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Merge branch 'master' into dr-support-pip-cm

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Dr.Lt.Data 2025-06-20 22:12:07 +09:00
commit 4e95c0c104
12 changed files with 112 additions and 29 deletions
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3
README.md
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@ -65,12 +65,13 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
- [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
- [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
- [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)
- [Cosmos Predict2](https://comfyanonymous.github.io/ComfyUI_examples/cosmos_predict2/)
- Video Models
- [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
- [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
- [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
- [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/)
- [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/) and [Cosmos Predict2](https://comfyanonymous.github.io/ComfyUI_examples/cosmos_predict2/)
- [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
- Audio Models
- [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)

50
comfy/k_diffusion/sampling.py
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@ -781,6 +781,7 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
old_denoised = denoised
return x
@torch.no_grad()
def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
"""DPM-Solver++(2M) SDE."""
@ -796,9 +797,12 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
old_denoised = None
h_last = None
h = None
h, h_last = None, None
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
@ -809,26 +813,29 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
x = denoised
else:
# DPM-Solver++(2M) SDE
t, s = -sigmas[i].log(), -sigmas[i + 1].log()
h = s - t
eta_h = eta * h
lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
h = lambda_t - lambda_s
h_eta = h * (eta + 1)
x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised
alpha_t = sigmas[i + 1] * lambda_t.exp()
x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised
if old_denoised is not None:
r = h_last / h
if solver_type == 'heun':
x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised)
x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (denoised - old_denoised)
elif solver_type == 'midpoint':
x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (denoised - old_denoised)
if eta:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise
if eta > 0 and s_noise > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
old_denoised = denoised
h_last = h
return x
@torch.no_grad()
def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
"""DPM-Solver++(3M) SDE."""
@ -842,6 +849,10 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
denoised_1, denoised_2 = None, None
h, h_1, h_2 = None, None, None
@ -853,13 +864,16 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
# Denoising step
x = denoised
else:
t, s = -sigmas[i].log(), -sigmas[i + 1].log()
h = s - t
lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
h = lambda_t - lambda_s
h_eta = h * (eta + 1)
x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised
alpha_t = sigmas[i + 1] * lambda_t.exp()
x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised
if h_2 is not None:
# DPM-Solver++(3M) SDE
r0 = h_1 / h
r1 = h_2 / h
d1_0 = (denoised - denoised_1) / r0
@ -868,20 +882,22 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
d2 = (d1_0 - d1_1) / (r0 + r1)
phi_2 = h_eta.neg().expm1() / h_eta + 1
phi_3 = phi_2 / h_eta - 0.5
x = x + phi_2 * d1 - phi_3 * d2
x = x + (alpha_t * phi_2) * d1 - (alpha_t * phi_3) * d2
elif h_1 is not None:
# DPM-Solver++(2M) SDE
r = h_1 / h
d = (denoised - denoised_1) / r
phi_2 = h_eta.neg().expm1() / h_eta + 1
x = x + phi_2 * d
x = x + (alpha_t * phi_2) * d
if eta:
if eta > 0 and s_noise > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
denoised_1, denoised_2 = denoised, denoised_1
h_1, h_2 = h, h_1
return x
@torch.no_grad()
def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
if len(sigmas) <= 1:
@ -891,6 +907,7 @@ def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
@torch.no_grad()
def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
if len(sigmas) <= 1:
@ -900,6 +917,7 @@ def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
@torch.no_grad()
def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
if len(sigmas) <= 1:

2
comfy/ldm/flux/controlnet.py
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@ -123,6 +123,8 @@ class ControlNetFlux(Flux):
if y is None:
y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
else:
y = y[:, :self.params.vec_in_dim]
# running on sequences img
img = self.img_in(img)

2
comfy/ldm/flux/layers.py
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@ -118,7 +118,7 @@ class Modulation(nn.Module):
def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
if modulation_dims is None:
if m_add is not None:
return tensor * m_mult + m_add
return torch.addcmul(m_add, tensor, m_mult)
else:
return tensor * m_mult
else:

2
comfy/ldm/modules/sub_quadratic_attention.py
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@ -31,7 +31,7 @@ def dynamic_slice(
starts: List[int],
sizes: List[int],
) -> Tensor:
slicing = [slice(start, start + size) for start, size in zip(starts, sizes)]
slicing = tuple(slice(start, start + size) for start, size in zip(starts, sizes))
return x[slicing]
class AttnChunk(NamedTuple):

2
comfy/sd1_clip.py
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@ -462,7 +462,7 @@ class SDTokenizer:
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
self.max_length = tokenizer_data.get("{}_max_length".format(embedding_key), max_length)
self.min_length = min_length
self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length)
self.end_token = None
self.min_padding = min_padding

55
comfy_config/config_parser.py
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@ -11,6 +11,43 @@ from comfy_config.types import (
PyProjectSettings
)
def validate_and_extract_os_classifiers(classifiers: list) -> list:
os_classifiers = [c for c in classifiers if c.startswith("Operating System :: ")]
if not os_classifiers:
return []
os_values = [c[len("Operating System :: ") :] for c in os_classifiers]
valid_os_prefixes = {"Microsoft", "POSIX", "MacOS", "OS Independent"}
for os_value in os_values:
if not any(os_value.startswith(prefix) for prefix in valid_os_prefixes):
return []
return os_values
def validate_and_extract_accelerator_classifiers(classifiers: list) -> list:
accelerator_classifiers = [c for c in classifiers if c.startswith("Environment ::")]
if not accelerator_classifiers:
return []
accelerator_values = [c[len("Environment :: ") :] for c in accelerator_classifiers]
valid_accelerators = {
"GPU :: NVIDIA CUDA",
"GPU :: AMD ROCm",
"GPU :: Intel Arc",
"NPU :: Huawei Ascend",
"GPU :: Apple Metal",
}
for accelerator_value in accelerator_values:
if accelerator_value not in valid_accelerators:
return []
return accelerator_values
"""
Extract configuration from a custom node directory's pyproject.toml file or a Python file.
@ -78,6 +115,24 @@ def extract_node_configuration(path) -> Optional[PyProjectConfig]:
tool_data = raw_settings.tool
comfy_data = tool_data.get("comfy", {}) if tool_data else {}
dependencies = project_data.get("dependencies", [])
supported_comfyui_frontend_version = ""
for dep in dependencies:
if isinstance(dep, str) and dep.startswith("comfyui-frontend-package"):
supported_comfyui_frontend_version = dep.removeprefix("comfyui-frontend-package")
break
supported_comfyui_version = comfy_data.get("requires-comfyui", "")
classifiers = project_data.get('classifiers', [])
supported_os = validate_and_extract_os_classifiers(classifiers)
supported_accelerators = validate_and_extract_accelerator_classifiers(classifiers)
project_data['supported_os'] = supported_os
project_data['supported_accelerators'] = supported_accelerators
project_data['supported_comfyui_frontend_version'] = supported_comfyui_frontend_version
project_data['supported_comfyui_version'] = supported_comfyui_version
return PyProjectConfig(project=project_data, tool_comfy=comfy_data)

6
comfy_config/types.py
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@ -51,7 +51,7 @@ class ComfyConfig(BaseModel):
models: List[Model] = Field(default_factory=list, alias="Models")
includes: List[str] = Field(default_factory=list)
web: Optional[str] = None
banner_url: str = ""
class License(BaseModel):
file: str = ""
@ -66,6 +66,10 @@ class ProjectConfig(BaseModel):
dependencies: List[str] = Field(default_factory=list)
license: License = Field(default_factory=License)
urls: URLs = Field(default_factory=URLs)
supported_os: List[str] = Field(default_factory=list)
supported_accelerators: List[str] = Field(default_factory=list)
supported_comfyui_version: str = ""
supported_comfyui_frontend_version: str = ""
@field_validator('license', mode='before')
@classmethod

2
comfyui_version.py
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@ -1,3 +1,3 @@
# This file is automatically generated by the build process when version is
# updated in pyproject.toml.
__version__ = "0.3.40"
__version__ = "0.3.41"

11
execution.py
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@ -429,17 +429,20 @@ def execute(server, dynprompt, caches, current_item, extra_data, executed, promp
logging.error(f"!!! Exception during processing !!! {ex}")
logging.error(traceback.format_exc())
tips = ""
if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number."
logging.error("Got an OOM, unloading all loaded models.")
comfy.model_management.unload_all_models()
error_details = {
"node_id": real_node_id,
"exception_message": str(ex),
"exception_message": "{}\n{}".format(ex, tips),
"exception_type": exception_type,
"traceback": traceback.format_tb(tb),
"current_inputs": input_data_formatted
}
if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
logging.error("Got an OOM, unloading all loaded models.")
comfy.model_management.unload_all_models()
return (ExecutionResult.FAILURE, error_details, ex)

2
pyproject.toml
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@ -1,6 +1,6 @@
[project]
name = "ComfyUI"
version = "0.3.40"
version = "0.3.41"
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.9"

4
requirements.txt
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@ -1,5 +1,5 @@
comfyui-frontend-package==1.21.7
comfyui-workflow-templates==0.1.28
comfyui-frontend-package==1.22.2
comfyui-workflow-templates==0.1.29
comfyui-embedded-docs==0.2.2
comfyui_manager
torch