    if vram_set_state == VRAMState.NO_VRAM:
-        device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"})
-        accelerate.dispatch_model(real_model, device_map=device_map, main_device=torch_dev)
-        model_accelerated = True
-    elif vram_set_state == VRAMState.LOW_VRAM:
-        device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(lowvram_model_memory // (1024 * 1024)), "cpu": "16GiB"})
-        accelerate.dispatch_model(real_model, device_map=device_map, main_device=torch_dev)
-        model_accelerated = True
-
-    return current_loaded_model
-
-def load_controlnet_gpu(control_models):
-    global current_gpu_controlnets
-    global vram_state
-    if vram_state == VRAMState.DISABLED:
-        return
-
-    if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
-        for m in control_models:
-            if hasattr(m, 'set_lowvram'):
-                m.set_lowvram(True)
-        #don't load controlnets like this if low vram because they will be loaded right before running and unloaded right after
-        return
-
-    models = []
-    for m in control_models:
-        models += m.get_models()
-
-    for m in current_gpu_controlnets:
-        if m not in models:
-            m.cpu()
-
-    device = get_torch_device()
-    current_gpu_controlnets = []
-    for m in models:
-        current_gpu_controlnets.append(m.to(device))
-
-
-def load_if_low_vram(model):
-    global vram_state
-    if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
-        return model.to(get_torch_device())
-    return model
-
-def unload_if_low_vram(model):
-    global vram_state
-    if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
-        return model.cpu()
-    return model
 
 def unet_offload_device():
     if vram_state == VRAMState.HIGH_VRAM:
@@ -354,6 +400,28 @@ def unet_offload_device():
     else:
         return torch.device("cpu")
 
+def unet_inital_load_device(parameters, dtype):
+    torch_dev = get_torch_device()
+    if vram_state == VRAMState.HIGH_VRAM:
+        return torch_dev
+
+    cpu_dev = torch.device("cpu")
+    if DISABLE_SMART_MEMORY:
+        return cpu_dev
+
+    dtype_size = 4
+    if dtype == torch.float16 or dtype == torch.bfloat16:
+        dtype_size = 2
+
+    model_size = dtype_size * parameters
+
+    mem_dev = get_free_memory(torch_dev)
+    mem_cpu = get_free_memory(cpu_dev)
+    if mem_dev > mem_cpu and model_size < mem_dev:
+        return torch_dev
+    else:
+        return cpu_dev
+
 def text_encoder_offload_device():
     if args.gpu_only:
         return get_torch_device()
@@ -441,8 +509,12 @@ def get_free_memory(dev=None, torch_free_too=False):
             mem_free_total = 1024 * 1024 * 1024 #TODO
             mem_free_torch = mem_free_total
         elif xpu_available:
-            mem_free_total = torch.xpu.get_device_properties(dev).total_memory - torch.xpu.memory_allocated(dev)
+            stats = torch.xpu.memory_stats(dev)
-            mem_free_torch = mem_free_total
+            mem_active = stats['active_bytes.all.current']
+            mem_allocated = stats['allocated_bytes.all.current']
+            mem_reserved = stats['reserved_bytes.all.current']
+            mem_free_torch = mem_reserved - mem_active
+            mem_free_total = torch.xpu.get_device_properties(dev).total_memory - mem_allocated
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_active = stats['active_bytes.all.current']
@@ -456,6 +528,13 @@ def get_free_memory(dev=None, torch_free_too=False):
     else:
         return mem_free_total
 
+def batch_area_memory(area):
+    if xformers_enabled() or pytorch_attention_flash_attention():
+        #TODO: these formulas are copied from maximum_batch_area below
+        return (area / 20) * (1024 * 1024)
+    else:
+        return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
+
 def maximum_batch_area():
     global vram_state
     if vram_state == VRAMState.NO_VRAM:
@@ -507,9 +586,12 @@ def should_use_fp16(device=None, model_params=0):
     if directml_enabled:
         return False
 
-    if cpu_mode() or mps_mode() or xpu_available:
+    if cpu_mode() or mps_mode():
         return False #TODO ?
 
+    if xpu_available:
+        return True
+
     if torch.cuda.is_bf16_supported():
         return True
 

comfy/nodes/package.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import importlib
+import os
 import pkgutil
 import time
 import types
@@ -14,6 +15,7 @@ except:
     custom_nodes = None
 from comfy.nodes.package_typing import ExportedNodes
 from functools import reduce
+from pkg_resources import resource_filename
 
 _comfy_nodes = ExportedNodes()
 
@@ -21,10 +23,19 @@ _comfy_nodes = ExportedNodes()
 def _import_nodes_in_module(exported_nodes: ExportedNodes, module: types.ModuleType):
     node_class_mappings = getattr(module, 'NODE_CLASS_MAPPINGS', None)
     node_display_names = getattr(module, 'NODE_DISPLAY_NAME_MAPPINGS', None)
+    web_directory = getattr(module, "WEB_DIRECTORY", None)
     if node_class_mappings:
         exported_nodes.NODE_CLASS_MAPPINGS.update(node_class_mappings)
     if node_display_names:
         exported_nodes.NODE_DISPLAY_NAME_MAPPINGS.update(node_display_names)
+    if web_directory:
+        # load the extension resources path
+        abs_web_directory = os.path.abspath(resource_filename(module.__name__, web_directory))
+        if not os.path.isdir(abs_web_directory):
+            abs_web_directory = os.path.abspath(os.path.join(os.path.dirname(module.__file__), web_directory))
+        if not os.path.isdir(abs_web_directory):
+            raise ImportError(path=abs_web_directory)
+        exported_nodes.EXTENSION_WEB_DIRS[module.__name__] = abs_web_directory
 
 
 def _import_and_enumerate_nodes_in_module(module: types.ModuleType, print_import_times=False) -> ExportedNodes:

comfy/nodes/package_typing.py

@@ -22,22 +22,27 @@ class CustomNode(Protocol):
 class ExportedNodes:
     NODE_CLASS_MAPPINGS: Dict[str, CustomNode] = field(default_factory=dict)
     NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = field(default_factory=dict)
+    EXTENSION_WEB_DIRS: Dict[str, str] = field(default_factory=dict)
 
     def update(self, exported_nodes: ExportedNodes) -> ExportedNodes:
         self.NODE_CLASS_MAPPINGS.update(exported_nodes.NODE_CLASS_MAPPINGS)
         self.NODE_DISPLAY_NAME_MAPPINGS.update(exported_nodes.NODE_DISPLAY_NAME_MAPPINGS)
+        self.EXTENSION_WEB_DIRS.update(exported_nodes.EXTENSION_WEB_DIRS)
         return self
 
     def __len__(self):
         return len(self.NODE_CLASS_MAPPINGS)
 
-    def __sub__(self, other):
+    def __sub__(self, other: ExportedNodes):
         exported_nodes = ExportedNodes().update(self)
         for self_key in exported_nodes.NODE_CLASS_MAPPINGS:
             if self_key in other.NODE_CLASS_MAPPINGS:
                 exported_nodes.NODE_CLASS_MAPPINGS.pop(self_key)
             if self_key in other.NODE_DISPLAY_NAME_MAPPINGS:
                 exported_nodes.NODE_DISPLAY_NAME_MAPPINGS.pop(self_key)
+        for self_key in exported_nodes.EXTENSION_WEB_DIRS:
+            if self_key in other.EXTENSION_WEB_DIRS:
+                exported_nodes.EXTENSION_WEB_DIRS.pop(self_key)
         return exported_nodes
 
     def __add__(self, other):

comfy/ops.py

@@ -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

comfy/sample.py

@@ -51,19 +51,24 @@ def get_models_from_cond(cond, model_type):
             models += [c[1][model_type]]
     return models
 
-def load_additional_models(positive, negative, dtype):
+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:
+        control_models += m.get_models()
+
     gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen")
-    gligen = [x[1].to(dtype) for x in gligen]
+    gligen = [x[1] for x in gligen]
-    models = control_nets + gligen
+    models = control_models + gligen
-    comfy.model_management.load_controlnet_gpu(models)
     return models
 
 def cleanup_additional_models(models):
     """cleanup additional models that were loaded"""
     for m in models:
-        m.cleanup()
+        if hasattr(m, 'cleanup'):
+            m.cleanup()
 
 def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
     device = comfy.model_management.get_torch_device()
@@ -72,7 +77,8 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
         noise_mask = prepare_mask(noise_mask, noise.shape, device)
 
     real_model = None
-    comfy.model_management.load_model_gpu(model)
+    models = get_additional_models(positive, negative)
+    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)
@@ -81,7 +87,6 @@ def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative
     positive_copy = broadcast_cond(positive, noise.shape[0], device)
     negative_copy = broadcast_cond(negative, noise.shape[0], device)
 
-    models = load_additional_models(positive, negative, model.model_dtype())
 
     sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
 

comfy/samplers.py

@@ -88,9 +88,9 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
                 gligen_type = gligen[0]
                 gligen_model = gligen[1]
                 if gligen_type == "position":
-                    gligen_patch = gligen_model.set_position(input_x.shape, gligen[2], input_x.device)
+                    gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device)
                 else:
-                    gligen_patch = gligen_model.set_empty(input_x.shape, input_x.device)
+                    gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device)
 
                 patches['middle_patch'] = [gligen_patch]
 
@@ -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 = []

comfy/sd.py

@@ -243,8 +243,15 @@ def set_attr(obj, attr, value):
     setattr(obj, attrs[-1], torch.nn.Parameter(value))
     del prev
 
+def get_attr(obj, attr):
+    attrs = attr.split(".")
+    for name in attrs:
+        obj = getattr(obj, name)
+    return obj
+
+
 class ModelPatcher:
-    def __init__(self, model, load_device, offload_device, size=0):
+    def __init__(self, model, load_device, offload_device, size=0, current_device=None):
         self.size = size
         self.model = model
         self.patches = {}
@@ -253,6 +260,10 @@ class ModelPatcher:
         self.model_size()
         self.load_device = load_device
         self.offload_device = offload_device
+        if current_device is None:
+            self.current_device = self.offload_device
+        else:
+            self.current_device = current_device
 
     def model_size(self):
         if self.size > 0:
@@ -267,7 +278,7 @@ class ModelPatcher:
         return size
 
     def clone(self):
-        n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size)
+        n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device)
         n.patches = {}
         for k in self.patches:
             n.patches[k] = self.patches[k][:]
@@ -276,6 +287,11 @@ class ModelPatcher:
         n.model_keys = self.model_keys
         return n
 
+    def is_clone(self, other):
+        if hasattr(other, 'model') and self.model is other.model:
+            return True
+        return False
+
     def set_model_sampler_cfg_function(self, sampler_cfg_function):
         if len(inspect.signature(sampler_cfg_function).parameters) == 3:
             self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way
@@ -390,6 +406,11 @@ class ModelPatcher:
             out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
             set_attr(self.model, key, out_weight)
             del temp_weight
+
+        if device_to is not None:
+            self.model.to(device_to)
+            self.current_device = device_to
+
         return self.model
 
     def calculate_weight(self, patches, weight, key):
@@ -482,7 +503,7 @@ class ModelPatcher:
 
         return weight
 
-    def unpatch_model(self):
+    def unpatch_model(self, device_to=None):
         keys = list(self.backup.keys())
 
         for k in keys:
@@ -490,6 +511,11 @@ class ModelPatcher:
 
         self.backup = {}
 
+        if device_to is not None:
+            self.model.to(device_to)
+            self.current_device = device_to
+
+
 def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
     key_map = model_lora_keys_unet(model.model)
     key_map = model_lora_keys_clip(clip.cond_stage_model, key_map)
@@ -555,7 +581,7 @@ class CLIP:
         else:
             self.cond_stage_model.reset_clip_layer()
 
-        model_management.load_model_gpu(self.patcher)
+        self.load_model()
         cond, pooled = self.cond_stage_model.encode_token_weights(tokens)
         if return_pooled:
             return cond, pooled
@@ -571,11 +597,9 @@ class CLIP:
     def get_sd(self):
         return self.cond_stage_model.state_dict()
 
-    def patch_model(self):
+    def load_model(self):
-        self.patcher.patch_model()
+        model_management.load_model_gpu(self.patcher)
-
+        return self.patcher
-    def unpatch_model(self):
-        self.patcher.unpatch_model()
 
     def get_key_patches(self):
         return self.patcher.get_key_patches()
@@ -630,11 +654,12 @@ class VAE:
         return samples
 
     def decode(self, samples_in):
-        model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
         try:
+            memory_used = (2562 * samples_in.shape[2] * samples_in.shape[3] * 64) * 1.7
+            model_management.free_memory(memory_used, self.device)
             free_memory = model_management.get_free_memory(self.device)
-            batch_number = int((free_memory * 0.7) / (2562 * samples_in.shape[2] * samples_in.shape[3] * 64))
+            batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
 
             pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu")
@@ -650,19 +675,19 @@ class VAE:
         return pixel_samples
 
     def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16):
-        model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
         output = self.decode_tiled_(samples, tile_x, tile_y, overlap)
         self.first_stage_model = self.first_stage_model.to(self.offload_device)
         return output.movedim(1,-1)
 
     def encode(self, pixel_samples):
-        model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
         pixel_samples = pixel_samples.movedim(-1,1)
         try:
+            memory_used = (2078 * pixel_samples.shape[2] * pixel_samples.shape[3]) * 1.7 #NOTE: this constant along with the one in the decode above are estimated from the mem usage for the VAE and could change.
+            model_management.free_memory(memory_used, self.device)
             free_memory = model_management.get_free_memory(self.device)
-            batch_number = int((free_memory * 0.7) / (2078 * pixel_samples.shape[2] * pixel_samples.shape[3])) #NOTE: this constant along with the one in the decode above are estimated from the mem usage for the VAE and could change.
+            batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
             samples = torch.empty((pixel_samples.shape[0], 4, round(pixel_samples.shape[2] // 8), round(pixel_samples.shape[3] // 8)), device="cpu")
             for x in range(0, pixel_samples.shape[0], batch_number):
@@ -677,7 +702,6 @@ class VAE:
         return samples
 
     def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
-        model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
         pixel_samples = pixel_samples.movedim(-1,1)
         samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap)
@@ -757,6 +781,7 @@ class ControlNet(ControlBase):
     def __init__(self, control_model, global_average_pooling=False, device=None):
         super().__init__(device)
         self.control_model = control_model
+        self.control_model_wrapped = ModelPatcher(self.control_model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
         self.global_average_pooling = global_average_pooling
 
     def get_control(self, x_noisy, t, cond, batched_number):
@@ -780,19 +805,14 @@ class ControlNet(ControlBase):
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
             self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
 
-        if self.control_model.dtype == torch.float16:
-            precision_scope = torch.autocast
-        else:
-            precision_scope = contextlib.nullcontext
 
-        with precision_scope(model_management.get_autocast_device(self.device)):
+        context = torch.cat(cond['c_crossattn'], 1)
-            self.control_model = model_management.load_if_low_vram(self.control_model)
+        y = cond.get('c_adm', None)
-            context = torch.cat(cond['c_crossattn'], 1)
+        if y is not None:
-            y = cond.get('c_adm', None)
+            y = y.to(self.control_model.dtype)
-            control = self.control_model(x=x_noisy, hint=self.cond_hint, timesteps=t, context=context, y=y)
+        control = self.control_model(x=x_noisy.to(self.control_model.dtype), hint=self.cond_hint, timesteps=t, context=context.to(self.control_model.dtype), y=y)
-            self.control_model = model_management.unload_if_low_vram(self.control_model)
+
         out = {'middle':[], 'output': []}
-        autocast_enabled = torch.is_autocast_enabled()
 
         for i in range(len(control)):
             if i == (len(control) - 1):
@@ -806,7 +826,7 @@ class ControlNet(ControlBase):
                 x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
 
             x *= self.strength
-            if x.dtype != output_dtype and not autocast_enabled:
+            if x.dtype != output_dtype:
                 x = x.to(output_dtype)
 
             if control_prev is not None and key in control_prev:
@@ -825,17 +845,133 @@ class ControlNet(ControlBase):
 
     def get_models(self):
         out = super().get_models()
-        out.append(self.control_model)
+        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.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias)
+            else:
+                return torch.nn.functional.linear(input, self.weight.to(input.device), 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.to(input.device) + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), self.bias, self.stride, self.padding, self.dilation, self.groups)
+            else:
+                return torch.nn.functional.conv2d(input, self.weight.to(input.device), 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:
+            weight = sd[k]
+            if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break.
+                key_split = k.split('.')              # I have no idea why they don't just leave the weight there instead of using the meta device.
+                op = get_attr(diffusion_model, '.'.join(key_split[:-1]))
+                weight = op._hf_hook.weights_map[key_split[-1]]
+
+            try:
+                set_attr(self.control_model, k, weight)
+            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
         use_fp16 = model_management.should_use_fp16()
-        controlnet_config = model_detection.model_config_from_diffusers_unet(controlnet_data, use_fp16).unet_config
+        controlnet_config = model_detection.unet_config_from_diffusers_unet(controlnet_data, use_fp16)
         diffusers_keys = utils.unet_to_diffusers(controlnet_config)
         diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
         diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
@@ -874,6 +1010,9 @@ def load_controlnet(ckpt_path, model=None):
             if k in controlnet_data:
                 new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
 
+        leftover_keys = controlnet_data.keys()
+        if len(leftover_keys) > 0:
+            print("leftover keys:", leftover_keys)
         controlnet_data = new_sd
 
     pth_key = 'control_model.zero_convs.0.0.weight'
@@ -901,8 +1040,8 @@ def load_controlnet(ckpt_path, model=None):
     if pth:
         if 'difference' in controlnet_data:
             if model is not None:
-                m = model.patch_model()
+                model_management.load_models_gpu([model])
-                model_sd = m.state_dict()
+                model_sd = model.model_state_dict()
                 for x in controlnet_data:
                     c_m = "control_model."
                     if x.startswith(c_m):
@@ -910,7 +1049,6 @@ def load_controlnet(ckpt_path, model=None):
                         if sd_key in model_sd:
                             cd = controlnet_data[x]
                             cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
-                model.unpatch_model()
             else:
                 print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
 
@@ -970,11 +1108,10 @@ class T2IAdapter(ControlBase):
         output_dtype = x_noisy.dtype
         out = {'input':[]}
 
-        autocast_enabled = torch.is_autocast_enabled()
         for i in range(len(self.control_input)):
             key = 'input'
             x = self.control_input[i] * self.strength
-            if x.dtype != output_dtype and not autocast_enabled:
+            if x.dtype != output_dtype:
                 x = x.to(output_dtype)
 
             if control_prev is not None and key in control_prev:
@@ -1001,7 +1138,6 @@ class T2IAdapter(ControlBase):
         self.copy_to(c)
         return c
 
-
 def load_t2i_adapter(t2i_data):
     keys = t2i_data.keys()
     if 'adapter' in keys:
@@ -1087,7 +1223,7 @@ def load_gligen(ckpt_path):
     model = gligen.load_gligen(data)
     if model_management.should_use_fp16():
         model = model.half()
-    return model
+    return ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
 
 def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
     #TODO: this function is a mess and should be removed eventually
@@ -1199,8 +1335,13 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         if output_clipvision:
             clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
 
+    dtype = torch.float32
+    if fp16:
+        dtype = torch.float16
+
+    inital_load_device = model_management.unet_inital_load_device(parameters, dtype)
     offload_device = model_management.unet_offload_device()
-    model = model_config.get_model(sd, "model.diffusion_model.", device=offload_device)
+    model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device)
     model.load_model_weights(sd, "model.diffusion_model.")
 
     if output_vae:
@@ -1221,7 +1362,12 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     if len(left_over) > 0:
         print("left over keys:", left_over)
 
-    return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae, clipvision)
+    model_patcher = ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device(), current_device=inital_load_device)
+    if inital_load_device != torch.device("cpu"):
+        print("loaded straight to GPU")
+        model_management.load_model_gpu(model_patcher)
+
+    return (model_patcher, clip, vae, clipvision)
 
 
 def load_unet(unet_path): #load unet in diffusers format
@@ -1249,14 +1395,6 @@ def load_unet(unet_path): #load unet in diffusers format
     return ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device)
 
 def save_checkpoint(output_path, model, clip, vae, metadata=None):
-    try:
+    model_management.load_models_gpu([model, clip.load_model()])
-        model.patch_model()
+    sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd())
-        clip.patch_model()
+    utils.save_torch_file(sd, output_path, metadata=metadata)
-        sd = model.model.state_dict_for_saving(clip.get_sd(), vae.get_sd())
-        utils.save_torch_file(sd, output_path, metadata=metadata)
-        model.unpatch_model()
-        clip.unpatch_model()
-    except Exception as e:
-        model.unpatch_model()
-        clip.unpatch_model()
-        raise e

comfy_extras/nodes/nodes_mask.py

@@ -1,3 +1,5 @@
+import numpy as np
+from scipy.ndimage import grey_dilation
 import torch
 
 from comfy.nodes.common import MAX_RESOLUTION
@@ -277,6 +279,35 @@ class FeatherMask:
             output[-y, :] *= feather_rate
 
         return (output,)
+    
+class GrowMask:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "expand": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "tapered_corners": ("BOOLEAN", {"default": True}),
+            },
+        }
+    
+    CATEGORY = "mask"
+
+    RETURN_TYPES = ("MASK",)
+
+    FUNCTION = "expand_mask"
+
+    def expand_mask(self, mask, expand, tapered_corners):
+        c = 0 if tapered_corners else 1
+        kernel = np.array([[c, 1, c],
+                           [1, 1, 1],
+                           [c, 1, c]])
+        output = mask.numpy().copy()
+        while expand > 0:
+            output = grey_dilation(output, footprint=kernel)
+            expand -= 1
+        output = torch.from_numpy(output)
+        return (output,)
 
 
 
@@ -290,6 +321,7 @@ NODE_CLASS_MAPPINGS = {
     "CropMask": CropMask,
     "MaskComposite": MaskComposite,
     "FeatherMask": FeatherMask,
+    "GrowMask": GrowMask,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {

comfy_extras/nodes/nodes_post_processing.py

@@ -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,
 }

web/index.html

@@ -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">

web/scripts/app.js

@@ -1026,18 +1026,21 @@ export class ComfyApp {
 	}
 
 	/**
-	 * Loads all extensions from the API into the window
+	 * Loads all extensions from the API into the window in parallel
 	 */
 	async #loadExtensions() {
-		const extensions = await api.getExtensions();
+	    const extensions = await api.getExtensions();
-		this.logging.addEntry("Comfy.App", "debug", { Extensions: extensions });
+	    this.logging.addEntry("Comfy.App", "debug", { Extensions: extensions });
-		for (const ext of extensions) {
+	
-			try {
+	    const extensionPromises = extensions.map(async ext => {
-				await import(api.apiURL(ext));
+	        try {
-			} catch (error) {
+	            await import(api.apiURL(ext));
-				console.error("Error loading extension", ext, error);
+	        } catch (error) {
-			}
+	            console.error("Error loading extension", ext, error);
-		}
+	        }
+	    });
+	
+	    await Promise.all(extensionPromises);
 	}
 
 	/**

web/user.css

@@ -0,0 +1 @@
+/* Put custom styles here */