diff --git a/.ci/update_windows/update.py b/.ci/update_windows/update.py
index 51a263203..59ece5130 100755
--- a/.ci/update_windows/update.py
+++ b/.ci/update_windows/update.py
@@ -66,8 +66,10 @@ if branch is None:
try:
ref = repo.lookup_reference('refs/remotes/origin/master')
except:
- print("pulling.") # noqa: T201
- pull(repo)
+ print("fetching.") # noqa: T201
+ for remote in repo.remotes:
+ if remote.name == "origin":
+ remote.fetch()
ref = repo.lookup_reference('refs/remotes/origin/master')
repo.checkout(ref)
branch = repo.lookup_branch('master')
@@ -149,3 +151,4 @@ try:
shutil.copy(stable_update_script, stable_update_script_to)
except:
pass
+
diff --git a/CODEOWNERS b/CODEOWNERS
index b7aca9b26..4d5448636 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -1,3 +1,2 @@
# Admins
-* @comfyanonymous
-* @kosinkadink
+* @comfyanonymous @kosinkadink @guill
diff --git a/README.md b/README.md
index 91fb510e1..bae955b1b 100644
--- a/README.md
+++ b/README.md
@@ -81,6 +81,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
- [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
- [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
- [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)
+ - [Hunyuan Video 1.5](https://docs.comfy.org/tutorials/video/hunyuan/hunyuan-video-1-5)
- Audio Models
- [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
- [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
@@ -319,6 +320,32 @@ For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step
1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536)
2. Launch ComfyUI by running `python main.py`
+
+## [ComfyUI-Manager](https://github.com/Comfy-Org/ComfyUI-Manager/tree/manager-v4)
+
+**ComfyUI-Manager** is an extension that allows you to easily install, update, and manage custom nodes for ComfyUI.
+
+### Setup
+
+1. Install the manager dependencies:
+ ```bash
+ pip install -r manager_requirements.txt
+ ```
+
+2. Enable the manager with the `--enable-manager` flag when running ComfyUI:
+ ```bash
+ python main.py --enable-manager
+ ```
+
+### Command Line Options
+
+| Flag | Description |
+|------|-------------|
+| `--enable-manager` | Enable ComfyUI-Manager |
+| `--enable-manager-legacy-ui` | Use the legacy manager UI instead of the new UI (requires `--enable-manager`) |
+| `--disable-manager-ui` | Disable the manager UI and endpoints while keeping background features like security checks and scheduled installation completion (requires `--enable-manager`) |
+
+
# Running
```python main.py```
diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index 2ceac81a2..5a1602fe3 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -122,6 +122,12 @@ upcast.add_argument("--force-upcast-attention", action="store_true", help="Force
upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.")
+parser.add_argument("--enable-manager", action="store_true", help="Enable the ComfyUI-Manager feature.")
+manager_group = parser.add_mutually_exclusive_group()
+manager_group.add_argument("--disable-manager-ui", action="store_true", help="Disables only the ComfyUI-Manager UI and endpoints. Scheduled installations and similar background tasks will still operate.")
+manager_group.add_argument("--enable-manager-legacy-ui", action="store_true", help="Enables the legacy UI of ComfyUI-Manager")
+
+
vram_group = parser.add_mutually_exclusive_group()
vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
@@ -169,6 +175,7 @@ parser.add_argument("--multi-user", action="store_true", help="Enables per-user
parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).")
+
# The default built-in provider hosted under web/
DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
diff --git a/comfy/context_windows.py b/comfy/context_windows.py
index 041f380f9..5c412d1c2 100644
--- a/comfy/context_windows.py
+++ b/comfy/context_windows.py
@@ -51,26 +51,36 @@ class ContextHandlerABC(ABC):
class IndexListContextWindow(ContextWindowABC):
- def __init__(self, index_list: list[int], dim: int=0):
+ def __init__(self, index_list: list[int], dim: int=0, total_frames: int=0):
self.index_list = index_list
self.context_length = len(index_list)
self.dim = dim
+ self.total_frames = total_frames
+ self.center_ratio = (min(index_list) + max(index_list)) / (2 * total_frames)
- def get_tensor(self, full: torch.Tensor, device=None, dim=None) -> torch.Tensor:
+ def get_tensor(self, full: torch.Tensor, device=None, dim=None, retain_index_list=[]) -> torch.Tensor:
if dim is None:
dim = self.dim
if dim == 0 and full.shape[dim] == 1:
return full
- idx = [slice(None)] * dim + [self.index_list]
- return full[idx].to(device)
+ idx = tuple([slice(None)] * dim + [self.index_list])
+ window = full[idx]
+ if retain_index_list:
+ idx = tuple([slice(None)] * dim + [retain_index_list])
+ window[idx] = full[idx]
+ return window.to(device)
def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=None) -> torch.Tensor:
if dim is None:
dim = self.dim
- idx = [slice(None)] * dim + [self.index_list]
+ idx = tuple([slice(None)] * dim + [self.index_list])
full[idx] += to_add
return full
+ def get_region_index(self, num_regions: int) -> int:
+ region_idx = int(self.center_ratio * num_regions)
+ return min(max(region_idx, 0), num_regions - 1)
+
class IndexListCallbacks:
EVALUATE_CONTEXT_WINDOWS = "evaluate_context_windows"
@@ -94,7 +104,8 @@ class ContextFuseMethod:
ContextResults = collections.namedtuple("ContextResults", ['window_idx', 'sub_conds_out', 'sub_conds', 'window'])
class IndexListContextHandler(ContextHandlerABC):
- def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1, closed_loop=False, dim=0):
+ def __init__(self, context_schedule: ContextSchedule, fuse_method: ContextFuseMethod, context_length: int=1, context_overlap: int=0, context_stride: int=1,
+ closed_loop: bool=False, dim:int=0, freenoise: bool=False, cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False):
self.context_schedule = context_schedule
self.fuse_method = fuse_method
self.context_length = context_length
@@ -103,13 +114,18 @@ class IndexListContextHandler(ContextHandlerABC):
self.closed_loop = closed_loop
self.dim = dim
self._step = 0
+ self.freenoise = freenoise
+ self.cond_retain_index_list = [int(x.strip()) for x in cond_retain_index_list.split(",")] if cond_retain_index_list else []
+ self.split_conds_to_windows = split_conds_to_windows
self.callbacks = {}
def should_use_context(self, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]) -> bool:
# for now, assume first dim is batch - should have stored on BaseModel in actual implementation
if x_in.size(self.dim) > self.context_length:
- logging.info(f"Using context windows {self.context_length} for {x_in.size(self.dim)} frames.")
+ logging.info(f"Using context windows {self.context_length} with overlap {self.context_overlap} for {x_in.size(self.dim)} frames.")
+ if self.cond_retain_index_list:
+ logging.info(f"Retaining original cond for indexes: {self.cond_retain_index_list}")
return True
return False
@@ -123,6 +139,11 @@ class IndexListContextHandler(ContextHandlerABC):
return None
# reuse or resize cond items to match context requirements
resized_cond = []
+ # if multiple conds, split based on primary region
+ if self.split_conds_to_windows and len(cond_in) > 1:
+ region = window.get_region_index(len(cond_in))
+ logging.info(f"Splitting conds to windows; using region {region} for window {window[0]}-{window[-1]} with center ratio {window.center_ratio:.3f}")
+ cond_in = [cond_in[region]]
# cond object is a list containing a dict - outer list is irrelevant, so just loop through it
for actual_cond in cond_in:
resized_actual_cond = actual_cond.copy()
@@ -146,12 +167,19 @@ class IndexListContextHandler(ContextHandlerABC):
# when in dictionary, look for tensors and CONDCrossAttn [comfy/conds.py] (has cond attr that is a tensor)
for cond_key, cond_value in new_cond_item.items():
if isinstance(cond_value, torch.Tensor):
- if cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim):
+ if (self.dim < cond_value.ndim and cond_value(self.dim) == x_in.size(self.dim)) or \
+ (cond_value.ndim < self.dim and cond_value.size(0) == x_in.size(self.dim)):
new_cond_item[cond_key] = window.get_tensor(cond_value, device)
+ # Handle audio_embed (temporal dim is 1)
+ elif cond_key == "audio_embed" and hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
+ audio_cond = cond_value.cond
+ if audio_cond.ndim > 1 and audio_cond.size(1) == x_in.size(self.dim):
+ new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(audio_cond, device, dim=1))
# if has cond that is a Tensor, check if needs to be subset
elif hasattr(cond_value, "cond") and isinstance(cond_value.cond, torch.Tensor):
- if cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim):
- new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device))
+ if (self.dim < cond_value.cond.ndim and cond_value.cond.size(self.dim) == x_in.size(self.dim)) or \
+ (cond_value.cond.ndim < self.dim and cond_value.cond.size(0) == x_in.size(self.dim)):
+ new_cond_item[cond_key] = cond_value._copy_with(window.get_tensor(cond_value.cond, device, retain_index_list=self.cond_retain_index_list))
elif cond_key == "num_video_frames": # for SVD
new_cond_item[cond_key] = cond_value._copy_with(cond_value.cond)
new_cond_item[cond_key].cond = window.context_length
@@ -164,7 +192,7 @@ class IndexListContextHandler(ContextHandlerABC):
return resized_cond
def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
- mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep, rtol=0.0001)
+ mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep[0], rtol=0.0001)
matches = torch.nonzero(mask)
if torch.numel(matches) == 0:
raise Exception("No sample_sigmas matched current timestep; something went wrong.")
@@ -173,7 +201,7 @@ class IndexListContextHandler(ContextHandlerABC):
def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]:
full_length = x_in.size(self.dim) # TODO: choose dim based on model
context_windows = self.context_schedule.func(full_length, self, model_options)
- context_windows = [IndexListContextWindow(window, dim=self.dim) for window in context_windows]
+ context_windows = [IndexListContextWindow(window, dim=self.dim, total_frames=full_length) for window in context_windows]
return context_windows
def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: list[list[dict]], x_in: torch.Tensor, timestep: torch.Tensor, model_options: dict[str]):
@@ -250,8 +278,8 @@ class IndexListContextHandler(ContextHandlerABC):
prev_weight = (bias_total / (bias_total + bias))
new_weight = (bias / (bias_total + bias))
# account for dims of tensors
- idx_window = [slice(None)] * self.dim + [idx]
- pos_window = [slice(None)] * self.dim + [pos]
+ idx_window = tuple([slice(None)] * self.dim + [idx])
+ pos_window = tuple([slice(None)] * self.dim + [pos])
# apply new values
conds_final[i][idx_window] = conds_final[i][idx_window] * prev_weight + sub_conds_out[i][pos_window] * new_weight
biases_final[i][idx] = bias_total + bias
@@ -287,6 +315,28 @@ def create_prepare_sampling_wrapper(model: ModelPatcher):
)
+def _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callback, noise, *args, **kwargs):
+ model_options = extra_args.get("model_options", None)
+ if model_options is None:
+ raise Exception("model_options not found in sampler_sample_wrapper; this should never happen, something went wrong.")
+ handler: IndexListContextHandler = model_options.get("context_handler", None)
+ if handler is None:
+ raise Exception("context_handler not found in sampler_sample_wrapper; this should never happen, something went wrong.")
+ if not handler.freenoise:
+ return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
+ noise = apply_freenoise(noise, handler.dim, handler.context_length, handler.context_overlap, extra_args["seed"])
+
+ return executor(guider, sigmas, extra_args, callback, noise, *args, **kwargs)
+
+
+def create_sampler_sample_wrapper(model: ModelPatcher):
+ model.add_wrapper_with_key(
+ comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE,
+ "ContextWindows_sampler_sample",
+ _sampler_sample_wrapper
+ )
+
+
def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: int, device=None) -> torch.Tensor:
total_dims = len(x_in.shape)
weights_tensor = torch.Tensor(weights).to(device=device)
@@ -538,3 +588,29 @@ def shift_window_to_end(window: list[int], num_frames: int):
for i in range(len(window)):
# 2) add end_delta to each val to slide windows to end
window[i] = window[i] + end_delta
+
+
+# https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved/blob/90fb1331201a4b29488089e4fbffc0d82cc6d0a9/animatediff/sample_settings.py#L465
+def apply_freenoise(noise: torch.Tensor, dim: int, context_length: int, context_overlap: int, seed: int):
+ logging.info("Context windows: Applying FreeNoise")
+ generator = torch.Generator(device='cpu').manual_seed(seed)
+ latent_video_length = noise.shape[dim]
+ delta = context_length - context_overlap
+
+ for start_idx in range(0, latent_video_length - context_length, delta):
+ place_idx = start_idx + context_length
+
+ actual_delta = min(delta, latent_video_length - place_idx)
+ if actual_delta <= 0:
+ break
+
+ list_idx = torch.randperm(actual_delta, generator=generator, device='cpu') + start_idx
+
+ source_slice = [slice(None)] * noise.ndim
+ source_slice[dim] = list_idx
+ target_slice = [slice(None)] * noise.ndim
+ target_slice[dim] = slice(place_idx, place_idx + actual_delta)
+
+ noise[tuple(target_slice)] = noise[tuple(source_slice)]
+
+ return noise
diff --git a/comfy/ldm/chroma/model.py b/comfy/ldm/chroma/model.py
index a72f8cc47..2e8ef0687 100644
--- a/comfy/ldm/chroma/model.py
+++ b/comfy/ldm/chroma/model.py
@@ -40,7 +40,8 @@ class ChromaParams:
out_dim: int
hidden_dim: int
n_layers: int
-
+ txt_ids_dims: list
+ vec_in_dim: int
diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py
index 2472ab79c..60f2bdae2 100644
--- a/comfy/ldm/flux/layers.py
+++ b/comfy/ldm/flux/layers.py
@@ -57,6 +57,35 @@ class MLPEmbedder(nn.Module):
def forward(self, x: Tensor) -> Tensor:
return self.out_layer(self.silu(self.in_layer(x)))
+class YakMLP(nn.Module):
+ def __init__(self, hidden_size: int, intermediate_size: int, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.gate_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device)
+ self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=True, dtype=dtype, device=device)
+ self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=True, dtype=dtype, device=device)
+ self.act_fn = nn.SiLU()
+
+ def forward(self, x: Tensor) -> Tensor:
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+ return down_proj
+
+def build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=False, yak_mlp=False, dtype=None, device=None, operations=None):
+ if yak_mlp:
+ return YakMLP(hidden_size, mlp_hidden_dim, dtype=dtype, device=device, operations=operations)
+ if mlp_silu_act:
+ return nn.Sequential(
+ operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device),
+ SiLUActivation(),
+ operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device),
+ )
+ else:
+ return nn.Sequential(
+ operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+ nn.GELU(approximate="tanh"),
+ operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+ )
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, dtype=None, device=None, operations=None):
@@ -140,7 +169,7 @@ class SiLUActivation(nn.Module):
class DoubleStreamBlock(nn.Module):
- def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, mlp_silu_act=False, proj_bias=True, dtype=None, device=None, operations=None):
+ def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=False, modulation=True, mlp_silu_act=False, proj_bias=True, yak_mlp=False, dtype=None, device=None, operations=None):
super().__init__()
mlp_hidden_dim = int(hidden_size * mlp_ratio)
@@ -156,18 +185,7 @@ class DoubleStreamBlock(nn.Module):
self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
- if mlp_silu_act:
- self.img_mlp = nn.Sequential(
- operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device),
- SiLUActivation(),
- operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device),
- )
- else:
- self.img_mlp = nn.Sequential(
- operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
- nn.GELU(approximate="tanh"),
- operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
- )
+ self.img_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations)
if self.modulation:
self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
@@ -177,18 +195,7 @@ class DoubleStreamBlock(nn.Module):
self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
- if mlp_silu_act:
- self.txt_mlp = nn.Sequential(
- operations.Linear(hidden_size, mlp_hidden_dim * 2, bias=False, dtype=dtype, device=device),
- SiLUActivation(),
- operations.Linear(mlp_hidden_dim, hidden_size, bias=False, dtype=dtype, device=device),
- )
- else:
- self.txt_mlp = nn.Sequential(
- operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
- nn.GELU(approximate="tanh"),
- operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
- )
+ self.txt_mlp = build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=mlp_silu_act, yak_mlp=yak_mlp, dtype=dtype, device=device, operations=operations)
self.flipped_img_txt = flipped_img_txt
@@ -275,6 +282,7 @@ class SingleStreamBlock(nn.Module):
modulation=True,
mlp_silu_act=False,
bias=True,
+ yak_mlp=False,
dtype=None,
device=None,
operations=None
@@ -288,12 +296,17 @@ class SingleStreamBlock(nn.Module):
self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
self.mlp_hidden_dim_first = self.mlp_hidden_dim
+ self.yak_mlp = yak_mlp
if mlp_silu_act:
self.mlp_hidden_dim_first = int(hidden_size * mlp_ratio * 2)
self.mlp_act = SiLUActivation()
else:
self.mlp_act = nn.GELU(approximate="tanh")
+ if self.yak_mlp:
+ self.mlp_hidden_dim_first *= 2
+ self.mlp_act = nn.SiLU()
+
# qkv and mlp_in
self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim_first, bias=bias, dtype=dtype, device=device)
# proj and mlp_out
@@ -325,7 +338,10 @@ class SingleStreamBlock(nn.Module):
attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
del q, k, v
# compute activation in mlp stream, cat again and run second linear layer
- mlp = self.mlp_act(mlp)
+ if self.yak_mlp:
+ mlp = self.mlp_act(mlp[..., self.mlp_hidden_dim_first // 2:]) * mlp[..., :self.mlp_hidden_dim_first // 2]
+ else:
+ mlp = self.mlp_act(mlp)
output = self.linear2(torch.cat((attn, mlp), 2))
x += apply_mod(output, mod.gate, None, modulation_dims)
if x.dtype == torch.float16:
diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
index d5674dea6..f40c2a7a9 100644
--- a/comfy/ldm/flux/model.py
+++ b/comfy/ldm/flux/model.py
@@ -15,7 +15,8 @@ from .layers import (
MLPEmbedder,
SingleStreamBlock,
timestep_embedding,
- Modulation
+ Modulation,
+ RMSNorm
)
@dataclass
@@ -34,11 +35,14 @@ class FluxParams:
patch_size: int
qkv_bias: bool
guidance_embed: bool
+ txt_ids_dims: list
global_modulation: bool = False
mlp_silu_act: bool = False
ops_bias: bool = True
default_ref_method: str = "offset"
ref_index_scale: float = 1.0
+ yak_mlp: bool = False
+ txt_norm: bool = False
class Flux(nn.Module):
@@ -76,6 +80,11 @@ class Flux(nn.Module):
)
self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, bias=params.ops_bias, dtype=dtype, device=device)
+ if params.txt_norm:
+ self.txt_norm = RMSNorm(params.context_in_dim, dtype=dtype, device=device, operations=operations)
+ else:
+ self.txt_norm = None
+
self.double_blocks = nn.ModuleList(
[
DoubleStreamBlock(
@@ -86,6 +95,7 @@ class Flux(nn.Module):
modulation=params.global_modulation is False,
mlp_silu_act=params.mlp_silu_act,
proj_bias=params.ops_bias,
+ yak_mlp=params.yak_mlp,
dtype=dtype, device=device, operations=operations
)
for _ in range(params.depth)
@@ -94,7 +104,7 @@ class Flux(nn.Module):
self.single_blocks = nn.ModuleList(
[
- SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=params.global_modulation is False, mlp_silu_act=params.mlp_silu_act, bias=params.ops_bias, dtype=dtype, device=device, operations=operations)
+ SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, modulation=params.global_modulation is False, mlp_silu_act=params.mlp_silu_act, bias=params.ops_bias, yak_mlp=params.yak_mlp, dtype=dtype, device=device, operations=operations)
for _ in range(params.depth_single_blocks)
]
)
@@ -150,6 +160,8 @@ class Flux(nn.Module):
y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
+ if self.txt_norm is not None:
+ txt = self.txt_norm(txt)
txt = self.txt_in(txt)
vec_orig = vec
@@ -332,8 +344,9 @@ class Flux(nn.Module):
txt_ids = torch.zeros((bs, context.shape[1], len(self.params.axes_dim)), device=x.device, dtype=torch.float32)
- if len(self.params.axes_dim) == 4: # Flux 2
- txt_ids[:, :, 3] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32)
+ if len(self.params.txt_ids_dims) > 0:
+ for i in self.params.txt_ids_dims:
+ txt_ids[:, :, i] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32)
out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
out = out[:, :img_tokens]
diff --git a/comfy/ldm/hunyuan_video/upsampler.py b/comfy/ldm/hunyuan_video/upsampler.py
index 9f5e91a59..85f515f67 100644
--- a/comfy/ldm/hunyuan_video/upsampler.py
+++ b/comfy/ldm/hunyuan_video/upsampler.py
@@ -1,7 +1,8 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
-from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm, ResnetBlock, VideoConv3d
+from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, VideoConv3d
+from comfy.ldm.hunyuan_video.vae_refiner import RMS_norm
import model_management, model_patcher
class SRResidualCausalBlock3D(nn.Module):
diff --git a/comfy/ldm/hunyuan_video/vae_refiner.py b/comfy/ldm/hunyuan_video/vae_refiner.py
index 9f750dcc4..ddf77cd0e 100644
--- a/comfy/ldm/hunyuan_video/vae_refiner.py
+++ b/comfy/ldm/hunyuan_video/vae_refiner.py
@@ -1,42 +1,12 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
-from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d, Normalize
+from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, CarriedConv3d, Normalize, conv_carry_causal_3d, torch_cat_if_needed
import comfy.ops
import comfy.ldm.models.autoencoder
import comfy.model_management
ops = comfy.ops.disable_weight_init
-class NoPadConv3d(nn.Module):
- def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs):
- super().__init__()
- self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)
-
- def forward(self, x):
- return self.conv(x)
-
-
-def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):
-
- x = xl[0]
- xl.clear()
-
- if conv_carry_out is not None:
- to_push = x[:, :, -2:, :, :].clone()
- conv_carry_out.append(to_push)
-
- if isinstance(op, NoPadConv3d):
- if conv_carry_in is None:
- x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate')
- else:
- carry_len = conv_carry_in[0].shape[2]
- x = torch.cat([conv_carry_in.pop(0), x], dim=2)
- x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate')
-
- out = op(x)
-
- return out
-
class RMS_norm(nn.Module):
def __init__(self, dim):
@@ -49,7 +19,7 @@ class RMS_norm(nn.Module):
return F.normalize(x, dim=1) * self.scale * comfy.model_management.cast_to(self.gamma, dtype=x.dtype, device=x.device)
class DnSmpl(nn.Module):
- def __init__(self, ic, oc, tds=True, refiner_vae=True, op=VideoConv3d):
+ def __init__(self, ic, oc, tds, refiner_vae, op):
super().__init__()
fct = 2 * 2 * 2 if tds else 1 * 2 * 2
assert oc % fct == 0
@@ -109,7 +79,7 @@ class DnSmpl(nn.Module):
class UpSmpl(nn.Module):
- def __init__(self, ic, oc, tus=True, refiner_vae=True, op=VideoConv3d):
+ def __init__(self, ic, oc, tus, refiner_vae, op):
super().__init__()
fct = 2 * 2 * 2 if tus else 1 * 2 * 2
self.conv = op(ic, oc * fct, kernel_size=3, stride=1, padding=1)
@@ -163,23 +133,6 @@ class UpSmpl(nn.Module):
return h + x
-class HunyuanRefinerResnetBlock(ResnetBlock):
- def __init__(self, in_channels, out_channels, conv_op=NoPadConv3d, norm_op=RMS_norm):
- super().__init__(in_channels=in_channels, out_channels=out_channels, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
-
- def forward(self, x, conv_carry_in=None, conv_carry_out=None):
- h = x
- h = [ self.swish(self.norm1(x)) ]
- h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
-
- h = [ self.dropout(self.swish(self.norm2(h))) ]
- h = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
-
- if self.in_channels != self.out_channels:
- x = self.nin_shortcut(x)
-
- return x+h
-
class Encoder(nn.Module):
def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,
ffactor_spatial, ffactor_temporal, downsample_match_channel=True, refiner_vae=True, **_):
@@ -191,7 +144,7 @@ class Encoder(nn.Module):
self.refiner_vae = refiner_vae
if self.refiner_vae:
- conv_op = NoPadConv3d
+ conv_op = CarriedConv3d
norm_op = RMS_norm
else:
conv_op = ops.Conv3d
@@ -206,9 +159,10 @@ class Encoder(nn.Module):
for i, tgt in enumerate(block_out_channels):
stage = nn.Module()
- stage.block = nn.ModuleList([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt,
- out_channels=tgt,
- conv_op=conv_op, norm_op=norm_op)
+ stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
+ out_channels=tgt,
+ temb_channels=0,
+ conv_op=conv_op, norm_op=norm_op)
for j in range(num_res_blocks)])
ch = tgt
if i < depth:
@@ -218,9 +172,9 @@ class Encoder(nn.Module):
self.down.append(stage)
self.mid = nn.Module()
- self.mid.block_1 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
+ self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
- self.mid.block_2 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
+ self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
self.norm_out = norm_op(ch)
self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1)
@@ -246,22 +200,20 @@ class Encoder(nn.Module):
conv_carry_out = []
if i == len(x) - 1:
conv_carry_out = None
+
x1 = [ x1 ]
x1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)
for stage in self.down:
for blk in stage.block:
- x1 = blk(x1, conv_carry_in, conv_carry_out)
+ x1 = blk(x1, None, conv_carry_in, conv_carry_out)
if hasattr(stage, 'downsample'):
x1 = stage.downsample(x1, conv_carry_in, conv_carry_out)
out.append(x1)
conv_carry_in = conv_carry_out
- if len(out) > 1:
- out = torch.cat(out, dim=2)
- else:
- out = out[0]
+ out = torch_cat_if_needed(out, dim=2)
x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(out)))
del out
@@ -288,7 +240,7 @@ class Decoder(nn.Module):
self.refiner_vae = refiner_vae
if self.refiner_vae:
- conv_op = NoPadConv3d
+ conv_op = CarriedConv3d
norm_op = RMS_norm
else:
conv_op = ops.Conv3d
@@ -298,9 +250,9 @@ class Decoder(nn.Module):
self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1)
self.mid = nn.Module()
- self.mid.block_1 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
+ self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
- self.mid.block_2 = HunyuanRefinerResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
+ self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, conv_op=conv_op, norm_op=norm_op)
self.up = nn.ModuleList()
depth = (ffactor_spatial >> 1).bit_length()
@@ -308,9 +260,10 @@ class Decoder(nn.Module):
for i, tgt in enumerate(block_out_channels):
stage = nn.Module()
- stage.block = nn.ModuleList([HunyuanRefinerResnetBlock(in_channels=ch if j == 0 else tgt,
- out_channels=tgt,
- conv_op=conv_op, norm_op=norm_op)
+ stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
+ out_channels=tgt,
+ temb_channels=0,
+ conv_op=conv_op, norm_op=norm_op)
for j in range(num_res_blocks + 1)])
ch = tgt
if i < depth:
@@ -340,7 +293,7 @@ class Decoder(nn.Module):
conv_carry_out = None
for stage in self.up:
for blk in stage.block:
- x1 = blk(x1, conv_carry_in, conv_carry_out)
+ x1 = blk(x1, None, conv_carry_in, conv_carry_out)
if hasattr(stage, 'upsample'):
x1 = stage.upsample(x1, conv_carry_in, conv_carry_out)
@@ -350,10 +303,7 @@ class Decoder(nn.Module):
conv_carry_in = conv_carry_out
del x
- if len(out) > 1:
- out = torch.cat(out, dim=2)
- else:
- out = out[0]
+ out = torch_cat_if_needed(out, dim=2)
if not self.refiner_vae:
if z.shape[-3] == 1:
diff --git a/comfy/ldm/kandinsky5/model.py b/comfy/ldm/kandinsky5/model.py
new file mode 100644
index 000000000..1509de2f8
--- /dev/null
+++ b/comfy/ldm/kandinsky5/model.py
@@ -0,0 +1,413 @@
+import torch
+from torch import nn
+import math
+
+import comfy.ldm.common_dit
+from comfy.ldm.modules.attention import optimized_attention
+from comfy.ldm.flux.math import apply_rope1
+from comfy.ldm.flux.layers import EmbedND
+
+def attention(q, k, v, heads, transformer_options={}):
+ return optimized_attention(
+ q.transpose(1, 2),
+ k.transpose(1, 2),
+ v.transpose(1, 2),
+ heads=heads,
+ skip_reshape=True,
+ transformer_options=transformer_options
+ )
+
+def apply_scale_shift_norm(norm, x, scale, shift):
+ return torch.addcmul(shift, norm(x), scale + 1.0)
+
+def apply_gate_sum(x, out, gate):
+ return torch.addcmul(x, gate, out)
+
+def get_shift_scale_gate(params):
+ shift, scale, gate = torch.chunk(params, 3, dim=-1)
+ return tuple(x.unsqueeze(1) for x in (shift, scale, gate))
+
+def get_freqs(dim, max_period=10000.0):
+ return torch.exp(-math.log(max_period) * torch.arange(start=0, end=dim, dtype=torch.float32) / dim)
+
+
+class TimeEmbeddings(nn.Module):
+ def __init__(self, model_dim, time_dim, max_period=10000.0, operation_settings=None):
+ super().__init__()
+ assert model_dim % 2 == 0
+ self.model_dim = model_dim
+ self.max_period = max_period
+ self.register_buffer("freqs", get_freqs(model_dim // 2, max_period), persistent=False)
+ operations = operation_settings.get("operations")
+ self.in_layer = operations.Linear(model_dim, time_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.activation = nn.SiLU()
+ self.out_layer = operations.Linear(time_dim, time_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+ def forward(self, timestep, dtype):
+ args = torch.outer(timestep, self.freqs.to(device=timestep.device))
+ time_embed = torch.cat([torch.cos(args), torch.sin(args)], dim=-1).to(dtype)
+ time_embed = self.out_layer(self.activation(self.in_layer(time_embed)))
+ return time_embed
+
+
+class TextEmbeddings(nn.Module):
+ def __init__(self, text_dim, model_dim, operation_settings=None):
+ super().__init__()
+ operations = operation_settings.get("operations")
+ self.in_layer = operations.Linear(text_dim, model_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.norm = operations.LayerNorm(model_dim, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+ def forward(self, text_embed):
+ text_embed = self.in_layer(text_embed)
+ return self.norm(text_embed).type_as(text_embed)
+
+
+class VisualEmbeddings(nn.Module):
+ def __init__(self, visual_dim, model_dim, patch_size, operation_settings=None):
+ super().__init__()
+ self.patch_size = patch_size
+ operations = operation_settings.get("operations")
+ self.in_layer = operations.Linear(visual_dim, model_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+ def forward(self, x):
+ x = x.movedim(1, -1) # B C T H W -> B T H W C
+ B, T, H, W, dim = x.shape
+ pt, ph, pw = self.patch_size
+
+ x = x.view(
+ B,
+ T // pt, pt,
+ H // ph, ph,
+ W // pw, pw,
+ dim,
+ ).permute(0, 1, 3, 5, 2, 4, 6, 7).flatten(4, 7)
+
+ return self.in_layer(x)
+
+
+class Modulation(nn.Module):
+ def __init__(self, time_dim, model_dim, num_params, operation_settings=None):
+ super().__init__()
+ self.activation = nn.SiLU()
+ self.out_layer = operation_settings.get("operations").Linear(time_dim, num_params * model_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+ def forward(self, x):
+ return self.out_layer(self.activation(x))
+
+
+class SelfAttention(nn.Module):
+ def __init__(self, num_channels, head_dim, operation_settings=None):
+ super().__init__()
+ assert num_channels % head_dim == 0
+ self.num_heads = num_channels // head_dim
+ self.head_dim = head_dim
+
+ operations = operation_settings.get("operations")
+ self.to_query = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.to_key = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.to_value = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.query_norm = operations.RMSNorm(head_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.key_norm = operations.RMSNorm(head_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+ self.out_layer = operations.Linear(num_channels, num_channels, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.num_chunks = 2
+
+ def _compute_qk(self, x, freqs, proj_fn, norm_fn):
+ result = proj_fn(x).view(*x.shape[:-1], self.num_heads, -1)
+ return apply_rope1(norm_fn(result), freqs)
+
+ def _forward(self, x, freqs, transformer_options={}):
+ q = self._compute_qk(x, freqs, self.to_query, self.query_norm)
+ k = self._compute_qk(x, freqs, self.to_key, self.key_norm)
+ v = self.to_value(x).view(*x.shape[:-1], self.num_heads, -1)
+ out = attention(q, k, v, self.num_heads, transformer_options=transformer_options)
+ return self.out_layer(out)
+
+ def _forward_chunked(self, x, freqs, transformer_options={}):
+ def process_chunks(proj_fn, norm_fn):
+ x_chunks = torch.chunk(x, self.num_chunks, dim=1)
+ freqs_chunks = torch.chunk(freqs, self.num_chunks, dim=1)
+ chunks = []
+ for x_chunk, freqs_chunk in zip(x_chunks, freqs_chunks):
+ chunks.append(self._compute_qk(x_chunk, freqs_chunk, proj_fn, norm_fn))
+ return torch.cat(chunks, dim=1)
+
+ q = process_chunks(self.to_query, self.query_norm)
+ k = process_chunks(self.to_key, self.key_norm)
+ v = self.to_value(x).view(*x.shape[:-1], self.num_heads, -1)
+ out = attention(q, k, v, self.num_heads, transformer_options=transformer_options)
+ return self.out_layer(out)
+
+ def forward(self, x, freqs, transformer_options={}):
+ if x.shape[1] > 8192:
+ return self._forward_chunked(x, freqs, transformer_options=transformer_options)
+ else:
+ return self._forward(x, freqs, transformer_options=transformer_options)
+
+
+class CrossAttention(SelfAttention):
+ def get_qkv(self, x, context):
+ q = self.to_query(x).view(*x.shape[:-1], self.num_heads, -1)
+ k = self.to_key(context).view(*context.shape[:-1], self.num_heads, -1)
+ v = self.to_value(context).view(*context.shape[:-1], self.num_heads, -1)
+ return q, k, v
+
+ def forward(self, x, context, transformer_options={}):
+ q, k, v = self.get_qkv(x, context)
+ out = attention(self.query_norm(q), self.key_norm(k), v, self.num_heads, transformer_options=transformer_options)
+ return self.out_layer(out)
+
+
+class FeedForward(nn.Module):
+ def __init__(self, dim, ff_dim, operation_settings=None):
+ super().__init__()
+ operations = operation_settings.get("operations")
+ self.in_layer = operations.Linear(dim, ff_dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.activation = nn.GELU()
+ self.out_layer = operations.Linear(ff_dim, dim, bias=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.num_chunks = 4
+
+ def _forward(self, x):
+ return self.out_layer(self.activation(self.in_layer(x)))
+
+ def _forward_chunked(self, x):
+ chunks = torch.chunk(x, self.num_chunks, dim=1)
+ output_chunks = []
+ for chunk in chunks:
+ output_chunks.append(self._forward(chunk))
+ return torch.cat(output_chunks, dim=1)
+
+ def forward(self, x):
+ if x.shape[1] > 8192:
+ return self._forward_chunked(x)
+ else:
+ return self._forward(x)
+
+
+class OutLayer(nn.Module):
+ def __init__(self, model_dim, time_dim, visual_dim, patch_size, operation_settings=None):
+ super().__init__()
+ self.patch_size = patch_size
+ self.modulation = Modulation(time_dim, model_dim, 2, operation_settings=operation_settings)
+ operations = operation_settings.get("operations")
+ self.norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.out_layer = operations.Linear(model_dim, math.prod(patch_size) * visual_dim, bias=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+ def forward(self, visual_embed, time_embed):
+ B, T, H, W, _ = visual_embed.shape
+ shift, scale = torch.chunk(self.modulation(time_embed), 2, dim=-1)
+ scale = scale[:, None, None, None, :]
+ shift = shift[:, None, None, None, :]
+ visual_embed = apply_scale_shift_norm(self.norm, visual_embed, scale, shift)
+ x = self.out_layer(visual_embed)
+
+ out_dim = x.shape[-1] // (self.patch_size[0] * self.patch_size[1] * self.patch_size[2])
+ x = x.view(
+ B, T, H, W,
+ out_dim,
+ self.patch_size[0], self.patch_size[1], self.patch_size[2]
+ )
+ return x.permute(0, 4, 1, 5, 2, 6, 3, 7).flatten(2, 3).flatten(3, 4).flatten(4, 5)
+
+
+class TransformerEncoderBlock(nn.Module):
+ def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_settings=None):
+ super().__init__()
+ self.text_modulation = Modulation(time_dim, model_dim, 6, operation_settings=operation_settings)
+ operations = operation_settings.get("operations")
+
+ self.self_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.self_attention = SelfAttention(model_dim, head_dim, operation_settings=operation_settings)
+
+ self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings)
+
+ def forward(self, x, time_embed, freqs, transformer_options={}):
+ self_attn_params, ff_params = torch.chunk(self.text_modulation(time_embed), 2, dim=-1)
+ shift, scale, gate = get_shift_scale_gate(self_attn_params)
+ out = apply_scale_shift_norm(self.self_attention_norm, x, scale, shift)
+ out = self.self_attention(out, freqs, transformer_options=transformer_options)
+ x = apply_gate_sum(x, out, gate)
+
+ shift, scale, gate = get_shift_scale_gate(ff_params)
+ out = apply_scale_shift_norm(self.feed_forward_norm, x, scale, shift)
+ out = self.feed_forward(out)
+ x = apply_gate_sum(x, out, gate)
+ return x
+
+
+class TransformerDecoderBlock(nn.Module):
+ def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_settings=None):
+ super().__init__()
+ self.visual_modulation = Modulation(time_dim, model_dim, 9, operation_settings=operation_settings)
+
+ operations = operation_settings.get("operations")
+ self.self_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.self_attention = SelfAttention(model_dim, head_dim, operation_settings=operation_settings)
+
+ self.cross_attention_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.cross_attention = CrossAttention(model_dim, head_dim, operation_settings=operation_settings)
+
+ self.feed_forward_norm = operations.LayerNorm(model_dim, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.feed_forward = FeedForward(model_dim, ff_dim, operation_settings=operation_settings)
+
+ def forward(self, visual_embed, text_embed, time_embed, freqs, transformer_options={}):
+ self_attn_params, cross_attn_params, ff_params = torch.chunk(self.visual_modulation(time_embed), 3, dim=-1)
+ # self attention
+ shift, scale, gate = get_shift_scale_gate(self_attn_params)
+ visual_out = apply_scale_shift_norm(self.self_attention_norm, visual_embed, scale, shift)
+ visual_out = self.self_attention(visual_out, freqs, transformer_options=transformer_options)
+ visual_embed = apply_gate_sum(visual_embed, visual_out, gate)
+ # cross attention
+ shift, scale, gate = get_shift_scale_gate(cross_attn_params)
+ visual_out = apply_scale_shift_norm(self.cross_attention_norm, visual_embed, scale, shift)
+ visual_out = self.cross_attention(visual_out, text_embed, transformer_options=transformer_options)
+ visual_embed = apply_gate_sum(visual_embed, visual_out, gate)
+ # feed forward
+ shift, scale, gate = get_shift_scale_gate(ff_params)
+ visual_out = apply_scale_shift_norm(self.feed_forward_norm, visual_embed, scale, shift)
+ visual_out = self.feed_forward(visual_out)
+ visual_embed = apply_gate_sum(visual_embed, visual_out, gate)
+ return visual_embed
+
+
+class Kandinsky5(nn.Module):
+ def __init__(
+ self,
+ in_visual_dim=16, out_visual_dim=16, in_text_dim=3584, in_text_dim2=768, time_dim=512,
+ model_dim=1792, ff_dim=7168, visual_embed_dim=132, patch_size=(1, 2, 2), num_text_blocks=2, num_visual_blocks=32,
+ axes_dims=(16, 24, 24), rope_scale_factor=(1.0, 2.0, 2.0),
+ dtype=None, device=None, operations=None, **kwargs
+ ):
+ super().__init__()
+ head_dim = sum(axes_dims)
+ self.rope_scale_factor = rope_scale_factor
+ self.in_visual_dim = in_visual_dim
+ self.model_dim = model_dim
+ self.patch_size = patch_size
+ self.visual_embed_dim = visual_embed_dim
+ self.dtype = dtype
+ self.device = device
+ operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+
+ self.time_embeddings = TimeEmbeddings(model_dim, time_dim, operation_settings=operation_settings)
+ self.text_embeddings = TextEmbeddings(in_text_dim, model_dim, operation_settings=operation_settings)
+ self.pooled_text_embeddings = TextEmbeddings(in_text_dim2, time_dim, operation_settings=operation_settings)
+ self.visual_embeddings = VisualEmbeddings(visual_embed_dim, model_dim, patch_size, operation_settings=operation_settings)
+
+ self.text_transformer_blocks = nn.ModuleList(
+ [TransformerEncoderBlock(model_dim, time_dim, ff_dim, head_dim, operation_settings=operation_settings) for _ in range(num_text_blocks)]
+ )
+
+ self.visual_transformer_blocks = nn.ModuleList(
+ [TransformerDecoderBlock(model_dim, time_dim, ff_dim, head_dim, operation_settings=operation_settings) for _ in range(num_visual_blocks)]
+ )
+
+ self.out_layer = OutLayer(model_dim, time_dim, out_visual_dim, patch_size, operation_settings=operation_settings)
+
+ self.rope_embedder_3d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=axes_dims)
+ self.rope_embedder_1d = EmbedND(dim=head_dim, theta=10000.0, axes_dim=[head_dim])
+
+ def rope_encode_1d(self, seq_len, seq_start=0, steps=None, device=None, dtype=None, transformer_options={}):
+ steps = seq_len if steps is None else steps
+ seq_ids = torch.linspace(seq_start, seq_start + (seq_len - 1), steps=steps, device=device, dtype=dtype)
+ seq_ids = seq_ids.reshape(-1, 1).unsqueeze(0) # Shape: (1, steps, 1)
+ freqs = self.rope_embedder_1d(seq_ids).movedim(1, 2)
+ return freqs
+
+ def rope_encode_3d(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, transformer_options={}):
+
+ patch_size = self.patch_size
+ t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
+ h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
+ w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
+
+ if steps_t is None:
+ steps_t = t_len
+ if steps_h is None:
+ steps_h = h_len
+ if steps_w is None:
+ steps_w = w_len
+
+ h_start = 0
+ w_start = 0
+ rope_options = transformer_options.get("rope_options", None)
+ if rope_options is not None:
+ t_len = (t_len - 1.0) * rope_options.get("scale_t", 1.0) + 1.0
+ h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0
+ w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0
+
+ t_start += rope_options.get("shift_t", 0.0)
+ h_start += rope_options.get("shift_y", 0.0)
+ w_start += rope_options.get("shift_x", 0.0)
+ else:
+ rope_scale_factor = self.rope_scale_factor
+ if self.model_dim == 4096: # pro video model uses different rope scaling at higher resolutions
+ if h * w >= 14080:
+ rope_scale_factor = (1.0, 3.16, 3.16)
+
+ t_len = (t_len - 1.0) / rope_scale_factor[0] + 1.0
+ h_len = (h_len - 1.0) / rope_scale_factor[1] + 1.0
+ w_len = (w_len - 1.0) / rope_scale_factor[2] + 1.0
+
+ img_ids = torch.zeros((steps_t, steps_h, steps_w, 3), device=device, dtype=dtype)
+ img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(t_start, t_start + (t_len - 1), steps=steps_t, device=device, dtype=dtype).reshape(-1, 1, 1)
+ img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(h_start, h_start + (h_len - 1), steps=steps_h, device=device, dtype=dtype).reshape(1, -1, 1)
+ img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(w_start, w_start + (w_len - 1), steps=steps_w, device=device, dtype=dtype).reshape(1, 1, -1)
+ img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])
+
+ freqs = self.rope_embedder_3d(img_ids).movedim(1, 2)
+ return freqs
+
+ def forward_orig(self, x, timestep, context, y, freqs, freqs_text, transformer_options={}, **kwargs):
+ patches_replace = transformer_options.get("patches_replace", {})
+ context = self.text_embeddings(context)
+ time_embed = self.time_embeddings(timestep, x.dtype) + self.pooled_text_embeddings(y)
+
+ for block in self.text_transformer_blocks:
+ context = block(context, time_embed, freqs_text, transformer_options=transformer_options)
+
+ visual_embed = self.visual_embeddings(x)
+ visual_shape = visual_embed.shape[:-1]
+ visual_embed = visual_embed.flatten(1, -2)
+
+ blocks_replace = patches_replace.get("dit", {})
+ transformer_options["total_blocks"] = len(self.visual_transformer_blocks)
+ transformer_options["block_type"] = "double"
+ for i, block in enumerate(self.visual_transformer_blocks):
+ transformer_options["block_index"] = i
+ if ("double_block", i) in blocks_replace:
+ def block_wrap(args):
+ return block(x=args["x"], context=args["context"], time_embed=args["time_embed"], freqs=args["freqs"], transformer_options=args.get("transformer_options"))
+ visual_embed = blocks_replace[("double_block", i)]({"x": visual_embed, "context": context, "time_embed": time_embed, "freqs": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})["x"]
+ else:
+ visual_embed = block(visual_embed, context, time_embed, freqs=freqs, transformer_options=transformer_options)
+
+ visual_embed = visual_embed.reshape(*visual_shape, -1)
+ return self.out_layer(visual_embed, time_embed)
+
+ def _forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options={}, **kwargs):
+ original_dims = x.ndim
+ if original_dims == 4:
+ x = x.unsqueeze(2)
+ bs, c, t_len, h, w = x.shape
+ x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
+
+ if time_dim_replace is not None:
+ time_dim_replace = comfy.ldm.common_dit.pad_to_patch_size(time_dim_replace, self.patch_size)
+ x[:, :time_dim_replace.shape[1], :time_dim_replace.shape[2]] = time_dim_replace
+
+ freqs = self.rope_encode_3d(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options)
+ freqs_text = self.rope_encode_1d(context.shape[1], device=x.device, dtype=x.dtype, transformer_options=transformer_options)
+
+ out = self.forward_orig(x, timestep, context, y, freqs, freqs_text, transformer_options=transformer_options, **kwargs)
+ if original_dims == 4:
+ out = out.squeeze(2)
+ return out
+
+ def forward(self, x, timestep, context, y, time_dim_replace=None, transformer_options={}, **kwargs):
+ return comfy.patcher_extension.WrapperExecutor.new_class_executor(
+ self._forward,
+ self,
+ comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
+ ).execute(x, timestep, context, y, time_dim_replace=time_dim_replace, transformer_options=transformer_options, **kwargs)
diff --git a/comfy/ldm/lumina/controlnet.py b/comfy/ldm/lumina/controlnet.py
new file mode 100644
index 000000000..fd7ce3b5c
--- /dev/null
+++ b/comfy/ldm/lumina/controlnet.py
@@ -0,0 +1,113 @@
+import torch
+from torch import nn
+
+from .model import JointTransformerBlock
+
+class ZImageControlTransformerBlock(JointTransformerBlock):
+ def __init__(
+ self,
+ layer_id: int,
+ dim: int,
+ n_heads: int,
+ n_kv_heads: int,
+ multiple_of: int,
+ ffn_dim_multiplier: float,
+ norm_eps: float,
+ qk_norm: bool,
+ modulation=True,
+ block_id=0,
+ operation_settings=None,
+ ):
+ super().__init__(layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation, z_image_modulation=True, operation_settings=operation_settings)
+ self.block_id = block_id
+ if block_id == 0:
+ self.before_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+ self.after_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+ def forward(self, c, x, **kwargs):
+ if self.block_id == 0:
+ c = self.before_proj(c) + x
+ c = super().forward(c, **kwargs)
+ c_skip = self.after_proj(c)
+ return c_skip, c
+
+class ZImage_Control(torch.nn.Module):
+ def __init__(
+ self,
+ dim: int = 3840,
+ n_heads: int = 30,
+ n_kv_heads: int = 30,
+ multiple_of: int = 256,
+ ffn_dim_multiplier: float = (8.0 / 3.0),
+ norm_eps: float = 1e-5,
+ qk_norm: bool = True,
+ dtype=None,
+ device=None,
+ operations=None,
+ **kwargs
+ ):
+ super().__init__()
+ operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+
+ self.additional_in_dim = 0
+ self.control_in_dim = 16
+ n_refiner_layers = 2
+ self.n_control_layers = 6
+ self.control_layers = nn.ModuleList(
+ [
+ ZImageControlTransformerBlock(
+ i,
+ dim,
+ n_heads,
+ n_kv_heads,
+ multiple_of,
+ ffn_dim_multiplier,
+ norm_eps,
+ qk_norm,
+ block_id=i,
+ operation_settings=operation_settings,
+ )
+ for i in range(self.n_control_layers)
+ ]
+ )
+
+ all_x_embedder = {}
+ patch_size = 2
+ f_patch_size = 1
+ x_embedder = operations.Linear(f_patch_size * patch_size * patch_size * self.control_in_dim, dim, bias=True, device=device, dtype=dtype)
+ all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
+
+ self.control_all_x_embedder = nn.ModuleDict(all_x_embedder)
+ self.control_noise_refiner = nn.ModuleList(
+ [
+ JointTransformerBlock(
+ layer_id,
+ dim,
+ n_heads,
+ n_kv_heads,
+ multiple_of,
+ ffn_dim_multiplier,
+ norm_eps,
+ qk_norm,
+ modulation=True,
+ z_image_modulation=True,
+ operation_settings=operation_settings,
+ )
+ for layer_id in range(n_refiner_layers)
+ ]
+ )
+
+ def forward(self, cap_feats, control_context, x_freqs_cis, adaln_input):
+ patch_size = 2
+ f_patch_size = 1
+ pH = pW = patch_size
+ B, C, H, W = control_context.shape
+ control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))
+
+ x_attn_mask = None
+ for layer in self.control_noise_refiner:
+ control_context = layer(control_context, x_attn_mask, x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input)
+ return control_context
+
+ def forward_control_block(self, layer_id, control_context, x, x_attn_mask, x_freqs_cis, adaln_input):
+ return self.control_layers[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)
diff --git a/comfy/ldm/lumina/model.py b/comfy/ldm/lumina/model.py
index 7d7e9112c..6c24fed9b 100644
--- a/comfy/ldm/lumina/model.py
+++ b/comfy/ldm/lumina/model.py
@@ -22,6 +22,10 @@ def modulate(x, scale):
# Core NextDiT Model #
#############################################################################
+def clamp_fp16(x):
+ if x.dtype == torch.float16:
+ return torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
+ return x
class JointAttention(nn.Module):
"""Multi-head attention module."""
@@ -169,7 +173,7 @@ class FeedForward(nn.Module):
# @torch.compile
def _forward_silu_gating(self, x1, x3):
- return F.silu(x1) * x3
+ return clamp_fp16(F.silu(x1) * x3)
def forward(self, x):
return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
@@ -273,27 +277,27 @@ class JointTransformerBlock(nn.Module):
scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)
x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2(
- self.attention(
+ clamp_fp16(self.attention(
modulate(self.attention_norm1(x), scale_msa),
x_mask,
freqs_cis,
transformer_options=transformer_options,
- )
+ ))
)
x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
- self.feed_forward(
+ clamp_fp16(self.feed_forward(
modulate(self.ffn_norm1(x), scale_mlp),
- )
+ ))
)
else:
assert adaln_input is None
x = x + self.attention_norm2(
- self.attention(
+ clamp_fp16(self.attention(
self.attention_norm1(x),
x_mask,
freqs_cis,
transformer_options=transformer_options,
- )
+ ))
)
x = x + self.ffn_norm2(
self.feed_forward(
@@ -564,7 +568,7 @@ class NextDiT(nn.Module):
).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
# def forward(self, x, t, cap_feats, cap_mask):
- def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
+ def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, transformer_options={}, **kwargs):
t = 1.0 - timesteps
cap_feats = context
cap_mask = attention_mask
@@ -581,16 +585,23 @@ class NextDiT(nn.Module):
cap_feats = self.cap_embedder(cap_feats) # (N, L, D) # todo check if able to batchify w.o. redundant compute
- transformer_options = kwargs.get("transformer_options", {})
+ patches = transformer_options.get("patches", {})
x_is_tensor = isinstance(x, torch.Tensor)
- x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
- freqs_cis = freqs_cis.to(x.device)
+ img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
+ freqs_cis = freqs_cis.to(img.device)
- for layer in self.layers:
- x = layer(x, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+ for i, layer in enumerate(self.layers):
+ img = layer(img, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+ if "double_block" in patches:
+ for p in patches["double_block"]:
+ out = p({"img": img[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
+ if "img" in out:
+ img[:, cap_size[0]:] = out["img"]
+ if "txt" in out:
+ img[:, :cap_size[0]] = out["txt"]
- x = self.final_layer(x, adaln_input)
- x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]
+ img = self.final_layer(img, adaln_input)
+ img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]
- return -x
+ return -img
diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 7437e0567..a8800ded0 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -517,6 +517,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
@wrap_attn
def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+ exception_fallback = False
if skip_reshape:
b, _, _, dim_head = q.shape
tensor_layout = "HND"
@@ -541,6 +542,8 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
except Exception as e:
logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e))
+ exception_fallback = True
+ if exception_fallback:
if tensor_layout == "NHD":
q, k, v = map(
lambda t: t.transpose(1, 2),
diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py
index 4245eedca..681a55db5 100644
--- a/comfy/ldm/modules/diffusionmodules/model.py
+++ b/comfy/ldm/modules/diffusionmodules/model.py
@@ -13,6 +13,12 @@ if model_management.xformers_enabled_vae():
import xformers
import xformers.ops
+def torch_cat_if_needed(xl, dim):
+ if len(xl) > 1:
+ return torch.cat(xl, dim)
+ else:
+ return xl[0]
+
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
@@ -43,6 +49,37 @@ def Normalize(in_channels, num_groups=32):
return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+class CarriedConv3d(nn.Module):
+ def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding=0, **kwargs):
+ super().__init__()
+ self.conv = ops.Conv3d(n_channels, out_channels, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+ def forward(self, x):
+ return self.conv(x)
+
+
+def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):
+
+ x = xl[0]
+ xl.clear()
+
+ if isinstance(op, CarriedConv3d):
+ if conv_carry_in is None:
+ x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2, 0), mode = 'replicate')
+ else:
+ carry_len = conv_carry_in[0].shape[2]
+ x = torch.nn.functional.pad(x, (1, 1, 1, 1, 2 - carry_len, 0), mode = 'replicate')
+ x = torch.cat([conv_carry_in.pop(0), x], dim=2)
+
+ if conv_carry_out is not None:
+ to_push = x[:, :, -2:, :, :].clone()
+ conv_carry_out.append(to_push)
+
+ out = op(x)
+
+ return out
+
+
class VideoConv3d(nn.Module):
def __init__(self, n_channels, out_channels, kernel_size, stride=1, dilation=1, padding_mode='replicate', padding=1, **kwargs):
super().__init__()
@@ -89,29 +126,24 @@ class Upsample(nn.Module):
stride=1,
padding=1)
- def forward(self, x):
+ def forward(self, x, conv_carry_in=None, conv_carry_out=None):
scale_factor = self.scale_factor
if isinstance(scale_factor, (int, float)):
scale_factor = (scale_factor,) * (x.ndim - 2)
if x.ndim == 5 and scale_factor[0] > 1.0:
- t = x.shape[2]
- if t > 1:
- a, b = x.split((1, t - 1), dim=2)
- del x
- b = interpolate_up(b, scale_factor)
- else:
- a = x
-
- a = interpolate_up(a.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2)
- if t > 1:
- x = torch.cat((a, b), dim=2)
- else:
- x = a
+ results = []
+ if conv_carry_in is None:
+ first = x[:, :, :1, :, :]
+ results.append(interpolate_up(first.squeeze(2), scale_factor=scale_factor[1:]).unsqueeze(2))
+ x = x[:, :, 1:, :, :]
+ if x.shape[2] > 0:
+ results.append(interpolate_up(x, scale_factor))
+ x = torch_cat_if_needed(results, dim=2)
else:
x = interpolate_up(x, scale_factor)
if self.with_conv:
- x = self.conv(x)
+ x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
return x
@@ -127,17 +159,20 @@ class Downsample(nn.Module):
stride=stride,
padding=0)
- def forward(self, x):
+ def forward(self, x, conv_carry_in=None, conv_carry_out=None):
if self.with_conv:
- if x.ndim == 4:
+ if isinstance(self.conv, CarriedConv3d):
+ x = conv_carry_causal_3d([x], self.conv, conv_carry_in, conv_carry_out)
+ elif x.ndim == 4:
pad = (0, 1, 0, 1)
mode = "constant"
x = torch.nn.functional.pad(x, pad, mode=mode, value=0)
+ x = self.conv(x)
elif x.ndim == 5:
pad = (1, 1, 1, 1, 2, 0)
mode = "replicate"
x = torch.nn.functional.pad(x, pad, mode=mode)
- x = self.conv(x)
+ x = self.conv(x)
else:
x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
return x
@@ -183,23 +218,23 @@ class ResnetBlock(nn.Module):
stride=1,
padding=0)
- def forward(self, x, temb=None):
+ def forward(self, x, temb=None, conv_carry_in=None, conv_carry_out=None):
h = x
h = self.norm1(h)
- h = self.swish(h)
- h = self.conv1(h)
+ h = [ self.swish(h) ]
+ h = conv_carry_causal_3d(h, self.conv1, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
if temb is not None:
h = h + self.temb_proj(self.swish(temb))[:,:,None,None]
h = self.norm2(h)
h = self.swish(h)
- h = self.dropout(h)
- h = self.conv2(h)
+ h = [ self.dropout(h) ]
+ h = conv_carry_causal_3d(h, self.conv2, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
- x = self.conv_shortcut(x)
+ x = conv_carry_causal_3d([x], self.conv_shortcut, conv_carry_in=conv_carry_in, conv_carry_out=conv_carry_out)
else:
x = self.nin_shortcut(x)
@@ -279,6 +314,7 @@ def pytorch_attention(q, k, v):
orig_shape = q.shape
B = orig_shape[0]
C = orig_shape[1]
+ oom_fallback = False
q, k, v = map(
lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
(q, k, v),
@@ -289,6 +325,8 @@ def pytorch_attention(q, k, v):
out = out.transpose(2, 3).reshape(orig_shape)
except model_management.OOM_EXCEPTION:
logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
+ oom_fallback = True
+ if oom_fallback:
out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape)
return out
@@ -517,9 +555,14 @@ class Encoder(nn.Module):
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
+ self.carried = False
if conv3d:
- conv_op = VideoConv3d
+ if not attn_resolutions:
+ conv_op = CarriedConv3d
+ self.carried = True
+ else:
+ conv_op = VideoConv3d
mid_attn_conv_op = ops.Conv3d
else:
conv_op = ops.Conv2d
@@ -532,6 +575,7 @@ class Encoder(nn.Module):
stride=1,
padding=1)
+ self.time_compress = 1
curr_res = resolution
in_ch_mult = (1,)+tuple(ch_mult)
self.in_ch_mult = in_ch_mult
@@ -558,10 +602,15 @@ class Encoder(nn.Module):
if time_compress is not None:
if (self.num_resolutions - 1 - i_level) > math.log2(time_compress):
stride = (1, 2, 2)
+ else:
+ self.time_compress *= 2
down.downsample = Downsample(block_in, resamp_with_conv, stride=stride, conv_op=conv_op)
curr_res = curr_res // 2
self.down.append(down)
+ if time_compress is not None:
+ self.time_compress = time_compress
+
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in,
@@ -587,15 +636,42 @@ class Encoder(nn.Module):
def forward(self, x):
# timestep embedding
temb = None
- # downsampling
- h = self.conv_in(x)
- for i_level in range(self.num_resolutions):
- for i_block in range(self.num_res_blocks):
- h = self.down[i_level].block[i_block](h, temb)
- if len(self.down[i_level].attn) > 0:
- h = self.down[i_level].attn[i_block](h)
- if i_level != self.num_resolutions-1:
- h = self.down[i_level].downsample(h)
+
+ if self.carried:
+ xl = [x[:, :, :1, :, :]]
+ if x.shape[2] > self.time_compress:
+ tc = self.time_compress
+ xl += torch.split(x[:, :, 1: 1 + ((x.shape[2] - 1) // tc) * tc, :, :], tc * 2, dim = 2)
+ x = xl
+ else:
+ x = [x]
+ out = []
+
+ conv_carry_in = None
+
+ for i, x1 in enumerate(x):
+ conv_carry_out = []
+ if i == len(x) - 1:
+ conv_carry_out = None
+
+ # downsampling
+ x1 = [ x1 ]
+ h1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)
+
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h1 = self.down[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out)
+ if len(self.down[i_level].attn) > 0:
+ assert i == 0 #carried should not happen if attn exists
+ h1 = self.down[i_level].attn[i_block](h1)
+ if i_level != self.num_resolutions-1:
+ h1 = self.down[i_level].downsample(h1, conv_carry_in, conv_carry_out)
+
+ out.append(h1)
+ conv_carry_in = conv_carry_out
+
+ h = torch_cat_if_needed(out, dim=2)
+ del out
# middle
h = self.mid.block_1(h, temb)
@@ -604,15 +680,15 @@ class Encoder(nn.Module):
# end
h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
+ h = [ nonlinearity(h) ]
+ h = conv_carry_causal_3d(h, self.conv_out)
return h
class Decoder(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+ resolution, z_channels, tanh_out=False, use_linear_attn=False,
conv_out_op=ops.Conv2d,
resnet_op=ResnetBlock,
attn_op=AttnBlock,
@@ -626,12 +702,18 @@ class Decoder(nn.Module):
self.num_res_blocks = num_res_blocks
self.resolution = resolution
self.in_channels = in_channels
- self.give_pre_end = give_pre_end
self.tanh_out = tanh_out
+ self.carried = False
if conv3d:
- conv_op = VideoConv3d
- conv_out_op = VideoConv3d
+ if not attn_resolutions and resnet_op == ResnetBlock:
+ conv_op = CarriedConv3d
+ conv_out_op = CarriedConv3d
+ self.carried = True
+ else:
+ conv_op = VideoConv3d
+ conv_out_op = VideoConv3d
+
mid_attn_conv_op = ops.Conv3d
else:
conv_op = ops.Conv2d
@@ -706,29 +788,43 @@ class Decoder(nn.Module):
temb = None
# z to block_in
- h = self.conv_in(z)
+ h = conv_carry_causal_3d([z], self.conv_in)
# middle
h = self.mid.block_1(h, temb, **kwargs)
h = self.mid.attn_1(h, **kwargs)
h = self.mid.block_2(h, temb, **kwargs)
+ if self.carried:
+ h = torch.split(h, 2, dim=2)
+ else:
+ h = [ h ]
+ out = []
+
+ conv_carry_in = None
+
# upsampling
- for i_level in reversed(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks+1):
- h = self.up[i_level].block[i_block](h, temb, **kwargs)
- if len(self.up[i_level].attn) > 0:
- h = self.up[i_level].attn[i_block](h, **kwargs)
- if i_level != 0:
- h = self.up[i_level].upsample(h)
+ for i, h1 in enumerate(h):
+ conv_carry_out = []
+ if i == len(h) - 1:
+ conv_carry_out = None
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks+1):
+ h1 = self.up[i_level].block[i_block](h1, temb, conv_carry_in, conv_carry_out, **kwargs)
+ if len(self.up[i_level].attn) > 0:
+ assert i == 0 #carried should not happen if attn exists
+ h1 = self.up[i_level].attn[i_block](h1, **kwargs)
+ if i_level != 0:
+ h1 = self.up[i_level].upsample(h1, conv_carry_in, conv_carry_out)
- # end
- if self.give_pre_end:
- return h
+ h1 = self.norm_out(h1)
+ h1 = [ nonlinearity(h1) ]
+ h1 = conv_carry_causal_3d(h1, self.conv_out, conv_carry_in, conv_carry_out)
+ if self.tanh_out:
+ h1 = torch.tanh(h1)
+ out.append(h1)
+ conv_carry_in = conv_carry_out
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h, **kwargs)
- if self.tanh_out:
- h = torch.tanh(h)
- return h
+ out = torch_cat_if_needed(out, dim=2)
+
+ return out
diff --git a/comfy/lora.py b/comfy/lora.py
index 3a9077869..e7202ce97 100644
--- a/comfy/lora.py
+++ b/comfy/lora.py
@@ -322,6 +322,13 @@ def model_lora_keys_unet(model, key_map={}):
key_map["diffusion_model.{}".format(key_lora)] = to
key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = to
+ if isinstance(model, comfy.model_base.Kandinsky5):
+ for k in sdk:
+ if k.startswith("diffusion_model.") and k.endswith(".weight"):
+ key_lora = k[len("diffusion_model."):-len(".weight")]
+ key_map["{}".format(key_lora)] = k
+ key_map["transformer.{}".format(key_lora)] = k
+
return key_map
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 9b76c285e..0be006cc2 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -47,6 +47,7 @@ import comfy.ldm.chroma_radiance.model
import comfy.ldm.ace.model
import comfy.ldm.omnigen.omnigen2
import comfy.ldm.qwen_image.model
+import comfy.ldm.kandinsky5.model
import comfy.model_management
import comfy.patcher_extension
@@ -134,7 +135,7 @@ class BaseModel(torch.nn.Module):
if not unet_config.get("disable_unet_model_creation", False):
if model_config.custom_operations is None:
fp8 = model_config.optimizations.get("fp8", False)
- operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, scaled_fp8=model_config.scaled_fp8, model_config=model_config)
+ operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype, fp8_optimizations=fp8, model_config=model_config)
else:
operations = model_config.custom_operations
self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
@@ -329,18 +330,6 @@ class BaseModel(torch.nn.Module):
extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict))
unet_state_dict = self.diffusion_model.state_dict()
-
- if self.model_config.scaled_fp8 is not None:
- unet_state_dict["scaled_fp8"] = torch.tensor([], dtype=self.model_config.scaled_fp8)
-
- # Save mixed precision metadata
- if hasattr(self.model_config, 'layer_quant_config') and self.model_config.layer_quant_config:
- metadata = {
- "format_version": "1.0",
- "layers": self.model_config.layer_quant_config
- }
- unet_state_dict["_quantization_metadata"] = metadata
-
unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
if self.model_type == ModelType.V_PREDICTION:
@@ -1642,3 +1631,49 @@ class HunyuanVideo15_SR_Distilled(HunyuanVideo15):
out = super().extra_conds(**kwargs)
out['disable_time_r'] = comfy.conds.CONDConstant(False)
return out
+
+class Kandinsky5(BaseModel):
+ def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+ super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.kandinsky5.model.Kandinsky5)
+
+ def encode_adm(self, **kwargs):
+ return kwargs["pooled_output"]
+
+ def concat_cond(self, **kwargs):
+ noise = kwargs.get("noise", None)
+ device = kwargs["device"]
+ image = torch.zeros_like(noise)
+
+ mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
+ if mask is None:
+ mask = torch.zeros_like(noise)[:, :1]
+ else:
+ mask = 1.0 - mask
+ mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+ if mask.shape[-3] < noise.shape[-3]:
+ mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
+ mask = utils.resize_to_batch_size(mask, noise.shape[0])
+
+ return torch.cat((image, mask), dim=1)
+
+ 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)
+
+ time_dim_replace = kwargs.get("time_dim_replace", None)
+ if time_dim_replace is not None:
+ out['time_dim_replace'] = comfy.conds.CONDRegular(self.process_latent_in(time_dim_replace))
+
+ return out
+
+class Kandinsky5Image(Kandinsky5):
+ def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+ super().__init__(model_config, model_type, device=device)
+
+ def concat_cond(self, **kwargs):
+ return None
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 7afe4a798..30b33a486 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -6,20 +6,6 @@ import math
import logging
import torch
-
-def detect_layer_quantization(metadata):
- quant_key = "_quantization_metadata"
- if metadata is not None and quant_key in metadata:
- quant_metadata = metadata.pop(quant_key)
- quant_metadata = json.loads(quant_metadata)
- if isinstance(quant_metadata, dict) and "layers" in quant_metadata:
- logging.info(f"Found quantization metadata (version {quant_metadata.get('format_version', 'unknown')})")
- return quant_metadata["layers"]
- else:
- raise ValueError("Invalid quantization metadata format")
- return None
-
-
def count_blocks(state_dict_keys, prefix_string):
count = 0
while True:
@@ -208,12 +194,12 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["theta"] = 2000
dit_config["out_channels"] = 128
dit_config["global_modulation"] = True
- dit_config["vec_in_dim"] = None
dit_config["mlp_silu_act"] = True
dit_config["qkv_bias"] = False
dit_config["ops_bias"] = False
dit_config["default_ref_method"] = "index"
dit_config["ref_index_scale"] = 10.0
+ dit_config["txt_ids_dims"] = [3]
patch_size = 1
else:
dit_config["image_model"] = "flux"
@@ -223,6 +209,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["theta"] = 10000
dit_config["out_channels"] = 16
dit_config["qkv_bias"] = True
+ dit_config["txt_ids_dims"] = []
patch_size = 2
dit_config["in_channels"] = 16
@@ -245,6 +232,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix)
if vec_in_key in state_dict_keys:
dit_config["vec_in_dim"] = state_dict[vec_in_key].shape[1]
+ else:
+ dit_config["vec_in_dim"] = None
dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
@@ -270,6 +259,11 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["nerf_embedder_dtype"] = torch.float32
else:
dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
+ dit_config["yak_mlp"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys
+ dit_config["txt_norm"] = "{}txt_norm.scale".format(key_prefix) in state_dict_keys
+ if dit_config["yak_mlp"] and dit_config["txt_norm"]: # Ovis model
+ dit_config["txt_ids_dims"] = [1, 2]
+
return dit_config
if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview
@@ -617,6 +611,24 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.')
return dit_config
+ if '{}visual_transformer_blocks.0.cross_attention.key_norm.weight'.format(key_prefix) in state_dict_keys: # Kandinsky 5
+ dit_config = {}
+ model_dim = state_dict['{}visual_embeddings.in_layer.bias'.format(key_prefix)].shape[0]
+ dit_config["model_dim"] = model_dim
+ if model_dim in [4096, 2560]: # pro video and lite image
+ dit_config["axes_dims"] = (32, 48, 48)
+ if model_dim == 2560: # lite image
+ dit_config["rope_scale_factor"] = (1.0, 1.0, 1.0)
+ elif model_dim == 1792: # lite video
+ dit_config["axes_dims"] = (16, 24, 24)
+ dit_config["time_dim"] = state_dict['{}time_embeddings.in_layer.bias'.format(key_prefix)].shape[0]
+ dit_config["image_model"] = "kandinsky5"
+ dit_config["ff_dim"] = state_dict['{}visual_transformer_blocks.0.feed_forward.in_layer.weight'.format(key_prefix)].shape[0]
+ dit_config["visual_embed_dim"] = state_dict['{}visual_embeddings.in_layer.weight'.format(key_prefix)].shape[1]
+ dit_config["num_text_blocks"] = count_blocks(state_dict_keys, '{}text_transformer_blocks.'.format(key_prefix) + '{}.')
+ dit_config["num_visual_blocks"] = count_blocks(state_dict_keys, '{}visual_transformer_blocks.'.format(key_prefix) + '{}.')
+ return dit_config
+
if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
return None
@@ -759,22 +771,11 @@ 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_key = "{}scaled_fp8".format(unet_key_prefix)
- 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
- if scaled_fp8_weight.nelement() == 2:
- model_config.optimizations["fp8"] = False
- else:
- model_config.optimizations["fp8"] = True
-
# Detect per-layer quantization (mixed precision)
- layer_quant_config = detect_layer_quantization(metadata)
- if layer_quant_config:
- model_config.layer_quant_config = layer_quant_config
- logging.info(f"Detected mixed precision quantization: {len(layer_quant_config)} layers quantized")
+ quant_config = comfy.utils.detect_layer_quantization(state_dict, unet_key_prefix)
+ if quant_config:
+ model_config.quant_config = quant_config
+ logging.info("Detected mixed precision quantization")
return model_config
diff --git a/comfy/model_management.py b/comfy/model_management.py
index aeddbaefe..40717b1e4 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1492,6 +1492,20 @@ def extended_fp16_support():
return True
+LORA_COMPUTE_DTYPES = {}
+def lora_compute_dtype(device):
+ dtype = LORA_COMPUTE_DTYPES.get(device, None)
+ if dtype is not None:
+ return dtype
+
+ if should_use_fp16(device):
+ dtype = torch.float16
+ else:
+ dtype = torch.float32
+
+ LORA_COMPUTE_DTYPES[device] = dtype
+ return dtype
+
def soft_empty_cache(force=False):
global cpu_state
if cpu_state == CPUState.MPS:
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 3eac77275..5b1ccb824 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -126,27 +126,11 @@ class LowVramPatch:
def __init__(self, key, patches, convert_func=None, set_func=None):
self.key = key
self.patches = patches
- self.convert_func = convert_func
+ self.convert_func = convert_func # TODO: remove
self.set_func = set_func
def __call__(self, weight):
- intermediate_dtype = weight.dtype
- if self.convert_func is not None:
- weight = self.convert_func(weight, inplace=False)
-
- if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
- intermediate_dtype = torch.float32
- out = comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype)
- if self.set_func is None:
- return comfy.float.stochastic_rounding(out, weight.dtype, seed=string_to_seed(self.key))
- else:
- return self.set_func(out, seed=string_to_seed(self.key), return_weight=True)
-
- out = comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
- if self.set_func is not None:
- return self.set_func(out, seed=string_to_seed(self.key), return_weight=True).to(dtype=intermediate_dtype)
- else:
- return out
+ return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
#The above patch logic may cast up the weight to fp32, and do math. Go with fp32 x 3
LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR = 3
@@ -630,10 +614,11 @@ class ModelPatcher:
if key not in self.backup:
self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update)
+ temp_dtype = comfy.model_management.lora_compute_dtype(device_to)
if device_to is not None:
- temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
+ temp_weight = comfy.model_management.cast_to_device(weight, device_to, temp_dtype, copy=True)
else:
- temp_weight = weight.to(torch.float32, copy=True)
+ temp_weight = weight.to(temp_dtype, copy=True)
if convert_func is not None:
temp_weight = convert_func(temp_weight, inplace=True)
@@ -699,12 +684,12 @@ class ModelPatcher:
offloaded = []
offload_buffer = 0
loading.sort(reverse=True)
- for x in loading:
+ for i, x in enumerate(loading):
module_offload_mem, module_mem, n, m, params = x
lowvram_weight = False
- potential_offload = max(offload_buffer, module_offload_mem * (comfy.model_management.NUM_STREAMS + 1))
+ potential_offload = max(offload_buffer, module_offload_mem + sum([ x1[1] for x1 in loading[i+1:i+1+comfy.model_management.NUM_STREAMS]]))
lowvram_fits = mem_counter + module_mem + potential_offload < lowvram_model_memory
weight_key = "{}.weight".format(n)
@@ -777,6 +762,8 @@ class ModelPatcher:
key = "{}.{}".format(n, param)
self.unpin_weight(key)
self.patch_weight_to_device(key, device_to=device_to)
+ if comfy.model_management.is_device_cuda(device_to):
+ torch.cuda.synchronize()
logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
m.comfy_patched_weights = True
@@ -876,14 +863,18 @@ class ModelPatcher:
patch_counter = 0
unload_list = self._load_list()
unload_list.sort()
+
offload_buffer = self.model.model_offload_buffer_memory
+ if len(unload_list) > 0:
+ NS = comfy.model_management.NUM_STREAMS
+ offload_weight_factor = [ min(offload_buffer / (NS + 1), unload_list[0][1]) ] * NS
for unload in unload_list:
if memory_to_free + offload_buffer - self.model.model_offload_buffer_memory < memory_freed:
break
module_offload_mem, module_mem, n, m, params = unload
- potential_offload = (comfy.model_management.NUM_STREAMS + 1) * module_offload_mem
+ potential_offload = module_offload_mem + sum(offload_weight_factor)
lowvram_possible = hasattr(m, "comfy_cast_weights")
if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True:
@@ -935,6 +926,8 @@ class ModelPatcher:
m.comfy_patched_weights = False
memory_freed += module_mem
offload_buffer = max(offload_buffer, potential_offload)
+ offload_weight_factor.append(module_mem)
+ offload_weight_factor.pop(0)
logging.debug("freed {}".format(n))
for param in params:
diff --git a/comfy/ops.py b/comfy/ops.py
index 61a2f0754..35237c9f7 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -23,6 +23,7 @@ from comfy.cli_args import args, PerformanceFeature
import comfy.float
import comfy.rmsnorm
import contextlib
+import json
def run_every_op():
if torch.compiler.is_compiling():
@@ -111,22 +112,24 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
if s.bias is not None:
bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=bias_has_function, stream=offload_stream)
- if bias_has_function:
- with wf_context:
- for f in s.bias_function:
- bias = f(bias)
+ comfy.model_management.sync_stream(device, offload_stream)
+
+ bias_a = bias
+ weight_a = weight
+
+ if s.bias is not None:
+ for f in s.bias_function:
+ bias = f(bias)
if weight_has_function or weight.dtype != dtype:
- with wf_context:
- weight = weight.to(dtype=dtype)
- if isinstance(weight, QuantizedTensor):
- weight = weight.dequantize()
- for f in s.weight_function:
- weight = f(weight)
+ weight = weight.to(dtype=dtype)
+ if isinstance(weight, QuantizedTensor):
+ weight = weight.dequantize()
+ for f in s.weight_function:
+ weight = f(weight)
- comfy.model_management.sync_stream(device, offload_stream)
if offloadable:
- return weight, bias, offload_stream
+ return weight, bias, (offload_stream, weight_a, bias_a)
else:
#Legacy function signature
return weight, bias
@@ -135,13 +138,16 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
def uncast_bias_weight(s, weight, bias, offload_stream):
if offload_stream is None:
return
- if weight is not None:
- device = weight.device
+ os, weight_a, bias_a = offload_stream
+ if os is None:
+ return
+ if weight_a is not None:
+ device = weight_a.device
else:
- if bias is None:
+ if bias_a is None:
return
- device = bias.device
- offload_stream.wait_stream(comfy.model_management.current_stream(device))
+ device = bias_a.device
+ os.wait_stream(comfy.model_management.current_stream(device))
class CastWeightBiasOp:
@@ -417,22 +423,12 @@ def fp8_linear(self, input):
if input.ndim == 3 or input.ndim == 2:
w, bias, offload_stream = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype, offloadable=True)
+ scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
- scale_weight = self.scale_weight
- scale_input = self.scale_input
- if scale_weight is None:
- scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
- else:
- scale_weight = scale_weight.to(input.device)
-
- if scale_input is None:
- scale_input = torch.ones((), device=input.device, dtype=torch.float32)
- input = torch.clamp(input, min=-448, max=448, out=input)
- layout_params_weight = {'scale': scale_input, 'orig_dtype': input_dtype}
- quantized_input = QuantizedTensor(input.to(dtype).contiguous(), "TensorCoreFP8Layout", layout_params_weight)
- else:
- scale_input = scale_input.to(input.device)
- quantized_input = QuantizedTensor.from_float(input, "TensorCoreFP8Layout", scale=scale_input, dtype=dtype)
+ scale_input = torch.ones((), device=input.device, dtype=torch.float32)
+ input = torch.clamp(input, min=-448, max=448, out=input)
+ layout_params_weight = {'scale': scale_input, 'orig_dtype': input_dtype}
+ quantized_input = QuantizedTensor(input.to(dtype).contiguous(), "TensorCoreFP8Layout", layout_params_weight)
# Wrap weight in QuantizedTensor - this enables unified dispatch
# Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!
@@ -453,7 +449,7 @@ class fp8_ops(manual_cast):
return None
def forward_comfy_cast_weights(self, input):
- if not self.training:
+ if len(self.weight_function) == 0 and len(self.bias_function) == 0:
try:
out = fp8_linear(self, input)
if out is not None:
@@ -466,59 +462,6 @@ class fp8_ops(manual_cast):
uncast_bias_weight(self, weight, bias, offload_stream)
return x
-def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None):
- logging.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input))
- class scaled_fp8_op(manual_cast):
- class Linear(manual_cast.Linear):
- def __init__(self, *args, **kwargs):
- if override_dtype is not None:
- kwargs['dtype'] = override_dtype
- super().__init__(*args, **kwargs)
-
- def reset_parameters(self):
- if not hasattr(self, 'scale_weight'):
- self.scale_weight = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
-
- if not scale_input:
- self.scale_input = None
-
- if not hasattr(self, 'scale_input'):
- self.scale_input = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
- return None
-
- def forward_comfy_cast_weights(self, input):
- if fp8_matrix_mult:
- out = fp8_linear(self, input)
- if out is not None:
- return out
-
- weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
-
- if weight.numel() < input.numel(): #TODO: optimize
- x = torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
- else:
- x = torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
- uncast_bias_weight(self, weight, bias, offload_stream)
- return x
-
- def convert_weight(self, weight, inplace=False, **kwargs):
- if inplace:
- weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype)
- return weight
- else:
- return weight.to(dtype=torch.float32) * self.scale_weight.to(device=weight.device, dtype=torch.float32)
-
- def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
- weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
- if return_weight:
- return weight
- if inplace_update:
- self.weight.data.copy_(weight)
- else:
- self.weight = torch.nn.Parameter(weight, requires_grad=False)
-
- return scaled_fp8_op
-
CUBLAS_IS_AVAILABLE = False
try:
from cublas_ops import CublasLinear
@@ -545,9 +488,9 @@ if CUBLAS_IS_AVAILABLE:
from .quant_ops import QuantizedTensor, QUANT_ALGOS
-def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False):
+def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False):
class MixedPrecisionOps(manual_cast):
- _layer_quant_config = layer_quant_config
+ _quant_config = quant_config
_compute_dtype = compute_dtype
_full_precision_mm = full_precision_mm
@@ -590,27 +533,38 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
manually_loaded_keys = [weight_key]
- if layer_name not in MixedPrecisionOps._layer_quant_config:
+ layer_conf = state_dict.pop(f"{prefix}comfy_quant", None)
+ if layer_conf is not None:
+ layer_conf = json.loads(layer_conf.numpy().tobytes())
+
+ if layer_conf is None:
self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False)
else:
- quant_format = MixedPrecisionOps._layer_quant_config[layer_name].get("format", None)
- if quant_format is None:
+ self.quant_format = layer_conf.get("format", None)
+ if not self._full_precision_mm:
+ self._full_precision_mm = layer_conf.get("full_precision_matrix_mult", False)
+
+ if self.quant_format is None:
raise ValueError(f"Unknown quantization format for layer {layer_name}")
- qconfig = QUANT_ALGOS[quant_format]
+ qconfig = QUANT_ALGOS[self.quant_format]
self.layout_type = qconfig["comfy_tensor_layout"]
weight_scale_key = f"{prefix}weight_scale"
+ scale = state_dict.pop(weight_scale_key, None)
+ if scale is not None:
+ scale = scale.to(device)
layout_params = {
- 'scale': state_dict.pop(weight_scale_key, None),
+ 'scale': scale,
'orig_dtype': MixedPrecisionOps._compute_dtype,
'block_size': qconfig.get("group_size", None),
}
- if layout_params['scale'] is not None:
+
+ if scale is not None:
manually_loaded_keys.append(weight_scale_key)
self.weight = torch.nn.Parameter(
- QuantizedTensor(weight.to(device=device), self.layout_type, layout_params),
+ QuantizedTensor(weight.to(device=device, dtype=qconfig.get("storage_t", None)), self.layout_type, layout_params),
requires_grad=False
)
@@ -619,7 +573,7 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
_v = state_dict.pop(param_key, None)
if _v is None:
continue
- setattr(self, param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False))
+ self.register_parameter(param_name, torch.nn.Parameter(_v.to(device=device), requires_grad=False))
manually_loaded_keys.append(param_key)
super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
@@ -628,6 +582,16 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
if key in missing_keys:
missing_keys.remove(key)
+ def state_dict(self, *args, destination=None, prefix="", **kwargs):
+ sd = super().state_dict(*args, destination=destination, prefix=prefix, **kwargs)
+ if isinstance(self.weight, QuantizedTensor):
+ sd["{}weight_scale".format(prefix)] = self.weight._layout_params['scale']
+ quant_conf = {"format": self.quant_format}
+ if self._full_precision_mm:
+ quant_conf["full_precision_matrix_mult"] = True
+ sd["{}comfy_quant".format(prefix)] = torch.frombuffer(json.dumps(quant_conf).encode('utf-8'), dtype=torch.uint8)
+ return sd
+
def _forward(self, input, weight, bias):
return torch.nn.functional.linear(input, weight, bias)
@@ -643,9 +607,8 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
if self._full_precision_mm or self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
return self.forward_comfy_cast_weights(input, *args, **kwargs)
if (getattr(self, 'layout_type', None) is not None and
- getattr(self, 'input_scale', None) is not None and
not isinstance(input, QuantizedTensor)):
- input = QuantizedTensor.from_float(input, self.layout_type, scale=self.input_scale, dtype=self.weight.dtype)
+ input = QuantizedTensor.from_float(input, self.layout_type, scale=getattr(self, 'input_scale', None), dtype=self.weight.dtype)
return self._forward(input, self.weight, self.bias)
def convert_weight(self, weight, inplace=False, **kwargs):
@@ -656,7 +619,7 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
if getattr(self, 'layout_type', None) is not None:
- weight = QuantizedTensor.from_float(weight, self.layout_type, scale=None, dtype=self.weight.dtype, stochastic_rounding=seed, inplace_ops=True)
+ weight = QuantizedTensor.from_float(weight, self.layout_type, scale="recalculate", dtype=self.weight.dtype, stochastic_rounding=seed, inplace_ops=True)
else:
weight = weight.to(self.weight.dtype)
if return_weight:
@@ -665,17 +628,28 @@ def mixed_precision_ops(layer_quant_config={}, compute_dtype=torch.bfloat16, ful
assert inplace_update is False # TODO: eventually remove the inplace_update stuff
self.weight = torch.nn.Parameter(weight, requires_grad=False)
+ def _apply(self, fn, recurse=True): # This is to get torch.compile + moving weights to another device working
+ if recurse:
+ for module in self.children():
+ module._apply(fn)
+
+ for key, param in self._parameters.items():
+ if param is None:
+ continue
+ self.register_parameter(key, torch.nn.Parameter(fn(param), requires_grad=False))
+ for key, buf in self._buffers.items():
+ if buf is not None:
+ self._buffers[key] = fn(buf)
+ return self
+
return MixedPrecisionOps
-def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None, model_config=None):
+def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None):
fp8_compute = comfy.model_management.supports_fp8_compute(load_device) # TODO: if we support more ops this needs to be more granular
- if model_config and hasattr(model_config, 'layer_quant_config') and model_config.layer_quant_config:
- logging.info(f"Using mixed precision operations: {len(model_config.layer_quant_config)} quantized layers")
- return mixed_precision_ops(model_config.layer_quant_config, compute_dtype, full_precision_mm=not fp8_compute)
-
- if scaled_fp8 is not None:
- return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8)
+ if model_config and hasattr(model_config, 'quant_config') and model_config.quant_config:
+ logging.info("Using mixed precision operations")
+ return mixed_precision_ops(model_config.quant_config, compute_dtype, full_precision_mm=not fp8_compute)
if (
fp8_compute and
diff --git a/comfy/quant_ops.py b/comfy/quant_ops.py
index bb1fb860c..571d3f760 100644
--- a/comfy/quant_ops.py
+++ b/comfy/quant_ops.py
@@ -238,6 +238,9 @@ class QuantizedTensor(torch.Tensor):
def is_contiguous(self, *arg, **kwargs):
return self._qdata.is_contiguous(*arg, **kwargs)
+ def storage(self):
+ return self._qdata.storage()
+
# ==============================================================================
# Generic Utilities (Layout-Agnostic Operations)
# ==============================================================================
@@ -249,12 +252,6 @@ def _create_transformed_qtensor(qt, transform_fn):
def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=None, op_name="to"):
- if target_dtype is not None and target_dtype != qt.dtype:
- logging.warning(
- f"QuantizedTensor: dtype conversion requested to {target_dtype}, "
- f"but not supported for quantized tensors. Ignoring dtype."
- )
-
if target_layout is not None and target_layout != torch.strided:
logging.warning(
f"QuantizedTensor: layout change requested to {target_layout}, "
@@ -274,6 +271,8 @@ def _handle_device_transfer(qt, target_device, target_dtype=None, target_layout=
logging.debug(f"QuantizedTensor.{op_name}: Moving from {current_device} to {target_device}")
new_q_data = qt._qdata.to(device=target_device)
new_params = _move_layout_params_to_device(qt._layout_params, target_device)
+ if target_dtype is not None:
+ new_params["orig_dtype"] = target_dtype
new_qt = QuantizedTensor(new_q_data, qt._layout_type, new_params)
logging.debug(f"QuantizedTensor.{op_name}: Created new tensor on {target_device}")
return new_qt
@@ -339,7 +338,9 @@ def generic_copy_(func, args, kwargs):
# Copy from another quantized tensor
qt_dest._qdata.copy_(src._qdata, non_blocking=non_blocking)
qt_dest._layout_type = src._layout_type
+ orig_dtype = qt_dest._layout_params["orig_dtype"]
_copy_layout_params_inplace(src._layout_params, qt_dest._layout_params, non_blocking=non_blocking)
+ qt_dest._layout_params["orig_dtype"] = orig_dtype
else:
# Copy from regular tensor - just copy raw data
qt_dest._qdata.copy_(src)
@@ -397,17 +398,20 @@ class TensorCoreFP8Layout(QuantizedLayout):
def quantize(cls, tensor, scale=None, dtype=torch.float8_e4m3fn, stochastic_rounding=0, inplace_ops=False):
orig_dtype = tensor.dtype
- if scale is None:
+ if isinstance(scale, str) and scale == "recalculate":
scale = torch.amax(tensor.abs()) / torch.finfo(dtype).max
- if not isinstance(scale, torch.Tensor):
- scale = torch.tensor(scale)
- scale = scale.to(device=tensor.device, dtype=torch.float32)
+ if scale is not None:
+ if not isinstance(scale, torch.Tensor):
+ scale = torch.tensor(scale)
+ scale = scale.to(device=tensor.device, dtype=torch.float32)
- if inplace_ops:
- tensor *= (1.0 / scale).to(tensor.dtype)
+ if inplace_ops:
+ tensor *= (1.0 / scale).to(tensor.dtype)
+ else:
+ tensor = tensor * (1.0 / scale).to(tensor.dtype)
else:
- tensor = tensor * (1.0 / scale).to(tensor.dtype)
+ scale = torch.ones((), device=tensor.device, dtype=torch.float32)
if stochastic_rounding > 0:
tensor = comfy.float.stochastic_rounding(tensor, dtype=dtype, seed=stochastic_rounding)
diff --git a/comfy/sd.py b/comfy/sd.py
index 9eeb0c45a..754b1703d 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -53,6 +53,8 @@ import comfy.text_encoders.omnigen2
import comfy.text_encoders.qwen_image
import comfy.text_encoders.hunyuan_image
import comfy.text_encoders.z_image
+import comfy.text_encoders.ovis
+import comfy.text_encoders.kandinsky5
import comfy.model_patcher
import comfy.lora
@@ -97,7 +99,7 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
class CLIP:
- def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, model_options={}):
+ def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}, parameters=0, state_dict=[], model_options={}):
if no_init:
return
params = target.params.copy()
@@ -128,6 +130,27 @@ class CLIP:
self.patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram
self.patcher.is_clip = True
self.apply_hooks_to_conds = None
+ if len(state_dict) > 0:
+ if isinstance(state_dict, list):
+ for c in state_dict:
+ m, u = self.load_sd(c)
+ if len(m) > 0:
+ logging.warning("clip missing: {}".format(m))
+
+ if len(u) > 0:
+ logging.debug("clip unexpected: {}".format(u))
+ else:
+ m, u = self.load_sd(state_dict, full_model=True)
+ if len(m) > 0:
+ m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
+ if len(m_filter) > 0:
+ logging.warning("clip missing: {}".format(m))
+ else:
+ logging.debug("clip missing: {}".format(m))
+
+ if len(u) > 0:
+ logging.debug("clip unexpected {}:".format(u))
+
if params['device'] == load_device:
model_management.load_models_gpu([self.patcher], force_full_load=True)
self.layer_idx = None
@@ -192,6 +215,7 @@ class CLIP:
self.cond_stage_model.set_clip_options({"projected_pooled": False})
self.load_model()
+ self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
all_hooks.reset()
self.patcher.patch_hooks(None)
if show_pbar:
@@ -239,6 +263,7 @@ class CLIP:
self.cond_stage_model.set_clip_options({"projected_pooled": False})
self.load_model()
+ self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
o = self.cond_stage_model.encode_token_weights(tokens)
cond, pooled = o[:2]
if return_dict:
@@ -468,7 +493,7 @@ class VAE:
decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig})
self.memory_used_encode = lambda shape, dtype: (1400 * 9 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)
- self.memory_used_decode = lambda shape, dtype: (2800 * 4 * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)
+ self.memory_used_decode = lambda shape, dtype: (3600 * 4 * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)
elif "decoder.conv_in.conv.weight" in sd:
ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
ddconfig["conv3d"] = True
@@ -480,8 +505,10 @@ class VAE:
self.latent_dim = 3
self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
- self.memory_used_decode = lambda shape, dtype: (1500 * shape[2] * shape[3] * shape[4] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
- self.memory_used_encode = lambda shape, dtype: (900 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
+ #This is likely to significantly over-estimate with single image or low frame counts as the
+ #implementation is able to completely skip caching. Rework if used as an image only VAE
+ self.memory_used_decode = lambda shape, dtype: (2800 * min(8, ((shape[2] - 1) * 4) + 1) * shape[3] * shape[4] * (8 * 8)) * model_management.dtype_size(dtype)
+ self.memory_used_encode = lambda shape, dtype: (1400 * min(9, shape[2]) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
elif "decoder.unpatcher3d.wavelets" in sd:
self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 8, 8)
@@ -740,6 +767,8 @@ class VAE:
self.throw_exception_if_invalid()
pixel_samples = None
do_tile = False
+ if self.latent_dim == 2 and samples_in.ndim == 5:
+ samples_in = samples_in[:, :, 0]
try:
memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
@@ -956,16 +985,17 @@ class CLIPType(Enum):
QWEN_IMAGE = 18
HUNYUAN_IMAGE = 19
HUNYUAN_VIDEO_15 = 20
+ OVIS = 21
+ KANDINSKY5 = 22
+ KANDINSKY5_IMAGE = 23
def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
clip_data = []
for p in ckpt_paths:
sd, metadata = comfy.utils.load_torch_file(p, safe_load=True, return_metadata=True)
- if metadata is not None:
- quant_metadata = metadata.get("_quantization_metadata", None)
- if quant_metadata is not None:
- sd["_quantization_metadata"] = quant_metadata
+ if model_options.get("custom_operations", None) is None:
+ sd, metadata = comfy.utils.convert_old_quants(sd, model_prefix="", metadata=metadata)
clip_data.append(sd)
return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options)
@@ -987,6 +1017,7 @@ class TEModel(Enum):
MISTRAL3_24B = 14
MISTRAL3_24B_PRUNED_FLUX2 = 15
QWEN3_4B = 16
+ QWEN3_2B = 17
def detect_te_model(sd):
@@ -1020,9 +1051,12 @@ def detect_te_model(sd):
if weight.shape[0] == 512:
return TEModel.QWEN25_7B
if "model.layers.0.post_attention_layernorm.weight" in sd:
- if 'model.layers.0.self_attn.q_norm.weight' in sd:
- return TEModel.QWEN3_4B
weight = sd['model.layers.0.post_attention_layernorm.weight']
+ if 'model.layers.0.self_attn.q_norm.weight' in sd:
+ if weight.shape[0] == 2560:
+ return TEModel.QWEN3_4B
+ elif weight.shape[0] == 2048:
+ return TEModel.QWEN3_2B
if weight.shape[0] == 5120:
if "model.layers.39.post_attention_layernorm.weight" in sd:
return TEModel.MISTRAL3_24B
@@ -1078,7 +1112,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=True, t5=False)
clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
elif clip_type == CLIPType.HIDREAM:
- clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None)
+ clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=False, clip_g=True, t5=False, llama=False, dtype_t5=None, dtype_llama=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
else:
clip_target.clip = sdxl_clip.SDXLRefinerClipModel
@@ -1102,7 +1136,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
elif clip_type == CLIPType.HIDREAM:
clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data),
- clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None)
+ clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
else: #CLIPType.MOCHI
clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
@@ -1131,7 +1165,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
elif te_model == TEModel.LLAMA3_8:
clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),
- clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)
+ clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
elif te_model == TEModel.QWEN25_3B:
clip_target.clip = comfy.text_encoders.omnigen2.te(**llama_detect(clip_data))
@@ -1150,13 +1184,16 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
elif te_model == TEModel.QWEN3_4B:
clip_target.clip = comfy.text_encoders.z_image.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.z_image.ZImageTokenizer
+ elif te_model == TEModel.QWEN3_2B:
+ clip_target.clip = comfy.text_encoders.ovis.te(**llama_detect(clip_data))
+ clip_target.tokenizer = comfy.text_encoders.ovis.OvisTokenizer
else:
# clip_l
if clip_type == CLIPType.SD3:
clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
elif clip_type == CLIPType.HIDREAM:
- clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None)
+ clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
else:
clip_target.clip = sd1_clip.SD1ClipModel
@@ -1199,6 +1236,12 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
elif clip_type == CLIPType.HUNYUAN_VIDEO_15:
clip_target.clip = comfy.text_encoders.hunyuan_image.te(**llama_detect(clip_data))
clip_target.tokenizer = comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer
+ elif clip_type == CLIPType.KANDINSKY5:
+ clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))
+ clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5Tokenizer
+ elif clip_type == CLIPType.KANDINSKY5_IMAGE:
+ clip_target.clip = comfy.text_encoders.kandinsky5.te(**llama_detect(clip_data))
+ clip_target.tokenizer = comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage
else:
clip_target.clip = sdxl_clip.SDXLClipModel
clip_target.tokenizer = sdxl_clip.SDXLTokenizer
@@ -1211,19 +1254,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
parameters = 0
for c in clip_data:
- if "_quantization_metadata" in c:
- c.pop("_quantization_metadata")
parameters += comfy.utils.calculate_parameters(c)
tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
- clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options)
- for c in clip_data:
- m, u = clip.load_sd(c)
- if len(m) > 0:
- logging.warning("clip missing: {}".format(m))
-
- if len(u) > 0:
- logging.debug("clip unexpected: {}".format(u))
+ clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, state_dict=clip_data, model_options=model_options)
return clip
def load_gligen(ckpt_path):
@@ -1282,6 +1316,10 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix)
load_device = model_management.get_torch_device()
+ custom_operations = model_options.get("custom_operations", None)
+ if custom_operations is None:
+ sd, metadata = comfy.utils.convert_old_quants(sd, diffusion_model_prefix, metadata=metadata)
+
model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix, metadata=metadata)
if model_config is None:
logging.warning("Warning, This is not a checkpoint file, trying to load it as a diffusion model only.")
@@ -1290,18 +1328,22 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
return None
return (diffusion_model, None, VAE(sd={}), None) # The VAE object is there to throw an exception if it's actually used'
-
unet_weight_dtype = list(model_config.supported_inference_dtypes)
- if model_config.scaled_fp8 is not None:
+ if model_config.quant_config is not None:
weight_dtype = None
- model_config.custom_operations = model_options.get("custom_operations", None)
+ if custom_operations is not None:
+ model_config.custom_operations = custom_operations
+
unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None))
if unet_dtype is None:
unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)
- manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
+ if model_config.quant_config is not None:
+ manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
+ else:
+ manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
if model_config.clip_vision_prefix is not None:
@@ -1319,22 +1361,33 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
vae = VAE(sd=vae_sd, metadata=metadata)
if output_clip:
+ if te_model_options.get("custom_operations", None) is None:
+ scaled_fp8_list = []
+ for k in list(sd.keys()): # Convert scaled fp8 to mixed ops
+ if k.endswith(".scaled_fp8"):
+ scaled_fp8_list.append(k[:-len("scaled_fp8")])
+
+ if len(scaled_fp8_list) > 0:
+ out_sd = {}
+ for k in sd:
+ skip = False
+ for pref in scaled_fp8_list:
+ skip = skip or k.startswith(pref)
+ if not skip:
+ out_sd[k] = sd[k]
+
+ for pref in scaled_fp8_list:
+ quant_sd, qmetadata = comfy.utils.convert_old_quants(sd, pref, metadata={})
+ for k in quant_sd:
+ out_sd[k] = quant_sd[k]
+ sd = out_sd
+
clip_target = model_config.clip_target(state_dict=sd)
if clip_target is not None:
clip_sd = model_config.process_clip_state_dict(sd)
if len(clip_sd) > 0:
parameters = comfy.utils.calculate_parameters(clip_sd)
- clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, model_options=te_model_options)
- m, u = clip.load_sd(clip_sd, full_model=True)
- if len(m) > 0:
- m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
- if len(m_filter) > 0:
- logging.warning("clip missing: {}".format(m))
- else:
- logging.debug("clip missing: {}".format(m))
-
- if len(u) > 0:
- logging.debug("clip unexpected {}:".format(u))
+ clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd, parameters=parameters, state_dict=clip_sd, model_options=te_model_options)
else:
logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
@@ -1381,6 +1434,9 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
if len(temp_sd) > 0:
sd = temp_sd
+ custom_operations = model_options.get("custom_operations", None)
+ if custom_operations is None:
+ sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata)
parameters = comfy.utils.calculate_parameters(sd)
weight_dtype = comfy.utils.weight_dtype(sd)
@@ -1411,7 +1467,7 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
offload_device = model_management.unet_offload_device()
unet_weight_dtype = list(model_config.supported_inference_dtypes)
- if model_config.scaled_fp8 is not None:
+ if model_config.quant_config is not None:
weight_dtype = None
if dtype is None:
@@ -1419,12 +1475,15 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None):
else:
unet_dtype = dtype
- if model_config.layer_quant_config is not None:
+ if model_config.quant_config is not None:
manual_cast_dtype = model_management.unet_manual_cast(None, load_device, model_config.supported_inference_dtypes)
else:
manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
- model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
+
+ if custom_operations is not None:
+ model_config.custom_operations = custom_operations
+
if model_options.get("fp8_optimizations", False):
model_config.optimizations["fp8"] = True
@@ -1463,6 +1522,9 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m
if vae is not None:
vae_sd = vae.get_sd()
+ if metadata is None:
+ metadata = {}
+
model_management.load_models_gpu(load_models, force_patch_weights=True)
clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None
sd = model.model.state_dict_for_saving(clip_sd, vae_sd, clip_vision_sd)
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 0fc9ab3db..962948dae 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -107,29 +107,17 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
config[k] = v
operations = model_options.get("custom_operations", None)
- scaled_fp8 = None
- quantization_metadata = model_options.get("quantization_metadata", None)
+ quant_config = model_options.get("quantization_metadata", None)
if operations is None:
- layer_quant_config = None
- if quantization_metadata is not None:
- layer_quant_config = json.loads(quantization_metadata).get("layers", None)
-
- if layer_quant_config is not None:
- operations = comfy.ops.mixed_precision_ops(layer_quant_config, dtype, full_precision_mm=True)
- logging.info(f"Using MixedPrecisionOps for text encoder: {len(layer_quant_config)} quantized layers")
+ if quant_config is not None:
+ operations = comfy.ops.mixed_precision_ops(quant_config, dtype, full_precision_mm=True)
+ logging.info("Using MixedPrecisionOps for text encoder")
else:
- # Fallback to scaled_fp8_ops for backward compatibility
- scaled_fp8 = model_options.get("scaled_fp8", None)
- if scaled_fp8 is not None:
- operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8)
- else:
- operations = comfy.ops.manual_cast
+ operations = comfy.ops.manual_cast
self.operations = operations
self.transformer = model_class(config, dtype, device, self.operations)
- if scaled_fp8 is not None:
- self.transformer.scaled_fp8 = torch.nn.Parameter(torch.tensor([], dtype=scaled_fp8))
self.num_layers = self.transformer.num_layers
@@ -147,6 +135,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
self.layer_norm_hidden_state = layer_norm_hidden_state
self.return_projected_pooled = return_projected_pooled
self.return_attention_masks = return_attention_masks
+ self.execution_device = None
if layer == "hidden":
assert layer_idx is not None
@@ -163,6 +152,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
def set_clip_options(self, options):
layer_idx = options.get("layer", self.layer_idx)
self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
+ self.execution_device = options.get("execution_device", self.execution_device)
if isinstance(self.layer, list) or self.layer == "all":
pass
elif layer_idx is None or abs(layer_idx) > self.num_layers:
@@ -175,6 +165,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
self.layer = self.options_default[0]
self.layer_idx = self.options_default[1]
self.return_projected_pooled = self.options_default[2]
+ self.execution_device = None
def process_tokens(self, tokens, device):
end_token = self.special_tokens.get("end", None)
@@ -258,7 +249,11 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens, embeds_info
def forward(self, tokens):
- device = self.transformer.get_input_embeddings().weight.device
+ if self.execution_device is None:
+ device = self.transformer.get_input_embeddings().weight.device
+ else:
+ device = self.execution_device
+
embeds, attention_mask, num_tokens, embeds_info = self.process_tokens(tokens, device)
attention_mask_model = None
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index af8120400..383c82c3e 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -21,6 +21,7 @@ import comfy.text_encoders.ace
import comfy.text_encoders.omnigen2
import comfy.text_encoders.qwen_image
import comfy.text_encoders.hunyuan_image
+import comfy.text_encoders.kandinsky5
import comfy.text_encoders.z_image
from . import supported_models_base
@@ -1027,6 +1028,8 @@ class ZImage(Lumina2):
memory_usage_factor = 1.7
+ supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+
def clip_target(self, state_dict={}):
pref = self.text_encoder_key_prefix[0]
hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_4b.transformer.".format(pref))
@@ -1472,7 +1475,60 @@ class HunyuanVideo15_SR_Distilled(HunyuanVideo):
hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideo15Tokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect))
-models = [LotusD, 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, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2]
+class Kandinsky5(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "kandinsky5",
+ }
+
+ sampling_settings = {
+ "shift": 10.0,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.HunyuanVideo
+
+ memory_usage_factor = 1.1 #TODO
+
+ 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.Kandinsky5(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5Tokenizer, comfy.text_encoders.kandinsky5.te(**hunyuan_detect))
+
+
+class Kandinsky5Image(Kandinsky5):
+ unet_config = {
+ "image_model": "kandinsky5",
+ "model_dim": 2560,
+ "visual_embed_dim": 64,
+ }
+
+ sampling_settings = {
+ "shift": 3.0,
+ }
+
+ latent_format = latent_formats.Flux
+ memory_usage_factor = 1.1 #TODO
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.Kandinsky5Image(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage, comfy.text_encoders.kandinsky5.te(**hunyuan_detect))
+
+
+models = [LotusD, 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, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5]
models += [SVD_img2vid]
diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py
index e4bd74514..0e7a829ba 100644
--- a/comfy/supported_models_base.py
+++ b/comfy/supported_models_base.py
@@ -17,6 +17,7 @@
"""
import torch
+import logging
from . import model_base
from . import utils
from . import latent_formats
@@ -49,8 +50,7 @@ class BASE:
manual_cast_dtype = None
custom_operations = None
- scaled_fp8 = None
- layer_quant_config = None # Per-layer quantization configuration for mixed precision
+ quant_config = None # quantization configuration for mixed precision
optimizations = {"fp8": False}
@classmethod
@@ -118,3 +118,7 @@ class BASE:
def set_inference_dtype(self, dtype, manual_cast_dtype):
self.unet_config['dtype'] = dtype
self.manual_cast_dtype = manual_cast_dtype
+
+ def __getattr__(self, name):
+ logging.warning("\nWARNING, you accessed {} from the model config object which doesn't exist. Please fix your code.\n".format(name))
+ return None
diff --git a/comfy/text_encoders/cosmos.py b/comfy/text_encoders/cosmos.py
index a1adb5242..448381fa9 100644
--- a/comfy/text_encoders/cosmos.py
+++ b/comfy/text_encoders/cosmos.py
@@ -7,10 +7,10 @@ from transformers import T5TokenizerFast
class T5XXLModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_old_config_xxl.json")
- t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None)
- if t5xxl_scaled_fp8 is not None:
+ t5xxl_quantization_metadata = model_options.get("t5xxl_quantization_metadata", None)
+ if t5xxl_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["scaled_fp8"] = t5xxl_scaled_fp8
+ model_options["quantization_metadata"] = t5xxl_quantization_metadata
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, zero_out_masked=attention_mask, model_options=model_options)
@@ -30,12 +30,12 @@ class CosmosT5Tokenizer(sd1_clip.SD1Tokenizer):
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
-def te(dtype_t5=None, t5xxl_scaled_fp8=None):
+def te(dtype_t5=None, t5_quantization_metadata=None):
class CosmosTEModel_(CosmosT5XXL):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ if t5_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
if dtype is None:
dtype = dtype_t5
super().__init__(device=device, dtype=dtype, model_options=model_options)
diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py
index 99f4812bb..21d93d757 100644
--- a/comfy/text_encoders/flux.py
+++ b/comfy/text_encoders/flux.py
@@ -63,12 +63,12 @@ class FluxClipModel(torch.nn.Module):
else:
return self.t5xxl.load_sd(sd)
-def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None):
+def flux_clip(dtype_t5=None, t5_quantization_metadata=None):
class FluxClipModel_(FluxClipModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ if t5_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
return FluxClipModel_
@@ -159,15 +159,13 @@ class Flux2TEModel(sd1_clip.SD1ClipModel):
out = out.reshape(out.shape[0], out.shape[1], -1)
return out, pooled, extra
-def flux2_te(dtype_llama=None, llama_scaled_fp8=None, llama_quantization_metadata=None, pruned=False):
+def flux2_te(dtype_llama=None, llama_quantization_metadata=None, pruned=False):
class Flux2TEModel_(Flux2TEModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
- model_options = model_options.copy()
- model_options["scaled_fp8"] = llama_scaled_fp8
if dtype_llama is not None:
dtype = dtype_llama
if llama_quantization_metadata is not None:
+ model_options = model_options.copy()
model_options["quantization_metadata"] = llama_quantization_metadata
if pruned:
model_options = model_options.copy()
diff --git a/comfy/text_encoders/genmo.py b/comfy/text_encoders/genmo.py
index 9dcf190a2..5daea8135 100644
--- a/comfy/text_encoders/genmo.py
+++ b/comfy/text_encoders/genmo.py
@@ -26,12 +26,12 @@ class MochiT5Tokenizer(sd1_clip.SD1Tokenizer):
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
-def mochi_te(dtype_t5=None, t5xxl_scaled_fp8=None):
+def mochi_te(dtype_t5=None, t5_quantization_metadata=None):
class MochiTEModel_(MochiT5XXL):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ if t5_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
if dtype is None:
dtype = dtype_t5
super().__init__(device=device, dtype=dtype, model_options=model_options)
diff --git a/comfy/text_encoders/hidream.py b/comfy/text_encoders/hidream.py
index dbcf52784..600b34480 100644
--- a/comfy/text_encoders/hidream.py
+++ b/comfy/text_encoders/hidream.py
@@ -142,14 +142,14 @@ class HiDreamTEModel(torch.nn.Module):
return self.llama.load_sd(sd)
-def hidream_clip(clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None):
+def hidream_clip(clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, t5_quantization_metadata=None, llama_quantization_metadata=None):
class HiDreamTEModel_(HiDreamTEModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ if t5_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
- if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options:
+ model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
+ if llama_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["llama_scaled_fp8"] = llama_scaled_fp8
+ model_options["llama_quantization_metadata"] = llama_quantization_metadata
super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, dtype_t5=dtype_t5, dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
return HiDreamTEModel_
diff --git a/comfy/text_encoders/hunyuan_image.py b/comfy/text_encoders/hunyuan_image.py
index ff04726e1..cd198036c 100644
--- a/comfy/text_encoders/hunyuan_image.py
+++ b/comfy/text_encoders/hunyuan_image.py
@@ -40,10 +40,10 @@ class HunyuanImageTokenizer(QwenImageTokenizer):
class Qwen25_7BVLIModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}):
- llama_scaled_fp8 = model_options.get("qwen_scaled_fp8", None)
- if llama_scaled_fp8 is not None:
+ llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+ if llama_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["scaled_fp8"] = llama_scaled_fp8
+ model_options["quantization_metadata"] = llama_quantization_metadata
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
@@ -91,12 +91,12 @@ class HunyuanImageTEModel(QwenImageTEModel):
else:
return super().load_sd(sd)
-def te(byt5=True, dtype_llama=None, llama_scaled_fp8=None):
+def te(byt5=True, dtype_llama=None, llama_quantization_metadata=None):
class QwenImageTEModel_(HunyuanImageTEModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+ if llama_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["qwen_scaled_fp8"] = llama_scaled_fp8
+ model_options["llama_quantization_metadata"] = llama_quantization_metadata
if dtype_llama is not None:
dtype = dtype_llama
super().__init__(byt5=byt5, device=device, dtype=dtype, model_options=model_options)
diff --git a/comfy/text_encoders/hunyuan_video.py b/comfy/text_encoders/hunyuan_video.py
index 0110517bb..a9a6c525e 100644
--- a/comfy/text_encoders/hunyuan_video.py
+++ b/comfy/text_encoders/hunyuan_video.py
@@ -6,7 +6,7 @@ from transformers import LlamaTokenizerFast
import torch
import os
import numbers
-
+import comfy.utils
def llama_detect(state_dict, prefix=""):
out = {}
@@ -14,12 +14,9 @@ def llama_detect(state_dict, prefix=""):
if t5_key in state_dict:
out["dtype_llama"] = state_dict[t5_key].dtype
- scaled_fp8_key = "{}scaled_fp8".format(prefix)
- if scaled_fp8_key in state_dict:
- out["llama_scaled_fp8"] = state_dict[scaled_fp8_key].dtype
-
- if "_quantization_metadata" in state_dict:
- out["llama_quantization_metadata"] = state_dict["_quantization_metadata"]
+ quant = comfy.utils.detect_layer_quantization(state_dict, prefix)
+ if quant is not None:
+ out["llama_quantization_metadata"] = quant
return out
@@ -31,10 +28,10 @@ class LLAMA3Tokenizer(sd1_clip.SDTokenizer):
class LLAMAModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}, special_tokens={"start": 128000, "pad": 128258}):
- llama_scaled_fp8 = model_options.get("llama_scaled_fp8", None)
- if llama_scaled_fp8 is not None:
+ llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+ if llama_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["scaled_fp8"] = llama_scaled_fp8
+ model_options["quantization_metadata"] = llama_quantization_metadata
textmodel_json_config = {}
vocab_size = model_options.get("vocab_size", None)
@@ -161,11 +158,11 @@ class HunyuanVideoClipModel(torch.nn.Module):
return self.llama.load_sd(sd)
-def hunyuan_video_clip(dtype_llama=None, llama_scaled_fp8=None):
+def hunyuan_video_clip(dtype_llama=None, llama_quantization_metadata=None):
class HunyuanVideoClipModel_(HunyuanVideoClipModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options:
+ if llama_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["llama_scaled_fp8"] = llama_scaled_fp8
+ model_options["llama_quantization_metadata"] = llama_quantization_metadata
super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
return HunyuanVideoClipModel_
diff --git a/comfy/text_encoders/kandinsky5.py b/comfy/text_encoders/kandinsky5.py
new file mode 100644
index 000000000..be086458c
--- /dev/null
+++ b/comfy/text_encoders/kandinsky5.py
@@ -0,0 +1,68 @@
+from comfy import sd1_clip
+from .qwen_image import QwenImageTokenizer, QwenImageTEModel
+from .llama import Qwen25_7BVLI
+
+
+class Kandinsky5Tokenizer(QwenImageTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+ self.llama_template = "<|im_start|>system\nYou are a prompt engineer. Describe the video in detail.\nDescribe how the camera moves or shakes, describe the zoom and view angle, whether it follows the objects.\nDescribe the location of the video, main characters or objects and their action.\nDescribe the dynamism of the video and presented actions.\nName the visual style of the video: whether it is a professional footage, user generated content, some kind of animation, video game or screen content.\nDescribe the visual effects, postprocessing and transitions if they are presented in the video.\nPay attention to the order of key actions shown in the scene.<|im_end|>\n<|im_start|>user\n{}<|im_end|>"
+ self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+ out = super().tokenize_with_weights(text, return_word_ids, **kwargs)
+ out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
+
+ return out
+
+
+class Kandinsky5TokenizerImage(Kandinsky5Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+ self.llama_template = "<|im_start|>system\nYou are a promt engineer. Describe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>"
+
+
+class Qwen25_7BVLIModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None, attention_mask=True, model_options={}):
+ llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
+ if llama_quantization_metadata is not None:
+ model_options = model_options.copy()
+ model_options["quantization_metadata"] = llama_quantization_metadata
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class Kandinsky5TEModel(QwenImageTEModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super(QwenImageTEModel, self).__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options)
+ self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)
+
+ def encode_token_weights(self, token_weight_pairs):
+ cond, p, extra = super().encode_token_weights(token_weight_pairs, template_end=-1)
+ l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs["l"])
+
+ return cond, l_pooled, extra
+
+ def set_clip_options(self, options):
+ super().set_clip_options(options)
+ self.clip_l.set_clip_options(options)
+
+ def reset_clip_options(self):
+ super().reset_clip_options()
+ self.clip_l.reset_clip_options()
+
+ def load_sd(self, sd):
+ if "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+ return self.clip_l.load_sd(sd)
+ else:
+ return super().load_sd(sd)
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+ class Kandinsky5TEModel_(Kandinsky5TEModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if llama_quantization_metadata is not None:
+ model_options = model_options.copy()
+ model_options["llama_quantization_metadata"] = llama_quantization_metadata
+ if dtype_llama is not None:
+ dtype = dtype_llama
+ super().__init__(device=device, dtype=dtype, model_options=model_options)
+ return Kandinsky5TEModel_
diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py
index cd4b5f76c..0d07ac8c6 100644
--- a/comfy/text_encoders/llama.py
+++ b/comfy/text_encoders/llama.py
@@ -100,6 +100,28 @@ class Qwen3_4BConfig:
rope_scale = None
final_norm: bool = True
+@dataclass
+class Ovis25_2BConfig:
+ vocab_size: int = 151936
+ hidden_size: int = 2048
+ intermediate_size: int = 6144
+ num_hidden_layers: int = 28
+ num_attention_heads: int = 16
+ num_key_value_heads: int = 8
+ max_position_embeddings: int = 40960
+ rms_norm_eps: float = 1e-6
+ rope_theta: float = 1000000.0
+ transformer_type: str = "llama"
+ head_dim = 128
+ rms_norm_add = False
+ mlp_activation = "silu"
+ qkv_bias = False
+ rope_dims = None
+ q_norm = "gemma3"
+ k_norm = "gemma3"
+ rope_scale = None
+ final_norm: bool = True
+
@dataclass
class Qwen25_7BVLI_Config:
vocab_size: int = 152064
@@ -542,6 +564,15 @@ class Qwen3_4B(BaseLlama, torch.nn.Module):
self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
self.dtype = dtype
+class Ovis25_2B(BaseLlama, torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ config = Ovis25_2BConfig(**config_dict)
+ self.num_layers = config.num_hidden_layers
+
+ self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+ self.dtype = dtype
+
class Qwen25_7BVLI(BaseLlama, torch.nn.Module):
def __init__(self, config_dict, dtype, device, operations):
super().__init__()
diff --git a/comfy/text_encoders/lumina2.py b/comfy/text_encoders/lumina2.py
index fd986e2c1..7a6cfdab2 100644
--- a/comfy/text_encoders/lumina2.py
+++ b/comfy/text_encoders/lumina2.py
@@ -40,7 +40,7 @@ class LuminaModel(sd1_clip.SD1ClipModel):
super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)
-def te(dtype_llama=None, llama_scaled_fp8=None, model_type="gemma2_2b"):
+def te(dtype_llama=None, llama_quantization_metadata=None, model_type="gemma2_2b"):
if model_type == "gemma2_2b":
model = Gemma2_2BModel
elif model_type == "gemma3_4b":
@@ -48,9 +48,9 @@ def te(dtype_llama=None, llama_scaled_fp8=None, model_type="gemma2_2b"):
class LuminaTEModel_(LuminaModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+ if llama_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["scaled_fp8"] = llama_scaled_fp8
+ model_options["quantization_metadata"] = llama_quantization_metadata
if dtype_llama is not None:
dtype = dtype_llama
super().__init__(device=device, dtype=dtype, name=model_type, model_options=model_options, clip_model=model)
diff --git a/comfy/text_encoders/omnigen2.py b/comfy/text_encoders/omnigen2.py
index 1a01b2dd4..50aa4121f 100644
--- a/comfy/text_encoders/omnigen2.py
+++ b/comfy/text_encoders/omnigen2.py
@@ -32,12 +32,12 @@ class Omnigen2Model(sd1_clip.SD1ClipModel):
super().__init__(device=device, dtype=dtype, name="qwen25_3b", clip_model=Qwen25_3BModel, model_options=model_options)
-def te(dtype_llama=None, llama_scaled_fp8=None):
+def te(dtype_llama=None, llama_quantization_metadata=None):
class Omnigen2TEModel_(Omnigen2Model):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+ if llama_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["scaled_fp8"] = llama_scaled_fp8
+ model_options["quantization_metadata"] = llama_quantization_metadata
if dtype_llama is not None:
dtype = dtype_llama
super().__init__(device=device, dtype=dtype, model_options=model_options)
diff --git a/comfy/text_encoders/ovis.py b/comfy/text_encoders/ovis.py
new file mode 100644
index 000000000..5754424d2
--- /dev/null
+++ b/comfy/text_encoders/ovis.py
@@ -0,0 +1,66 @@
+from transformers import Qwen2Tokenizer
+import comfy.text_encoders.llama
+from comfy import sd1_clip
+import os
+import torch
+import numbers
+
+class Qwen3Tokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
+ super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen3_2b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=284, pad_token=151643, tokenizer_data=tokenizer_data)
+
+
+class OvisTokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen3_2b", tokenizer=Qwen3Tokenizer)
+ self.llama_template = "<|im_start|>user\nDescribe the image by detailing the color, quantity, text, shape, size, texture, spatial relationships of the objects and background: {}<|im_end|>\n<|im_start|>assistant\n\n\n\n\n"
+
+ def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs):
+ if llama_template is None:
+ llama_text = self.llama_template.format(text)
+ else:
+ llama_text = llama_template.format(text)
+
+ tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
+ return tokens
+
+class Ovis25_2BModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Ovis25_2B, enable_attention_masks=attention_mask, return_attention_masks=False, zero_out_masked=True, model_options=model_options)
+
+
+class OvisTEModel(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__(device=device, dtype=dtype, name="qwen3_2b", clip_model=Ovis25_2BModel, model_options=model_options)
+
+ def encode_token_weights(self, token_weight_pairs, template_end=-1):
+ out, pooled = super().encode_token_weights(token_weight_pairs)
+ tok_pairs = token_weight_pairs["qwen3_2b"][0]
+ count_im_start = 0
+ if template_end == -1:
+ for i, v in enumerate(tok_pairs):
+ elem = v[0]
+ if not torch.is_tensor(elem):
+ if isinstance(elem, numbers.Integral):
+ if elem == 4004 and count_im_start < 1:
+ template_end = i
+ count_im_start += 1
+
+ if out.shape[1] > (template_end + 1):
+ if tok_pairs[template_end + 1][0] == 25:
+ template_end += 1
+
+ out = out[:, template_end:]
+ return out, pooled, {}
+
+
+def te(dtype_llama=None, llama_quantization_metadata=None):
+ class OvisTEModel_(OvisTEModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if dtype_llama is not None:
+ dtype = dtype_llama
+ if llama_quantization_metadata is not None:
+ model_options["quantization_metadata"] = llama_quantization_metadata
+ super().__init__(device=device, dtype=dtype, model_options=model_options)
+ return OvisTEModel_
diff --git a/comfy/text_encoders/pixart_t5.py b/comfy/text_encoders/pixart_t5.py
index 5f383de07..e5e5f18be 100644
--- a/comfy/text_encoders/pixart_t5.py
+++ b/comfy/text_encoders/pixart_t5.py
@@ -30,12 +30,12 @@ class PixArtTokenizer(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 pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None):
+def pixart_te(dtype_t5=None, t5_quantization_metadata=None):
class PixArtTEModel_(PixArtT5XXL):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ if t5_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
if dtype is None:
dtype = dtype_t5
super().__init__(device=device, dtype=dtype, model_options=model_options)
diff --git a/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json b/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json
index 67688e82c..df5b5d7fe 100644
--- a/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json
+++ b/comfy/text_encoders/qwen25_tokenizer/tokenizer_config.json
@@ -179,36 +179,36 @@
"special": false
},
"151665": {
- "content": "<|img|>",
+ "content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
- "special": true
+ "special": false
},
"151666": {
- "content": "<|endofimg|>",
+ "content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
- "special": true
+ "special": false
},
"151667": {
- "content": "<|meta|>",
+ "content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
- "special": true
+ "special": false
},
"151668": {
- "content": "<|endofmeta|>",
+ "content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
- "special": true
+ "special": false
}
},
"additional_special_tokens": [
diff --git a/comfy/text_encoders/qwen_image.py b/comfy/text_encoders/qwen_image.py
index c0d32a6ef..5c14dec23 100644
--- a/comfy/text_encoders/qwen_image.py
+++ b/comfy/text_encoders/qwen_image.py
@@ -85,12 +85,12 @@ class QwenImageTEModel(sd1_clip.SD1ClipModel):
return out, pooled, extra
-def te(dtype_llama=None, llama_scaled_fp8=None):
+def te(dtype_llama=None, llama_quantization_metadata=None):
class QwenImageTEModel_(QwenImageTEModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+ if llama_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["scaled_fp8"] = llama_scaled_fp8
+ model_options["quantization_metadata"] = llama_quantization_metadata
if dtype_llama is not None:
dtype = dtype_llama
super().__init__(device=device, dtype=dtype, model_options=model_options)
diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py
index ff5d412db..8b153c72b 100644
--- a/comfy/text_encoders/sd3_clip.py
+++ b/comfy/text_encoders/sd3_clip.py
@@ -6,14 +6,15 @@ import torch
import os
import comfy.model_management
import logging
+import comfy.utils
class T5XXLModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}):
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
- t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None)
- if t5xxl_scaled_fp8 is not None:
+ t5xxl_quantization_metadata = model_options.get("t5xxl_quantization_metadata", None)
+ if t5xxl_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["scaled_fp8"] = t5xxl_scaled_fp8
+ model_options["quantization_metadata"] = t5xxl_quantization_metadata
model_options = {**model_options, "model_name": "t5xxl"}
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
@@ -25,9 +26,9 @@ def t5_xxl_detect(state_dict, prefix=""):
if t5_key in state_dict:
out["dtype_t5"] = state_dict[t5_key].dtype
- scaled_fp8_key = "{}scaled_fp8".format(prefix)
- if scaled_fp8_key in state_dict:
- out["t5xxl_scaled_fp8"] = state_dict[scaled_fp8_key].dtype
+ quant = comfy.utils.detect_layer_quantization(state_dict, prefix)
+ if quant is not None:
+ out["t5_quantization_metadata"] = quant
return out
@@ -156,11 +157,11 @@ class SD3ClipModel(torch.nn.Module):
else:
return self.t5xxl.load_sd(sd)
-def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5xxl_scaled_fp8=None, t5_attention_mask=False):
+def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_quantization_metadata=None, t5_attention_mask=False):
class SD3ClipModel_(SD3ClipModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ if t5_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ model_options["t5xxl_quantization_metadata"] = t5_quantization_metadata
super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options)
return SD3ClipModel_
diff --git a/comfy/text_encoders/wan.py b/comfy/text_encoders/wan.py
index d50fa4b28..164a57edd 100644
--- a/comfy/text_encoders/wan.py
+++ b/comfy/text_encoders/wan.py
@@ -25,12 +25,12 @@ class WanT5Model(sd1_clip.SD1ClipModel):
def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
super().__init__(device=device, dtype=dtype, model_options=model_options, name="umt5xxl", clip_model=UMT5XXlModel, **kwargs)
-def te(dtype_t5=None, t5xxl_scaled_fp8=None):
+def te(dtype_t5=None, t5_quantization_metadata=None):
class WanTEModel(WanT5Model):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if t5xxl_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+ if t5_quantization_metadata is not None:
model_options = model_options.copy()
- model_options["scaled_fp8"] = t5xxl_scaled_fp8
+ model_options["quantization_metadata"] = t5_quantization_metadata
if dtype_t5 is not None:
dtype = dtype_t5
super().__init__(device=device, dtype=dtype, model_options=model_options)
diff --git a/comfy/text_encoders/z_image.py b/comfy/text_encoders/z_image.py
index bb9273b20..19adde0b7 100644
--- a/comfy/text_encoders/z_image.py
+++ b/comfy/text_encoders/z_image.py
@@ -34,12 +34,9 @@ class ZImageTEModel(sd1_clip.SD1ClipModel):
super().__init__(device=device, dtype=dtype, name="qwen3_4b", clip_model=Qwen3_4BModel, model_options=model_options)
-def te(dtype_llama=None, llama_scaled_fp8=None, llama_quantization_metadata=None):
+def te(dtype_llama=None, llama_quantization_metadata=None):
class ZImageTEModel_(ZImageTEModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
- if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
- model_options = model_options.copy()
- model_options["scaled_fp8"] = llama_scaled_fp8
if dtype_llama is not None:
dtype = dtype_llama
if llama_quantization_metadata is not None:
diff --git a/comfy/utils.py b/comfy/utils.py
index 37485e497..89846bc95 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -29,6 +29,7 @@ import itertools
from torch.nn.functional import interpolate
from einops import rearrange
from comfy.cli_args import args
+import json
MMAP_TORCH_FILES = args.mmap_torch_files
DISABLE_MMAP = args.disable_mmap
@@ -1194,3 +1195,68 @@ def unpack_latents(combined_latent, latent_shapes):
else:
output_tensors = combined_latent
return output_tensors
+
+def detect_layer_quantization(state_dict, prefix):
+ for k in state_dict:
+ if k.startswith(prefix) and k.endswith(".comfy_quant"):
+ logging.info("Found quantization metadata version 1")
+ return {"mixed_ops": True}
+ return None
+
+def convert_old_quants(state_dict, model_prefix="", metadata={}):
+ if metadata is None:
+ metadata = {}
+
+ quant_metadata = None
+ if "_quantization_metadata" not in metadata:
+ scaled_fp8_key = "{}scaled_fp8".format(model_prefix)
+
+ if scaled_fp8_key in state_dict:
+ scaled_fp8_weight = state_dict[scaled_fp8_key]
+ scaled_fp8_dtype = scaled_fp8_weight.dtype
+ if scaled_fp8_dtype == torch.float32:
+ scaled_fp8_dtype = torch.float8_e4m3fn
+
+ if scaled_fp8_weight.nelement() == 2:
+ full_precision_matrix_mult = True
+ else:
+ full_precision_matrix_mult = False
+
+ out_sd = {}
+ layers = {}
+ for k in list(state_dict.keys()):
+ if not k.startswith(model_prefix):
+ out_sd[k] = state_dict[k]
+ continue
+ k_out = k
+ w = state_dict.pop(k)
+ layer = None
+ if k_out.endswith(".scale_weight"):
+ layer = k_out[:-len(".scale_weight")]
+ k_out = "{}.weight_scale".format(layer)
+
+ if layer is not None:
+ layer_conf = {"format": "float8_e4m3fn"} # TODO: check if anyone did some non e4m3fn scaled checkpoints
+ if full_precision_matrix_mult:
+ layer_conf["full_precision_matrix_mult"] = full_precision_matrix_mult
+ layers[layer] = layer_conf
+
+ if k_out.endswith(".scale_input"):
+ layer = k_out[:-len(".scale_input")]
+ k_out = "{}.input_scale".format(layer)
+ if w.item() == 1.0:
+ continue
+
+ out_sd[k_out] = w
+
+ state_dict = out_sd
+ quant_metadata = {"layers": layers}
+ else:
+ quant_metadata = json.loads(metadata["_quantization_metadata"])
+
+ if quant_metadata is not None:
+ layers = quant_metadata["layers"]
+ for k, v in layers.items():
+ state_dict["{}.comfy_quant".format(k)] = torch.frombuffer(json.dumps(v).encode('utf-8'), dtype=torch.uint8)
+
+ return state_dict, metadata
diff --git a/comfy_api/feature_flags.py b/comfy_api/feature_flags.py
index 0d4389a6e..bfb77eb5f 100644
--- a/comfy_api/feature_flags.py
+++ b/comfy_api/feature_flags.py
@@ -13,6 +13,7 @@ from comfy.cli_args import args
SERVER_FEATURE_FLAGS: Dict[str, Any] = {
"supports_preview_metadata": True,
"max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
+ "extension": {"manager": {"supports_v4": True}},
}
diff --git a/comfy_api/latest/__init__.py b/comfy_api/latest/__init__.py
index 176ae36e0..0fa01d1e7 100644
--- a/comfy_api/latest/__init__.py
+++ b/comfy_api/latest/__init__.py
@@ -8,8 +8,8 @@ from comfy_api.internal.async_to_sync import create_sync_class
from comfy_api.latest._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents
from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents, MESH, VOXEL
-from . import _io as io
-from . import _ui as ui
+from . import _io_public as io
+from . import _ui_public as ui
# from comfy_api.latest._resources import _RESOURCES as resources #noqa: F401
from comfy_execution.utils import get_executing_context
from comfy_execution.progress import get_progress_state, PreviewImageTuple
diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py
index bde37f90a..a4cd3737d 100644
--- a/comfy_api/latest/_input_impl/video_types.py
+++ b/comfy_api/latest/_input_impl/video_types.py
@@ -336,7 +336,10 @@ class VideoFromComponents(VideoInput):
raise ValueError("Only MP4 format is supported for now")
if codec != VideoCodec.AUTO and codec != VideoCodec.H264:
raise ValueError("Only H264 codec is supported for now")
- with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}) as output:
+ extra_kwargs = {}
+ if isinstance(format, VideoContainer) and format != VideoContainer.AUTO:
+ extra_kwargs["format"] = format.value
+ with av.open(path, mode='w', options={'movflags': 'use_metadata_tags'}, **extra_kwargs) as output:
# Add metadata before writing any streams
if metadata is not None:
for key, value in metadata.items():
diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py
index 79c0722a9..d7cbe68cf 100644
--- a/comfy_api/latest/_io.py
+++ b/comfy_api/latest/_io.py
@@ -4,7 +4,8 @@ import copy
import inspect
from abc import ABC, abstractmethod
from collections import Counter
-from dataclasses import asdict, dataclass
+from collections.abc import Iterable
+from dataclasses import asdict, dataclass, field
from enum import Enum
from typing import Any, Callable, Literal, TypedDict, TypeVar, TYPE_CHECKING
from typing_extensions import NotRequired, final
@@ -150,6 +151,9 @@ class _IO_V3:
def __init__(self):
pass
+ def validate(self):
+ pass
+
@property
def io_type(self):
return self.Parent.io_type
@@ -182,6 +186,9 @@ class Input(_IO_V3):
def get_io_type(self):
return _StringIOType(self.io_type)
+ def get_all(self) -> list[Input]:
+ return [self]
+
class WidgetInput(Input):
'''
Base class for a V3 Input with widget.
@@ -561,6 +568,8 @@ class Conditioning(ComfyTypeIO):
'''Used by WAN Camera.'''
time_dim_concat: NotRequired[torch.Tensor]
'''Used by WAN Phantom Subject.'''
+ time_dim_replace: NotRequired[torch.Tensor]
+ '''Used by Kandinsky5 I2V.'''
CondList = list[tuple[torch.Tensor, PooledDict]]
Type = CondList
@@ -814,13 +823,61 @@ class MultiType:
else:
return super().as_dict()
+@comfytype(io_type="COMFY_MATCHTYPE_V3")
+class MatchType(ComfyTypeIO):
+ class Template:
+ def __init__(self, template_id: str, allowed_types: _ComfyType | list[_ComfyType] = AnyType):
+ self.template_id = template_id
+ # account for syntactic sugar
+ if not isinstance(allowed_types, Iterable):
+ allowed_types = [allowed_types]
+ for t in allowed_types:
+ if not isinstance(t, type):
+ if not isinstance(t, _ComfyType):
+ raise ValueError(f"Allowed types must be a ComfyType or a list of ComfyTypes, got {t.__class__.__name__}")
+ else:
+ if not issubclass(t, _ComfyType):
+ raise ValueError(f"Allowed types must be a ComfyType or a list of ComfyTypes, got {t.__name__}")
+ self.allowed_types = allowed_types
+
+ def as_dict(self):
+ return {
+ "template_id": self.template_id,
+ "allowed_types": ",".join([t.io_type for t in self.allowed_types]),
+ }
+
+ class Input(Input):
+ def __init__(self, id: str, template: MatchType.Template,
+ display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+ super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+ self.template = template
+
+ def as_dict(self):
+ return super().as_dict() | prune_dict({
+ "template": self.template.as_dict(),
+ })
+
+ class Output(Output):
+ def __init__(self, template: MatchType.Template, id: str=None, display_name: str=None, tooltip: str=None,
+ is_output_list=False):
+ super().__init__(id, display_name, tooltip, is_output_list)
+ self.template = template
+
+ def as_dict(self):
+ return super().as_dict() | prune_dict({
+ "template": self.template.as_dict(),
+ })
+
class DynamicInput(Input, ABC):
'''
Abstract class for dynamic input registration.
'''
- @abstractmethod
def get_dynamic(self) -> list[Input]:
- ...
+ return []
+
+ def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+ pass
+
class DynamicOutput(Output, ABC):
'''
@@ -830,99 +887,223 @@ class DynamicOutput(Output, ABC):
is_output_list=False):
super().__init__(id, display_name, tooltip, is_output_list)
- @abstractmethod
def get_dynamic(self) -> list[Output]:
- ...
+ return []
@comfytype(io_type="COMFY_AUTOGROW_V3")
-class AutogrowDynamic(ComfyTypeI):
- Type = list[Any]
- class Input(DynamicInput):
- def __init__(self, id: str, template_input: Input, min: int=1, max: int=None,
- display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
- super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
- self.template_input = template_input
- if min is not None:
- assert(min >= 1)
- if max is not None:
- assert(max >= 1)
+class Autogrow(ComfyTypeI):
+ Type = dict[str, Any]
+ _MaxNames = 100 # NOTE: max 100 names for sanity
+
+ class _AutogrowTemplate:
+ def __init__(self, input: Input):
+ # dynamic inputs are not allowed as the template input
+ assert(not isinstance(input, DynamicInput))
+ self.input = copy.copy(input)
+ if isinstance(self.input, WidgetInput):
+ self.input.force_input = True
+ self.names: list[str] = []
+ self.cached_inputs = {}
+
+ def _create_input(self, input: Input, name: str):
+ new_input = copy.copy(self.input)
+ new_input.id = name
+ return new_input
+
+ def _create_cached_inputs(self):
+ for name in self.names:
+ self.cached_inputs[name] = self._create_input(self.input, name)
+
+ def get_all(self) -> list[Input]:
+ return list(self.cached_inputs.values())
+
+ def as_dict(self):
+ return prune_dict({
+ "input": create_input_dict_v1([self.input]),
+ })
+
+ def validate(self):
+ self.input.validate()
+
+ def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+ real_inputs = []
+ for name, input in self.cached_inputs.items():
+ if name in live_inputs:
+ real_inputs.append(input)
+ add_to_input_dict_v1(d, real_inputs, live_inputs, curr_prefix)
+ add_dynamic_id_mapping(d, real_inputs, curr_prefix)
+
+ class TemplatePrefix(_AutogrowTemplate):
+ def __init__(self, input: Input, prefix: str, min: int=1, max: int=10):
+ super().__init__(input)
+ self.prefix = prefix
+ assert(min >= 0)
+ assert(max >= 1)
+ assert(max <= Autogrow._MaxNames)
self.min = min
self.max = max
+ self.names = [f"{self.prefix}{i}" for i in range(self.max)]
+ self._create_cached_inputs()
+
+ def as_dict(self):
+ return super().as_dict() | prune_dict({
+ "prefix": self.prefix,
+ "min": self.min,
+ "max": self.max,
+ })
+
+ class TemplateNames(_AutogrowTemplate):
+ def __init__(self, input: Input, names: list[str], min: int=1):
+ super().__init__(input)
+ self.names = names[:Autogrow._MaxNames]
+ assert(min >= 0)
+ self.min = min
+ self._create_cached_inputs()
+
+ def as_dict(self):
+ return super().as_dict() | prune_dict({
+ "names": self.names,
+ "min": self.min,
+ })
+
+ class Input(DynamicInput):
+ def __init__(self, id: str, template: Autogrow.TemplatePrefix | Autogrow.TemplateNames,
+ display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
+ super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
+ self.template = template
+
+ def as_dict(self):
+ return super().as_dict() | prune_dict({
+ "template": self.template.as_dict(),
+ })
def get_dynamic(self) -> list[Input]:
- curr_count = 1
- new_inputs = []
- for i in range(self.min):
- new_input = copy.copy(self.template_input)
- new_input.id = f"{new_input.id}{curr_count}_${self.id}_ag$"
- if new_input.display_name is not None:
- new_input.display_name = f"{new_input.display_name}{curr_count}"
- new_input.optional = self.optional or new_input.optional
- if isinstance(self.template_input, WidgetInput):
- new_input.force_input = True
- new_inputs.append(new_input)
- curr_count += 1
- # pretend to expand up to max
- for i in range(curr_count-1, self.max):
- new_input = copy.copy(self.template_input)
- new_input.id = f"{new_input.id}{curr_count}_${self.id}_ag$"
- if new_input.display_name is not None:
- new_input.display_name = f"{new_input.display_name}{curr_count}"
- new_input.optional = True
- if isinstance(self.template_input, WidgetInput):
- new_input.force_input = True
- new_inputs.append(new_input)
- curr_count += 1
- return new_inputs
+ return self.template.get_all()
-@comfytype(io_type="COMFY_COMBODYNAMIC_V3")
-class ComboDynamic(ComfyTypeI):
- class Input(DynamicInput):
- def __init__(self, id: str):
- pass
+ def get_all(self) -> list[Input]:
+ return [self] + self.template.get_all()
-@comfytype(io_type="COMFY_MATCHTYPE_V3")
-class MatchType(ComfyTypeIO):
- class Template:
- def __init__(self, template_id: str, allowed_types: _ComfyType | list[_ComfyType]):
- self.template_id = template_id
- self.allowed_types = [allowed_types] if isinstance(allowed_types, _ComfyType) else allowed_types
+ def validate(self):
+ self.template.validate()
+
+ def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+ curr_prefix = f"{curr_prefix}{self.id}."
+ # need to remove self from expected inputs dictionary; replaced by template inputs in frontend
+ for inner_dict in d.values():
+ if self.id in inner_dict:
+ del inner_dict[self.id]
+ self.template.expand_schema_for_dynamic(d, live_inputs, curr_prefix)
+
+@comfytype(io_type="COMFY_DYNAMICCOMBO_V3")
+class DynamicCombo(ComfyTypeI):
+ Type = dict[str, Any]
+
+ class Option:
+ def __init__(self, key: str, inputs: list[Input]):
+ self.key = key
+ self.inputs = inputs
def as_dict(self):
return {
- "template_id": self.template_id,
- "allowed_types": "".join(t.io_type for t in self.allowed_types),
+ "key": self.key,
+ "inputs": create_input_dict_v1(self.inputs),
}
class Input(DynamicInput):
- def __init__(self, id: str, template: MatchType.Template,
+ def __init__(self, id: str, options: list[DynamicCombo.Option],
display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None, extra_dict=None):
super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
- self.template = template
+ self.options = options
+
+ def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+ # check if dynamic input's id is in live_inputs
+ if self.id in live_inputs:
+ curr_prefix = f"{curr_prefix}{self.id}."
+ key = live_inputs[self.id]
+ selected_option = None
+ for option in self.options:
+ if option.key == key:
+ selected_option = option
+ break
+ if selected_option is not None:
+ add_to_input_dict_v1(d, selected_option.inputs, live_inputs, curr_prefix)
+ add_dynamic_id_mapping(d, selected_option.inputs, curr_prefix, self)
def get_dynamic(self) -> list[Input]:
- return [self]
+ return [input for option in self.options for input in option.inputs]
+
+ def get_all(self) -> list[Input]:
+ return [self] + [input for option in self.options for input in option.inputs]
def as_dict(self):
return super().as_dict() | prune_dict({
- "template": self.template.as_dict(),
+ "options": [o.as_dict() for o in self.options],
})
- class Output(DynamicOutput):
- def __init__(self, id: str, template: MatchType.Template, display_name: str=None, tooltip: str=None,
- is_output_list=False):
- super().__init__(id, display_name, tooltip, is_output_list)
- self.template = template
+ def validate(self):
+ # make sure all nested inputs are validated
+ for option in self.options:
+ for input in option.inputs:
+ input.validate()
- def get_dynamic(self) -> list[Output]:
- return [self]
+@comfytype(io_type="COMFY_DYNAMICSLOT_V3")
+class DynamicSlot(ComfyTypeI):
+ Type = dict[str, Any]
+
+ class Input(DynamicInput):
+ def __init__(self, slot: Input, inputs: list[Input],
+ display_name: str=None, tooltip: str=None, lazy: bool=None, extra_dict=None):
+ assert(not isinstance(slot, DynamicInput))
+ self.slot = copy.copy(slot)
+ self.slot.display_name = slot.display_name if slot.display_name is not None else display_name
+ optional = True
+ self.slot.tooltip = slot.tooltip if slot.tooltip is not None else tooltip
+ self.slot.lazy = slot.lazy if slot.lazy is not None else lazy
+ self.slot.extra_dict = slot.extra_dict if slot.extra_dict is not None else extra_dict
+ super().__init__(slot.id, self.slot.display_name, optional, self.slot.tooltip, self.slot.lazy, self.slot.extra_dict)
+ self.inputs = inputs
+ self.force_input = None
+ # force widget inputs to have no widgets, otherwise this would be awkward
+ if isinstance(self.slot, WidgetInput):
+ self.force_input = True
+ self.slot.force_input = True
+
+ def expand_schema_for_dynamic(self, d: dict[str, Any], live_inputs: dict[str, Any], curr_prefix=''):
+ if self.id in live_inputs:
+ curr_prefix = f"{curr_prefix}{self.id}."
+ add_to_input_dict_v1(d, self.inputs, live_inputs, curr_prefix)
+ add_dynamic_id_mapping(d, [self.slot] + self.inputs, curr_prefix)
+
+ def get_dynamic(self) -> list[Input]:
+ return [self.slot] + self.inputs
+
+ def get_all(self) -> list[Input]:
+ return [self] + [self.slot] + self.inputs
def as_dict(self):
return super().as_dict() | prune_dict({
- "template": self.template.as_dict(),
+ "slotType": str(self.slot.get_io_type()),
+ "inputs": create_input_dict_v1(self.inputs),
+ "forceInput": self.force_input,
})
+ def validate(self):
+ self.slot.validate()
+ for input in self.inputs:
+ input.validate()
+
+def add_dynamic_id_mapping(d: dict[str, Any], inputs: list[Input], curr_prefix: str, self: DynamicInput=None):
+ dynamic = d.setdefault("dynamic_paths", {})
+ if self is not None:
+ dynamic[self.id] = f"{curr_prefix}{self.id}"
+ for i in inputs:
+ if not isinstance(i, DynamicInput):
+ dynamic[f"{i.id}"] = f"{curr_prefix}{i.id}"
+
+class V3Data(TypedDict):
+ hidden_inputs: dict[str, Any]
+ dynamic_paths: dict[str, Any]
class HiddenHolder:
def __init__(self, unique_id: str, prompt: Any,
@@ -984,6 +1165,7 @@ class NodeInfoV1:
output_is_list: list[bool]=None
output_name: list[str]=None
output_tooltips: list[str]=None
+ output_matchtypes: list[str]=None
name: str=None
display_name: str=None
description: str=None
@@ -1019,9 +1201,9 @@ class Schema:
"""Display name of node."""
category: str = "sd"
"""The category of the node, as per the "Add Node" menu."""
- inputs: list[Input]=None
- outputs: list[Output]=None
- hidden: list[Hidden]=None
+ inputs: list[Input] = field(default_factory=list)
+ outputs: list[Output] = field(default_factory=list)
+ hidden: list[Hidden] = field(default_factory=list)
description: str=""
"""Node description, shown as a tooltip when hovering over the node."""
is_input_list: bool = False
@@ -1061,7 +1243,11 @@ class Schema:
'''Validate the schema:
- verify ids on inputs and outputs are unique - both internally and in relation to each other
'''
- input_ids = [i.id for i in self.inputs] if self.inputs is not None else []
+ nested_inputs: list[Input] = []
+ if self.inputs is not None:
+ for input in self.inputs:
+ nested_inputs.extend(input.get_all())
+ input_ids = [i.id for i in nested_inputs] if nested_inputs is not None else []
output_ids = [o.id for o in self.outputs] if self.outputs is not None else []
input_set = set(input_ids)
output_set = set(output_ids)
@@ -1077,6 +1263,13 @@ class Schema:
issues.append(f"Ids must be unique between inputs and outputs, but {intersection} are not.")
if len(issues) > 0:
raise ValueError("\n".join(issues))
+ # validate inputs and outputs
+ if self.inputs is not None:
+ for input in self.inputs:
+ input.validate()
+ if self.outputs is not None:
+ for output in self.outputs:
+ output.validate()
def finalize(self):
"""Add hidden based on selected schema options, and give outputs without ids default ids."""
@@ -1102,19 +1295,10 @@ class Schema:
if output.id is None:
output.id = f"_{i}_{output.io_type}_"
- def get_v1_info(self, cls) -> NodeInfoV1:
+ def get_v1_info(self, cls, live_inputs: dict[str, Any]=None) -> NodeInfoV1:
+ # NOTE: live_inputs will not be used anymore very soon and this will be done another way
# get V1 inputs
- input = {
- "required": {}
- }
- if self.inputs:
- for i in self.inputs:
- if isinstance(i, DynamicInput):
- dynamic_inputs = i.get_dynamic()
- for d in dynamic_inputs:
- add_to_dict_v1(d, input)
- else:
- add_to_dict_v1(i, input)
+ input = create_input_dict_v1(self.inputs, live_inputs)
if self.hidden:
for hidden in self.hidden:
input.setdefault("hidden", {})[hidden.name] = (hidden.value,)
@@ -1123,12 +1307,24 @@ class Schema:
output_is_list = []
output_name = []
output_tooltips = []
+ output_matchtypes = []
+ any_matchtypes = False
if self.outputs:
for o in self.outputs:
output.append(o.io_type)
output_is_list.append(o.is_output_list)
output_name.append(o.display_name if o.display_name else o.io_type)
output_tooltips.append(o.tooltip if o.tooltip else None)
+ # special handling for MatchType
+ if isinstance(o, MatchType.Output):
+ output_matchtypes.append(o.template.template_id)
+ any_matchtypes = True
+ else:
+ output_matchtypes.append(None)
+
+ # clear out lists that are all None
+ if not any_matchtypes:
+ output_matchtypes = None
info = NodeInfoV1(
input=input,
@@ -1137,6 +1333,7 @@ class Schema:
output_is_list=output_is_list,
output_name=output_name,
output_tooltips=output_tooltips,
+ output_matchtypes=output_matchtypes,
name=self.node_id,
display_name=self.display_name,
category=self.category,
@@ -1182,16 +1379,57 @@ class Schema:
return info
-def add_to_dict_v1(i: Input, input: dict):
+def create_input_dict_v1(inputs: list[Input], live_inputs: dict[str, Any]=None) -> dict:
+ input = {
+ "required": {}
+ }
+ add_to_input_dict_v1(input, inputs, live_inputs)
+ return input
+
+def add_to_input_dict_v1(d: dict[str, Any], inputs: list[Input], live_inputs: dict[str, Any]=None, curr_prefix=''):
+ for i in inputs:
+ if isinstance(i, DynamicInput):
+ add_to_dict_v1(i, d)
+ if live_inputs is not None:
+ i.expand_schema_for_dynamic(d, live_inputs, curr_prefix)
+ else:
+ add_to_dict_v1(i, d)
+
+def add_to_dict_v1(i: Input, d: dict, dynamic_dict: dict=None):
key = "optional" if i.optional else "required"
as_dict = i.as_dict()
# for v1, we don't want to include the optional key
as_dict.pop("optional", None)
- input.setdefault(key, {})[i.id] = (i.get_io_type(), as_dict)
+ if dynamic_dict is None:
+ value = (i.get_io_type(), as_dict)
+ else:
+ value = (i.get_io_type(), as_dict, dynamic_dict)
+ d.setdefault(key, {})[i.id] = value
def add_to_dict_v3(io: Input | Output, d: dict):
d[io.id] = (io.get_io_type(), io.as_dict())
+def build_nested_inputs(values: dict[str, Any], v3_data: V3Data):
+ paths = v3_data.get("dynamic_paths", None)
+ if paths is None:
+ return values
+ values = values.copy()
+ result = {}
+
+ for key, path in paths.items():
+ parts = path.split(".")
+ current = result
+
+ for i, p in enumerate(parts):
+ is_last = (i == len(parts) - 1)
+
+ if is_last:
+ current[p] = values.pop(key, None)
+ else:
+ current = current.setdefault(p, {})
+
+ values.update(result)
+ return values
class _ComfyNodeBaseInternal(_ComfyNodeInternal):
@@ -1311,12 +1549,12 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
@final
@classmethod
- def PREPARE_CLASS_CLONE(cls, hidden_inputs: dict) -> type[ComfyNode]:
+ def PREPARE_CLASS_CLONE(cls, v3_data: V3Data) -> type[ComfyNode]:
"""Creates clone of real node class to prevent monkey-patching."""
c_type: type[ComfyNode] = cls if is_class(cls) else type(cls)
type_clone: type[ComfyNode] = shallow_clone_class(c_type)
# set hidden
- type_clone.hidden = HiddenHolder.from_dict(hidden_inputs)
+ type_clone.hidden = HiddenHolder.from_dict(v3_data["hidden_inputs"])
return type_clone
@final
@@ -1433,14 +1671,18 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
@final
@classmethod
- def INPUT_TYPES(cls, include_hidden=True, return_schema=False) -> dict[str, dict] | tuple[dict[str, dict], Schema]:
+ def INPUT_TYPES(cls, include_hidden=True, return_schema=False, live_inputs=None) -> dict[str, dict] | tuple[dict[str, dict], Schema, V3Data]:
schema = cls.FINALIZE_SCHEMA()
- info = schema.get_v1_info(cls)
+ info = schema.get_v1_info(cls, live_inputs)
input = info.input
if not include_hidden:
input.pop("hidden", None)
if return_schema:
- return input, schema
+ v3_data: V3Data = {}
+ dynamic = input.pop("dynamic_paths", None)
+ if dynamic is not None:
+ v3_data["dynamic_paths"] = dynamic
+ return input, schema, v3_data
return input
@final
@@ -1513,7 +1755,7 @@ class ComfyNode(_ComfyNodeBaseInternal):
raise NotImplementedError
@classmethod
- def validate_inputs(cls, **kwargs) -> bool:
+ def validate_inputs(cls, **kwargs) -> bool | str:
"""Optionally, define this function to validate inputs; equivalent to V1's VALIDATE_INPUTS."""
raise NotImplementedError
@@ -1628,6 +1870,7 @@ __all__ = [
"StyleModel",
"Gligen",
"UpscaleModel",
+ "LatentUpscaleModel",
"Audio",
"Video",
"SVG",
@@ -1651,6 +1894,10 @@ __all__ = [
"SEGS",
"AnyType",
"MultiType",
+ # Dynamic Types
+ "MatchType",
+ # "DynamicCombo",
+ # "Autogrow",
# Other classes
"HiddenHolder",
"Hidden",
@@ -1661,4 +1908,5 @@ __all__ = [
"NodeOutput",
"add_to_dict_v1",
"add_to_dict_v3",
+ "V3Data",
]
diff --git a/comfy_api/latest/_io_public.py b/comfy_api/latest/_io_public.py
new file mode 100644
index 000000000..43c7680f3
--- /dev/null
+++ b/comfy_api/latest/_io_public.py
@@ -0,0 +1 @@
+from ._io import * # noqa: F403
diff --git a/comfy_api/latest/_ui.py b/comfy_api/latest/_ui.py
index b0bbabe2a..5a75a3aae 100644
--- a/comfy_api/latest/_ui.py
+++ b/comfy_api/latest/_ui.py
@@ -3,6 +3,7 @@ from __future__ import annotations
import json
import os
import random
+import uuid
from io import BytesIO
from typing import Type
@@ -318,9 +319,10 @@ class AudioSaveHelper:
for key, value in metadata.items():
output_container.metadata[key] = value
+ layout = "mono" if waveform.shape[0] == 1 else "stereo"
# Set up the output stream with appropriate properties
if format == "opus":
- out_stream = output_container.add_stream("libopus", rate=sample_rate)
+ out_stream = output_container.add_stream("libopus", rate=sample_rate, layout=layout)
if quality == "64k":
out_stream.bit_rate = 64000
elif quality == "96k":
@@ -332,7 +334,7 @@ class AudioSaveHelper:
elif quality == "320k":
out_stream.bit_rate = 320000
elif format == "mp3":
- out_stream = output_container.add_stream("libmp3lame", rate=sample_rate)
+ out_stream = output_container.add_stream("libmp3lame", rate=sample_rate, layout=layout)
if quality == "V0":
# TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
out_stream.codec_context.qscale = 1
@@ -341,12 +343,12 @@ class AudioSaveHelper:
elif quality == "320k":
out_stream.bit_rate = 320000
else: # format == "flac":
- out_stream = output_container.add_stream("flac", rate=sample_rate)
+ out_stream = output_container.add_stream("flac", rate=sample_rate, layout=layout)
frame = av.AudioFrame.from_ndarray(
waveform.movedim(0, 1).reshape(1, -1).float().numpy(),
format="flt",
- layout="mono" if waveform.shape[0] == 1 else "stereo",
+ layout=layout,
)
frame.sample_rate = sample_rate
frame.pts = 0
@@ -436,9 +438,19 @@ class PreviewUI3D(_UIOutput):
def __init__(self, model_file, camera_info, **kwargs):
self.model_file = model_file
self.camera_info = camera_info
+ self.bg_image_path = None
+ bg_image = kwargs.get("bg_image", None)
+ if bg_image is not None:
+ img_array = (bg_image[0].cpu().numpy() * 255).astype(np.uint8)
+ img = PILImage.fromarray(img_array)
+ temp_dir = folder_paths.get_temp_directory()
+ filename = f"bg_{uuid.uuid4().hex}.png"
+ bg_image_path = os.path.join(temp_dir, filename)
+ img.save(bg_image_path, compress_level=1)
+ self.bg_image_path = f"temp/{filename}"
def as_dict(self):
- return {"result": [self.model_file, self.camera_info]}
+ return {"result": [self.model_file, self.camera_info, self.bg_image_path]}
class PreviewText(_UIOutput):
diff --git a/comfy_api/latest/_ui_public.py b/comfy_api/latest/_ui_public.py
new file mode 100644
index 000000000..85b11d78b
--- /dev/null
+++ b/comfy_api/latest/_ui_public.py
@@ -0,0 +1 @@
+from ._ui import * # noqa: F403
diff --git a/comfy_api/v0_0_2/__init__.py b/comfy_api/v0_0_2/__init__.py
index de0f95001..c4fa1d971 100644
--- a/comfy_api/v0_0_2/__init__.py
+++ b/comfy_api/v0_0_2/__init__.py
@@ -6,7 +6,7 @@ from comfy_api.latest import (
)
from typing import Type, TYPE_CHECKING
from comfy_api.internal.async_to_sync import create_sync_class
-from comfy_api.latest import io, ui, ComfyExtension #noqa: F401
+from comfy_api.latest import io, ui, IO, UI, ComfyExtension #noqa: F401
class ComfyAPIAdapter_v0_0_2(ComfyAPI_latest):
@@ -42,4 +42,8 @@ __all__ = [
"InputImpl",
"Types",
"ComfyExtension",
+ "io",
+ "IO",
+ "ui",
+ "UI",
]
diff --git a/comfy_api_nodes/apis/kling_api.py b/comfy_api_nodes/apis/kling_api.py
new file mode 100644
index 000000000..d8949f8ac
--- /dev/null
+++ b/comfy_api_nodes/apis/kling_api.py
@@ -0,0 +1,86 @@
+from pydantic import BaseModel, Field
+
+
+class OmniProText2VideoRequest(BaseModel):
+ model_name: str = Field(..., description="kling-video-o1")
+ aspect_ratio: str = Field(..., description="'16:9', '9:16' or '1:1'")
+ duration: str = Field(..., description="'5' or '10'")
+ prompt: str = Field(...)
+ mode: str = Field("pro")
+
+
+class OmniParamImage(BaseModel):
+ image_url: str = Field(...)
+ type: str | None = Field(None, description="Can be 'first_frame' or 'end_frame'")
+
+
+class OmniParamVideo(BaseModel):
+ video_url: str = Field(...)
+ refer_type: str | None = Field(..., description="Can be 'base' or 'feature'")
+ keep_original_sound: str = Field(..., description="'yes' or 'no'")
+
+
+class OmniProFirstLastFrameRequest(BaseModel):
+ model_name: str = Field(..., description="kling-video-o1")
+ image_list: list[OmniParamImage] = Field(..., min_length=1, max_length=7)
+ duration: str = Field(..., description="'5' or '10'")
+ prompt: str = Field(...)
+ mode: str = Field("pro")
+
+
+class OmniProReferences2VideoRequest(BaseModel):
+ model_name: str = Field(..., description="kling-video-o1")
+ aspect_ratio: str | None = Field(..., description="'16:9', '9:16' or '1:1'")
+ image_list: list[OmniParamImage] | None = Field(
+ None, max_length=7, description="Max length 4 when video is present."
+ )
+ video_list: list[OmniParamVideo] | None = Field(None, max_length=1)
+ duration: str | None = Field(..., description="From 3 to 10.")
+ prompt: str = Field(...)
+ mode: str = Field("pro")
+
+
+class TaskStatusVideoResult(BaseModel):
+ duration: str | None = Field(None, description="Total video duration")
+ id: str | None = Field(None, description="Generated video ID")
+ url: str | None = Field(None, description="URL for generated video")
+
+
+class TaskStatusImageResult(BaseModel):
+ index: int = Field(..., description="Image Number，0-9")
+ url: str = Field(..., description="URL for generated image")
+
+
+class OmniTaskStatusResults(BaseModel):
+ videos: list[TaskStatusVideoResult] | None = Field(None)
+ images: list[TaskStatusImageResult] | None = Field(None)
+
+
+class OmniTaskStatusResponseData(BaseModel):
+ created_at: int | None = Field(None, description="Task creation time")
+ updated_at: int | None = Field(None, description="Task update time")
+ task_status: str | None = None
+ task_status_msg: str | None = Field(None, description="Additional failure reason. Only for polling endpoint.")
+ task_id: str | None = Field(None, description="Task ID")
+ task_result: OmniTaskStatusResults | None = Field(None)
+
+
+class OmniTaskStatusResponse(BaseModel):
+ code: int | None = Field(None, description="Error code")
+ message: str | None = Field(None, description="Error message")
+ request_id: str | None = Field(None, description="Request ID")
+ data: OmniTaskStatusResponseData | None = Field(None)
+
+
+class OmniImageParamImage(BaseModel):
+ image: str = Field(...)
+
+
+class OmniProImageRequest(BaseModel):
+ model_name: str = Field(..., description="kling-image-o1")
+ resolution: str = Field(..., description="'1k' or '2k'")
+ aspect_ratio: str | None = Field(...)
+ prompt: str = Field(...)
+ mode: str = Field("pro")
+ n: int | None = Field(1, le=9)
+ image_list: list[OmniImageParamImage] | None = Field(..., max_length=10)
diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py
index 23a7f55f1..6c840dc47 100644
--- a/comfy_api_nodes/nodes_kling.py
+++ b/comfy_api_nodes/nodes_kling.py
@@ -4,13 +4,14 @@ For source of truth on the allowed permutations of request fields, please refere
- [Compatibility Table](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap)
"""
-import math
import logging
-
-from typing_extensions import override
+import math
+import re
import torch
+from typing_extensions import override
+from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
from comfy_api_nodes.apis import (
KlingCameraControl,
KlingCameraConfig,
@@ -48,23 +49,33 @@ from comfy_api_nodes.apis import (
KlingCharacterEffectModelName,
KlingSingleImageEffectModelName,
)
+from comfy_api_nodes.apis.kling_api import (
+ OmniImageParamImage,
+ OmniParamImage,
+ OmniParamVideo,
+ OmniProFirstLastFrameRequest,
+ OmniProImageRequest,
+ OmniProReferences2VideoRequest,
+ OmniProText2VideoRequest,
+ OmniTaskStatusResponse,
+)
from comfy_api_nodes.util import (
- validate_image_dimensions,
+ ApiEndpoint,
+ download_url_to_image_tensor,
+ download_url_to_video_output,
+ get_number_of_images,
+ poll_op,
+ sync_op,
+ tensor_to_base64_string,
+ upload_audio_to_comfyapi,
+ upload_images_to_comfyapi,
+ upload_video_to_comfyapi,
validate_image_aspect_ratio,
+ validate_image_dimensions,
+ validate_string,
validate_video_dimensions,
validate_video_duration,
- tensor_to_base64_string,
- validate_string,
- upload_audio_to_comfyapi,
- download_url_to_image_tensor,
- upload_video_to_comfyapi,
- download_url_to_video_output,
- sync_op,
- ApiEndpoint,
- poll_op,
)
-from comfy_api.input_impl import VideoFromFile
-from comfy_api.latest import ComfyExtension, IO, Input
KLING_API_VERSION = "v1"
PATH_TEXT_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/text2video"
@@ -202,6 +213,50 @@ VOICES_CONFIG = {
}
+def normalize_omni_prompt_references(prompt: str) -> str:
+ """
+ Rewrites Kling Omni-style placeholders used in the app, like:
+
+ @image, @image1, @image2, ... @imageN
+ @video, @video1, @video2, ... @videoN
+
+ into the API-compatible form:
+
+ <<>>, <<>>, ...
+ <<>>, <<>>, ...
+
+ This is a UX shim for ComfyUI so users can type the same syntax as in the Kling app.
+ """
+ if not prompt:
+ return prompt
+
+ def _image_repl(match):
+ return f"<<>>"
+
+ def _video_repl(match):
+ return f"<<>>"
+
+ # (? and not @imageFoo
+ prompt = re.sub(r"(?\d*)(?!\w)", _image_repl, prompt)
+ return re.sub(r"(?\d*)(?!\w)", _video_repl, prompt)
+
+
+async def finish_omni_video_task(cls: type[IO.ComfyNode], response: OmniTaskStatusResponse) -> IO.NodeOutput:
+ if response.code:
+ raise RuntimeError(
+ f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+ )
+ final_response = await poll_op(
+ cls,
+ ApiEndpoint(path=f"/proxy/kling/v1/videos/omni-video/{response.data.task_id}"),
+ response_model=OmniTaskStatusResponse,
+ status_extractor=lambda r: (r.data.task_status if r.data else None),
+ max_poll_attempts=160,
+ )
+ return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url))
+
+
def is_valid_camera_control_configs(configs: list[float]) -> bool:
"""Verifies that at least one camera control configuration is non-zero."""
return any(not math.isclose(value, 0.0) for value in configs)
@@ -449,7 +504,7 @@ async def execute_video_effect(
image_1: torch.Tensor,
image_2: torch.Tensor | None = None,
model_mode: KlingVideoGenMode | None = None,
-) -> tuple[VideoFromFile, str, str]:
+) -> tuple[InputImpl.VideoFromFile, str, str]:
if dual_character:
request_input_field = KlingDualCharacterEffectInput(
model_name=model_name,
@@ -736,6 +791,474 @@ class KlingTextToVideoNode(IO.ComfyNode):
)
+class OmniProTextToVideoNode(IO.ComfyNode):
+
+ @classmethod
+ def define_schema(cls) -> IO.Schema:
+ return IO.Schema(
+ node_id="KlingOmniProTextToVideoNode",
+ display_name="Kling Omni Text to Video (Pro)",
+ category="api node/video/Kling",
+ description="Use text prompts to generate videos with the latest Kling model.",
+ inputs=[
+ IO.Combo.Input("model_name", options=["kling-video-o1"]),
+ IO.String.Input(
+ "prompt",
+ multiline=True,
+ tooltip="A text prompt describing the video content. "
+ "This can include both positive and negative descriptions.",
+ ),
+ IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+ IO.Combo.Input("duration", options=[5, 10]),
+ ],
+ outputs=[
+ IO.Video.Output(),
+ ],
+ hidden=[
+ IO.Hidden.auth_token_comfy_org,
+ IO.Hidden.api_key_comfy_org,
+ IO.Hidden.unique_id,
+ ],
+ is_api_node=True,
+ )
+
+ @classmethod
+ async def execute(
+ cls,
+ model_name: str,
+ prompt: str,
+ aspect_ratio: str,
+ duration: int,
+ ) -> IO.NodeOutput:
+ validate_string(prompt, min_length=1, max_length=2500)
+ response = await sync_op(
+ cls,
+ ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+ response_model=OmniTaskStatusResponse,
+ data=OmniProText2VideoRequest(
+ model_name=model_name,
+ prompt=prompt,
+ aspect_ratio=aspect_ratio,
+ duration=str(duration),
+ ),
+ )
+ return await finish_omni_video_task(cls, response)
+
+
+class OmniProFirstLastFrameNode(IO.ComfyNode):
+
+ @classmethod
+ def define_schema(cls) -> IO.Schema:
+ return IO.Schema(
+ node_id="KlingOmniProFirstLastFrameNode",
+ display_name="Kling Omni First-Last-Frame to Video (Pro)",
+ category="api node/video/Kling",
+ description="Use a start frame, an optional end frame, or reference images with the latest Kling model.",
+ inputs=[
+ IO.Combo.Input("model_name", options=["kling-video-o1"]),
+ IO.String.Input(
+ "prompt",
+ multiline=True,
+ tooltip="A text prompt describing the video content. "
+ "This can include both positive and negative descriptions.",
+ ),
+ IO.Combo.Input("duration", options=["5", "10"]),
+ IO.Image.Input("first_frame"),
+ IO.Image.Input(
+ "end_frame",
+ optional=True,
+ tooltip="An optional end frame for the video. "
+ "This cannot be used simultaneously with 'reference_images'.",
+ ),
+ IO.Image.Input(
+ "reference_images",
+ optional=True,
+ tooltip="Up to 6 additional reference images.",
+ ),
+ ],
+ outputs=[
+ IO.Video.Output(),
+ ],
+ hidden=[
+ IO.Hidden.auth_token_comfy_org,
+ IO.Hidden.api_key_comfy_org,
+ IO.Hidden.unique_id,
+ ],
+ is_api_node=True,
+ )
+
+ @classmethod
+ async def execute(
+ cls,
+ model_name: str,
+ prompt: str,
+ duration: int,
+ first_frame: Input.Image,
+ end_frame: Input.Image | None = None,
+ reference_images: Input.Image | None = None,
+ ) -> IO.NodeOutput:
+ prompt = normalize_omni_prompt_references(prompt)
+ validate_string(prompt, min_length=1, max_length=2500)
+ if end_frame is not None and reference_images is not None:
+ raise ValueError("The 'end_frame' input cannot be used simultaneously with 'reference_images'.")
+ validate_image_dimensions(first_frame, min_width=300, min_height=300)
+ validate_image_aspect_ratio(first_frame, (1, 2.5), (2.5, 1))
+ image_list: list[OmniParamImage] = [
+ OmniParamImage(
+ image_url=(await upload_images_to_comfyapi(cls, first_frame, wait_label="Uploading first frame"))[0],
+ type="first_frame",
+ )
+ ]
+ if end_frame is not None:
+ validate_image_dimensions(end_frame, min_width=300, min_height=300)
+ validate_image_aspect_ratio(end_frame, (1, 2.5), (2.5, 1))
+ image_list.append(
+ OmniParamImage(
+ image_url=(await upload_images_to_comfyapi(cls, end_frame, wait_label="Uploading end frame"))[0],
+ type="end_frame",
+ )
+ )
+ if reference_images is not None:
+ if get_number_of_images(reference_images) > 6:
+ raise ValueError("The maximum number of reference images allowed is 6.")
+ for i in reference_images:
+ validate_image_dimensions(i, min_width=300, min_height=300)
+ validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+ for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference frame(s)"):
+ image_list.append(OmniParamImage(image_url=i))
+ response = await sync_op(
+ cls,
+ ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+ response_model=OmniTaskStatusResponse,
+ data=OmniProFirstLastFrameRequest(
+ model_name=model_name,
+ prompt=prompt,
+ duration=str(duration),
+ image_list=image_list,
+ ),
+ )
+ return await finish_omni_video_task(cls, response)
+
+
+class OmniProImageToVideoNode(IO.ComfyNode):
+
+ @classmethod
+ def define_schema(cls) -> IO.Schema:
+ return IO.Schema(
+ node_id="KlingOmniProImageToVideoNode",
+ display_name="Kling Omni Image to Video (Pro)",
+ category="api node/video/Kling",
+ description="Use up to 7 reference images to generate a video with the latest Kling model.",
+ inputs=[
+ IO.Combo.Input("model_name", options=["kling-video-o1"]),
+ IO.String.Input(
+ "prompt",
+ multiline=True,
+ tooltip="A text prompt describing the video content. "
+ "This can include both positive and negative descriptions.",
+ ),
+ IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+ IO.Int.Input("duration", default=3, min=3, max=10, display_mode=IO.NumberDisplay.slider),
+ IO.Image.Input(
+ "reference_images",
+ tooltip="Up to 7 reference images.",
+ ),
+ ],
+ outputs=[
+ IO.Video.Output(),
+ ],
+ hidden=[
+ IO.Hidden.auth_token_comfy_org,
+ IO.Hidden.api_key_comfy_org,
+ IO.Hidden.unique_id,
+ ],
+ is_api_node=True,
+ )
+
+ @classmethod
+ async def execute(
+ cls,
+ model_name: str,
+ prompt: str,
+ aspect_ratio: str,
+ duration: int,
+ reference_images: Input.Image,
+ ) -> IO.NodeOutput:
+ prompt = normalize_omni_prompt_references(prompt)
+ validate_string(prompt, min_length=1, max_length=2500)
+ if get_number_of_images(reference_images) > 7:
+ raise ValueError("The maximum number of reference images is 7.")
+ for i in reference_images:
+ validate_image_dimensions(i, min_width=300, min_height=300)
+ validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+ image_list: list[OmniParamImage] = []
+ for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+ image_list.append(OmniParamImage(image_url=i))
+ response = await sync_op(
+ cls,
+ ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+ response_model=OmniTaskStatusResponse,
+ data=OmniProReferences2VideoRequest(
+ model_name=model_name,
+ prompt=prompt,
+ aspect_ratio=aspect_ratio,
+ duration=str(duration),
+ image_list=image_list,
+ ),
+ )
+ return await finish_omni_video_task(cls, response)
+
+
+class OmniProVideoToVideoNode(IO.ComfyNode):
+
+ @classmethod
+ def define_schema(cls) -> IO.Schema:
+ return IO.Schema(
+ node_id="KlingOmniProVideoToVideoNode",
+ display_name="Kling Omni Video to Video (Pro)",
+ category="api node/video/Kling",
+ description="Use a video and up to 4 reference images to generate a video with the latest Kling model.",
+ inputs=[
+ IO.Combo.Input("model_name", options=["kling-video-o1"]),
+ IO.String.Input(
+ "prompt",
+ multiline=True,
+ tooltip="A text prompt describing the video content. "
+ "This can include both positive and negative descriptions.",
+ ),
+ IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]),
+ IO.Int.Input("duration", default=3, min=3, max=10, display_mode=IO.NumberDisplay.slider),
+ IO.Video.Input("reference_video", tooltip="Video to use as a reference."),
+ IO.Boolean.Input("keep_original_sound", default=True),
+ IO.Image.Input(
+ "reference_images",
+ tooltip="Up to 4 additional reference images.",
+ optional=True,
+ ),
+ ],
+ outputs=[
+ IO.Video.Output(),
+ ],
+ hidden=[
+ IO.Hidden.auth_token_comfy_org,
+ IO.Hidden.api_key_comfy_org,
+ IO.Hidden.unique_id,
+ ],
+ is_api_node=True,
+ )
+
+ @classmethod
+ async def execute(
+ cls,
+ model_name: str,
+ prompt: str,
+ aspect_ratio: str,
+ duration: int,
+ reference_video: Input.Video,
+ keep_original_sound: bool,
+ reference_images: Input.Image | None = None,
+ ) -> IO.NodeOutput:
+ prompt = normalize_omni_prompt_references(prompt)
+ validate_string(prompt, min_length=1, max_length=2500)
+ validate_video_duration(reference_video, min_duration=3.0, max_duration=10.05)
+ validate_video_dimensions(reference_video, min_width=720, min_height=720, max_width=2160, max_height=2160)
+ image_list: list[OmniParamImage] = []
+ if reference_images is not None:
+ if get_number_of_images(reference_images) > 4:
+ raise ValueError("The maximum number of reference images allowed with a video input is 4.")
+ for i in reference_images:
+ validate_image_dimensions(i, min_width=300, min_height=300)
+ validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+ for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+ image_list.append(OmniParamImage(image_url=i))
+ video_list = [
+ OmniParamVideo(
+ video_url=await upload_video_to_comfyapi(cls, reference_video, wait_label="Uploading reference video"),
+ refer_type="feature",
+ keep_original_sound="yes" if keep_original_sound else "no",
+ )
+ ]
+ response = await sync_op(
+ cls,
+ ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+ response_model=OmniTaskStatusResponse,
+ data=OmniProReferences2VideoRequest(
+ model_name=model_name,
+ prompt=prompt,
+ aspect_ratio=aspect_ratio,
+ duration=str(duration),
+ image_list=image_list if image_list else None,
+ video_list=video_list,
+ ),
+ )
+ return await finish_omni_video_task(cls, response)
+
+
+class OmniProEditVideoNode(IO.ComfyNode):
+
+ @classmethod
+ def define_schema(cls) -> IO.Schema:
+ return IO.Schema(
+ node_id="KlingOmniProEditVideoNode",
+ display_name="Kling Omni Edit Video (Pro)",
+ category="api node/video/Kling",
+ description="Edit an existing video with the latest model from Kling.",
+ inputs=[
+ IO.Combo.Input("model_name", options=["kling-video-o1"]),
+ IO.String.Input(
+ "prompt",
+ multiline=True,
+ tooltip="A text prompt describing the video content. "
+ "This can include both positive and negative descriptions.",
+ ),
+ IO.Video.Input("video", tooltip="Video for editing. The output video length will be the same."),
+ IO.Boolean.Input("keep_original_sound", default=True),
+ IO.Image.Input(
+ "reference_images",
+ tooltip="Up to 4 additional reference images.",
+ optional=True,
+ ),
+ ],
+ outputs=[
+ IO.Video.Output(),
+ ],
+ hidden=[
+ IO.Hidden.auth_token_comfy_org,
+ IO.Hidden.api_key_comfy_org,
+ IO.Hidden.unique_id,
+ ],
+ is_api_node=True,
+ )
+
+ @classmethod
+ async def execute(
+ cls,
+ model_name: str,
+ prompt: str,
+ video: Input.Video,
+ keep_original_sound: bool,
+ reference_images: Input.Image | None = None,
+ ) -> IO.NodeOutput:
+ prompt = normalize_omni_prompt_references(prompt)
+ validate_string(prompt, min_length=1, max_length=2500)
+ validate_video_duration(video, min_duration=3.0, max_duration=10.05)
+ validate_video_dimensions(video, min_width=720, min_height=720, max_width=2160, max_height=2160)
+ image_list: list[OmniParamImage] = []
+ if reference_images is not None:
+ if get_number_of_images(reference_images) > 4:
+ raise ValueError("The maximum number of reference images allowed with a video input is 4.")
+ for i in reference_images:
+ validate_image_dimensions(i, min_width=300, min_height=300)
+ validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+ for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+ image_list.append(OmniParamImage(image_url=i))
+ video_list = [
+ OmniParamVideo(
+ video_url=await upload_video_to_comfyapi(cls, video, wait_label="Uploading base video"),
+ refer_type="base",
+ keep_original_sound="yes" if keep_original_sound else "no",
+ )
+ ]
+ response = await sync_op(
+ cls,
+ ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"),
+ response_model=OmniTaskStatusResponse,
+ data=OmniProReferences2VideoRequest(
+ model_name=model_name,
+ prompt=prompt,
+ aspect_ratio=None,
+ duration=None,
+ image_list=image_list if image_list else None,
+ video_list=video_list,
+ ),
+ )
+ return await finish_omni_video_task(cls, response)
+
+
+class OmniProImageNode(IO.ComfyNode):
+
+ @classmethod
+ def define_schema(cls) -> IO.Schema:
+ return IO.Schema(
+ node_id="KlingOmniProImageNode",
+ display_name="Kling Omni Image (Pro)",
+ category="api node/image/Kling",
+ description="Create or edit images with the latest model from Kling.",
+ inputs=[
+ IO.Combo.Input("model_name", options=["kling-image-o1"]),
+ IO.String.Input(
+ "prompt",
+ multiline=True,
+ tooltip="A text prompt describing the image content. "
+ "This can include both positive and negative descriptions.",
+ ),
+ IO.Combo.Input("resolution", options=["1K", "2K"]),
+ IO.Combo.Input(
+ "aspect_ratio",
+ options=["16:9", "9:16", "1:1", "4:3", "3:4", "3:2", "2:3", "21:9"],
+ ),
+ IO.Image.Input(
+ "reference_images",
+ tooltip="Up to 10 additional reference images.",
+ optional=True,
+ ),
+ ],
+ outputs=[
+ IO.Image.Output(),
+ ],
+ hidden=[
+ IO.Hidden.auth_token_comfy_org,
+ IO.Hidden.api_key_comfy_org,
+ IO.Hidden.unique_id,
+ ],
+ is_api_node=True,
+ )
+
+ @classmethod
+ async def execute(
+ cls,
+ model_name: str,
+ prompt: str,
+ resolution: str,
+ aspect_ratio: str,
+ reference_images: Input.Image | None = None,
+ ) -> IO.NodeOutput:
+ prompt = normalize_omni_prompt_references(prompt)
+ validate_string(prompt, min_length=1, max_length=2500)
+ image_list: list[OmniImageParamImage] = []
+ if reference_images is not None:
+ if get_number_of_images(reference_images) > 10:
+ raise ValueError("The maximum number of reference images is 10.")
+ for i in reference_images:
+ validate_image_dimensions(i, min_width=300, min_height=300)
+ validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1))
+ for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"):
+ image_list.append(OmniImageParamImage(image=i))
+ response = await sync_op(
+ cls,
+ ApiEndpoint(path="/proxy/kling/v1/images/omni-image", method="POST"),
+ response_model=OmniTaskStatusResponse,
+ data=OmniProImageRequest(
+ model_name=model_name,
+ prompt=prompt,
+ resolution=resolution.lower(),
+ aspect_ratio=aspect_ratio,
+ image_list=image_list if image_list else None,
+ ),
+ )
+ if response.code:
+ raise RuntimeError(
+ f"Kling request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+ )
+ final_response = await poll_op(
+ cls,
+ ApiEndpoint(path=f"/proxy/kling/v1/images/omni-image/{response.data.task_id}"),
+ response_model=OmniTaskStatusResponse,
+ status_extractor=lambda r: (r.data.task_status if r.data else None),
+ )
+ return IO.NodeOutput(await download_url_to_image_tensor(final_response.data.task_result.images[0].url))
+
+
class KlingCameraControlT2VNode(IO.ComfyNode):
"""
Kling Text to Video Camera Control Node. This node is a text to video node, but it supports controlling the camera.
@@ -1162,7 +1685,10 @@ class KlingSingleImageVideoEffectNode(IO.ComfyNode):
category="api node/video/Kling",
description="Achieve different special effects when generating a video based on the effect_scene.",
inputs=[
- IO.Image.Input("image", tooltip=" Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1"),
+ IO.Image.Input(
+ "image",
+ tooltip=" Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1",
+ ),
IO.Combo.Input(
"effect_scene",
options=[i.value for i in KlingSingleImageEffectsScene],
@@ -1525,6 +2051,12 @@ class KlingExtension(ComfyExtension):
KlingImageGenerationNode,
KlingSingleImageVideoEffectNode,
KlingDualCharacterVideoEffectNode,
+ OmniProTextToVideoNode,
+ OmniProFirstLastFrameNode,
+ OmniProImageToVideoNode,
+ OmniProVideoToVideoNode,
+ OmniProEditVideoNode,
+ # OmniProImageNode, # need support from backend
]
diff --git a/comfy_api_nodes/nodes_pika.py b/comfy_api_nodes/nodes_pika.py
index 51148211b..acd88c391 100644
--- a/comfy_api_nodes/nodes_pika.py
+++ b/comfy_api_nodes/nodes_pika.py
@@ -92,6 +92,7 @@ class PikaImageToVideo(IO.ComfyNode):
IO.Hidden.unique_id,
],
is_api_node=True,
+ is_deprecated=True,
)
@classmethod
@@ -152,6 +153,7 @@ class PikaTextToVideoNode(IO.ComfyNode):
IO.Hidden.unique_id,
],
is_api_node=True,
+ is_deprecated=True,
)
@classmethod
@@ -239,6 +241,7 @@ class PikaScenes(IO.ComfyNode):
IO.Hidden.unique_id,
],
is_api_node=True,
+ is_deprecated=True,
)
@classmethod
@@ -323,6 +326,7 @@ class PikAdditionsNode(IO.ComfyNode):
IO.Hidden.unique_id,
],
is_api_node=True,
+ is_deprecated=True,
)
@classmethod
@@ -399,6 +403,7 @@ class PikaSwapsNode(IO.ComfyNode):
IO.Hidden.unique_id,
],
is_api_node=True,
+ is_deprecated=True,
)
@classmethod
@@ -466,6 +471,7 @@ class PikaffectsNode(IO.ComfyNode):
IO.Hidden.unique_id,
],
is_api_node=True,
+ is_deprecated=True,
)
@classmethod
@@ -515,6 +521,7 @@ class PikaStartEndFrameNode(IO.ComfyNode):
IO.Hidden.unique_id,
],
is_api_node=True,
+ is_deprecated=True,
)
@classmethod
diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py
index 80292fb3c..4cc22abfb 100644
--- a/comfy_api_nodes/util/__init__.py
+++ b/comfy_api_nodes/util/__init__.py
@@ -47,6 +47,7 @@ from .validation_utils import (
validate_string,
validate_video_dimensions,
validate_video_duration,
+ validate_video_frame_count,
)
__all__ = [
@@ -94,6 +95,7 @@ __all__ = [
"validate_string",
"validate_video_dimensions",
"validate_video_duration",
+ "validate_video_frame_count",
# Misc functions
"get_fs_object_size",
]
diff --git a/comfy_api_nodes/util/_helpers.py b/comfy_api_nodes/util/_helpers.py
index 328fe5227..491e6b6a8 100644
--- a/comfy_api_nodes/util/_helpers.py
+++ b/comfy_api_nodes/util/_helpers.py
@@ -2,8 +2,8 @@ import asyncio
import contextlib
import os
import time
+from collections.abc import Callable
from io import BytesIO
-from typing import Callable, Optional, Union
from comfy.cli_args import args
from comfy.model_management import processing_interrupted
@@ -35,12 +35,12 @@ def default_base_url() -> str:
async def sleep_with_interrupt(
seconds: float,
- node_cls: Optional[type[IO.ComfyNode]],
- label: Optional[str] = None,
- start_ts: Optional[float] = None,
- estimated_total: Optional[int] = None,
+ node_cls: type[IO.ComfyNode] | None,
+ label: str | None = None,
+ start_ts: float | None = None,
+ estimated_total: int | None = None,
*,
- display_callback: Optional[Callable[[type[IO.ComfyNode], str, int, Optional[int]], None]] = None,
+ display_callback: Callable[[type[IO.ComfyNode], str, int, int | None], None] | None = None,
):
"""
Sleep in 1s slices while:
@@ -65,7 +65,7 @@ def mimetype_to_extension(mime_type: str) -> str:
return mime_type.split("/")[-1].lower()
-def get_fs_object_size(path_or_object: Union[str, BytesIO]) -> int:
+def get_fs_object_size(path_or_object: str | BytesIO) -> int:
if isinstance(path_or_object, str):
return os.path.getsize(path_or_object)
return len(path_or_object.getvalue())
diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py
index bf01d7d36..bf37cba5f 100644
--- a/comfy_api_nodes/util/client.py
+++ b/comfy_api_nodes/util/client.py
@@ -4,10 +4,11 @@ import json
import logging
import time
import uuid
+from collections.abc import Callable, Iterable
from dataclasses import dataclass
from enum import Enum
from io import BytesIO
-from typing import Any, Callable, Iterable, Literal, Optional, Type, TypeVar, Union
+from typing import Any, Literal, TypeVar
from urllib.parse import urljoin, urlparse
import aiohttp
@@ -37,8 +38,8 @@ class ApiEndpoint:
path: str,
method: Literal["GET", "POST", "PUT", "DELETE", "PATCH"] = "GET",
*,
- query_params: Optional[dict[str, Any]] = None,
- headers: Optional[dict[str, str]] = None,
+ query_params: dict[str, Any] | None = None,
+ headers: dict[str, str] | None = None,
):
self.path = path
self.method = method
@@ -52,18 +53,18 @@ class _RequestConfig:
endpoint: ApiEndpoint
timeout: float
content_type: str
- data: Optional[dict[str, Any]]
- files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]]
- multipart_parser: Optional[Callable]
+ data: dict[str, Any] | None
+ files: dict[str, Any] | list[tuple[str, Any]] | None
+ multipart_parser: Callable | None
max_retries: int
retry_delay: float
retry_backoff: float
wait_label: str = "Waiting"
monitor_progress: bool = True
- estimated_total: Optional[int] = None
- final_label_on_success: Optional[str] = "Completed"
- progress_origin_ts: Optional[float] = None
- price_extractor: Optional[Callable[[dict[str, Any]], Optional[float]]] = None
+ estimated_total: int | None = None
+ final_label_on_success: str | None = "Completed"
+ progress_origin_ts: float | None = None
+ price_extractor: Callable[[dict[str, Any]], float | None] | None = None
@dataclass
@@ -71,10 +72,10 @@ class _PollUIState:
started: float
status_label: str = "Queued"
is_queued: bool = True
- price: Optional[float] = None
- estimated_duration: Optional[int] = None
+ price: float | None = None
+ estimated_duration: int | None = None
base_processing_elapsed: float = 0.0 # sum of completed active intervals
- active_since: Optional[float] = None # start time of current active interval (None if queued)
+ active_since: float | None = None # start time of current active interval (None if queued)
_RETRY_STATUS = {408, 429, 500, 502, 503, 504}
@@ -87,20 +88,20 @@ async def sync_op(
cls: type[IO.ComfyNode],
endpoint: ApiEndpoint,
*,
- response_model: Type[M],
- price_extractor: Optional[Callable[[M], Optional[float]]] = None,
- data: Optional[BaseModel] = None,
- files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]] = None,
+ response_model: type[M],
+ price_extractor: Callable[[M | Any], float | None] | None = None,
+ data: BaseModel | None = None,
+ files: dict[str, Any] | list[tuple[str, Any]] | None = None,
content_type: str = "application/json",
timeout: float = 3600.0,
- multipart_parser: Optional[Callable] = None,
+ multipart_parser: Callable | None = None,
max_retries: int = 3,
retry_delay: float = 1.0,
retry_backoff: float = 2.0,
wait_label: str = "Waiting for server",
- estimated_duration: Optional[int] = None,
- final_label_on_success: Optional[str] = "Completed",
- progress_origin_ts: Optional[float] = None,
+ estimated_duration: int | None = None,
+ final_label_on_success: str | None = "Completed",
+ progress_origin_ts: float | None = None,
monitor_progress: bool = True,
) -> M:
raw = await sync_op_raw(
@@ -131,22 +132,22 @@ async def poll_op(
cls: type[IO.ComfyNode],
poll_endpoint: ApiEndpoint,
*,
- response_model: Type[M],
- status_extractor: Callable[[M], Optional[Union[str, int]]],
- progress_extractor: Optional[Callable[[M], Optional[int]]] = None,
- price_extractor: Optional[Callable[[M], Optional[float]]] = None,
- completed_statuses: Optional[list[Union[str, int]]] = None,
- failed_statuses: Optional[list[Union[str, int]]] = None,
- queued_statuses: Optional[list[Union[str, int]]] = None,
- data: Optional[BaseModel] = None,
+ response_model: type[M],
+ status_extractor: Callable[[M | Any], str | int | None],
+ progress_extractor: Callable[[M | Any], int | None] | None = None,
+ price_extractor: Callable[[M | Any], float | None] | None = None,
+ completed_statuses: list[str | int] | None = None,
+ failed_statuses: list[str | int] | None = None,
+ queued_statuses: list[str | int] | None = None,
+ data: BaseModel | None = None,
poll_interval: float = 5.0,
max_poll_attempts: int = 120,
timeout_per_poll: float = 120.0,
max_retries_per_poll: int = 3,
retry_delay_per_poll: float = 1.0,
retry_backoff_per_poll: float = 2.0,
- estimated_duration: Optional[int] = None,
- cancel_endpoint: Optional[ApiEndpoint] = None,
+ estimated_duration: int | None = None,
+ cancel_endpoint: ApiEndpoint | None = None,
cancel_timeout: float = 10.0,
) -> M:
raw = await poll_op_raw(
@@ -178,22 +179,22 @@ async def sync_op_raw(
cls: type[IO.ComfyNode],
endpoint: ApiEndpoint,
*,
- price_extractor: Optional[Callable[[dict[str, Any]], Optional[float]]] = None,
- data: Optional[Union[dict[str, Any], BaseModel]] = None,
- files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]] = None,
+ price_extractor: Callable[[dict[str, Any]], float | None] | None = None,
+ data: dict[str, Any] | BaseModel | None = None,
+ files: dict[str, Any] | list[tuple[str, Any]] | None = None,
content_type: str = "application/json",
timeout: float = 3600.0,
- multipart_parser: Optional[Callable] = None,
+ multipart_parser: Callable | None = None,
max_retries: int = 3,
retry_delay: float = 1.0,
retry_backoff: float = 2.0,
wait_label: str = "Waiting for server",
- estimated_duration: Optional[int] = None,
+ estimated_duration: int | None = None,
as_binary: bool = False,
- final_label_on_success: Optional[str] = "Completed",
- progress_origin_ts: Optional[float] = None,
+ final_label_on_success: str | None = "Completed",
+ progress_origin_ts: float | None = None,
monitor_progress: bool = True,
-) -> Union[dict[str, Any], bytes]:
+) -> dict[str, Any] | bytes:
"""
Make a single network request.
- If as_binary=False (default): returns JSON dict (or {'_raw': ''} if non-JSON).
@@ -229,21 +230,21 @@ async def poll_op_raw(
cls: type[IO.ComfyNode],
poll_endpoint: ApiEndpoint,
*,
- status_extractor: Callable[[dict[str, Any]], Optional[Union[str, int]]],
- progress_extractor: Optional[Callable[[dict[str, Any]], Optional[int]]] = None,
- price_extractor: Optional[Callable[[dict[str, Any]], Optional[float]]] = None,
- completed_statuses: Optional[list[Union[str, int]]] = None,
- failed_statuses: Optional[list[Union[str, int]]] = None,
- queued_statuses: Optional[list[Union[str, int]]] = None,
- data: Optional[Union[dict[str, Any], BaseModel]] = None,
+ status_extractor: Callable[[dict[str, Any]], str | int | None],
+ progress_extractor: Callable[[dict[str, Any]], int | None] | None = None,
+ price_extractor: Callable[[dict[str, Any]], float | None] | None = None,
+ completed_statuses: list[str | int] | None = None,
+ failed_statuses: list[str | int] | None = None,
+ queued_statuses: list[str | int] | None = None,
+ data: dict[str, Any] | BaseModel | None = None,
poll_interval: float = 5.0,
max_poll_attempts: int = 120,
timeout_per_poll: float = 120.0,
max_retries_per_poll: int = 3,
retry_delay_per_poll: float = 1.0,
retry_backoff_per_poll: float = 2.0,
- estimated_duration: Optional[int] = None,
- cancel_endpoint: Optional[ApiEndpoint] = None,
+ estimated_duration: int | None = None,
+ cancel_endpoint: ApiEndpoint | None = None,
cancel_timeout: float = 10.0,
) -> dict[str, Any]:
"""
@@ -261,7 +262,7 @@ async def poll_op_raw(
consumed_attempts = 0 # counts only non-queued polls
progress_bar = utils.ProgressBar(100) if progress_extractor else None
- last_progress: Optional[int] = None
+ last_progress: int | None = None
state = _PollUIState(started=started, estimated_duration=estimated_duration)
stop_ticker = asyncio.Event()
@@ -420,10 +421,10 @@ async def poll_op_raw(
def _display_text(
node_cls: type[IO.ComfyNode],
- text: Optional[str],
+ text: str | None,
*,
- status: Optional[Union[str, int]] = None,
- price: Optional[float] = None,
+ status: str | int | None = None,
+ price: float | None = None,
) -> None:
display_lines: list[str] = []
if status:
@@ -440,13 +441,13 @@ def _display_text(
def _display_time_progress(
node_cls: type[IO.ComfyNode],
- status: Optional[Union[str, int]],
+ status: str | int | None,
elapsed_seconds: int,
- estimated_total: Optional[int] = None,
+ estimated_total: int | None = None,
*,
- price: Optional[float] = None,
- is_queued: Optional[bool] = None,
- processing_elapsed_seconds: Optional[int] = None,
+ price: float | None = None,
+ is_queued: bool | None = None,
+ processing_elapsed_seconds: int | None = None,
) -> None:
if estimated_total is not None and estimated_total > 0 and is_queued is False:
pe = processing_elapsed_seconds if processing_elapsed_seconds is not None else elapsed_seconds
@@ -488,7 +489,7 @@ def _unpack_tuple(t: tuple) -> tuple[str, Any, str]:
raise ValueError("files tuple must be (filename, file[, content_type])")
-def _merge_params(endpoint_params: dict[str, Any], method: str, data: Optional[dict[str, Any]]) -> dict[str, Any]:
+def _merge_params(endpoint_params: dict[str, Any], method: str, data: dict[str, Any] | None) -> dict[str, Any]:
params = dict(endpoint_params or {})
if method.upper() == "GET" and data:
for k, v in data.items():
@@ -534,9 +535,9 @@ def _generate_operation_id(method: str, path: str, attempt: int) -> str:
def _snapshot_request_body_for_logging(
content_type: str,
method: str,
- data: Optional[dict[str, Any]],
- files: Optional[Union[dict[str, Any], list[tuple[str, Any]]]],
-) -> Optional[Union[dict[str, Any], str]]:
+ data: dict[str, Any] | None,
+ files: dict[str, Any] | list[tuple[str, Any]] | None,
+) -> dict[str, Any] | str | None:
if method.upper() == "GET":
return None
if content_type == "multipart/form-data":
@@ -586,13 +587,13 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
attempt = 0
delay = cfg.retry_delay
operation_succeeded: bool = False
- final_elapsed_seconds: Optional[int] = None
- extracted_price: Optional[float] = None
+ final_elapsed_seconds: int | None = None
+ extracted_price: float | None = None
while True:
attempt += 1
stop_event = asyncio.Event()
- monitor_task: Optional[asyncio.Task] = None
- sess: Optional[aiohttp.ClientSession] = None
+ monitor_task: asyncio.Task | None = None
+ sess: aiohttp.ClientSession | None = None
operation_id = _generate_operation_id(method, cfg.endpoint.path, attempt)
logging.debug("[DEBUG] HTTP %s %s (attempt %d)", method, url, attempt)
@@ -887,7 +888,7 @@ async def _request_base(cfg: _RequestConfig, expect_binary: bool):
)
-def _validate_or_raise(response_model: Type[M], payload: Any) -> M:
+def _validate_or_raise(response_model: type[M], payload: Any) -> M:
try:
return response_model.model_validate(payload)
except Exception as e:
@@ -902,9 +903,9 @@ def _validate_or_raise(response_model: Type[M], payload: Any) -> M:
def _wrap_model_extractor(
- response_model: Type[M],
- extractor: Optional[Callable[[M], Any]],
-) -> Optional[Callable[[dict[str, Any]], Any]]:
+ response_model: type[M],
+ extractor: Callable[[M], Any] | None,
+) -> Callable[[dict[str, Any]], Any] | None:
"""Wrap a typed extractor so it can be used by the dict-based poller.
Validates the dict into `response_model` before invoking `extractor`.
Uses a small per-wrapper cache keyed by `id(dict)` to avoid re-validating
@@ -929,10 +930,10 @@ def _wrap_model_extractor(
return _wrapped
-def _normalize_statuses(values: Optional[Iterable[Union[str, int]]]) -> set[Union[str, int]]:
+def _normalize_statuses(values: Iterable[str | int] | None) -> set[str | int]:
if not values:
return set()
- out: set[Union[str, int]] = set()
+ out: set[str | int] = set()
for v in values:
nv = _normalize_status_value(v)
if nv is not None:
@@ -940,7 +941,7 @@ def _normalize_statuses(values: Optional[Iterable[Union[str, int]]]) -> set[Unio
return out
-def _normalize_status_value(val: Union[str, int, None]) -> Union[str, int, None]:
+def _normalize_status_value(val: str | int | None) -> str | int | None:
if isinstance(val, str):
return val.strip().lower()
return val
diff --git a/comfy_api_nodes/util/conversions.py b/comfy_api_nodes/util/conversions.py
index 971dc57de..c57457580 100644
--- a/comfy_api_nodes/util/conversions.py
+++ b/comfy_api_nodes/util/conversions.py
@@ -4,7 +4,6 @@ import math
import mimetypes
import uuid
from io import BytesIO
-from typing import Optional
import av
import numpy as np
@@ -12,8 +11,7 @@ import torch
from PIL import Image
from comfy.utils import common_upscale
-from comfy_api.latest import Input, InputImpl
-from comfy_api.util import VideoCodec, VideoContainer
+from comfy_api.latest import Input, InputImpl, Types
from ._helpers import mimetype_to_extension
@@ -57,7 +55,7 @@ def image_tensor_pair_to_batch(image1: torch.Tensor, image2: torch.Tensor) -> to
def tensor_to_bytesio(
image: torch.Tensor,
- name: Optional[str] = None,
+ name: str | None = None,
total_pixels: int = 2048 * 2048,
mime_type: str = "image/png",
) -> BytesIO:
@@ -177,8 +175,8 @@ def audio_to_base64_string(audio: Input.Audio, container_format: str = "mp4", co
def video_to_base64_string(
video: Input.Video,
- container_format: VideoContainer = None,
- codec: VideoCodec = None
+ container_format: Types.VideoContainer | None = None,
+ codec: Types.VideoCodec | None = None,
) -> str:
"""
Converts a video input to a base64 string.
@@ -189,12 +187,11 @@ def video_to_base64_string(
codec: Optional codec to use (defaults to video.codec if available)
"""
video_bytes_io = BytesIO()
-
- # Use provided format/codec if specified, otherwise use video's own if available
- format_to_use = container_format if container_format is not None else getattr(video, 'container', VideoContainer.MP4)
- codec_to_use = codec if codec is not None else getattr(video, 'codec', VideoCodec.H264)
-
- video.save_to(video_bytes_io, format=format_to_use, codec=codec_to_use)
+ video.save_to(
+ video_bytes_io,
+ format=container_format or getattr(video, "container", Types.VideoContainer.MP4),
+ codec=codec or getattr(video, "codec", Types.VideoCodec.H264),
+ )
video_bytes_io.seek(0)
return base64.b64encode(video_bytes_io.getvalue()).decode("utf-8")
diff --git a/comfy_api_nodes/util/download_helpers.py b/comfy_api_nodes/util/download_helpers.py
index 14207dc68..3e0d0352d 100644
--- a/comfy_api_nodes/util/download_helpers.py
+++ b/comfy_api_nodes/util/download_helpers.py
@@ -3,15 +3,15 @@ import contextlib
import uuid
from io import BytesIO
from pathlib import Path
-from typing import IO, Optional, Union
+from typing import IO
from urllib.parse import urljoin, urlparse
import aiohttp
import torch
from aiohttp.client_exceptions import ClientError, ContentTypeError
-from comfy_api.input_impl import VideoFromFile
from comfy_api.latest import IO as COMFY_IO
+from comfy_api.latest import InputImpl
from . import request_logger
from ._helpers import (
@@ -29,9 +29,9 @@ _RETRY_STATUS = {408, 429, 500, 502, 503, 504}
async def download_url_to_bytesio(
url: str,
- dest: Optional[Union[BytesIO, IO[bytes], str, Path]],
+ dest: BytesIO | IO[bytes] | str | Path | None,
*,
- timeout: Optional[float] = None,
+ timeout: float | None = None,
max_retries: int = 5,
retry_delay: float = 1.0,
retry_backoff: float = 2.0,
@@ -71,10 +71,10 @@ async def download_url_to_bytesio(
is_path_sink = isinstance(dest, (str, Path))
fhandle = None
- session: Optional[aiohttp.ClientSession] = None
- stop_evt: Optional[asyncio.Event] = None
- monitor_task: Optional[asyncio.Task] = None
- req_task: Optional[asyncio.Task] = None
+ session: aiohttp.ClientSession | None = None
+ stop_evt: asyncio.Event | None = None
+ monitor_task: asyncio.Task | None = None
+ req_task: asyncio.Task | None = None
try:
with contextlib.suppress(Exception):
@@ -234,11 +234,11 @@ async def download_url_to_video_output(
timeout: float = None,
max_retries: int = 5,
cls: type[COMFY_IO.ComfyNode] = None,
-) -> VideoFromFile:
+) -> InputImpl.VideoFromFile:
"""Downloads a video from a URL and returns a `VIDEO` output."""
result = BytesIO()
await download_url_to_bytesio(video_url, result, timeout=timeout, max_retries=max_retries, cls=cls)
- return VideoFromFile(result)
+ return InputImpl.VideoFromFile(result)
async def download_url_as_bytesio(
diff --git a/comfy_api_nodes/util/request_logger.py b/comfy_api_nodes/util/request_logger.py
index ac52e2eab..e0cb4428d 100644
--- a/comfy_api_nodes/util/request_logger.py
+++ b/comfy_api_nodes/util/request_logger.py
@@ -1,5 +1,3 @@
-from __future__ import annotations
-
import datetime
import hashlib
import json
diff --git a/comfy_api_nodes/util/upload_helpers.py b/comfy_api_nodes/util/upload_helpers.py
index b9019841f..b8d33f4d1 100644
--- a/comfy_api_nodes/util/upload_helpers.py
+++ b/comfy_api_nodes/util/upload_helpers.py
@@ -4,15 +4,13 @@ import logging
import time
import uuid
from io import BytesIO
-from typing import Optional
from urllib.parse import urlparse
import aiohttp
import torch
from pydantic import BaseModel, Field
-from comfy_api.latest import IO, Input
-from comfy_api.util import VideoCodec, VideoContainer
+from comfy_api.latest import IO, Input, Types
from . import request_logger
from ._helpers import is_processing_interrupted, sleep_with_interrupt
@@ -32,7 +30,7 @@ from .conversions import (
class UploadRequest(BaseModel):
file_name: str = Field(..., description="Filename to upload")
- content_type: Optional[str] = Field(
+ content_type: str | None = Field(
None,
description="Mime type of the file. For example: image/png, image/jpeg, video/mp4, etc.",
)
@@ -56,7 +54,7 @@ async def upload_images_to_comfyapi(
Uploads images to ComfyUI API and returns download URLs.
To upload multiple images, stack them in the batch dimension first.
"""
- # if batch, try to upload each file if max_images is greater than 0
+ # if batched, try to upload each file if max_images is greater than 0
download_urls: list[str] = []
is_batch = len(image.shape) > 3
batch_len = image.shape[0] if is_batch else 1
@@ -100,9 +98,10 @@ async def upload_video_to_comfyapi(
cls: type[IO.ComfyNode],
video: Input.Video,
*,
- container: VideoContainer = VideoContainer.MP4,
- codec: VideoCodec = VideoCodec.H264,
- max_duration: Optional[int] = None,
+ container: Types.VideoContainer = Types.VideoContainer.MP4,
+ codec: Types.VideoCodec = Types.VideoCodec.H264,
+ max_duration: int | None = None,
+ wait_label: str | None = "Uploading",
) -> str:
"""
Uploads a single video to ComfyUI API and returns its download URL.
@@ -127,7 +126,7 @@ async def upload_video_to_comfyapi(
video.save_to(video_bytes_io, format=container, codec=codec)
video_bytes_io.seek(0)
- return await upload_file_to_comfyapi(cls, video_bytes_io, filename, upload_mime_type)
+ return await upload_file_to_comfyapi(cls, video_bytes_io, filename, upload_mime_type, wait_label)
async def upload_file_to_comfyapi(
@@ -219,7 +218,7 @@ async def upload_file(
return
monitor_task = asyncio.create_task(_monitor())
- sess: Optional[aiohttp.ClientSession] = None
+ sess: aiohttp.ClientSession | None = None
try:
try:
request_logger.log_request_response(
diff --git a/comfy_api_nodes/util/validation_utils.py b/comfy_api_nodes/util/validation_utils.py
index ec7006aed..f01edea96 100644
--- a/comfy_api_nodes/util/validation_utils.py
+++ b/comfy_api_nodes/util/validation_utils.py
@@ -1,9 +1,7 @@
import logging
-from typing import Optional
import torch
-from comfy_api.input.video_types import VideoInput
from comfy_api.latest import Input
@@ -18,10 +16,10 @@ def get_image_dimensions(image: torch.Tensor) -> tuple[int, int]:
def validate_image_dimensions(
image: torch.Tensor,
- min_width: Optional[int] = None,
- max_width: Optional[int] = None,
- min_height: Optional[int] = None,
- max_height: Optional[int] = None,
+ min_width: int | None = None,
+ max_width: int | None = None,
+ min_height: int | None = None,
+ max_height: int | None = None,
):
height, width = get_image_dimensions(image)
@@ -37,8 +35,8 @@ def validate_image_dimensions(
def validate_image_aspect_ratio(
image: torch.Tensor,
- min_ratio: Optional[tuple[float, float]] = None, # e.g. (1, 4)
- max_ratio: Optional[tuple[float, float]] = None, # e.g. (4, 1)
+ min_ratio: tuple[float, float] | None = None, # e.g. (1, 4)
+ max_ratio: tuple[float, float] | None = None, # e.g. (4, 1)
*,
strict: bool = True, # True -> (min, max); False -> [min, max]
) -> float:
@@ -54,8 +52,8 @@ def validate_image_aspect_ratio(
def validate_images_aspect_ratio_closeness(
first_image: torch.Tensor,
second_image: torch.Tensor,
- min_rel: float, # e.g. 0.8
- max_rel: float, # e.g. 1.25
+ min_rel: float, # e.g. 0.8
+ max_rel: float, # e.g. 1.25
*,
strict: bool = False, # True -> (min, max); False -> [min, max]
) -> float:
@@ -84,8 +82,8 @@ def validate_images_aspect_ratio_closeness(
def validate_aspect_ratio_string(
aspect_ratio: str,
- min_ratio: Optional[tuple[float, float]] = None, # e.g. (1, 4)
- max_ratio: Optional[tuple[float, float]] = None, # e.g. (4, 1)
+ min_ratio: tuple[float, float] | None = None, # e.g. (1, 4)
+ max_ratio: tuple[float, float] | None = None, # e.g. (4, 1)
*,
strict: bool = False, # True -> (min, max); False -> [min, max]
) -> float:
@@ -97,10 +95,10 @@ def validate_aspect_ratio_string(
def validate_video_dimensions(
video: Input.Video,
- min_width: Optional[int] = None,
- max_width: Optional[int] = None,
- min_height: Optional[int] = None,
- max_height: Optional[int] = None,
+ min_width: int | None = None,
+ max_width: int | None = None,
+ min_height: int | None = None,
+ max_height: int | None = None,
):
try:
width, height = video.get_dimensions()
@@ -120,8 +118,8 @@ def validate_video_dimensions(
def validate_video_duration(
video: Input.Video,
- min_duration: Optional[float] = None,
- max_duration: Optional[float] = None,
+ min_duration: float | None = None,
+ max_duration: float | None = None,
):
try:
duration = video.get_duration()
@@ -136,6 +134,23 @@ def validate_video_duration(
raise ValueError(f"Video duration must be at most {max_duration}s, got {duration}s")
+def validate_video_frame_count(
+ video: Input.Video,
+ min_frame_count: int | None = None,
+ max_frame_count: int | None = None,
+):
+ try:
+ frame_count = video.get_frame_count()
+ except Exception as e:
+ logging.error("Error getting frame count of video: %s", e)
+ return
+
+ if min_frame_count is not None and min_frame_count > frame_count:
+ raise ValueError(f"Video frame count must be at least {min_frame_count}, got {frame_count}")
+ if max_frame_count is not None and frame_count > max_frame_count:
+ raise ValueError(f"Video frame count must be at most {max_frame_count}, got {frame_count}")
+
+
def get_number_of_images(images):
if isinstance(images, torch.Tensor):
return images.shape[0] if images.ndim >= 4 else 1
@@ -144,8 +159,8 @@ def get_number_of_images(images):
def validate_audio_duration(
audio: Input.Audio,
- min_duration: Optional[float] = None,
- max_duration: Optional[float] = None,
+ min_duration: float | None = None,
+ max_duration: float | None = None,
) -> None:
sr = int(audio["sample_rate"])
dur = int(audio["waveform"].shape[-1]) / sr
@@ -177,7 +192,7 @@ def validate_string(
)
-def validate_container_format_is_mp4(video: VideoInput) -> None:
+def validate_container_format_is_mp4(video: Input.Video) -> None:
"""Validates video container format is MP4."""
container_format = video.get_container_format()
if container_format not in ["mp4", "mov,mp4,m4a,3gp,3g2,mj2"]:
@@ -194,8 +209,8 @@ def _ratio_from_tuple(r: tuple[float, float]) -> float:
def _assert_ratio_bounds(
ar: float,
*,
- min_ratio: Optional[tuple[float, float]] = None,
- max_ratio: Optional[tuple[float, float]] = None,
+ min_ratio: tuple[float, float] | None = None,
+ max_ratio: tuple[float, float] | None = None,
strict: bool = True,
) -> None:
"""Validate a numeric aspect ratio against optional min/max ratio bounds."""
diff --git a/comfy_execution/validation.py b/comfy_execution/validation.py
index cec105fc9..24c0b4ed7 100644
--- a/comfy_execution/validation.py
+++ b/comfy_execution/validation.py
@@ -1,4 +1,5 @@
from __future__ import annotations
+from comfy_api.latest import IO
def validate_node_input(
@@ -23,6 +24,11 @@ def validate_node_input(
if not received_type != input_type:
return True
+ # If the received type or input_type is a MatchType, we can return True immediately;
+ # validation for this is handled by the frontend
+ if received_type == IO.MatchType.io_type or input_type == IO.MatchType.io_type:
+ return True
+
# Not equal, and not strings
if not isinstance(received_type, str) or not isinstance(input_type, str):
return False
diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 2ed7e0b22..c7916443c 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -6,65 +6,80 @@ import torch
import comfy.model_management
import folder_paths
import os
-import io
-import json
-import random
import hashlib
import node_helpers
import logging
-from comfy.cli_args import args
-from comfy.comfy_types import FileLocator
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO, UI
-class EmptyLatentAudio:
- def __init__(self):
- self.device = comfy.model_management.intermediate_device()
+class EmptyLatentAudio(IO.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="EmptyLatentAudio",
+ display_name="Empty Latent Audio",
+ category="latent/audio",
+ inputs=[
+ IO.Float.Input("seconds", default=47.6, min=1.0, max=1000.0, step=0.1),
+ IO.Int.Input(
+ "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."
+ ),
+ ],
+ outputs=[IO.Latent.Output()],
+ )
@classmethod
- def INPUT_TYPES(s):
- return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1}),
- "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}),
- }}
- RETURN_TYPES = ("LATENT",)
- FUNCTION = "generate"
-
- CATEGORY = "latent/audio"
-
- def generate(self, seconds, batch_size):
+ def execute(cls, seconds, batch_size) -> IO.NodeOutput:
length = round((seconds * 44100 / 2048) / 2) * 2
- latent = torch.zeros([batch_size, 64, length], device=self.device)
- return ({"samples":latent, "type": "audio"}, )
+ latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device())
+ return IO.NodeOutput({"samples":latent, "type": "audio"})
-class ConditioningStableAudio:
+ generate = execute # TODO: remove
+
+
+class ConditioningStableAudio(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {"required": {"positive": ("CONDITIONING", ),
- "negative": ("CONDITIONING", ),
- "seconds_start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
- "seconds_total": ("FLOAT", {"default": 47.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
- }}
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="ConditioningStableAudio",
+ category="conditioning",
+ inputs=[
+ IO.Conditioning.Input("positive"),
+ IO.Conditioning.Input("negative"),
+ IO.Float.Input("seconds_start", default=0.0, min=0.0, max=1000.0, step=0.1),
+ IO.Float.Input("seconds_total", default=47.0, min=0.0, max=1000.0, step=0.1),
+ ],
+ outputs=[
+ IO.Conditioning.Output(display_name="positive"),
+ IO.Conditioning.Output(display_name="negative"),
+ ],
+ )
- RETURN_TYPES = ("CONDITIONING","CONDITIONING")
- RETURN_NAMES = ("positive", "negative")
-
- FUNCTION = "append"
-
- CATEGORY = "conditioning"
-
- def append(self, positive, negative, seconds_start, seconds_total):
+ @classmethod
+ def execute(cls, positive, negative, seconds_start, seconds_total) -> IO.NodeOutput:
positive = node_helpers.conditioning_set_values(positive, {"seconds_start": seconds_start, "seconds_total": seconds_total})
negative = node_helpers.conditioning_set_values(negative, {"seconds_start": seconds_start, "seconds_total": seconds_total})
- return (positive, negative)
+ return IO.NodeOutput(positive, negative)
-class VAEEncodeAudio:
+ append = execute # TODO: remove
+
+
+class VAEEncodeAudio(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}}
- RETURN_TYPES = ("LATENT",)
- FUNCTION = "encode"
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="VAEEncodeAudio",
+ display_name="VAE Encode Audio",
+ category="latent/audio",
+ inputs=[
+ IO.Audio.Input("audio"),
+ IO.Vae.Input("vae"),
+ ],
+ outputs=[IO.Latent.Output()],
+ )
- CATEGORY = "latent/audio"
-
- def encode(self, vae, audio):
+ @classmethod
+ def execute(cls, vae, audio) -> IO.NodeOutput:
sample_rate = audio["sample_rate"]
if 44100 != sample_rate:
waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100)
@@ -72,213 +87,134 @@ class VAEEncodeAudio:
waveform = audio["waveform"]
t = vae.encode(waveform.movedim(1, -1))
- return ({"samples":t}, )
+ return IO.NodeOutput({"samples":t})
-class VAEDecodeAudio:
+ encode = execute # TODO: remove
+
+
+class VAEDecodeAudio(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
- RETURN_TYPES = ("AUDIO",)
- FUNCTION = "decode"
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="VAEDecodeAudio",
+ display_name="VAE Decode Audio",
+ category="latent/audio",
+ inputs=[
+ IO.Latent.Input("samples"),
+ IO.Vae.Input("vae"),
+ ],
+ outputs=[IO.Audio.Output()],
+ )
- CATEGORY = "latent/audio"
-
- def decode(self, vae, samples):
+ @classmethod
+ def execute(cls, vae, samples) -> IO.NodeOutput:
audio = vae.decode(samples["samples"]).movedim(-1, 1)
std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0
std[std < 1.0] = 1.0
audio /= std
- return ({"waveform": audio, "sample_rate": 44100}, )
+ return IO.NodeOutput({"waveform": audio, "sample_rate": 44100})
+
+ decode = execute # TODO: remove
-def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None, quality="128k"):
-
- filename_prefix += self.prefix_append
- full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
- results: list[FileLocator] = []
-
- # Prepare metadata dictionary
- metadata = {}
- if not args.disable_metadata:
- if prompt is not None:
- metadata["prompt"] = json.dumps(prompt)
- if extra_pnginfo is not None:
- for x in extra_pnginfo:
- metadata[x] = json.dumps(extra_pnginfo[x])
-
- # Opus supported sample rates
- OPUS_RATES = [8000, 12000, 16000, 24000, 48000]
-
- for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
- filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
- file = f"{filename_with_batch_num}_{counter:05}_.{format}"
- output_path = os.path.join(full_output_folder, file)
-
- # Use original sample rate initially
- sample_rate = audio["sample_rate"]
-
- # Handle Opus sample rate requirements
- if format == "opus":
- if sample_rate > 48000:
- sample_rate = 48000
- elif sample_rate not in OPUS_RATES:
- # Find the next highest supported rate
- for rate in sorted(OPUS_RATES):
- if rate > sample_rate:
- sample_rate = rate
- break
- if sample_rate not in OPUS_RATES: # Fallback if still not supported
- sample_rate = 48000
-
- # Resample if necessary
- if sample_rate != audio["sample_rate"]:
- waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate)
-
- # Create output with specified format
- output_buffer = io.BytesIO()
- output_container = av.open(output_buffer, mode='w', format=format)
-
- # Set metadata on the container
- for key, value in metadata.items():
- output_container.metadata[key] = value
-
- layout = 'mono' if waveform.shape[0] == 1 else 'stereo'
- # Set up the output stream with appropriate properties
- if format == "opus":
- out_stream = output_container.add_stream("libopus", rate=sample_rate, layout=layout)
- if quality == "64k":
- out_stream.bit_rate = 64000
- elif quality == "96k":
- out_stream.bit_rate = 96000
- elif quality == "128k":
- out_stream.bit_rate = 128000
- elif quality == "192k":
- out_stream.bit_rate = 192000
- elif quality == "320k":
- out_stream.bit_rate = 320000
- elif format == "mp3":
- out_stream = output_container.add_stream("libmp3lame", rate=sample_rate, layout=layout)
- if quality == "V0":
- #TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
- out_stream.codec_context.qscale = 1
- elif quality == "128k":
- out_stream.bit_rate = 128000
- elif quality == "320k":
- out_stream.bit_rate = 320000
- else: #format == "flac":
- out_stream = output_container.add_stream("flac", rate=sample_rate, layout=layout)
-
- frame = av.AudioFrame.from_ndarray(waveform.movedim(0, 1).reshape(1, -1).float().numpy(), format='flt', layout=layout)
- frame.sample_rate = sample_rate
- frame.pts = 0
- output_container.mux(out_stream.encode(frame))
-
- # Flush encoder
- output_container.mux(out_stream.encode(None))
-
- # Close containers
- output_container.close()
-
- # Write the output to file
- output_buffer.seek(0)
- with open(output_path, 'wb') as f:
- f.write(output_buffer.getbuffer())
-
- results.append({
- "filename": file,
- "subfolder": subfolder,
- "type": self.type
- })
- counter += 1
-
- return { "ui": { "audio": results } }
-
-class SaveAudio:
- def __init__(self):
- self.output_dir = folder_paths.get_output_directory()
- self.type = "output"
- self.prefix_append = ""
+class SaveAudio(IO.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="SaveAudio",
+ display_name="Save Audio (FLAC)",
+ category="audio",
+ inputs=[
+ IO.Audio.Input("audio"),
+ IO.String.Input("filename_prefix", default="audio/ComfyUI"),
+ ],
+ hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+ is_output_node=True,
+ )
@classmethod
- def INPUT_TYPES(s):
- return {"required": { "audio": ("AUDIO", ),
- "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
- },
- "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
- }
+ def execute(cls, audio, filename_prefix="ComfyUI", format="flac") -> IO.NodeOutput:
+ return IO.NodeOutput(
+ ui=UI.AudioSaveHelper.get_save_audio_ui(audio, filename_prefix=filename_prefix, cls=cls, format=format)
+ )
- RETURN_TYPES = ()
- FUNCTION = "save_flac"
+ save_flac = execute # TODO: remove
- OUTPUT_NODE = True
- CATEGORY = "audio"
-
- def save_flac(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None):
- return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo)
-
-class SaveAudioMP3:
- def __init__(self):
- self.output_dir = folder_paths.get_output_directory()
- self.type = "output"
- self.prefix_append = ""
+class SaveAudioMP3(IO.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="SaveAudioMP3",
+ display_name="Save Audio (MP3)",
+ category="audio",
+ inputs=[
+ IO.Audio.Input("audio"),
+ IO.String.Input("filename_prefix", default="audio/ComfyUI"),
+ IO.Combo.Input("quality", options=["V0", "128k", "320k"], default="V0"),
+ ],
+ hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+ is_output_node=True,
+ )
@classmethod
- def INPUT_TYPES(s):
- return {"required": { "audio": ("AUDIO", ),
- "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
- "quality": (["V0", "128k", "320k"], {"default": "V0"}),
- },
- "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
- }
+ def execute(cls, audio, filename_prefix="ComfyUI", format="mp3", quality="128k") -> IO.NodeOutput:
+ return IO.NodeOutput(
+ ui=UI.AudioSaveHelper.get_save_audio_ui(
+ audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality
+ )
+ )
- RETURN_TYPES = ()
- FUNCTION = "save_mp3"
+ save_mp3 = execute # TODO: remove
- OUTPUT_NODE = True
- CATEGORY = "audio"
-
- def save_mp3(self, audio, filename_prefix="ComfyUI", format="mp3", prompt=None, extra_pnginfo=None, quality="128k"):
- return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality)
-
-class SaveAudioOpus:
- def __init__(self):
- self.output_dir = folder_paths.get_output_directory()
- self.type = "output"
- self.prefix_append = ""
+class SaveAudioOpus(IO.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="SaveAudioOpus",
+ display_name="Save Audio (Opus)",
+ category="audio",
+ inputs=[
+ IO.Audio.Input("audio"),
+ IO.String.Input("filename_prefix", default="audio/ComfyUI"),
+ IO.Combo.Input("quality", options=["64k", "96k", "128k", "192k", "320k"], default="128k"),
+ ],
+ hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+ is_output_node=True,
+ )
@classmethod
- def INPUT_TYPES(s):
- return {"required": { "audio": ("AUDIO", ),
- "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
- "quality": (["64k", "96k", "128k", "192k", "320k"], {"default": "128k"}),
- },
- "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
- }
+ def execute(cls, audio, filename_prefix="ComfyUI", format="opus", quality="V3") -> IO.NodeOutput:
+ return IO.NodeOutput(
+ ui=UI.AudioSaveHelper.get_save_audio_ui(
+ audio, filename_prefix=filename_prefix, cls=cls, format=format, quality=quality
+ )
+ )
- RETURN_TYPES = ()
- FUNCTION = "save_opus"
+ save_opus = execute # TODO: remove
- OUTPUT_NODE = True
- CATEGORY = "audio"
-
- def save_opus(self, audio, filename_prefix="ComfyUI", format="opus", prompt=None, extra_pnginfo=None, quality="V3"):
- return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality)
-
-class PreviewAudio(SaveAudio):
- def __init__(self):
- self.output_dir = folder_paths.get_temp_directory()
- self.type = "temp"
- self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
+class PreviewAudio(IO.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="PreviewAudio",
+ display_name="Preview Audio",
+ category="audio",
+ inputs=[
+ IO.Audio.Input("audio"),
+ ],
+ hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+ is_output_node=True,
+ )
@classmethod
- def INPUT_TYPES(s):
- return {"required":
- {"audio": ("AUDIO", ), },
- "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
- }
+ def execute(cls, audio) -> IO.NodeOutput:
+ return IO.NodeOutput(ui=UI.PreviewAudio(audio, cls=cls))
+
+ save_flac = execute # TODO: remove
+
def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
"""Convert audio to float 32 bits PCM format."""
@@ -316,26 +252,30 @@ def load(filepath: str) -> tuple[torch.Tensor, int]:
wav = f32_pcm(wav)
return wav, sr
-class LoadAudio:
+class LoadAudio(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
+ def define_schema(cls):
input_dir = folder_paths.get_input_directory()
files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"])
- return {"required": {"audio": (sorted(files), {"audio_upload": True})}}
+ return IO.Schema(
+ node_id="LoadAudio",
+ display_name="Load Audio",
+ category="audio",
+ inputs=[
+ IO.Combo.Input("audio", upload=IO.UploadType.audio, options=sorted(files)),
+ ],
+ outputs=[IO.Audio.Output()],
+ )
- CATEGORY = "audio"
-
- RETURN_TYPES = ("AUDIO", )
- FUNCTION = "load"
-
- def load(self, audio):
+ @classmethod
+ def execute(cls, audio) -> IO.NodeOutput:
audio_path = folder_paths.get_annotated_filepath(audio)
waveform, sample_rate = load(audio_path)
audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
- return (audio, )
+ return IO.NodeOutput(audio)
@classmethod
- def IS_CHANGED(s, audio):
+ def fingerprint_inputs(cls, audio):
image_path = folder_paths.get_annotated_filepath(audio)
m = hashlib.sha256()
with open(image_path, 'rb') as f:
@@ -343,46 +283,69 @@ class LoadAudio:
return m.digest().hex()
@classmethod
- def VALIDATE_INPUTS(s, audio):
+ def validate_inputs(cls, audio):
if not folder_paths.exists_annotated_filepath(audio):
return "Invalid audio file: {}".format(audio)
return True
-class RecordAudio:
+ load = execute # TODO: remove
+
+
+class RecordAudio(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {"required": {"audio": ("AUDIO_RECORD", {})}}
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="RecordAudio",
+ display_name="Record Audio",
+ category="audio",
+ inputs=[
+ IO.Custom("AUDIO_RECORD").Input("audio"),
+ ],
+ outputs=[IO.Audio.Output()],
+ )
- CATEGORY = "audio"
-
- RETURN_TYPES = ("AUDIO", )
- FUNCTION = "load"
-
- def load(self, audio):
+ @classmethod
+ def execute(cls, audio) -> IO.NodeOutput:
audio_path = folder_paths.get_annotated_filepath(audio)
waveform, sample_rate = load(audio_path)
audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
- return (audio, )
+ return IO.NodeOutput(audio)
+
+ load = execute # TODO: remove
-class TrimAudioDuration:
+class TrimAudioDuration(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(cls):
- return {
- "required": {
- "audio": ("AUDIO",),
- "start_index": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.01, "tooltip": "Start time in seconds, can be negative to count from the end (supports sub-seconds)."}),
- "duration": ("FLOAT", {"default": 60.0, "min": 0.0, "step": 0.01, "tooltip": "Duration in seconds"}),
- },
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="TrimAudioDuration",
+ display_name="Trim Audio Duration",
+ description="Trim audio tensor into chosen time range.",
+ category="audio",
+ inputs=[
+ IO.Audio.Input("audio"),
+ IO.Float.Input(
+ "start_index",
+ default=0.0,
+ min=-0xffffffffffffffff,
+ max=0xffffffffffffffff,
+ step=0.01,
+ tooltip="Start time in seconds, can be negative to count from the end (supports sub-seconds).",
+ ),
+ IO.Float.Input(
+ "duration",
+ default=60.0,
+ min=0.0,
+ step=0.01,
+ tooltip="Duration in seconds",
+ ),
+ ],
+ outputs=[IO.Audio.Output()],
+ )
- FUNCTION = "trim"
- RETURN_TYPES = ("AUDIO",)
- CATEGORY = "audio"
- DESCRIPTION = "Trim audio tensor into chosen time range."
-
- def trim(self, audio, start_index, duration):
+ @classmethod
+ def execute(cls, audio, start_index, duration) -> IO.NodeOutput:
waveform = audio["waveform"]
sample_rate = audio["sample_rate"]
audio_length = waveform.shape[-1]
@@ -399,23 +362,30 @@ class TrimAudioDuration:
if start_frame >= end_frame:
raise ValueError("AudioTrim: Start time must be less than end time and be within the audio length.")
- return ({"waveform": waveform[..., start_frame:end_frame], "sample_rate": sample_rate},)
+ return IO.NodeOutput({"waveform": waveform[..., start_frame:end_frame], "sample_rate": sample_rate})
+
+ trim = execute # TODO: remove
-class SplitAudioChannels:
+class SplitAudioChannels(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {"required": {
- "audio": ("AUDIO",),
- }}
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="SplitAudioChannels",
+ display_name="Split Audio Channels",
+ description="Separates the audio into left and right channels.",
+ category="audio",
+ inputs=[
+ IO.Audio.Input("audio"),
+ ],
+ outputs=[
+ IO.Audio.Output(display_name="left"),
+ IO.Audio.Output(display_name="right"),
+ ],
+ )
- RETURN_TYPES = ("AUDIO", "AUDIO")
- RETURN_NAMES = ("left", "right")
- FUNCTION = "separate"
- CATEGORY = "audio"
- DESCRIPTION = "Separates the audio into left and right channels."
-
- def separate(self, audio):
+ @classmethod
+ def execute(cls, audio) -> IO.NodeOutput:
waveform = audio["waveform"]
sample_rate = audio["sample_rate"]
@@ -425,7 +395,9 @@ class SplitAudioChannels:
left_channel = waveform[..., 0:1, :]
right_channel = waveform[..., 1:2, :]
- return ({"waveform": left_channel, "sample_rate": sample_rate}, {"waveform": right_channel, "sample_rate": sample_rate})
+ return IO.NodeOutput({"waveform": left_channel, "sample_rate": sample_rate}, {"waveform": right_channel, "sample_rate": sample_rate})
+
+ separate = execute # TODO: remove
def match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2):
@@ -443,21 +415,29 @@ def match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_
return waveform_1, waveform_2, output_sample_rate
-class AudioConcat:
+class AudioConcat(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {"required": {
- "audio1": ("AUDIO",),
- "audio2": ("AUDIO",),
- "direction": (['after', 'before'], {"default": 'after', "tooltip": "Whether to append audio2 after or before audio1."}),
- }}
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="AudioConcat",
+ display_name="Audio Concat",
+ description="Concatenates the audio1 to audio2 in the specified direction.",
+ category="audio",
+ inputs=[
+ IO.Audio.Input("audio1"),
+ IO.Audio.Input("audio2"),
+ IO.Combo.Input(
+ "direction",
+ options=['after', 'before'],
+ default="after",
+ tooltip="Whether to append audio2 after or before audio1.",
+ )
+ ],
+ outputs=[IO.Audio.Output()],
+ )
- RETURN_TYPES = ("AUDIO",)
- FUNCTION = "concat"
- CATEGORY = "audio"
- DESCRIPTION = "Concatenates the audio1 to audio2 in the specified direction."
-
- def concat(self, audio1, audio2, direction):
+ @classmethod
+ def execute(cls, audio1, audio2, direction) -> IO.NodeOutput:
waveform_1 = audio1["waveform"]
waveform_2 = audio2["waveform"]
sample_rate_1 = audio1["sample_rate"]
@@ -477,26 +457,33 @@ class AudioConcat:
elif direction == 'before':
concatenated_audio = torch.cat((waveform_2, waveform_1), dim=2)
- return ({"waveform": concatenated_audio, "sample_rate": output_sample_rate},)
+ return IO.NodeOutput({"waveform": concatenated_audio, "sample_rate": output_sample_rate})
+
+ concat = execute # TODO: remove
-class AudioMerge:
+class AudioMerge(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(cls):
- return {
- "required": {
- "audio1": ("AUDIO",),
- "audio2": ("AUDIO",),
- "merge_method": (["add", "mean", "subtract", "multiply"], {"tooltip": "The method used to combine the audio waveforms."}),
- },
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="AudioMerge",
+ display_name="Audio Merge",
+ description="Combine two audio tracks by overlaying their waveforms.",
+ category="audio",
+ inputs=[
+ IO.Audio.Input("audio1"),
+ IO.Audio.Input("audio2"),
+ IO.Combo.Input(
+ "merge_method",
+ options=["add", "mean", "subtract", "multiply"],
+ tooltip="The method used to combine the audio waveforms.",
+ )
+ ],
+ outputs=[IO.Audio.Output()],
+ )
- FUNCTION = "merge"
- RETURN_TYPES = ("AUDIO",)
- CATEGORY = "audio"
- DESCRIPTION = "Combine two audio tracks by overlaying their waveforms."
-
- def merge(self, audio1, audio2, merge_method):
+ @classmethod
+ def execute(cls, audio1, audio2, merge_method) -> IO.NodeOutput:
waveform_1 = audio1["waveform"]
waveform_2 = audio2["waveform"]
sample_rate_1 = audio1["sample_rate"]
@@ -530,85 +517,110 @@ class AudioMerge:
if max_val > 1.0:
waveform = waveform / max_val
- return ({"waveform": waveform, "sample_rate": output_sample_rate},)
+ return IO.NodeOutput({"waveform": waveform, "sample_rate": output_sample_rate})
+
+ merge = execute # TODO: remove
-class AudioAdjustVolume:
+class AudioAdjustVolume(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {"required": {
- "audio": ("AUDIO",),
- "volume": ("INT", {"default": 1.0, "min": -100, "max": 100, "tooltip": "Volume adjustment in decibels (dB). 0 = no change, +6 = double, -6 = half, etc"}),
- }}
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="AudioAdjustVolume",
+ display_name="Audio Adjust Volume",
+ category="audio",
+ inputs=[
+ IO.Audio.Input("audio"),
+ IO.Int.Input(
+ "volume",
+ default=1,
+ min=-100,
+ max=100,
+ tooltip="Volume adjustment in decibels (dB). 0 = no change, +6 = double, -6 = half, etc",
+ )
+ ],
+ outputs=[IO.Audio.Output()],
+ )
- RETURN_TYPES = ("AUDIO",)
- FUNCTION = "adjust_volume"
- CATEGORY = "audio"
-
- def adjust_volume(self, audio, volume):
+ @classmethod
+ def execute(cls, audio, volume) -> IO.NodeOutput:
if volume == 0:
- return (audio,)
+ return IO.NodeOutput(audio)
waveform = audio["waveform"]
sample_rate = audio["sample_rate"]
gain = 10 ** (volume / 20)
waveform = waveform * gain
- return ({"waveform": waveform, "sample_rate": sample_rate},)
+ return IO.NodeOutput({"waveform": waveform, "sample_rate": sample_rate})
+
+ adjust_volume = execute # TODO: remove
-class EmptyAudio:
+class EmptyAudio(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {"required": {
- "duration": ("FLOAT", {"default": 60.0, "min": 0.0, "max": 0xffffffffffffffff, "step": 0.01, "tooltip": "Duration of the empty audio clip in seconds"}),
- "sample_rate": ("INT", {"default": 44100, "tooltip": "Sample rate of the empty audio clip."}),
- "channels": ("INT", {"default": 2, "min": 1, "max": 2, "tooltip": "Number of audio channels (1 for mono, 2 for stereo)."}),
- }}
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="EmptyAudio",
+ display_name="Empty Audio",
+ category="audio",
+ inputs=[
+ IO.Float.Input(
+ "duration",
+ default=60.0,
+ min=0.0,
+ max=0xffffffffffffffff,
+ step=0.01,
+ tooltip="Duration of the empty audio clip in seconds",
+ ),
+ IO.Int.Input(
+ "sample_rate",
+ default=44100,
+ tooltip="Sample rate of the empty audio clip.",
+ min=1,
+ max=192000,
+ ),
+ IO.Int.Input(
+ "channels",
+ default=2,
+ min=1,
+ max=2,
+ tooltip="Number of audio channels (1 for mono, 2 for stereo).",
+ ),
+ ],
+ outputs=[IO.Audio.Output()],
+ )
- RETURN_TYPES = ("AUDIO",)
- FUNCTION = "create_empty_audio"
- CATEGORY = "audio"
-
- def create_empty_audio(self, duration, sample_rate, channels):
+ @classmethod
+ def execute(cls, duration, sample_rate, channels) -> IO.NodeOutput:
num_samples = int(round(duration * sample_rate))
waveform = torch.zeros((1, channels, num_samples), dtype=torch.float32)
- return ({"waveform": waveform, "sample_rate": sample_rate},)
+ return IO.NodeOutput({"waveform": waveform, "sample_rate": sample_rate})
+
+ create_empty_audio = execute # TODO: remove
-NODE_CLASS_MAPPINGS = {
- "EmptyLatentAudio": EmptyLatentAudio,
- "VAEEncodeAudio": VAEEncodeAudio,
- "VAEDecodeAudio": VAEDecodeAudio,
- "SaveAudio": SaveAudio,
- "SaveAudioMP3": SaveAudioMP3,
- "SaveAudioOpus": SaveAudioOpus,
- "LoadAudio": LoadAudio,
- "PreviewAudio": PreviewAudio,
- "ConditioningStableAudio": ConditioningStableAudio,
- "RecordAudio": RecordAudio,
- "TrimAudioDuration": TrimAudioDuration,
- "SplitAudioChannels": SplitAudioChannels,
- "AudioConcat": AudioConcat,
- "AudioMerge": AudioMerge,
- "AudioAdjustVolume": AudioAdjustVolume,
- "EmptyAudio": EmptyAudio,
-}
+class AudioExtension(ComfyExtension):
+ @override
+ async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+ return [
+ EmptyLatentAudio,
+ VAEEncodeAudio,
+ VAEDecodeAudio,
+ SaveAudio,
+ SaveAudioMP3,
+ SaveAudioOpus,
+ LoadAudio,
+ PreviewAudio,
+ ConditioningStableAudio,
+ RecordAudio,
+ TrimAudioDuration,
+ SplitAudioChannels,
+ AudioConcat,
+ AudioMerge,
+ AudioAdjustVolume,
+ EmptyAudio,
+ ]
-NODE_DISPLAY_NAME_MAPPINGS = {
- "EmptyLatentAudio": "Empty Latent Audio",
- "VAEEncodeAudio": "VAE Encode Audio",
- "VAEDecodeAudio": "VAE Decode Audio",
- "PreviewAudio": "Preview Audio",
- "LoadAudio": "Load Audio",
- "SaveAudio": "Save Audio (FLAC)",
- "SaveAudioMP3": "Save Audio (MP3)",
- "SaveAudioOpus": "Save Audio (Opus)",
- "RecordAudio": "Record Audio",
- "TrimAudioDuration": "Trim Audio Duration",
- "SplitAudioChannels": "Split Audio Channels",
- "AudioConcat": "Audio Concat",
- "AudioMerge": "Audio Merge",
- "AudioAdjustVolume": "Audio Adjust Volume",
- "EmptyAudio": "Empty Audio",
-}
+async def comfy_entrypoint() -> AudioExtension:
+ return AudioExtension()
diff --git a/comfy_extras/nodes_context_windows.py b/comfy_extras/nodes_context_windows.py
index 1c3d9e697..3799a9004 100644
--- a/comfy_extras/nodes_context_windows.py
+++ b/comfy_extras/nodes_context_windows.py
@@ -26,6 +26,9 @@ class ContextWindowsManualNode(io.ComfyNode):
io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
io.Int.Input("dim", min=0, max=5, default=0, tooltip="The dimension to apply the context windows to."),
+ io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),
+ #io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window, for example setting this to '0' will use the initial start image for each window."),
+ #io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."),
],
outputs=[
io.Model.Output(tooltip="The model with context windows applied during sampling."),
@@ -34,7 +37,8 @@ class ContextWindowsManualNode(io.ComfyNode):
)
@classmethod
- def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, dim: int) -> io.Model:
+ def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, dim: int, freenoise: bool,
+ cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False) -> io.Model:
model = model.clone()
model.model_options["context_handler"] = comfy.context_windows.IndexListContextHandler(
context_schedule=comfy.context_windows.get_matching_context_schedule(context_schedule),
@@ -43,9 +47,15 @@ class ContextWindowsManualNode(io.ComfyNode):
context_overlap=context_overlap,
context_stride=context_stride,
closed_loop=closed_loop,
- dim=dim)
+ dim=dim,
+ freenoise=freenoise,
+ cond_retain_index_list=cond_retain_index_list,
+ split_conds_to_windows=split_conds_to_windows
+ )
# make memory usage calculation only take into account the context window latents
comfy.context_windows.create_prepare_sampling_wrapper(model)
+ if freenoise: # no other use for this wrapper at this time
+ comfy.context_windows.create_sampler_sample_wrapper(model)
return io.NodeOutput(model)
class WanContextWindowsManualNode(ContextWindowsManualNode):
@@ -68,14 +78,18 @@ class WanContextWindowsManualNode(ContextWindowsManualNode):
io.Int.Input("context_stride", min=1, default=1, tooltip="The stride of the context window; only applicable to uniform schedules."),
io.Boolean.Input("closed_loop", default=False, tooltip="Whether to close the context window loop; only applicable to looped schedules."),
io.Combo.Input("fuse_method", options=comfy.context_windows.ContextFuseMethods.LIST_STATIC, default=comfy.context_windows.ContextFuseMethods.PYRAMID, tooltip="The method to use to fuse the context windows."),
+ io.Boolean.Input("freenoise", default=False, tooltip="Whether to apply FreeNoise noise shuffling, improves window blending."),
+ #io.String.Input("cond_retain_index_list", default="", tooltip="List of latent indices to retain in the conditioning tensors for each window, for example setting this to '0' will use the initial start image for each window."),
+ #io.Boolean.Input("split_conds_to_windows", default=False, tooltip="Whether to split multiple conditionings (created by ConditionCombine) to each window based on region index."),
]
return schema
@classmethod
- def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str) -> io.Model:
+ def execute(cls, model: io.Model.Type, context_length: int, context_overlap: int, context_schedule: str, context_stride: int, closed_loop: bool, fuse_method: str, freenoise: bool,
+ cond_retain_index_list: list[int]=[], split_conds_to_windows: bool=False) -> io.Model:
context_length = max(((context_length - 1) // 4) + 1, 1) # at least length 1
context_overlap = max(((context_overlap - 1) // 4) + 1, 0) # at least overlap 0
- return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2)
+ return super().execute(model, context_length, context_overlap, context_schedule, context_stride, closed_loop, fuse_method, dim=2, freenoise=freenoise, cond_retain_index_list=cond_retain_index_list, split_conds_to_windows=split_conds_to_windows)
class ContextWindowsExtension(ComfyExtension):
diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py
index e3ac58447..3429b731e 100644
--- a/comfy_extras/nodes_freelunch.py
+++ b/comfy_extras/nodes_freelunch.py
@@ -2,6 +2,8 @@
import torch
import logging
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO
def Fourier_filter(x, threshold, scale):
# FFT
@@ -22,21 +24,26 @@ def Fourier_filter(x, threshold, scale):
return x_filtered.to(x.dtype)
-class FreeU:
+class FreeU(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {"required": { "model": ("MODEL",),
- "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}),
- "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}),
- "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}),
- "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}),
- }}
- RETURN_TYPES = ("MODEL",)
- FUNCTION = "patch"
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="FreeU",
+ category="model_patches/unet",
+ inputs=[
+ IO.Model.Input("model"),
+ IO.Float.Input("b1", default=1.1, min=0.0, max=10.0, step=0.01),
+ IO.Float.Input("b2", default=1.2, min=0.0, max=10.0, step=0.01),
+ IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
+ IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
+ ],
+ outputs=[
+ IO.Model.Output(),
+ ],
+ )
- CATEGORY = "model_patches/unet"
-
- def patch(self, model, b1, b2, s1, s2):
+ @classmethod
+ def execute(cls, model, b1, b2, s1, s2) -> IO.NodeOutput:
model_channels = model.model.model_config.unet_config["model_channels"]
scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
on_cpu_devices = {}
@@ -59,23 +66,31 @@ class FreeU:
m = model.clone()
m.set_model_output_block_patch(output_block_patch)
- return (m, )
+ return IO.NodeOutput(m)
-class FreeU_V2:
+ patch = execute # TODO: remove
+
+
+class FreeU_V2(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {"required": { "model": ("MODEL",),
- "b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}),
- "b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}),
- "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}),
- "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}),
- }}
- RETURN_TYPES = ("MODEL",)
- FUNCTION = "patch"
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="FreeU_V2",
+ category="model_patches/unet",
+ inputs=[
+ IO.Model.Input("model"),
+ IO.Float.Input("b1", default=1.3, min=0.0, max=10.0, step=0.01),
+ IO.Float.Input("b2", default=1.4, min=0.0, max=10.0, step=0.01),
+ IO.Float.Input("s1", default=0.9, min=0.0, max=10.0, step=0.01),
+ IO.Float.Input("s2", default=0.2, min=0.0, max=10.0, step=0.01),
+ ],
+ outputs=[
+ IO.Model.Output(),
+ ],
+ )
- CATEGORY = "model_patches/unet"
-
- def patch(self, model, b1, b2, s1, s2):
+ @classmethod
+ def execute(cls, model, b1, b2, s1, s2) -> IO.NodeOutput:
model_channels = model.model.model_config.unet_config["model_channels"]
scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)}
on_cpu_devices = {}
@@ -105,9 +120,19 @@ class FreeU_V2:
m = model.clone()
m.set_model_output_block_patch(output_block_patch)
- return (m, )
+ return IO.NodeOutput(m)
-NODE_CLASS_MAPPINGS = {
- "FreeU": FreeU,
- "FreeU_V2": FreeU_V2,
-}
+ patch = execute # TODO: remove
+
+
+class FreelunchExtension(ComfyExtension):
+ @override
+ async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+ return [
+ FreeU,
+ FreeU_V2,
+ ]
+
+
+async def comfy_entrypoint() -> FreelunchExtension:
+ return FreelunchExtension()
diff --git a/comfy_extras/nodes_kandinsky5.py b/comfy_extras/nodes_kandinsky5.py
new file mode 100644
index 000000000..9cb234be1
--- /dev/null
+++ b/comfy_extras/nodes_kandinsky5.py
@@ -0,0 +1,136 @@
+import nodes
+import node_helpers
+import torch
+import comfy.model_management
+import comfy.utils
+
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class Kandinsky5ImageToVideo(io.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ return io.Schema(
+ node_id="Kandinsky5ImageToVideo",
+ category="conditioning/video_models",
+ inputs=[
+ io.Conditioning.Input("positive"),
+ io.Conditioning.Input("negative"),
+ io.Vae.Input("vae"),
+ io.Int.Input("width", default=768, min=16, max=nodes.MAX_RESOLUTION, step=16),
+ io.Int.Input("height", default=512, min=16, max=nodes.MAX_RESOLUTION, step=16),
+ io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=4),
+ io.Int.Input("batch_size", default=1, min=1, max=4096),
+ io.Image.Input("start_image", optional=True),
+ ],
+ outputs=[
+ io.Conditioning.Output(display_name="positive"),
+ io.Conditioning.Output(display_name="negative"),
+ io.Latent.Output(display_name="latent", tooltip="Empty video latent"),
+ io.Latent.Output(display_name="cond_latent", tooltip="Clean encoded start images, used to replace the noisy start of the model output latents"),
+ ],
+ )
+
+ @classmethod
+ def execute(cls, positive, negative, vae, width, height, length, batch_size, start_image=None) -> io.NodeOutput:
+ latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+ cond_latent_out = {}
+ if start_image is not None:
+ start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+ encoded = vae.encode(start_image[:, :, :, :3])
+ cond_latent_out["samples"] = encoded
+
+ mask = torch.ones((1, 1, latent.shape[2], latent.shape[-2], latent.shape[-1]), device=start_image.device, dtype=start_image.dtype)
+ mask[:, :, :((start_image.shape[0] - 1) // 4) + 1] = 0.0
+
+ positive = node_helpers.conditioning_set_values(positive, {"time_dim_replace": encoded, "concat_mask": mask})
+ negative = node_helpers.conditioning_set_values(negative, {"time_dim_replace": encoded, "concat_mask": mask})
+
+ out_latent = {}
+ out_latent["samples"] = latent
+ return io.NodeOutput(positive, negative, out_latent, cond_latent_out)
+
+
+def adaptive_mean_std_normalization(source, reference, clump_mean_low=0.3, clump_mean_high=0.35, clump_std_low=0.35, clump_std_high=0.5):
+ source_mean = source.mean(dim=(1, 3, 4), keepdim=True) # mean over C, H, W
+ source_std = source.std(dim=(1, 3, 4), keepdim=True) # std over C, H, W
+
+ reference_mean = torch.clamp(reference.mean(), source_mean - clump_mean_low, source_mean + clump_mean_high)
+ reference_std = torch.clamp(reference.std(), source_std - clump_std_low, source_std + clump_std_high)
+
+ # normalization
+ normalized = (source - source_mean) / (source_std + 1e-8)
+ normalized = normalized * reference_std + reference_mean
+
+ return normalized
+
+
+class NormalizeVideoLatentStart(io.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ return io.Schema(
+ node_id="NormalizeVideoLatentStart",
+ category="conditioning/video_models",
+ description="Normalizes the initial frames of a video latent to match the mean and standard deviation of subsequent reference frames. Helps reduce differences between the starting frames and the rest of the video.",
+ inputs=[
+ io.Latent.Input("latent"),
+ io.Int.Input("start_frame_count", default=4, min=1, max=nodes.MAX_RESOLUTION, step=1, tooltip="Number of latent frames to normalize, counted from the start"),
+ io.Int.Input("reference_frame_count", default=5, min=1, max=nodes.MAX_RESOLUTION, step=1, tooltip="Number of latent frames after the start frames to use as reference"),
+ ],
+ outputs=[
+ io.Latent.Output(display_name="latent"),
+ ],
+ )
+
+ @classmethod
+ def execute(cls, latent, start_frame_count, reference_frame_count) -> io.NodeOutput:
+ if latent["samples"].shape[2] <= 1:
+ return io.NodeOutput(latent)
+ s = latent.copy()
+ samples = latent["samples"].clone()
+
+ first_frames = samples[:, :, :start_frame_count]
+ reference_frames_data = samples[:, :, start_frame_count:start_frame_count+min(reference_frame_count, samples.shape[2]-1)]
+ normalized_first_frames = adaptive_mean_std_normalization(first_frames, reference_frames_data)
+
+ samples[:, :, :start_frame_count] = normalized_first_frames
+ s["samples"] = samples
+ return io.NodeOutput(s)
+
+
+class CLIPTextEncodeKandinsky5(io.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ return io.Schema(
+ node_id="CLIPTextEncodeKandinsky5",
+ category="advanced/conditioning/kandinsky5",
+ inputs=[
+ io.Clip.Input("clip"),
+ io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
+ io.String.Input("qwen25_7b", multiline=True, dynamic_prompts=True),
+ ],
+ outputs=[
+ io.Conditioning.Output(),
+ ],
+ )
+
+ @classmethod
+ def execute(cls, clip, clip_l, qwen25_7b) -> io.NodeOutput:
+ tokens = clip.tokenize(clip_l)
+ tokens["qwen25_7b"] = clip.tokenize(qwen25_7b)["qwen25_7b"]
+
+ return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
+
+
+class Kandinsky5Extension(ComfyExtension):
+ @override
+ async def get_node_list(self) -> list[type[io.ComfyNode]]:
+ return [
+ Kandinsky5ImageToVideo,
+ NormalizeVideoLatentStart,
+ CLIPTextEncodeKandinsky5,
+ ]
+
+async def comfy_entrypoint() -> Kandinsky5Extension:
+ return Kandinsky5Extension()
diff --git a/comfy_extras/nodes_latent.py b/comfy_extras/nodes_latent.py
index d2df07ff9..e439b18ef 100644
--- a/comfy_extras/nodes_latent.py
+++ b/comfy_extras/nodes_latent.py
@@ -4,7 +4,7 @@ import torch
import nodes
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
-
+import logging
def reshape_latent_to(target_shape, latent, repeat_batch=True):
if latent.shape[1:] != target_shape[1:]:
@@ -388,6 +388,42 @@ class LatentOperationSharpen(io.ComfyNode):
return luminance * sharpened
return io.NodeOutput(sharpen)
+class ReplaceVideoLatentFrames(io.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ return io.Schema(
+ node_id="ReplaceVideoLatentFrames",
+ category="latent/batch",
+ inputs=[
+ io.Latent.Input("destination", tooltip="The destination latent where frames will be replaced."),
+ io.Latent.Input("source", optional=True, tooltip="The source latent providing frames to insert into the destination latent. If not provided, the destination latent is returned unchanged."),
+ io.Int.Input("index", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1, tooltip="The starting latent frame index in the destination latent where the source latent frames will be placed. Negative values count from the end."),
+ ],
+ outputs=[
+ io.Latent.Output(),
+ ],
+ )
+
+ @classmethod
+ def execute(cls, destination, index, source=None) -> io.NodeOutput:
+ if source is None:
+ return io.NodeOutput(destination)
+ dest_frames = destination["samples"].shape[2]
+ source_frames = source["samples"].shape[2]
+ if index < 0:
+ index = dest_frames + index
+ if index > dest_frames:
+ logging.warning(f"ReplaceVideoLatentFrames: Index {index} is out of bounds for destination latent frames {dest_frames}.")
+ return io.NodeOutput(destination)
+ if index + source_frames > dest_frames:
+ logging.warning(f"ReplaceVideoLatentFrames: Source latent frames {source_frames} do not fit within destination latent frames {dest_frames} at the specified index {index}.")
+ return io.NodeOutput(destination)
+ s = source.copy()
+ s_source = source["samples"]
+ s_destination = destination["samples"].clone()
+ s_destination[:, :, index:index + s_source.shape[2]] = s_source
+ s["samples"] = s_destination
+ return io.NodeOutput(s)
class LatentExtension(ComfyExtension):
@override
@@ -405,6 +441,7 @@ class LatentExtension(ComfyExtension):
LatentApplyOperationCFG,
LatentOperationTonemapReinhard,
LatentOperationSharpen,
+ ReplaceVideoLatentFrames
]
diff --git a/comfy_extras/nodes_load_3d.py b/comfy_extras/nodes_load_3d.py
index 54c66ef68..545588ef8 100644
--- a/comfy_extras/nodes_load_3d.py
+++ b/comfy_extras/nodes_load_3d.py
@@ -2,22 +2,18 @@ import nodes
import folder_paths
import os
-from comfy.comfy_types import IO
-from comfy_api.input_impl import VideoFromFile
+from typing_extensions import override
+from comfy_api.latest import IO, ComfyExtension, InputImpl, UI
from pathlib import Path
-from PIL import Image
-import numpy as np
-
-import uuid
def normalize_path(path):
return path.replace('\\', '/')
-class Load3D():
+class Load3D(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
+ def define_schema(cls):
input_dir = os.path.join(folder_paths.get_input_directory(), "3d")
os.makedirs(input_dir, exist_ok=True)
@@ -30,23 +26,29 @@ class Load3D():
for file_path in input_path.rglob("*")
if file_path.suffix.lower() in {'.gltf', '.glb', '.obj', '.fbx', '.stl'}
]
+ return IO.Schema(
+ node_id="Load3D",
+ display_name="Load 3D & Animation",
+ category="3d",
+ is_experimental=True,
+ inputs=[
+ IO.Combo.Input("model_file", options=sorted(files), upload=IO.UploadType.model),
+ IO.Load3D.Input("image"),
+ IO.Int.Input("width", default=1024, min=1, max=4096, step=1),
+ IO.Int.Input("height", default=1024, min=1, max=4096, step=1),
+ ],
+ outputs=[
+ IO.Image.Output(display_name="image"),
+ IO.Mask.Output(display_name="mask"),
+ IO.String.Output(display_name="mesh_path"),
+ IO.Image.Output(display_name="normal"),
+ IO.Load3DCamera.Output(display_name="camera_info"),
+ IO.Video.Output(display_name="recording_video"),
+ ],
+ )
- return {"required": {
- "model_file": (sorted(files), {"file_upload": True}),
- "image": ("LOAD_3D", {}),
- "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
- "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
- }}
-
- RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "LOAD3D_CAMERA", IO.VIDEO)
- RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "camera_info", "recording_video")
-
- FUNCTION = "process"
- EXPERIMENTAL = True
-
- CATEGORY = "3d"
-
- def process(self, model_file, image, **kwargs):
+ @classmethod
+ def execute(cls, model_file, image, **kwargs) -> IO.NodeOutput:
image_path = folder_paths.get_annotated_filepath(image['image'])
mask_path = folder_paths.get_annotated_filepath(image['mask'])
normal_path = folder_paths.get_annotated_filepath(image['normal'])
@@ -61,58 +63,47 @@ class Load3D():
if image['recording'] != "":
recording_video_path = folder_paths.get_annotated_filepath(image['recording'])
- video = VideoFromFile(recording_video_path)
+ video = InputImpl.VideoFromFile(recording_video_path)
- return output_image, output_mask, model_file, normal_image, image['camera_info'], video
+ return IO.NodeOutput(output_image, output_mask, model_file, normal_image, image['camera_info'], video)
-class Preview3D():
+ process = execute # TODO: remove
+
+
+class Preview3D(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {"required": {
- "model_file": ("STRING", {"default": "", "multiline": False}),
- },
- "optional": {
- "camera_info": ("LOAD3D_CAMERA", {}),
- "bg_image": ("IMAGE", {})
- }}
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="Preview3D",
+ display_name="Preview 3D & Animation",
+ category="3d",
+ is_experimental=True,
+ is_output_node=True,
+ inputs=[
+ IO.String.Input("model_file", default="", multiline=False),
+ IO.Load3DCamera.Input("camera_info", optional=True),
+ IO.Image.Input("bg_image", optional=True),
+ ],
+ outputs=[],
+ )
- OUTPUT_NODE = True
- RETURN_TYPES = ()
-
- CATEGORY = "3d"
-
- FUNCTION = "process"
- EXPERIMENTAL = True
-
- def process(self, model_file, **kwargs):
+ @classmethod
+ def execute(cls, model_file, **kwargs) -> IO.NodeOutput:
camera_info = kwargs.get("camera_info", None)
bg_image = kwargs.get("bg_image", None)
+ return IO.NodeOutput(ui=UI.PreviewUI3D(model_file, camera_info, bg_image=bg_image))
- bg_image_path = None
- if bg_image is not None:
+ process = execute # TODO: remove
- img_array = (bg_image[0].cpu().numpy() * 255).astype(np.uint8)
- img = Image.fromarray(img_array)
- temp_dir = folder_paths.get_temp_directory()
- filename = f"bg_{uuid.uuid4().hex}.png"
- bg_image_path = os.path.join(temp_dir, filename)
- img.save(bg_image_path, compress_level=1)
+class Load3DExtension(ComfyExtension):
+ @override
+ async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+ return [
+ Load3D,
+ Preview3D,
+ ]
- bg_image_path = f"temp/{filename}"
- return {
- "ui": {
- "result": [model_file, camera_info, bg_image_path]
- }
- }
-
-NODE_CLASS_MAPPINGS = {
- "Load3D": Load3D,
- "Preview3D": Preview3D,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
- "Load3D": "Load 3D & Animation",
- "Preview3D": "Preview 3D & Animation",
-}
+async def comfy_entrypoint() -> Load3DExtension:
+ return Load3DExtension()
diff --git a/comfy_extras/nodes_logic.py b/comfy_extras/nodes_logic.py
new file mode 100644
index 000000000..95a6ba788
--- /dev/null
+++ b/comfy_extras/nodes_logic.py
@@ -0,0 +1,155 @@
+from typing import TypedDict
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+from comfy_api.latest import _io
+
+
+
+class SwitchNode(io.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ template = io.MatchType.Template("switch")
+ return io.Schema(
+ node_id="ComfySwitchNode",
+ display_name="Switch",
+ category="logic",
+ is_experimental=True,
+ inputs=[
+ io.Boolean.Input("switch"),
+ io.MatchType.Input("on_false", template=template, lazy=True, optional=True),
+ io.MatchType.Input("on_true", template=template, lazy=True, optional=True),
+ ],
+ outputs=[
+ io.MatchType.Output(template=template, display_name="output"),
+ ],
+ )
+
+ @classmethod
+ def check_lazy_status(cls, switch, on_false=..., on_true=...):
+ # We use ... instead of None, as None is passed for connected-but-unevaluated inputs.
+ # This trick allows us to ignore the value of the switch and still be able to run execute().
+
+ # One of the inputs may be missing, in which case we need to evaluate the other input
+ if on_false is ...:
+ return ["on_true"]
+ if on_true is ...:
+ return ["on_false"]
+ # Normal lazy switch operation
+ if switch and on_true is None:
+ return ["on_true"]
+ if not switch and on_false is None:
+ return ["on_false"]
+
+ @classmethod
+ def validate_inputs(cls, switch, on_false=..., on_true=...):
+ # This check happens before check_lazy_status(), so we can eliminate the case where
+ # both inputs are missing.
+ if on_false is ... and on_true is ...:
+ return "At least one of on_false or on_true must be connected to Switch node"
+ return True
+
+ @classmethod
+ def execute(cls, switch, on_true=..., on_false=...) -> io.NodeOutput:
+ if on_true is ...:
+ return io.NodeOutput(on_false)
+ if on_false is ...:
+ return io.NodeOutput(on_true)
+ return io.NodeOutput(on_true if switch else on_false)
+
+
+class DCTestNode(io.ComfyNode):
+ class DCValues(TypedDict):
+ combo: str
+ string: str
+ integer: int
+ image: io.Image.Type
+ subcombo: dict[str]
+
+ @classmethod
+ def define_schema(cls):
+ return io.Schema(
+ node_id="DCTestNode",
+ display_name="DCTest",
+ category="logic",
+ is_output_node=True,
+ inputs=[_io.DynamicCombo.Input("combo", options=[
+ _io.DynamicCombo.Option("option1", [io.String.Input("string")]),
+ _io.DynamicCombo.Option("option2", [io.Int.Input("integer")]),
+ _io.DynamicCombo.Option("option3", [io.Image.Input("image")]),
+ _io.DynamicCombo.Option("option4", [
+ _io.DynamicCombo.Input("subcombo", options=[
+ _io.DynamicCombo.Option("opt1", [io.Float.Input("float_x"), io.Float.Input("float_y")]),
+ _io.DynamicCombo.Option("opt2", [io.Mask.Input("mask1", optional=True)]),
+ ])
+ ])]
+ )],
+ outputs=[io.AnyType.Output()],
+ )
+
+ @classmethod
+ def execute(cls, combo: DCValues) -> io.NodeOutput:
+ combo_val = combo["combo"]
+ if combo_val == "option1":
+ return io.NodeOutput(combo["string"])
+ elif combo_val == "option2":
+ return io.NodeOutput(combo["integer"])
+ elif combo_val == "option3":
+ return io.NodeOutput(combo["image"])
+ elif combo_val == "option4":
+ return io.NodeOutput(f"{combo['subcombo']}")
+ else:
+ raise ValueError(f"Invalid combo: {combo_val}")
+
+
+class AutogrowNamesTestNode(io.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ template = _io.Autogrow.TemplateNames(input=io.Float.Input("float"), names=["a", "b", "c"])
+ return io.Schema(
+ node_id="AutogrowNamesTestNode",
+ display_name="AutogrowNamesTest",
+ category="logic",
+ inputs=[
+ _io.Autogrow.Input("autogrow", template=template)
+ ],
+ outputs=[io.String.Output()],
+ )
+
+ @classmethod
+ def execute(cls, autogrow: _io.Autogrow.Type) -> io.NodeOutput:
+ vals = list(autogrow.values())
+ combined = ",".join([str(x) for x in vals])
+ return io.NodeOutput(combined)
+
+class AutogrowPrefixTestNode(io.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ template = _io.Autogrow.TemplatePrefix(input=io.Float.Input("float"), prefix="float", min=1, max=10)
+ return io.Schema(
+ node_id="AutogrowPrefixTestNode",
+ display_name="AutogrowPrefixTest",
+ category="logic",
+ inputs=[
+ _io.Autogrow.Input("autogrow", template=template)
+ ],
+ outputs=[io.String.Output()],
+ )
+
+ @classmethod
+ def execute(cls, autogrow: _io.Autogrow.Type) -> io.NodeOutput:
+ vals = list(autogrow.values())
+ combined = ",".join([str(x) for x in vals])
+ return io.NodeOutput(combined)
+
+class LogicExtension(ComfyExtension):
+ @override
+ async def get_node_list(self) -> list[type[io.ComfyNode]]:
+ return [
+ # SwitchNode,
+ # DCTestNode,
+ # AutogrowNamesTestNode,
+ # AutogrowPrefixTestNode,
+ ]
+
+async def comfy_entrypoint() -> LogicExtension:
+ return LogicExtension()
diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py
index a5e405008..290e6f55e 100644
--- a/comfy_extras/nodes_mask.py
+++ b/comfy_extras/nodes_mask.py
@@ -3,11 +3,10 @@ import scipy.ndimage
import torch
import comfy.utils
import node_helpers
-import folder_paths
-import random
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, IO, UI
import nodes
-from nodes import MAX_RESOLUTION
def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
source = source.to(destination.device)
@@ -46,202 +45,213 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
destination[..., top:bottom, left:right] = source_portion + destination_portion
return destination
-class LatentCompositeMasked:
+class LatentCompositeMasked(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {
- "required": {
- "destination": ("LATENT",),
- "source": ("LATENT",),
- "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
- "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
- "resize_source": ("BOOLEAN", {"default": False}),
- },
- "optional": {
- "mask": ("MASK",),
- }
- }
- RETURN_TYPES = ("LATENT",)
- FUNCTION = "composite"
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="LatentCompositeMasked",
+ category="latent",
+ inputs=[
+ IO.Latent.Input("destination"),
+ IO.Latent.Input("source"),
+ IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
+ IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=8),
+ IO.Boolean.Input("resize_source", default=False),
+ IO.Mask.Input("mask", optional=True),
+ ],
+ outputs=[IO.Latent.Output()],
+ )
- CATEGORY = "latent"
-
- def composite(self, destination, source, x, y, resize_source, mask = None):
+ @classmethod
+ def execute(cls, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
output = destination.copy()
destination = destination["samples"].clone()
source = source["samples"]
output["samples"] = composite(destination, source, x, y, mask, 8, resize_source)
- return (output,)
+ return IO.NodeOutput(output)
-class ImageCompositeMasked:
+ composite = execute # TODO: remove
+
+
+class ImageCompositeMasked(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {
- "required": {
- "destination": ("IMAGE",),
- "source": ("IMAGE",),
- "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
- "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
- "resize_source": ("BOOLEAN", {"default": False}),
- },
- "optional": {
- "mask": ("MASK",),
- }
- }
- RETURN_TYPES = ("IMAGE",)
- FUNCTION = "composite"
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="ImageCompositeMasked",
+ category="image",
+ inputs=[
+ IO.Image.Input("destination"),
+ IO.Image.Input("source"),
+ IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Boolean.Input("resize_source", default=False),
+ IO.Mask.Input("mask", optional=True),
+ ],
+ outputs=[IO.Image.Output()],
+ )
- CATEGORY = "image"
-
- def composite(self, destination, source, x, y, resize_source, mask = None):
+ @classmethod
+ def execute(cls, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
destination, source = node_helpers.image_alpha_fix(destination, source)
destination = destination.clone().movedim(-1, 1)
output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
- return (output,)
+ return IO.NodeOutput(output)
-class MaskToImage:
+ composite = execute # TODO: remove
+
+
+class MaskToImage(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {
- "required": {
- "mask": ("MASK",),
- }
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="MaskToImage",
+ display_name="Convert Mask to Image",
+ category="mask",
+ inputs=[
+ IO.Mask.Input("mask"),
+ ],
+ outputs=[IO.Image.Output()],
+ )
- CATEGORY = "mask"
-
- RETURN_TYPES = ("IMAGE",)
- FUNCTION = "mask_to_image"
-
- def mask_to_image(self, mask):
+ @classmethod
+ def execute(cls, mask) -> IO.NodeOutput:
result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
- return (result,)
+ return IO.NodeOutput(result)
-class ImageToMask:
+ mask_to_image = execute # TODO: remove
+
+
+class ImageToMask(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {
- "required": {
- "image": ("IMAGE",),
- "channel": (["red", "green", "blue", "alpha"],),
- }
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="ImageToMask",
+ display_name="Convert Image to Mask",
+ category="mask",
+ inputs=[
+ IO.Image.Input("image"),
+ IO.Combo.Input("channel", options=["red", "green", "blue", "alpha"]),
+ ],
+ outputs=[IO.Mask.Output()],
+ )
- CATEGORY = "mask"
-
- RETURN_TYPES = ("MASK",)
- FUNCTION = "image_to_mask"
-
- def image_to_mask(self, image, channel):
+ @classmethod
+ def execute(cls, image, channel) -> IO.NodeOutput:
channels = ["red", "green", "blue", "alpha"]
mask = image[:, :, :, channels.index(channel)]
- return (mask,)
+ return IO.NodeOutput(mask)
-class ImageColorToMask:
+ image_to_mask = execute # TODO: remove
+
+
+class ImageColorToMask(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {
- "required": {
- "image": ("IMAGE",),
- "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}),
- }
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="ImageColorToMask",
+ category="mask",
+ inputs=[
+ IO.Image.Input("image"),
+ IO.Int.Input("color", default=0, min=0, max=0xFFFFFF, step=1, display_mode=IO.NumberDisplay.number),
+ ],
+ outputs=[IO.Mask.Output()],
+ )
- CATEGORY = "mask"
-
- RETURN_TYPES = ("MASK",)
- FUNCTION = "image_to_mask"
-
- def image_to_mask(self, image, color):
+ @classmethod
+ def execute(cls, image, color) -> IO.NodeOutput:
temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)
temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2]
mask = torch.where(temp == color, 1.0, 0).float()
- return (mask,)
+ return IO.NodeOutput(mask)
-class SolidMask:
+ image_to_mask = execute # TODO: remove
+
+
+class SolidMask(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(cls):
- return {
- "required": {
- "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
- "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
- "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
- }
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="SolidMask",
+ category="mask",
+ inputs=[
+ IO.Float.Input("value", default=1.0, min=0.0, max=1.0, step=0.01),
+ IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+ ],
+ outputs=[IO.Mask.Output()],
+ )
- CATEGORY = "mask"
-
- RETURN_TYPES = ("MASK",)
-
- FUNCTION = "solid"
-
- def solid(self, value, width, height):
+ @classmethod
+ def execute(cls, value, width, height) -> IO.NodeOutput:
out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu")
- return (out,)
+ return IO.NodeOutput(out)
-class InvertMask:
+ solid = execute # TODO: remove
+
+
+class InvertMask(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(cls):
- return {
- "required": {
- "mask": ("MASK",),
- }
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="InvertMask",
+ category="mask",
+ inputs=[
+ IO.Mask.Input("mask"),
+ ],
+ outputs=[IO.Mask.Output()],
+ )
- CATEGORY = "mask"
-
- RETURN_TYPES = ("MASK",)
-
- FUNCTION = "invert"
-
- def invert(self, mask):
+ @classmethod
+ def execute(cls, mask) -> IO.NodeOutput:
out = 1.0 - mask
- return (out,)
+ return IO.NodeOutput(out)
-class CropMask:
+ invert = execute # TODO: remove
+
+
+class CropMask(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(cls):
- return {
- "required": {
- "mask": ("MASK",),
- "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
- "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
- "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
- "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
- }
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="CropMask",
+ category="mask",
+ inputs=[
+ IO.Mask.Input("mask"),
+ IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Int.Input("width", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Int.Input("height", default=512, min=1, max=nodes.MAX_RESOLUTION, step=1),
+ ],
+ outputs=[IO.Mask.Output()],
+ )
- CATEGORY = "mask"
-
- RETURN_TYPES = ("MASK",)
-
- FUNCTION = "crop"
-
- def crop(self, mask, x, y, width, height):
+ @classmethod
+ def execute(cls, mask, x, y, width, height) -> IO.NodeOutput:
mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
out = mask[:, y:y + height, x:x + width]
- return (out,)
+ return IO.NodeOutput(out)
-class MaskComposite:
+ crop = execute # TODO: remove
+
+
+class MaskComposite(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(cls):
- return {
- "required": {
- "destination": ("MASK",),
- "source": ("MASK",),
- "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
- "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
- "operation": (["multiply", "add", "subtract", "and", "or", "xor"],),
- }
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="MaskComposite",
+ category="mask",
+ inputs=[
+ IO.Mask.Input("destination"),
+ IO.Mask.Input("source"),
+ IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Combo.Input("operation", options=["multiply", "add", "subtract", "and", "or", "xor"]),
+ ],
+ outputs=[IO.Mask.Output()],
+ )
- CATEGORY = "mask"
-
- RETURN_TYPES = ("MASK",)
-
- FUNCTION = "combine"
-
- def combine(self, destination, source, x, y, operation):
+ @classmethod
+ def execute(cls, destination, source, x, y, operation) -> IO.NodeOutput:
output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
source = source.reshape((-1, source.shape[-2], source.shape[-1]))
@@ -267,28 +277,29 @@ class MaskComposite:
output = torch.clamp(output, 0.0, 1.0)
- return (output,)
+ return IO.NodeOutput(output)
-class FeatherMask:
+ combine = execute # TODO: remove
+
+
+class FeatherMask(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(cls):
- return {
- "required": {
- "mask": ("MASK",),
- "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
- "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
- "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
- "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
- }
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="FeatherMask",
+ category="mask",
+ inputs=[
+ IO.Mask.Input("mask"),
+ IO.Int.Input("left", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Int.Input("top", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Int.Input("right", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Int.Input("bottom", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
+ ],
+ outputs=[IO.Mask.Output()],
+ )
- CATEGORY = "mask"
-
- RETURN_TYPES = ("MASK",)
-
- FUNCTION = "feather"
-
- def feather(self, mask, left, top, right, bottom):
+ @classmethod
+ def execute(cls, mask, left, top, right, bottom) -> IO.NodeOutput:
output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone()
left = min(left, output.shape[-1])
@@ -312,26 +323,28 @@ class FeatherMask:
feather_rate = (y + 1) / bottom
output[:, -y, :] *= feather_rate
- return (output,)
+ return IO.NodeOutput(output)
-class GrowMask:
+ feather = execute # TODO: remove
+
+
+class GrowMask(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(cls):
- return {
- "required": {
- "mask": ("MASK",),
- "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}),
- "tapered_corners": ("BOOLEAN", {"default": True}),
- },
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="GrowMask",
+ display_name="Grow Mask",
+ category="mask",
+ inputs=[
+ IO.Mask.Input("mask"),
+ IO.Int.Input("expand", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
+ IO.Boolean.Input("tapered_corners", default=True),
+ ],
+ outputs=[IO.Mask.Output()],
+ )
- CATEGORY = "mask"
-
- RETURN_TYPES = ("MASK",)
-
- FUNCTION = "expand_mask"
-
- def expand_mask(self, mask, expand, tapered_corners):
+ @classmethod
+ def execute(cls, mask, expand, tapered_corners) -> IO.NodeOutput:
c = 0 if tapered_corners else 1
kernel = np.array([[c, 1, c],
[1, 1, 1],
@@ -347,69 +360,74 @@ class GrowMask:
output = scipy.ndimage.grey_dilation(output, footprint=kernel)
output = torch.from_numpy(output)
out.append(output)
- return (torch.stack(out, dim=0),)
+ return IO.NodeOutput(torch.stack(out, dim=0))
-class ThresholdMask:
+ expand_mask = execute # TODO: remove
+
+
+class ThresholdMask(IO.ComfyNode):
@classmethod
- def INPUT_TYPES(s):
- return {
- "required": {
- "mask": ("MASK",),
- "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
- }
- }
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="ThresholdMask",
+ category="mask",
+ inputs=[
+ IO.Mask.Input("mask"),
+ IO.Float.Input("value", default=0.5, min=0.0, max=1.0, step=0.01),
+ ],
+ outputs=[IO.Mask.Output()],
+ )
- CATEGORY = "mask"
-
- RETURN_TYPES = ("MASK",)
- FUNCTION = "image_to_mask"
-
- def image_to_mask(self, mask, value):
+ @classmethod
+ def execute(cls, mask, value) -> IO.NodeOutput:
mask = (mask > value).float()
- return (mask,)
+ return IO.NodeOutput(mask)
+
+ image_to_mask = execute # TODO: remove
+
# Mask Preview - original implement from
# https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81
# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes
-class MaskPreview(nodes.SaveImage):
- def __init__(self):
- self.output_dir = folder_paths.get_temp_directory()
- self.type = "temp"
- self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
- self.compress_level = 4
+class MaskPreview(IO.ComfyNode):
+ @classmethod
+ def define_schema(cls):
+ return IO.Schema(
+ node_id="MaskPreview",
+ display_name="Preview Mask",
+ category="mask",
+ description="Saves the input images to your ComfyUI output directory.",
+ inputs=[
+ IO.Mask.Input("mask"),
+ ],
+ hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo],
+ is_output_node=True,
+ )
@classmethod
- def INPUT_TYPES(s):
- return {
- "required": {"mask": ("MASK",), },
- "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
- }
-
- FUNCTION = "execute"
- CATEGORY = "mask"
-
- def execute(self, mask, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
- preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
- return self.save_images(preview, filename_prefix, prompt, extra_pnginfo)
+ def execute(cls, mask, filename_prefix="ComfyUI") -> IO.NodeOutput:
+ return IO.NodeOutput(ui=UI.PreviewMask(mask))
-NODE_CLASS_MAPPINGS = {
- "LatentCompositeMasked": LatentCompositeMasked,
- "ImageCompositeMasked": ImageCompositeMasked,
- "MaskToImage": MaskToImage,
- "ImageToMask": ImageToMask,
- "ImageColorToMask": ImageColorToMask,
- "SolidMask": SolidMask,
- "InvertMask": InvertMask,
- "CropMask": CropMask,
- "MaskComposite": MaskComposite,
- "FeatherMask": FeatherMask,
- "GrowMask": GrowMask,
- "ThresholdMask": ThresholdMask,
- "MaskPreview": MaskPreview
-}
+class MaskExtension(ComfyExtension):
+ @override
+ async def get_node_list(self) -> list[type[IO.ComfyNode]]:
+ return [
+ LatentCompositeMasked,
+ ImageCompositeMasked,
+ MaskToImage,
+ ImageToMask,
+ ImageColorToMask,
+ SolidMask,
+ InvertMask,
+ CropMask,
+ MaskComposite,
+ FeatherMask,
+ GrowMask,
+ ThresholdMask,
+ MaskPreview,
+ ]
-NODE_DISPLAY_NAME_MAPPINGS = {
- "ImageToMask": "Convert Image to Mask",
- "MaskToImage": "Convert Mask to Image",
-}
+
+async def comfy_entrypoint() -> MaskExtension:
+ return MaskExtension()
diff --git a/comfy_extras/nodes_model_downscale.py b/comfy_extras/nodes_model_downscale.py
index f7ca9699d..dec2ae841 100644
--- a/comfy_extras/nodes_model_downscale.py
+++ b/comfy_extras/nodes_model_downscale.py
@@ -53,11 +53,6 @@ class PatchModelAddDownscale(io.ComfyNode):
return io.NodeOutput(m)
-NODE_DISPLAY_NAME_MAPPINGS = {
- # Sampling
- "PatchModelAddDownscale": "",
-}
-
class ModelDownscaleExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
diff --git a/comfy_extras/nodes_model_patch.py b/comfy_extras/nodes_model_patch.py
index 783c59b6b..c61810dbf 100644
--- a/comfy_extras/nodes_model_patch.py
+++ b/comfy_extras/nodes_model_patch.py
@@ -6,6 +6,7 @@ import comfy.ops
import comfy.model_management
import comfy.ldm.common_dit
import comfy.latent_formats
+import comfy.ldm.lumina.controlnet
class BlockWiseControlBlock(torch.nn.Module):
@@ -189,6 +190,35 @@ class SigLIPMultiFeatProjModel(torch.nn.Module):
return embedding
+def z_image_convert(sd):
+ replace_keys = {".attention.to_out.0.bias": ".attention.out.bias",
+ ".attention.norm_k.weight": ".attention.k_norm.weight",
+ ".attention.norm_q.weight": ".attention.q_norm.weight",
+ ".attention.to_out.0.weight": ".attention.out.weight"
+ }
+
+ out_sd = {}
+ for k in sorted(sd.keys()):
+ w = sd[k]
+
+ k_out = k
+ if k_out.endswith(".attention.to_k.weight"):
+ cc = [w]
+ continue
+ if k_out.endswith(".attention.to_q.weight"):
+ cc = [w] + cc
+ continue
+ if k_out.endswith(".attention.to_v.weight"):
+ cc = cc + [w]
+ w = torch.cat(cc, dim=0)
+ k_out = k_out.replace(".attention.to_v.weight", ".attention.qkv.weight")
+
+ for r, rr in replace_keys.items():
+ k_out = k_out.replace(r, rr)
+ out_sd[k_out] = w
+
+ return out_sd
+
class ModelPatchLoader:
@classmethod
def INPUT_TYPES(s):
@@ -211,6 +241,9 @@ class ModelPatchLoader:
elif 'feature_embedder.mid_layer_norm.bias' in sd:
sd = comfy.utils.state_dict_prefix_replace(sd, {"feature_embedder.": ""}, filter_keys=True)
model = SigLIPMultiFeatProjModel(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
+ elif 'control_all_x_embedder.2-1.weight' in sd: # alipai z image fun controlnet
+ sd = z_image_convert(sd)
+ model = comfy.ldm.lumina.controlnet.ZImage_Control(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
model.load_state_dict(sd)
model = comfy.model_patcher.ModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
@@ -263,6 +296,69 @@ class DiffSynthCnetPatch:
def models(self):
return [self.model_patch]
+class ZImageControlPatch:
+ def __init__(self, model_patch, vae, image, strength):
+ self.model_patch = model_patch
+ self.vae = vae
+ self.image = image
+ self.strength = strength
+ self.encoded_image = self.encode_latent_cond(image)
+ self.encoded_image_size = (image.shape[1], image.shape[2])
+ self.temp_data = None
+
+ def encode_latent_cond(self, image):
+ latent_image = comfy.latent_formats.Flux().process_in(self.vae.encode(image))
+ return latent_image
+
+ def __call__(self, kwargs):
+ x = kwargs.get("x")
+ img = kwargs.get("img")
+ txt = kwargs.get("txt")
+ pe = kwargs.get("pe")
+ vec = kwargs.get("vec")
+ block_index = kwargs.get("block_index")
+ spacial_compression = self.vae.spacial_compression_encode()
+ if self.encoded_image is None or self.encoded_image_size != (x.shape[-2] * spacial_compression, x.shape[-1] * spacial_compression):
+ image_scaled = comfy.utils.common_upscale(self.image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center")
+ loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
+ self.encoded_image = self.encode_latent_cond(image_scaled.movedim(1, -1))
+ self.encoded_image_size = (image_scaled.shape[-2], image_scaled.shape[-1])
+ comfy.model_management.load_models_gpu(loaded_models)
+
+ cnet_index = (block_index // 5)
+ cnet_index_float = (block_index / 5)
+
+ kwargs.pop("img") # we do ops in place
+ kwargs.pop("txt")
+
+ cnet_blocks = self.model_patch.model.n_control_layers
+ if cnet_index_float > (cnet_blocks - 1):
+ self.temp_data = None
+ return kwargs
+
+ if self.temp_data is None or self.temp_data[0] > cnet_index:
+ self.temp_data = (-1, (None, self.model_patch.model(txt, self.encoded_image.to(img.dtype), pe, vec)))
+
+ while self.temp_data[0] < cnet_index and (self.temp_data[0] + 1) < cnet_blocks:
+ next_layer = self.temp_data[0] + 1
+ self.temp_data = (next_layer, self.model_patch.model.forward_control_block(next_layer, self.temp_data[1][1], img[:, :self.temp_data[1][1].shape[1]], None, pe, vec))
+
+ if cnet_index_float == self.temp_data[0]:
+ img[:, :self.temp_data[1][0].shape[1]] += (self.temp_data[1][0] * self.strength)
+ if cnet_blocks == self.temp_data[0] + 1:
+ self.temp_data = None
+
+ return kwargs
+
+ def to(self, device_or_dtype):
+ if isinstance(device_or_dtype, torch.device):
+ self.encoded_image = self.encoded_image.to(device_or_dtype)
+ self.temp_data = None
+ return self
+
+ def models(self):
+ return [self.model_patch]
+
class QwenImageDiffsynthControlnet:
@classmethod
def INPUT_TYPES(s):
@@ -289,7 +385,10 @@ class QwenImageDiffsynthControlnet:
mask = mask.unsqueeze(2)
mask = 1.0 - mask
- model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask))
+ if isinstance(model_patch.model, comfy.ldm.lumina.controlnet.ZImage_Control):
+ model_patched.set_model_double_block_patch(ZImageControlPatch(model_patch, vae, image, strength))
+ else:
+ model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask))
return (model_patched,)
diff --git a/comfy_extras/nodes_train.py b/comfy_extras/nodes_train.py
index cb24ab709..19b8baaf4 100644
--- a/comfy_extras/nodes_train.py
+++ b/comfy_extras/nodes_train.py
@@ -623,7 +623,7 @@ class TrainLoraNode(io.ComfyNode):
noise = comfy_extras.nodes_custom_sampler.Noise_RandomNoise(seed)
if multi_res:
# use first latent as dummy latent if multi_res
- latents = latents[0].repeat(num_images, 1, 1, 1)
+ latents = latents[0].repeat((num_images,) + ((1,) * (latents[0].ndim - 1)))
guider.sample(
noise.generate_noise({"samples": latents}),
latents,
diff --git a/comfy_extras/nodes_video.py b/comfy_extras/nodes_video.py
index 69fabb12e..6cf6e39bf 100644
--- a/comfy_extras/nodes_video.py
+++ b/comfy_extras/nodes_video.py
@@ -88,7 +88,7 @@ class SaveVideo(io.ComfyNode):
)
@classmethod
- def execute(cls, video: VideoInput, filename_prefix, format, codec) -> io.NodeOutput:
+ def execute(cls, video: VideoInput, filename_prefix, format: str, codec) -> io.NodeOutput:
width, height = video.get_dimensions()
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
filename_prefix,
@@ -108,7 +108,7 @@ class SaveVideo(io.ComfyNode):
file = f"{filename}_{counter:05}_.{VideoContainer.get_extension(format)}"
video.save_to(
os.path.join(full_output_folder, file),
- format=format,
+ format=VideoContainer(format),
codec=codec,
metadata=saved_metadata
)
diff --git a/comfyui_version.py b/comfyui_version.py
index fa4b4f4b0..4b039356e 100644
--- a/comfyui_version.py
+++ b/comfyui_version.py
@@ -1,3 +1,3 @@
# This file is automatically generated by the build process when version is
# updated in pyproject.toml.
-__version__ = "0.3.75"
+__version__ = "0.3.76"
diff --git a/cuda_malloc.py b/cuda_malloc.py
index 6520d5123..ee2bc4b69 100644
--- a/cuda_malloc.py
+++ b/cuda_malloc.py
@@ -63,18 +63,22 @@ def cuda_malloc_supported():
return True
+version = ""
+
+try:
+ torch_spec = importlib.util.find_spec("torch")
+ for folder in torch_spec.submodule_search_locations:
+ ver_file = os.path.join(folder, "version.py")
+ if os.path.isfile(ver_file):
+ spec = importlib.util.spec_from_file_location("torch_version_import", ver_file)
+ module = importlib.util.module_from_spec(spec)
+ spec.loader.exec_module(module)
+ version = module.__version__
+except:
+ pass
+
if not args.cuda_malloc:
try:
- version = ""
- torch_spec = importlib.util.find_spec("torch")
- for folder in torch_spec.submodule_search_locations:
- ver_file = os.path.join(folder, "version.py")
- if os.path.isfile(ver_file):
- spec = importlib.util.spec_from_file_location("torch_version_import", ver_file)
- module = importlib.util.module_from_spec(spec)
- spec.loader.exec_module(module)
- version = module.__version__
-
if int(version[0]) >= 2 and "+cu" in version: # enable by default for torch version 2.0 and up only on cuda torch
if PerformanceFeature.AutoTune not in args.fast: # Autotune has issues with cuda malloc
args.cuda_malloc = cuda_malloc_supported()
@@ -90,3 +94,6 @@ if args.cuda_malloc and not args.disable_cuda_malloc:
env_var += ",backend:cudaMallocAsync"
os.environ['PYTORCH_CUDA_ALLOC_CONF'] = env_var
+
+def get_torch_version_noimport():
+ return str(version)
diff --git a/execution.py b/execution.py
index 17c77beab..c2186ac98 100644
--- a/execution.py
+++ b/execution.py
@@ -34,7 +34,7 @@ from comfy_execution.validation import validate_node_input
from comfy_execution.progress import get_progress_state, reset_progress_state, add_progress_handler, WebUIProgressHandler
from comfy_execution.utils import CurrentNodeContext
from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func
-from comfy_api.latest import io
+from comfy_api.latest import io, _io
class ExecutionResult(Enum):
@@ -76,7 +76,7 @@ class IsChangedCache:
return self.is_changed[node_id]
# Intentionally do not use cached outputs here. We only want constants in IS_CHANGED
- input_data_all, _, hidden_inputs = get_input_data(node["inputs"], class_def, node_id, None)
+ input_data_all, _, v3_data = get_input_data(node["inputs"], class_def, node_id, None)
try:
is_changed = await _async_map_node_over_list(self.prompt_id, node_id, class_def, input_data_all, is_changed_name)
is_changed = await resolve_map_node_over_list_results(is_changed)
@@ -146,8 +146,9 @@ SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org")
def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}):
is_v3 = issubclass(class_def, _ComfyNodeInternal)
+ v3_data: io.V3Data = {}
if is_v3:
- valid_inputs, schema = class_def.INPUT_TYPES(include_hidden=False, return_schema=True)
+ valid_inputs, schema, v3_data = class_def.INPUT_TYPES(include_hidden=False, return_schema=True, live_inputs=inputs)
else:
valid_inputs = class_def.INPUT_TYPES()
input_data_all = {}
@@ -207,7 +208,8 @@ def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=
input_data_all[x] = [extra_data.get("auth_token_comfy_org", None)]
if h[x] == "API_KEY_COMFY_ORG":
input_data_all[x] = [extra_data.get("api_key_comfy_org", None)]
- return input_data_all, missing_keys, hidden_inputs_v3
+ v3_data["hidden_inputs"] = hidden_inputs_v3
+ return input_data_all, missing_keys, v3_data
map_node_over_list = None #Don't hook this please
@@ -223,7 +225,7 @@ async def resolve_map_node_over_list_results(results):
raise exc
return [x.result() if isinstance(x, asyncio.Task) else x for x in results]
-async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, hidden_inputs=None):
+async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, v3_data=None):
# check if node wants the lists
input_is_list = getattr(obj, "INPUT_IS_LIST", False)
@@ -259,13 +261,16 @@ async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, f
if is_class(obj):
type_obj = obj
obj.VALIDATE_CLASS()
- class_clone = obj.PREPARE_CLASS_CLONE(hidden_inputs)
+ class_clone = obj.PREPARE_CLASS_CLONE(v3_data)
# otherwise, use class instance to populate/reuse some fields
else:
type_obj = type(obj)
type_obj.VALIDATE_CLASS()
- class_clone = type_obj.PREPARE_CLASS_CLONE(hidden_inputs)
+ class_clone = type_obj.PREPARE_CLASS_CLONE(v3_data)
f = make_locked_method_func(type_obj, func, class_clone)
+ # in case of dynamic inputs, restructure inputs to expected nested dict
+ if v3_data is not None:
+ inputs = _io.build_nested_inputs(inputs, v3_data)
# V1
else:
f = getattr(obj, func)
@@ -320,8 +325,8 @@ def merge_result_data(results, obj):
output.append([o[i] for o in results])
return output
-async def get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=None, pre_execute_cb=None, hidden_inputs=None):
- return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, hidden_inputs=hidden_inputs)
+async def get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=None, pre_execute_cb=None, v3_data=None):
+ return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
has_pending_task = any(isinstance(r, asyncio.Task) and not r.done() for r in return_values)
if has_pending_task:
return return_values, {}, False, has_pending_task
@@ -460,7 +465,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
has_subgraph = False
else:
get_progress_state().start_progress(unique_id)
- input_data_all, missing_keys, hidden_inputs = get_input_data(inputs, class_def, unique_id, execution_list, dynprompt, extra_data)
+ input_data_all, missing_keys, v3_data = get_input_data(inputs, class_def, unique_id, execution_list, dynprompt, extra_data)
if server.client_id is not None:
server.last_node_id = display_node_id
server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id)
@@ -475,7 +480,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
else:
lazy_status_present = getattr(obj, "check_lazy_status", None) is not None
if lazy_status_present:
- required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, hidden_inputs=hidden_inputs)
+ required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, v3_data=v3_data)
required_inputs = await resolve_map_node_over_list_results(required_inputs)
required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], []))
required_inputs = [x for x in required_inputs if isinstance(x,str) and (
@@ -507,7 +512,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed,
def pre_execute_cb(call_index):
# TODO - How to handle this with async functions without contextvars (which requires Python 3.12)?
GraphBuilder.set_default_prefix(unique_id, call_index, 0)
- output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, hidden_inputs=hidden_inputs)
+ output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
if has_pending_tasks:
pending_async_nodes[unique_id] = output_data
unblock = execution_list.add_external_block(unique_id)
@@ -745,18 +750,17 @@ async def validate_inputs(prompt_id, prompt, item, validated):
class_type = prompt[unique_id]['class_type']
obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
- class_inputs = obj_class.INPUT_TYPES()
- valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{})))
-
errors = []
valid = True
validate_function_inputs = []
validate_has_kwargs = False
if issubclass(obj_class, _ComfyNodeInternal):
+ class_inputs, _, _ = obj_class.INPUT_TYPES(include_hidden=False, return_schema=True, live_inputs=inputs)
validate_function_name = "validate_inputs"
validate_function = first_real_override(obj_class, validate_function_name)
else:
+ class_inputs = obj_class.INPUT_TYPES()
validate_function_name = "VALIDATE_INPUTS"
validate_function = getattr(obj_class, validate_function_name, None)
if validate_function is not None:
@@ -765,6 +769,8 @@ async def validate_inputs(prompt_id, prompt, item, validated):
validate_has_kwargs = argspec.varkw is not None
received_types = {}
+ valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{})))
+
for x in valid_inputs:
input_type, input_category, extra_info = get_input_info(obj_class, x, class_inputs)
assert extra_info is not None
@@ -935,7 +941,7 @@ async def validate_inputs(prompt_id, prompt, item, validated):
continue
if len(validate_function_inputs) > 0 or validate_has_kwargs:
- input_data_all, _, hidden_inputs = get_input_data(inputs, obj_class, unique_id)
+ input_data_all, _, v3_data = get_input_data(inputs, obj_class, unique_id)
input_filtered = {}
for x in input_data_all:
if x in validate_function_inputs or validate_has_kwargs:
@@ -943,7 +949,7 @@ async def validate_inputs(prompt_id, prompt, item, validated):
if 'input_types' in validate_function_inputs:
input_filtered['input_types'] = [received_types]
- ret = await _async_map_node_over_list(prompt_id, unique_id, obj_class, input_filtered, validate_function_name, hidden_inputs=hidden_inputs)
+ ret = await _async_map_node_over_list(prompt_id, unique_id, obj_class, input_filtered, validate_function_name, v3_data=v3_data)
ret = await resolve_map_node_over_list_results(ret)
for x in input_filtered:
for i, r in enumerate(ret):
diff --git a/main.py b/main.py
index db4bf6c9d..349009c96 100644
--- a/main.py
+++ b/main.py
@@ -15,6 +15,7 @@ from comfy_execution.progress import get_progress_state
from comfy_execution.utils import get_executing_context
from comfy_api import feature_flags
+
if __name__ == "__main__":
#NOTE: These do not do anything on core ComfyUI, they are for custom nodes.
os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'
@@ -22,6 +23,23 @@ if __name__ == "__main__":
setup_logger(log_level=args.verbose, use_stdout=args.log_stdout)
+
+def handle_comfyui_manager_unavailable():
+ if not args.windows_standalone_build:
+ logging.warning(f"\n\nYou appear to be running comfyui-manager from source, this is not recommended. Please install comfyui-manager using the following command:\ncommand:\n\t{sys.executable} -m pip install --pre comfyui_manager\n")
+ args.enable_manager = False
+
+
+if args.enable_manager:
+ if importlib.util.find_spec("comfyui_manager"):
+ import comfyui_manager
+
+ if not comfyui_manager.__file__ or not comfyui_manager.__file__.endswith('__init__.py'):
+ handle_comfyui_manager_unavailable()
+ else:
+ handle_comfyui_manager_unavailable()
+
+
def apply_custom_paths():
# extra model paths
extra_model_paths_config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extra_model_paths.yaml")
@@ -84,6 +102,11 @@ def execute_prestartup_script():
for possible_module in possible_modules:
module_path = os.path.join(custom_node_path, possible_module)
+
+ if args.enable_manager:
+ if comfyui_manager.should_be_disabled(module_path):
+ continue
+
if os.path.isfile(module_path) or module_path.endswith(".disabled") or module_path == "__pycache__":
continue
@@ -106,6 +129,10 @@ def execute_prestartup_script():
logging.info("")
apply_custom_paths()
+
+if args.enable_manager:
+ comfyui_manager.prestartup()
+
execute_prestartup_script()
@@ -145,6 +172,9 @@ if __name__ == "__main__":
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ":4096:8"
import cuda_malloc
+ if "rocm" in cuda_malloc.get_torch_version_noimport():
+ os.environ['OCL_SET_SVM_SIZE'] = '262144' # set at the request of AMD
+
if 'torch' in sys.modules:
logging.warning("WARNING: Potential Error in code: Torch already imported, torch should never be imported before this point.")
@@ -328,6 +358,9 @@ def start_comfyui(asyncio_loop=None):
asyncio.set_event_loop(asyncio_loop)
prompt_server = server.PromptServer(asyncio_loop)
+ if args.enable_manager and not args.disable_manager_ui:
+ comfyui_manager.start()
+
hook_breaker_ac10a0.save_functions()
asyncio_loop.run_until_complete(nodes.init_extra_nodes(
init_custom_nodes=(not args.disable_all_custom_nodes) or len(args.whitelist_custom_nodes) > 0,
diff --git a/manager_requirements.txt b/manager_requirements.txt
new file mode 100644
index 000000000..b95cefb74
--- /dev/null
+++ b/manager_requirements.txt
@@ -0,0 +1 @@
+comfyui_manager==4.0.3b4
diff --git a/nodes.py b/nodes.py
index 495dec806..8d28a725d 100644
--- a/nodes.py
+++ b/nodes.py
@@ -43,6 +43,9 @@ import folder_paths
import latent_preview
import node_helpers
+if args.enable_manager:
+ import comfyui_manager
+
def before_node_execution():
comfy.model_management.throw_exception_if_processing_interrupted()
@@ -939,7 +942,7 @@ 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", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2"], ),
+ "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos", "lumina2", "wan", "hidream", "chroma", "ace", "omnigen2", "qwen_image", "hunyuan_image", "flux2", "ovis"], ),
},
"optional": {
"device": (["default", "cpu"], {"advanced": True}),
@@ -967,7 +970,7 @@ class DualCLIPLoader:
def INPUT_TYPES(s):
return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
"clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
- "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hidream", "hunyuan_image", "hunyuan_video_15"], ),
+ "type": (["sdxl", "sd3", "flux", "hunyuan_video", "hidream", "hunyuan_image", "hunyuan_video_15", "kandinsky5", "kandinsky5_image"], ),
},
"optional": {
"device": (["default", "cpu"], {"advanced": True}),
@@ -2243,6 +2246,12 @@ async def init_external_custom_nodes():
if args.disable_all_custom_nodes and possible_module not in args.whitelist_custom_nodes:
logging.info(f"Skipping {possible_module} due to disable_all_custom_nodes and whitelist_custom_nodes")
continue
+
+ if args.enable_manager:
+ if comfyui_manager.should_be_disabled(module_path):
+ logging.info(f"Blocked by policy: {module_path}")
+ continue
+
time_before = time.perf_counter()
success = await load_custom_node(module_path, base_node_names, module_parent="custom_nodes")
node_import_times.append((time.perf_counter() - time_before, module_path, success))
@@ -2346,7 +2355,9 @@ async def init_builtin_extra_nodes():
"nodes_easycache.py",
"nodes_audio_encoder.py",
"nodes_rope.py",
+ "nodes_logic.py",
"nodes_nop.py",
+ "nodes_kandinsky5.py",
]
import_failed = []
diff --git a/pyproject.toml b/pyproject.toml
index 9009e65fe..02b94a0ce 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[project]
name = "ComfyUI"
-version = "0.3.75"
+version = "0.3.76"
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.9"
diff --git a/requirements.txt b/requirements.txt
index 386477808..f98848e20 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.32.9
+comfyui-frontend-package==1.33.10
comfyui-workflow-templates==0.7.25
comfyui-embedded-docs==0.3.1
torch
diff --git a/server.py b/server.py
index fca5050bd..ac4f42222 100644
--- a/server.py
+++ b/server.py
@@ -44,6 +44,9 @@ from protocol import BinaryEventTypes
# Import cache control middleware
from middleware.cache_middleware import cache_control
+if args.enable_manager:
+ import comfyui_manager
+
async def send_socket_catch_exception(function, message):
try:
await function(message)
@@ -95,7 +98,7 @@ def create_cors_middleware(allowed_origin: str):
response = await handler(request)
response.headers['Access-Control-Allow-Origin'] = allowed_origin
- response.headers['Access-Control-Allow-Methods'] = 'POST, GET, DELETE, PUT, OPTIONS'
+ response.headers['Access-Control-Allow-Methods'] = 'POST, GET, DELETE, PUT, OPTIONS, PATCH'
response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization'
response.headers['Access-Control-Allow-Credentials'] = 'true'
return response
@@ -212,6 +215,9 @@ class PromptServer():
if args.disable_api_nodes:
middlewares.append(create_block_external_middleware())
+ if args.enable_manager:
+ middlewares.append(comfyui_manager.create_middleware())
+
max_upload_size = round(args.max_upload_size * 1024 * 1024)
self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares)
self.sockets = dict()
@@ -599,7 +605,7 @@ class PromptServer():
system_stats = {
"system": {
- "os": os.name,
+ "os": sys.platform,
"ram_total": ram_total,
"ram_free": ram_free,
"comfyui_version": __version__,
diff --git a/tests-unit/comfy_quant/test_mixed_precision.py b/tests-unit/comfy_quant/test_mixed_precision.py
index 63361309f..3a54941e6 100644
--- a/tests-unit/comfy_quant/test_mixed_precision.py
+++ b/tests-unit/comfy_quant/test_mixed_precision.py
@@ -2,6 +2,7 @@ import unittest
import torch
import sys
import os
+import json
# Add comfy to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
@@ -15,6 +16,7 @@ if not has_gpu():
from comfy import ops
from comfy.quant_ops import QuantizedTensor
+import comfy.utils
class SimpleModel(torch.nn.Module):
@@ -94,8 +96,9 @@ class TestMixedPrecisionOps(unittest.TestCase):
"layer3.weight_scale": torch.tensor(1.5, dtype=torch.float32),
}
+ state_dict, _ = comfy.utils.convert_old_quants(state_dict, metadata={"_quantization_metadata": json.dumps({"layers": layer_quant_config})})
# Create model and load state dict (strict=False because custom loading pops keys)
- model = SimpleModel(operations=ops.mixed_precision_ops(layer_quant_config))
+ model = SimpleModel(operations=ops.mixed_precision_ops({}))
model.load_state_dict(state_dict, strict=False)
# Verify weights are wrapped in QuantizedTensor
@@ -115,7 +118,8 @@ class TestMixedPrecisionOps(unittest.TestCase):
# Forward pass
input_tensor = torch.randn(5, 10, dtype=torch.bfloat16)
- output = model(input_tensor)
+ with torch.inference_mode():
+ output = model(input_tensor)
self.assertEqual(output.shape, (5, 40))
@@ -141,7 +145,8 @@ class TestMixedPrecisionOps(unittest.TestCase):
"layer3.bias": torch.randn(40, dtype=torch.bfloat16),
}
- model = SimpleModel(operations=ops.mixed_precision_ops(layer_quant_config))
+ state_dict1, _ = comfy.utils.convert_old_quants(state_dict1, metadata={"_quantization_metadata": json.dumps({"layers": layer_quant_config})})
+ model = SimpleModel(operations=ops.mixed_precision_ops({}))
model.load_state_dict(state_dict1, strict=False)
# Save state dict
@@ -178,7 +183,8 @@ class TestMixedPrecisionOps(unittest.TestCase):
"layer3.bias": torch.randn(40, dtype=torch.bfloat16),
}
- model = SimpleModel(operations=ops.mixed_precision_ops(layer_quant_config))
+ state_dict, _ = comfy.utils.convert_old_quants(state_dict, metadata={"_quantization_metadata": json.dumps({"layers": layer_quant_config})})
+ model = SimpleModel(operations=ops.mixed_precision_ops({}))
model.load_state_dict(state_dict, strict=False)
# Add a weight function (simulating LoRA)
@@ -215,8 +221,10 @@ class TestMixedPrecisionOps(unittest.TestCase):
"layer3.bias": torch.randn(40, dtype=torch.bfloat16),
}
+ state_dict, _ = comfy.utils.convert_old_quants(state_dict, metadata={"_quantization_metadata": json.dumps({"layers": layer_quant_config})})
+
# Load should raise KeyError for unknown format in QUANT_FORMAT_MIXINS
- model = SimpleModel(operations=ops.mixed_precision_ops(layer_quant_config))
+ model = SimpleModel(operations=ops.mixed_precision_ops({}))
with self.assertRaises(KeyError):
model.load_state_dict(state_dict, strict=False)