mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-12-17 18:13:01 +08:00
Merge branch 'master' into dr-support-pip-cm
This commit is contained in:
Dr.Lt.Data
commit
35a294431f
8
.github/ISSUE_TEMPLATE/bug-report.yml
vendored
8
.github/ISSUE_TEMPLATE/bug-report.yml
vendored
@ -15,6 +15,14 @@ body:
|
||||
steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
|
||||
|
||||
If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
|
||||
- type: checkboxes
|
||||
id: custom-nodes-test
|
||||
attributes:
|
||||
label: Custom Node Testing
|
||||
description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
|
||||
options:
|
||||
- label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
|
||||
required: true
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Expected Behavior
|
||||
|
||||
8
.github/ISSUE_TEMPLATE/user-support.yml
vendored
8
.github/ISSUE_TEMPLATE/user-support.yml
vendored
@ -11,6 +11,14 @@ body:
|
||||
**2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics.
|
||||
|
||||
If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
|
||||
- type: checkboxes
|
||||
id: custom-nodes-test
|
||||
attributes:
|
||||
label: Custom Node Testing
|
||||
description: Please confirm you have tried to reproduce the issue with all custom nodes disabled.
|
||||
options:
|
||||
- label: I have tried disabling custom nodes and the issue persists (see [how to disable custom nodes](https://docs.comfy.org/troubleshooting/custom-node-issues#step-1%3A-test-with-all-custom-nodes-disabled) if you need help)
|
||||
required: true
|
||||
- type: textarea
|
||||
attributes:
|
||||
label: Your question
|
||||
|
||||
@ -6,6 +6,7 @@
|
||||
|
||||
[![Website][website-shield]][website-url]
|
||||
[![Dynamic JSON Badge][discord-shield]][discord-url]
|
||||
[![Twitter][twitter-shield]][twitter-url]
|
||||
[![Matrix][matrix-shield]][matrix-url]
|
||||
<br>
|
||||
[![][github-release-shield]][github-release-link]
|
||||
@ -20,6 +21,8 @@
|
||||
<!-- Workaround to display total user from https://github.com/badges/shields/issues/4500#issuecomment-2060079995 -->
|
||||
[discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fcomfyorg%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total
|
||||
[discord-url]: https://www.comfy.org/discord
|
||||
[twitter-shield]: https://img.shields.io/twitter/follow/ComfyUI
|
||||
[twitter-url]: https://x.com/ComfyUI
|
||||
|
||||
[github-release-shield]: https://img.shields.io/github/v/release/comfyanonymous/ComfyUI?style=flat&sort=semver
|
||||
[github-release-link]: https://github.com/comfyanonymous/ComfyUI/releases
|
||||
@ -95,7 +98,8 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
|
||||
- [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
|
||||
- Latent previews with [TAESD](#how-to-show-high-quality-previews)
|
||||
- Starts up very fast.
|
||||
- Works fully offline: will never download anything.
|
||||
- Works fully offline: core will never download anything unless you want to.
|
||||
- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview).
|
||||
- [Config file](extra_model_paths.yaml.example) to set the search paths for models.
|
||||
|
||||
Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
|
||||
|
||||
@ -205,6 +205,19 @@ comfyui-workflow-templates is not installed.
|
||||
""".strip()
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def embedded_docs_path(cls) -> str:
|
||||
"""Get the path to embedded documentation"""
|
||||
try:
|
||||
import comfyui_embedded_docs
|
||||
|
||||
return str(
|
||||
importlib.resources.files(comfyui_embedded_docs) / "docs"
|
||||
)
|
||||
except ImportError:
|
||||
logging.info("comfyui-embedded-docs package not found")
|
||||
return None
|
||||
|
||||
@classmethod
|
||||
def parse_version_string(cls, value: str) -> tuple[str, str, str]:
|
||||
"""
|
||||
|
||||
@ -86,3 +86,45 @@ class CONDConstant(CONDRegular):
|
||||
|
||||
def size(self):
|
||||
return [1]
|
||||
|
||||
|
||||
class CONDList(CONDRegular):
|
||||
def __init__(self, cond):
|
||||
self.cond = cond
|
||||
|
||||
def process_cond(self, batch_size, device, **kwargs):
|
||||
out = []
|
||||
for c in self.cond:
|
||||
out.append(comfy.utils.repeat_to_batch_size(c, batch_size).to(device))
|
||||
|
||||
return self._copy_with(out)
|
||||
|
||||
def can_concat(self, other):
|
||||
if len(self.cond) != len(other.cond):
|
||||
return False
|
||||
for i in range(len(self.cond)):
|
||||
if self.cond[i].shape != other.cond[i].shape:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def concat(self, others):
|
||||
out = []
|
||||
for i in range(len(self.cond)):
|
||||
o = [self.cond[i]]
|
||||
for x in others:
|
||||
o.append(x.cond[i])
|
||||
out.append(torch.cat(o))
|
||||
|
||||
return out
|
||||
|
||||
def size(self): # hackish implementation to make the mem estimation work
|
||||
o = 0
|
||||
c = 1
|
||||
for c in self.cond:
|
||||
size = c.size()
|
||||
o += math.prod(size)
|
||||
if len(size) > 1:
|
||||
c = size[1]
|
||||
|
||||
return [1, c, o // c]
|
||||
|
||||
@ -390,8 +390,9 @@ class ControlLora(ControlNet):
|
||||
pass
|
||||
|
||||
for k in self.control_weights:
|
||||
if k not in {"lora_controlnet"}:
|
||||
comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
|
||||
if (k not in {"lora_controlnet"}):
|
||||
if (k.endswith(".up") or k.endswith(".down") or k.endswith(".weight") or k.endswith(".bias")) and ("__" not in k):
|
||||
comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
|
||||
|
||||
def copy(self):
|
||||
c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
|
||||
|
||||
@ -121,6 +121,9 @@ class ControlNetFlux(Flux):
|
||||
if img.ndim != 3 or txt.ndim != 3:
|
||||
raise ValueError("Input img and txt tensors must have 3 dimensions.")
|
||||
|
||||
if y is None:
|
||||
y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
|
||||
|
||||
# running on sequences img
|
||||
img = self.img_in(img)
|
||||
|
||||
@ -174,7 +177,7 @@ class ControlNetFlux(Flux):
|
||||
out["output"] = out_output[:self.main_model_single]
|
||||
return out
|
||||
|
||||
def forward(self, x, timesteps, context, y, guidance=None, hint=None, **kwargs):
|
||||
def forward(self, x, timesteps, context, y=None, guidance=None, hint=None, **kwargs):
|
||||
patch_size = 2
|
||||
if self.latent_input:
|
||||
hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))
|
||||
|
||||
@ -101,6 +101,10 @@ class Flux(nn.Module):
|
||||
transformer_options={},
|
||||
attn_mask: Tensor = None,
|
||||
) -> Tensor:
|
||||
|
||||
if y is None:
|
||||
y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
|
||||
|
||||
patches_replace = transformer_options.get("patches_replace", {})
|
||||
if img.ndim != 3 or txt.ndim != 3:
|
||||
raise ValueError("Input img and txt tensors must have 3 dimensions.")
|
||||
@ -188,7 +192,7 @@ class Flux(nn.Module):
|
||||
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
|
||||
return img
|
||||
|
||||
def forward(self, x, timestep, context, y, guidance=None, control=None, transformer_options={}, **kwargs):
|
||||
def forward(self, x, timestep, context, y=None, guidance=None, control=None, transformer_options={}, **kwargs):
|
||||
bs, c, h, w = x.shape
|
||||
patch_size = self.patch_size
|
||||
x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
|
||||
|
||||
@ -102,6 +102,13 @@ def model_sampling(model_config, model_type):
|
||||
return ModelSampling(model_config)
|
||||
|
||||
|
||||
def convert_tensor(extra, dtype):
|
||||
if hasattr(extra, "dtype"):
|
||||
if extra.dtype != torch.int and extra.dtype != torch.long:
|
||||
extra = extra.to(dtype)
|
||||
return extra
|
||||
|
||||
|
||||
class BaseModel(torch.nn.Module):
|
||||
def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel):
|
||||
super().__init__()
|
||||
@ -165,9 +172,14 @@ class BaseModel(torch.nn.Module):
|
||||
extra_conds = {}
|
||||
for o in kwargs:
|
||||
extra = kwargs[o]
|
||||
|
||||
if hasattr(extra, "dtype"):
|
||||
if extra.dtype != torch.int and extra.dtype != torch.long:
|
||||
extra = extra.to(dtype)
|
||||
extra = convert_tensor(extra, dtype)
|
||||
elif isinstance(extra, list):
|
||||
ex = []
|
||||
for ext in extra:
|
||||
ex.append(convert_tensor(ext, dtype))
|
||||
extra = ex
|
||||
extra_conds[o] = extra
|
||||
|
||||
t = self.process_timestep(t, x=x, **extra_conds)
|
||||
|
||||
@ -295,6 +295,7 @@ except:
|
||||
pass
|
||||
|
||||
|
||||
SUPPORT_FP8_OPS = args.supports_fp8_compute
|
||||
try:
|
||||
if is_amd():
|
||||
try:
|
||||
@ -305,9 +306,13 @@ try:
|
||||
logging.info("AMD arch: {}".format(arch))
|
||||
logging.info("ROCm version: {}".format(rocm_version))
|
||||
if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
|
||||
if torch_version_numeric[0] >= 2 and torch_version_numeric[1] >= 7: # works on 2.6 but doesn't actually seem to improve much
|
||||
if torch_version_numeric >= (2, 7): # works on 2.6 but doesn't actually seem to improve much
|
||||
if any((a in arch) for a in ["gfx1100", "gfx1101", "gfx1151"]): # TODO: more arches
|
||||
ENABLE_PYTORCH_ATTENTION = True
|
||||
if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4):
|
||||
if any((a in arch) for a in ["gfx1201"]): # TODO: more arches
|
||||
SUPPORT_FP8_OPS = True
|
||||
|
||||
except:
|
||||
pass
|
||||
|
||||
@ -328,7 +333,7 @@ except:
|
||||
pass
|
||||
|
||||
try:
|
||||
if torch_version_numeric[0] == 2 and torch_version_numeric[1] >= 5:
|
||||
if torch_version_numeric >= (2, 5):
|
||||
torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
|
||||
except:
|
||||
logging.warning("Warning, could not set allow_fp16_bf16_reduction_math_sdp")
|
||||
@ -1262,7 +1267,7 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
|
||||
return False
|
||||
|
||||
def supports_fp8_compute(device=None):
|
||||
if args.supports_fp8_compute:
|
||||
if SUPPORT_FP8_OPS:
|
||||
return True
|
||||
|
||||
if not is_nvidia():
|
||||
@ -1276,11 +1281,11 @@ def supports_fp8_compute(device=None):
|
||||
if props.minor < 9:
|
||||
return False
|
||||
|
||||
if torch_version_numeric[0] < 2 or (torch_version_numeric[0] == 2 and torch_version_numeric[1] < 3):
|
||||
if torch_version_numeric < (2, 3):
|
||||
return False
|
||||
|
||||
if WINDOWS:
|
||||
if (torch_version_numeric[0] == 2 and torch_version_numeric[1] < 4):
|
||||
if torch_version_numeric < (2, 4):
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
@ -324,7 +324,7 @@ class IdeogramV1(ComfyNodeABC):
|
||||
|
||||
RETURN_TYPES = (IO.IMAGE,)
|
||||
FUNCTION = "api_call"
|
||||
CATEGORY = "api node/image/Ideogram/v1"
|
||||
CATEGORY = "api node/image/Ideogram"
|
||||
DESCRIPTION = cleandoc(__doc__ or "")
|
||||
API_NODE = True
|
||||
|
||||
@ -483,7 +483,7 @@ class IdeogramV2(ComfyNodeABC):
|
||||
|
||||
RETURN_TYPES = (IO.IMAGE,)
|
||||
FUNCTION = "api_call"
|
||||
CATEGORY = "api node/image/Ideogram/v2"
|
||||
CATEGORY = "api node/image/Ideogram"
|
||||
DESCRIPTION = cleandoc(__doc__ or "")
|
||||
API_NODE = True
|
||||
|
||||
@ -649,7 +649,7 @@ class IdeogramV3(ComfyNodeABC):
|
||||
|
||||
RETURN_TYPES = (IO.IMAGE,)
|
||||
FUNCTION = "api_call"
|
||||
CATEGORY = "api node/image/Ideogram/v3"
|
||||
CATEGORY = "api node/image/Ideogram"
|
||||
DESCRIPTION = cleandoc(__doc__ or "")
|
||||
API_NODE = True
|
||||
|
||||
|
||||
97
comfy_config/config_parser.py
Normal file
97
comfy_config/config_parser.py
Normal file
@ -0,0 +1,97 @@
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from pydantic_settings import PydanticBaseSettingsSource, TomlConfigSettingsSource
|
||||
|
||||
from comfy_config.types import (
|
||||
ComfyConfig,
|
||||
ProjectConfig,
|
||||
PyProjectConfig,
|
||||
PyProjectSettings
|
||||
)
|
||||
|
||||
"""
|
||||
Extract configuration from a custom node directory's pyproject.toml file or a Python file.
|
||||
|
||||
This function reads and parses the pyproject.toml file in the specified directory
|
||||
to extract project and ComfyUI-specific configuration information. If no
|
||||
pyproject.toml file is found, it creates a minimal configuration using the
|
||||
folder name as the project name. If a Python file is provided, it uses the
|
||||
file name (without extension) as the project name.
|
||||
|
||||
Args:
|
||||
path (str): Path to the directory containing the pyproject.toml file, or
|
||||
path to a .py file. If pyproject.toml doesn't exist in a directory,
|
||||
the folder name will be used as the default project name. If a .py
|
||||
file is provided, the filename (without .py extension) will be used
|
||||
as the project name.
|
||||
|
||||
Returns:
|
||||
Optional[PyProjectConfig]: A PyProjectConfig object containing:
|
||||
- project: Basic project information (name, version, dependencies, etc.)
|
||||
- tool_comfy: ComfyUI-specific configuration (publisher_id, models, etc.)
|
||||
Returns None if configuration extraction fails or if the provided file
|
||||
is not a Python file.
|
||||
|
||||
Notes:
|
||||
- If pyproject.toml is missing in a directory, creates a default config with folder name
|
||||
- If a .py file is provided, creates a default config with filename (without extension)
|
||||
- Returns None for non-Python files
|
||||
|
||||
Example:
|
||||
>>> from comfy_config import config_parser
|
||||
>>> # For directory
|
||||
>>> custom_node_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
>>> project_config = config_parser.extract_node_configuration(custom_node_dir)
|
||||
>>> print(project_config.project.name) # "my_custom_node" or name from pyproject.toml
|
||||
>>>
|
||||
>>> # For single-file Python node file
|
||||
>>> py_file_path = os.path.realpath(__file__) # "/path/to/my_node.py"
|
||||
>>> project_config = config_parser.extract_node_configuration(py_file_path)
|
||||
>>> print(project_config.project.name) # "my_node"
|
||||
"""
|
||||
def extract_node_configuration(path) -> Optional[PyProjectConfig]:
|
||||
if os.path.isfile(path):
|
||||
file_path = Path(path)
|
||||
|
||||
if file_path.suffix.lower() != '.py':
|
||||
return None
|
||||
|
||||
project_name = file_path.stem
|
||||
project = ProjectConfig(name=project_name)
|
||||
comfy = ComfyConfig()
|
||||
return PyProjectConfig(project=project, tool_comfy=comfy)
|
||||
|
||||
folder_name = os.path.basename(path)
|
||||
toml_path = Path(path) / "pyproject.toml"
|
||||
|
||||
if not toml_path.exists():
|
||||
project = ProjectConfig(name=folder_name)
|
||||
comfy = ComfyConfig()
|
||||
return PyProjectConfig(project=project, tool_comfy=comfy)
|
||||
|
||||
raw_settings = load_pyproject_settings(toml_path)
|
||||
|
||||
project_data = raw_settings.project
|
||||
|
||||
tool_data = raw_settings.tool
|
||||
comfy_data = tool_data.get("comfy", {}) if tool_data else {}
|
||||
|
||||
return PyProjectConfig(project=project_data, tool_comfy=comfy_data)
|
||||
|
||||
|
||||
def load_pyproject_settings(toml_path: Path) -> PyProjectSettings:
|
||||
class PyProjectLoader(PyProjectSettings):
|
||||
@classmethod
|
||||
def settings_customise_sources(
|
||||
cls,
|
||||
settings_cls,
|
||||
init_settings: PydanticBaseSettingsSource,
|
||||
env_settings: PydanticBaseSettingsSource,
|
||||
dotenv_settings: PydanticBaseSettingsSource,
|
||||
file_secret_settings: PydanticBaseSettingsSource,
|
||||
):
|
||||
return (TomlConfigSettingsSource(settings_cls, toml_path),)
|
||||
|
||||
return PyProjectLoader()
|
||||
80
comfy_config/types.py
Normal file
80
comfy_config/types.py
Normal file
@ -0,0 +1,80 @@
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic_settings import BaseSettings, SettingsConfigDict
|
||||
from typing import List, Optional
|
||||
|
||||
# IMPORTANT: The type definitions specified in pyproject.toml for custom nodes
|
||||
# must remain synchronized with the corresponding files in the https://github.com/Comfy-Org/comfy-cli/blob/main/comfy_cli/registry/types.py.
|
||||
# Any changes to one must be reflected in the other to maintain consistency.
|
||||
|
||||
class NodeVersion(BaseModel):
|
||||
changelog: str
|
||||
dependencies: List[str]
|
||||
deprecated: bool
|
||||
id: str
|
||||
version: str
|
||||
download_url: str
|
||||
|
||||
|
||||
class Node(BaseModel):
|
||||
id: str
|
||||
name: str
|
||||
description: str
|
||||
author: Optional[str] = None
|
||||
license: Optional[str] = None
|
||||
icon: Optional[str] = None
|
||||
repository: Optional[str] = None
|
||||
tags: List[str] = Field(default_factory=list)
|
||||
latest_version: Optional[NodeVersion] = None
|
||||
|
||||
|
||||
class PublishNodeVersionResponse(BaseModel):
|
||||
node_version: NodeVersion
|
||||
signedUrl: str
|
||||
|
||||
|
||||
class URLs(BaseModel):
|
||||
homepage: str = Field(default="", alias="Homepage")
|
||||
documentation: str = Field(default="", alias="Documentation")
|
||||
repository: str = Field(default="", alias="Repository")
|
||||
issues: str = Field(default="", alias="Issues")
|
||||
|
||||
|
||||
class Model(BaseModel):
|
||||
location: str
|
||||
model_url: str
|
||||
|
||||
|
||||
class ComfyConfig(BaseModel):
|
||||
publisher_id: str = Field(default="", alias="PublisherId")
|
||||
display_name: str = Field(default="", alias="DisplayName")
|
||||
icon: str = Field(default="", alias="Icon")
|
||||
models: List[Model] = Field(default_factory=list, alias="Models")
|
||||
includes: List[str] = Field(default_factory=list)
|
||||
|
||||
|
||||
class License(BaseModel):
|
||||
file: str = ""
|
||||
text: str = ""
|
||||
|
||||
|
||||
class ProjectConfig(BaseModel):
|
||||
name: str = ""
|
||||
description: str = ""
|
||||
version: str = "1.0.0"
|
||||
requires_python: str = Field(default=">= 3.9", alias="requires-python")
|
||||
dependencies: List[str] = Field(default_factory=list)
|
||||
license: License = Field(default_factory=License)
|
||||
urls: URLs = Field(default_factory=URLs)
|
||||
|
||||
|
||||
class PyProjectConfig(BaseModel):
|
||||
project: ProjectConfig = Field(default_factory=ProjectConfig)
|
||||
tool_comfy: ComfyConfig = Field(default_factory=ComfyConfig)
|
||||
|
||||
|
||||
class PyProjectSettings(BaseSettings):
|
||||
project: dict = Field(default_factory=dict)
|
||||
|
||||
tool: dict = Field(default_factory=dict)
|
||||
|
||||
model_config = SettingsConfigDict()
|
||||
@ -14,8 +14,10 @@ import re
|
||||
from io import BytesIO
|
||||
from inspect import cleandoc
|
||||
import torch
|
||||
import comfy.utils
|
||||
|
||||
from comfy.comfy_types import FileLocator
|
||||
from comfy.comfy_types import FileLocator, IO
|
||||
from server import PromptServer
|
||||
|
||||
MAX_RESOLUTION = nodes.MAX_RESOLUTION
|
||||
|
||||
@ -229,6 +231,186 @@ class SVG:
|
||||
all_svgs_list.extend(svg_item.data)
|
||||
return SVG(all_svgs_list)
|
||||
|
||||
|
||||
class ImageStitch:
|
||||
"""Upstreamed from https://github.com/kijai/ComfyUI-KJNodes"""
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"image1": ("IMAGE",),
|
||||
"direction": (["right", "down", "left", "up"], {"default": "right"}),
|
||||
"match_image_size": ("BOOLEAN", {"default": True}),
|
||||
"spacing_width": (
|
||||
"INT",
|
||||
{"default": 0, "min": 0, "max": 1024, "step": 2},
|
||||
),
|
||||
"spacing_color": (
|
||||
["white", "black", "red", "green", "blue"],
|
||||
{"default": "white"},
|
||||
),
|
||||
},
|
||||
"optional": {
|
||||
"image2": ("IMAGE",),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "stitch"
|
||||
CATEGORY = "image/transform"
|
||||
DESCRIPTION = """
|
||||
Stitches image2 to image1 in the specified direction.
|
||||
If image2 is not provided, returns image1 unchanged.
|
||||
Optional spacing can be added between images.
|
||||
"""
|
||||
|
||||
def stitch(
|
||||
self,
|
||||
image1,
|
||||
direction,
|
||||
match_image_size,
|
||||
spacing_width,
|
||||
spacing_color,
|
||||
image2=None,
|
||||
):
|
||||
if image2 is None:
|
||||
return (image1,)
|
||||
|
||||
# Handle batch size differences
|
||||
if image1.shape[0] != image2.shape[0]:
|
||||
max_batch = max(image1.shape[0], image2.shape[0])
|
||||
if image1.shape[0] < max_batch:
|
||||
image1 = torch.cat(
|
||||
[image1, image1[-1:].repeat(max_batch - image1.shape[0], 1, 1, 1)]
|
||||
)
|
||||
if image2.shape[0] < max_batch:
|
||||
image2 = torch.cat(
|
||||
[image2, image2[-1:].repeat(max_batch - image2.shape[0], 1, 1, 1)]
|
||||
)
|
||||
|
||||
# Match image sizes if requested
|
||||
if match_image_size:
|
||||
h1, w1 = image1.shape[1:3]
|
||||
h2, w2 = image2.shape[1:3]
|
||||
aspect_ratio = w2 / h2
|
||||
|
||||
if direction in ["left", "right"]:
|
||||
target_h, target_w = h1, int(h1 * aspect_ratio)
|
||||
else: # up, down
|
||||
target_w, target_h = w1, int(w1 / aspect_ratio)
|
||||
|
||||
image2 = comfy.utils.common_upscale(
|
||||
image2.movedim(-1, 1), target_w, target_h, "lanczos", "disabled"
|
||||
).movedim(1, -1)
|
||||
|
||||
# When not matching sizes, pad to align non-concat dimensions
|
||||
if not match_image_size:
|
||||
h1, w1 = image1.shape[1:3]
|
||||
h2, w2 = image2.shape[1:3]
|
||||
|
||||
if direction in ["left", "right"]:
|
||||
# For horizontal concat, pad heights to match
|
||||
if h1 != h2:
|
||||
target_h = max(h1, h2)
|
||||
if h1 < target_h:
|
||||
pad_h = target_h - h1
|
||||
pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
|
||||
image1 = torch.nn.functional.pad(image1, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0)
|
||||
if h2 < target_h:
|
||||
pad_h = target_h - h2
|
||||
pad_top, pad_bottom = pad_h // 2, pad_h - pad_h // 2
|
||||
image2 = torch.nn.functional.pad(image2, (0, 0, 0, 0, pad_top, pad_bottom), mode='constant', value=0.0)
|
||||
else: # up, down
|
||||
# For vertical concat, pad widths to match
|
||||
if w1 != w2:
|
||||
target_w = max(w1, w2)
|
||||
if w1 < target_w:
|
||||
pad_w = target_w - w1
|
||||
pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
|
||||
image1 = torch.nn.functional.pad(image1, (0, 0, pad_left, pad_right), mode='constant', value=0.0)
|
||||
if w2 < target_w:
|
||||
pad_w = target_w - w2
|
||||
pad_left, pad_right = pad_w // 2, pad_w - pad_w // 2
|
||||
image2 = torch.nn.functional.pad(image2, (0, 0, pad_left, pad_right), mode='constant', value=0.0)
|
||||
|
||||
# Ensure same number of channels
|
||||
if image1.shape[-1] != image2.shape[-1]:
|
||||
max_channels = max(image1.shape[-1], image2.shape[-1])
|
||||
if image1.shape[-1] < max_channels:
|
||||
image1 = torch.cat(
|
||||
[
|
||||
image1,
|
||||
torch.ones(
|
||||
*image1.shape[:-1],
|
||||
max_channels - image1.shape[-1],
|
||||
device=image1.device,
|
||||
),
|
||||
],
|
||||
dim=-1,
|
||||
)
|
||||
if image2.shape[-1] < max_channels:
|
||||
image2 = torch.cat(
|
||||
[
|
||||
image2,
|
||||
torch.ones(
|
||||
*image2.shape[:-1],
|
||||
max_channels - image2.shape[-1],
|
||||
device=image2.device,
|
||||
),
|
||||
],
|
||||
dim=-1,
|
||||
)
|
||||
|
||||
# Add spacing if specified
|
||||
if spacing_width > 0:
|
||||
spacing_width = spacing_width + (spacing_width % 2) # Ensure even
|
||||
|
||||
color_map = {
|
||||
"white": 1.0,
|
||||
"black": 0.0,
|
||||
"red": (1.0, 0.0, 0.0),
|
||||
"green": (0.0, 1.0, 0.0),
|
||||
"blue": (0.0, 0.0, 1.0),
|
||||
}
|
||||
color_val = color_map[spacing_color]
|
||||
|
||||
if direction in ["left", "right"]:
|
||||
spacing_shape = (
|
||||
image1.shape[0],
|
||||
max(image1.shape[1], image2.shape[1]),
|
||||
spacing_width,
|
||||
image1.shape[-1],
|
||||
)
|
||||
else:
|
||||
spacing_shape = (
|
||||
image1.shape[0],
|
||||
spacing_width,
|
||||
max(image1.shape[2], image2.shape[2]),
|
||||
image1.shape[-1],
|
||||
)
|
||||
|
||||
spacing = torch.full(spacing_shape, 0.0, device=image1.device)
|
||||
if isinstance(color_val, tuple):
|
||||
for i, c in enumerate(color_val):
|
||||
if i < spacing.shape[-1]:
|
||||
spacing[..., i] = c
|
||||
if spacing.shape[-1] == 4: # Add alpha
|
||||
spacing[..., 3] = 1.0
|
||||
else:
|
||||
spacing[..., : min(3, spacing.shape[-1])] = color_val
|
||||
if spacing.shape[-1] == 4:
|
||||
spacing[..., 3] = 1.0
|
||||
|
||||
# Concatenate images
|
||||
images = [image2, image1] if direction in ["left", "up"] else [image1, image2]
|
||||
if spacing_width > 0:
|
||||
images.insert(1, spacing)
|
||||
|
||||
concat_dim = 2 if direction in ["left", "right"] else 1
|
||||
return (torch.cat(images, dim=concat_dim),)
|
||||
|
||||
|
||||
class SaveSVGNode:
|
||||
"""
|
||||
Save SVG files on disk.
|
||||
@ -310,6 +492,37 @@ class SaveSVGNode:
|
||||
counter += 1
|
||||
return { "ui": { "images": results } }
|
||||
|
||||
class GetImageSize:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"image": (IO.IMAGE,),
|
||||
},
|
||||
"hidden": {
|
||||
"unique_id": "UNIQUE_ID",
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = (IO.INT, IO.INT, IO.INT)
|
||||
RETURN_NAMES = ("width", "height", "batch_size")
|
||||
FUNCTION = "get_size"
|
||||
|
||||
CATEGORY = "image"
|
||||
DESCRIPTION = """Returns width and height of the image, and passes it through unchanged."""
|
||||
|
||||
def get_size(self, image, unique_id=None) -> tuple[int, int]:
|
||||
height = image.shape[1]
|
||||
width = image.shape[2]
|
||||
batch_size = image.shape[0]
|
||||
|
||||
# Send progress text to display size on the node
|
||||
if unique_id:
|
||||
PromptServer.instance.send_progress_text(f"width: {width}, height: {height}\n batch size: {batch_size}", unique_id)
|
||||
|
||||
return width, height, batch_size
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"ImageCrop": ImageCrop,
|
||||
"RepeatImageBatch": RepeatImageBatch,
|
||||
@ -318,4 +531,6 @@ NODE_CLASS_MAPPINGS = {
|
||||
"SaveAnimatedWEBP": SaveAnimatedWEBP,
|
||||
"SaveAnimatedPNG": SaveAnimatedPNG,
|
||||
"SaveSVGNode": SaveSVGNode,
|
||||
"ImageStitch": ImageStitch,
|
||||
"GetImageSize": GetImageSize,
|
||||
}
|
||||
|
||||
@ -1,3 +1,3 @@
|
||||
# This file is automatically generated by the build process when version is
|
||||
# updated in pyproject.toml.
|
||||
__version__ = "0.3.39"
|
||||
__version__ = "0.3.40"
|
||||
|
||||
2
nodes.py
2
nodes.py
@ -2064,11 +2064,13 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"ImagePadForOutpaint": "Pad Image for Outpainting",
|
||||
"ImageBatch": "Batch Images",
|
||||
"ImageCrop": "Image Crop",
|
||||
"ImageStitch": "Image Stitch",
|
||||
"ImageBlend": "Image Blend",
|
||||
"ImageBlur": "Image Blur",
|
||||
"ImageQuantize": "Image Quantize",
|
||||
"ImageSharpen": "Image Sharpen",
|
||||
"ImageScaleToTotalPixels": "Scale Image to Total Pixels",
|
||||
"GetImageSize": "Get Image Size",
|
||||
# _for_testing
|
||||
"VAEDecodeTiled": "VAE Decode (Tiled)",
|
||||
"VAEEncodeTiled": "VAE Encode (Tiled)",
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "ComfyUI"
|
||||
version = "0.3.39"
|
||||
version = "0.3.40"
|
||||
readme = "README.md"
|
||||
license = { file = "LICENSE" }
|
||||
requires-python = ">=3.9"
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
comfyui-frontend-package==1.20.7
|
||||
comfyui-workflow-templates==0.1.23
|
||||
comfyui-frontend-package==1.21.7
|
||||
comfyui-workflow-templates==0.1.25
|
||||
comfyui-embedded-docs==0.2.0
|
||||
comfyui_manager
|
||||
torch
|
||||
torchsde
|
||||
|
||||
16
server.py
16
server.py
@ -390,7 +390,7 @@ class PromptServer():
|
||||
async def view_image(request):
|
||||
if "filename" in request.rel_url.query:
|
||||
filename = request.rel_url.query["filename"]
|
||||
filename,output_dir = folder_paths.annotated_filepath(filename)
|
||||
filename, output_dir = folder_paths.annotated_filepath(filename)
|
||||
|
||||
if not filename:
|
||||
return web.Response(status=400)
|
||||
@ -476,9 +476,8 @@ class PromptServer():
|
||||
# Get content type from mimetype, defaulting to 'application/octet-stream'
|
||||
content_type = mimetypes.guess_type(filename)[0] or 'application/octet-stream'
|
||||
|
||||
# For security, force certain extensions to download instead of display
|
||||
file_extension = os.path.splitext(filename)[1].lower()
|
||||
if file_extension in {'.html', '.htm', '.js', '.css'}:
|
||||
# For security, force certain mimetypes to download instead of display
|
||||
if content_type in {'text/html', 'text/html-sandboxed', 'application/xhtml+xml', 'text/javascript', 'text/css'}:
|
||||
content_type = 'application/octet-stream' # Forces download
|
||||
|
||||
return web.FileResponse(
|
||||
@ -746,6 +745,13 @@ class PromptServer():
|
||||
web.static('/templates', workflow_templates_path)
|
||||
])
|
||||
|
||||
# Serve embedded documentation from the package
|
||||
embedded_docs_path = FrontendManager.embedded_docs_path()
|
||||
if embedded_docs_path:
|
||||
self.app.add_routes([
|
||||
web.static('/docs', embedded_docs_path)
|
||||
])
|
||||
|
||||
self.app.add_routes([
|
||||
web.static('/', self.web_root),
|
||||
])
|
||||
@ -782,7 +788,7 @@ class PromptServer():
|
||||
if hasattr(Image, 'Resampling'):
|
||||
resampling = Image.Resampling.BILINEAR
|
||||
else:
|
||||
resampling = Image.ANTIALIAS
|
||||
resampling = Image.Resampling.LANCZOS
|
||||
|
||||
image = ImageOps.contain(image, (max_size, max_size), resampling)
|
||||
type_num = 1
|
||||
|
||||
0
tests-unit/comfy_extras_test/__init__.py
Normal file
0
tests-unit/comfy_extras_test/__init__.py
Normal file
243
tests-unit/comfy_extras_test/image_stitch_test.py
Normal file
243
tests-unit/comfy_extras_test/image_stitch_test.py
Normal file
@ -0,0 +1,243 @@
|
||||
import torch
|
||||
from unittest.mock import patch, MagicMock
|
||||
|
||||
# Mock nodes module to prevent CUDA initialization during import
|
||||
mock_nodes = MagicMock()
|
||||
mock_nodes.MAX_RESOLUTION = 16384
|
||||
|
||||
# Mock server module for PromptServer
|
||||
mock_server = MagicMock()
|
||||
|
||||
with patch.dict('sys.modules', {'nodes': mock_nodes, 'server': mock_server}):
|
||||
from comfy_extras.nodes_images import ImageStitch
|
||||
|
||||
|
||||
class TestImageStitch:
|
||||
|
||||
def create_test_image(self, batch_size=1, height=64, width=64, channels=3):
|
||||
"""Helper to create test images with specific dimensions"""
|
||||
return torch.rand(batch_size, height, width, channels)
|
||||
|
||||
def test_no_image2_passthrough(self):
|
||||
"""Test that when image2 is None, image1 is returned unchanged"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image()
|
||||
|
||||
result = node.stitch(image1, "right", True, 0, "white", image2=None)
|
||||
|
||||
assert len(result) == 1
|
||||
assert torch.equal(result[0], image1)
|
||||
|
||||
def test_basic_horizontal_stitch_right(self):
|
||||
"""Test basic horizontal stitching to the right"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=32, width=32)
|
||||
image2 = self.create_test_image(height=32, width=24)
|
||||
|
||||
result = node.stitch(image1, "right", False, 0, "white", image2)
|
||||
|
||||
assert result[0].shape == (1, 32, 56, 3) # 32 + 24 width
|
||||
|
||||
def test_basic_horizontal_stitch_left(self):
|
||||
"""Test basic horizontal stitching to the left"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=32, width=32)
|
||||
image2 = self.create_test_image(height=32, width=24)
|
||||
|
||||
result = node.stitch(image1, "left", False, 0, "white", image2)
|
||||
|
||||
assert result[0].shape == (1, 32, 56, 3) # 24 + 32 width
|
||||
|
||||
def test_basic_vertical_stitch_down(self):
|
||||
"""Test basic vertical stitching downward"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=32, width=32)
|
||||
image2 = self.create_test_image(height=24, width=32)
|
||||
|
||||
result = node.stitch(image1, "down", False, 0, "white", image2)
|
||||
|
||||
assert result[0].shape == (1, 56, 32, 3) # 32 + 24 height
|
||||
|
||||
def test_basic_vertical_stitch_up(self):
|
||||
"""Test basic vertical stitching upward"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=32, width=32)
|
||||
image2 = self.create_test_image(height=24, width=32)
|
||||
|
||||
result = node.stitch(image1, "up", False, 0, "white", image2)
|
||||
|
||||
assert result[0].shape == (1, 56, 32, 3) # 24 + 32 height
|
||||
|
||||
def test_size_matching_horizontal(self):
|
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"""Test size matching for horizontal concatenation"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=64, width=64)
|
||||
image2 = self.create_test_image(height=32, width=32) # Different aspect ratio
|
||||
|
||||
result = node.stitch(image1, "right", True, 0, "white", image2)
|
||||
|
||||
# image2 should be resized to match image1's height (64) with preserved aspect ratio
|
||||
expected_width = 64 + 64 # original + resized (32*64/32 = 64)
|
||||
assert result[0].shape == (1, 64, expected_width, 3)
|
||||
|
||||
def test_size_matching_vertical(self):
|
||||
"""Test size matching for vertical concatenation"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=64, width=64)
|
||||
image2 = self.create_test_image(height=32, width=32)
|
||||
|
||||
result = node.stitch(image1, "down", True, 0, "white", image2)
|
||||
|
||||
# image2 should be resized to match image1's width (64) with preserved aspect ratio
|
||||
expected_height = 64 + 64 # original + resized (32*64/32 = 64)
|
||||
assert result[0].shape == (1, expected_height, 64, 3)
|
||||
|
||||
def test_padding_for_mismatched_heights_horizontal(self):
|
||||
"""Test padding when heights don't match in horizontal concatenation"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=64, width=32)
|
||||
image2 = self.create_test_image(height=48, width=24) # Shorter height
|
||||
|
||||
result = node.stitch(image1, "right", False, 0, "white", image2)
|
||||
|
||||
# Both images should be padded to height 64
|
||||
assert result[0].shape == (1, 64, 56, 3) # 32 + 24 width, max(64,48) height
|
||||
|
||||
def test_padding_for_mismatched_widths_vertical(self):
|
||||
"""Test padding when widths don't match in vertical concatenation"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=32, width=64)
|
||||
image2 = self.create_test_image(height=24, width=48) # Narrower width
|
||||
|
||||
result = node.stitch(image1, "down", False, 0, "white", image2)
|
||||
|
||||
# Both images should be padded to width 64
|
||||
assert result[0].shape == (1, 56, 64, 3) # 32 + 24 height, max(64,48) width
|
||||
|
||||
def test_spacing_horizontal(self):
|
||||
"""Test spacing addition in horizontal concatenation"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=32, width=32)
|
||||
image2 = self.create_test_image(height=32, width=24)
|
||||
spacing_width = 16
|
||||
|
||||
result = node.stitch(image1, "right", False, spacing_width, "white", image2)
|
||||
|
||||
# Expected width: 32 + 16 (spacing) + 24 = 72
|
||||
assert result[0].shape == (1, 32, 72, 3)
|
||||
|
||||
def test_spacing_vertical(self):
|
||||
"""Test spacing addition in vertical concatenation"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=32, width=32)
|
||||
image2 = self.create_test_image(height=24, width=32)
|
||||
spacing_width = 16
|
||||
|
||||
result = node.stitch(image1, "down", False, spacing_width, "white", image2)
|
||||
|
||||
# Expected height: 32 + 16 (spacing) + 24 = 72
|
||||
assert result[0].shape == (1, 72, 32, 3)
|
||||
|
||||
def test_spacing_color_values(self):
|
||||
"""Test that spacing colors are applied correctly"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=32, width=32)
|
||||
image2 = self.create_test_image(height=32, width=32)
|
||||
|
||||
# Test white spacing
|
||||
result_white = node.stitch(image1, "right", False, 16, "white", image2)
|
||||
# Check that spacing region contains white values (close to 1.0)
|
||||
spacing_region = result_white[0][:, :, 32:48, :] # Middle 16 pixels
|
||||
assert torch.all(spacing_region >= 0.9) # Should be close to white
|
||||
|
||||
# Test black spacing
|
||||
result_black = node.stitch(image1, "right", False, 16, "black", image2)
|
||||
spacing_region = result_black[0][:, :, 32:48, :]
|
||||
assert torch.all(spacing_region <= 0.1) # Should be close to black
|
||||
|
||||
def test_odd_spacing_width_made_even(self):
|
||||
"""Test that odd spacing widths are made even"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=32, width=32)
|
||||
image2 = self.create_test_image(height=32, width=32)
|
||||
|
||||
# Use odd spacing width
|
||||
result = node.stitch(image1, "right", False, 15, "white", image2)
|
||||
|
||||
# Should be made even (16), so total width = 32 + 16 + 32 = 80
|
||||
assert result[0].shape == (1, 32, 80, 3)
|
||||
|
||||
def test_batch_size_matching(self):
|
||||
"""Test that different batch sizes are handled correctly"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(batch_size=2, height=32, width=32)
|
||||
image2 = self.create_test_image(batch_size=1, height=32, width=32)
|
||||
|
||||
result = node.stitch(image1, "right", False, 0, "white", image2)
|
||||
|
||||
# Should match larger batch size
|
||||
assert result[0].shape == (2, 32, 64, 3)
|
||||
|
||||
def test_channel_matching_rgb_to_rgba(self):
|
||||
"""Test that channel differences are handled (RGB + alpha)"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(channels=3) # RGB
|
||||
image2 = self.create_test_image(channels=4) # RGBA
|
||||
|
||||
result = node.stitch(image1, "right", False, 0, "white", image2)
|
||||
|
||||
# Should have 4 channels (RGBA)
|
||||
assert result[0].shape[-1] == 4
|
||||
|
||||
def test_channel_matching_rgba_to_rgb(self):
|
||||
"""Test that channel differences are handled (RGBA + RGB)"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(channels=4) # RGBA
|
||||
image2 = self.create_test_image(channels=3) # RGB
|
||||
|
||||
result = node.stitch(image1, "right", False, 0, "white", image2)
|
||||
|
||||
# Should have 4 channels (RGBA)
|
||||
assert result[0].shape[-1] == 4
|
||||
|
||||
def test_all_color_options(self):
|
||||
"""Test all available color options"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=32, width=32)
|
||||
image2 = self.create_test_image(height=32, width=32)
|
||||
|
||||
colors = ["white", "black", "red", "green", "blue"]
|
||||
|
||||
for color in colors:
|
||||
result = node.stitch(image1, "right", False, 16, color, image2)
|
||||
assert result[0].shape == (1, 32, 80, 3) # Basic shape check
|
||||
|
||||
def test_all_directions(self):
|
||||
"""Test all direction options"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(height=32, width=32)
|
||||
image2 = self.create_test_image(height=32, width=32)
|
||||
|
||||
directions = ["right", "left", "up", "down"]
|
||||
|
||||
for direction in directions:
|
||||
result = node.stitch(image1, direction, False, 0, "white", image2)
|
||||
assert result[0].shape == (1, 32, 64, 3) if direction in ["right", "left"] else (1, 64, 32, 3)
|
||||
|
||||
def test_batch_size_channel_spacing_integration(self):
|
||||
"""Test integration of batch matching, channel matching, size matching, and spacings"""
|
||||
node = ImageStitch()
|
||||
image1 = self.create_test_image(batch_size=2, height=64, width=48, channels=3)
|
||||
image2 = self.create_test_image(batch_size=1, height=32, width=32, channels=4)
|
||||
|
||||
result = node.stitch(image1, "right", True, 8, "red", image2)
|
||||
|
||||
# Should handle: batch matching, size matching, channel matching, spacing
|
||||
assert result[0].shape[0] == 2 # Batch size matched
|
||||
assert result[0].shape[-1] == 4 # Channels matched to max
|
||||
assert result[0].shape[1] == 64 # Height from image1 (size matching)
|
||||
# Width should be: 48 + 8 (spacing) + resized_image2_width
|
||||
expected_image2_width = int(64 * (32/32)) # Resized to height 64
|
||||
expected_total_width = 48 + 8 + expected_image2_width
|
||||
assert result[0].shape[2] == expected_total_width
|
||||
|
||||
Loading…
Reference in New Issue
Block a user