Initial exploration of weight zipper

* Call unpatch_hooks at the start of ModelPatcher.partially_unload

* Only call unpatch_hooks in partially_unload if lowvram is possible

comfy/cli_args.py

@@ -106,6 +106,7 @@ attn_group.add_argument("--use-split-cross-attention", action="store_true", help
 attn_group.add_argument("--use-quad-cross-attention", action="store_true", help="Use the sub-quadratic cross attention optimization . Ignored when xformers is used.")
 attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", help="Use the new pytorch 2.0 cross attention function.")
 attn_group.add_argument("--use-sage-attention", action="store_true", help="Use sage attention.")
+attn_group.add_argument("--use-flash-attention", action="store_true", help="Use FlashAttention.")
 
 parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
 

comfy/clip_vision.py

@@ -9,6 +9,7 @@ import comfy.model_patcher
 import comfy.model_management
 import comfy.utils
 import comfy.clip_model
+import comfy.image_encoders.dino2
 
 class Output:
     def __getitem__(self, key):
@@ -34,6 +35,12 @@ def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], s
     image = torch.clip((255. * image), 0, 255).round() / 255.0
     return (image - mean.view([3,1,1])) / std.view([3,1,1])
 
+IMAGE_ENCODERS = {
+    "clip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
+    "siglip_vision_model": comfy.clip_model.CLIPVisionModelProjection,
+    "dinov2": comfy.image_encoders.dino2.Dinov2Model,
+}
+
 class ClipVisionModel():
     def __init__(self, json_config):
         with open(json_config) as f:
@@ -42,10 +49,11 @@ class ClipVisionModel():
         self.image_size = config.get("image_size", 224)
         self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
         self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711])
+        model_class = IMAGE_ENCODERS.get(config.get("model_type", "clip_vision_model"))
         self.load_device = comfy.model_management.text_encoder_device()
         offload_device = comfy.model_management.text_encoder_offload_device()
         self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
-        self.model = comfy.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, comfy.ops.manual_cast)
+        self.model = model_class(config, self.dtype, offload_device, comfy.ops.manual_cast)
         self.model.eval()
 
         self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
@@ -111,6 +119,8 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
                 json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
         else:
             json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
+    elif "embeddings.patch_embeddings.projection.weight" in sd:
+        json_config = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "image_encoders"), "dino2_giant.json")
     else:
         return None
 

comfy/image_encoders/dino2.py

@@ -0,0 +1,141 @@
+import torch
+from comfy.text_encoders.bert import BertAttention
+import comfy.model_management
+from comfy.ldm.modules.attention import optimized_attention_for_device
+
+
+class Dino2AttentionOutput(torch.nn.Module):
+    def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device)
+
+    def forward(self, x):
+        return self.dense(x)
+
+
+class Dino2AttentionBlock(torch.nn.Module):
+    def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.attention = BertAttention(embed_dim, heads, dtype, device, operations)
+        self.output = Dino2AttentionOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+
+    def forward(self, x, mask, optimized_attention):
+        return self.output(self.attention(x, mask, optimized_attention))
+
+
+class LayerScale(torch.nn.Module):
+    def __init__(self, dim, dtype, device, operations):
+        super().__init__()
+        self.lambda1 = torch.nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+
+    def forward(self, x):
+        return x * comfy.model_management.cast_to_device(self.lambda1, x.device, x.dtype)
+
+
+class SwiGLUFFN(torch.nn.Module):
+    def __init__(self, dim, dtype, device, operations):
+        super().__init__()
+        in_features = out_features = dim
+        hidden_features = int(dim * 4)
+        hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
+
+        self.weights_in = operations.Linear(in_features, 2 * hidden_features, bias=True, device=device, dtype=dtype)
+        self.weights_out = operations.Linear(hidden_features, out_features, bias=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.weights_in(x)
+        x1, x2 = x.chunk(2, dim=-1)
+        x = torch.nn.functional.silu(x1) * x2
+        return self.weights_out(x)
+
+
+class Dino2Block(torch.nn.Module):
+    def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations)
+        self.layer_scale1 = LayerScale(dim, dtype, device, operations)
+        self.layer_scale2 = LayerScale(dim, dtype, device, operations)
+        self.mlp = SwiGLUFFN(dim, dtype, device, operations)
+        self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
+        self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
+
+    def forward(self, x, optimized_attention):
+        x = x + self.layer_scale1(self.attention(self.norm1(x), None, optimized_attention))
+        x = x + self.layer_scale2(self.mlp(self.norm2(x)))
+        return x
+
+
+class Dino2Encoder(torch.nn.Module):
+    def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations):
+        super().__init__()
+        self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations) for _ in range(num_layers)])
+
+    def forward(self, x, intermediate_output=None):
+        optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
+
+        if intermediate_output is not None:
+            if intermediate_output < 0:
+                intermediate_output = len(self.layer) + intermediate_output
+
+        intermediate = None
+        for i, l in enumerate(self.layer):
+            x = l(x, optimized_attention)
+            if i == intermediate_output:
+                intermediate = x.clone()
+        return x, intermediate
+
+
+class Dino2PatchEmbeddings(torch.nn.Module):
+    def __init__(self, dim, num_channels=3, patch_size=14, image_size=518, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.projection = operations.Conv2d(
+            in_channels=num_channels,
+            out_channels=dim,
+            kernel_size=patch_size,
+            stride=patch_size,
+            bias=True,
+            dtype=dtype,
+            device=device
+        )
+
+    def forward(self, pixel_values):
+        return self.projection(pixel_values).flatten(2).transpose(1, 2)
+
+
+class Dino2Embeddings(torch.nn.Module):
+    def __init__(self, dim, dtype, device, operations):
+        super().__init__()
+        patch_size = 14
+        image_size = 518
+
+        self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations)
+        self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device))
+        self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device))
+        self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))
+
+    def forward(self, pixel_values):
+        x = self.patch_embeddings(pixel_values)
+        # TODO: mask_token?
+        x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
+        x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype)
+        return x
+
+
+class Dinov2Model(torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        num_layers = config_dict["num_hidden_layers"]
+        dim = config_dict["hidden_size"]
+        heads = config_dict["num_attention_heads"]
+        layer_norm_eps = config_dict["layer_norm_eps"]
+
+        self.embeddings = Dino2Embeddings(dim, dtype, device, operations)
+        self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations)
+        self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
+
+    def forward(self, pixel_values, attention_mask=None, intermediate_output=None):
+        x = self.embeddings(pixel_values)
+        x, i = self.encoder(x, intermediate_output=intermediate_output)
+        x = self.layernorm(x)
+        pooled_output = x[:, 0, :]
+        return x, i, pooled_output, None

comfy/image_encoders/dino2_giant.json

@@ -0,0 +1,21 @@
+{
+  "attention_probs_dropout_prob": 0.0,
+  "drop_path_rate": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 1536,
+  "image_size": 518,
+  "initializer_range": 0.02,
+  "layer_norm_eps": 1e-06,
+  "layerscale_value": 1.0,
+  "mlp_ratio": 4,
+  "model_type": "dinov2",
+  "num_attention_heads": 24,
+  "num_channels": 3,
+  "num_hidden_layers": 40,
+  "patch_size": 14,
+  "qkv_bias": true,
+  "use_swiglu_ffn": true,
+  "image_mean": [0.485, 0.456, 0.406],
+  "image_std": [0.229, 0.224, 0.225]
+}

comfy/k_diffusion/sampling.py

@@ -1419,6 +1419,6 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
             old_denoised_d = denoised_d
 
         if s_noise != 0 and sigmas[i + 1] > 0:
-            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (sigmas[i + 1] ** 2 - sigmas[i] ** 2 * r ** 2).sqrt()
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (sigmas[i + 1] ** 2 - sigmas[i] ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0)
         old_denoised = denoised
     return x

comfy/ldm/flux/math.py

@@ -10,10 +10,11 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
     q_shape = q.shape
     k_shape = k.shape
 
-    q = q.float().reshape(*q.shape[:-1], -1, 1, 2)
-    k = k.float().reshape(*k.shape[:-1], -1, 1, 2)
-    q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v)
-    k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v)
+    if pe is not None:
+        q = q.to(dtype=pe.dtype).reshape(*q.shape[:-1], -1, 1, 2)
+        k = k.to(dtype=pe.dtype).reshape(*k.shape[:-1], -1, 1, 2)
+        q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v)
+        k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v)
 
     heads = q.shape[1]
     x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask)
@@ -36,8 +37,8 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
 
 
 def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
-    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
-    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_ = xq.to(dtype=freqs_cis.dtype).reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.to(dtype=freqs_cis.dtype).reshape(*xk.shape[:-1], -1, 1, 2)
     xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
     xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
     return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)

comfy/ldm/flux/model.py

@@ -115,8 +115,11 @@ class Flux(nn.Module):
         vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
         txt = self.txt_in(txt)
 
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        pe = self.pe_embedder(ids)
+        if img_ids is not None:
+            ids = torch.cat((txt_ids, img_ids), dim=1)
+            pe = self.pe_embedder(ids)
+        else:
+            pe = None
 
         blocks_replace = patches_replace.get("dit", {})
         for i, block in enumerate(self.double_blocks):

comfy/ldm/modules/attention.py

@@ -24,6 +24,13 @@ if model_management.sage_attention_enabled():
         logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
         exit(-1)
 
+if model_management.flash_attention_enabled():
+    try:
+        from flash_attn import flash_attn_func
+    except ModuleNotFoundError:
+        logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn")
+        exit(-1)
+
 from comfy.cli_args import args
 import comfy.ops
 ops = comfy.ops.disable_weight_init
@@ -496,6 +503,63 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
     return out
 
 
+try:
+    @torch.library.custom_op("flash_attention::flash_attn", mutates_args=())
+    def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
+                    dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
+        return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal)
+
+
+    @flash_attn_wrapper.register_fake
+    def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False):
+        # Output shape is the same as q
+        return q.new_empty(q.shape)
+except AttributeError as error:
+    FLASH_ATTN_ERROR = error
+
+    def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
+                    dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
+        assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}"
+
+
+def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
+
+    if mask is not None:
+        # add a batch dimension if there isn't already one
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0)
+        # add a heads dimension if there isn't already one
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(1)
+
+    try:
+        assert mask is None
+        out = flash_attn_wrapper(
+            q.transpose(1, 2),
+            k.transpose(1, 2),
+            v.transpose(1, 2),
+            dropout_p=0.0,
+            causal=False,
+        ).transpose(1, 2)
+    except Exception as e:
+        logging.warning(f"Flash Attention failed, using default SDPA: {e}")
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+    if not skip_output_reshape:
+        out = (
+            out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+        )
+    return out
+
+
 optimized_attention = attention_basic
 
 if model_management.sage_attention_enabled():
@@ -504,6 +568,9 @@ if model_management.sage_attention_enabled():
 elif model_management.xformers_enabled():
     logging.info("Using xformers attention")
     optimized_attention = attention_xformers
+elif model_management.flash_attention_enabled():
+    logging.info("Using Flash Attention")
+    optimized_attention = attention_flash
 elif model_management.pytorch_attention_enabled():
     logging.info("Using pytorch attention")
     optimized_attention = attention_pytorch

comfy/ldm/wan/model.py

@@ -384,6 +384,7 @@ class WanModel(torch.nn.Module):
         context,
         clip_fea=None,
         freqs=None,
+        transformer_options={},
     ):
         r"""
         Forward pass through the diffusion model
@@ -423,14 +424,18 @@ class WanModel(torch.nn.Module):
             context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
             context = torch.concat([context_clip, context], dim=1)
 
-        # arguments
-        kwargs = dict(
-            e=e0,
-            freqs=freqs,
-            context=context)
-
-        for block in self.blocks:
-            x = block(x, **kwargs)
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.blocks):
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"])
+                    return out
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(x, e=e0, freqs=freqs, context=context)
 
         # head
         x = self.head(x, e)
@@ -439,7 +444,7 @@ class WanModel(torch.nn.Module):
         x = self.unpatchify(x, grid_sizes)
         return x
 
-    def forward(self, x, timestep, context, clip_fea=None, **kwargs):
+    def forward(self, x, timestep, context, clip_fea=None, transformer_options={},**kwargs):
         bs, c, t, h, w = x.shape
         x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
         patch_size = self.patch_size
@@ -453,7 +458,7 @@ class WanModel(torch.nn.Module):
         img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
 
         freqs = self.rope_embedder(img_ids).movedim(1, 2)
-        return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs)[:, :, :t, :h, :w]
+        return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options)[:, :, :t, :h, :w]
 
     def unpatchify(self, x, grid_sizes):
         r"""

comfy/model_base.py

@@ -16,6 +16,7 @@
     along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
 
+from __future__ import annotations
 import torch
 import logging
 from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
@@ -104,7 +105,7 @@ class BaseModel(torch.nn.Module):
         self.model_config = model_config
         self.manual_cast_dtype = model_config.manual_cast_dtype
         self.device = device
-        self.current_patcher: 'ModelPatcher' = None
+        self.current_patcher: ModelPatcher = None
 
         if not unet_config.get("disable_unet_model_creation", False):
             if model_config.custom_operations is None:
@@ -128,6 +129,7 @@ class BaseModel(torch.nn.Module):
         logging.info("model_type {}".format(model_type.name))
         logging.debug("adm {}".format(self.adm_channels))
         self.memory_usage_factor = model_config.memory_usage_factor
+        self.zipper_initialized = False
 
     def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
         return comfy.patcher_extension.WrapperExecutor.new_class_executor(
@@ -137,6 +139,16 @@ class BaseModel(torch.nn.Module):
         ).execute(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)
 
     def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
+        # handle lowvram zipper initialization, if required
+        if self.model_lowvram and not self.zipper_initialized:
+            if self.current_patcher:
+                self.zipper_initialized = True
+                with self.current_patcher.use_ejected():
+                    loading = self.current_patcher._load_list_lowvram_only()
+                    
+        return self._apply_model_inner(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)
+
+    def _apply_model_inner(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
         sigma = t
         xc = self.model_sampling.calculate_input(sigma, x)
         if c_concat is not None:

comfy/model_management.py

@@ -930,6 +930,9 @@ def cast_to_device(tensor, device, dtype, copy=False):
 def sage_attention_enabled():
     return args.use_sage_attention
 
+def flash_attention_enabled():
+    return args.use_flash_attention
+
 def xformers_enabled():
     global directml_enabled
     global cpu_state

comfy/model_patcher.py

@@ -17,7 +17,7 @@
 """
 
 from __future__ import annotations
-from typing import Optional, Callable
+from typing import Optional, Callable, TYPE_CHECKING
 import torch
 import copy
 import inspect
@@ -26,6 +26,7 @@ import uuid
 import collections
 import math
 
+import comfy.ops
 import comfy.utils
 import comfy.float
 import comfy.model_management
@@ -34,6 +35,9 @@ import comfy.hooks
 import comfy.patcher_extension
 from comfy.patcher_extension import CallbacksMP, WrappersMP, PatcherInjection
 from comfy.comfy_types import UnetWrapperFunction
+if TYPE_CHECKING:
+    from comfy.model_base import BaseModel
+
 
 def string_to_seed(data):
     crc = 0xFFFFFFFF
@@ -201,7 +205,7 @@ class MemoryCounter:
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
         self.size = size
-        self.model = model
+        self.model: BaseModel = model
         if not hasattr(self.model, 'device'):
             logging.debug("Model doesn't have a device attribute.")
             self.model.device = offload_device
@@ -568,6 +572,14 @@ class ModelPatcher:
         else:
             set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key))
 
+    def _zipper_dict_lowvram_only(self):
+        loading = self._load_list_lowvram_only()
+        
+
+    def _load_list_lowvram_only(self):
+        loading = self._load_list()
+        return [x for x in loading if hasattr(x[2], "prev_comfy_cast_weights")]
+
     def _load_list(self):
         loading = []
         for n, m in self.model.named_modules():
@@ -583,6 +595,35 @@ class ModelPatcher:
                 loading.append((comfy.model_management.module_size(m), n, m, params))
         return loading
 
+    def prepare_teeth(self):
+        ordered_list = self._load_list_lowvram_only()
+        prev_i = None
+        next_i = None
+        # first, create teeth on modules in list
+        for l in ordered_list:
+            m: comfy.ops.CastWeightBiasOp = l[2]
+            m.init_tooth(self.load_device, self.offload_device, l[1])
+        # create teeth linked list
+        for i in range(len(ordered_list)):
+            if i+1 == len(ordered_list):
+                next_i = None
+            else:
+                next_i = i+1
+            m: comfy.ops.CastWeightBiasOp = ordered_list[i][2]
+            if prev_i is not None:
+                m.zipper_tooth.prev_tooth = ordered_list[prev_i][2].zipper_tooth
+            else:
+                m.zipper_tooth.start = True
+            if next_i is not None:
+                m.zipper_tooth.next_tooth = ordered_list[next_i][2].zipper_tooth
+            prev_i = i
+
+    def clean_teeth(self):
+        ordered_list = self._load_list_lowvram_only()
+        for l in ordered_list:
+            m: comfy.ops.CastWeightBiasOp = l[2]
+            m.clean_tooth()
+
     def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
         with self.use_ejected():
             self.unpatch_hooks()
@@ -591,6 +632,8 @@ class ModelPatcher:
             lowvram_counter = 0
             loading = self._load_list()
 
+            logging.info(f"total size of _load_list: {sum([x[0] for x in loading])}")
+
             load_completely = []
             loading.sort(reverse=True)
             for x in loading:
@@ -672,6 +715,7 @@ class ModelPatcher:
             if lowvram_counter > 0:
                 logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
                 self.model.model_lowvram = True
+                self.model.zipper_initialized = False
             else:
                 logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
                 self.model.model_lowvram = False
@@ -684,6 +728,9 @@ class ModelPatcher:
             self.model.model_loaded_weight_memory = mem_counter
             self.model.current_weight_patches_uuid = self.patches_uuid
 
+            if self.model.model_lowvram:
+                self.prepare_teeth()
+
             for callback in self.get_all_callbacks(CallbacksMP.ON_LOAD):
                 callback(self, device_to, lowvram_model_memory, force_patch_weights, full_load)
 
@@ -715,6 +762,7 @@ class ModelPatcher:
                     move_weight_functions(m, device_to)
                     wipe_lowvram_weight(m)
 
+                self.clean_teeth()
                 self.model.model_lowvram = False
                 self.model.lowvram_patch_counter = 0
 
@@ -747,6 +795,7 @@ class ModelPatcher:
 
     def partially_unload(self, device_to, memory_to_free=0):
         with self.use_ejected():
+            hooks_unpatched = False
             memory_freed = 0
             patch_counter = 0
             unload_list = self._load_list()
@@ -770,6 +819,10 @@ class ModelPatcher:
                                 move_weight = False
                                 break
 
+                            if not hooks_unpatched:
+                                self.unpatch_hooks()
+                                hooks_unpatched = True
+
                             if bk.inplace_update:
                                 comfy.utils.copy_to_param(self.model, key, bk.weight)
                             else:
@@ -799,8 +852,10 @@ class ModelPatcher:
                         logging.debug("freed {}".format(n))
 
             self.model.model_lowvram = True
+            self.model.zipper_initialized = False
             self.model.lowvram_patch_counter += patch_counter
             self.model.model_loaded_weight_memory -= memory_freed
+            self.prepare_teeth()
             return memory_freed
 
     def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):

comfy/ops.py

@@ -16,6 +16,7 @@
     along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
 
+from __future__ import annotations
 import torch
 import logging
 import comfy.model_management
@@ -56,6 +57,79 @@ class CastWeightBiasOp:
     comfy_cast_weights = False
     weight_function = []
     bias_function = []
+    zipper_init: dict = None
+    zipper_tooth: ZipperTooth = None
+    _zipper_tooth: ZipperTooth = None
+
+    def init_tooth(self, load_device, offload_device, key: str=None):
+        if self.zipper_tooth:
+            self.clean_tooth()
+        self.zipper_tooth = ZipperTooth(self, load_device, offload_device, key)
+
+    def clean_tooth(self):
+        if self.zipper_tooth:
+            del self.zipper_tooth
+            self.zipper_tooth = None
+
+    def connect_teeth(self):
+        if self.zipper_init is not None:
+
+            self.zipper_init[self.zipper_key] = (hasattr(self, "prev_comfy_cast_weights"), self.zipper_dict.get("prev_zipper_key", None))
+            self.zipper_dict["prev_zipper_key"] = self.zipper_key
+
+    # def zipper_connect(self):
+    #     if self.zipper_dict is not None:
+    #         self.zipper_dict[self.zipper_key] = (hasattr(self, "prev_comfy_cast_weights"), self.zipper_dict.get("prev_zipper_key", None))
+    #         self.zipper_dict["prev_zipper_key"] = self.zipper_key
+
+class ZipperTooth:
+    def __init__(self, op: CastWeightBiasOp, load_device, offload_device, key: str=None):
+        self.op = op
+        self.key: str = key
+        self.weight_preloaded: torch.Tensor = None
+        self.bias_preloaded: torch.Tensor = None
+        self.load_device = load_device
+        self.offload_device = offload_device
+        self.start = False
+
+        self.prev_tooth: ZipperTooth = None
+        self.next_tooth: ZipperTooth = None
+
+    def get_bias_weight(self, input: torch.Tensor=None, dtype=None, device=None, bias_dtype=None):
+        try:
+            if self.start:
+                return cast_bias_weight(self.op, input, dtype, device, bias_dtype)
+            return self.weight_preloaded, self.bias_preloaded
+        finally:
+            # if self.prev_tooth:
+            #     self.prev_tooth.offload_previous(0)
+            self.next_tooth.preload_next(0, input, dtype, device, bias_dtype)
+
+    def preload_next(self, teeth_count=1, input: torch.Tensor=None, dtype=None, device=None, bias_dtype=None):
+        # TODO: queue load of tensors
+        if input is not None:
+            if dtype is None:
+                dtype = input.dtype
+            if bias_dtype is None:
+                bias_dtype = dtype
+            if device is None:
+                device = input.device
+
+        non_blocking = comfy.model_management.device_supports_non_blocking(self.load_device)
+        
+        if self.op.bias is not None:
+            self.bias_preloaded = comfy.model_management.cast_to(self.op.bias, bias_dtype, device, non_blocking=non_blocking)
+
+        self.weight_preloaded = comfy.model_management.cast_to(self.op.weight, dtype, device, non_blocking=non_blocking)
+        if self.next_tooth and teeth_count:
+            self.next_tooth.preload_next(teeth_count-1)
+
+    def offload_previous(self, teeth_count=1):
+        # TODO: queue offload of tensors
+        self.weight_preloaded = None
+        self.bias_preloaded = None
+        if self.prev_tooth and teeth_count:
+            self.prev_tooth.offload_previous(teeth_count-1)
 
 class disable_weight_init:
     class Linear(torch.nn.Linear, CastWeightBiasOp):
@@ -63,7 +137,11 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
+            #if self.zipper_init:
+            if self.zipper_tooth:
+                weight, bias = self.zipper_tooth.get_bias_weight(input)
+            else:
+                weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.linear(input, weight, bias)
 
         def forward(self, *args, **kwargs):
@@ -77,7 +155,10 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
+            if self.zipper_tooth:
+                weight, bias = self.zipper_tooth.get_bias_weight(input)
+            else:
+                weight, bias = cast_bias_weight(self, input)
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
@@ -91,7 +172,10 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
+            if self.zipper_tooth:
+                weight, bias = self.zipper_tooth.get_bias_weight(input)
+            else:
+                weight, bias = cast_bias_weight(self, input)
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
@@ -105,7 +189,10 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
+            if self.zipper_tooth:
+                weight, bias = self.zipper_tooth.get_bias_weight(input)
+            else:
+                weight, bias = cast_bias_weight(self, input)
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
@@ -119,7 +206,10 @@ class disable_weight_init:
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
+            if self.zipper_tooth:
+                weight, bias = self.zipper_tooth.get_bias_weight(input)
+            else:
+                weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
@@ -134,7 +224,10 @@ class disable_weight_init:
 
         def forward_comfy_cast_weights(self, input):
             if self.weight is not None:
-                weight, bias = cast_bias_weight(self, input)
+                if self.zipper_tooth:
+                    weight, bias = self.zipper_tooth.get_bias_weight(input)
+                else:
+                    weight, bias = cast_bias_weight(self, input)
             else:
                 weight = None
                 bias = None
@@ -156,7 +249,10 @@ class disable_weight_init:
                 input, output_size, self.stride, self.padding, self.kernel_size,
                 num_spatial_dims, self.dilation)
 
-            weight, bias = cast_bias_weight(self, input)
+            if self.zipper_tooth:
+                weight, bias = self.zipper_tooth.get_bias_weight(input)
+            else:
+                weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.conv_transpose2d(
                 input, weight, bias, self.stride, self.padding,
                 output_padding, self.groups, self.dilation)
@@ -177,7 +273,10 @@ class disable_weight_init:
                 input, output_size, self.stride, self.padding, self.kernel_size,
                 num_spatial_dims, self.dilation)
 
-            weight, bias = cast_bias_weight(self, input)
+            if self.zipper_tooth:
+                weight, bias = self.zipper_tooth.get_bias_weight(input)
+            else:
+                weight, bias = cast_bias_weight(self, input)
             return torch.nn.functional.conv_transpose1d(
                 input, weight, bias, self.stride, self.padding,
                 output_padding, self.groups, self.dilation)
@@ -197,7 +296,10 @@ class disable_weight_init:
             output_dtype = out_dtype
             if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
                 out_dtype = None
-            weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
+            if self.zipper_tooth:
+                weight, bias = self.zipper_tooth.get_bias_weight(device=input.device, dtype=out_dtype)
+            else:
+                weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
             return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
 
         def forward(self, *args, **kwargs):

comfy/samplers.py

@@ -6,6 +6,7 @@ if TYPE_CHECKING:
     from comfy.model_patcher import ModelPatcher
     from comfy.model_base import BaseModel
     from comfy.controlnet import ControlBase
+    from comfy.ops import CastWeightBiasOp
 import torch
 from functools import partial
 import collections
@@ -18,6 +19,7 @@ import comfy.patcher_extension
 import comfy.hooks
 import scipy.stats
 import numpy
+import comfy.ops
 
 
 def add_area_dims(area, num_dims):
@@ -360,15 +362,38 @@ def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_o
 
 #The main sampling function shared by all the samplers
 #Returns denoised
-def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
+def sampling_function(model: BaseModel, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
     if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
         uncond_ = None
     else:
         uncond_ = uncond
 
+    do_cleanup = False
+    if "weight_zipper" not in model_options:
+        do_cleanup = True
+        #zipper_dict = {}
+        model_options["weight_zipper"] = True
+        loaded_modules = model.current_patcher._load_list_lowvram_only()
+        low_m = [x for x in loaded_modules if hasattr(x[2], "prev_comfy_cast_weights")]
+        sum_m = sum([x[0] for x in low_m])
+        for l in loaded_modules:
+            m: CastWeightBiasOp = l[2]
+            if hasattr(m, "comfy_cast_weights"):
+                m.zipper_tooth = comfy.ops.ZipperTooth
+                #m.zipper_dict = zipper_dict
+                m.zipper_key = l[1]
+
     conds = [cond, uncond_]
     out = calc_cond_batch(model, conds, x, timestep, model_options)
 
+    if do_cleanup:
+        zzz = 20
+        for l in loaded_modules:
+            m: CastWeightBiasOp = l[2]
+            if hasattr(l[2], "comfy_cast_weights"):
+                #m.zipper_dict = None
+                m.zipper_key = None
+
     for fn in model_options.get("sampler_pre_cfg_function", []):
         args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep,
                 "input": x, "sigma": timestep, "model": model, "model_options": model_options}

comfy/sd.py

@@ -440,6 +440,10 @@ class VAE:
         self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
         logging.info("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
 
+    def throw_exception_if_invalid(self):
+        if self.first_stage_model is None:
+            raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.")
+
     def vae_encode_crop_pixels(self, pixels):
         downscale_ratio = self.spacial_compression_encode()
 
@@ -495,6 +499,7 @@ class VAE:
         return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)
 
     def decode(self, samples_in):
+        self.throw_exception_if_invalid()
         pixel_samples = None
         try:
             memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
@@ -525,6 +530,7 @@ class VAE:
         return pixel_samples
 
     def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
+        self.throw_exception_if_invalid()
         memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile
         model_management.load_models_gpu([self.patcher], memory_required=memory_used)
         dims = samples.ndim - 2
@@ -553,6 +559,7 @@ class VAE:
         return output.movedim(1, -1)
 
     def encode(self, pixel_samples):
+        self.throw_exception_if_invalid()
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         pixel_samples = pixel_samples.movedim(-1, 1)
         if self.latent_dim == 3 and pixel_samples.ndim < 5:
@@ -585,6 +592,7 @@ class VAE:
         return samples
 
     def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
+        self.throw_exception_if_invalid()
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         dims = self.latent_dim
         pixel_samples = pixel_samples.movedim(-1, 1)
@@ -899,7 +907,12 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
 
     model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata)
     if model_config is None:
-        return None
+        logging.warning("Warning, This is not a checkpoint file, trying to load it as a diffusion model only.")
+        diffusion_model = load_diffusion_model_state_dict(sd, model_options={})
+        if diffusion_model is None:
+            return None
+        return (diffusion_model, None, VAE(sd={}), None)  # The VAE object is there to throw an exception if it's actually used'
+
 
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
     if model_config.scaled_fp8 is not None:

nodes.py

@@ -770,6 +770,7 @@ class VAELoader:
             vae_path = folder_paths.get_full_path_or_raise("vae", vae_name)
             sd = comfy.utils.load_torch_file(vae_path)
         vae = comfy.sd.VAE(sd=sd)
+        vae.throw_exception_if_invalid()
         return (vae,)
 
 class ControlNetLoader:

requirements.txt

@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.11.8
+comfyui-frontend-package==1.12.14
 torch
 torchsde
 torchvision