Compare commits
10 Commits
required_f
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annoate_ge
| Author | SHA1 | Date | |
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522d923948 | ||
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c05c9b552b | ||
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27598702e9 | ||
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8edc1f44c1 | ||
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eade1551bb | ||
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581a9991ff | ||
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e471c726e5 | ||
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75c1c757d9 | ||
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ce9b084279 | ||
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2206246055 |
@@ -69,6 +69,8 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
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- [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
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- [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/)
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- [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
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- 3D Models
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- [Hunyuan3D 2.0](https://docs.comfy.org/tutorials/3d/hunyuan3D-2)
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- [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
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- Asynchronous Queue system
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- Many optimizations: Only re-executes the parts of the workflow that changes between executions.
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@@ -22,13 +22,21 @@ import app.logger
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# The path to the requirements.txt file
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req_path = Path(__file__).parents[1] / "requirements.txt"
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def frontend_install_warning_message():
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"""The warning message to display when the frontend version is not up to date."""
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extra = ""
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if sys.flags.no_user_site:
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extra = "-s "
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return f"Please install the updated requirements.txt file by running:\n{sys.executable} {extra}-m pip install -r {req_path}\n\nThis error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.\n\nIf you are on the portable package you can run: update\\update_comfyui.bat to solve this problem"
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return f"""
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Please install the updated requirements.txt file by running:
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{sys.executable} {extra}-m pip install -r {req_path}
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This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.
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If you are on the portable package you can run: update\\update_comfyui.bat to solve this problem
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""".strip()
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def check_frontend_version():
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@@ -43,7 +51,17 @@ def check_frontend_version():
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with open(req_path, "r", encoding="utf-8") as f:
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required_frontend = parse_version(f.readline().split("=")[-1])
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if frontend_version < required_frontend:
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app.logger.log_startup_warning("________________________________________________________________________\nWARNING WARNING WARNING WARNING WARNING\n\nInstalled frontend version {} is lower than the recommended version {}.\n\n{}\n________________________________________________________________________".format('.'.join(map(str, frontend_version)), '.'.join(map(str, required_frontend)), frontend_install_warning_message()))
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app.logger.log_startup_warning(
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f"""
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________________________________________________________________________
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WARNING WARNING WARNING WARNING WARNING
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Installed frontend version {".".join(map(str, frontend_version))} is lower than the recommended version {".".join(map(str, required_frontend))}.
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{frontend_install_warning_message()}
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________________________________________________________________________
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""".strip()
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)
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else:
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logging.info("ComfyUI frontend version: {}".format(frontend_version_str))
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except Exception as e:
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@@ -150,9 +168,20 @@ class FrontendManager:
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def default_frontend_path(cls) -> str:
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try:
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import comfyui_frontend_package
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return str(importlib.resources.files(comfyui_frontend_package) / "static")
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except ImportError:
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logging.error(f"\n\n********** ERROR ***********\n\ncomfyui-frontend-package is not installed. {frontend_install_warning_message()}\n********** ERROR **********\n")
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logging.error(
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f"""
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********** ERROR ***********
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comfyui-frontend-package is not installed.
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{frontend_install_warning_message()}
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********** ERROR ***********
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""".strip()
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)
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sys.exit(-1)
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@classmethod
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@@ -175,7 +204,9 @@ class FrontendManager:
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return match_result.group(1), match_result.group(2), match_result.group(3)
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@classmethod
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def init_frontend_unsafe(cls, version_string: str, provider: Optional[FrontEndProvider] = None) -> str:
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def init_frontend_unsafe(
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cls, version_string: str, provider: Optional[FrontEndProvider] = None
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) -> str:
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"""
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Initializes the frontend for the specified version.
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@@ -197,12 +228,20 @@ class FrontendManager:
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repo_owner, repo_name, version = cls.parse_version_string(version_string)
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if version.startswith("v"):
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expected_path = str(Path(cls.CUSTOM_FRONTENDS_ROOT) / f"{repo_owner}_{repo_name}" / version.lstrip("v"))
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expected_path = str(
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Path(cls.CUSTOM_FRONTENDS_ROOT)
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/ f"{repo_owner}_{repo_name}"
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/ version.lstrip("v")
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)
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if os.path.exists(expected_path):
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logging.info(f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}")
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logging.info(
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f"Using existing copy of specific frontend version tag: {repo_owner}/{repo_name}@{version}"
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)
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return expected_path
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logging.info(f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub...")
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logging.info(
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f"Initializing frontend: {repo_owner}/{repo_name}@{version}, requesting version details from GitHub..."
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)
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provider = provider or FrontEndProvider(repo_owner, repo_name)
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release = provider.get_release(version)
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@@ -489,7 +489,17 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
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if mask.ndim == 3:
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mask = mask.unsqueeze(1)
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try:
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out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
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except Exception as e:
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logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e))
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if tensor_layout == "NHD":
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q, k, v = map(
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lambda t: t.transpose(1, 2),
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(q, k, v),
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)
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return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape)
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if tensor_layout == "HND":
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if not skip_output_reshape:
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out = (
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@@ -46,6 +46,32 @@ cpu_state = CPUState.GPU
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total_vram = 0
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def get_supported_float8_types():
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float8_types = []
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try:
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float8_types.append(torch.float8_e4m3fn)
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except:
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pass
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try:
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float8_types.append(torch.float8_e4m3fnuz)
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except:
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pass
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try:
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float8_types.append(torch.float8_e5m2)
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except:
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pass
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try:
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float8_types.append(torch.float8_e5m2fnuz)
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except:
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pass
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try:
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float8_types.append(torch.float8_e8m0fnu)
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except:
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pass
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return float8_types
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FLOAT8_TYPES = get_supported_float8_types()
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xpu_available = False
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torch_version = ""
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try:
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@@ -701,11 +727,8 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor
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return torch.float8_e5m2
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fp8_dtype = None
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try:
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if weight_dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
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if weight_dtype in FLOAT8_TYPES:
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fp8_dtype = weight_dtype
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except:
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pass
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if fp8_dtype is not None:
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if supports_fp8_compute(device): #if fp8 compute is supported the casting is most likely not expensive
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@@ -1,6 +1,9 @@
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import nodes
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from __future__ import annotations
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from typing import Type, Literal
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import nodes
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from comfy_execution.graph_utils import is_link
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from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions
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class DependencyCycleError(Exception):
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pass
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@@ -54,7 +57,22 @@ class DynamicPrompt:
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def get_original_prompt(self):
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return self.original_prompt
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def get_input_info(class_def, input_name, valid_inputs=None):
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def get_input_info(
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class_def: Type[ComfyNodeABC],
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input_name: str,
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valid_inputs: InputTypeDict | None = None
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) -> tuple[str, Literal["required", "optional", "hidden"], InputTypeOptions] | tuple[None, None, None]:
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"""Get the input type, category, and extra info for a given input name.
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Arguments:
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class_def: The class definition of the node.
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input_name: The name of the input to get info for.
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valid_inputs: The valid inputs for the node, or None to use the class_def.INPUT_TYPES().
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Returns:
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tuple[str, str, dict] | tuple[None, None, None]: The input type, category, and extra info for the input name.
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"""
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valid_inputs = valid_inputs or class_def.INPUT_TYPES()
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input_info = None
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input_category = None
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@@ -126,7 +144,7 @@ class TopologicalSort:
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from_node_id, from_socket = value
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if subgraph_nodes is not None and from_node_id not in subgraph_nodes:
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continue
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input_type, input_category, input_info = self.get_input_info(unique_id, input_name)
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_, _, input_info = self.get_input_info(unique_id, input_name)
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is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"]
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if (include_lazy or not is_lazy) and not self.is_cached(from_node_id):
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node_ids.append(from_node_id)
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@@ -21,8 +21,8 @@ class Load3D():
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"height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
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}}
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RETURN_TYPES = ("IMAGE", "MASK", "STRING")
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RETURN_NAMES = ("image", "mask", "mesh_path")
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RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE", "IMAGE")
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RETURN_NAMES = ("image", "mask", "mesh_path", "normal", "lineart")
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FUNCTION = "process"
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EXPERIMENTAL = True
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@@ -32,12 +32,16 @@ class Load3D():
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def process(self, model_file, image, **kwargs):
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image_path = folder_paths.get_annotated_filepath(image['image'])
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mask_path = folder_paths.get_annotated_filepath(image['mask'])
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normal_path = folder_paths.get_annotated_filepath(image['normal'])
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lineart_path = folder_paths.get_annotated_filepath(image['lineart'])
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load_image_node = nodes.LoadImage()
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output_image, ignore_mask = load_image_node.load_image(image=image_path)
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ignore_image, output_mask = load_image_node.load_image(image=mask_path)
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normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
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lineart_image, ignore_mask3 = load_image_node.load_image(image=lineart_path)
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return output_image, output_mask, model_file,
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return output_image, output_mask, model_file, normal_image, lineart_image
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class Load3DAnimation():
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@classmethod
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@@ -55,8 +59,8 @@ class Load3DAnimation():
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"height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
|
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}}
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|
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RETURN_TYPES = ("IMAGE", "MASK", "STRING")
|
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RETURN_NAMES = ("image", "mask", "mesh_path")
|
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RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE")
|
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RETURN_NAMES = ("image", "mask", "mesh_path", "normal")
|
||||
|
||||
FUNCTION = "process"
|
||||
EXPERIMENTAL = True
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@@ -66,12 +70,14 @@ class Load3DAnimation():
|
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def process(self, model_file, image, **kwargs):
|
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image_path = folder_paths.get_annotated_filepath(image['image'])
|
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mask_path = folder_paths.get_annotated_filepath(image['mask'])
|
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normal_path = folder_paths.get_annotated_filepath(image['normal'])
|
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|
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load_image_node = nodes.LoadImage()
|
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output_image, ignore_mask = load_image_node.load_image(image=image_path)
|
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ignore_image, output_mask = load_image_node.load_image(image=mask_path)
|
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normal_image, ignore_mask2 = load_image_node.load_image(image=normal_path)
|
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|
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return output_image, output_mask, model_file,
|
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return output_image, output_mask, model_file, normal_image
|
||||
|
||||
class Preview3D():
|
||||
@classmethod
|
||||
|
||||
@@ -244,6 +244,30 @@ class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
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|
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return {"required": arg_dict}
|
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|
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class ModelMergeWAN2_1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
|
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CATEGORY = "advanced/model_merging/model_specific"
|
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DESCRIPTION = "1.3B model has 30 blocks, 14B model has 40 blocks. Image to video model has the extra img_emb."
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
arg_dict = { "model1": ("MODEL",),
|
||||
"model2": ("MODEL",)}
|
||||
|
||||
argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
|
||||
|
||||
arg_dict["patch_embedding."] = argument
|
||||
arg_dict["time_embedding."] = argument
|
||||
arg_dict["time_projection."] = argument
|
||||
arg_dict["text_embedding."] = argument
|
||||
arg_dict["img_emb."] = argument
|
||||
|
||||
for i in range(40):
|
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arg_dict["blocks.{}.".format(i)] = argument
|
||||
|
||||
arg_dict["head."] = argument
|
||||
|
||||
return {"required": arg_dict}
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
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"ModelMergeSD1": ModelMergeSD1,
|
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"ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
|
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@@ -256,4 +280,5 @@ NODE_CLASS_MAPPINGS = {
|
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"ModelMergeLTXV": ModelMergeLTXV,
|
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"ModelMergeCosmos7B": ModelMergeCosmos7B,
|
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"ModelMergeCosmos14B": ModelMergeCosmos14B,
|
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"ModelMergeWAN2_1": ModelMergeWAN2_1,
|
||||
}
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
# This file is automatically generated by the build process when version is
|
||||
# updated in pyproject.toml.
|
||||
__version__ = "0.3.26"
|
||||
__version__ = "0.3.27"
|
||||
|
||||
31
execution.py
31
execution.py
@@ -93,7 +93,7 @@ def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, e
|
||||
missing_keys = {}
|
||||
for x in inputs:
|
||||
input_data = inputs[x]
|
||||
input_type, input_category, input_info = get_input_info(class_def, x, valid_inputs)
|
||||
_, input_category, input_info = get_input_info(class_def, x, valid_inputs)
|
||||
def mark_missing():
|
||||
missing_keys[x] = True
|
||||
input_data_all[x] = (None,)
|
||||
@@ -555,7 +555,7 @@ def validate_inputs(prompt, item, validated):
|
||||
received_types = {}
|
||||
|
||||
for x in valid_inputs:
|
||||
type_input, input_category, extra_info = get_input_info(obj_class, x, class_inputs)
|
||||
input_type, input_category, extra_info = get_input_info(obj_class, x, class_inputs)
|
||||
assert extra_info is not None
|
||||
if x not in inputs:
|
||||
if input_category == "required":
|
||||
@@ -571,7 +571,7 @@ def validate_inputs(prompt, item, validated):
|
||||
continue
|
||||
|
||||
val = inputs[x]
|
||||
info = (type_input, extra_info)
|
||||
info = (input_type, extra_info)
|
||||
if isinstance(val, list):
|
||||
if len(val) != 2:
|
||||
error = {
|
||||
@@ -592,8 +592,8 @@ def validate_inputs(prompt, item, validated):
|
||||
r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
|
||||
received_type = r[val[1]]
|
||||
received_types[x] = received_type
|
||||
if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, type_input):
|
||||
details = f"{x}, received_type({received_type}) mismatch input_type({type_input})"
|
||||
if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, input_type):
|
||||
details = f"{x}, received_type({received_type}) mismatch input_type({input_type})"
|
||||
error = {
|
||||
"type": "return_type_mismatch",
|
||||
"message": "Return type mismatch between linked nodes",
|
||||
@@ -641,22 +641,22 @@ def validate_inputs(prompt, item, validated):
|
||||
val = val["__value__"]
|
||||
inputs[x] = val
|
||||
|
||||
if type_input == "INT":
|
||||
if input_type == "INT":
|
||||
val = int(val)
|
||||
inputs[x] = val
|
||||
if type_input == "FLOAT":
|
||||
if input_type == "FLOAT":
|
||||
val = float(val)
|
||||
inputs[x] = val
|
||||
if type_input == "STRING":
|
||||
if input_type == "STRING":
|
||||
val = str(val)
|
||||
inputs[x] = val
|
||||
if type_input == "BOOLEAN":
|
||||
if input_type == "BOOLEAN":
|
||||
val = bool(val)
|
||||
inputs[x] = val
|
||||
except Exception as ex:
|
||||
error = {
|
||||
"type": "invalid_input_type",
|
||||
"message": f"Failed to convert an input value to a {type_input} value",
|
||||
"message": f"Failed to convert an input value to a {input_type} value",
|
||||
"details": f"{x}, {val}, {ex}",
|
||||
"extra_info": {
|
||||
"input_name": x,
|
||||
@@ -696,18 +696,19 @@ def validate_inputs(prompt, item, validated):
|
||||
errors.append(error)
|
||||
continue
|
||||
|
||||
if isinstance(type_input, list):
|
||||
if val not in type_input:
|
||||
if isinstance(input_type, list):
|
||||
combo_options = input_type
|
||||
if val not in combo_options:
|
||||
input_config = info
|
||||
list_info = ""
|
||||
|
||||
# Don't send back gigantic lists like if they're lots of
|
||||
# scanned model filepaths
|
||||
if len(type_input) > 20:
|
||||
list_info = f"(list of length {len(type_input)})"
|
||||
if len(combo_options) > 20:
|
||||
list_info = f"(list of length {len(combo_options)})"
|
||||
input_config = None
|
||||
else:
|
||||
list_info = str(type_input)
|
||||
list_info = str(combo_options)
|
||||
|
||||
error = {
|
||||
"type": "value_not_in_list",
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "ComfyUI"
|
||||
version = "0.3.26"
|
||||
version = "0.3.27"
|
||||
readme = "README.md"
|
||||
license = { file = "LICENSE" }
|
||||
requires-python = ">=3.9"
|
||||
|
||||
Reference in New Issue
Block a user