make dpm_2_ancestral work with rectified flow.

https://github.com/comfyanonymous/ComfyUI/issues/5813

README.md

@@ -39,6 +39,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
 - Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
+- [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
 - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
 - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
 - Asynchronous Queue system
@@ -74,37 +75,37 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 
 | Keybind                            | Explanation                                                                                                        |
 |------------------------------------|--------------------------------------------------------------------------------------------------------------------|
-| Ctrl + Enter                       | Queue up current graph for generation                                                                              |
-| Ctrl + Shift + Enter               | Queue up current graph as first for generation                                                                     |
-| Ctrl + Alt + Enter                 | Cancel current generation                                                                                          |
-| Ctrl + Z/Ctrl + Y                  | Undo/Redo                                                                                                          |
-| Ctrl + S                           | Save workflow                                                                                                      |
-| Ctrl + O                           | Load workflow                                                                                                      |
-| Ctrl + A                           | Select all nodes                                                                                                   |
-| Alt + C                            | Collapse/uncollapse selected nodes                                                                                 |
-| Ctrl + M                           | Mute/unmute selected nodes                                                                                         |
-| Ctrl + B                           | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)            |
-| Delete/Backspace                   | Delete selected nodes                                                                                              |
-| Ctrl + Backspace                   | Delete the current graph                                                                                           |
-| Space                              | Move the canvas around when held and moving the cursor                                                             |
-| Ctrl/Shift + Click                 | Add clicked node to selection                                                                                      |
-| Ctrl + C/Ctrl + V                  | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes)                     |
-| Ctrl + C/Ctrl + Shift + V          | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
-| Shift + Drag                       | Move multiple selected nodes at the same time                                                                      |
-| Ctrl + D                           | Load default graph                                                                                                 |
-| Alt + `+`                          | Canvas Zoom in                                                                                                     |
-| Alt + `-`                          | Canvas Zoom out                                                                                                    |
-| Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out                                                                                                 |
-| P                                  | Pin/Unpin selected nodes                                                                                           |
-| Ctrl + G                           | Group selected nodes                                                                                               |
-| Q                                  | Toggle visibility of the queue                                                                                     |
-| H                                  | Toggle visibility of history                                                                                       |
-| R                                  | Refresh graph                                                                                                      |
+| `Ctrl` + `Enter`                      | Queue up current graph for generation                                                                              |
+| `Ctrl` + `Shift` + `Enter`              | Queue up current graph as first for generation                                                                     |
+| `Ctrl` + `Alt` + `Enter`                | Cancel current generation                                                                                          |
+| `Ctrl` + `Z`/`Ctrl` + `Y`                 | Undo/Redo                                                                                                          |
+| `Ctrl` + `S`                          | Save workflow                                                                                                      |
+| `Ctrl` + `O`                          | Load workflow                                                                                                      |
+| `Ctrl` + `A`                          | Select all nodes                                                                                                   |
+| `Alt `+ `C`                           | Collapse/uncollapse selected nodes                                                                                 |
+| `Ctrl` + `M`                          | Mute/unmute selected nodes                                                                                         |
+| `Ctrl` + `B`                           | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)            |
+| `Delete`/`Backspace`                   | Delete selected nodes                                                                                              |
+| `Ctrl` + `Backspace`                   | Delete the current graph                                                                                           |
+| `Space`                              | Move the canvas around when held and moving the cursor                                                             |
+| `Ctrl`/`Shift` + `Click`                 | Add clicked node to selection                                                                                      |
+| `Ctrl` + `C`/`Ctrl` + `V`                  | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes)                     |
+| `Ctrl` + `C`/`Ctrl` + `Shift` + `V`          | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
+| `Shift` + `Drag`                       | Move multiple selected nodes at the same time                                                                      |
+| `Ctrl` + `D`                           | Load default graph                                                                                                 |
+| `Alt` + `+`                          | Canvas Zoom in                                                                                                     |
+| `Alt` + `-`                          | Canvas Zoom out                                                                                                    |
+| `Ctrl` + `Shift` + LMB + Vertical drag | Canvas Zoom in/out                                                                                                 |
+| `P`                                  | Pin/Unpin selected nodes                                                                                           |
+| `Ctrl` + `G`                           | Group selected nodes                                                                                               |
+| `Q`                                 | Toggle visibility of the queue                                                                                     |
+| `H`                                  | Toggle visibility of history                                                                                       |
+| `R`                                  | Refresh graph                                                                                                      |
 | Double-Click LMB                   | Open node quick search palette                                                                                     |
-| Shift + Drag                       | Move multiple wires at once                                                                                        |
-| Ctrl + Alt + LMB                   | Disconnect all wires from clicked slot                                                                             |
+| `Shift` + Drag                       | Move multiple wires at once                                                                                        |
+| `Ctrl` + `Alt` + LMB                   | Disconnect all wires from clicked slot                                                                             |
 
-Ctrl can also be replaced with Cmd instead for macOS users
+`Ctrl` can also be replaced with `Cmd` instead for macOS users
 
 # Installing
 
@@ -212,6 +213,12 @@ For 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.
 
 For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py```
 
+### AMD ROCm Tips
+
+You can enable experimental memory efficient attention on pytorch 2.5 in ComfyUI on RDNA3 and potentially other AMD GPUs using this command:
+
+```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
+
 # Notes
 
 Only parts of the graph that have an output with all the correct inputs will be executed.

api_server/routes/internal/internal_routes.py

@@ -10,7 +10,6 @@ class InternalRoutes:
     The top level web router for internal routes: /internal/*
     The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only.
     Check README.md for more information.
-    
     '''
 
     def __init__(self, prompt_server):

app/user_manager.py

@@ -36,7 +36,7 @@ class UserManager():
 
         self.settings = AppSettings(self)
         if not os.path.exists(user_directory):
-            os.mkdir(user_directory)
+            os.makedirs(user_directory, exist_ok=True)
             if not args.multi_user:
                 print("****** User settings have been changed to be stored on the server instead of browser storage. ******")
                 print("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")

comfy/cldm/dit_embedder.py

@@ -0,0 +1,122 @@
+import math
+from typing import List, Optional, Tuple
+
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from torch import Tensor
+
+from comfy.ldm.modules.diffusionmodules.mmdit import DismantledBlock, PatchEmbed, VectorEmbedder, TimestepEmbedder, get_2d_sincos_pos_embed_torch
+
+
+class ControlNetEmbedder(nn.Module):
+
+    def __init__(
+        self,
+        img_size: int,
+        patch_size: int,
+        in_chans: int,
+        attention_head_dim: int,
+        num_attention_heads: int,
+        adm_in_channels: int,
+        num_layers: int,
+        main_model_double: int,
+        double_y_emb: bool,
+        device: torch.device,
+        dtype: torch.dtype,
+        pos_embed_max_size: Optional[int] = None,
+        operations = None,
+    ):
+        super().__init__()
+        self.main_model_double = main_model_double
+        self.dtype = dtype
+        self.hidden_size = num_attention_heads * attention_head_dim
+        self.patch_size = patch_size
+        self.x_embedder = PatchEmbed(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=self.hidden_size,
+            strict_img_size=pos_embed_max_size is None,
+            device=device,
+            dtype=dtype,
+            operations=operations,
+        )
+
+        self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations)
+
+        self.double_y_emb = double_y_emb
+        if self.double_y_emb:
+            self.orig_y_embedder = VectorEmbedder(
+                adm_in_channels, self.hidden_size, dtype, device, operations=operations
+            )
+            self.y_embedder = VectorEmbedder(
+                self.hidden_size, self.hidden_size, dtype, device, operations=operations
+            )
+        else:
+            self.y_embedder = VectorEmbedder(
+                adm_in_channels, self.hidden_size, dtype, device, operations=operations
+            )
+
+        self.transformer_blocks = nn.ModuleList(
+            DismantledBlock(
+                hidden_size=self.hidden_size, num_heads=num_attention_heads, qkv_bias=True,
+                dtype=dtype, device=device, operations=operations
+            )
+            for _ in range(num_layers)
+        )
+
+        # self.use_y_embedder = pooled_projection_dim != self.time_text_embed.text_embedder.linear_1.in_features
+        # TODO double check this logic when 8b
+        self.use_y_embedder = True
+
+        self.controlnet_blocks = nn.ModuleList([])
+        for _ in range(len(self.transformer_blocks)):
+            controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+            self.controlnet_blocks.append(controlnet_block)
+
+        self.pos_embed_input = PatchEmbed(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=self.hidden_size,
+            strict_img_size=False,
+            device=device,
+            dtype=dtype,
+            operations=operations,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        y: Optional[torch.Tensor] = None,
+        context: Optional[torch.Tensor] = None,
+        hint = None,
+    ) -> Tuple[Tensor, List[Tensor]]:
+        x_shape = list(x.shape)
+        x = self.x_embedder(x)
+        if not self.double_y_emb:
+            h = (x_shape[-2] + 1) // self.patch_size
+            w = (x_shape[-1] + 1) // self.patch_size
+            x += get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=x.device)
+        c = self.t_embedder(timesteps, dtype=x.dtype)
+        if y is not None and self.y_embedder is not None:
+            if self.double_y_emb:
+                y = self.orig_y_embedder(y)
+            y = self.y_embedder(y)
+            c = c + y
+
+        x = x + self.pos_embed_input(hint)
+
+        block_out = ()
+
+        repeat = math.ceil(self.main_model_double / len(self.transformer_blocks))
+        for i in range(len(self.transformer_blocks)):
+            out = self.transformer_blocks[i](x, c)
+            if not self.double_y_emb:
+                x = out
+            block_out += (self.controlnet_blocks[i](out),) * repeat
+
+        return {"output": block_out}

comfy/cli_args.py

@@ -60,8 +60,10 @@ fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If
 fp_group.add_argument("--force-fp16", action="store_true", help="Force fp16.")
 
 fpunet_group = parser.add_mutually_exclusive_group()
-fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the UNET in bf16. This should only be used for testing stuff.")
-fpunet_group.add_argument("--fp16-unet", action="store_true", help="Store unet weights in fp16.")
+fpunet_group.add_argument("--fp32-unet", action="store_true", help="Run the diffusion model in fp32.")
+fpunet_group.add_argument("--fp64-unet", action="store_true", help="Run the diffusion model in fp64.")
+fpunet_group.add_argument("--bf16-unet", action="store_true", help="Run the diffusion model in bf16.")
+fpunet_group.add_argument("--fp16-unet", action="store_true", help="Run the diffusion model in fp16")
 fpunet_group.add_argument("--fp8_e4m3fn-unet", action="store_true", help="Store unet weights in fp8_e4m3fn.")
 fpunet_group.add_argument("--fp8_e5m2-unet", action="store_true", help="Store unet weights in fp8_e5m2.")
 

comfy/clip_vision.py

@@ -16,13 +16,18 @@ class Output:
     def __setitem__(self, key, item):
         setattr(self, key, item)
 
-def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]):
+def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711], crop=True):
     mean = torch.tensor(mean, device=image.device, dtype=image.dtype)
     std = torch.tensor(std, device=image.device, dtype=image.dtype)
     image = image.movedim(-1, 1)
     if not (image.shape[2] == size and image.shape[3] == size):
-        scale = (size / min(image.shape[2], image.shape[3]))
-        image = torch.nn.functional.interpolate(image, size=(round(scale * image.shape[2]), round(scale * image.shape[3])), mode="bicubic", antialias=True)
+        if crop:
+            scale = (size / min(image.shape[2], image.shape[3]))
+            scale_size = (round(scale * image.shape[2]), round(scale * image.shape[3]))
+        else:
+            scale_size = (size, size)
+
+        image = torch.nn.functional.interpolate(image, size=scale_size, mode="bicubic", antialias=True)
         h = (image.shape[2] - size)//2
         w = (image.shape[3] - size)//2
         image = image[:,:,h:h+size,w:w+size]
@@ -51,9 +56,9 @@ class ClipVisionModel():
     def get_sd(self):
         return self.model.state_dict()
 
-    def encode_image(self, image):
+    def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std).float()
+        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
         out = self.model(pixel_values=pixel_values, intermediate_output=-2)
 
         outputs = Output()

comfy/controlnet.py

@@ -35,7 +35,7 @@ import comfy.ldm.cascade.controlnet
 import comfy.cldm.mmdit
 import comfy.ldm.hydit.controlnet
 import comfy.ldm.flux.controlnet
-
+import comfy.cldm.dit_embedder
 
 def broadcast_image_to(tensor, target_batch_size, batched_number):
     current_batch_size = tensor.shape[0]
@@ -78,6 +78,7 @@ class ControlBase:
         self.concat_mask = False
         self.extra_concat_orig = []
         self.extra_concat = None
+        self.preprocess_image = lambda a: a
 
     def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None, extra_concat=[]):
         self.cond_hint_original = cond_hint
@@ -129,6 +130,7 @@ class ControlBase:
         c.strength_type = self.strength_type
         c.concat_mask = self.concat_mask
         c.extra_concat_orig = self.extra_concat_orig.copy()
+        c.preprocess_image = self.preprocess_image
 
     def inference_memory_requirements(self, dtype):
         if self.previous_controlnet is not None:
@@ -181,7 +183,7 @@ class ControlBase:
 
 
 class ControlNet(ControlBase):
-    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT, concat_mask=False):
+    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, load_device=None, manual_cast_dtype=None, extra_conds=["y"], strength_type=StrengthType.CONSTANT, concat_mask=False, preprocess_image=lambda a: a):
         super().__init__()
         self.control_model = control_model
         self.load_device = load_device
@@ -196,6 +198,7 @@ class ControlNet(ControlBase):
         self.extra_conds += extra_conds
         self.strength_type = strength_type
         self.concat_mask = concat_mask
+        self.preprocess_image = preprocess_image
 
     def get_control(self, x_noisy, t, cond, batched_number):
         control_prev = None
@@ -224,6 +227,7 @@ class ControlNet(ControlBase):
                 if self.latent_format is not None:
                     raise ValueError("This Controlnet needs a VAE but none was provided, please use a ControlNetApply node with a VAE input and connect it.")
             self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
+            self.cond_hint = self.preprocess_image(self.cond_hint)
             if self.vae is not None:
                 loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
                 self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1))
@@ -427,6 +431,7 @@ def controlnet_load_state_dict(control_model, sd):
         logging.debug("unexpected controlnet keys: {}".format(unexpected))
     return control_model
 
+
 def load_controlnet_mmdit(sd, model_options={}):
     new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
     model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd, model_options=model_options)
@@ -448,6 +453,82 @@ def load_controlnet_mmdit(sd, model_options={}):
     return control
 
 
+class ControlNetSD35(ControlNet):
+    def pre_run(self, model, percent_to_timestep_function):
+        if self.control_model.double_y_emb:
+            missing, unexpected = self.control_model.orig_y_embedder.load_state_dict(model.diffusion_model.y_embedder.state_dict(), strict=False)
+        else:
+            missing, unexpected = self.control_model.x_embedder.load_state_dict(model.diffusion_model.x_embedder.state_dict(), strict=False)
+        super().pre_run(model, percent_to_timestep_function)
+
+    def copy(self):
+        c = ControlNetSD35(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
+        c.control_model = self.control_model
+        c.control_model_wrapped = self.control_model_wrapped
+        self.copy_to(c)
+        return c
+
+def load_controlnet_sd35(sd, model_options={}):
+    control_type = -1
+    if "control_type" in sd:
+        control_type = round(sd.pop("control_type").item())
+
+    # blur_cnet = control_type == 0
+    canny_cnet = control_type == 1
+    depth_cnet = control_type == 2
+
+    new_sd = {}
+    for k in comfy.utils.MMDIT_MAP_BASIC:
+        if k[1] in sd:
+            new_sd[k[0]] = sd.pop(k[1])
+    for k in sd:
+        new_sd[k] = sd[k]
+    sd = new_sd
+
+    y_emb_shape = sd["y_embedder.mlp.0.weight"].shape
+    depth = y_emb_shape[0] // 64
+    hidden_size = 64 * depth
+    num_heads = depth
+    head_dim = hidden_size // num_heads
+    num_blocks = comfy.model_detection.count_blocks(new_sd, 'transformer_blocks.{}.')
+
+    load_device = comfy.model_management.get_torch_device()
+    offload_device = comfy.model_management.unet_offload_device()
+    unet_dtype = comfy.model_management.unet_dtype(model_params=-1)
+
+    manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
+
+    operations = model_options.get("custom_operations", None)
+    if operations is None:
+        operations = comfy.ops.pick_operations(unet_dtype, manual_cast_dtype, disable_fast_fp8=True)
+
+    control_model = comfy.cldm.dit_embedder.ControlNetEmbedder(img_size=None,
+                                                               patch_size=2,
+                                                               in_chans=16,
+                                                               num_layers=num_blocks,
+                                                               main_model_double=depth,
+                                                               double_y_emb=y_emb_shape[0] == y_emb_shape[1],
+                                                               attention_head_dim=head_dim,
+                                                               num_attention_heads=num_heads,
+                                                               adm_in_channels=2048,
+                                                               device=offload_device,
+                                                               dtype=unet_dtype,
+                                                               operations=operations)
+
+    control_model = controlnet_load_state_dict(control_model, sd)
+
+    latent_format = comfy.latent_formats.SD3()
+    preprocess_image = lambda a: a
+    if canny_cnet:
+        preprocess_image = lambda a: (a * 255 * 0.5 + 0.5)
+    elif depth_cnet:
+        preprocess_image = lambda a: 1.0 - a
+
+    control = ControlNetSD35(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, preprocess_image=preprocess_image)
+    return control
+
+
+
 def load_controlnet_hunyuandit(controlnet_data, model_options={}):
     model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data, model_options=model_options)
 
@@ -560,7 +641,10 @@ def load_controlnet_state_dict(state_dict, model=None, model_options={}):
         if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data:
             return load_controlnet_flux_xlabs_mistoline(controlnet_data, model_options=model_options)
         elif "pos_embed_input.proj.weight" in controlnet_data:
-            return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet
+            if "transformer_blocks.0.adaLN_modulation.1.bias" in controlnet_data:
+                return load_controlnet_sd35(controlnet_data, model_options=model_options) #Stability sd3.5 format
+            else:
+                return load_controlnet_mmdit(controlnet_data, model_options=model_options) #SD3 diffusers controlnet
         elif "controlnet_x_embedder.weight" in controlnet_data:
             return load_controlnet_flux_instantx(controlnet_data, model_options=model_options)
     elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux

comfy/k_diffusion/sampling.py

@@ -280,6 +280,9 @@ def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None,
 
 @torch.no_grad()
 def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
+        return sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
+
     """Ancestral sampling with DPM-Solver second-order steps."""
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@@ -306,6 +309,39 @@ def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, dis
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
     return x
 
+@torch.no_grad()
+def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """Ancestral sampling with DPM-Solver second-order steps."""
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
+        sigma_down = sigmas[i+1] * downstep_ratio
+        alpha_ip1 = 1 - sigmas[i+1]
+        alpha_down = 1 - sigma_down
+        renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
+
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        d = to_d(x, sigmas[i], denoised)
+        if sigma_down == 0:
+            # Euler method
+            dt = sigma_down - sigmas[i]
+            x = x + d * dt
+        else:
+            # DPM-Solver-2
+            sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp()
+            dt_1 = sigma_mid - sigmas[i]
+            dt_2 = sigma_down - sigmas[i]
+            x_2 = x + d * dt_1
+            denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
+            d_2 = to_d(x_2, sigma_mid, denoised_2)
+            x = x + d_2 * dt_2
+            x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
+    return x
 
 def linear_multistep_coeff(order, t, i, j):
     if order - 1 > i:

comfy/latent_formats.py

@@ -216,3 +216,139 @@ class Mochi(LatentFormat):
         latents_mean = self.latents_mean.to(latent.device, latent.dtype)
         latents_std = self.latents_std.to(latent.device, latent.dtype)
         return latent * latents_std / self.scale_factor + latents_mean
+
+class LTXV(LatentFormat):
+    latent_channels = 128
+    def __init__(self):
+        self.latent_rgb_factors = [
+            [ 1.1202e-02, -6.3815e-04, -1.0021e-02],
+            [ 8.6031e-02,  6.5813e-02,  9.5409e-04],
+            [-1.2576e-02, -7.5734e-03, -4.0528e-03],
+            [ 9.4063e-03, -2.1688e-03,  2.6093e-03],
+            [ 3.7636e-03,  1.2765e-02,  9.1548e-03],
+            [ 2.1024e-02, -5.2973e-03,  3.4373e-03],
+            [-8.8896e-03, -1.9703e-02, -1.8761e-02],
+            [-1.3160e-02, -1.0523e-02,  1.9709e-03],
+            [-1.5152e-03, -6.9891e-03, -7.5810e-03],
+            [-1.7247e-03,  4.6560e-04, -3.3839e-03],
+            [ 1.3617e-02,  4.7077e-03, -2.0045e-03],
+            [ 1.0256e-02,  7.7318e-03,  1.3948e-02],
+            [-1.6108e-02, -6.2151e-03,  1.1561e-03],
+            [ 7.3407e-03,  1.5628e-02,  4.4865e-04],
+            [ 9.5357e-04, -2.9518e-03, -1.4760e-02],
+            [ 1.9143e-02,  1.0868e-02,  1.2264e-02],
+            [ 4.4575e-03,  3.6682e-05, -6.8508e-03],
+            [-4.5681e-04,  3.2570e-03,  7.7929e-03],
+            [ 3.3902e-02,  3.3405e-02,  3.7454e-02],
+            [-2.3001e-02, -2.4877e-03, -3.1033e-03],
+            [ 5.0265e-02,  3.8841e-02,  3.3539e-02],
+            [-4.1018e-03, -1.1095e-03,  1.5859e-03],
+            [-1.2689e-01, -1.3107e-01, -2.1005e-01],
+            [ 2.6276e-02,  1.4189e-02, -3.5963e-03],
+            [-4.8679e-03,  8.8486e-03,  7.8029e-03],
+            [-1.6610e-03, -4.8597e-03, -5.2060e-03],
+            [-2.1010e-03,  2.3610e-03,  9.3796e-03],
+            [-2.2482e-02, -2.1305e-02, -1.5087e-02],
+            [-1.5753e-02, -1.0646e-02, -6.5083e-03],
+            [-4.6975e-03,  5.0288e-03, -6.7390e-03],
+            [ 1.1951e-02,  2.0712e-02,  1.6191e-02],
+            [-6.3704e-03, -8.4827e-03, -9.5483e-03],
+            [ 7.2610e-03, -9.9326e-03, -2.2978e-02],
+            [-9.1904e-04,  6.2882e-03,  9.5720e-03],
+            [-3.7178e-02, -3.7123e-02, -5.6713e-02],
+            [-1.3373e-01, -1.0720e-01, -5.3801e-02],
+            [-5.3702e-03,  8.1256e-03,  8.8397e-03],
+            [-1.5247e-01, -2.1437e-01, -2.1843e-01],
+            [ 3.1441e-02,  7.0335e-03, -9.7541e-03],
+            [ 2.1528e-03, -8.9817e-03, -2.1023e-02],
+            [ 3.8461e-03, -5.8957e-03, -1.5014e-02],
+            [-4.3470e-03, -1.2940e-02, -1.5972e-02],
+            [-5.4781e-03, -1.0842e-02, -3.0204e-03],
+            [-6.5347e-03,  3.0806e-03, -1.0163e-02],
+            [-5.0414e-03, -7.1503e-03, -8.9686e-04],
+            [-8.5851e-03, -2.4351e-03,  1.0674e-03],
+            [-9.0016e-03, -9.6493e-03,  1.5692e-03],
+            [ 5.0914e-03,  1.2099e-02,  1.9968e-02],
+            [ 1.3758e-02,  1.1669e-02,  8.1958e-03],
+            [-1.0518e-02, -1.1575e-02, -4.1307e-03],
+            [-2.8410e-02, -3.1266e-02, -2.2149e-02],
+            [ 2.9336e-03,  3.6511e-02,  1.8717e-02],
+            [-1.6703e-02, -1.6696e-02, -4.4529e-03],
+            [ 4.8818e-02,  4.0063e-02,  8.7410e-03],
+            [-1.5066e-02, -5.7328e-04,  2.9785e-03],
+            [-1.7613e-02, -8.1034e-03,  1.3086e-02],
+            [-9.2633e-03,  1.0803e-02, -6.3489e-03],
+            [ 3.0851e-03,  4.7750e-04,  1.2347e-02],
+            [-2.2785e-02, -2.3043e-02, -2.6005e-02],
+            [-2.4787e-02, -1.5389e-02, -2.2104e-02],
+            [-2.3572e-02,  1.0544e-03,  1.2361e-02],
+            [-7.8915e-03, -1.2271e-03, -6.0968e-03],
+            [-1.1478e-02, -1.2543e-03,  6.2679e-03],
+            [-5.4229e-02,  2.6644e-02,  6.3394e-03],
+            [ 4.4216e-03, -7.3338e-03, -1.0464e-02],
+            [-4.5013e-03,  1.6082e-03,  1.4420e-02],
+            [ 1.3673e-02,  8.8877e-03,  4.1253e-03],
+            [-1.0145e-02,  9.0072e-03,  1.5695e-02],
+            [-5.6234e-03,  1.1847e-03,  8.1261e-03],
+            [-3.7171e-03, -5.3538e-03,  1.2590e-03],
+            [ 2.9476e-02,  2.1424e-02,  3.0424e-02],
+            [-3.4925e-02, -2.4340e-02, -2.5316e-02],
+            [-3.4127e-02, -2.2406e-02, -1.0589e-02],
+            [-1.7342e-02, -1.3249e-02, -1.0719e-02],
+            [-2.1478e-03, -8.6051e-03, -2.9878e-03],
+            [ 1.2089e-03, -4.2391e-03, -6.8569e-03],
+            [ 9.0411e-04, -6.6886e-03, -6.7547e-05],
+            [ 1.6048e-02, -1.0057e-02, -2.8929e-02],
+            [ 1.2290e-03,  1.0163e-02,  1.8861e-02],
+            [ 1.7264e-02,  2.7257e-04,  1.3785e-02],
+            [-1.3482e-02, -3.6427e-03,  6.7481e-04],
+            [ 4.6782e-03, -5.2423e-03,  2.4467e-03],
+            [-5.9113e-03, -6.2244e-03, -1.8162e-03],
+            [ 1.5496e-02,  1.4582e-02,  1.9514e-03],
+            [ 7.4958e-03,  1.5886e-03, -8.2305e-03],
+            [ 1.9086e-02,  1.6360e-03, -3.9674e-03],
+            [-5.7021e-03, -2.7307e-03, -4.1066e-03],
+            [ 1.7450e-03,  1.4602e-02,  2.5794e-02],
+            [-8.2788e-04,  2.2902e-03,  4.5161e-03],
+            [ 1.1632e-02,  8.9193e-03, -7.2813e-03],
+            [ 7.5721e-03,  2.6784e-03,  1.1393e-02],
+            [ 5.1939e-03,  3.6903e-03,  1.4049e-02],
+            [-1.8383e-02, -2.2529e-02, -2.4477e-02],
+            [ 5.8842e-04, -5.7874e-03, -1.4770e-02],
+            [-1.6125e-02, -8.6101e-03, -1.4533e-02],
+            [ 2.0540e-02,  2.0729e-02,  6.4338e-03],
+            [ 3.3587e-03, -1.1226e-02, -1.6444e-02],
+            [-1.4742e-03, -1.0489e-02,  1.7097e-03],
+            [ 2.8130e-02,  2.3546e-02,  3.2791e-02],
+            [-1.8532e-02, -1.2842e-02, -8.7756e-03],
+            [-8.0533e-03, -1.0771e-02, -1.7536e-02],
+            [-3.9009e-03,  1.6150e-02,  3.3359e-02],
+            [-7.4554e-03, -1.4154e-02, -6.1910e-03],
+            [ 3.4734e-03, -1.1370e-02, -1.0581e-02],
+            [ 1.1476e-02,  3.9281e-03,  2.8231e-03],
+            [ 7.1639e-03, -1.4741e-03, -3.8066e-03],
+            [ 2.2250e-03, -8.7552e-03, -9.5719e-03],
+            [ 2.4146e-02,  2.1696e-02,  2.8056e-02],
+            [-5.4365e-03, -2.4291e-02, -1.7802e-02],
+            [ 7.4263e-03,  1.0510e-02,  1.2705e-02],
+            [ 6.2669e-03,  6.2658e-03,  1.9211e-02],
+            [ 1.6378e-02,  9.4933e-03,  6.6971e-03],
+            [ 1.7173e-02,  2.3601e-02,  2.3296e-02],
+            [-1.4568e-02, -9.8279e-03, -1.1556e-02],
+            [ 1.4431e-02,  1.4430e-02,  6.6362e-03],
+            [-6.8230e-03,  1.8863e-02,  1.4555e-02],
+            [ 6.1156e-03,  3.4700e-03, -2.6662e-03],
+            [-2.6983e-03, -5.9402e-03, -9.2276e-03],
+            [ 1.0235e-02,  7.4173e-03, -7.6243e-03],
+            [-1.3255e-02,  1.9322e-02, -9.2153e-04],
+            [ 2.4222e-03, -4.8039e-03, -1.5759e-02],
+            [ 2.6244e-02,  2.5951e-02,  2.0249e-02],
+            [ 1.5711e-02,  1.8498e-02,  2.7407e-03],
+            [-2.1714e-03,  4.7214e-03, -2.2443e-02],
+            [-7.4747e-03,  7.4166e-03,  1.4430e-02],
+            [-8.3906e-03, -7.9776e-03,  9.7927e-03],
+            [ 3.8321e-02,  9.6622e-03, -1.9268e-02],
+            [-1.4605e-02, -6.7032e-03,  3.9675e-03]
+        ]
+
+        self.latent_rgb_factors_bias = [-0.0571, -0.1657, -0.2512]

comfy/ldm/common_dit.py

@@ -2,7 +2,7 @@ import torch
 import comfy.ops
 
 def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"):
-    if padding_mode == "circular" and torch.jit.is_tracing() or torch.jit.is_scripting():
+    if padding_mode == "circular" and (torch.jit.is_tracing() or torch.jit.is_scripting()):
         padding_mode = "reflect"
     pad_h = (patch_size[0] - img.shape[-2] % patch_size[0]) % patch_size[0]
     pad_w = (patch_size[1] - img.shape[-1] % patch_size[1]) % patch_size[1]

comfy/ldm/hydit/models.py

@@ -287,7 +287,7 @@ class HunYuanDiT(nn.Module):
                 style=None,
                 return_dict=False,
                 control=None,
-                transformer_options=None,
+                transformer_options={},
                 ):
         """
         Forward pass of the encoder.
@@ -315,8 +315,7 @@ class HunYuanDiT(nn.Module):
         return_dict: bool
             Whether to return a dictionary.
         """
-        #import pdb
-        #pdb.set_trace()
+        patches_replace = transformer_options.get("patches_replace", {})
         encoder_hidden_states = context
         text_states = encoder_hidden_states                     # 2,77,1024
         text_states_t5 = encoder_hidden_states_t5               # 2,256,2048
@@ -364,6 +363,8 @@ class HunYuanDiT(nn.Module):
         # Concatenate all extra vectors
         c = t + self.extra_embedder(extra_vec)  # [B, D]
 
+        blocks_replace = patches_replace.get("dit", {})
+
         controls = None
         if control:
             controls = control.get("output", None)
@@ -375,9 +376,20 @@ class HunYuanDiT(nn.Module):
                     skip = skips.pop() + controls.pop().to(dtype=x.dtype)
                 else:
                     skip = skips.pop()
-                x = block(x, c, text_states, freqs_cis_img, skip)   # (N, L, D)
             else:
-                x = block(x, c, text_states, freqs_cis_img)         # (N, L, D)
+                skip = None
+
+            if ("double_block", layer) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], args["vec"], args["txt"], args["pe"], args["skip"])
+                    return out
+
+                out = blocks_replace[("double_block", layer)]({"img": x, "txt": text_states, "vec": c, "pe": freqs_cis_img, "skip": skip}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(x, c, text_states, freqs_cis_img, skip)   # (N, L, D)
+
 
             if layer < (self.depth // 2 - 1):
                 skips.append(x)

comfy/ldm/lightricks/model.py

@@ -0,0 +1,514 @@
+import torch
+from torch import nn
+import comfy.ldm.modules.attention
+from comfy.ldm.genmo.joint_model.layers import RMSNorm
+import comfy.ldm.common_dit
+from einops import rearrange
+import math
+from typing import Dict, Optional, Tuple
+
+from .symmetric_patchifier import SymmetricPatchifier
+
+
+def get_timestep_embedding(
+    timesteps: torch.Tensor,
+    embedding_dim: int,
+    flip_sin_to_cos: bool = False,
+    downscale_freq_shift: float = 1,
+    scale: float = 1,
+    max_period: int = 10000,
+):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
+
+    Args
+        timesteps (torch.Tensor):
+            a 1-D Tensor of N indices, one per batch element. These may be fractional.
+        embedding_dim (int):
+            the dimension of the output.
+        flip_sin_to_cos (bool):
+            Whether the embedding order should be `cos, sin` (if True) or `sin, cos` (if False)
+        downscale_freq_shift (float):
+            Controls the delta between frequencies between dimensions
+        scale (float):
+            Scaling factor applied to the embeddings.
+        max_period (int):
+            Controls the maximum frequency of the embeddings
+    Returns
+        torch.Tensor: an [N x dim] Tensor of positional embeddings.
+    """
+    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+
+    half_dim = embedding_dim // 2
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+    )
+    exponent = exponent / (half_dim - downscale_freq_shift)
+
+    emb = torch.exp(exponent)
+    emb = timesteps[:, None].float() * emb[None, :]
+
+    # scale embeddings
+    emb = scale * emb
+
+    # concat sine and cosine embeddings
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+    # flip sine and cosine embeddings
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+    # zero pad
+    if embedding_dim % 2 == 1:
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        time_embed_dim: int,
+        act_fn: str = "silu",
+        out_dim: int = None,
+        post_act_fn: Optional[str] = None,
+        cond_proj_dim=None,
+        sample_proj_bias=True,
+        dtype=None, device=None, operations=None,
+    ):
+        super().__init__()
+
+        self.linear_1 = operations.Linear(in_channels, time_embed_dim, sample_proj_bias, dtype=dtype, device=device)
+
+        if cond_proj_dim is not None:
+            self.cond_proj = operations.Linear(cond_proj_dim, in_channels, bias=False, dtype=dtype, device=device)
+        else:
+            self.cond_proj = None
+
+        self.act = nn.SiLU()
+
+        if out_dim is not None:
+            time_embed_dim_out = out_dim
+        else:
+            time_embed_dim_out = time_embed_dim
+        self.linear_2 = operations.Linear(time_embed_dim, time_embed_dim_out, sample_proj_bias, dtype=dtype, device=device)
+
+        if post_act_fn is None:
+            self.post_act = None
+        # else:
+        #     self.post_act = get_activation(post_act_fn)
+
+    def forward(self, sample, condition=None):
+        if condition is not None:
+            sample = sample + self.cond_proj(condition)
+        sample = self.linear_1(sample)
+
+        if self.act is not None:
+            sample = self.act(sample)
+
+        sample = self.linear_2(sample)
+
+        if self.post_act is not None:
+            sample = self.post_act(sample)
+        return sample
+
+
+class Timesteps(nn.Module):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+        self.scale = scale
+
+    def forward(self, timesteps):
+        t_emb = get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+            scale=self.scale,
+        )
+        return t_emb
+
+
+class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
+    """
+    For PixArt-Alpha.
+
+    Reference:
+    https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L164C9-L168C29
+    """
+
+    def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.outdim = size_emb_dim
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, timestep, resolution, aspect_ratio, batch_size, hidden_dtype):
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)
+        return timesteps_emb
+
+
+class AdaLayerNormSingle(nn.Module):
+    r"""
+    Norm layer adaptive layer norm single (adaLN-single).
+
+    As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        use_additional_conditions (`bool`): To use additional conditions for normalization or not.
+    """
+
+    def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
+            embedding_dim, size_emb_dim=embedding_dim // 3, use_additional_conditions=use_additional_conditions, dtype=dtype, device=device, operations=operations
+        )
+
+        self.silu = nn.SiLU()
+        self.linear = operations.Linear(embedding_dim, 6 * embedding_dim, bias=True, dtype=dtype, device=device)
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        batch_size: Optional[int] = None,
+        hidden_dtype: Optional[torch.dtype] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        # No modulation happening here.
+        added_cond_kwargs = added_cond_kwargs or {"resolution": None, "aspect_ratio": None}
+        embedded_timestep = self.emb(timestep, **added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_dtype)
+        return self.linear(self.silu(embedded_timestep)), embedded_timestep
+
+class PixArtAlphaTextProjection(nn.Module):
+    """
+    Projects caption embeddings. Also handles dropout for classifier-free guidance.
+
+    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
+    """
+
+    def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", dtype=None, device=None, operations=None):
+        super().__init__()
+        if out_features is None:
+            out_features = hidden_size
+        self.linear_1 = operations.Linear(in_features=in_features, out_features=hidden_size, bias=True, dtype=dtype, device=device)
+        if act_fn == "gelu_tanh":
+            self.act_1 = nn.GELU(approximate="tanh")
+        elif act_fn == "silu":
+            self.act_1 = nn.SiLU()
+        else:
+            raise ValueError(f"Unknown activation function: {act_fn}")
+        self.linear_2 = operations.Linear(in_features=hidden_size, out_features=out_features, bias=True, dtype=dtype, device=device)
+
+    def forward(self, caption):
+        hidden_states = self.linear_1(caption)
+        hidden_states = self.act_1(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class GELU_approx(nn.Module):
+    def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.proj = operations.Linear(dim_in, dim_out, dtype=dtype, device=device)
+
+    def forward(self, x):
+        return torch.nn.functional.gelu(self.proj(x), approximate="tanh")
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        project_in = GELU_approx(dim, inner_dim, dtype=dtype, device=device, operations=operations)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+def apply_rotary_emb(input_tensor, freqs_cis): #TODO: remove duplicate funcs and pick the best/fastest one
+    cos_freqs = freqs_cis[0]
+    sin_freqs = freqs_cis[1]
+
+    t_dup = rearrange(input_tensor, "... (d r) -> ... d r", r=2)
+    t1, t2 = t_dup.unbind(dim=-1)
+    t_dup = torch.stack((-t2, t1), dim=-1)
+    input_tensor_rot = rearrange(t_dup, "... d r -> ... (d r)")
+
+    out = input_tensor * cos_freqs + input_tensor_rot * sin_freqs
+
+    return out
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=None):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = query_dim if context_dim is None else context_dim
+        self.attn_precision = attn_precision
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.q_norm = RMSNorm(inner_dim, dtype=dtype, device=device)
+        self.k_norm = RMSNorm(inner_dim, dtype=dtype, device=device)
+
+        self.to_q = operations.Linear(query_dim, inner_dim, bias=True, dtype=dtype, device=device)
+        self.to_k = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
+        self.to_v = operations.Linear(context_dim, inner_dim, bias=True, dtype=dtype, device=device)
+
+        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
+
+    def forward(self, x, context=None, mask=None, pe=None):
+        q = self.to_q(x)
+        context = x if context is None else context
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        if pe is not None:
+            q = apply_rotary_emb(q, pe)
+            k = apply_rotary_emb(k, pe)
+
+        if mask is None:
+            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
+        else:
+            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
+        return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(self, dim, n_heads, d_head, context_dim=None, attn_precision=None, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.attn_precision = attn_precision
+        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, context_dim=None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+        self.ff = FeedForward(dim, dim_out=dim, glu=True, dtype=dtype, device=device, operations=operations)
+
+        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)
+
+        self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
+
+    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None):
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
+
+        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa
+
+        x += self.attn2(x, context=context, mask=attention_mask)
+
+        y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp
+        x += self.ff(y) * gate_mlp
+
+        return x
+
+def get_fractional_positions(indices_grid, max_pos):
+    fractional_positions = torch.stack(
+        [
+            indices_grid[:, i] / max_pos[i]
+            for i in range(3)
+        ],
+        dim=-1,
+    )
+    return fractional_positions
+
+
+def precompute_freqs_cis(indices_grid, dim, out_dtype, theta=10000.0, max_pos=[20, 2048, 2048]):
+    dtype = torch.float32 #self.dtype
+
+    fractional_positions = get_fractional_positions(indices_grid, max_pos)
+
+    start = 1
+    end = theta
+    device = fractional_positions.device
+
+    indices = theta ** (
+        torch.linspace(
+            math.log(start, theta),
+            math.log(end, theta),
+            dim // 6,
+            device=device,
+            dtype=dtype,
+        )
+    )
+    indices = indices.to(dtype=dtype)
+
+    indices = indices * math.pi / 2
+
+    freqs = (
+        (indices * (fractional_positions.unsqueeze(-1) * 2 - 1))
+        .transpose(-1, -2)
+        .flatten(2)
+    )
+
+    cos_freq = freqs.cos().repeat_interleave(2, dim=-1)
+    sin_freq = freqs.sin().repeat_interleave(2, dim=-1)
+    if dim % 6 != 0:
+        cos_padding = torch.ones_like(cos_freq[:, :, : dim % 6])
+        sin_padding = torch.zeros_like(cos_freq[:, :, : dim % 6])
+        cos_freq = torch.cat([cos_padding, cos_freq], dim=-1)
+        sin_freq = torch.cat([sin_padding, sin_freq], dim=-1)
+    return cos_freq.to(out_dtype), sin_freq.to(out_dtype)
+
+
+class LTXVModel(torch.nn.Module):
+    def __init__(self,
+                 in_channels=128,
+                 cross_attention_dim=2048,
+                 attention_head_dim=64,
+                 num_attention_heads=32,
+
+                 caption_channels=4096,
+                 num_layers=28,
+
+
+                 positional_embedding_theta=10000.0,
+                 positional_embedding_max_pos=[20, 2048, 2048],
+                 dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        self.dtype = dtype
+        self.out_channels = in_channels
+        self.inner_dim = num_attention_heads * attention_head_dim
+
+        self.patchify_proj = operations.Linear(in_channels, self.inner_dim, bias=True, dtype=dtype, device=device)
+
+        self.adaln_single = AdaLayerNormSingle(
+            self.inner_dim, use_additional_conditions=False, dtype=dtype, device=device, operations=operations
+        )
+
+        # self.adaln_single.linear = operations.Linear(self.inner_dim, 4 * self.inner_dim, bias=True, dtype=dtype, device=device)
+
+        self.caption_projection = PixArtAlphaTextProjection(
+            in_features=caption_channels, hidden_size=self.inner_dim, dtype=dtype, device=device, operations=operations
+        )
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    self.inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    context_dim=cross_attention_dim,
+                    # attn_precision=attn_precision,
+                    dtype=dtype, device=device, operations=operations
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        self.scale_shift_table = nn.Parameter(torch.empty(2, self.inner_dim, dtype=dtype, device=device))
+        self.norm_out = operations.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.proj_out = operations.Linear(self.inner_dim, self.out_channels, dtype=dtype, device=device)
+
+        self.patchifier = SymmetricPatchifier(1)
+
+    def forward(self, x, timestep, context, attention_mask, frame_rate=25, guiding_latent=None, transformer_options={}, **kwargs):
+        patches_replace = transformer_options.get("patches_replace", {})
+
+        indices_grid = self.patchifier.get_grid(
+            orig_num_frames=x.shape[2],
+            orig_height=x.shape[3],
+            orig_width=x.shape[4],
+            batch_size=x.shape[0],
+            scale_grid=((1 / frame_rate) * 8, 32, 32),
+            device=x.device,
+        )
+
+        if guiding_latent is not None:
+            ts = torch.ones([x.shape[0], 1, x.shape[2], x.shape[3], x.shape[4]], device=x.device, dtype=x.dtype)
+            input_ts = timestep.view([timestep.shape[0]] + [1] * (x.ndim - 1))
+            ts *= input_ts
+            ts[:, :, 0] = 0.0
+            timestep = self.patchifier.patchify(ts)
+            input_x = x.clone()
+            x[:, :, 0] = guiding_latent[:, :, 0]
+
+        orig_shape = list(x.shape)
+
+        x = self.patchifier.patchify(x)
+
+        x = self.patchify_proj(x)
+        timestep = timestep * 1000.0
+
+        attention_mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1]))
+        attention_mask = attention_mask.masked_fill(attention_mask.to(torch.bool), float("-inf"))  # not sure about this
+        # attention_mask = (context != 0).any(dim=2).to(dtype=x.dtype)
+
+        pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype)
+
+        batch_size = x.shape[0]
+        timestep, embedded_timestep = self.adaln_single(
+            timestep.flatten(),
+            {"resolution": None, "aspect_ratio": None},
+            batch_size=batch_size,
+            hidden_dtype=x.dtype,
+        )
+        # Second dimension is 1 or number of tokens (if timestep_per_token)
+        timestep = timestep.view(batch_size, -1, timestep.shape[-1])
+        embedded_timestep = embedded_timestep.view(
+            batch_size, -1, embedded_timestep.shape[-1]
+        )
+
+        # 2. Blocks
+        if self.caption_projection is not None:
+            batch_size = x.shape[0]
+            context = self.caption_projection(context)
+            context = context.view(
+                batch_size, -1, x.shape[-1]
+            )
+
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.transformer_blocks):
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"])
+                    return out
+
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(
+                    x,
+                    context=context,
+                    attention_mask=attention_mask,
+                    timestep=timestep,
+                    pe=pe
+                )
+
+        # 3. Output
+        scale_shift_values = (
+            self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + embedded_timestep[:, :, None]
+        )
+        shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
+        x = self.norm_out(x)
+        # Modulation
+        x = x * (1 + scale) + shift
+        x = self.proj_out(x)
+
+        x = self.patchifier.unpatchify(
+            latents=x,
+            output_height=orig_shape[3],
+            output_width=orig_shape[4],
+            output_num_frames=orig_shape[2],
+            out_channels=orig_shape[1] // math.prod(self.patchifier.patch_size),
+        )
+
+        if guiding_latent is not None:
+            x[:, :, 0] = (input_x[:, :, 0] - guiding_latent[:, :, 0]) / input_ts[:, :, 0]
+
+        # print("res", x)
+        return x

comfy/ldm/lightricks/symmetric_patchifier.py

@@ -0,0 +1,105 @@
+from abc import ABC, abstractmethod
+from typing import Tuple
+
+import torch
+from einops import rearrange
+from torch import Tensor
+
+
+def append_dims(x: torch.Tensor, target_dims: int) -> torch.Tensor:
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(
+            f"input has {x.ndim} dims but target_dims is {target_dims}, which is less"
+        )
+    elif dims_to_append == 0:
+        return x
+    return x[(...,) + (None,) * dims_to_append]
+
+
+class Patchifier(ABC):
+    def __init__(self, patch_size: int):
+        super().__init__()
+        self._patch_size = (1, patch_size, patch_size)
+
+    @abstractmethod
+    def patchify(
+        self, latents: Tensor, frame_rates: Tensor, scale_grid: bool
+    ) -> Tuple[Tensor, Tensor]:
+        pass
+
+    @abstractmethod
+    def unpatchify(
+        self,
+        latents: Tensor,
+        output_height: int,
+        output_width: int,
+        output_num_frames: int,
+        out_channels: int,
+    ) -> Tuple[Tensor, Tensor]:
+        pass
+
+    @property
+    def patch_size(self):
+        return self._patch_size
+
+    def get_grid(
+        self, orig_num_frames, orig_height, orig_width, batch_size, scale_grid, device
+    ):
+        f = orig_num_frames // self._patch_size[0]
+        h = orig_height // self._patch_size[1]
+        w = orig_width // self._patch_size[2]
+        grid_h = torch.arange(h, dtype=torch.float32, device=device)
+        grid_w = torch.arange(w, dtype=torch.float32, device=device)
+        grid_f = torch.arange(f, dtype=torch.float32, device=device)
+        grid = torch.meshgrid(grid_f, grid_h, grid_w)
+        grid = torch.stack(grid, dim=0)
+        grid = grid.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
+
+        if scale_grid is not None:
+            for i in range(3):
+                if isinstance(scale_grid[i], Tensor):
+                    scale = append_dims(scale_grid[i], grid.ndim - 1)
+                else:
+                    scale = scale_grid[i]
+                grid[:, i, ...] = grid[:, i, ...] * scale * self._patch_size[i]
+
+        grid = rearrange(grid, "b c f h w -> b c (f h w)", b=batch_size)
+        return grid
+
+
+class SymmetricPatchifier(Patchifier):
+    def patchify(
+        self,
+        latents: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        latents = rearrange(
+            latents,
+            "b c (f p1) (h p2) (w p3) -> b (f h w) (c p1 p2 p3)",
+            p1=self._patch_size[0],
+            p2=self._patch_size[1],
+            p3=self._patch_size[2],
+        )
+        return latents
+
+    def unpatchify(
+        self,
+        latents: Tensor,
+        output_height: int,
+        output_width: int,
+        output_num_frames: int,
+        out_channels: int,
+    ) -> Tuple[Tensor, Tensor]:
+        output_height = output_height // self._patch_size[1]
+        output_width = output_width // self._patch_size[2]
+        latents = rearrange(
+            latents,
+            "b (f h w) (c p q) -> b c f (h p) (w q) ",
+            f=output_num_frames,
+            h=output_height,
+            w=output_width,
+            p=self._patch_size[1],
+            q=self._patch_size[2],
+        )
+        return latents

comfy/ldm/lightricks/vae/causal_conv3d.py

@@ -0,0 +1,64 @@
+from typing import Tuple, Union
+
+import torch
+import torch.nn as nn
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+
+class CausalConv3d(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size: int = 3,
+        stride: Union[int, Tuple[int]] = 1,
+        dilation: int = 1,
+        groups: int = 1,
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        kernel_size = (kernel_size, kernel_size, kernel_size)
+        self.time_kernel_size = kernel_size[0]
+
+        dilation = (dilation, 1, 1)
+
+        height_pad = kernel_size[1] // 2
+        width_pad = kernel_size[2] // 2
+        padding = (0, height_pad, width_pad)
+
+        self.conv = ops.Conv3d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            dilation=dilation,
+            padding=padding,
+            padding_mode="zeros",
+            groups=groups,
+        )
+
+    def forward(self, x, causal: bool = True):
+        if causal:
+            first_frame_pad = x[:, :, :1, :, :].repeat(
+                (1, 1, self.time_kernel_size - 1, 1, 1)
+            )
+            x = torch.concatenate((first_frame_pad, x), dim=2)
+        else:
+            first_frame_pad = x[:, :, :1, :, :].repeat(
+                (1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
+            )
+            last_frame_pad = x[:, :, -1:, :, :].repeat(
+                (1, 1, (self.time_kernel_size - 1) // 2, 1, 1)
+            )
+            x = torch.concatenate((first_frame_pad, x, last_frame_pad), dim=2)
+        x = self.conv(x)
+        return x
+
+    @property
+    def weight(self):
+        return self.conv.weight

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -0,0 +1,698 @@
+import torch
+from torch import nn
+from functools import partial
+import math
+from einops import rearrange
+from typing import Any, Mapping, Optional, Tuple, Union, List
+from .conv_nd_factory import make_conv_nd, make_linear_nd
+from .pixel_norm import PixelNorm
+
+
+class Encoder(nn.Module):
+    r"""
+    The `Encoder` layer of a variational autoencoder that encodes its input into a latent representation.
+
+    Args:
+        dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3):
+            The number of dimensions to use in convolutions.
+        in_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        out_channels (`int`, *optional*, defaults to 3):
+            The number of output channels.
+        blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`):
+            The blocks to use. Each block is a tuple of the block name and the number of layers.
+        base_channels (`int`, *optional*, defaults to 128):
+            The number of output channels for the first convolutional layer.
+        norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups for normalization.
+        patch_size (`int`, *optional*, defaults to 1):
+            The patch size to use. Should be a power of 2.
+        norm_layer (`str`, *optional*, defaults to `group_norm`):
+            The normalization layer to use. Can be either `group_norm` or `pixel_norm`.
+        latent_log_var (`str`, *optional*, defaults to `per_channel`):
+            The number of channels for the log variance. Can be either `per_channel`, `uniform`, or `none`.
+    """
+
+    def __init__(
+        self,
+        dims: Union[int, Tuple[int, int]] = 3,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        blocks=[("res_x", 1)],
+        base_channels: int = 128,
+        norm_num_groups: int = 32,
+        patch_size: Union[int, Tuple[int]] = 1,
+        norm_layer: str = "group_norm",  # group_norm, pixel_norm
+        latent_log_var: str = "per_channel",
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.norm_layer = norm_layer
+        self.latent_channels = out_channels
+        self.latent_log_var = latent_log_var
+        self.blocks_desc = blocks
+
+        in_channels = in_channels * patch_size**2
+        output_channel = base_channels
+
+        self.conv_in = make_conv_nd(
+            dims=dims,
+            in_channels=in_channels,
+            out_channels=output_channel,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            causal=True,
+        )
+
+        self.down_blocks = nn.ModuleList([])
+
+        for block_name, block_params in blocks:
+            input_channel = output_channel
+            if isinstance(block_params, int):
+                block_params = {"num_layers": block_params}
+
+            if block_name == "res_x":
+                block = UNetMidBlock3D(
+                    dims=dims,
+                    in_channels=input_channel,
+                    num_layers=block_params["num_layers"],
+                    resnet_eps=1e-6,
+                    resnet_groups=norm_num_groups,
+                    norm_layer=norm_layer,
+                )
+            elif block_name == "res_x_y":
+                output_channel = block_params.get("multiplier", 2) * output_channel
+                block = ResnetBlock3D(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    eps=1e-6,
+                    groups=norm_num_groups,
+                    norm_layer=norm_layer,
+                )
+            elif block_name == "compress_time":
+                block = make_conv_nd(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    kernel_size=3,
+                    stride=(2, 1, 1),
+                    causal=True,
+                )
+            elif block_name == "compress_space":
+                block = make_conv_nd(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    kernel_size=3,
+                    stride=(1, 2, 2),
+                    causal=True,
+                )
+            elif block_name == "compress_all":
+                block = make_conv_nd(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    kernel_size=3,
+                    stride=(2, 2, 2),
+                    causal=True,
+                )
+            elif block_name == "compress_all_x_y":
+                output_channel = block_params.get("multiplier", 2) * output_channel
+                block = make_conv_nd(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    kernel_size=3,
+                    stride=(2, 2, 2),
+                    causal=True,
+                )
+            else:
+                raise ValueError(f"unknown block: {block_name}")
+
+            self.down_blocks.append(block)
+
+        # out
+        if norm_layer == "group_norm":
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6
+            )
+        elif norm_layer == "pixel_norm":
+            self.conv_norm_out = PixelNorm()
+        elif norm_layer == "layer_norm":
+            self.conv_norm_out = LayerNorm(output_channel, eps=1e-6)
+
+        self.conv_act = nn.SiLU()
+
+        conv_out_channels = out_channels
+        if latent_log_var == "per_channel":
+            conv_out_channels *= 2
+        elif latent_log_var == "uniform":
+            conv_out_channels += 1
+        elif latent_log_var != "none":
+            raise ValueError(f"Invalid latent_log_var: {latent_log_var}")
+        self.conv_out = make_conv_nd(
+            dims, output_channel, conv_out_channels, 3, padding=1, causal=True
+        )
+
+        self.gradient_checkpointing = False
+
+    def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+        r"""The forward method of the `Encoder` class."""
+
+        sample = patchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
+        sample = self.conv_in(sample)
+
+        checkpoint_fn = (
+            partial(torch.utils.checkpoint.checkpoint, use_reentrant=False)
+            if self.gradient_checkpointing and self.training
+            else lambda x: x
+        )
+
+        for down_block in self.down_blocks:
+            sample = checkpoint_fn(down_block)(sample)
+
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if self.latent_log_var == "uniform":
+            last_channel = sample[:, -1:, ...]
+            num_dims = sample.dim()
+
+            if num_dims == 4:
+                # For shape (B, C, H, W)
+                repeated_last_channel = last_channel.repeat(
+                    1, sample.shape[1] - 2, 1, 1
+                )
+                sample = torch.cat([sample, repeated_last_channel], dim=1)
+            elif num_dims == 5:
+                # For shape (B, C, F, H, W)
+                repeated_last_channel = last_channel.repeat(
+                    1, sample.shape[1] - 2, 1, 1, 1
+                )
+                sample = torch.cat([sample, repeated_last_channel], dim=1)
+            else:
+                raise ValueError(f"Invalid input shape: {sample.shape}")
+
+        return sample
+
+
+class Decoder(nn.Module):
+    r"""
+    The `Decoder` layer of a variational autoencoder that decodes its latent representation into an output sample.
+
+    Args:
+        dims (`int` or `Tuple[int, int]`, *optional*, defaults to 3):
+            The number of dimensions to use in convolutions.
+        in_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        out_channels (`int`, *optional*, defaults to 3):
+            The number of output channels.
+        blocks (`List[Tuple[str, int]]`, *optional*, defaults to `[("res_x", 1)]`):
+            The blocks to use. Each block is a tuple of the block name and the number of layers.
+        base_channels (`int`, *optional*, defaults to 128):
+            The number of output channels for the first convolutional layer.
+        norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups for normalization.
+        patch_size (`int`, *optional*, defaults to 1):
+            The patch size to use. Should be a power of 2.
+        norm_layer (`str`, *optional*, defaults to `group_norm`):
+            The normalization layer to use. Can be either `group_norm` or `pixel_norm`.
+        causal (`bool`, *optional*, defaults to `True`):
+            Whether to use causal convolutions or not.
+    """
+
+    def __init__(
+        self,
+        dims,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        blocks=[("res_x", 1)],
+        base_channels: int = 128,
+        layers_per_block: int = 2,
+        norm_num_groups: int = 32,
+        patch_size: int = 1,
+        norm_layer: str = "group_norm",
+        causal: bool = True,
+    ):
+        super().__init__()
+        self.patch_size = patch_size
+        self.layers_per_block = layers_per_block
+        out_channels = out_channels * patch_size**2
+        self.causal = causal
+        self.blocks_desc = blocks
+
+        # Compute output channel to be product of all channel-multiplier blocks
+        output_channel = base_channels
+        for block_name, block_params in list(reversed(blocks)):
+            block_params = block_params if isinstance(block_params, dict) else {}
+            if block_name == "res_x_y":
+                output_channel = output_channel * block_params.get("multiplier", 2)
+
+        self.conv_in = make_conv_nd(
+            dims,
+            in_channels,
+            output_channel,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            causal=True,
+        )
+
+        self.up_blocks = nn.ModuleList([])
+
+        for block_name, block_params in list(reversed(blocks)):
+            input_channel = output_channel
+            if isinstance(block_params, int):
+                block_params = {"num_layers": block_params}
+
+            if block_name == "res_x":
+                block = UNetMidBlock3D(
+                    dims=dims,
+                    in_channels=input_channel,
+                    num_layers=block_params["num_layers"],
+                    resnet_eps=1e-6,
+                    resnet_groups=norm_num_groups,
+                    norm_layer=norm_layer,
+                )
+            elif block_name == "res_x_y":
+                output_channel = output_channel // block_params.get("multiplier", 2)
+                block = ResnetBlock3D(
+                    dims=dims,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    eps=1e-6,
+                    groups=norm_num_groups,
+                    norm_layer=norm_layer,
+                )
+            elif block_name == "compress_time":
+                block = DepthToSpaceUpsample(
+                    dims=dims, in_channels=input_channel, stride=(2, 1, 1)
+                )
+            elif block_name == "compress_space":
+                block = DepthToSpaceUpsample(
+                    dims=dims, in_channels=input_channel, stride=(1, 2, 2)
+                )
+            elif block_name == "compress_all":
+                block = DepthToSpaceUpsample(
+                    dims=dims,
+                    in_channels=input_channel,
+                    stride=(2, 2, 2),
+                    residual=block_params.get("residual", False),
+                )
+            else:
+                raise ValueError(f"unknown layer: {block_name}")
+
+            self.up_blocks.append(block)
+
+        if norm_layer == "group_norm":
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=output_channel, num_groups=norm_num_groups, eps=1e-6
+            )
+        elif norm_layer == "pixel_norm":
+            self.conv_norm_out = PixelNorm()
+        elif norm_layer == "layer_norm":
+            self.conv_norm_out = LayerNorm(output_channel, eps=1e-6)
+
+        self.conv_act = nn.SiLU()
+        self.conv_out = make_conv_nd(
+            dims, output_channel, out_channels, 3, padding=1, causal=True
+        )
+
+        self.gradient_checkpointing = False
+
+    # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor:
+    def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+        r"""The forward method of the `Decoder` class."""
+        # assert target_shape is not None, "target_shape must be provided"
+
+        sample = self.conv_in(sample, causal=self.causal)
+
+        upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
+
+        checkpoint_fn = (
+            partial(torch.utils.checkpoint.checkpoint, use_reentrant=False)
+            if self.gradient_checkpointing and self.training
+            else lambda x: x
+        )
+
+        sample = sample.to(upscale_dtype)
+
+        for up_block in self.up_blocks:
+            sample = checkpoint_fn(up_block)(sample, causal=self.causal)
+
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample, causal=self.causal)
+
+        sample = unpatchify(sample, patch_size_hw=self.patch_size, patch_size_t=1)
+
+        return sample
+
+
+class UNetMidBlock3D(nn.Module):
+    """
+    A 3D UNet mid-block [`UNetMidBlock3D`] with multiple residual blocks.
+
+    Args:
+        in_channels (`int`): The number of input channels.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
+        num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
+        resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
+        resnet_groups (`int`, *optional*, defaults to 32):
+            The number of groups to use in the group normalization layers of the resnet blocks.
+
+    Returns:
+        `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
+        in_channels, height, width)`.
+
+    """
+
+    def __init__(
+        self,
+        dims: Union[int, Tuple[int, int]],
+        in_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_groups: int = 32,
+        norm_layer: str = "group_norm",
+    ):
+        super().__init__()
+        resnet_groups = (
+            resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+        )
+
+        self.res_blocks = nn.ModuleList(
+            [
+                ResnetBlock3D(
+                    dims=dims,
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    norm_layer=norm_layer,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+    def forward(
+        self, hidden_states: torch.FloatTensor, causal: bool = True
+    ) -> torch.FloatTensor:
+        for resnet in self.res_blocks:
+            hidden_states = resnet(hidden_states, causal=causal)
+
+        return hidden_states
+
+
+class DepthToSpaceUpsample(nn.Module):
+    def __init__(self, dims, in_channels, stride, residual=False):
+        super().__init__()
+        self.stride = stride
+        self.out_channels = math.prod(stride) * in_channels
+        self.conv = make_conv_nd(
+            dims=dims,
+            in_channels=in_channels,
+            out_channels=self.out_channels,
+            kernel_size=3,
+            stride=1,
+            causal=True,
+        )
+        self.residual = residual
+
+    def forward(self, x, causal: bool = True):
+        if self.residual:
+            # Reshape and duplicate the input to match the output shape
+            x_in = rearrange(
+                x,
+                "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+                p1=self.stride[0],
+                p2=self.stride[1],
+                p3=self.stride[2],
+            )
+            x_in = x_in.repeat(1, math.prod(self.stride), 1, 1, 1)
+            if self.stride[0] == 2:
+                x_in = x_in[:, :, 1:, :, :]
+        x = self.conv(x, causal=causal)
+        x = rearrange(
+            x,
+            "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)",
+            p1=self.stride[0],
+            p2=self.stride[1],
+            p3=self.stride[2],
+        )
+        if self.stride[0] == 2:
+            x = x[:, :, 1:, :, :]
+        if self.residual:
+            x = x + x_in
+        return x
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, dim, eps, elementwise_affine=True) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine)
+
+    def forward(self, x):
+        x = rearrange(x, "b c d h w -> b d h w c")
+        x = self.norm(x)
+        x = rearrange(x, "b d h w c -> b c d h w")
+        return x
+
+
+class ResnetBlock3D(nn.Module):
+    r"""
+    A Resnet block.
+
+    Parameters:
+        in_channels (`int`): The number of channels in the input.
+        out_channels (`int`, *optional*, default to be `None`):
+            The number of output channels for the first conv layer. If None, same as `in_channels`.
+        dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
+        groups (`int`, *optional*, default to `32`): The number of groups to use for the first normalization layer.
+        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
+    """
+
+    def __init__(
+        self,
+        dims: Union[int, Tuple[int, int]],
+        in_channels: int,
+        out_channels: Optional[int] = None,
+        dropout: float = 0.0,
+        groups: int = 32,
+        eps: float = 1e-6,
+        norm_layer: str = "group_norm",
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        if norm_layer == "group_norm":
+            self.norm1 = nn.GroupNorm(
+                num_groups=groups, num_channels=in_channels, eps=eps, affine=True
+            )
+        elif norm_layer == "pixel_norm":
+            self.norm1 = PixelNorm()
+        elif norm_layer == "layer_norm":
+            self.norm1 = LayerNorm(in_channels, eps=eps, elementwise_affine=True)
+
+        self.non_linearity = nn.SiLU()
+
+        self.conv1 = make_conv_nd(
+            dims,
+            in_channels,
+            out_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            causal=True,
+        )
+
+        if norm_layer == "group_norm":
+            self.norm2 = nn.GroupNorm(
+                num_groups=groups, num_channels=out_channels, eps=eps, affine=True
+            )
+        elif norm_layer == "pixel_norm":
+            self.norm2 = PixelNorm()
+        elif norm_layer == "layer_norm":
+            self.norm2 = LayerNorm(out_channels, eps=eps, elementwise_affine=True)
+
+        self.dropout = torch.nn.Dropout(dropout)
+
+        self.conv2 = make_conv_nd(
+            dims,
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            causal=True,
+        )
+
+        self.conv_shortcut = (
+            make_linear_nd(
+                dims=dims, in_channels=in_channels, out_channels=out_channels
+            )
+            if in_channels != out_channels
+            else nn.Identity()
+        )
+
+        self.norm3 = (
+            LayerNorm(in_channels, eps=eps, elementwise_affine=True)
+            if in_channels != out_channels
+            else nn.Identity()
+        )
+
+    def forward(
+        self,
+        input_tensor: torch.FloatTensor,
+        causal: bool = True,
+    ) -> torch.FloatTensor:
+        hidden_states = input_tensor
+
+        hidden_states = self.norm1(hidden_states)
+
+        hidden_states = self.non_linearity(hidden_states)
+
+        hidden_states = self.conv1(hidden_states, causal=causal)
+
+        hidden_states = self.norm2(hidden_states)
+
+        hidden_states = self.non_linearity(hidden_states)
+
+        hidden_states = self.dropout(hidden_states)
+
+        hidden_states = self.conv2(hidden_states, causal=causal)
+
+        input_tensor = self.norm3(input_tensor)
+
+        input_tensor = self.conv_shortcut(input_tensor)
+
+        output_tensor = input_tensor + hidden_states
+
+        return output_tensor
+
+
+def patchify(x, patch_size_hw, patch_size_t=1):
+    if patch_size_hw == 1 and patch_size_t == 1:
+        return x
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size_hw, r=patch_size_hw
+        )
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b c (f p) (h q) (w r) -> b (c p r q) f h w",
+            p=patch_size_t,
+            q=patch_size_hw,
+            r=patch_size_hw,
+        )
+    else:
+        raise ValueError(f"Invalid input shape: {x.shape}")
+
+    return x
+
+
+def unpatchify(x, patch_size_hw, patch_size_t=1):
+    if patch_size_hw == 1 and patch_size_t == 1:
+        return x
+
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size_hw, r=patch_size_hw
+        )
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b (c p r q) f h w -> b c (f p) (h q) (w r)",
+            p=patch_size_t,
+            q=patch_size_hw,
+            r=patch_size_hw,
+        )
+
+    return x
+
+class processor(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.register_buffer("std-of-means", torch.empty(128))
+        self.register_buffer("mean-of-means", torch.empty(128))
+        self.register_buffer("mean-of-stds", torch.empty(128))
+        self.register_buffer("mean-of-stds_over_std-of-means", torch.empty(128))
+        self.register_buffer("channel", torch.empty(128))
+
+    def un_normalize(self, x):
+        return (x * self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)) + self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)
+
+    def normalize(self, x):
+        return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)
+
+class VideoVAE(nn.Module):
+    def __init__(self):
+        super().__init__()
+        config = {
+            "_class_name": "CausalVideoAutoencoder",
+            "dims": 3,
+            "in_channels": 3,
+            "out_channels": 3,
+            "latent_channels": 128,
+            "blocks": [
+                ["res_x", 4],
+                ["compress_all", 1],
+                ["res_x_y", 1],
+                ["res_x", 3],
+                ["compress_all", 1],
+                ["res_x_y", 1],
+                ["res_x", 3],
+                ["compress_all", 1],
+                ["res_x", 3],
+                ["res_x", 4],
+            ],
+            "scaling_factor": 1.0,
+            "norm_layer": "pixel_norm",
+            "patch_size": 4,
+            "latent_log_var": "uniform",
+            "use_quant_conv": False,
+            "causal_decoder": False,
+        }
+
+        double_z = config.get("double_z", True)
+        latent_log_var = config.get(
+            "latent_log_var", "per_channel" if double_z else "none"
+        )
+
+        self.encoder = Encoder(
+            dims=config["dims"],
+            in_channels=config.get("in_channels", 3),
+            out_channels=config["latent_channels"],
+            blocks=config.get("encoder_blocks", config.get("blocks")),
+            patch_size=config.get("patch_size", 1),
+            latent_log_var=latent_log_var,
+            norm_layer=config.get("norm_layer", "group_norm"),
+        )
+
+        self.decoder = Decoder(
+            dims=config["dims"],
+            in_channels=config["latent_channels"],
+            out_channels=config.get("out_channels", 3),
+            blocks=config.get("decoder_blocks", config.get("blocks")),
+            patch_size=config.get("patch_size", 1),
+            norm_layer=config.get("norm_layer", "group_norm"),
+            causal=config.get("causal_decoder", False),
+        )
+
+        self.per_channel_statistics = processor()
+
+    def encode(self, x):
+        means, logvar = torch.chunk(self.encoder(x), 2, dim=1)
+        return self.per_channel_statistics.normalize(means)
+
+    def decode(self, x):
+        return self.decoder(self.per_channel_statistics.un_normalize(x))
+

comfy/ldm/lightricks/vae/conv_nd_factory.py

@@ -0,0 +1,83 @@
+from typing import Tuple, Union
+
+import torch
+
+from .dual_conv3d import DualConv3d
+from .causal_conv3d import CausalConv3d
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def make_conv_nd(
+    dims: Union[int, Tuple[int, int]],
+    in_channels: int,
+    out_channels: int,
+    kernel_size: int,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+    bias=True,
+    causal=False,
+):
+    if dims == 2:
+        return ops.Conv2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+    elif dims == 3:
+        if causal:
+            return CausalConv3d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                groups=groups,
+                bias=bias,
+            )
+        return ops.Conv3d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+        )
+    elif dims == (2, 1):
+        return DualConv3d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=bias,
+        )
+    else:
+        raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def make_linear_nd(
+    dims: int,
+    in_channels: int,
+    out_channels: int,
+    bias=True,
+):
+    if dims == 2:
+        return ops.Conv2d(
+            in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias
+        )
+    elif dims == 3 or dims == (2, 1):
+        return ops.Conv3d(
+            in_channels=in_channels, out_channels=out_channels, kernel_size=1, bias=bias
+        )
+    else:
+        raise ValueError(f"unsupported dimensions: {dims}")

comfy/ldm/lightricks/vae/dual_conv3d.py

@@ -0,0 +1,195 @@
+import math
+from typing import Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+
+class DualConv3d(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride: Union[int, Tuple[int, int, int]] = 1,
+        padding: Union[int, Tuple[int, int, int]] = 0,
+        dilation: Union[int, Tuple[int, int, int]] = 1,
+        groups=1,
+        bias=True,
+    ):
+        super(DualConv3d, self).__init__()
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        # Ensure kernel_size, stride, padding, and dilation are tuples of length 3
+        if isinstance(kernel_size, int):
+            kernel_size = (kernel_size, kernel_size, kernel_size)
+        if kernel_size == (1, 1, 1):
+            raise ValueError(
+                "kernel_size must be greater than 1. Use make_linear_nd instead."
+            )
+        if isinstance(stride, int):
+            stride = (stride, stride, stride)
+        if isinstance(padding, int):
+            padding = (padding, padding, padding)
+        if isinstance(dilation, int):
+            dilation = (dilation, dilation, dilation)
+
+        # Set parameters for convolutions
+        self.groups = groups
+        self.bias = bias
+
+        # Define the size of the channels after the first convolution
+        intermediate_channels = (
+            out_channels if in_channels < out_channels else in_channels
+        )
+
+        # Define parameters for the first convolution
+        self.weight1 = nn.Parameter(
+            torch.Tensor(
+                intermediate_channels,
+                in_channels // groups,
+                1,
+                kernel_size[1],
+                kernel_size[2],
+            )
+        )
+        self.stride1 = (1, stride[1], stride[2])
+        self.padding1 = (0, padding[1], padding[2])
+        self.dilation1 = (1, dilation[1], dilation[2])
+        if bias:
+            self.bias1 = nn.Parameter(torch.Tensor(intermediate_channels))
+        else:
+            self.register_parameter("bias1", None)
+
+        # Define parameters for the second convolution
+        self.weight2 = nn.Parameter(
+            torch.Tensor(
+                out_channels, intermediate_channels // groups, kernel_size[0], 1, 1
+            )
+        )
+        self.stride2 = (stride[0], 1, 1)
+        self.padding2 = (padding[0], 0, 0)
+        self.dilation2 = (dilation[0], 1, 1)
+        if bias:
+            self.bias2 = nn.Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter("bias2", None)
+
+        # Initialize weights and biases
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.kaiming_uniform_(self.weight1, a=math.sqrt(5))
+        nn.init.kaiming_uniform_(self.weight2, a=math.sqrt(5))
+        if self.bias:
+            fan_in1, _ = nn.init._calculate_fan_in_and_fan_out(self.weight1)
+            bound1 = 1 / math.sqrt(fan_in1)
+            nn.init.uniform_(self.bias1, -bound1, bound1)
+            fan_in2, _ = nn.init._calculate_fan_in_and_fan_out(self.weight2)
+            bound2 = 1 / math.sqrt(fan_in2)
+            nn.init.uniform_(self.bias2, -bound2, bound2)
+
+    def forward(self, x, use_conv3d=False, skip_time_conv=False):
+        if use_conv3d:
+            return self.forward_with_3d(x=x, skip_time_conv=skip_time_conv)
+        else:
+            return self.forward_with_2d(x=x, skip_time_conv=skip_time_conv)
+
+    def forward_with_3d(self, x, skip_time_conv):
+        # First convolution
+        x = F.conv3d(
+            x,
+            self.weight1,
+            self.bias1,
+            self.stride1,
+            self.padding1,
+            self.dilation1,
+            self.groups,
+        )
+
+        if skip_time_conv:
+            return x
+
+        # Second convolution
+        x = F.conv3d(
+            x,
+            self.weight2,
+            self.bias2,
+            self.stride2,
+            self.padding2,
+            self.dilation2,
+            self.groups,
+        )
+
+        return x
+
+    def forward_with_2d(self, x, skip_time_conv):
+        b, c, d, h, w = x.shape
+
+        # First 2D convolution
+        x = rearrange(x, "b c d h w -> (b d) c h w")
+        # Squeeze the depth dimension out of weight1 since it's 1
+        weight1 = self.weight1.squeeze(2)
+        # Select stride, padding, and dilation for the 2D convolution
+        stride1 = (self.stride1[1], self.stride1[2])
+        padding1 = (self.padding1[1], self.padding1[2])
+        dilation1 = (self.dilation1[1], self.dilation1[2])
+        x = F.conv2d(x, weight1, self.bias1, stride1, padding1, dilation1, self.groups)
+
+        _, _, h, w = x.shape
+
+        if skip_time_conv:
+            x = rearrange(x, "(b d) c h w -> b c d h w", b=b)
+            return x
+
+        # Second convolution which is essentially treated as a 1D convolution across the 'd' dimension
+        x = rearrange(x, "(b d) c h w -> (b h w) c d", b=b)
+
+        # Reshape weight2 to match the expected dimensions for conv1d
+        weight2 = self.weight2.squeeze(-1).squeeze(-1)
+        # Use only the relevant dimension for stride, padding, and dilation for the 1D convolution
+        stride2 = self.stride2[0]
+        padding2 = self.padding2[0]
+        dilation2 = self.dilation2[0]
+        x = F.conv1d(x, weight2, self.bias2, stride2, padding2, dilation2, self.groups)
+        x = rearrange(x, "(b h w) c d -> b c d h w", b=b, h=h, w=w)
+
+        return x
+
+    @property
+    def weight(self):
+        return self.weight2
+
+
+def test_dual_conv3d_consistency():
+    # Initialize parameters
+    in_channels = 3
+    out_channels = 5
+    kernel_size = (3, 3, 3)
+    stride = (2, 2, 2)
+    padding = (1, 1, 1)
+
+    # Create an instance of the DualConv3d class
+    dual_conv3d = DualConv3d(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=padding,
+        bias=True,
+    )
+
+    # Example input tensor
+    test_input = torch.randn(1, 3, 10, 10, 10)
+
+    # Perform forward passes with both 3D and 2D settings
+    output_conv3d = dual_conv3d(test_input, use_conv3d=True)
+    output_2d = dual_conv3d(test_input, use_conv3d=False)
+
+    # Assert that the outputs from both methods are sufficiently close
+    assert torch.allclose(
+        output_conv3d, output_2d, atol=1e-6
+    ), "Outputs are not consistent between 3D and 2D convolutions."

comfy/ldm/lightricks/vae/pixel_norm.py

@@ -0,0 +1,12 @@
+import torch
+from torch import nn
+
+
+class PixelNorm(nn.Module):
+    def __init__(self, dim=1, eps=1e-8):
+        super(PixelNorm, self).__init__()
+        self.dim = dim
+        self.eps = eps
+
+    def forward(self, x):
+        return x / torch.sqrt(torch.mean(x**2, dim=self.dim, keepdim=True) + self.eps)

comfy/ldm/modules/attention.py

@@ -299,7 +299,10 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
                     if len(mask.shape) == 2:
                         s1 += mask[i:end]
                     else:
-                        s1 += mask[:, i:end]
+                        if mask.shape[1] == 1:
+                            s1 += mask
+                        else:
+                            s1 += mask[:, i:end]
 
                 s2 = s1.softmax(dim=-1).to(v.dtype)
                 del s1

comfy/ldm/modules/sub_quadratic_attention.py

@@ -234,6 +234,8 @@ def efficient_dot_product_attention(
     def get_mask_chunk(chunk_idx: int) -> Tensor:
         if mask is None:
             return None
+        if mask.shape[1] == 1:
+            return mask
         chunk = min(query_chunk_size, q_tokens)
         return mask[:,chunk_idx:chunk_idx + chunk]
 

comfy/lora.py

@@ -62,6 +62,7 @@ def load_lora(lora, to_load):
         diffusers_lora = "{}_lora.up.weight".format(x)
         diffusers2_lora = "{}.lora_B.weight".format(x)
         diffusers3_lora = "{}.lora.up.weight".format(x)
+        mochi_lora = "{}.lora_B".format(x)
         transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
         A_name = None
 
@@ -81,6 +82,10 @@ def load_lora(lora, to_load):
             A_name = diffusers3_lora
             B_name = "{}.lora.down.weight".format(x)
             mid_name = None
+        elif mochi_lora in lora.keys():
+            A_name = mochi_lora
+            B_name = "{}.lora_A".format(x)
+            mid_name = None
         elif transformers_lora in lora.keys():
             A_name = transformers_lora
             B_name ="{}.lora_linear_layer.down.weight".format(x)
@@ -362,6 +367,12 @@ def model_lora_keys_unet(model, key_map={}):
                 key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris
                 key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer
 
+    if isinstance(model, comfy.model_base.GenmoMochi):
+        for k in sdk:
+            if k.startswith("diffusion_model.") and k.endswith(".weight"): #Official Mochi lora format
+                key_lora = k[len("diffusion_model."):-len(".weight")]
+                key_map["{}".format(key_lora)] = k
+
     return key_map
 
 

comfy/model_base.py

@@ -30,6 +30,7 @@ import comfy.ldm.hydit.models
 import comfy.ldm.audio.dit
 import comfy.ldm.audio.embedders
 import comfy.ldm.flux.model
+import comfy.ldm.lightricks.model
 
 import comfy.model_management
 import comfy.conds
@@ -711,7 +712,13 @@ class Flux(BaseModel):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux)
 
     def concat_cond(self, **kwargs):
-        num_channels = self.diffusion_model.img_in.weight.shape[1] // (self.diffusion_model.patch_size * self.diffusion_model.patch_size)
+        try:
+            #Handle Flux control loras dynamically changing the img_in weight.
+            num_channels = self.diffusion_model.img_in.weight.shape[1] // (self.diffusion_model.patch_size * self.diffusion_model.patch_size)
+        except:
+            #Some cases like tensorrt might not have the weights accessible
+            num_channels = self.model_config.unet_config["in_channels"]
+
         out_channels = self.model_config.unet_config["out_channels"]
 
         if num_channels <= out_channels:
@@ -767,3 +774,23 @@ class GenmoMochi(BaseModel):
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out
+
+class LTXV(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lightricks.model.LTXVModel) #TODO
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        guiding_latent = kwargs.get("guiding_latent", None)
+        if guiding_latent is not None:
+            out['guiding_latent'] = comfy.conds.CONDRegular(guiding_latent)
+
+        out['frame_rate'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", 25))
+        return out

comfy/model_detection.py

@@ -183,6 +183,10 @@ def detect_unet_config(state_dict, key_prefix):
         dit_config["rope_theta"] = 10000.0
         return dit_config
 
+    if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv
+        dit_config = {}
+        dit_config["image_model"] = "ltxv"
+        return dit_config
 
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None

comfy/model_management.py

@@ -628,6 +628,10 @@ def maximum_vram_for_weights(device=None):
 def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
     if model_params < 0:
         model_params = 1000000000000000000000
+    if args.fp32_unet:
+        return torch.float32
+    if args.fp64_unet:
+        return torch.float64
     if args.bf16_unet:
         return torch.bfloat16
     if args.fp16_unet:
@@ -674,7 +678,7 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor
 
 # None means no manual cast
 def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
-    if weight_dtype == torch.float32:
+    if weight_dtype == torch.float32 or weight_dtype == torch.float64:
         return None
 
     fp16_supported = should_use_fp16(inference_device, prioritize_performance=False)

comfy/model_patcher.py

@@ -367,17 +367,26 @@ class ModelPatcher:
         else:
             set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key))
 
+    def _load_list(self):
+        loading = []
+        for n, m in self.model.named_modules():
+            params = []
+            skip = False
+            for name, param in m.named_parameters(recurse=False):
+                params.append(name)
+            for name, param in m.named_parameters(recurse=True):
+                if name not in params:
+                    skip = True # skip random weights in non leaf modules
+                    break
+            if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0):
+                loading.append((comfy.model_management.module_size(m), n, m, params))
+        return loading
+
     def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
         mem_counter = 0
         patch_counter = 0
         lowvram_counter = 0
-        loading = []
-        for n, m in self.model.named_modules():
-            params = []
-            for name, param in m.named_parameters(recurse=False):
-                params.append(name)
-            if hasattr(m, "comfy_cast_weights") or len(params) > 0:
-                loading.append((comfy.model_management.module_size(m), n, m, params))
+        loading = self._load_list()
 
         load_completely = []
         loading.sort(reverse=True)
@@ -420,8 +429,9 @@ class ModelPatcher:
                     if m.comfy_cast_weights:
                         wipe_lowvram_weight(m)
 
-                mem_counter += module_mem
-                load_completely.append((module_mem, n, m, params))
+                if full_load or mem_counter + module_mem < lowvram_model_memory:
+                    mem_counter += module_mem
+                    load_completely.append((module_mem, n, m, params))
 
         load_completely.sort(reverse=True)
         for x in load_completely:
@@ -508,14 +518,7 @@ class ModelPatcher:
     def partially_unload(self, device_to, memory_to_free=0):
         memory_freed = 0
         patch_counter = 0
-        unload_list = []
-
-        for n, m in self.model.named_modules():
-            shift_lowvram = False
-            if hasattr(m, "comfy_cast_weights"):
-                module_mem = comfy.model_management.module_size(m)
-                unload_list.append((module_mem, n, m))
-
+        unload_list = self._load_list()
         unload_list.sort()
         for unload in unload_list:
             if memory_to_free < memory_freed:
@@ -523,32 +526,42 @@ class ModelPatcher:
             module_mem = unload[0]
             n = unload[1]
             m = unload[2]
-            weight_key = "{}.weight".format(n)
-            bias_key = "{}.bias".format(n)
+            params = unload[3]
 
+            lowvram_possible = hasattr(m, "comfy_cast_weights")
             if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True:
-                for key in [weight_key, bias_key]:
+                move_weight = True
+                for param in params:
+                    key = "{}.{}".format(n, param)
                     bk = self.backup.get(key, None)
                     if bk is not None:
+                        if not lowvram_possible:
+                            move_weight = False
+                            break
+
                         if bk.inplace_update:
                             comfy.utils.copy_to_param(self.model, key, bk.weight)
                         else:
                             comfy.utils.set_attr_param(self.model, key, bk.weight)
                         self.backup.pop(key)
 
-                m.to(device_to)
-                if weight_key in self.patches:
-                    m.weight_function = LowVramPatch(weight_key, self.patches)
-                    patch_counter += 1
-                if bias_key in self.patches:
-                    m.bias_function = LowVramPatch(bias_key, self.patches)
-                    patch_counter += 1
+                weight_key = "{}.weight".format(n)
+                bias_key = "{}.bias".format(n)
+                if move_weight:
+                    m.to(device_to)
+                    if lowvram_possible:
+                        if weight_key in self.patches:
+                            m.weight_function = LowVramPatch(weight_key, self.patches)
+                            patch_counter += 1
+                        if bias_key in self.patches:
+                            m.bias_function = LowVramPatch(bias_key, self.patches)
+                            patch_counter += 1
 
-                m.prev_comfy_cast_weights = m.comfy_cast_weights
-                m.comfy_cast_weights = True
-                m.comfy_patched_weights = False
-                memory_freed += module_mem
-                logging.debug("freed {}".format(n))
+                        m.prev_comfy_cast_weights = m.comfy_cast_weights
+                        m.comfy_cast_weights = True
+                    m.comfy_patched_weights = False
+                    memory_freed += module_mem
+                    logging.debug("freed {}".format(n))
 
         self.model.model_lowvram = True
         self.model.lowvram_patch_counter += patch_counter

comfy/sd.py

@@ -8,6 +8,7 @@ from .ldm.cascade.stage_a import StageA
 from .ldm.cascade.stage_c_coder import StageC_coder
 from .ldm.audio.autoencoder import AudioOobleckVAE
 import comfy.ldm.genmo.vae.model
+import comfy.ldm.lightricks.vae.causal_video_autoencoder
 import yaml
 
 import comfy.utils
@@ -27,6 +28,7 @@ import comfy.text_encoders.hydit
 import comfy.text_encoders.flux
 import comfy.text_encoders.long_clipl
 import comfy.text_encoders.genmo
+import comfy.text_encoders.lt
 
 import comfy.model_patcher
 import comfy.lora
@@ -261,6 +263,14 @@ class VAE:
                 self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8)
                 self.working_dtypes = [torch.float16, torch.float32]
+            elif "decoder.up_blocks.0.res_blocks.0.conv1.conv.weight" in sd: #lightricks ltxv
+                self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE()
+                self.latent_channels = 128
+                self.latent_dim = 3
+                self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
+                self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32)
+                self.working_dtypes = [torch.bfloat16, torch.float32]
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -360,7 +370,9 @@ class VAE:
             elif dims == 2:
                 pixel_samples = self.decode_tiled_(samples_in)
             elif dims == 3:
-                pixel_samples = self.decode_tiled_3d(samples_in)
+                tile = 256 // self.spacial_compression_decode()
+                overlap = tile // 4
+                pixel_samples = self.decode_tiled_3d(samples_in, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
 
         pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples
@@ -424,6 +436,12 @@ class VAE:
     def get_sd(self):
         return self.first_stage_model.state_dict()
 
+    def spacial_compression_decode(self):
+        try:
+            return self.upscale_ratio[-1]
+        except:
+            return self.upscale_ratio
+
 class StyleModel:
     def __init__(self, model, device="cpu"):
         self.model = model
@@ -452,6 +470,7 @@ class CLIPType(Enum):
     HUNYUAN_DIT = 5
     FLUX = 6
     MOCHI = 7
+    LTXV = 8
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
     clip_data = []
@@ -530,6 +549,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             if clip_type == CLIPType.SD3:
                 clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, **t5xxl_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+            elif clip_type == CLIPType.LTXV:
+                clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer
             else: #CLIPType.MOCHI
                 clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer

comfy/supported_models.py

@@ -11,6 +11,7 @@ import comfy.text_encoders.aura_t5
 import comfy.text_encoders.hydit
 import comfy.text_encoders.flux
 import comfy.text_encoders.genmo
+import comfy.text_encoders.lt
 
 from . import supported_models_base
 from . import latent_formats
@@ -658,6 +659,15 @@ class Flux(supported_models_base.BASE):
         t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect))
 
+class FluxInpaint(Flux):
+    unet_config = {
+        "image_model": "flux",
+        "guidance_embed": True,
+        "in_channels": 96,
+    }
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
 class FluxSchnell(Flux):
     unet_config = {
         "image_model": "flux",
@@ -702,7 +712,34 @@ class GenmoMochi(supported_models_base.BASE):
         t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.genmo.MochiT5Tokenizer, comfy.text_encoders.genmo.mochi_te(**t5_detect))
 
+class LTXV(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "ltxv",
+    }
 
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell, GenmoMochi]
+    sampling_settings = {
+        "shift": 2.37,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.LTXV
+
+    memory_usage_factor = 2.7
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.LTXV(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.lt.LTXVT5Tokenizer, comfy.text_encoders.lt.ltxv_te(**t5_detect))
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV]
 
 models += [SVD_img2vid]

comfy/text_encoders/lt.py

@@ -0,0 +1,18 @@
+from comfy import sd1_clip
+import os
+from transformers import T5TokenizerFast
+import comfy.text_encoders.genmo
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128) #pad to 128?
+
+
+class LTXVT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+
+def ltxv_te(*args, **kwargs):
+    return comfy.text_encoders.genmo.mochi_te(*args, **kwargs)

comfy_extras/nodes_lt.py

@@ -0,0 +1,181 @@
+import nodes
+import node_helpers
+import torch
+import comfy.model_management
+import comfy.model_sampling
+import math
+
+class EmptyLTXVLatentVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                              "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                              "length": ("INT", {"default": 97, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "latent/video/ltxv"
+
+    def generate(self, width, height, length, batch_size=1):
+        latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
+        return ({"samples": latent}, )
+
+
+class LTXVImgToVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "vae": ("VAE",),
+                             "image": ("IMAGE",),
+                             "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                             "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
+                             "length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
+
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+
+    CATEGORY = "conditioning/video_models"
+    FUNCTION = "generate"
+
+    def generate(self, positive, negative, image, vae, width, height, length, batch_size):
+        pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+        encode_pixels = pixels[:, :, :, :3]
+        t = vae.encode(encode_pixels)
+        positive = node_helpers.conditioning_set_values(positive, {"guiding_latent": t})
+        negative = node_helpers.conditioning_set_values(negative, {"guiding_latent": t})
+
+        latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
+        latent[:, :, :t.shape[2]] = t
+        return (positive, negative, {"samples": latent}, )
+
+
+class LTXVConditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "frame_rate": ("FLOAT", {"default": 25.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
+    RETURN_NAMES = ("positive", "negative")
+    FUNCTION = "append"
+
+    CATEGORY = "conditioning/video_models"
+
+    def append(self, positive, negative, frame_rate):
+        positive = node_helpers.conditioning_set_values(positive, {"frame_rate": frame_rate})
+        negative = node_helpers.conditioning_set_values(negative, {"frame_rate": frame_rate})
+        return (positive, negative)
+
+
+class ModelSamplingLTXV:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}),
+                              "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}),
+                              },
+                "optional": {"latent": ("LATENT",), }
+                }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, max_shift, base_shift, latent=None):
+        m = model.clone()
+
+        if latent is None:
+            tokens = 4096
+        else:
+            tokens = math.prod(latent["samples"].shape[2:])
+
+        x1 = 1024
+        x2 = 4096
+        mm = (max_shift - base_shift) / (x2 - x1)
+        b = base_shift - mm * x1
+        shift = (tokens) * mm + b
+
+        sampling_base = comfy.model_sampling.ModelSamplingFlux
+        sampling_type = comfy.model_sampling.CONST
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(shift=shift)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
+
+class LTXVScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                     "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}),
+                     "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}),
+                     "stretch": ("BOOLEAN", {
+                        "default": True,
+                        "tooltip": "Stretch the sigmas to be in the range [terminal, 1]."
+                    }),
+                     "terminal": (
+                        "FLOAT",
+                        {
+                            "default": 0.1, "min": 0.0, "max": 0.99, "step": 0.01,
+                            "tooltip": "The terminal value of the sigmas after stretching."
+                        },
+                    ),
+                    },
+                "optional": {"latent": ("LATENT",), }
+               }
+
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, steps, max_shift, base_shift, stretch, terminal, latent=None):
+        if latent is None:
+            tokens = 4096
+        else:
+            tokens = math.prod(latent["samples"].shape[2:])
+
+        sigmas = torch.linspace(1.0, 0.0, steps + 1)
+
+        x1 = 1024
+        x2 = 4096
+        mm = (max_shift - base_shift) / (x2 - x1)
+        b = base_shift - mm * x1
+        sigma_shift = (tokens) * mm + b
+
+        power = 1
+        sigmas = torch.where(
+            sigmas != 0,
+            math.exp(sigma_shift) / (math.exp(sigma_shift) + (1 / sigmas - 1) ** power),
+            0,
+        )
+
+        # Stretch sigmas so that its final value matches the given terminal value.
+        if stretch:
+            non_zero_mask = sigmas != 0
+            non_zero_sigmas = sigmas[non_zero_mask]
+            one_minus_z = 1.0 - non_zero_sigmas
+            scale_factor = one_minus_z[-1] / (1.0 - terminal)
+            stretched = 1.0 - (one_minus_z / scale_factor)
+            sigmas[non_zero_mask] = stretched
+
+        return (sigmas,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "EmptyLTXVLatentVideo": EmptyLTXVLatentVideo,
+    "LTXVImgToVideo": LTXVImgToVideo,
+    "ModelSamplingLTXV": ModelSamplingLTXV,
+    "LTXVConditioning": LTXVConditioning,
+    "LTXVScheduler": LTXVScheduler,
+}

comfy_extras/nodes_model_merging_model_specific.py

@@ -174,6 +174,28 @@ class ModelMergeMochiPreview(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
         return {"required": arg_dict}
 
+class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    CATEGORY = "advanced/model_merging/model_specific"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["patchify_proj."] = argument
+        arg_dict["adaln_single."] = argument
+        arg_dict["caption_projection."] = argument
+
+        for i in range(28):
+            arg_dict["transformer_blocks.{}.".format(i)] = argument
+
+        arg_dict["scale_shift_table"] = argument
+        arg_dict["proj_out."] = argument
+
+        return {"required": arg_dict}
+
 NODE_CLASS_MAPPINGS = {
     "ModelMergeSD1": ModelMergeSD1,
     "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
@@ -183,4 +205,5 @@ NODE_CLASS_MAPPINGS = {
     "ModelMergeFlux1": ModelMergeFlux1,
     "ModelMergeSD35_Large": ModelMergeSD35_Large,
     "ModelMergeMochiPreview": ModelMergeMochiPreview,
+    "ModelMergeLTXV": ModelMergeLTXV,
 }

model_filemanager/__init__.py

@@ -1,2 +0,0 @@
-# model_manager/__init__.py
-from .download_models import download_model, DownloadModelStatus, DownloadStatusType, create_model_path, check_file_exists, track_download_progress, validate_filename

model_filemanager/download_models.py

@@ -1,234 +0,0 @@
-#NOTE: This was an experiment and WILL BE REMOVED
-from __future__ import annotations
-import aiohttp
-import os
-import traceback
-import logging
-from folder_paths import folder_names_and_paths, get_folder_paths
-import re
-from typing import Callable, Any, Optional, Awaitable, Dict
-from enum import Enum
-import time
-from dataclasses import dataclass
-
-
-class DownloadStatusType(Enum):
-    PENDING = "pending"
-    IN_PROGRESS = "in_progress"
-    COMPLETED = "completed"
-    ERROR = "error"
-
-
-@dataclass
-class DownloadModelStatus():
-    status: str
-    progress_percentage: float
-    message: str
-    already_existed: bool = False
-
-    def __init__(self, status: DownloadStatusType, progress_percentage: float, message: str, already_existed: bool):
-        self.status = status.value  # Store the string value of the Enum
-        self.progress_percentage = progress_percentage
-        self.message = message
-        self.already_existed = already_existed
-
-    def to_dict(self) -> Dict[str, Any]:
-        return {
-            "status": self.status,
-            "progress_percentage": self.progress_percentage,
-            "message": self.message,
-            "already_existed": self.already_existed
-        }
-
-
-async def download_model(model_download_request: Callable[[str], Awaitable[aiohttp.ClientResponse]],
-                         model_name: str,
-                         model_url: str,
-                         model_directory: str,
-                         folder_path: str,
-                         progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]],
-                         progress_interval: float = 1.0) -> DownloadModelStatus:
-    """
-    Download a model file from a given URL into the models directory.
-
-    Args:
-        model_download_request (Callable[[str], Awaitable[aiohttp.ClientResponse]]):
-            A function that makes an HTTP request. This makes it easier to mock in unit tests.
-        model_name (str):
-            The name of the model file to be downloaded. This will be the filename on disk.
-        model_url (str):
-            The URL from which to download the model.
-        model_directory (str):
-            The subdirectory within the main models directory where the model
-            should be saved (e.g., 'checkpoints', 'loras', etc.).
-        progress_callback (Callable[[str, DownloadModelStatus], Awaitable[Any]]):
-            An asynchronous function to call with progress updates.
-        folder_path (str);
-            Path to which model folder should be used as the root.
-
-    Returns:
-        DownloadModelStatus: The result of the download operation.
-    """
-    if not validate_filename(model_name):
-        return DownloadModelStatus(
-            DownloadStatusType.ERROR,
-            0,
-            "Invalid model name",
-            False
-        )
-
-    if not model_directory in folder_names_and_paths:
-        return DownloadModelStatus(
-            DownloadStatusType.ERROR,
-            0,
-            "Invalid or unrecognized model directory. model_directory must be a known model type (eg 'checkpoints'). If you are seeing this error for a custom model type, ensure the relevant custom nodes are installed and working.",
-            False
-        )
-
-    if not folder_path in get_folder_paths(model_directory):
-        return DownloadModelStatus(
-            DownloadStatusType.ERROR,
-            0,
-            f"Invalid folder path '{folder_path}', does not match the list of known directories ({get_folder_paths(model_directory)}). If you're seeing this in the downloader UI, you may need to refresh the page.",
-            False
-        )
-
-    file_path = create_model_path(model_name, folder_path)
-    existing_file = await check_file_exists(file_path, model_name, progress_callback)
-    if existing_file:
-        return existing_file
-
-    try:
-        logging.info(f"Downloading {model_name} from {model_url}")
-        status = DownloadModelStatus(DownloadStatusType.PENDING, 0, f"Starting download of {model_name}", False)
-        await progress_callback(model_name, status)
-
-        response = await model_download_request(model_url)
-        if response.status != 200:
-            error_message = f"Failed to download {model_name}. Status code: {response.status}"
-            logging.error(error_message)
-            status = DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
-            await progress_callback(model_name, status)
-            return DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
-
-        return await track_download_progress(response, file_path, model_name, progress_callback, progress_interval)
-
-    except Exception as e:
-        logging.error(f"Error in downloading model: {e}")
-        return await handle_download_error(e, model_name, progress_callback)
-
-
-def create_model_path(model_name: str, folder_path: str) -> tuple[str, str]:
-    os.makedirs(folder_path, exist_ok=True)
-    file_path = os.path.join(folder_path, model_name)
-
-    # Ensure the resulting path is still within the base directory
-    abs_file_path = os.path.abspath(file_path)
-    abs_base_dir = os.path.abspath(folder_path)
-    if os.path.commonprefix([abs_file_path, abs_base_dir]) != abs_base_dir:
-        raise Exception(f"Invalid model directory: {folder_path}/{model_name}")
-
-    return file_path
-
-
-async def check_file_exists(file_path: str,
-                            model_name: str,
-                            progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]]
-                            ) -> Optional[DownloadModelStatus]:
-    if os.path.exists(file_path):
-        status = DownloadModelStatus(DownloadStatusType.COMPLETED, 100, f"{model_name} already exists", True)
-        await progress_callback(model_name, status)
-        return status
-    return None
-
-
-async def track_download_progress(response: aiohttp.ClientResponse,
-                                  file_path: str,
-                                  model_name: str,
-                                  progress_callback: Callable[[str, DownloadModelStatus], Awaitable[Any]],
-                                  interval: float = 1.0) -> DownloadModelStatus:
-    try:
-        total_size = int(response.headers.get('Content-Length', 0))
-        downloaded = 0
-        last_update_time = time.time()
-
-        async def update_progress():
-            nonlocal last_update_time
-            progress = (downloaded / total_size) * 100 if total_size > 0 else 0
-            status = DownloadModelStatus(DownloadStatusType.IN_PROGRESS, progress, f"Downloading {model_name}", False)
-            await progress_callback(model_name, status)
-            last_update_time = time.time()
-
-        temp_file_path = file_path + '.tmp'
-        with open(temp_file_path, 'wb') as f:
-            chunk_iterator = response.content.iter_chunked(8192)
-            while True:
-                try:
-                    chunk = await chunk_iterator.__anext__()
-                except StopAsyncIteration:
-                    break
-                f.write(chunk)
-                downloaded += len(chunk)
-
-                if time.time() - last_update_time >= interval:
-                    await update_progress()
-
-        os.rename(temp_file_path, file_path)
-
-        await update_progress()
-
-        logging.info(f"Successfully downloaded {model_name}. Total downloaded: {downloaded}")
-        status = DownloadModelStatus(DownloadStatusType.COMPLETED, 100, f"Successfully downloaded {model_name}", False)
-        await progress_callback(model_name, status)
-
-        return status
-    except Exception as e:
-        logging.error(f"Error in track_download_progress: {e}")
-        logging.error(traceback.format_exc())
-        return await handle_download_error(e, model_name, progress_callback)
-
-
-async def handle_download_error(e: Exception,
-                                model_name: str,
-                                progress_callback: Callable[[str, DownloadModelStatus], Any]
-                                ) -> DownloadModelStatus:
-    error_message = f"Error downloading {model_name}: {str(e)}"
-    status = DownloadModelStatus(DownloadStatusType.ERROR, 0, error_message, False)
-    await progress_callback(model_name, status)
-    return status
-
-
-def validate_filename(filename: str)-> bool:
-    """
-    Validate a filename to ensure it's safe and doesn't contain any path traversal attempts.
-
-    Args:
-    filename (str): The filename to validate
-
-    Returns:
-    bool: True if the filename is valid, False otherwise
-    """
-    if not filename.lower().endswith(('.sft', '.safetensors')):
-        return False
-
-    # Check if the filename is empty, None, or just whitespace
-    if not filename or not filename.strip():
-        return False
-
-    # Check for any directory traversal attempts or invalid characters
-    if any(char in filename for char in ['..', '/', '\\', '\n', '\r', '\t', '\0']):
-        return False
-
-    # Check if the filename starts with a dot (hidden file)
-    if filename.startswith('.'):
-        return False
-
-    # Use a whitelist of allowed characters
-    if not re.match(r'^[a-zA-Z0-9_\-. ]+$', filename):
-        return False
-
-    # Ensure the filename isn't too long
-    if len(filename) > 255:
-        return False
-
-    return True

nodes.py

@@ -301,7 +301,8 @@ class VAEDecodeTiled:
     def decode(self, vae, samples, tile_size, overlap=64):
         if tile_size < overlap * 4:
             overlap = tile_size // 4
-        images = vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, overlap=overlap // 8)
+        compression = vae.spacial_compression_decode()
+        images = vae.decode_tiled(samples["samples"], tile_x=tile_size // compression, tile_y=tile_size // compression, overlap=overlap // compression)
         if len(images.shape) == 5: #Combine batches
             images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
         return (images, )
@@ -391,7 +392,7 @@ class InpaintModelConditioning:
 
     CATEGORY = "conditioning/inpaint"
 
-    def encode(self, positive, negative, pixels, vae, mask, noise_mask):
+    def encode(self, positive, negative, pixels, vae, mask, noise_mask=True):
         x = (pixels.shape[1] // 8) * 8
         y = (pixels.shape[2] // 8) * 8
         mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
@@ -897,7 +898,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -915,6 +916,8 @@ class CLIPLoader:
             clip_type = comfy.sd.CLIPType.STABLE_AUDIO
         elif type == "mochi":
             clip_type = comfy.sd.CLIPType.MOCHI
+        elif type == "ltxv":
+            clip_type = comfy.sd.CLIPType.LTXV
         else:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
@@ -968,15 +971,19 @@ class CLIPVisionEncode:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_vision": ("CLIP_VISION",),
-                              "image": ("IMAGE",)
+                              "image": ("IMAGE",),
+                              "crop": (["center", "none"],)
                              }}
     RETURN_TYPES = ("CLIP_VISION_OUTPUT",)
     FUNCTION = "encode"
 
     CATEGORY = "conditioning"
 
-    def encode(self, clip_vision, image):
-        output = clip_vision.encode_image(image)
+    def encode(self, clip_vision, image, crop):
+        crop_image = True
+        if crop != "center":
+            crop_image = False
+        output = clip_vision.encode_image(image, crop=crop_image)
         return (output,)
 
 class StyleModelLoader:
@@ -1001,14 +1008,19 @@ class StyleModelApply:
         return {"required": {"conditioning": ("CONDITIONING", ),
                              "style_model": ("STYLE_MODEL", ),
                              "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
+                             "strength_type": (["multiply"], ),
                              }}
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "apply_stylemodel"
 
     CATEGORY = "conditioning/style_model"
 
-    def apply_stylemodel(self, clip_vision_output, style_model, conditioning):
+    def apply_stylemodel(self, clip_vision_output, style_model, conditioning, strength, strength_type):
         cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0)
+        if strength_type == "multiply":
+            cond *= strength
+
         c = []
         for t in conditioning:
             n = [torch.cat((t[0], cond), dim=1), t[1].copy()]
@@ -2136,6 +2148,7 @@ def init_builtin_extra_nodes():
         "nodes_torch_compile.py",
         "nodes_mochi.py",
         "nodes_slg.py",
+        "nodes_lt.py",
     ]
 
     import_failed = []

server.py

@@ -29,7 +29,6 @@ import comfy.model_management
 import node_helpers
 from app.frontend_management import FrontendManager
 from app.user_manager import UserManager
-from model_filemanager import download_model, DownloadModelStatus
 from typing import Optional
 from api_server.routes.internal.internal_routes import InternalRoutes
 
@@ -676,36 +675,6 @@ class PromptServer():
                     self.prompt_queue.delete_history_item(id_to_delete)
 
             return web.Response(status=200)
-        
-        # Internal route. Should not be depended upon and is subject to change at any time.
-        # TODO(robinhuang): Move to internal route table class once we refactor PromptServer to pass around Websocket.
-        # NOTE: This was an experiment and WILL BE REMOVED
-        @routes.post("/internal/models/download")
-        async def download_handler(request):
-            async def report_progress(filename: str, status: DownloadModelStatus):
-                payload = status.to_dict()
-                payload['download_path'] = filename
-                await self.send_json("download_progress", payload)
-
-            data = await request.json()
-            url = data.get('url')
-            model_directory = data.get('model_directory')
-            folder_path = data.get('folder_path')
-            model_filename = data.get('model_filename')
-            progress_interval = data.get('progress_interval', 1.0) # In seconds, how often to report download progress.
-
-            if not url or not model_directory or not model_filename or not folder_path:
-                return web.json_response({"status": "error", "message": "Missing URL or folder path or filename"}, status=400)
-
-            session = self.client_session
-            if session is None:
-                logging.error("Client session is not initialized")
-                return web.Response(status=500)
-            
-            task = asyncio.create_task(download_model(lambda url: session.get(url), model_filename, url, model_directory, folder_path, report_progress, progress_interval))
-            await task
-
-            return web.json_response(task.result().to_dict())
 
     async def setup(self):
         timeout = aiohttp.ClientTimeout(total=None) # no timeout

tests-unit/prompt_server_test/download_models_test.py

@@ -1,337 +0,0 @@
-import pytest
-import tempfile
-import aiohttp
-from aiohttp import ClientResponse
-import itertools
-import os
-from unittest.mock import AsyncMock, patch, MagicMock
-from model_filemanager import download_model, track_download_progress, create_model_path, check_file_exists, DownloadStatusType, DownloadModelStatus, validate_filename
-import folder_paths
-
-@pytest.fixture
-def temp_dir():
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        yield tmpdirname
-
-class AsyncIteratorMock:
-    """
-    A mock class that simulates an asynchronous iterator.
-    This is used to mimic the behavior of aiohttp's content iterator.
-    """
-    def __init__(self, seq):
-        # Convert the input sequence into an iterator
-        self.iter = iter(seq)
-
-    def __aiter__(self):
-        # This method is called when 'async for' is used
-        return self
-
-    async def __anext__(self):
-        # This method is called for each iteration in an 'async for' loop
-        try:
-            return next(self.iter)
-        except StopIteration:
-            # This is the asynchronous equivalent of StopIteration
-            raise StopAsyncIteration
-
-class ContentMock:
-    """
-    A mock class that simulates the content attribute of an aiohttp ClientResponse.
-    This class provides the iter_chunked method which returns an async iterator of chunks.
-    """
-    def __init__(self, chunks):
-        # Store the chunks that will be returned by the iterator
-        self.chunks = chunks
-
-    def iter_chunked(self, chunk_size):
-        # This method mimics aiohttp's content.iter_chunked()
-        # For simplicity in testing, we ignore chunk_size and just return our predefined chunks
-        return AsyncIteratorMock(self.chunks)
-
-@pytest.mark.asyncio
-async def test_download_model_success(temp_dir):
-    mock_response = AsyncMock(spec=aiohttp.ClientResponse)
-    mock_response.status = 200
-    mock_response.headers = {'Content-Length': '1000'}
-    # Create a mock for content that returns an async iterator directly
-    chunks = [b'a' * 500, b'b' * 300, b'c' * 200]
-    mock_response.content = ContentMock(chunks)
-
-    mock_make_request = AsyncMock(return_value=mock_response)
-    mock_progress_callback = AsyncMock()
-
-    time_values = itertools.count(0, 0.1)
-
-    fake_paths = {'checkpoints': ([temp_dir], folder_paths.supported_pt_extensions)}
-
-    with patch('model_filemanager.create_model_path', return_value=('models/checkpoints/model.sft', 'model.sft')), \
-         patch('model_filemanager.check_file_exists', return_value=None), \
-         patch('folder_paths.folder_names_and_paths', fake_paths), \
-         patch('time.time', side_effect=time_values):  # Simulate time passing
-
-        result = await download_model(
-            mock_make_request,
-            'model.sft',
-            'http://example.com/model.sft',
-            'checkpoints',
-            temp_dir,
-            mock_progress_callback
-        )
-
-    # Assert the result
-    assert isinstance(result, DownloadModelStatus)
-    assert result.message == 'Successfully downloaded model.sft'
-    assert result.status == 'completed'
-    assert result.already_existed is False
-
-    # Check progress callback calls
-    assert mock_progress_callback.call_count >= 3  # At least start, one progress update, and completion
-    
-    # Check initial call
-    mock_progress_callback.assert_any_call(
-        'model.sft',
-        DownloadModelStatus(DownloadStatusType.PENDING, 0, "Starting download of model.sft", False)
-    )
-
-    # Check final call
-    mock_progress_callback.assert_any_call(
-        'model.sft',
-        DownloadModelStatus(DownloadStatusType.COMPLETED, 100, "Successfully downloaded model.sft", False)
-    )
-
-    mock_file_path = os.path.join(temp_dir, 'model.sft')
-    assert os.path.exists(mock_file_path)
-    with open(mock_file_path, 'rb') as mock_file:
-        assert mock_file.read() == b''.join(chunks)
-    os.remove(mock_file_path)
-
-    # Verify request was made
-    mock_make_request.assert_called_once_with('http://example.com/model.sft')
-
-@pytest.mark.asyncio
-async def test_download_model_url_request_failure(temp_dir):
-    # Mock dependencies
-    mock_response = AsyncMock(spec=ClientResponse)
-    mock_response.status = 404  # Simulate a "Not Found" error
-    mock_get = AsyncMock(return_value=mock_response)
-    mock_progress_callback = AsyncMock()
-    
-    fake_paths = {'checkpoints': ([temp_dir], folder_paths.supported_pt_extensions)}
-
-    # Mock the create_model_path function
-    with patch('model_filemanager.create_model_path', return_value='/mock/path/model.safetensors'), \
-         patch('model_filemanager.check_file_exists', return_value=None), \
-         patch('folder_paths.folder_names_and_paths', fake_paths):
-        # Call the function
-        result = await download_model(
-            mock_get,
-            'model.safetensors',
-            'http://example.com/model.safetensors',
-            'checkpoints',
-            temp_dir,
-            mock_progress_callback
-        )
-
-    # Assert the expected behavior
-    assert isinstance(result, DownloadModelStatus)
-    assert result.status == 'error'
-    assert result.message == 'Failed to download model.safetensors. Status code: 404'
-    assert result.already_existed is False
-
-    # Check that progress_callback was called with the correct arguments
-    mock_progress_callback.assert_any_call(
-        'model.safetensors',
-        DownloadModelStatus(
-            status=DownloadStatusType.PENDING,
-            progress_percentage=0,
-            message='Starting download of model.safetensors',
-            already_existed=False
-        )
-    )
-    mock_progress_callback.assert_called_with(
-        'model.safetensors',
-        DownloadModelStatus(
-            status=DownloadStatusType.ERROR,
-            progress_percentage=0,
-            message='Failed to download model.safetensors. Status code: 404',
-            already_existed=False
-        )
-    )
-
-    # Verify that the get method was called with the correct URL
-    mock_get.assert_called_once_with('http://example.com/model.safetensors')
-
-@pytest.mark.asyncio
-async def test_download_model_invalid_model_subdirectory():
-    mock_make_request = AsyncMock()
-    mock_progress_callback = AsyncMock()
-
-    result = await download_model(
-        mock_make_request,
-        'model.sft',
-        'http://example.com/model.sft',
-        '../bad_path',
-        '../bad_path',
-        mock_progress_callback
-    )
-
-    # Assert the result
-    assert isinstance(result, DownloadModelStatus)
-    assert result.message.startswith('Invalid or unrecognized model directory')
-    assert result.status == 'error'
-    assert result.already_existed is False
-
-@pytest.mark.asyncio
-async def test_download_model_invalid_folder_path():
-    mock_make_request = AsyncMock()
-    mock_progress_callback = AsyncMock()
-
-    result = await download_model(
-        mock_make_request,
-        'model.sft',
-        'http://example.com/model.sft',
-        'checkpoints',
-        'invalid_path',
-        mock_progress_callback
-    )
-
-    # Assert the result
-    assert isinstance(result, DownloadModelStatus)
-    assert result.message.startswith("Invalid folder path")
-    assert result.status == 'error'
-    assert result.already_existed is False
-
-def test_create_model_path(tmp_path, monkeypatch):
-    model_name = "model.safetensors"
-    folder_path = os.path.join(tmp_path, "mock_dir")
-
-    file_path = create_model_path(model_name, folder_path)
-
-    assert file_path == os.path.join(folder_path, "model.safetensors")
-    assert os.path.exists(os.path.dirname(file_path))
-
-    with pytest.raises(Exception, match="Invalid model directory"):
-        create_model_path("../path_traversal.safetensors", folder_path)
-
-    with pytest.raises(Exception, match="Invalid model directory"):
-        create_model_path("/etc/some_root_path", folder_path)
-
-
-@pytest.mark.asyncio
-async def test_check_file_exists_when_file_exists(tmp_path):
-    file_path = tmp_path / "existing_model.sft"
-    file_path.touch()  # Create an empty file
-
-    mock_callback = AsyncMock()
-
-    result = await check_file_exists(str(file_path), "existing_model.sft", mock_callback)
-
-    assert result is not None
-    assert result.status == "completed"
-    assert result.message == "existing_model.sft already exists"
-    assert result.already_existed is True
-
-    mock_callback.assert_called_once_with(
-        "existing_model.sft",
-        DownloadModelStatus(DownloadStatusType.COMPLETED, 100, "existing_model.sft already exists", already_existed=True)
-    )
-
-@pytest.mark.asyncio
-async def test_check_file_exists_when_file_does_not_exist(tmp_path):
-    file_path = tmp_path / "non_existing_model.sft"
-
-    mock_callback = AsyncMock()
-
-    result = await check_file_exists(str(file_path), "non_existing_model.sft", mock_callback)
-
-    assert result is None
-    mock_callback.assert_not_called()
-
-@pytest.mark.asyncio
-async def test_track_download_progress_no_content_length(temp_dir):
-    mock_response = AsyncMock(spec=aiohttp.ClientResponse)
-    mock_response.headers = {}  # No Content-Length header
-    chunks = [b'a' * 500, b'b' * 500]
-    mock_response.content.iter_chunked.return_value = AsyncIteratorMock(chunks)
-
-    mock_callback = AsyncMock()
-
-    full_path = os.path.join(temp_dir, 'model.sft')
-
-    result = await track_download_progress(
-        mock_response, full_path, 'model.sft',
-        mock_callback, interval=0.1
-    )
-
-    assert result.status == "completed"
-
-    assert os.path.exists(full_path)
-    with open(full_path, 'rb') as f:
-        assert f.read() == b''.join(chunks)
-    os.remove(full_path)
-
-    # Check that progress was reported even without knowing the total size
-    mock_callback.assert_any_call(
-        'model.sft',
-        DownloadModelStatus(DownloadStatusType.IN_PROGRESS, 0, "Downloading model.sft", already_existed=False)
-    )
-
-@pytest.mark.asyncio
-async def test_track_download_progress_interval(temp_dir):
-    mock_response = AsyncMock(spec=aiohttp.ClientResponse)
-    mock_response.headers = {'Content-Length': '1000'}
-    chunks = [b'a' * 100] * 10
-    mock_response.content.iter_chunked.return_value = AsyncIteratorMock(chunks)
-
-    mock_callback = AsyncMock()
-    mock_open = MagicMock(return_value=MagicMock())
-
-    # Create a mock time function that returns incremental float values
-    mock_time = MagicMock()
-    mock_time.side_effect = [i * 0.5 for i in range(30)]  # This should be enough for 10 chunks
-
-    full_path = os.path.join(temp_dir, 'model.sft')
-
-    with patch('time.time', mock_time):
-        await track_download_progress(
-            mock_response, full_path, 'model.sft',
-            mock_callback, interval=1.0
-        )
-    
-    assert os.path.exists(full_path)
-    with open(full_path, 'rb') as f:
-        assert f.read() == b''.join(chunks)
-    os.remove(full_path)
-
-    # Assert that progress was updated at least 3 times (start, at least one interval, and end)
-    assert mock_callback.call_count >= 3, f"Expected at least 3 calls, but got {mock_callback.call_count}"
-
-    # Verify the first and last calls
-    first_call = mock_callback.call_args_list[0]
-    assert first_call[0][1].status == "in_progress"
-    # Allow for some initial progress, but it should be less than 50%
-    assert 0 <= first_call[0][1].progress_percentage < 50, f"First call progress was {first_call[0][1].progress_percentage}%"
-
-    last_call = mock_callback.call_args_list[-1]
-    assert last_call[0][1].status == "completed"
-    assert last_call[0][1].progress_percentage == 100
-
-@pytest.mark.parametrize("filename, expected", [
-    ("valid_model.safetensors", True),
-    ("valid_model.sft", True),
-    ("valid model.safetensors", True), # Test with space
-    ("UPPERCASE_MODEL.SAFETENSORS", True),
-    ("model_with.multiple.dots.pt", False),
-    ("", False),  # Empty string
-    ("../../../etc/passwd", False),  # Path traversal attempt
-    ("/etc/passwd", False),  # Absolute path
-    ("\\windows\\system32\\config\\sam", False),  # Windows path
-    (".hidden_file.pt", False),  # Hidden file
-    ("invalid<char>.ckpt", False),  # Invalid character
-    ("invalid?.ckpt", False),  # Another invalid character
-    ("very" * 100 + ".safetensors", False),  # Too long filename
-    ("\nmodel_with_newline.pt", False),  # Newline character
-    ("model_with_emoji😊.pt", False),  # Emoji in filename
-])
-def test_validate_filename(filename, expected):
-    assert validate_filename(filename) == expected

web/assets/ExtensionPanel-CfMfcLgI.js

@@ -1,103 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, c6 as useExtensionStore, u as useSettingStore, r as ref, o as onMounted, q as computed, g as openBlock, h as createElementBlock, i as createVNode, y as withCtx, z as unref, bT as script$1, A as createBaseVNode, x as createBlock, N as Fragment, O as renderList, a6 as toDisplayString, aw as createTextVNode, bR as script$3, j as createCommentVNode, D as script$4 } from "./index-B6dYHNhg.js";
-import { s as script, a as script$2 } from "./index-CjwCGacA.js";
-import "./index-MX9DEi8Q.js";
-const _hoisted_1 = { class: "extension-panel" };
-const _hoisted_2 = { class: "mt-4" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "ExtensionPanel",
-  setup(__props) {
-    const extensionStore = useExtensionStore();
-    const settingStore = useSettingStore();
-    const editingEnabledExtensions = ref({});
-    onMounted(() => {
-      extensionStore.extensions.forEach((ext) => {
-        editingEnabledExtensions.value[ext.name] = extensionStore.isExtensionEnabled(ext.name);
-      });
-    });
-    const changedExtensions = computed(() => {
-      return extensionStore.extensions.filter(
-        (ext) => editingEnabledExtensions.value[ext.name] !== extensionStore.isExtensionEnabled(ext.name)
-      );
-    });
-    const hasChanges = computed(() => {
-      return changedExtensions.value.length > 0;
-    });
-    const updateExtensionStatus = /* @__PURE__ */ __name(() => {
-      const editingDisabledExtensionNames = Object.entries(
-        editingEnabledExtensions.value
-      ).filter(([_, enabled]) => !enabled).map(([name]) => name);
-      settingStore.set("Comfy.Extension.Disabled", [
-        ...extensionStore.inactiveDisabledExtensionNames,
-        ...editingDisabledExtensionNames
-      ]);
-    }, "updateExtensionStatus");
-    const applyChanges = /* @__PURE__ */ __name(() => {
-      window.location.reload();
-    }, "applyChanges");
-    return (_ctx, _cache) => {
-      return openBlock(), createElementBlock("div", _hoisted_1, [
-        createVNode(unref(script$2), {
-          value: unref(extensionStore).extensions,
-          stripedRows: "",
-          size: "small"
-        }, {
-          default: withCtx(() => [
-            createVNode(unref(script), {
-              field: "name",
-              header: _ctx.$t("extensionName"),
-              sortable: ""
-            }, null, 8, ["header"]),
-            createVNode(unref(script), { pt: {
-              bodyCell: "flex items-center justify-end"
-            } }, {
-              body: withCtx((slotProps) => [
-                createVNode(unref(script$1), {
-                  modelValue: editingEnabledExtensions.value[slotProps.data.name],
-                  "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => editingEnabledExtensions.value[slotProps.data.name] = $event, "onUpdate:modelValue"),
-                  onChange: updateExtensionStatus
-                }, null, 8, ["modelValue", "onUpdate:modelValue"])
-              ]),
-              _: 1
-            })
-          ]),
-          _: 1
-        }, 8, ["value"]),
-        createBaseVNode("div", _hoisted_2, [
-          hasChanges.value ? (openBlock(), createBlock(unref(script$3), {
-            key: 0,
-            severity: "info"
-          }, {
-            default: withCtx(() => [
-              createBaseVNode("ul", null, [
-                (openBlock(true), createElementBlock(Fragment, null, renderList(changedExtensions.value, (ext) => {
-                  return openBlock(), createElementBlock("li", {
-                    key: ext.name
-                  }, [
-                    createBaseVNode("span", null, toDisplayString(unref(extensionStore).isExtensionEnabled(ext.name) ? "[-]" : "[+]"), 1),
-                    createTextVNode(" " + toDisplayString(ext.name), 1)
-                  ]);
-                }), 128))
-              ])
-            ]),
-            _: 1
-          })) : createCommentVNode("", true),
-          createVNode(unref(script$4), {
-            label: _ctx.$t("reloadToApplyChanges"),
-            icon: "pi pi-refresh",
-            onClick: applyChanges,
-            disabled: !hasChanges.value,
-            text: "",
-            fluid: "",
-            severity: "danger"
-          }, null, 8, ["label", "disabled"])
-        ])
-      ]);
-    };
-  }
-});
-export {
-  _sfc_main as default
-};
-//# sourceMappingURL=ExtensionPanel-CfMfcLgI.js.map

web/assets/ExtensionPanel-CfMfcLgI.js.map

@@ -1 +0,0 @@
-{"version":3,"file":"ExtensionPanel-CfMfcLgI.js","sources":["../../src/components/dialog/content/setting/ExtensionPanel.vue"],"sourcesContent":["<template>\n  <div class=\"extension-panel\">\n    <DataTable :value=\"extensionStore.extensions\" stripedRows size=\"small\">\n      <Column field=\"name\" :header=\"$t('extensionName')\" sortable></Column>\n      <Column\n        :pt=\"{\n          bodyCell: 'flex items-center justify-end'\n        }\"\n      >\n        <template #body=\"slotProps\">\n          <ToggleSwitch\n            v-model=\"editingEnabledExtensions[slotProps.data.name]\"\n            @change=\"updateExtensionStatus\"\n          />\n        </template>\n      </Column>\n    </DataTable>\n    <div class=\"mt-4\">\n      <Message v-if=\"hasChanges\" severity=\"info\">\n        <ul>\n          <li v-for=\"ext in changedExtensions\" :key=\"ext.name\">\n            <span>\n              {{ extensionStore.isExtensionEnabled(ext.name) ? '[-]' : '[+]' }}\n            </span>\n            {{ ext.name }}\n          </li>\n        </ul>\n      </Message>\n      <Button\n        :label=\"$t('reloadToApplyChanges')\"\n        icon=\"pi pi-refresh\"\n        @click=\"applyChanges\"\n        :disabled=\"!hasChanges\"\n        text\n        fluid\n        severity=\"danger\"\n      />\n    </div>\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport { ref, computed, onMounted } from 'vue'\nimport { useExtensionStore } from '@/stores/extensionStore'\nimport { useSettingStore } from '@/stores/settingStore'\nimport DataTable from 'primevue/datatable'\nimport Column from 'primevue/column'\nimport ToggleSwitch from 'primevue/toggleswitch'\nimport Button from 'primevue/button'\nimport Message from 'primevue/message'\n\nconst extensionStore = useExtensionStore()\nconst settingStore = useSettingStore()\n\nconst editingEnabledExtensions = ref<Record<string, boolean>>({})\n\nonMounted(() => {\n  extensionStore.extensions.forEach((ext) => {\n    editingEnabledExtensions.value[ext.name] =\n      extensionStore.isExtensionEnabled(ext.name)\n  })\n})\n\nconst changedExtensions = computed(() => {\n  return extensionStore.extensions.filter(\n    (ext) =>\n      editingEnabledExtensions.value[ext.name] !==\n      extensionStore.isExtensionEnabled(ext.name)\n  )\n})\n\nconst hasChanges = computed(() => {\n  return changedExtensions.value.length > 0\n})\n\nconst updateExtensionStatus = () => {\n  const editingDisabledExtensionNames = Object.entries(\n    editingEnabledExtensions.value\n  )\n    .filter(([_, enabled]) => !enabled)\n    .map(([name]) => name)\n\n  settingStore.set('Comfy.Extension.Disabled', [\n    ...extensionStore.inactiveDisabledExtensionNames,\n    ...editingDisabledExtensionNames\n  ])\n}\n\nconst applyChanges = () => {\n  // Refresh the page to apply changes\n  window.location.reload()\n}\n</script>\n"],"names":[],"mappings":";;;;;;;;;;AAmDA,UAAM,iBAAiB;AACvB,UAAM,eAAe;AAEf,UAAA,2BAA2B,IAA6B,CAAA,CAAE;AAEhE,cAAU,MAAM;AACC,qBAAA,WAAW,QAAQ,CAAC,QAAQ;AACzC,iCAAyB,MAAM,IAAI,IAAI,IACrC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA,CAC7C;AAAA,IAAA,CACF;AAEK,UAAA,oBAAoB,SAAS,MAAM;AACvC,aAAO,eAAe,WAAW;AAAA,QAC/B,CAAC,QACC,yBAAyB,MAAM,IAAI,IAAI,MACvC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA;AAAA,IAC9C,CACD;AAEK,UAAA,aAAa,SAAS,MAAM;AACzB,aAAA,kBAAkB,MAAM,SAAS;AAAA,IAAA,CACzC;AAED,UAAM,wBAAwB,6BAAM;AAClC,YAAM,gCAAgC,OAAO;AAAA,QAC3C,yBAAyB;AAAA,MAExB,EAAA,OAAO,CAAC,CAAC,GAAG,OAAO,MAAM,CAAC,OAAO,EACjC,IAAI,CAAC,CAAC,IAAI,MAAM,IAAI;AAEvB,mBAAa,IAAI,4BAA4B;AAAA,QAC3C,GAAG,eAAe;AAAA,QAClB,GAAG;AAAA,MAAA,CACJ;AAAA,IAAA,GAV2B;AAa9B,UAAM,eAAe,6BAAM;AAEzB,aAAO,SAAS;IAAO,GAFJ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/ExtensionPanel-DsD42OtO.js

@@ -0,0 +1,117 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, r as ref, c6 as FilterMatchMode, ca as useExtensionStore, u as useSettingStore, o as onMounted, q as computed, g as openBlock, x as createBlock, y as withCtx, i as createVNode, c7 as SearchBox, z as unref, bT as script, A as createBaseVNode, h as createElementBlock, O as renderList, a6 as toDisplayString, aw as createTextVNode, N as Fragment, D as script$1, j as createCommentVNode, bV as script$3, c8 as _sfc_main$1 } from "./index-CoOvI8ZH.js";
+import { s as script$2, a as script$4 } from "./index-DK6Kev7f.js";
+import "./index-D4DWQPPQ.js";
+const _hoisted_1 = { class: "flex justify-end" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "ExtensionPanel",
+  setup(__props) {
+    const filters = ref({
+      global: { value: "", matchMode: FilterMatchMode.CONTAINS }
+    });
+    const extensionStore = useExtensionStore();
+    const settingStore = useSettingStore();
+    const editingEnabledExtensions = ref({});
+    onMounted(() => {
+      extensionStore.extensions.forEach((ext) => {
+        editingEnabledExtensions.value[ext.name] = extensionStore.isExtensionEnabled(ext.name);
+      });
+    });
+    const changedExtensions = computed(() => {
+      return extensionStore.extensions.filter(
+        (ext) => editingEnabledExtensions.value[ext.name] !== extensionStore.isExtensionEnabled(ext.name)
+      );
+    });
+    const hasChanges = computed(() => {
+      return changedExtensions.value.length > 0;
+    });
+    const updateExtensionStatus = /* @__PURE__ */ __name(() => {
+      const editingDisabledExtensionNames = Object.entries(
+        editingEnabledExtensions.value
+      ).filter(([_, enabled]) => !enabled).map(([name]) => name);
+      settingStore.set("Comfy.Extension.Disabled", [
+        ...extensionStore.inactiveDisabledExtensionNames,
+        ...editingDisabledExtensionNames
+      ]);
+    }, "updateExtensionStatus");
+    const applyChanges = /* @__PURE__ */ __name(() => {
+      window.location.reload();
+    }, "applyChanges");
+    return (_ctx, _cache) => {
+      return openBlock(), createBlock(_sfc_main$1, {
+        value: "Extension",
+        class: "extension-panel"
+      }, {
+        header: withCtx(() => [
+          createVNode(SearchBox, {
+            modelValue: filters.value["global"].value,
+            "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => filters.value["global"].value = $event),
+            placeholder: _ctx.$t("searchExtensions") + "..."
+          }, null, 8, ["modelValue", "placeholder"]),
+          hasChanges.value ? (openBlock(), createBlock(unref(script), {
+            key: 0,
+            severity: "info",
+            "pt:text": "w-full"
+          }, {
+            default: withCtx(() => [
+              createBaseVNode("ul", null, [
+                (openBlock(true), createElementBlock(Fragment, null, renderList(changedExtensions.value, (ext) => {
+                  return openBlock(), createElementBlock("li", {
+                    key: ext.name
+                  }, [
+                    createBaseVNode("span", null, toDisplayString(unref(extensionStore).isExtensionEnabled(ext.name) ? "[-]" : "[+]"), 1),
+                    createTextVNode(" " + toDisplayString(ext.name), 1)
+                  ]);
+                }), 128))
+              ]),
+              createBaseVNode("div", _hoisted_1, [
+                createVNode(unref(script$1), {
+                  label: _ctx.$t("reloadToApplyChanges"),
+                  onClick: applyChanges,
+                  outlined: "",
+                  severity: "danger"
+                }, null, 8, ["label"])
+              ])
+            ]),
+            _: 1
+          })) : createCommentVNode("", true)
+        ]),
+        default: withCtx(() => [
+          createVNode(unref(script$4), {
+            value: unref(extensionStore).extensions,
+            stripedRows: "",
+            size: "small",
+            filters: filters.value
+          }, {
+            default: withCtx(() => [
+              createVNode(unref(script$2), {
+                field: "name",
+                header: _ctx.$t("extensionName"),
+                sortable: ""
+              }, null, 8, ["header"]),
+              createVNode(unref(script$2), { pt: {
+                bodyCell: "flex items-center justify-end"
+              } }, {
+                body: withCtx((slotProps) => [
+                  createVNode(unref(script$3), {
+                    modelValue: editingEnabledExtensions.value[slotProps.data.name],
+                    "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => editingEnabledExtensions.value[slotProps.data.name] = $event, "onUpdate:modelValue"),
+                    onChange: updateExtensionStatus
+                  }, null, 8, ["modelValue", "onUpdate:modelValue"])
+                ]),
+                _: 1
+              })
+            ]),
+            _: 1
+          }, 8, ["value", "filters"])
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+export {
+  _sfc_main as default
+};
+//# sourceMappingURL=ExtensionPanel-DsD42OtO.js.map

web/assets/ExtensionPanel-DsD42OtO.js.map

@@ -0,0 +1 @@
+{"version":3,"file":"ExtensionPanel-DsD42OtO.js","sources":["../../src/components/dialog/content/setting/ExtensionPanel.vue"],"sourcesContent":["<template>\n  <PanelTemplate value=\"Extension\" class=\"extension-panel\">\n    <template #header>\n      <SearchBox\n        v-model=\"filters['global'].value\"\n        :placeholder=\"$t('searchExtensions') + '...'\"\n      />\n      <Message v-if=\"hasChanges\" severity=\"info\" pt:text=\"w-full\">\n        <ul>\n          <li v-for=\"ext in changedExtensions\" :key=\"ext.name\">\n            <span>\n              {{ extensionStore.isExtensionEnabled(ext.name) ? '[-]' : '[+]' }}\n            </span>\n            {{ ext.name }}\n          </li>\n        </ul>\n        <div class=\"flex justify-end\">\n          <Button\n            :label=\"$t('reloadToApplyChanges')\"\n            @click=\"applyChanges\"\n            outlined\n            severity=\"danger\"\n          />\n        </div>\n      </Message>\n    </template>\n    <DataTable\n      :value=\"extensionStore.extensions\"\n      stripedRows\n      size=\"small\"\n      :filters=\"filters\"\n    >\n      <Column field=\"name\" :header=\"$t('extensionName')\" sortable></Column>\n      <Column\n        :pt=\"{\n          bodyCell: 'flex items-center justify-end'\n        }\"\n      >\n        <template #body=\"slotProps\">\n          <ToggleSwitch\n            v-model=\"editingEnabledExtensions[slotProps.data.name]\"\n            @change=\"updateExtensionStatus\"\n          />\n        </template>\n      </Column>\n    </DataTable>\n  </PanelTemplate>\n</template>\n\n<script setup lang=\"ts\">\nimport { ref, computed, onMounted } from 'vue'\nimport { useExtensionStore } from '@/stores/extensionStore'\nimport { useSettingStore } from '@/stores/settingStore'\nimport DataTable from 'primevue/datatable'\nimport Column from 'primevue/column'\nimport ToggleSwitch from 'primevue/toggleswitch'\nimport Button from 'primevue/button'\nimport Message from 'primevue/message'\nimport { FilterMatchMode } from '@primevue/core/api'\nimport PanelTemplate from './PanelTemplate.vue'\nimport SearchBox from '@/components/common/SearchBox.vue'\n\nconst filters = ref({\n  global: { value: '', matchMode: FilterMatchMode.CONTAINS }\n})\n\nconst extensionStore = useExtensionStore()\nconst settingStore = useSettingStore()\n\nconst editingEnabledExtensions = ref<Record<string, boolean>>({})\n\nonMounted(() => {\n  extensionStore.extensions.forEach((ext) => {\n    editingEnabledExtensions.value[ext.name] =\n      extensionStore.isExtensionEnabled(ext.name)\n  })\n})\n\nconst changedExtensions = computed(() => {\n  return extensionStore.extensions.filter(\n    (ext) =>\n      editingEnabledExtensions.value[ext.name] !==\n      extensionStore.isExtensionEnabled(ext.name)\n  )\n})\n\nconst hasChanges = computed(() => {\n  return changedExtensions.value.length > 0\n})\n\nconst updateExtensionStatus = () => {\n  const editingDisabledExtensionNames = Object.entries(\n    editingEnabledExtensions.value\n  )\n    .filter(([_, enabled]) => !enabled)\n    .map(([name]) => name)\n\n  settingStore.set('Comfy.Extension.Disabled', [\n    ...extensionStore.inactiveDisabledExtensionNames,\n    ...editingDisabledExtensionNames\n  ])\n}\n\nconst applyChanges = () => {\n  // Refresh the page to apply changes\n  window.location.reload()\n}\n</script>\n"],"names":[],"mappings":";;;;;;;;;AA8DA,UAAM,UAAU,IAAI;AAAA,MAClB,QAAQ,EAAE,OAAO,IAAI,WAAW,gBAAgB,SAAS;AAAA,IAAA,CAC1D;AAED,UAAM,iBAAiB;AACvB,UAAM,eAAe;AAEf,UAAA,2BAA2B,IAA6B,CAAA,CAAE;AAEhE,cAAU,MAAM;AACC,qBAAA,WAAW,QAAQ,CAAC,QAAQ;AACzC,iCAAyB,MAAM,IAAI,IAAI,IACrC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA,CAC7C;AAAA,IAAA,CACF;AAEK,UAAA,oBAAoB,SAAS,MAAM;AACvC,aAAO,eAAe,WAAW;AAAA,QAC/B,CAAC,QACC,yBAAyB,MAAM,IAAI,IAAI,MACvC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA;AAAA,IAC9C,CACD;AAEK,UAAA,aAAa,SAAS,MAAM;AACzB,aAAA,kBAAkB,MAAM,SAAS;AAAA,IAAA,CACzC;AAED,UAAM,wBAAwB,6BAAM;AAClC,YAAM,gCAAgC,OAAO;AAAA,QAC3C,yBAAyB;AAAA,MAExB,EAAA,OAAO,CAAC,CAAC,GAAG,OAAO,MAAM,CAAC,OAAO,EACjC,IAAI,CAAC,CAAC,IAAI,MAAM,IAAI;AAEvB,mBAAa,IAAI,4BAA4B;AAAA,QAC3C,GAAG,eAAe;AAAA,QAClB,GAAG;AAAA,MAAA,CACJ;AAAA,IAAA,GAV2B;AAa9B,UAAM,eAAe,6BAAM;AAEzB,aAAO,SAAS;IAAO,GAFJ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/GraphView-DtkYXy38.css

@@ -106,32 +106,6 @@
   margin: -0.125rem 0.125rem;
 }
 
-.comfy-vue-node-search-container[data-v-2d409367] {
-  display: flex;
-  width: 100%;
-  min-width: 26rem;
-  align-items: center;
-  justify-content: center;
-}
-.comfy-vue-node-search-container[data-v-2d409367] * {
-  pointer-events: auto;
-}
-.comfy-vue-node-preview-container[data-v-2d409367] {
-  position: absolute;
-  left: -350px;
-  top: 50px;
-}
-.comfy-vue-node-search-box[data-v-2d409367] {
-  z-index: 10;
-  flex-grow: 1;
-}
-._filter-button[data-v-2d409367] {
-  z-index: 10;
-}
-._dialog[data-v-2d409367] {
-  min-width: 26rem;
-}
-
 .invisible-dialog-root {
   width: 60%;
   min-width: 24rem;
@@ -184,10 +158,10 @@
   z-index: 9999;
 }
 
-[data-v-9eb975c3] .p-togglebutton::before {
+[data-v-783f8efe] .p-togglebutton::before {
   display: none
 }
-[data-v-9eb975c3] .p-togglebutton {
+[data-v-783f8efe] .p-togglebutton {
   position: relative;
   flex-shrink: 0;
   border-radius: 0px;
@@ -195,14 +169,14 @@
   padding-left: 0.5rem;
   padding-right: 0.5rem
 }
-[data-v-9eb975c3] .p-togglebutton.p-togglebutton-checked {
+[data-v-783f8efe] .p-togglebutton.p-togglebutton-checked {
   border-bottom-width: 2px;
   border-bottom-color: var(--p-button-text-primary-color)
 }
-[data-v-9eb975c3] .p-togglebutton-checked .close-button,[data-v-9eb975c3] .p-togglebutton:hover .close-button {
+[data-v-783f8efe] .p-togglebutton-checked .close-button,[data-v-783f8efe] .p-togglebutton:hover .close-button {
   visibility: visible
 }
-.status-indicator[data-v-9eb975c3] {
+.status-indicator[data-v-783f8efe] {
   position: absolute;
   font-weight: 700;
   font-size: 1.5rem;
@@ -210,10 +184,10 @@
   left: 50%;
   transform: translate(-50%, -50%)
 }
-[data-v-9eb975c3] .p-togglebutton:hover .status-indicator {
+[data-v-783f8efe] .p-togglebutton:hover .status-indicator {
   display: none
 }
-[data-v-9eb975c3] .p-togglebutton .close-button {
+[data-v-783f8efe] .p-togglebutton .close-button {
   visibility: hidden
 }
 
@@ -241,26 +215,26 @@
   border-bottom-right-radius: 0;
 }
 
-.actionbar[data-v-eb6e9acf] {
+.actionbar[data-v-542a7001] {
   pointer-events: all;
   position: fixed;
   z-index: 1000;
 }
-.actionbar.is-docked[data-v-eb6e9acf] {
+.actionbar.is-docked[data-v-542a7001] {
   position: static;
   border-style: none;
   background-color: transparent;
   padding: 0px;
 }
-.actionbar.is-dragging[data-v-eb6e9acf] {
+.actionbar.is-dragging[data-v-542a7001] {
   -webkit-user-select: none;
      -moz-user-select: none;
           user-select: none;
 }
-[data-v-eb6e9acf] .p-panel-content {
+[data-v-542a7001] .p-panel-content {
   padding: 0.25rem;
 }
-[data-v-eb6e9acf] .p-panel-header {
+[data-v-542a7001] .p-panel-header {
   display: none;
 }
 

web/assets/KeybindingPanel-C-7KE-Kw.css

@@ -0,0 +1,8 @@
+
+[data-v-9d7e362e] .p-datatable-tbody > tr > td {
+  padding: 0.25rem;
+  min-height: 2rem
+}
+[data-v-9d7e362e] .p-datatable-row-selected .actions,[data-v-9d7e362e] .p-datatable-selectable-row:hover .actions {
+  visibility: visible
+}

web/assets/KeybindingPanel-CB_wEOHl.css

@@ -1,8 +0,0 @@
-
-[data-v-2d8b3a76] .p-datatable-tbody > tr > td {
-  padding: 0.25rem;
-  min-height: 2rem
-}
-[data-v-2d8b3a76] .p-datatable-row-selected .actions,[data-v-2d8b3a76] .p-datatable-selectable-row:hover .actions {
-  visibility: visible
-}

web/assets/KeybindingPanel-DcEfyPZZ.js

@@ -1,274 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, q as computed, g as openBlock, h as createElementBlock, N as Fragment, O as renderList, i as createVNode, y as withCtx, aw as createTextVNode, a6 as toDisplayString, z as unref, aA as script, j as createCommentVNode, r as ref, c3 as FilterMatchMode, M as useKeybindingStore, F as useCommandStore, aJ as watchEffect, be as useToast, t as resolveDirective, c4 as SearchBox, A as createBaseVNode, D as script$2, x as createBlock, ao as script$4, bi as withModifiers, bR as script$5, aH as script$6, v as withDirectives, P as pushScopeId, Q as popScopeId, b$ as KeyComboImpl, c5 as KeybindingImpl, _ as _export_sfc } from "./index-B6dYHNhg.js";
-import { s as script$1, a as script$3 } from "./index-CjwCGacA.js";
-import "./index-MX9DEi8Q.js";
-const _hoisted_1$1 = {
-  key: 0,
-  class: "px-2"
-};
-const _sfc_main$1 = /* @__PURE__ */ defineComponent({
-  __name: "KeyComboDisplay",
-  props: {
-    keyCombo: {},
-    isModified: { type: Boolean, default: false }
-  },
-  setup(__props) {
-    const props = __props;
-    const keySequences = computed(() => props.keyCombo.getKeySequences());
-    return (_ctx, _cache) => {
-      return openBlock(), createElementBlock("span", null, [
-        (openBlock(true), createElementBlock(Fragment, null, renderList(keySequences.value, (sequence, index) => {
-          return openBlock(), createElementBlock(Fragment, { key: index }, [
-            createVNode(unref(script), {
-              severity: _ctx.isModified ? "info" : "secondary"
-            }, {
-              default: withCtx(() => [
-                createTextVNode(toDisplayString(sequence), 1)
-              ]),
-              _: 2
-            }, 1032, ["severity"]),
-            index < keySequences.value.length - 1 ? (openBlock(), createElementBlock("span", _hoisted_1$1, "+")) : createCommentVNode("", true)
-          ], 64);
-        }), 128))
-      ]);
-    };
-  }
-});
-const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-2d8b3a76"), n = n(), popScopeId(), n), "_withScopeId");
-const _hoisted_1 = { class: "keybinding-panel" };
-const _hoisted_2 = { class: "actions invisible flex flex-row" };
-const _hoisted_3 = ["title"];
-const _hoisted_4 = { key: 1 };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "KeybindingPanel",
-  setup(__props) {
-    const filters = ref({
-      global: { value: "", matchMode: FilterMatchMode.CONTAINS }
-    });
-    const keybindingStore = useKeybindingStore();
-    const commandStore = useCommandStore();
-    const commandsData = computed(() => {
-      return Object.values(commandStore.commands).map((command) => ({
-        id: command.id,
-        keybinding: keybindingStore.getKeybindingByCommandId(command.id)
-      }));
-    });
-    const selectedCommandData = ref(null);
-    const editDialogVisible = ref(false);
-    const newBindingKeyCombo = ref(null);
-    const currentEditingCommand = ref(null);
-    const keybindingInput = ref(null);
-    const existingKeybindingOnCombo = computed(() => {
-      if (!currentEditingCommand.value) {
-        return null;
-      }
-      if (currentEditingCommand.value.keybinding?.combo?.equals(
-        newBindingKeyCombo.value
-      )) {
-        return null;
-      }
-      if (!newBindingKeyCombo.value) {
-        return null;
-      }
-      return keybindingStore.getKeybinding(newBindingKeyCombo.value);
-    });
-    function editKeybinding(commandData) {
-      currentEditingCommand.value = commandData;
-      newBindingKeyCombo.value = commandData.keybinding ? commandData.keybinding.combo : null;
-      editDialogVisible.value = true;
-    }
-    __name(editKeybinding, "editKeybinding");
-    watchEffect(() => {
-      if (editDialogVisible.value) {
-        setTimeout(() => {
-          keybindingInput.value?.$el?.focus();
-        }, 300);
-      }
-    });
-    function removeKeybinding(commandData) {
-      if (commandData.keybinding) {
-        keybindingStore.unsetKeybinding(commandData.keybinding);
-        keybindingStore.persistUserKeybindings();
-      }
-    }
-    __name(removeKeybinding, "removeKeybinding");
-    function captureKeybinding(event) {
-      const keyCombo = KeyComboImpl.fromEvent(event);
-      newBindingKeyCombo.value = keyCombo;
-    }
-    __name(captureKeybinding, "captureKeybinding");
-    function cancelEdit() {
-      editDialogVisible.value = false;
-      currentEditingCommand.value = null;
-      newBindingKeyCombo.value = null;
-    }
-    __name(cancelEdit, "cancelEdit");
-    function saveKeybinding() {
-      if (currentEditingCommand.value && newBindingKeyCombo.value) {
-        const updated = keybindingStore.updateKeybindingOnCommand(
-          new KeybindingImpl({
-            commandId: currentEditingCommand.value.id,
-            combo: newBindingKeyCombo.value
-          })
-        );
-        if (updated) {
-          keybindingStore.persistUserKeybindings();
-        }
-      }
-      cancelEdit();
-    }
-    __name(saveKeybinding, "saveKeybinding");
-    const toast = useToast();
-    async function resetKeybindings() {
-      keybindingStore.resetKeybindings();
-      await keybindingStore.persistUserKeybindings();
-      toast.add({
-        severity: "info",
-        summary: "Info",
-        detail: "Keybindings reset",
-        life: 3e3
-      });
-    }
-    __name(resetKeybindings, "resetKeybindings");
-    return (_ctx, _cache) => {
-      const _directive_tooltip = resolveDirective("tooltip");
-      return openBlock(), createElementBlock("div", _hoisted_1, [
-        createVNode(unref(script$3), {
-          value: commandsData.value,
-          selection: selectedCommandData.value,
-          "onUpdate:selection": _cache[1] || (_cache[1] = ($event) => selectedCommandData.value = $event),
-          "global-filter-fields": ["id"],
-          filters: filters.value,
-          selectionMode: "single",
-          stripedRows: "",
-          pt: {
-            header: "px-0"
-          }
-        }, {
-          header: withCtx(() => [
-            createVNode(SearchBox, {
-              modelValue: filters.value["global"].value,
-              "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => filters.value["global"].value = $event),
-              placeholder: _ctx.$t("searchKeybindings") + "..."
-            }, null, 8, ["modelValue", "placeholder"])
-          ]),
-          default: withCtx(() => [
-            createVNode(unref(script$1), {
-              field: "actions",
-              header: ""
-            }, {
-              body: withCtx((slotProps) => [
-                createBaseVNode("div", _hoisted_2, [
-                  createVNode(unref(script$2), {
-                    icon: "pi pi-pencil",
-                    class: "p-button-text",
-                    onClick: /* @__PURE__ */ __name(($event) => editKeybinding(slotProps.data), "onClick")
-                  }, null, 8, ["onClick"]),
-                  createVNode(unref(script$2), {
-                    icon: "pi pi-trash",
-                    class: "p-button-text p-button-danger",
-                    onClick: /* @__PURE__ */ __name(($event) => removeKeybinding(slotProps.data), "onClick"),
-                    disabled: !slotProps.data.keybinding
-                  }, null, 8, ["onClick", "disabled"])
-                ])
-              ]),
-              _: 1
-            }),
-            createVNode(unref(script$1), {
-              field: "id",
-              header: "Command ID",
-              sortable: "",
-              class: "max-w-64 2xl:max-w-full"
-            }, {
-              body: withCtx((slotProps) => [
-                createBaseVNode("div", {
-                  class: "overflow-hidden text-ellipsis whitespace-nowrap",
-                  title: slotProps.data.id
-                }, toDisplayString(slotProps.data.id), 9, _hoisted_3)
-              ]),
-              _: 1
-            }),
-            createVNode(unref(script$1), {
-              field: "keybinding",
-              header: "Keybinding"
-            }, {
-              body: withCtx((slotProps) => [
-                slotProps.data.keybinding ? (openBlock(), createBlock(_sfc_main$1, {
-                  key: 0,
-                  keyCombo: slotProps.data.keybinding.combo,
-                  isModified: unref(keybindingStore).isCommandKeybindingModified(slotProps.data.id)
-                }, null, 8, ["keyCombo", "isModified"])) : (openBlock(), createElementBlock("span", _hoisted_4, "-"))
-              ]),
-              _: 1
-            })
-          ]),
-          _: 1
-        }, 8, ["value", "selection", "filters"]),
-        createVNode(unref(script$6), {
-          class: "min-w-96",
-          visible: editDialogVisible.value,
-          "onUpdate:visible": _cache[2] || (_cache[2] = ($event) => editDialogVisible.value = $event),
-          modal: "",
-          header: currentEditingCommand.value?.id,
-          onHide: cancelEdit
-        }, {
-          footer: withCtx(() => [
-            createVNode(unref(script$2), {
-              label: "Save",
-              icon: "pi pi-check",
-              onClick: saveKeybinding,
-              disabled: !!existingKeybindingOnCombo.value,
-              autofocus: ""
-            }, null, 8, ["disabled"])
-          ]),
-          default: withCtx(() => [
-            createBaseVNode("div", null, [
-              createVNode(unref(script$4), {
-                class: "mb-2 text-center",
-                ref_key: "keybindingInput",
-                ref: keybindingInput,
-                modelValue: newBindingKeyCombo.value?.toString() ?? "",
-                placeholder: "Press keys for new binding",
-                onKeydown: withModifiers(captureKeybinding, ["stop", "prevent"]),
-                autocomplete: "off",
-                fluid: "",
-                invalid: !!existingKeybindingOnCombo.value
-              }, null, 8, ["modelValue", "invalid"]),
-              existingKeybindingOnCombo.value ? (openBlock(), createBlock(unref(script$5), {
-                key: 0,
-                severity: "error"
-              }, {
-                default: withCtx(() => [
-                  createTextVNode(" Keybinding already exists on "),
-                  createVNode(unref(script), {
-                    severity: "secondary",
-                    value: existingKeybindingOnCombo.value.commandId
-                  }, null, 8, ["value"])
-                ]),
-                _: 1
-              })) : createCommentVNode("", true)
-            ])
-          ]),
-          _: 1
-        }, 8, ["visible", "header"]),
-        withDirectives(createVNode(unref(script$2), {
-          class: "mt-4",
-          label: _ctx.$t("reset"),
-          icon: "pi pi-trash",
-          severity: "danger",
-          fluid: "",
-          text: "",
-          onClick: resetKeybindings
-        }, null, 8, ["label"]), [
-          [_directive_tooltip, _ctx.$t("resetKeybindingsTooltip")]
-        ])
-      ]);
-    };
-  }
-});
-const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-2d8b3a76"]]);
-export {
-  KeybindingPanel as default
-};
-//# sourceMappingURL=KeybindingPanel-DcEfyPZZ.js.map

web/assets/KeybindingPanel-lcJrxHwZ.js

@@ -0,0 +1,279 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, q as computed, g as openBlock, h as createElementBlock, N as Fragment, O as renderList, i as createVNode, y as withCtx, aw as createTextVNode, a6 as toDisplayString, z as unref, aA as script, j as createCommentVNode, r as ref, c6 as FilterMatchMode, M as useKeybindingStore, F as useCommandStore, aJ as watchEffect, bg as useToast, t as resolveDirective, x as createBlock, c7 as SearchBox, A as createBaseVNode, D as script$2, ao as script$4, bk as withModifiers, bT as script$5, aH as script$6, v as withDirectives, c8 as _sfc_main$2, P as pushScopeId, Q as popScopeId, c1 as KeyComboImpl, c9 as KeybindingImpl, _ as _export_sfc } from "./index-CoOvI8ZH.js";
+import { s as script$1, a as script$3 } from "./index-DK6Kev7f.js";
+import "./index-D4DWQPPQ.js";
+const _hoisted_1$1 = {
+  key: 0,
+  class: "px-2"
+};
+const _sfc_main$1 = /* @__PURE__ */ defineComponent({
+  __name: "KeyComboDisplay",
+  props: {
+    keyCombo: {},
+    isModified: { type: Boolean, default: false }
+  },
+  setup(__props) {
+    const props = __props;
+    const keySequences = computed(() => props.keyCombo.getKeySequences());
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("span", null, [
+        (openBlock(true), createElementBlock(Fragment, null, renderList(keySequences.value, (sequence, index) => {
+          return openBlock(), createElementBlock(Fragment, { key: index }, [
+            createVNode(unref(script), {
+              severity: _ctx.isModified ? "info" : "secondary"
+            }, {
+              default: withCtx(() => [
+                createTextVNode(toDisplayString(sequence), 1)
+              ]),
+              _: 2
+            }, 1032, ["severity"]),
+            index < keySequences.value.length - 1 ? (openBlock(), createElementBlock("span", _hoisted_1$1, "+")) : createCommentVNode("", true)
+          ], 64);
+        }), 128))
+      ]);
+    };
+  }
+});
+const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-9d7e362e"), n = n(), popScopeId(), n), "_withScopeId");
+const _hoisted_1 = { class: "actions invisible flex flex-row" };
+const _hoisted_2 = ["title"];
+const _hoisted_3 = { key: 1 };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "KeybindingPanel",
+  setup(__props) {
+    const filters = ref({
+      global: { value: "", matchMode: FilterMatchMode.CONTAINS }
+    });
+    const keybindingStore = useKeybindingStore();
+    const commandStore = useCommandStore();
+    const commandsData = computed(() => {
+      return Object.values(commandStore.commands).map((command) => ({
+        id: command.id,
+        keybinding: keybindingStore.getKeybindingByCommandId(command.id)
+      }));
+    });
+    const selectedCommandData = ref(null);
+    const editDialogVisible = ref(false);
+    const newBindingKeyCombo = ref(null);
+    const currentEditingCommand = ref(null);
+    const keybindingInput = ref(null);
+    const existingKeybindingOnCombo = computed(() => {
+      if (!currentEditingCommand.value) {
+        return null;
+      }
+      if (currentEditingCommand.value.keybinding?.combo?.equals(
+        newBindingKeyCombo.value
+      )) {
+        return null;
+      }
+      if (!newBindingKeyCombo.value) {
+        return null;
+      }
+      return keybindingStore.getKeybinding(newBindingKeyCombo.value);
+    });
+    function editKeybinding(commandData) {
+      currentEditingCommand.value = commandData;
+      newBindingKeyCombo.value = commandData.keybinding ? commandData.keybinding.combo : null;
+      editDialogVisible.value = true;
+    }
+    __name(editKeybinding, "editKeybinding");
+    watchEffect(() => {
+      if (editDialogVisible.value) {
+        setTimeout(() => {
+          keybindingInput.value?.$el?.focus();
+        }, 300);
+      }
+    });
+    function removeKeybinding(commandData) {
+      if (commandData.keybinding) {
+        keybindingStore.unsetKeybinding(commandData.keybinding);
+        keybindingStore.persistUserKeybindings();
+      }
+    }
+    __name(removeKeybinding, "removeKeybinding");
+    function captureKeybinding(event) {
+      const keyCombo = KeyComboImpl.fromEvent(event);
+      newBindingKeyCombo.value = keyCombo;
+    }
+    __name(captureKeybinding, "captureKeybinding");
+    function cancelEdit() {
+      editDialogVisible.value = false;
+      currentEditingCommand.value = null;
+      newBindingKeyCombo.value = null;
+    }
+    __name(cancelEdit, "cancelEdit");
+    function saveKeybinding() {
+      if (currentEditingCommand.value && newBindingKeyCombo.value) {
+        const updated = keybindingStore.updateKeybindingOnCommand(
+          new KeybindingImpl({
+            commandId: currentEditingCommand.value.id,
+            combo: newBindingKeyCombo.value
+          })
+        );
+        if (updated) {
+          keybindingStore.persistUserKeybindings();
+        }
+      }
+      cancelEdit();
+    }
+    __name(saveKeybinding, "saveKeybinding");
+    const toast = useToast();
+    async function resetKeybindings() {
+      keybindingStore.resetKeybindings();
+      await keybindingStore.persistUserKeybindings();
+      toast.add({
+        severity: "info",
+        summary: "Info",
+        detail: "Keybindings reset",
+        life: 3e3
+      });
+    }
+    __name(resetKeybindings, "resetKeybindings");
+    return (_ctx, _cache) => {
+      const _directive_tooltip = resolveDirective("tooltip");
+      return openBlock(), createBlock(_sfc_main$2, {
+        value: "Keybinding",
+        class: "keybinding-panel"
+      }, {
+        header: withCtx(() => [
+          createVNode(SearchBox, {
+            modelValue: filters.value["global"].value,
+            "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => filters.value["global"].value = $event),
+            placeholder: _ctx.$t("searchKeybindings") + "..."
+          }, null, 8, ["modelValue", "placeholder"])
+        ]),
+        default: withCtx(() => [
+          createVNode(unref(script$3), {
+            value: commandsData.value,
+            selection: selectedCommandData.value,
+            "onUpdate:selection": _cache[1] || (_cache[1] = ($event) => selectedCommandData.value = $event),
+            "global-filter-fields": ["id"],
+            filters: filters.value,
+            selectionMode: "single",
+            stripedRows: "",
+            pt: {
+              header: "px-0"
+            }
+          }, {
+            default: withCtx(() => [
+              createVNode(unref(script$1), {
+                field: "actions",
+                header: ""
+              }, {
+                body: withCtx((slotProps) => [
+                  createBaseVNode("div", _hoisted_1, [
+                    createVNode(unref(script$2), {
+                      icon: "pi pi-pencil",
+                      class: "p-button-text",
+                      onClick: /* @__PURE__ */ __name(($event) => editKeybinding(slotProps.data), "onClick")
+                    }, null, 8, ["onClick"]),
+                    createVNode(unref(script$2), {
+                      icon: "pi pi-trash",
+                      class: "p-button-text p-button-danger",
+                      onClick: /* @__PURE__ */ __name(($event) => removeKeybinding(slotProps.data), "onClick"),
+                      disabled: !slotProps.data.keybinding
+                    }, null, 8, ["onClick", "disabled"])
+                  ])
+                ]),
+                _: 1
+              }),
+              createVNode(unref(script$1), {
+                field: "id",
+                header: "Command ID",
+                sortable: "",
+                class: "max-w-64 2xl:max-w-full"
+              }, {
+                body: withCtx((slotProps) => [
+                  createBaseVNode("div", {
+                    class: "overflow-hidden text-ellipsis whitespace-nowrap",
+                    title: slotProps.data.id
+                  }, toDisplayString(slotProps.data.id), 9, _hoisted_2)
+                ]),
+                _: 1
+              }),
+              createVNode(unref(script$1), {
+                field: "keybinding",
+                header: "Keybinding"
+              }, {
+                body: withCtx((slotProps) => [
+                  slotProps.data.keybinding ? (openBlock(), createBlock(_sfc_main$1, {
+                    key: 0,
+                    keyCombo: slotProps.data.keybinding.combo,
+                    isModified: unref(keybindingStore).isCommandKeybindingModified(slotProps.data.id)
+                  }, null, 8, ["keyCombo", "isModified"])) : (openBlock(), createElementBlock("span", _hoisted_3, "-"))
+                ]),
+                _: 1
+              })
+            ]),
+            _: 1
+          }, 8, ["value", "selection", "filters"]),
+          createVNode(unref(script$6), {
+            class: "min-w-96",
+            visible: editDialogVisible.value,
+            "onUpdate:visible": _cache[2] || (_cache[2] = ($event) => editDialogVisible.value = $event),
+            modal: "",
+            header: currentEditingCommand.value?.id,
+            onHide: cancelEdit
+          }, {
+            footer: withCtx(() => [
+              createVNode(unref(script$2), {
+                label: "Save",
+                icon: "pi pi-check",
+                onClick: saveKeybinding,
+                disabled: !!existingKeybindingOnCombo.value,
+                autofocus: ""
+              }, null, 8, ["disabled"])
+            ]),
+            default: withCtx(() => [
+              createBaseVNode("div", null, [
+                createVNode(unref(script$4), {
+                  class: "mb-2 text-center",
+                  ref_key: "keybindingInput",
+                  ref: keybindingInput,
+                  modelValue: newBindingKeyCombo.value?.toString() ?? "",
+                  placeholder: "Press keys for new binding",
+                  onKeydown: withModifiers(captureKeybinding, ["stop", "prevent"]),
+                  autocomplete: "off",
+                  fluid: "",
+                  invalid: !!existingKeybindingOnCombo.value
+                }, null, 8, ["modelValue", "invalid"]),
+                existingKeybindingOnCombo.value ? (openBlock(), createBlock(unref(script$5), {
+                  key: 0,
+                  severity: "error"
+                }, {
+                  default: withCtx(() => [
+                    createTextVNode(" Keybinding already exists on "),
+                    createVNode(unref(script), {
+                      severity: "secondary",
+                      value: existingKeybindingOnCombo.value.commandId
+                    }, null, 8, ["value"])
+                  ]),
+                  _: 1
+                })) : createCommentVNode("", true)
+              ])
+            ]),
+            _: 1
+          }, 8, ["visible", "header"]),
+          withDirectives(createVNode(unref(script$2), {
+            class: "mt-4",
+            label: _ctx.$t("reset"),
+            icon: "pi pi-trash",
+            severity: "danger",
+            fluid: "",
+            text: "",
+            onClick: resetKeybindings
+          }, null, 8, ["label"]), [
+            [_directive_tooltip, _ctx.$t("resetKeybindingsTooltip")]
+          ])
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-9d7e362e"]]);
+export {
+  KeybindingPanel as default
+};
+//# sourceMappingURL=KeybindingPanel-lcJrxHwZ.js.map

web/assets/ServerConfigPanel-x68ubY-c.js

@@ -0,0 +1,150 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { A as createBaseVNode, g as openBlock, h as createElementBlock, aU as markRaw, d as defineComponent, u as useSettingStore, bw as storeToRefs, w as watch, cy as useCopyToClipboard, x as createBlock, y as withCtx, z as unref, bT as script, a6 as toDisplayString, O as renderList, N as Fragment, i as createVNode, D as script$1, j as createCommentVNode, bI as script$2, cz as formatCamelCase, cA as FormItem, c8 as _sfc_main$1, bN as electronAPI } from "./index-CoOvI8ZH.js";
+import { u as useServerConfigStore } from "./serverConfigStore-cctR8PGG.js";
+const _hoisted_1$1 = {
+  viewBox: "0 0 24 24",
+  width: "1.2em",
+  height: "1.2em"
+};
+const _hoisted_2$1 = /* @__PURE__ */ createBaseVNode("path", {
+  fill: "none",
+  stroke: "currentColor",
+  "stroke-linecap": "round",
+  "stroke-linejoin": "round",
+  "stroke-width": "2",
+  d: "m4 17l6-6l-6-6m8 14h8"
+}, null, -1);
+const _hoisted_3$1 = [
+  _hoisted_2$1
+];
+function render(_ctx, _cache) {
+  return openBlock(), createElementBlock("svg", _hoisted_1$1, [..._hoisted_3$1]);
+}
+__name(render, "render");
+const __unplugin_components_0 = markRaw({ name: "lucide-terminal", render });
+const _hoisted_1 = { class: "flex flex-col gap-2" };
+const _hoisted_2 = { class: "flex justify-end gap-2" };
+const _hoisted_3 = { class: "flex items-center justify-between" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "ServerConfigPanel",
+  setup(__props) {
+    const settingStore = useSettingStore();
+    const serverConfigStore = useServerConfigStore();
+    const {
+      serverConfigsByCategory,
+      serverConfigValues,
+      launchArgs,
+      commandLineArgs,
+      modifiedConfigs
+    } = storeToRefs(serverConfigStore);
+    const revertChanges = /* @__PURE__ */ __name(() => {
+      serverConfigStore.revertChanges();
+    }, "revertChanges");
+    const restartApp = /* @__PURE__ */ __name(() => {
+      electronAPI().restartApp();
+    }, "restartApp");
+    watch(launchArgs, (newVal) => {
+      settingStore.set("Comfy.Server.LaunchArgs", newVal);
+    });
+    watch(serverConfigValues, (newVal) => {
+      settingStore.set("Comfy.Server.ServerConfigValues", newVal);
+    });
+    const { copyToClipboard } = useCopyToClipboard();
+    const copyCommandLineArgs = /* @__PURE__ */ __name(async () => {
+      await copyToClipboard(commandLineArgs.value);
+    }, "copyCommandLineArgs");
+    return (_ctx, _cache) => {
+      const _component_i_lucide58terminal = __unplugin_components_0;
+      return openBlock(), createBlock(_sfc_main$1, {
+        value: "Server-Config",
+        class: "server-config-panel"
+      }, {
+        header: withCtx(() => [
+          createBaseVNode("div", _hoisted_1, [
+            unref(modifiedConfigs).length > 0 ? (openBlock(), createBlock(unref(script), {
+              key: 0,
+              severity: "info",
+              "pt:text": "w-full"
+            }, {
+              default: withCtx(() => [
+                createBaseVNode("p", null, toDisplayString(_ctx.$t("serverConfig.modifiedConfigs")), 1),
+                createBaseVNode("ul", null, [
+                  (openBlock(true), createElementBlock(Fragment, null, renderList(unref(modifiedConfigs), (config) => {
+                    return openBlock(), createElementBlock("li", {
+                      key: config.id
+                    }, toDisplayString(config.name) + ": " + toDisplayString(config.initialValue) + " → " + toDisplayString(config.value), 1);
+                  }), 128))
+                ]),
+                createBaseVNode("div", _hoisted_2, [
+                  createVNode(unref(script$1), {
+                    label: _ctx.$t("serverConfig.revertChanges"),
+                    onClick: revertChanges,
+                    outlined: ""
+                  }, null, 8, ["label"]),
+                  createVNode(unref(script$1), {
+                    label: _ctx.$t("serverConfig.restart"),
+                    onClick: restartApp,
+                    outlined: "",
+                    severity: "danger"
+                  }, null, 8, ["label"])
+                ])
+              ]),
+              _: 1
+            })) : createCommentVNode("", true),
+            unref(commandLineArgs) ? (openBlock(), createBlock(unref(script), {
+              key: 1,
+              severity: "secondary",
+              "pt:text": "w-full"
+            }, {
+              icon: withCtx(() => [
+                createVNode(_component_i_lucide58terminal, { class: "text-xl font-bold" })
+              ]),
+              default: withCtx(() => [
+                createBaseVNode("div", _hoisted_3, [
+                  createBaseVNode("p", null, toDisplayString(unref(commandLineArgs)), 1),
+                  createVNode(unref(script$1), {
+                    icon: "pi pi-clipboard",
+                    onClick: copyCommandLineArgs,
+                    severity: "secondary",
+                    text: ""
+                  })
+                ])
+              ]),
+              _: 1
+            })) : createCommentVNode("", true)
+          ])
+        ]),
+        default: withCtx(() => [
+          (openBlock(true), createElementBlock(Fragment, null, renderList(Object.entries(unref(serverConfigsByCategory)), ([label, items], i) => {
+            return openBlock(), createElementBlock("div", { key: label }, [
+              i > 0 ? (openBlock(), createBlock(unref(script$2), { key: 0 })) : createCommentVNode("", true),
+              createBaseVNode("h3", null, toDisplayString(unref(formatCamelCase)(label)), 1),
+              (openBlock(true), createElementBlock(Fragment, null, renderList(items, (item) => {
+                return openBlock(), createElementBlock("div", {
+                  key: item.name,
+                  class: "flex items-center mb-4"
+                }, [
+                  createVNode(FormItem, {
+                    item,
+                    formValue: item.value,
+                    "onUpdate:formValue": /* @__PURE__ */ __name(($event) => item.value = $event, "onUpdate:formValue"),
+                    id: item.id,
+                    labelClass: {
+                      "text-highlight": item.initialValue !== item.value
+                    }
+                  }, null, 8, ["item", "formValue", "onUpdate:formValue", "id", "labelClass"])
+                ]);
+              }), 128))
+            ]);
+          }), 128))
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+export {
+  _sfc_main as default
+};
+//# sourceMappingURL=ServerConfigPanel-x68ubY-c.js.map

web/assets/ServerConfigPanel-x68ubY-c.js.map

@@ -0,0 +1 @@
+{"version":3,"file":"ServerConfigPanel-x68ubY-c.js","sources":["../../src/components/dialog/content/setting/ServerConfigPanel.vue"],"sourcesContent":["<template>\n  <PanelTemplate value=\"Server-Config\" class=\"server-config-panel\">\n    <template #header>\n      <div class=\"flex flex-col gap-2\">\n        <Message\n          v-if=\"modifiedConfigs.length > 0\"\n          severity=\"info\"\n          pt:text=\"w-full\"\n        >\n          <p>\n            {{ $t('serverConfig.modifiedConfigs') }}\n          </p>\n          <ul>\n            <li v-for=\"config in modifiedConfigs\" :key=\"config.id\">\n              {{ config.name }}: {{ config.initialValue }} → {{ config.value }}\n            </li>\n          </ul>\n          <div class=\"flex justify-end gap-2\">\n            <Button\n              :label=\"$t('serverConfig.revertChanges')\"\n              @click=\"revertChanges\"\n              outlined\n            />\n            <Button\n              :label=\"$t('serverConfig.restart')\"\n              @click=\"restartApp\"\n              outlined\n              severity=\"danger\"\n            />\n          </div>\n        </Message>\n        <Message v-if=\"commandLineArgs\" severity=\"secondary\" pt:text=\"w-full\">\n          <template #icon>\n            <i-lucide:terminal class=\"text-xl font-bold\" />\n          </template>\n          <div class=\"flex items-center justify-between\">\n            <p>{{ commandLineArgs }}</p>\n            <Button\n              icon=\"pi pi-clipboard\"\n              @click=\"copyCommandLineArgs\"\n              severity=\"secondary\"\n              text\n            />\n          </div>\n        </Message>\n      </div>\n    </template>\n    <div\n      v-for=\"([label, items], i) in Object.entries(serverConfigsByCategory)\"\n      :key=\"label\"\n    >\n      <Divider v-if=\"i > 0\" />\n      <h3>{{ formatCamelCase(label) }}</h3>\n      <div\n        v-for=\"item in items\"\n        :key=\"item.name\"\n        class=\"flex items-center mb-4\"\n      >\n        <FormItem\n          :item=\"item\"\n          v-model:formValue=\"item.value\"\n          :id=\"item.id\"\n          :labelClass=\"{\n            'text-highlight': item.initialValue !== item.value\n          }\"\n        />\n      </div>\n    </div>\n  </PanelTemplate>\n</template>\n\n<script setup lang=\"ts\">\nimport Button from 'primevue/button'\nimport Message from 'primevue/message'\nimport Divider from 'primevue/divider'\nimport FormItem from '@/components/common/FormItem.vue'\nimport PanelTemplate from './PanelTemplate.vue'\nimport { formatCamelCase } from '@/utils/formatUtil'\nimport { useServerConfigStore } from '@/stores/serverConfigStore'\nimport { storeToRefs } from 'pinia'\nimport { electronAPI } from '@/utils/envUtil'\nimport { useSettingStore } from '@/stores/settingStore'\nimport { watch } from 'vue'\nimport { useCopyToClipboard } from '@/hooks/clipboardHooks'\n\nconst settingStore = useSettingStore()\nconst serverConfigStore = useServerConfigStore()\nconst {\n  serverConfigsByCategory,\n  serverConfigValues,\n  launchArgs,\n  commandLineArgs,\n  modifiedConfigs\n} = storeToRefs(serverConfigStore)\n\nconst revertChanges = () => {\n  serverConfigStore.revertChanges()\n}\n\nconst restartApp = () => {\n  electronAPI().restartApp()\n}\n\nwatch(launchArgs, (newVal) => {\n  settingStore.set('Comfy.Server.LaunchArgs', newVal)\n})\n\nwatch(serverConfigValues, (newVal) => {\n  settingStore.set('Comfy.Server.ServerConfigValues', newVal)\n})\n\nconst { copyToClipboard } = useCopyToClipboard()\nconst copyCommandLineArgs = async () => {\n  await copyToClipboard(commandLineArgs.value)\n}\n</script>\n"],"names":[],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AAqFA,UAAM,eAAe;AACrB,UAAM,oBAAoB;AACpB,UAAA;AAAA,MACJ;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,MACA;AAAA,IAAA,IACE,YAAY,iBAAiB;AAEjC,UAAM,gBAAgB,6BAAM;AAC1B,wBAAkB,cAAc;AAAA,IAAA,GADZ;AAItB,UAAM,aAAa,6BAAM;AACvB,kBAAA,EAAc;IAAW,GADR;AAIb,UAAA,YAAY,CAAC,WAAW;AACf,mBAAA,IAAI,2BAA2B,MAAM;AAAA,IAAA,CACnD;AAEK,UAAA,oBAAoB,CAAC,WAAW;AACvB,mBAAA,IAAI,mCAAmC,MAAM;AAAA,IAAA,CAC3D;AAEK,UAAA,EAAE,oBAAoB;AAC5B,UAAM,sBAAsB,mCAAY;AAChC,YAAA,gBAAgB,gBAAgB,KAAK;AAAA,IAAA,GADjB;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/ServerStartView-CqRVtr1h.js

@@ -0,0 +1,79 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, aD as useI18n, r as ref, o as onMounted, g as openBlock, h as createElementBlock, A as createBaseVNode, aw as createTextVNode, a6 as toDisplayString, z as unref, j as createCommentVNode, i as createVNode, D as script, bM as BaseTerminal, P as pushScopeId, Q as popScopeId, bN as electronAPI, _ as _export_sfc } from "./index-CoOvI8ZH.js";
+import { P as ProgressStatus } from "./index-BppSBmxJ.js";
+const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-f5429be7"), n = n(), popScopeId(), n), "_withScopeId");
+const _hoisted_1 = { class: "font-sans flex flex-col justify-center items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto" };
+const _hoisted_2 = { class: "text-2xl font-bold" };
+const _hoisted_3 = { key: 0 };
+const _hoisted_4 = {
+  key: 0,
+  class: "flex items-center my-4 gap-2"
+};
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "ServerStartView",
+  setup(__props) {
+    const electron = electronAPI();
+    const { t } = useI18n();
+    const status = ref(ProgressStatus.INITIAL_STATE);
+    const electronVersion = ref("");
+    let xterm;
+    const updateProgress = /* @__PURE__ */ __name(({ status: newStatus }) => {
+      status.value = newStatus;
+      xterm?.clear();
+    }, "updateProgress");
+    const terminalCreated = /* @__PURE__ */ __name(({ terminal, useAutoSize }, root) => {
+      xterm = terminal;
+      useAutoSize(root, true, true);
+      electron.onLogMessage((message) => {
+        terminal.write(message);
+      });
+      terminal.options.cursorBlink = false;
+      terminal.options.disableStdin = true;
+      terminal.options.cursorInactiveStyle = "block";
+    }, "terminalCreated");
+    const reinstall = /* @__PURE__ */ __name(() => electron.reinstall(), "reinstall");
+    const reportIssue = /* @__PURE__ */ __name(() => {
+      window.open("https://forum.comfy.org/c/v1-feedback/", "_blank");
+    }, "reportIssue");
+    const openLogs = /* @__PURE__ */ __name(() => electron.openLogsFolder(), "openLogs");
+    onMounted(async () => {
+      electron.sendReady();
+      electron.onProgressUpdate(updateProgress);
+      electronVersion.value = await electron.getElectronVersion();
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", _hoisted_1, [
+        createBaseVNode("h2", _hoisted_2, [
+          createTextVNode(toDisplayString(unref(t)(`serverStart.process.${status.value}`)) + " ", 1),
+          status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("span", _hoisted_3, " v" + toDisplayString(electronVersion.value), 1)) : createCommentVNode("", true)
+        ]),
+        status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("div", _hoisted_4, [
+          createVNode(unref(script), {
+            icon: "pi pi-flag",
+            severity: "secondary",
+            label: unref(t)("serverStart.reportIssue"),
+            onClick: reportIssue
+          }, null, 8, ["label"]),
+          createVNode(unref(script), {
+            icon: "pi pi-file",
+            severity: "secondary",
+            label: unref(t)("serverStart.openLogs"),
+            onClick: openLogs
+          }, null, 8, ["label"]),
+          createVNode(unref(script), {
+            icon: "pi pi-refresh",
+            label: unref(t)("serverStart.reinstall"),
+            onClick: reinstall
+          }, null, 8, ["label"])
+        ])) : createCommentVNode("", true),
+        createVNode(BaseTerminal, { onCreated: terminalCreated })
+      ]);
+    };
+  }
+});
+const ServerStartView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-f5429be7"]]);
+export {
+  ServerStartView as default
+};
+//# sourceMappingURL=ServerStartView-CqRVtr1h.js.map

web/assets/ServerStartView-CqRVtr1h.js.map

@@ -0,0 +1 @@
+{"version":3,"file":"ServerStartView-CqRVtr1h.js","sources":["../../src/views/ServerStartView.vue"],"sourcesContent":["<template>\n  <div\n    class=\"font-sans flex flex-col justify-center items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto\"\n  >\n    <h2 class=\"text-2xl font-bold\">\n      {{ t(`serverStart.process.${status}`) }}\n      <span v-if=\"status === ProgressStatus.ERROR\">\n        v{{ electronVersion }}\n      </span>\n    </h2>\n    <div\n      v-if=\"status === ProgressStatus.ERROR\"\n      class=\"flex items-center my-4 gap-2\"\n    >\n      <Button\n        icon=\"pi pi-flag\"\n        severity=\"secondary\"\n        :label=\"t('serverStart.reportIssue')\"\n        @click=\"reportIssue\"\n      />\n      <Button\n        icon=\"pi pi-file\"\n        severity=\"secondary\"\n        :label=\"t('serverStart.openLogs')\"\n        @click=\"openLogs\"\n      />\n      <Button\n        icon=\"pi pi-refresh\"\n        :label=\"t('serverStart.reinstall')\"\n        @click=\"reinstall\"\n      />\n    </div>\n    <BaseTerminal @created=\"terminalCreated\" />\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport Button from 'primevue/button'\nimport { ref, onMounted, Ref } from 'vue'\nimport BaseTerminal from '@/components/bottomPanel/tabs/terminal/BaseTerminal.vue'\nimport { ProgressStatus } from '@comfyorg/comfyui-electron-types'\nimport { electronAPI } from '@/utils/envUtil'\nimport type { useTerminal } from '@/hooks/bottomPanelTabs/useTerminal'\nimport { Terminal } from '@xterm/xterm'\nimport { useI18n } from 'vue-i18n'\n\nconst electron = electronAPI()\nconst { t } = useI18n()\n\nconst status = ref<ProgressStatus>(ProgressStatus.INITIAL_STATE)\nconst electronVersion = ref<string>('')\nlet xterm: Terminal | undefined\n\nconst updateProgress = ({ status: newStatus }: { status: ProgressStatus }) => {\n  status.value = newStatus\n  xterm?.clear()\n}\n\nconst terminalCreated = (\n  { terminal, useAutoSize }: ReturnType<typeof useTerminal>,\n  root: Ref<HTMLElement>\n) => {\n  xterm = terminal\n\n  useAutoSize(root, true, true)\n  electron.onLogMessage((message: string) => {\n    terminal.write(message)\n  })\n\n  terminal.options.cursorBlink = false\n  terminal.options.disableStdin = true\n  terminal.options.cursorInactiveStyle = 'block'\n}\n\nconst reinstall = () => electron.reinstall()\nconst reportIssue = () => {\n  window.open('https://forum.comfy.org/c/v1-feedback/', '_blank')\n}\nconst openLogs = () => electron.openLogsFolder()\n\nonMounted(async () => {\n  electron.sendReady()\n  electron.onProgressUpdate(updateProgress)\n  electronVersion.value = await electron.getElectronVersion()\n})\n</script>\n\n<style scoped>\n:deep(.xterm-helper-textarea) {\n  /* Hide this as it moves all over when uv is running */\n  display: none;\n}\n</style>\n"],"names":[],"mappings":";;;;;;;;;;;;;;;AA8CA,UAAM,WAAW;AACX,UAAA,EAAE,MAAM;AAER,UAAA,SAAS,IAAoB,eAAe,aAAa;AACzD,UAAA,kBAAkB,IAAY,EAAE;AAClC,QAAA;AAEJ,UAAM,iBAAiB,wBAAC,EAAE,QAAQ,gBAA4C;AAC5E,aAAO,QAAQ;AACf,aAAO,MAAM;AAAA,IAAA,GAFQ;AAKvB,UAAM,kBAAkB,wBACtB,EAAE,UAAU,YAAA,GACZ,SACG;AACK,cAAA;AAEI,kBAAA,MAAM,MAAM,IAAI;AACnB,eAAA,aAAa,CAAC,YAAoB;AACzC,iBAAS,MAAM,OAAO;AAAA,MAAA,CACvB;AAED,eAAS,QAAQ,cAAc;AAC/B,eAAS,QAAQ,eAAe;AAChC,eAAS,QAAQ,sBAAsB;AAAA,IAAA,GAbjB;AAgBlB,UAAA,YAAY,6BAAM,SAAS,aAAf;AAClB,UAAM,cAAc,6BAAM;AACjB,aAAA,KAAK,0CAA0C,QAAQ;AAAA,IAAA,GAD5C;AAGd,UAAA,WAAW,6BAAM,SAAS,kBAAf;AAEjB,cAAU,YAAY;AACpB,eAAS,UAAU;AACnB,eAAS,iBAAiB,cAAc;AACxB,sBAAA,QAAQ,MAAM,SAAS,mBAAmB;AAAA,IAAA,CAC3D;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/ServerStartView-Djq8v91B.css

@@ -0,0 +1,5 @@
+
+[data-v-f5429be7] .xterm-helper-textarea {
+  /* Hide this as it moves all over when uv is running */
+  display: none;
+}

web/assets/ServerStartView-e57oVZ6V.js

@@ -1,102 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, r as ref, o as onMounted, w as watch, I as onBeforeUnmount, g as openBlock, h as createElementBlock, i as createVNode, y as withCtx, A as createBaseVNode, a6 as toDisplayString, z as unref, bK as script, bL as electronAPI } from "./index-B6dYHNhg.js";
-import { t, s } from "./index-B4gmhi99.js";
-const _hoisted_1$1 = { class: "p-terminal rounded-none h-full w-full" };
-const _hoisted_2$1 = { class: "px-4 whitespace-pre-wrap" };
-const _sfc_main$1 = /* @__PURE__ */ defineComponent({
-  __name: "LogTerminal",
-  props: {
-    fetchLogs: { type: Function },
-    fetchInterval: {}
-  },
-  setup(__props) {
-    const props = __props;
-    const log = ref("");
-    const scrollPanelRef = ref(null);
-    const scrolledToBottom = ref(false);
-    let intervalId = 0;
-    onMounted(async () => {
-      const element = scrollPanelRef.value?.$el;
-      const scrollContainer = element?.querySelector(".p-scrollpanel-content");
-      if (scrollContainer) {
-        scrollContainer.addEventListener("scroll", () => {
-          scrolledToBottom.value = scrollContainer.scrollTop + scrollContainer.clientHeight === scrollContainer.scrollHeight;
-        });
-      }
-      const scrollToBottom = /* @__PURE__ */ __name(() => {
-        if (scrollContainer) {
-          scrollContainer.scrollTop = scrollContainer.scrollHeight;
-        }
-      }, "scrollToBottom");
-      watch(log, () => {
-        if (scrolledToBottom.value) {
-          scrollToBottom();
-        }
-      });
-      const fetchLogs = /* @__PURE__ */ __name(async () => {
-        log.value = await props.fetchLogs();
-      }, "fetchLogs");
-      await fetchLogs();
-      scrollToBottom();
-      intervalId = window.setInterval(fetchLogs, props.fetchInterval);
-    });
-    onBeforeUnmount(() => {
-      window.clearInterval(intervalId);
-    });
-    return (_ctx, _cache) => {
-      return openBlock(), createElementBlock("div", _hoisted_1$1, [
-        createVNode(unref(script), {
-          class: "h-full w-full",
-          ref_key: "scrollPanelRef",
-          ref: scrollPanelRef
-        }, {
-          default: withCtx(() => [
-            createBaseVNode("pre", _hoisted_2$1, toDisplayString(log.value), 1)
-          ]),
-          _: 1
-        }, 512)
-      ]);
-    };
-  }
-});
-const _hoisted_1 = { class: "font-sans flex flex-col justify-center items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto" };
-const _hoisted_2 = { class: "text-2xl font-bold" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "ServerStartView",
-  setup(__props) {
-    const electron = electronAPI();
-    const status = ref(t.INITIAL_STATE);
-    const logs = ref([]);
-    const updateProgress = /* @__PURE__ */ __name(({ status: newStatus }) => {
-      status.value = newStatus;
-      logs.value = [];
-    }, "updateProgress");
-    const addLogMessage = /* @__PURE__ */ __name((message) => {
-      logs.value = [...logs.value, message];
-    }, "addLogMessage");
-    const fetchLogs = /* @__PURE__ */ __name(async () => {
-      return logs.value.join("\n");
-    }, "fetchLogs");
-    onMounted(() => {
-      electron.sendReady();
-      electron.onProgressUpdate(updateProgress);
-      electron.onLogMessage((message) => {
-        addLogMessage(message);
-      });
-    });
-    return (_ctx, _cache) => {
-      return openBlock(), createElementBlock("div", _hoisted_1, [
-        createBaseVNode("h2", _hoisted_2, toDisplayString(unref(s)[status.value]), 1),
-        createVNode(_sfc_main$1, {
-          "fetch-logs": fetchLogs,
-          "fetch-interval": 500
-        })
-      ]);
-    };
-  }
-});
-export {
-  _sfc_main as default
-};
-//# sourceMappingURL=ServerStartView-e57oVZ6V.js.map

web/assets/ServerStartView-e57oVZ6V.js.map

@@ -1 +0,0 @@
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web/assets/WelcomeView-C4D1cggT.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineComponent, g as openBlock, h as createElementBlock, A as createBaseVNode, a6 as toDisplayString, i as createVNode, z as unref, D as script, P as pushScopeId, Q as popScopeId, _ as _export_sfc } from "./index-B6dYHNhg.js";
+import { d as defineComponent, g as openBlock, h as createElementBlock, A as createBaseVNode, a6 as toDisplayString, i as createVNode, z as unref, D as script, P as pushScopeId, Q as popScopeId, _ as _export_sfc } from "./index-CoOvI8ZH.js";
 const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-12b8b11b"), n = n(), popScopeId(), n), "_withScopeId");
 const _hoisted_1 = { class: "font-sans flex flex-col justify-center items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto" };
 const _hoisted_2 = { class: "flex flex-col items-center justify-center gap-8 p-8" };
@@ -30,4 +30,4 @@ const WelcomeView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-
 export {
   WelcomeView as default
 };
-//# sourceMappingURL=WelcomeView-DT4bj-QV.js.map
+//# sourceMappingURL=WelcomeView-C4D1cggT.js.map

web/assets/WelcomeView-C4D1cggT.js.map

@@ -1 +1 @@
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web/assets/index-B4gmhi99.js

@@ -1,62 +0,0 @@
-const o = {
-  LOADING_PROGRESS: "loading-progress",
-  IS_PACKAGED: "is-packaged",
-  RENDERER_READY: "renderer-ready",
-  RESTART_APP: "restart-app",
-  REINSTALL: "reinstall",
-  LOG_MESSAGE: "log-message",
-  OPEN_DIALOG: "open-dialog",
-  DOWNLOAD_PROGRESS: "download-progress",
-  START_DOWNLOAD: "start-download",
-  PAUSE_DOWNLOAD: "pause-download",
-  RESUME_DOWNLOAD: "resume-download",
-  CANCEL_DOWNLOAD: "cancel-download",
-  DELETE_MODEL: "delete-model",
-  GET_ALL_DOWNLOADS: "get-all-downloads",
-  GET_ELECTRON_VERSION: "get-electron-version",
-  SEND_ERROR_TO_SENTRY: "send-error-to-sentry",
-  GET_BASE_PATH: "get-base-path",
-  GET_MODEL_CONFIG_PATH: "get-model-config-path",
-  OPEN_PATH: "open-path",
-  OPEN_LOGS_PATH: "open-logs-path",
-  OPEN_DEV_TOOLS: "open-dev-tools",
-  IS_FIRST_TIME_SETUP: "is-first-time-setup",
-  GET_SYSTEM_PATHS: "get-system-paths",
-  VALIDATE_INSTALL_PATH: "validate-install-path",
-  VALIDATE_COMFYUI_SOURCE: "validate-comfyui-source",
-  SHOW_DIRECTORY_PICKER: "show-directory-picker",
-  INSTALL_COMFYUI: "install-comfyui"
-};
-var t = /* @__PURE__ */ ((e) => (e.INITIAL_STATE = "initial-state", e.PYTHON_SETUP = "python-setup", e.STARTING_SERVER = "starting-server", e.READY = "ready", e.ERROR = "error", e.ERROR_INSTALL_PATH = "error-install-path", e))(t || {});
-const s = {
-  "initial-state": "Loading...",
-  "python-setup": "Setting up Python Environment...",
-  "starting-server": "Starting ComfyUI server...",
-  ready: "Finishing...",
-  error: "Was not able to start ComfyUI. Please check the logs for more details. You can open it from the Help menu. Please report issues to: https://forum.comfy.org",
-  "error-install-path": "Installation path does not exist. Please reset the installation location."
-}, a = "electronAPI", n = "https://942cadba58d247c9cab96f45221aa813@o4507954455314432.ingest.us.sentry.io/4508007940685824", r = [
-  {
-    id: "user_files",
-    label: "User Files",
-    description: "Settings and user-created workflows"
-  },
-  {
-    id: "models",
-    label: "Models",
-    description: "Reference model files from existing ComfyUI installations. (No copy)"
-  }
-  // TODO: Decide whether we want to auto-migrate custom nodes, and install their dependencies.
-  // huchenlei: This is a very essential thing for migration experience.
-  // {
-  //   id: 'custom_nodes',
-  //   label: 'Custom Nodes',
-  //   description: 'Reference custom node files from existing ComfyUI installations. (No copy)',
-  // },
-];
-export {
-  r,
-  s,
-  t
-};
-//# sourceMappingURL=index-B4gmhi99.js.map

web/assets/index-B4gmhi99.js.map

@@ -1 +0,0 @@
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web/assets/index-BppSBmxJ.js

@@ -0,0 +1,106 @@
+const IPC_CHANNELS = {
+  LOADING_PROGRESS: "loading-progress",
+  IS_PACKAGED: "is-packaged",
+  RENDERER_READY: "renderer-ready",
+  RESTART_APP: "restart-app",
+  REINSTALL: "reinstall",
+  LOG_MESSAGE: "log-message",
+  OPEN_DIALOG: "open-dialog",
+  DOWNLOAD_PROGRESS: "download-progress",
+  START_DOWNLOAD: "start-download",
+  PAUSE_DOWNLOAD: "pause-download",
+  RESUME_DOWNLOAD: "resume-download",
+  CANCEL_DOWNLOAD: "cancel-download",
+  DELETE_MODEL: "delete-model",
+  GET_ALL_DOWNLOADS: "get-all-downloads",
+  GET_ELECTRON_VERSION: "get-electron-version",
+  SEND_ERROR_TO_SENTRY: "send-error-to-sentry",
+  GET_BASE_PATH: "get-base-path",
+  GET_MODEL_CONFIG_PATH: "get-model-config-path",
+  OPEN_PATH: "open-path",
+  OPEN_LOGS_PATH: "open-logs-path",
+  OPEN_DEV_TOOLS: "open-dev-tools",
+  TERMINAL_WRITE: "execute-terminal-command",
+  TERMINAL_RESIZE: "resize-terminal",
+  TERMINAL_RESTORE: "restore-terminal",
+  TERMINAL_ON_OUTPUT: "terminal-output",
+  IS_FIRST_TIME_SETUP: "is-first-time-setup",
+  GET_SYSTEM_PATHS: "get-system-paths",
+  VALIDATE_INSTALL_PATH: "validate-install-path",
+  VALIDATE_COMFYUI_SOURCE: "validate-comfyui-source",
+  SHOW_DIRECTORY_PICKER: "show-directory-picker",
+  INSTALL_COMFYUI: "install-comfyui"
+};
+var ProgressStatus = /* @__PURE__ */ ((ProgressStatus2) => {
+  ProgressStatus2["INITIAL_STATE"] = "initial-state";
+  ProgressStatus2["PYTHON_SETUP"] = "python-setup";
+  ProgressStatus2["STARTING_SERVER"] = "starting-server";
+  ProgressStatus2["READY"] = "ready";
+  ProgressStatus2["ERROR"] = "error";
+  return ProgressStatus2;
+})(ProgressStatus || {});
+const ProgressMessages = {
+  [
+    "initial-state"
+    /* INITIAL_STATE */
+  ]: "Loading...",
+  [
+    "python-setup"
+    /* PYTHON_SETUP */
+  ]: "Setting up Python Environment...",
+  [
+    "starting-server"
+    /* STARTING_SERVER */
+  ]: "Starting ComfyUI server...",
+  [
+    "ready"
+    /* READY */
+  ]: "Finishing...",
+  [
+    "error"
+    /* ERROR */
+  ]: "Was not able to start ComfyUI. Please check the logs for more details. You can open it from the Help menu. Please report issues to: https://forum.comfy.org"
+};
+const ELECTRON_BRIDGE_API = "electronAPI";
+const SENTRY_URL_ENDPOINT = "https://942cadba58d247c9cab96f45221aa813@o4507954455314432.ingest.us.sentry.io/4508007940685824";
+const MigrationItems = [
+  {
+    id: "user_files",
+    label: "User Files",
+    description: "Settings and user-created workflows"
+  },
+  {
+    id: "models",
+    label: "Models",
+    description: "Reference model files from existing ComfyUI installations. (No copy)"
+  }
+  // TODO: Decide whether we want to auto-migrate custom nodes, and install their dependencies.
+  // huchenlei: This is a very essential thing for migration experience.
+  // {
+  //   id: 'custom_nodes',
+  //   label: 'Custom Nodes',
+  //   description: 'Reference custom node files from existing ComfyUI installations. (No copy)',
+  // },
+];
+const DEFAULT_SERVER_ARGS = {
+  /** The host to use for the ComfyUI server. */
+  host: "127.0.0.1",
+  /** The port to use for the ComfyUI server. */
+  port: 8e3,
+  // Extra arguments to pass to the ComfyUI server.
+  extraServerArgs: {}
+};
+var DownloadStatus = /* @__PURE__ */ ((DownloadStatus2) => {
+  DownloadStatus2["PENDING"] = "pending";
+  DownloadStatus2["IN_PROGRESS"] = "in_progress";
+  DownloadStatus2["COMPLETED"] = "completed";
+  DownloadStatus2["PAUSED"] = "paused";
+  DownloadStatus2["ERROR"] = "error";
+  DownloadStatus2["CANCELLED"] = "cancelled";
+  return DownloadStatus2;
+})(DownloadStatus || {});
+export {
+  MigrationItems as M,
+  ProgressStatus as P
+};
+//# sourceMappingURL=index-BppSBmxJ.js.map

web/assets/index-D4DWQPPQ.js

@@ -1,6 +1,6 @@
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-import { c7 as script$2, A as createBaseVNode, g as openBlock, h as createElementBlock, m as mergeProps } from "./index-B6dYHNhg.js";
+import { cb as script$2, A as createBaseVNode, g as openBlock, h as createElementBlock, m as mergeProps } from "./index-CoOvI8ZH.js";
 var script$1 = {
   name: "BarsIcon",
   "extends": script$2
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   script as a,
   script$1 as s
 };
-//# sourceMappingURL=index-MX9DEi8Q.js.map
+//# sourceMappingURL=index-D4DWQPPQ.js.map

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+{"version":3,"file":"index-D4DWQPPQ.js","sources":["../../node_modules/@primevue/icons/bars/index.mjs","../../node_modules/@primevue/icons/plus/index.mjs"],"sourcesContent":["import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n  name: 'BarsIcon',\n  \"extends\": BaseIcon\n};\n\nvar _hoisted_1 = /*#__PURE__*/createElementVNode(\"path\", {\n  \"fill-rule\": \"evenodd\",\n  \"clip-rule\": \"evenodd\",\n  d: \"M13.3226 3.6129H0.677419C0.497757 3.6129 0.325452 3.54152 0.198411 3.41448C0.0713707 3.28744 0 3.11514 0 2.93548C0 2.75581 0.0713707 2.58351 0.198411 2.45647C0.325452 2.32943 0.497757 2.25806 0.677419 2.25806H13.3226C13.5022 2.25806 13.6745 2.32943 13.8016 2.45647C13.9286 2.58351 14 2.75581 14 2.93548C14 3.11514 13.9286 3.28744 13.8016 3.41448C13.6745 3.54152 13.5022 3.6129 13.3226 3.6129ZM13.3226 7.67741H0.677419C0.497757 7.67741 0.325452 7.60604 0.198411 7.479C0.0713707 7.35196 0 7.17965 0 6.99999C0 6.82033 0.0713707 6.64802 0.198411 6.52098C0.325452 6.39394 0.497757 6.32257 0.677419 6.32257H13.3226C13.5022 6.32257 13.6745 6.39394 13.8016 6.52098C13.9286 6.64802 14 6.82033 14 6.99999C14 7.17965 13.9286 7.35196 13.8016 7.479C13.6745 7.60604 13.5022 7.67741 13.3226 7.67741ZM0.677419 11.7419H13.3226C13.5022 11.7419 13.6745 11.6706 13.8016 11.5435C13.9286 11.4165 14 11.2442 14 11.0645C14 10.8848 13.9286 10.7125 13.8016 10.5855C13.6745 10.4585 13.5022 10.3871 13.3226 10.3871H0.677419C0.497757 10.3871 0.325452 10.4585 0.198411 10.5855C0.0713707 10.7125 0 10.8848 0 11.0645C0 11.2442 0.0713707 11.4165 0.198411 11.5435C0.325452 11.6706 0.497757 11.7419 0.677419 11.7419Z\",\n  fill: \"currentColor\"\n}, null, -1);\nvar _hoisted_2 = [_hoisted_1];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n  return openBlock(), createElementBlock(\"svg\", mergeProps({\n    width: \"14\",\n    height: \"14\",\n    viewBox: \"0 0 14 14\",\n    fill: \"none\",\n    xmlns: \"http://www.w3.org/2000/svg\"\n  }, _ctx.pti()), _hoisted_2, 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n","import BaseIcon from '@primevue/icons/baseicon';\nimport { openBlock, createElementBlock, mergeProps, createElementVNode } from 'vue';\n\nvar script = {\n  name: 'PlusIcon',\n  \"extends\": BaseIcon\n};\n\nvar _hoisted_1 = /*#__PURE__*/createElementVNode(\"path\", {\n  d: \"M7.67742 6.32258V0.677419C7.67742 0.497757 7.60605 0.325452 7.47901 0.198411C7.35197 0.0713707 7.17966 0 7 0C6.82034 0 6.64803 0.0713707 6.52099 0.198411C6.39395 0.325452 6.32258 0.497757 6.32258 0.677419V6.32258H0.677419C0.497757 6.32258 0.325452 6.39395 0.198411 6.52099C0.0713707 6.64803 0 6.82034 0 7C0 7.17966 0.0713707 7.35197 0.198411 7.47901C0.325452 7.60605 0.497757 7.67742 0.677419 7.67742H6.32258V13.3226C6.32492 13.5015 6.39704 13.6725 6.52358 13.799C6.65012 13.9255 6.82106 13.9977 7 14C7.17966 14 7.35197 13.9286 7.47901 13.8016C7.60605 13.6745 7.67742 13.5022 7.67742 13.3226V7.67742H13.3226C13.5022 7.67742 13.6745 7.60605 13.8016 7.47901C13.9286 7.35197 14 7.17966 14 7C13.9977 6.82106 13.9255 6.65012 13.799 6.52358C13.6725 6.39704 13.5015 6.32492 13.3226 6.32258H7.67742Z\",\n  fill: \"currentColor\"\n}, null, -1);\nvar _hoisted_2 = [_hoisted_1];\nfunction render(_ctx, _cache, $props, $setup, $data, $options) {\n  return openBlock(), createElementBlock(\"svg\", mergeProps({\n    width: \"14\",\n    height: \"14\",\n    viewBox: \"0 0 14 14\",\n    fill: \"none\",\n    xmlns: \"http://www.w3.org/2000/svg\"\n  }, _ctx.pti()), _hoisted_2, 16);\n}\n\nscript.render = render;\n\nexport { script as default };\n//# sourceMappingURL=index.mjs.map\n"],"names":["script","BaseIcon","_hoisted_1","createElementVNode","_hoisted_2","render"],"mappings":";;;AAGG,IAACA,WAAS;AAAA,EACX,MAAM;AAAA,EACN,WAAWC;AACb;AAEA,IAAIC,eAA0BC,gCAAmB,QAAQ;AAAA,EACvD,aAAa;AAAA,EACb,aAAa;AAAA,EACb,GAAG;AAAA,EACH,MAAM;AACR,GAAG,MAAM,EAAE;AACX,IAAIC,eAAa,CAACF,YAAU;AAC5B,SAASG,SAAO,MAAM,QAAQ,QAAQ,QAAQ,OAAO,UAAU;AAC7D,SAAO,UAAW,GAAE,mBAAmB,OAAO,WAAW;AAAA,IACvD,OAAO;AAAA,IACP,QAAQ;AAAA,IACR,SAAS;AAAA,IACT,MAAM;AAAA,IACN,OAAO;AAAA,EACR,GAAE,KAAK,IAAG,CAAE,GAAGD,cAAY,EAAE;AAChC;AARSC;AAUTL,SAAO,SAASK;ACtBb,IAAC,SAAS;AAAA,EACX,MAAM;AAAA,EACN,WAAWJ;AACb;AAEA,IAAI,aAA0BE,gCAAmB,QAAQ;AAAA,EACvD,GAAG;AAAA,EACH,MAAM;AACR,GAAG,MAAM,EAAE;AACX,IAAI,aAAa,CAAC,UAAU;AAC5B,SAAS,OAAO,MAAM,QAAQ,QAAQ,QAAQ,OAAO,UAAU;AAC7D,SAAO,UAAW,GAAE,mBAAmB,OAAO,WAAW;AAAA,IACvD,OAAO;AAAA,IACP,QAAQ;AAAA,IACR,SAAS;AAAA,IACT,MAAM;AAAA,IACN,OAAO;AAAA,EACR,GAAE,KAAK,IAAG,CAAE,GAAG,YAAY,EAAE;AAChC;AARS;AAUT,OAAO,SAAS;","x_google_ignoreList":[0,1]}

web/assets/index-DK6Kev7f.js

@@ -1,7 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { c7 as script$s, A as createBaseVNode, g as openBlock, h as createElementBlock, m as mergeProps, B as BaseStyle, R as script$t, a6 as toDisplayString, a1 as Ripple, t as resolveDirective, v as withDirectives, x as createBlock, J as resolveDynamicComponent, c8 as script$u, l as resolveComponent, C as normalizeClass, av as createSlots, y as withCtx, bw as script$v, bm as script$w, N as Fragment, O as renderList, aw as createTextVNode, bc as setAttribute, ad as UniqueComponentId, ba as normalizeProps, p as renderSlot, j as createCommentVNode, a4 as equals, b6 as script$x, bS as script$y, c9 as getFirstFocusableElement, ag as OverlayEventBus, a8 as getVNodeProp, af as resolveFieldData, ca as invokeElementMethod, a2 as getAttribute, cb as getNextElementSibling, Y as getOuterWidth, cc as getPreviousElementSibling, D as script$z, ar as script$A, a0 as script$B, b9 as script$D, ac as isNotEmpty, bi as withModifiers, W as getOuterHeight, cd as _default, ae as ZIndex, a3 as focus, ai as addStyle, ak as absolutePosition, al as ConnectedOverlayScrollHandler, am as isTouchDevice, ce as FilterOperator, aq as script$E, cf as FocusTrap, i as createVNode, au as Transition, cg as withKeys, ch as getIndex, s as script$G, ci as isClickable, cj as clearSelection, ck as localeComparator, cl as sort, cm as FilterService, c3 as FilterMatchMode, V as findSingle, bM as findIndexInList, bN as find, cn as exportCSV, X as getOffset, co as getHiddenElementOuterWidth, cp as getHiddenElementOuterHeight, cq as reorderArray, cr as getWindowScrollTop, cs as removeClass, ct as addClass, ah as isEmpty, ap as script$H, as as script$I } from "./index-B6dYHNhg.js";
-import { s as script$C, a as script$F } from "./index-MX9DEi8Q.js";
+import { cb as script$s, A as createBaseVNode, g as openBlock, h as createElementBlock, m as mergeProps, B as BaseStyle, R as script$t, a6 as toDisplayString, a1 as Ripple, t as resolveDirective, v as withDirectives, x as createBlock, J as resolveDynamicComponent, cc as script$u, l as resolveComponent, C as normalizeClass, av as createSlots, y as withCtx, by as script$v, bo as script$w, N as Fragment, O as renderList, aw as createTextVNode, be as setAttribute, ad as UniqueComponentId, bc as normalizeProps, p as renderSlot, j as createCommentVNode, a4 as equals, b8 as script$x, bU as script$y, cd as getFirstFocusableElement, ag as OverlayEventBus, a8 as getVNodeProp, af as resolveFieldData, ce as invokeElementMethod, a2 as getAttribute, cf as getNextElementSibling, Y as getOuterWidth, cg as getPreviousElementSibling, D as script$z, ar as script$A, a0 as script$B, bb as script$D, ac as isNotEmpty, bk as withModifiers, W as getOuterHeight, ch as _default, ae as ZIndex, a3 as focus, ai as addStyle, ak as absolutePosition, al as ConnectedOverlayScrollHandler, am as isTouchDevice, ci as FilterOperator, aq as script$E, cj as FocusTrap, i as createVNode, au as Transition, ck as withKeys, cl as getIndex, s as script$G, cm as isClickable, cn as clearSelection, co as localeComparator, cp as sort, cq as FilterService, c6 as FilterMatchMode, V as findSingle, bO as findIndexInList, bP as find, cr as exportCSV, X as getOffset, cs as getHiddenElementOuterWidth, ct as getHiddenElementOuterHeight, cu as reorderArray, cv as getWindowScrollTop, cw as removeClass, cx as addClass, ah as isEmpty, ap as script$H, as as script$I } from "./index-CoOvI8ZH.js";
+import { s as script$C, a as script$F } from "./index-D4DWQPPQ.js";
 var script$r = {
   name: "ArrowDownIcon",
   "extends": script$s
@@ -8782,4 +8782,4 @@ export {
   script$d as a,
   script as s
 };
-//# sourceMappingURL=index-CjwCGacA.js.map
+//# sourceMappingURL=index-DK6Kev7f.js.map

web/assets/serverConfigStore-cctR8PGG.js

@@ -0,0 +1,90 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { cB as defineStore, r as ref, q as computed } from "./index-CoOvI8ZH.js";
+const useServerConfigStore = defineStore("serverConfig", () => {
+  const serverConfigById = ref({});
+  const serverConfigs = computed(() => {
+    return Object.values(serverConfigById.value);
+  });
+  const modifiedConfigs = computed(
+    () => {
+      return serverConfigs.value.filter((config) => {
+        return config.initialValue !== config.value;
+      });
+    }
+  );
+  const revertChanges = /* @__PURE__ */ __name(() => {
+    for (const config of modifiedConfigs.value) {
+      config.value = config.initialValue;
+    }
+  }, "revertChanges");
+  const serverConfigsByCategory = computed(() => {
+    return serverConfigs.value.reduce(
+      (acc, config) => {
+        const category = config.category?.[0] ?? "General";
+        acc[category] = acc[category] || [];
+        acc[category].push(config);
+        return acc;
+      },
+      {}
+    );
+  });
+  const serverConfigValues = computed(() => {
+    return Object.fromEntries(
+      serverConfigs.value.map((config) => {
+        return [
+          config.id,
+          config.value === config.defaultValue || config.value === null || config.value === void 0 ? void 0 : config.value
+        ];
+      })
+    );
+  });
+  const launchArgs = computed(() => {
+    const args = Object.assign(
+      {},
+      ...serverConfigs.value.map((config) => {
+        if (config.value === config.defaultValue || config.value === null || config.value === void 0) {
+          return {};
+        }
+        return config.getValue ? config.getValue(config.value) : { [config.id]: config.value };
+      })
+    );
+    return Object.fromEntries(
+      Object.entries(args).map(([key, value]) => {
+        if (value === true) {
+          return [key, ""];
+        }
+        return [key, value.toString()];
+      })
+    );
+  });
+  const commandLineArgs = computed(() => {
+    return Object.entries(launchArgs.value).map(([key, value]) => [`--${key}`, value]).flat().filter((arg) => arg !== "").join(" ");
+  });
+  function loadServerConfig(configs, values) {
+    for (const config of configs) {
+      const value = values[config.id] ?? config.defaultValue;
+      serverConfigById.value[config.id] = {
+        ...config,
+        value,
+        initialValue: value
+      };
+    }
+  }
+  __name(loadServerConfig, "loadServerConfig");
+  return {
+    serverConfigById,
+    serverConfigs,
+    modifiedConfigs,
+    serverConfigsByCategory,
+    serverConfigValues,
+    launchArgs,
+    commandLineArgs,
+    revertChanges,
+    loadServerConfig
+  };
+});
+export {
+  useServerConfigStore as u
+};
+//# sourceMappingURL=serverConfigStore-cctR8PGG.js.map

web/assets/userSelection-C6c30qSU.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { bH as api, bW as $el } from "./index-B6dYHNhg.js";
+import { aZ as api, bY as $el } from "./index-CoOvI8ZH.js";
 function createSpinner() {
   const div = document.createElement("div");
   div.innerHTML = `<div class="lds-ring"><div></div><div></div><div></div><div></div></div>`;
@@ -126,4 +126,4 @@ window.comfyAPI.userSelection.UserSelectionScreen = UserSelectionScreen;
 export {
   UserSelectionScreen
 };
-//# sourceMappingURL=userSelection-BSkuSZyR.js.map
+//# sourceMappingURL=userSelection-C6c30qSU.js.map

web/assets/widgetInputs-CRPRgKEi.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { e as LGraphNode, c as app, c1 as applyTextReplacements, c0 as ComfyWidgets, c2 as addValueControlWidgets, k as LiteGraph } from "./index-B6dYHNhg.js";
+import { e as LGraphNode, c as app, c3 as applyTextReplacements, c2 as ComfyWidgets, c5 as addValueControlWidgets, k as LiteGraph } from "./index-CoOvI8ZH.js";
 const CONVERTED_TYPE = "converted-widget";
 const VALID_TYPES = [
   "STRING",
@@ -701,7 +701,7 @@ app.registerExtension({
       const r = origOnInputDblClick ? origOnInputDblClick.apply(this, arguments) : void 0;
       const input = this.inputs[slot];
       if (!input.widget || !input[ignoreDblClick]) {
-        if (!(input.type in ComfyWidgets) && !(input.widget[GET_CONFIG]?.()?.[0] instanceof Array)) {
+        if (!(input.type in ComfyWidgets) && !(input.widget?.[GET_CONFIG]?.()?.[0] instanceof Array)) {
           return r;
         }
       }
@@ -759,4 +759,4 @@ export {
   mergeIfValid,
   setWidgetConfig
 };
-//# sourceMappingURL=widgetInputs-BJ21PG7d.js.map
+//# sourceMappingURL=widgetInputs-CRPRgKEi.js.map

web/extensions/core/maskEditorOld.js

@@ -0,0 +1,2 @@
+// Shim for extensions/core/maskEditorOld.ts
+export const MaskEditorDialogOld = window.comfyAPI.maskEditorOld.MaskEditorDialogOld;

web/index.html

@@ -6,8 +6,8 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
     <link rel="stylesheet" type="text/css" href="user.css" />
     <link rel="stylesheet" type="text/css" href="materialdesignicons.min.css" />
-    <script type="module" crossorigin src="./assets/index-B6dYHNhg.js"></script>
-    <link rel="stylesheet" crossorigin href="./assets/index-BCoLUtIt.css">
+    <script type="module" crossorigin src="./assets/index-CoOvI8ZH.js"></script>
+    <link rel="stylesheet" crossorigin href="./assets/index-U_o182q3.css">
   </head>
   <body class="litegraph grid">
     <div id="vue-app"></div>