Merge branch 'master' into patch_hooks_improved_memory

README.md

@@ -40,6 +40,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 - 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/)
 - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
+- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
 - Smart memory management: can automatically run models on GPUs with as low as 1GB vram.

api_server/routes/internal/internal_routes.py

@@ -2,6 +2,7 @@ from aiohttp import web
 from typing import Optional
 from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths
 from api_server.services.file_service import FileService
+from api_server.services.terminal_service import TerminalService
 import app.logger
 
 class InternalRoutes:
@@ -11,7 +12,8 @@ class InternalRoutes:
     Check README.md for more information.
     
     '''
-    def __init__(self):
+
+    def __init__(self, prompt_server):
         self.routes: web.RouteTableDef = web.RouteTableDef()
         self._app: Optional[web.Application] = None
         self.file_service = FileService({
@@ -19,6 +21,8 @@ class InternalRoutes:
             "user": user_directory,
             "output": output_directory
         })
+        self.prompt_server = prompt_server
+        self.terminal_service = TerminalService(prompt_server)
 
     def setup_routes(self):
         @self.routes.get('/files')
@@ -34,7 +38,28 @@ class InternalRoutes:
 
         @self.routes.get('/logs')
         async def get_logs(request):
-            return web.json_response(app.logger.get_logs())
+            return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()]))
+
+        @self.routes.get('/logs/raw')
+        async def get_logs(request):
+            self.terminal_service.update_size()
+            return web.json_response({
+                "entries": list(app.logger.get_logs()),
+                "size": {"cols": self.terminal_service.cols, "rows": self.terminal_service.rows}
+            })
+
+        @self.routes.patch('/logs/subscribe')
+        async def subscribe_logs(request):
+            json_data = await request.json()
+            client_id = json_data["clientId"]
+            enabled = json_data["enabled"]
+            if enabled:
+                self.terminal_service.subscribe(client_id)
+            else:
+                self.terminal_service.unsubscribe(client_id)
+
+            return web.Response(status=200)
+
 
         @self.routes.get('/folder_paths')
         async def get_folder_paths(request):

api_server/services/terminal_service.py

@@ -0,0 +1,47 @@
+from app.logger import on_flush
+import os
+
+
+class TerminalService:
+    def __init__(self, server):
+        self.server = server
+        self.cols = None
+        self.rows = None
+        self.subscriptions = set()
+        on_flush(self.send_messages)
+
+    def update_size(self):
+        sz = os.get_terminal_size()
+        changed = False
+        if sz.columns != self.cols:
+            self.cols = sz.columns
+            changed = True 
+
+        if sz.lines != self.rows:
+            self.rows = sz.lines
+            changed = True
+
+        if changed:
+            return {"cols": self.cols, "rows": self.rows}
+
+        return None
+
+    def subscribe(self, client_id):
+        self.subscriptions.add(client_id)
+
+    def unsubscribe(self, client_id):
+        self.subscriptions.discard(client_id)
+
+    def send_messages(self, entries):
+        if not len(entries) or not len(self.subscriptions):
+            return
+        
+        new_size = self.update_size()
+        
+        for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration
+            if client_id not in self.server.sockets:
+                # Automatically unsub if the socket has disconnected
+                self.unsubscribe(client_id)
+                continue
+
+            self.server.send_sync("logs", {"entries": entries, "size": new_size}, client_id)

app/logger.py

@@ -1,20 +1,69 @@
-import logging
-from logging.handlers import MemoryHandler
 from collections import deque
+from datetime import datetime
+import io
+import logging
+import sys
+import threading
 
 logs = None
-formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+stdout_interceptor = None
+stderr_interceptor = None
+
+
+class LogInterceptor(io.TextIOWrapper):
+    def __init__(self, stream,  *args, **kwargs):
+        buffer = stream.buffer
+        encoding = stream.encoding
+        super().__init__(buffer, *args, **kwargs, encoding=encoding, line_buffering=stream.line_buffering)
+        self._lock = threading.Lock()
+        self._flush_callbacks = []
+        self._logs_since_flush = []
+
+    def write(self, data):
+        entry = {"t": datetime.now().isoformat(), "m": data}
+        with self._lock:
+            self._logs_since_flush.append(entry)
+
+            # Simple handling for cr to overwrite the last output if it isnt a full line
+            # else logs just get full of progress messages
+            if isinstance(data, str) and data.startswith("\r") and not logs[-1]["m"].endswith("\n"):
+                logs.pop()
+            logs.append(entry)
+        super().write(data)
+
+    def flush(self):
+        super().flush()
+        for cb in self._flush_callbacks:
+            cb(self._logs_since_flush)
+            self._logs_since_flush = []
+
+    def on_flush(self, callback):
+        self._flush_callbacks.append(callback)
 
 
 def get_logs():
-    return "\n".join([formatter.format(x) for x in logs])
+    return logs
 
 
+def on_flush(callback):
+    if stdout_interceptor is not None:
+        stdout_interceptor.on_flush(callback)
+    if stderr_interceptor is not None:
+        stderr_interceptor.on_flush(callback)
+
 def setup_logger(log_level: str = 'INFO', capacity: int = 300):
     global logs
     if logs:
         return
 
+    # Override output streams and log to buffer
+    logs = deque(maxlen=capacity)
+
+    global stdout_interceptor
+    global stderr_interceptor
+    stdout_interceptor = sys.stdout = LogInterceptor(sys.stdout)
+    stderr_interceptor = sys.stderr = LogInterceptor(sys.stderr)
+
     # Setup default global logger
     logger = logging.getLogger()
     logger.setLevel(log_level)
@@ -22,10 +71,3 @@ def setup_logger(log_level: str = 'INFO', capacity: int = 300):
     stream_handler = logging.StreamHandler()
     stream_handler.setFormatter(logging.Formatter("%(message)s"))
     logger.addHandler(stream_handler)
-
-    # Create a memory handler with a deque as its buffer
-    logs = deque(maxlen=capacity)
-    memory_handler = MemoryHandler(capacity, flushLevel=logging.INFO)
-    memory_handler.buffer = logs
-    memory_handler.setFormatter(formatter)
-    logger.addHandler(memory_handler)

app/user_manager.py

@@ -4,15 +4,31 @@ import re
 import uuid
 import glob
 import shutil
+import logging
 from aiohttp import web
 from urllib import parse
 from comfy.cli_args import args
 import folder_paths
 from .app_settings import AppSettings
+from typing import TypedDict
 
 default_user = "default"
 
 
+class FileInfo(TypedDict):
+    path: str
+    size: int
+    modified: int
+
+
+def get_file_info(path: str, relative_to: str) -> FileInfo:
+    return {
+        "path": os.path.relpath(path, relative_to).replace(os.sep, '/'),
+        "size": os.path.getsize(path),
+        "modified": os.path.getmtime(path)
+    }
+
+
 class UserManager():
     def __init__(self):
         user_directory = folder_paths.get_user_directory()
@@ -154,6 +170,7 @@ class UserManager():
 
             recurse = request.rel_url.query.get('recurse', '').lower() == "true"
             full_info = request.rel_url.query.get('full_info', '').lower() == "true"
+            split_path = request.rel_url.query.get('split', '').lower() == "true"
 
             # Use different patterns based on whether we're recursing or not
             if recurse:
@@ -161,26 +178,21 @@ class UserManager():
             else:
                 pattern = os.path.join(glob.escape(path), '*')
 
-            results = glob.glob(pattern, recursive=recurse)
+            def process_full_path(full_path: str) -> FileInfo | str | list[str]:
+                if full_info:
+                    return get_file_info(full_path, path)
 
-            if full_info:
+                rel_path = os.path.relpath(full_path, path).replace(os.sep, '/')
-                results = [
+                if split_path:
-                    {
+                    return [rel_path] + rel_path.split('/')
-                        'path': os.path.relpath(x, path).replace(os.sep, '/'),
-                        'size': os.path.getsize(x),
-                        'modified': os.path.getmtime(x)
-                    } for x in results if os.path.isfile(x)
-                ]
-            else:
-                results = [
-                    os.path.relpath(x, path).replace(os.sep, '/')
-                    for x in results
-                    if os.path.isfile(x)
-                ]
 
-            split_path = request.rel_url.query.get('split', '').lower() == "true"
+                return rel_path
-            if split_path and not full_info:
+
-                results = [[x] + x.split('/') for x in results]
+            results = [
+                process_full_path(full_path)
+                for full_path in glob.glob(pattern, recursive=recurse)
+                if os.path.isfile(full_path)
+            ]
 
             return web.json_response(results)
 
@@ -208,20 +220,51 @@ class UserManager():
 
         @routes.post("/userdata/{file}")
         async def post_userdata(request):
+            """
+            Upload or update a user data file.
+
+            This endpoint handles file uploads to a user's data directory, with options for
+            controlling overwrite behavior and response format.
+
+            Query Parameters:
+            - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true".
+            - full_info (optional): If "true", returns detailed file information (path, size, modified time).
+                                  If "false", returns only the relative file path.
+
+            Path Parameters:
+            - file: The target file path (URL encoded if necessary).
+
+            Returns:
+            - 400: If 'file' parameter is missing.
+            - 403: If the requested path is not allowed.
+            - 409: If overwrite=false and the file already exists.
+            - 200: JSON response with either:
+                  - Full file information (if full_info=true)
+                  - Relative file path (if full_info=false)
+
+            The request body should contain the raw file content to be written.
+            """
             path = get_user_data_path(request)
             if not isinstance(path, str):
                 return path
 
-            overwrite = request.query["overwrite"] != "false"
+            overwrite = request.query.get("overwrite", 'true') != "false"
+            full_info = request.query.get('full_info', 'false').lower() == "true"
+
             if not overwrite and os.path.exists(path):
-                return web.Response(status=409)
+                return web.Response(status=409, text="File already exists")
 
             body = await request.read()
 
             with open(path, "wb") as f:
                 f.write(body)
 
-            resp = os.path.relpath(path, self.get_request_user_filepath(request, None))
+            user_path = self.get_request_user_filepath(request, None)
+            if full_info:
+                resp = get_file_info(path, user_path)
+            else:
+                resp = os.path.relpath(path, user_path)
+
             return web.json_response(resp)
 
         @routes.delete("/userdata/{file}")
@@ -236,6 +279,30 @@ class UserManager():
 
         @routes.post("/userdata/{file}/move/{dest}")
         async def move_userdata(request):
+            """
+            Move or rename a user data file.
+
+            This endpoint handles moving or renaming files within a user's data directory, with options for
+            controlling overwrite behavior and response format.
+
+            Path Parameters:
+            - file: The source file path (URL encoded if necessary)
+            - dest: The destination file path (URL encoded if necessary)
+
+            Query Parameters:
+            - overwrite (optional): If "false", prevents overwriting existing files. Defaults to "true".
+            - full_info (optional): If "true", returns detailed file information (path, size, modified time).
+                                  If "false", returns only the relative file path.
+
+            Returns:
+            - 400: If either 'file' or 'dest' parameter is missing
+            - 403: If either requested path is not allowed
+            - 404: If the source file does not exist
+            - 409: If overwrite=false and the destination file already exists
+            - 200: JSON response with either:
+                  - Full file information (if full_info=true)
+                  - Relative file path (if full_info=false)
+            """
             source = get_user_data_path(request, check_exists=True)
             if not isinstance(source, str):
                 return source
@@ -244,12 +311,19 @@ class UserManager():
             if not isinstance(source, str):
                 return dest
 
-            overwrite = request.query["overwrite"] != "false"
+            overwrite = request.query.get("overwrite", 'true') != "false"
-            if not overwrite and os.path.exists(dest):
+            full_info = request.query.get('full_info', 'false').lower() == "true"
-                return web.Response(status=409)
 
-            print(f"moving '{source}' -> '{dest}'")
+            if not overwrite and os.path.exists(dest):
+                return web.Response(status=409, text="File already exists")
+
+            logging.info(f"moving '{source}' -> '{dest}'")
             shutil.move(source, dest)
 
-            resp = os.path.relpath(dest, self.get_request_user_filepath(request, None))
+            user_path = self.get_request_user_filepath(request, None)
+            if full_info:
+                resp = get_file_info(dest, user_path)
+            else:
+                resp = os.path.relpath(dest, user_path)
+
             return web.json_response(resp)

comfy/latent_formats.py

@@ -190,7 +190,21 @@ class Mochi(LatentFormat):
                                          0.9294154431013696, 1.3720942357788521, 0.881393668867029,
                                          0.9168315692124348, 0.9185249279345552, 0.9274757570805041]).view(1, self.latent_channels, 1, 1, 1)
 
-        self.latent_rgb_factors = None #TODO
+        self.latent_rgb_factors =[
+            [-0.0069, -0.0045,  0.0018],
+            [ 0.0154, -0.0692, -0.0274],
+            [ 0.0333,  0.0019,  0.0206],
+            [-0.1390,  0.0628,  0.1678],
+            [-0.0725,  0.0134, -0.1898],
+            [ 0.0074, -0.0270, -0.0209],
+            [-0.0176, -0.0277, -0.0221],
+            [ 0.5294,  0.5204,  0.3852],
+            [-0.0326, -0.0446, -0.0143],
+            [-0.0659,  0.0153, -0.0153],
+            [ 0.0185, -0.0217,  0.0014],
+            [-0.0396, -0.0495, -0.0281]
+        ]
+        self.latent_rgb_factors_bias = [-0.0940, -0.1418, -0.1453]
         self.taesd_decoder_name = None #TODO
 
     def process_in(self, latent):

comfy/ldm/flux/model.py

@@ -151,8 +151,8 @@ class Flux(nn.Module):
         h_len = ((h + (patch_size // 2)) // patch_size)
         w_len = ((w + (patch_size // 2)) // patch_size)
         img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
-        img_ids[:, :, 1] = torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
+        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
-        img_ids[:, :, 2] = torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
+        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
         img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)

comfy/ldm/genmo/vae/model.py

@@ -2,12 +2,16 @@
 #adapted to ComfyUI
 
 from typing import Callable, List, Optional, Tuple, Union
+from functools import partial
+import math
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
 
+from comfy.ldm.modules.attention import optimized_attention
+
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
@@ -158,8 +162,10 @@ class ResBlock(nn.Module):
         *,
         affine: bool = True,
         attn_block: Optional[nn.Module] = None,
-        padding_mode: str = "replicate",
         causal: bool = True,
+        prune_bottleneck: bool = False,
+        padding_mode: str,
+        bias: bool = True,
     ):
         super().__init__()
         self.channels = channels
@@ -170,23 +176,23 @@ class ResBlock(nn.Module):
             nn.SiLU(inplace=True),
             PConv3d(
                 in_channels=channels,
-                out_channels=channels,
+                out_channels=channels // 2 if prune_bottleneck else channels,
                 kernel_size=(3, 3, 3),
                 stride=(1, 1, 1),
                 padding_mode=padding_mode,
-                bias=True,
+                bias=bias,
-                # causal=causal,
+                causal=causal,
             ),
             norm_fn(channels, affine=affine),
             nn.SiLU(inplace=True),
             PConv3d(
-                in_channels=channels,
+                in_channels=channels // 2 if prune_bottleneck else channels,
                 out_channels=channels,
                 kernel_size=(3, 3, 3),
                 stride=(1, 1, 1),
                 padding_mode=padding_mode,
-                bias=True,
+                bias=bias,
-                # causal=causal,
+                causal=causal,
             ),
         )
 
@@ -206,6 +212,81 @@ class ResBlock(nn.Module):
         return self.attn_block(x)
 
 
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        head_dim: int = 32,
+        qkv_bias: bool = False,
+        out_bias: bool = True,
+        qk_norm: bool = True,
+    ) -> None:
+        super().__init__()
+        self.head_dim = head_dim
+        self.num_heads = dim // head_dim
+        self.qk_norm = qk_norm
+
+        self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias)
+        self.out = nn.Linear(dim, dim, bias=out_bias)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        """Compute temporal self-attention.
+
+        Args:
+            x: Input tensor. Shape: [B, C, T, H, W].
+            chunk_size: Chunk size for large tensors.
+
+        Returns:
+            x: Output tensor. Shape: [B, C, T, H, W].
+        """
+        B, _, T, H, W = x.shape
+
+        if T == 1:
+            # No attention for single frame.
+            x = x.movedim(1, -1)  # [B, C, T, H, W] -> [B, T, H, W, C]
+            qkv = self.qkv(x)
+            _, _, x = qkv.chunk(3, dim=-1)  # Throw away queries and keys.
+            x = self.out(x)
+            return x.movedim(-1, 1)  # [B, T, H, W, C] -> [B, C, T, H, W]
+
+        # 1D temporal attention.
+        x = rearrange(x, "B C t h w -> (B h w) t C")
+        qkv = self.qkv(x)
+
+        # Input: qkv with shape [B, t, 3 * num_heads * head_dim]
+        # Output: x with shape [B, num_heads, t, head_dim]
+        q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, self.head_dim).transpose(1, 3).unbind(2)
+
+        if self.qk_norm:
+            q = F.normalize(q, p=2, dim=-1)
+            k = F.normalize(k, p=2, dim=-1)
+
+        x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True)
+
+        assert x.size(0) == q.size(0)
+
+        x = self.out(x)
+        x = rearrange(x, "(B h w) t C -> B C t h w", B=B, h=H, w=W)
+        return x
+
+
+class AttentionBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        **attn_kwargs,
+    ) -> None:
+        super().__init__()
+        self.norm = norm_fn(dim)
+        self.attn = Attention(dim, **attn_kwargs)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x + self.attn(self.norm(x))
+
+
 class CausalUpsampleBlock(nn.Module):
     def __init__(
         self,
@@ -244,14 +325,9 @@ class CausalUpsampleBlock(nn.Module):
         return x
 
 
-def block_fn(channels, *, has_attention: bool = False, **block_kwargs):
+def block_fn(channels, *, affine: bool = True, has_attention: bool = False, **block_kwargs):
-    assert has_attention is False #NOTE: if this is ever true add back the attention code.
+    attn_block = AttentionBlock(channels) if has_attention else None
-
+    return ResBlock(channels, affine=affine, attn_block=attn_block, **block_kwargs)
-    attn_block = None #AttentionBlock(channels) if has_attention else None
-
-    return ResBlock(
-        channels, affine=True, attn_block=attn_block, **block_kwargs
-    )
 
 
 class DownsampleBlock(nn.Module):
@@ -288,8 +364,9 @@ class DownsampleBlock(nn.Module):
                 out_channels=out_channels,
                 kernel_size=(temporal_reduction, spatial_reduction, spatial_reduction),
                 stride=(temporal_reduction, spatial_reduction, spatial_reduction),
+                # First layer in each block always uses replicate padding
                 padding_mode="replicate",
-                bias=True,
+                bias=block_kwargs["bias"],
             )
         )
 
@@ -382,7 +459,7 @@ class Decoder(nn.Module):
         blocks = []
 
         first_block = [
-            nn.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1))
+            ops.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1))
         ]  # Input layer.
         # First set of blocks preserve channel count.
         for _ in range(num_res_blocks[-1]):
@@ -452,11 +529,165 @@ class Decoder(nn.Module):
 
         return self.output_proj(x).contiguous()
 
+class LatentDistribution:
+    def __init__(self, mean: torch.Tensor, logvar: torch.Tensor):
+        """Initialize latent distribution.
+
+        Args:
+            mean: Mean of the distribution. Shape: [B, C, T, H, W].
+            logvar: Logarithm of variance of the distribution. Shape: [B, C, T, H, W].
+        """
+        assert mean.shape == logvar.shape
+        self.mean = mean
+        self.logvar = logvar
+
+    def sample(self, temperature=1.0, generator: torch.Generator = None, noise=None):
+        if temperature == 0.0:
+            return self.mean
+
+        if noise is None:
+            noise = torch.randn(self.mean.shape, device=self.mean.device, dtype=self.mean.dtype, generator=generator)
+        else:
+            assert noise.device == self.mean.device
+            noise = noise.to(self.mean.dtype)
+
+        if temperature != 1.0:
+            raise NotImplementedError(f"Temperature {temperature} is not supported.")
+
+        # Just Gaussian sample with no scaling of variance.
+        return noise * torch.exp(self.logvar * 0.5) + self.mean
+
+    def mode(self):
+        return self.mean
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels: int,
+        base_channels: int,
+        channel_multipliers: List[int],
+        num_res_blocks: List[int],
+        latent_dim: int,
+        temporal_reductions: List[int],
+        spatial_reductions: List[int],
+        prune_bottlenecks: List[bool],
+        has_attentions: List[bool],
+        affine: bool = True,
+        bias: bool = True,
+        input_is_conv_1x1: bool = False,
+        padding_mode: str,
+    ):
+        super().__init__()
+        self.temporal_reductions = temporal_reductions
+        self.spatial_reductions = spatial_reductions
+        self.base_channels = base_channels
+        self.channel_multipliers = channel_multipliers
+        self.num_res_blocks = num_res_blocks
+        self.latent_dim = latent_dim
+
+        self.fourier_features = FourierFeatures()
+        ch = [mult * base_channels for mult in channel_multipliers]
+        num_down_blocks = len(ch) - 1
+        assert len(num_res_blocks) == num_down_blocks + 2
+
+        layers = (
+            [ops.Conv3d(in_channels, ch[0], kernel_size=(1, 1, 1), bias=True)]
+            if not input_is_conv_1x1
+            else [Conv1x1(in_channels, ch[0])]
+        )
+
+        assert len(prune_bottlenecks) == num_down_blocks + 2
+        assert len(has_attentions) == num_down_blocks + 2
+        block = partial(block_fn, padding_mode=padding_mode, affine=affine, bias=bias)
+
+        for _ in range(num_res_blocks[0]):
+            layers.append(block(ch[0], has_attention=has_attentions[0], prune_bottleneck=prune_bottlenecks[0]))
+        prune_bottlenecks = prune_bottlenecks[1:]
+        has_attentions = has_attentions[1:]
+
+        assert len(temporal_reductions) == len(spatial_reductions) == len(ch) - 1
+        for i in range(num_down_blocks):
+            layer = DownsampleBlock(
+                ch[i],
+                ch[i + 1],
+                num_res_blocks=num_res_blocks[i + 1],
+                temporal_reduction=temporal_reductions[i],
+                spatial_reduction=spatial_reductions[i],
+                prune_bottleneck=prune_bottlenecks[i],
+                has_attention=has_attentions[i],
+                affine=affine,
+                bias=bias,
+                padding_mode=padding_mode,
+            )
+
+            layers.append(layer)
+
+        # Additional blocks.
+        for _ in range(num_res_blocks[-1]):
+            layers.append(block(ch[-1], has_attention=has_attentions[-1], prune_bottleneck=prune_bottlenecks[-1]))
+
+        self.layers = nn.Sequential(*layers)
+
+        # Output layers.
+        self.output_norm = norm_fn(ch[-1])
+        self.output_proj = Conv1x1(ch[-1], 2 * latent_dim, bias=False)
+
+    @property
+    def temporal_downsample(self):
+        return math.prod(self.temporal_reductions)
+
+    @property
+    def spatial_downsample(self):
+        return math.prod(self.spatial_reductions)
+
+    def forward(self, x) -> LatentDistribution:
+        """Forward pass.
+
+        Args:
+            x: Input video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1]
+
+        Returns:
+            means: Latent tensor. Shape: [B, latent_dim, t, h, w]. Scaled [-1, 1].
+                   h = H // 8, w = W // 8, t - 1 = (T - 1) // 6
+            logvar: Shape: [B, latent_dim, t, h, w].
+        """
+        assert x.ndim == 5, f"Expected 5D input, got {x.shape}"
+        x = self.fourier_features(x)
+
+        x = self.layers(x)
+
+        x = self.output_norm(x)
+        x = F.silu(x, inplace=True)
+        x = self.output_proj(x)
+
+        means, logvar = torch.chunk(x, 2, dim=1)
+
+        assert means.ndim == 5
+        assert logvar.shape == means.shape
+        assert means.size(1) == self.latent_dim
+
+        return LatentDistribution(means, logvar)
+
 
 class VideoVAE(nn.Module):
     def __init__(self):
         super().__init__()
-        self.encoder = None #TODO once the model releases
+        self.encoder = Encoder(
+            in_channels=15,
+            base_channels=64,
+            channel_multipliers=[1, 2, 4, 6],
+            num_res_blocks=[3, 3, 4, 6, 3],
+            latent_dim=12,
+            temporal_reductions=[1, 2, 3],
+            spatial_reductions=[2, 2, 2],
+            prune_bottlenecks=[False, False, False, False, False],
+            has_attentions=[False, True, True, True, True],
+            affine=True,
+            bias=True,
+            input_is_conv_1x1=True,
+            padding_mode="replicate"
+        )
         self.decoder = Decoder(
             out_channels=3,
             base_channels=128,
@@ -474,7 +705,7 @@ class VideoVAE(nn.Module):
         )
 
     def encode(self, x):
-        return self.encoder(x)
+        return self.encoder(x).mode()
 
     def decode(self, x):
         return self.decoder(x)

comfy/ldm/modules/attention.py

@@ -393,6 +393,13 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
 
     return out
 
+if model_management.is_nvidia(): #pytorch 2.3 and up seem to have this issue.
+    SDP_BATCH_LIMIT = 2**15
+else:
+    #TODO: other GPUs ?
+    SDP_BATCH_LIMIT = 2**31
+
+
 def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
@@ -404,10 +411,15 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
             (q, k, v),
         )
 
-    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+    if SDP_BATCH_LIMIT >= q.shape[0]:
-    out = (
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
-        out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+        out = (
-    )
+            out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+        )
+    else:
+        out = torch.empty((q.shape[0], q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device)
+        for i in range(0, q.shape[0], SDP_BATCH_LIMIT):
+            out[i : i + SDP_BATCH_LIMIT] = torch.nn.functional.scaled_dot_product_attention(q[i : i + SDP_BATCH_LIMIT], k[i : i + SDP_BATCH_LIMIT], v[i : i + SDP_BATCH_LIMIT], attn_mask=mask, dropout_p=0.0, is_causal=False).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
     return out
 
 

comfy/model_detection.py

@@ -321,8 +321,9 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
     if model_config is None and use_base_if_no_match:
         model_config = comfy.supported_models_base.BASE(unet_config)
 
-    scaled_fp8_weight = state_dict.get("{}scaled_fp8".format(unet_key_prefix), None)
+    scaled_fp8_key = "{}scaled_fp8".format(unet_key_prefix)
-    if scaled_fp8_weight is not None:
+    if scaled_fp8_key in state_dict:
+        scaled_fp8_weight = state_dict.pop(scaled_fp8_key)
         model_config.scaled_fp8 = scaled_fp8_weight.dtype
         if model_config.scaled_fp8 == torch.float32:
             model_config.scaled_fp8 = torch.float8_e4m3fn

comfy/model_sampling.py

@@ -2,6 +2,25 @@ import torch
 from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
 import math
 
+def rescale_zero_terminal_snr_sigmas(sigmas):
+    alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
+    alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+    # Store old values.
+    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+    # Shift so the last timestep is zero.
+    alphas_bar_sqrt -= (alphas_bar_sqrt_T)
+
+    # Scale so the first timestep is back to the old value.
+    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+    # Convert alphas_bar_sqrt to betas
+    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
+    alphas_bar[-1] = 4.8973451890853435e-08
+    return ((1 - alphas_bar) / alphas_bar) ** 0.5
+
 class EPS:
     def calculate_input(self, sigma, noise):
         sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
@@ -48,7 +67,7 @@ class CONST:
         return latent / (1.0 - sigma)
 
 class ModelSamplingDiscrete(torch.nn.Module):
-    def __init__(self, model_config=None):
+    def __init__(self, model_config=None, zsnr=None):
         super().__init__()
 
         if model_config is not None:
@@ -61,11 +80,14 @@ class ModelSamplingDiscrete(torch.nn.Module):
         linear_end = sampling_settings.get("linear_end", 0.012)
         timesteps = sampling_settings.get("timesteps", 1000)
 
-        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3)
+        if zsnr is None:
+            zsnr = sampling_settings.get("zsnr", False)
+
+        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3, zsnr=zsnr)
         self.sigma_data = 1.0
 
     def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
-                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3, zsnr=False):
         if given_betas is not None:
             betas = given_betas
         else:
@@ -83,6 +105,9 @@ class ModelSamplingDiscrete(torch.nn.Module):
         # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32))
 
         sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5
+        if zsnr:
+            sigmas = rescale_zero_terminal_snr_sigmas(sigmas)
+
         self.set_sigmas(sigmas)
 
     def set_sigmas(self, sigmas):

comfy/sampler_helpers.py

@@ -3,6 +3,7 @@ import uuid
 import torch
 import comfy.model_management
 import comfy.conds
+import comfy.utils
 import comfy.hooks
 import comfy.patcher_extension
 from typing import TYPE_CHECKING
@@ -12,12 +13,7 @@ if TYPE_CHECKING:
     from comfy.controlnet import ControlBase
 
 def prepare_mask(noise_mask, shape, device):
-    """ensures noise mask is of proper dimensions"""
+    return comfy.utils.reshape_mask(noise_mask, shape).to(device)
-    noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
-    noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
-    noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
-    noise_mask = noise_mask.to(device)
-    return noise_mask
 
 def get_models_from_cond(cond, model_type):
     models = []

comfy/sd.py

@@ -218,6 +218,7 @@ class VAE:
         self.downscale_ratio = 8
         self.upscale_ratio = 8
         self.latent_channels = 4
+        self.latent_dim = 2
         self.output_channels = 3
         self.process_input = lambda image: image * 2.0 - 1.0
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
@@ -287,16 +288,22 @@ class VAE:
                 self.output_channels = 2
                 self.upscale_ratio = 2048
                 self.downscale_ratio =  2048
+                self.latent_dim = 1
                 self.process_output = lambda audio: audio
                 self.process_input = lambda audio: audio
                 self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
-            elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd: #genmo mochi vae
+            elif "blocks.2.blocks.3.stack.5.weight" in sd or "decoder.blocks.2.blocks.3.stack.5.weight" in sd or "layers.4.layers.1.attn_block.attn.qkv.weight" in sd or "encoder.layers.4.layers.1.attn_block.attn.qkv.weight" in sd: #genmo mochi vae
                 if "blocks.2.blocks.3.stack.5.weight" in sd:
                     sd = comfy.utils.state_dict_prefix_replace(sd, {"": "decoder."})
+                if "layers.4.layers.1.attn_block.attn.qkv.weight" in sd:
+                    sd = comfy.utils.state_dict_prefix_replace(sd, {"": "encoder."})
                 self.first_stage_model = comfy.ldm.genmo.vae.model.VideoVAE()
                 self.latent_channels = 12
+                self.latent_dim = 3
                 self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
+                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]
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -401,24 +408,45 @@ class VAE:
         pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples
 
-    def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16):
+    def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None):
-        model_management.load_model_gpu(self.patcher)
+        memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile
-        output = self.decode_tiled_(samples, tile_x, tile_y, overlap)
+        model_management.load_models_gpu([self.patcher], memory_required=memory_used)
-        return output.movedim(1,-1)
+        dims = samples.ndim - 2
+        args = {}
+        if tile_x is not None:
+            args["tile_x"] = tile_x
+        if tile_y is not None:
+            args["tile_y"] = tile_y
+        if overlap is not None:
+            args["overlap"] = overlap
+
+        if dims == 1:
+            args.pop("tile_y")
+            output = self.decode_tiled_1d(samples, **args)
+        elif dims == 2:
+            output = self.decode_tiled_(samples, **args)
+        elif dims == 3:
+            output = self.decode_tiled_3d(samples, **args)
+        return output.movedim(1, -1)
 
     def encode(self, pixel_samples):
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
-        pixel_samples = pixel_samples.movedim(-1,1)
+        pixel_samples = pixel_samples.movedim(-1, 1)
+        if self.latent_dim == 3:
+            pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
         try:
             memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
             model_management.load_models_gpu([self.patcher], memory_required=memory_used)
             free_memory = model_management.get_free_memory(self.device)
             batch_number = int(free_memory / max(1, memory_used))
             batch_number = max(1, batch_number)
-            samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a // self.downscale_ratio, pixel_samples.shape[2:])), device=self.output_device)
+            samples = None
             for x in range(0, pixel_samples.shape[0], batch_number):
-                pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device)
+                pixels_in = self.process_input(pixel_samples[x:x + batch_number]).to(self.vae_dtype).to(self.device)
-                samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
+                out = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
+                if samples is None:
+                    samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
+                samples[x:x + batch_number] = out
 
         except model_management.OOM_EXCEPTION as e:
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")

comfy/supported_models.py

@@ -197,6 +197,8 @@ class SDXL(supported_models_base.BASE):
                 self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item())
             return model_base.ModelType.V_PREDICTION_EDM
         elif "v_pred" in state_dict:
+            if "ztsnr" in state_dict: #Some zsnr anime checkpoints
+                self.sampling_settings["zsnr"] = True
             return model_base.ModelType.V_PREDICTION
         else:
             return model_base.ModelType.EPS

comfy/text_encoders/genmo.py

@@ -12,7 +12,7 @@ class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
 
 class MochiT5XXL(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}):
-        super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
+        super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
 
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):

comfy/utils.py

@@ -848,3 +848,24 @@ class ProgressBar:
 
     def update(self, value):
         self.update_absolute(self.current + value)
+
+def reshape_mask(input_mask, output_shape):
+    dims = len(output_shape) - 2
+
+    if dims == 1:
+        scale_mode = "linear"
+
+    if dims == 2:
+        mask = input_mask.reshape((-1, 1, input_mask.shape[-2], input_mask.shape[-1]))
+        scale_mode = "bilinear"
+
+    if dims == 3:
+        if len(input_mask.shape) < 5:
+            mask = input_mask.reshape((1, 1, -1, input_mask.shape[-2], input_mask.shape[-1]))
+        scale_mode = "trilinear"
+
+    mask = torch.nn.functional.interpolate(mask, size=output_shape[2:], mode=scale_mode)
+    if mask.shape[1] < output_shape[1]:
+        mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]]
+    mask = comfy.utils.repeat_to_batch_size(mask, output_shape[0])
+    return mask

comfy_extras/nodes_mochi.py

@@ -3,9 +3,6 @@ import torch
 import comfy.model_management
 
 class EmptyMochiLatentVideo:
-    def __init__(self):
-        self.device = comfy.model_management.intermediate_device()
-
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
@@ -15,10 +12,10 @@ class EmptyMochiLatentVideo:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "generate"
 
-    CATEGORY = "latent/mochi"
+    CATEGORY = "latent/video"
 
     def generate(self, width, height, length, batch_size=1):
-        latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=self.device)
+        latent = torch.zeros([batch_size, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
         return ({"samples":latent}, )
 
 NODE_CLASS_MAPPINGS = {

comfy_extras/nodes_model_advanced.py

@@ -51,25 +51,6 @@ class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete)
         return log_sigma.exp().to(timestep.device)
 
 
-def rescale_zero_terminal_snr_sigmas(sigmas):
-    alphas_cumprod = 1 / ((sigmas * sigmas) + 1)
-    alphas_bar_sqrt = alphas_cumprod.sqrt()
-
-    # Store old values.
-    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
-    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
-
-    # Shift so the last timestep is zero.
-    alphas_bar_sqrt -= (alphas_bar_sqrt_T)
-
-    # Scale so the first timestep is back to the old value.
-    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
-
-    # Convert alphas_bar_sqrt to betas
-    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
-    alphas_bar[-1] = 4.8973451890853435e-08
-    return ((1 - alphas_bar) / alphas_bar) ** 0.5
-
 class ModelSamplingDiscrete:
     @classmethod
     def INPUT_TYPES(s):
@@ -100,9 +81,7 @@ class ModelSamplingDiscrete:
         class ModelSamplingAdvanced(sampling_base, sampling_type):
             pass
 
-        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling = ModelSamplingAdvanced(model.model.model_config, zsnr=zsnr)
-        if zsnr:
-            model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))
 
         m.add_object_patch("model_sampling", model_sampling)
         return (m, )

comfy_extras/nodes_sag.py

@@ -57,12 +57,24 @@ def create_blur_map(x0, attn, sigma=3.0, threshold=1.0):
     attn = attn.reshape(b, -1, hw1, hw2)
     # Global Average Pool
     mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold
-    ratio = 2**(math.ceil(math.sqrt(lh * lw / hw1)) - 1).bit_length()
+
-    mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)]
+    total = mask.shape[-1]
+    x = round(math.sqrt((lh / lw) * total))
+    xx = None
+    for i in range(0, math.floor(math.sqrt(total) / 2)):
+        for j in [(x + i), max(1, x - i)]:
+            if total % j == 0:
+                xx = j
+                break
+        if xx is not None:
+            break
+
+    x = xx
+    y = total // x
 
     # Reshape
     mask = (
-        mask.reshape(b, *mid_shape)
+        mask.reshape(b, x, y)
         .unsqueeze(1)
         .type(attn.dtype)
     )

comfy_extras/nodes_sd3.py

@@ -7,17 +7,19 @@ import re
 class TripleCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "clip_name3": (folder_paths.get_filename_list("clip"), )
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ), "clip_name2": (folder_paths.get_filename_list("text_encoders"), ), "clip_name3": (folder_paths.get_filename_list("text_encoders"), )
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
     CATEGORY = "advanced/loaders"
 
+    DESCRIPTION = "[Recipes]\n\nsd3: clip-l, clip-g, t5"
+
     def load_clip(self, clip_name1, clip_name2, clip_name3):
-        clip_path1 = folder_paths.get_full_path_or_raise("clip", clip_name1)
+        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
-        clip_path2 = folder_paths.get_full_path_or_raise("clip", clip_name2)
+        clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
-        clip_path3 = folder_paths.get_full_path_or_raise("clip", clip_name3)
+        clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return (clip,)
 

folder_paths.py

@@ -18,7 +18,7 @@ folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".y
 
 folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions)
 folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions)
-folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions)
+folder_names_and_paths["text_encoders"] = ([os.path.join(models_dir, "text_encoders"), os.path.join(models_dir, "clip")], supported_pt_extensions)
 folder_names_and_paths["diffusion_models"] = ([os.path.join(models_dir, "unet"), os.path.join(models_dir, "diffusion_models")], supported_pt_extensions)
 folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions)
 folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions)
@@ -81,7 +81,8 @@ extension_mimetypes_cache = {
 }
 
 def map_legacy(folder_name: str) -> str:
-    legacy = {"unet": "diffusion_models"}
+    legacy = {"unet": "diffusion_models",
+              "clip": "text_encoders"}
     return legacy.get(folder_name, folder_name)
 
 if not os.path.exists(input_directory):

latent_preview.py

@@ -47,7 +47,12 @@ class Latent2RGBPreviewer(LatentPreviewer):
         if self.latent_rgb_factors_bias is not None:
             self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device)
 
-        latent_image = torch.nn.functional.linear(x0[0].permute(1, 2, 0), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias)
+        if x0.ndim == 5:
+            x0 = x0[0, :, 0]
+        else:
+            x0 = x0[0]
+
+        latent_image = torch.nn.functional.linear(x0.movedim(0, -1), self.latent_rgb_factors, bias=self.latent_rgb_factors_bias)
         # latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
 
         return preview_to_image(latent_image)

nodes.py

@@ -293,15 +293,21 @@ class VAEDecodeTiled:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ),
-                             "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64})
+                             "tile_size": ("INT", {"default": 512, "min": 128, "max": 4096, "step": 32}),
+                             "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
                             }}
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "decode"
 
     CATEGORY = "_for_testing"
 
-    def decode(self, vae, samples, tile_size):
+    def decode(self, vae, samples, tile_size, overlap=64):
-        return (vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, ), )
+        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)
+        if len(images.shape) == 5: #Combine batches
+            images = images.reshape(-1, images.shape[-3], images.shape[-2], images.shape[-1])
+        return (images, )
 
 class VAEEncode:
     @classmethod
@@ -891,7 +897,7 @@ class UNETLoader:
 class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
                               "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
@@ -899,6 +905,8 @@ class CLIPLoader:
 
     CATEGORY = "advanced/loaders"
 
+    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 / clip-g / clip-l\nstable_audio: t5\nmochi: t5"
+
     def load_clip(self, clip_name, type="stable_diffusion"):
         if type == "stable_cascade":
             clip_type = comfy.sd.CLIPType.STABLE_CASCADE
@@ -911,15 +919,15 @@ class CLIPLoader:
         else:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
-        clip_path = folder_paths.get_full_path_or_raise("clip", clip_name)
+        clip_path = folder_paths.get_full_path_or_raise("text_encoders", clip_name)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
         return (clip,)
 
 class DualCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
-                              "clip_name2": (folder_paths.get_filename_list("clip"), ),
+                              "clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
                               "type": (["sdxl", "sd3", "flux"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
@@ -927,9 +935,11 @@ class DualCLIPLoader:
 
     CATEGORY = "advanced/loaders"
 
+    DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5"
+
     def load_clip(self, clip_name1, clip_name2, type):
-        clip_path1 = folder_paths.get_full_path_or_raise("clip", clip_name1)
+        clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
-        clip_path2 = folder_paths.get_full_path_or_raise("clip", clip_name2)
+        clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
         if type == "sdxl":
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
         elif type == "sd3":

server.py

@@ -152,7 +152,7 @@ class PromptServer():
         mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8'
 
         self.user_manager = UserManager()
-        self.internal_routes = InternalRoutes()
+        self.internal_routes = InternalRoutes(self)
         self.supports = ["custom_nodes_from_web"]
         self.prompt_queue = None
         self.loop = loop

tests-unit/prompt_server_test/user_manager_test.py

@@ -14,7 +14,7 @@ def user_manager(tmp_path):
     um = UserManager()
     um.get_request_user_filepath = lambda req, file, **kwargs: os.path.join(
         tmp_path, file
-    )
+    ) if file else tmp_path
     return um
 
 
@@ -80,9 +80,7 @@ async def test_listuserdata_split_path(aiohttp_client, app, tmp_path):
     client = await aiohttp_client(app)
     resp = await client.get("/userdata?dir=test_dir&recurse=true&split=true")
     assert resp.status == 200
-    assert await resp.json() == [
+    assert await resp.json() == [["subdir/file1.txt", "subdir", "file1.txt"]]
-        ["subdir/file1.txt", "subdir", "file1.txt"]
-    ]
 
 
 async def test_listuserdata_invalid_directory(aiohttp_client, app):
@@ -118,3 +116,116 @@ async def test_listuserdata_normalized_separator(aiohttp_client, app, tmp_path):
             assert "/" in result[0]["path"]  # Ensure forward slash is used
             assert "\\" not in result[0]["path"]  # Ensure backslash is not present
             assert result[0]["path"] == "subdir/file1.txt"
+
+
+async def test_post_userdata_new_file(aiohttp_client, app, tmp_path):
+    client = await aiohttp_client(app)
+    content = b"test content"
+    resp = await client.post("/userdata/test.txt", data=content)
+
+    assert resp.status == 200
+    assert await resp.text() == '"test.txt"'
+
+    # Verify file was created with correct content
+    with open(tmp_path / "test.txt", "rb") as f:
+        assert f.read() == content
+
+
+async def test_post_userdata_overwrite_existing(aiohttp_client, app, tmp_path):
+    # Create initial file
+    with open(tmp_path / "test.txt", "w") as f:
+        f.write("initial content")
+
+    client = await aiohttp_client(app)
+    new_content = b"updated content"
+    resp = await client.post("/userdata/test.txt", data=new_content)
+
+    assert resp.status == 200
+    assert await resp.text() == '"test.txt"'
+
+    # Verify file was overwritten
+    with open(tmp_path / "test.txt", "rb") as f:
+        assert f.read() == new_content
+
+
+async def test_post_userdata_no_overwrite(aiohttp_client, app, tmp_path):
+    # Create initial file
+    with open(tmp_path / "test.txt", "w") as f:
+        f.write("initial content")
+
+    client = await aiohttp_client(app)
+    resp = await client.post("/userdata/test.txt?overwrite=false", data=b"new content")
+
+    assert resp.status == 409
+
+    # Verify original content unchanged
+    with open(tmp_path / "test.txt", "r") as f:
+        assert f.read() == "initial content"
+
+
+async def test_post_userdata_full_info(aiohttp_client, app, tmp_path):
+    client = await aiohttp_client(app)
+    content = b"test content"
+    resp = await client.post("/userdata/test.txt?full_info=true", data=content)
+
+    assert resp.status == 200
+    result = await resp.json()
+    assert result["path"] == "test.txt"
+    assert result["size"] == len(content)
+    assert "modified" in result
+
+
+async def test_move_userdata(aiohttp_client, app, tmp_path):
+    # Create initial file
+    with open(tmp_path / "source.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.post("/userdata/source.txt/move/dest.txt")
+
+    assert resp.status == 200
+    assert await resp.text() == '"dest.txt"'
+
+    # Verify file was moved
+    assert not os.path.exists(tmp_path / "source.txt")
+    with open(tmp_path / "dest.txt", "r") as f:
+        assert f.read() == "test content"
+
+
+async def test_move_userdata_no_overwrite(aiohttp_client, app, tmp_path):
+    # Create source and destination files
+    with open(tmp_path / "source.txt", "w") as f:
+        f.write("source content")
+    with open(tmp_path / "dest.txt", "w") as f:
+        f.write("destination content")
+
+    client = await aiohttp_client(app)
+    resp = await client.post("/userdata/source.txt/move/dest.txt?overwrite=false")
+
+    assert resp.status == 409
+
+    # Verify files remain unchanged
+    with open(tmp_path / "source.txt", "r") as f:
+        assert f.read() == "source content"
+    with open(tmp_path / "dest.txt", "r") as f:
+        assert f.read() == "destination content"
+
+
+async def test_move_userdata_full_info(aiohttp_client, app, tmp_path):
+    # Create initial file
+    with open(tmp_path / "source.txt", "w") as f:
+        f.write("test content")
+
+    client = await aiohttp_client(app)
+    resp = await client.post("/userdata/source.txt/move/dest.txt?full_info=true")
+
+    assert resp.status == 200
+    result = await resp.json()
+    assert result["path"] == "dest.txt"
+    assert result["size"] == len("test content")
+    assert "modified" in result
+
+    # Verify file was moved
+    assert not os.path.exists(tmp_path / "source.txt")
+    with open(tmp_path / "dest.txt", "r") as f:
+        assert f.read() == "test content"

tests-unit/server/routes/internal_routes_test.py

@@ -8,7 +8,7 @@ from folder_paths import models_dir, user_directory, output_directory
 
 @pytest.fixture
 def internal_routes():
-    return InternalRoutes()
+    return InternalRoutes(None)
 
 @pytest.fixture
 def aiohttp_client_factory(aiohttp_client, internal_routes):
@@ -102,7 +102,7 @@ async def test_file_service_initialization():
         # Create a mock instance
         mock_file_service_instance = MagicMock(spec=FileService)
         MockFileService.return_value = mock_file_service_instance
-        internal_routes = InternalRoutes()
+        internal_routes = InternalRoutes(None)
 
         # Check if FileService was initialized with the correct parameters
         MockFileService.assert_called_once_with({
@@ -112,4 +112,4 @@ async def test_file_service_initialization():
         })
 
         # Verify that the file_service attribute of InternalRoutes is set
-        assert internal_routes.file_service == mock_file_service_instance
+        assert internal_routes.file_service == mock_file_service_instance