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Fix and enforce no trailing whitespace.

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comfyanonymous 2024-12-31 03:16:37 -05:00
parent a90aafafc1
commit b7572b2f87
20 changed files with 51 additions and 50 deletions
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2
api_server/services/terminal_service.py
View File

@ -28,7 +28,7 @@ class TerminalService:
if columns != self.cols:
self.cols = columns
changed = True
changed = True
if lines != self.rows:
self.rows = lines

2
app/custom_node_manager.py
View File

@ -10,7 +10,7 @@ class CustomNodeManager:
Placeholder to refactor the custom node management features from ComfyUI-Manager.
Currently it only contains the custom workflow templates feature.
"""
def add_routes(self, routes, webapp, loadedModules):
def add_routes(self, routes, webapp, loadedModules):
@routes.get("/workflow_templates")
async def get_workflow_templates(request):

18
comfy/extra_samplers/uni_pc.py
View File

@ -226,7 +226,7 @@ def model_wrapper(
The input `model` has the following format:
``
model(x, t_input, **model_kwargs) -> noise | x_start | v | score
``
``
The input `classifier_fn` has the following format:
``
@ -240,7 +240,7 @@ def model_wrapper(
The input `model` has the following format:
``
model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
``
``
And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
[4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
@ -254,7 +254,7 @@ def model_wrapper(
``
def model_fn(x, t_continuous) -> noise:
t_input = get_model_input_time(t_continuous)
return noise_pred(model, x, t_input, **model_kwargs)
return noise_pred(model, x, t_input, **model_kwargs)
``
where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
@ -359,7 +359,7 @@ class UniPC:
max_val=1.,
variant='bh1',
):
"""Construct a UniPC.
"""Construct a UniPC.
We support both data_prediction and noise_prediction.
"""
@ -372,7 +372,7 @@ class UniPC:
def dynamic_thresholding_fn(self, x0, t=None):
"""
The dynamic thresholding method.
The dynamic thresholding method.
"""
dims = x0.dim()
p = self.dynamic_thresholding_ratio
@ -404,7 +404,7 @@ class UniPC:
def model_fn(self, x, t):
"""
Convert the model to the noise prediction model or the data prediction model.
Convert the model to the noise prediction model or the data prediction model.
"""
if self.predict_x0:
return self.data_prediction_fn(x, t)
@ -461,7 +461,7 @@ class UniPC:
def denoise_to_zero_fn(self, x, s):
"""
Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
"""
return self.data_prediction_fn(x, s)
@ -510,7 +510,7 @@ class UniPC:
col = torch.ones_like(rks)
for k in range(1, K + 1):
C.append(col)
col = col * rks / (k + 1)
col = col * rks / (k + 1)
C = torch.stack(C, dim=1)
if len(D1s) > 0:
@ -626,7 +626,7 @@ class UniPC:
R.append(torch.pow(rks, i - 1))
b.append(h_phi_k * factorial_i / B_h)
factorial_i *= (i + 1)
h_phi_k = h_phi_k / hh - 1 / factorial_i
h_phi_k = h_phi_k / hh - 1 / factorial_i
R = torch.stack(R)
b = torch.tensor(b, device=x.device)

4
comfy/ldm/cascade/stage_b.py
View File

@ -138,7 +138,7 @@ class StageB(nn.Module):
# nn.init.normal_(self.pixels_mapper[2].weight, std=0.02) # conditionings
# torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs
# nn.init.constant_(self.clf[1].weight, 0) # outputs
#
#
# # blocks
# for level_block in self.down_blocks + self.up_blocks:
# for block in level_block:
@ -148,7 +148,7 @@ class StageB(nn.Module):
# for layer in block.modules():
# if isinstance(layer, nn.Linear):
# nn.init.constant_(layer.weight, 0)
#
#
# def _init_weights(self, m):
# if isinstance(m, (nn.Conv2d, nn.Linear)):
# torch.nn.init.xavier_uniform_(m.weight)

4
comfy/ldm/cascade/stage_c.py
View File

@ -142,7 +142,7 @@ class StageC(nn.Module):
# nn.init.normal_(self.clip_img_mapper.weight, std=0.02) # conditionings
# torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02) # inputs
# nn.init.constant_(self.clf[1].weight, 0) # outputs
#
#
# # blocks
# for level_block in self.down_blocks + self.up_blocks:
# for block in level_block:
@ -152,7 +152,7 @@ class StageC(nn.Module):
# for layer in block.modules():
# if isinstance(layer, nn.Linear):
# nn.init.constant_(layer.weight, 0)
#
#
# def _init_weights(self, m):
# if isinstance(m, (nn.Conv2d, nn.Linear)):
# torch.nn.init.xavier_uniform_(m.weight)

2
comfy/ldm/flux/model.py
View File

@ -168,7 +168,7 @@ class Flux(nn.Module):
out = blocks_replace[("single_block", i)]({"img": img,
"vec": vec,
"pe": pe,
"attn_mask": attn_mask},
"attn_mask": attn_mask},
{"original_block": block_wrap})
img = out["img"]
else:

2
comfy/ldm/hydit/attn_layers.py
View File

@ -159,7 +159,7 @@ class CrossAttention(nn.Module):
q = q.transpose(-2, -3).contiguous() # q -> B, L1, H, C - B, H, L1, C
k = k.transpose(-2, -3).contiguous() # k -> B, L2, H, C - B, H, C, L2
v = v.transpose(-2, -3).contiguous()
v = v.transpose(-2, -3).contiguous()
context = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision)

2
comfy/model_base.py
View File

@ -787,7 +787,7 @@ class Flux(BaseModel):
cross_attn = kwargs.get("cross_attn", None)
if cross_attn is not None:
out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
# upscale the attention mask, since now we
# upscale the attention mask, since now we
attention_mask = kwargs.get("attention_mask", None)
if attention_mask is not None:
shape = kwargs["noise"].shape

2
comfy/model_detection.py
View File

@ -576,7 +576,7 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None):
'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}
'use_temporal_attention': False, 'use_temporal_resblock': False}
supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint]

2
comfy/utils.py
View File

@ -727,7 +727,7 @@ def bislerp(samples, width, height):
res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c)
#edge cases for same or polar opposites
res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5]
res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5]
res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]
return res

4
comfy_extras/nodes_gits.py
View File

@ -162,7 +162,7 @@ NOISE_LEVELS = {
[14.61464119, 7.49001646, 5.85520077, 4.45427561, 3.46139455, 2.84484982, 2.19988537, 1.72759056, 1.36964464, 1.08895338, 0.86115354, 0.69515091, 0.54755926, 0.43325692, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.46139455, 2.84484982, 2.19988537, 1.72759056, 1.36964464, 1.08895338, 0.86115354, 0.69515091, 0.54755926, 0.43325692, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.46139455, 2.84484982, 2.19988537, 1.72759056, 1.36964464, 1.08895338, 0.89115214, 0.72133851, 0.59516323, 0.4783645, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.09824532, 0.02916753],
],
],
1.15: [
[14.61464119, 0.83188516, 0.02916753],
[14.61464119, 1.84880662, 0.59516323, 0.02916753],
@ -246,7 +246,7 @@ NOISE_LEVELS = {
[14.61464119, 5.85520077, 2.84484982, 1.72759056, 1.162866, 0.83188516, 0.64427125, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 5.85520077, 2.84484982, 1.78698075, 1.24153244, 0.92192322, 0.72133851, 0.57119018, 0.45573691, 0.38853383, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 5.85520077, 2.84484982, 1.78698075, 1.24153244, 0.92192322, 0.72133851, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
],
],
1.35: [
[14.61464119, 0.69515091, 0.02916753],
[14.61464119, 0.95350921, 0.34370604, 0.02916753],

2
comfy_extras/nodes_perpneg.py
View File

@ -73,7 +73,7 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider):
comfy.samplers.calc_cond_batch(self.inner_model, [positive_cond, negative_cond, empty_cond], x, timestep, model_options)
cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg)
# normally this would be done in cfg_function, but we skipped
# normally this would be done in cfg_function, but we skipped
# that for efficiency: we can compute the noise predictions in
# a single call to calc_cond_batch() (rather than two)
# so we replicate the hook here

6
execution.py
View File

@ -62,7 +62,7 @@ class IsChangedCache:
class CacheSet:
def __init__(self, lru_size=None):
if lru_size is None or lru_size == 0:
self.init_classic_cache()
self.init_classic_cache()
else:
self.init_lru_cache(lru_size)
self.all = [self.outputs, self.ui, self.objects]
@ -168,7 +168,7 @@ def _map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execut
process_inputs(input_data_all, 0, input_is_list=input_is_list)
elif max_len_input == 0:
process_inputs({})
else:
else:
for i in range(max_len_input):
input_dict = slice_dict(input_data_all, i)
process_inputs(input_dict, i)
@ -232,7 +232,7 @@ def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb
output = merge_result_data(results, obj)
else:
output = []
ui = dict()
ui = dict()
if len(uis) > 0:
ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}
return output, ui, has_subgraph

16
nodes.py
View File

@ -51,7 +51,7 @@ class CLIPTextEncode(ComfyNodeABC):
def INPUT_TYPES(s) -> InputTypeDict:
return {
"required": {
"text": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}),
"text": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}),
"clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."})
}
}
@ -269,8 +269,8 @@ class VAEDecode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"samples": ("LATENT", {"tooltip": "The latent to be decoded."}),
"required": {
"samples": ("LATENT", {"tooltip": "The latent to be decoded."}),
"vae": ("VAE", {"tooltip": "The VAE model used for decoding the latent."})
}
}
@ -550,13 +550,13 @@ class CheckpointLoaderSimple:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"required": {
"ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}),
}
}
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
OUTPUT_TOOLTIPS = ("The model used for denoising latents.",
"The CLIP model used for encoding text prompts.",
OUTPUT_TOOLTIPS = ("The model used for denoising latents.",
"The CLIP model used for encoding text prompts.",
"The VAE model used for encoding and decoding images to and from latent space.")
FUNCTION = "load_checkpoint"
@ -633,7 +633,7 @@ class LoraLoader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"required": {
"model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
"clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
"lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
@ -1162,7 +1162,7 @@ class EmptyLatentImage:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"required": {
"width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The width of the latent images in pixels."}),
"height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8, "tooltip": "The height of the latent images in pixels."}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."})

1
ruff.toml
View File

@ -5,6 +5,7 @@ lint.ignore = ["ALL"]
lint.select = [
"S307", # suspicious-eval-usage
"T201", # print-usage
"W291",
"W292",
"W293",
# The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.

4
tests-unit/folder_paths_test/filter_by_content_types_test.py
View File

@ -6,8 +6,8 @@ from folder_paths import filter_files_content_types
@pytest.fixture(scope="module")
def file_extensions():
return {
'image': ['gif', 'heif', 'ico', 'jpeg', 'jpg', 'png', 'pnm', 'ppm', 'svg', 'tiff', 'webp', 'xbm', 'xpm'],
'audio': ['aif', 'aifc', 'aiff', 'au', 'flac', 'm4a', 'mp2', 'mp3', 'ogg', 'snd', 'wav'],
'image': ['gif', 'heif', 'ico', 'jpeg', 'jpg', 'png', 'pnm', 'ppm', 'svg', 'tiff', 'webp', 'xbm', 'xpm'],
'audio': ['aif', 'aifc', 'aiff', 'au', 'flac', 'm4a', 'mp2', 'mp3', 'ogg', 'snd', 'wav'],
'video': ['avi', 'm2v', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ogv', 'qt', 'webm', 'wmv']
}

8
tests-unit/utils/extra_config_test.py
View File

@ -57,8 +57,8 @@ def mock_yaml_safe_load(mock_yaml_content):
def test_load_extra_model_paths_expands_userpath(
mock_file,
monkeypatch,
mock_add_model_folder_path,
mock_expanduser,
mock_add_model_folder_path,
mock_expanduser,
mock_yaml_safe_load,
mock_expanded_home
):
@ -78,7 +78,7 @@ def test_load_extra_model_paths_expands_userpath(
# Check if add_model_folder_path was called with the correct arguments
for actual_call, expected_call in zip(mock_add_model_folder_path.call_args_list, expected_calls):
assert actual_call.args[0] == expected_call[0]
assert actual_call.args[0] == expected_call[0]
assert os.path.normpath(actual_call.args[1]) == os.path.normpath(expected_call[1]) # Normalize and check the path to check on multiple OS.
assert actual_call.args[2] == expected_call[2]
@ -97,7 +97,7 @@ def test_load_extra_model_paths_expands_appdata(
yaml_config_with_appdata,
mock_yaml_content_appdata
):
# Set the mock_file to return yaml with appdata as a variable
# Set the mock_file to return yaml with appdata as a variable
mock_file.return_value.read.return_value = yaml_config_with_appdata
# Attach mocks

6
tests/compare/test_quality.py
View File

@ -32,7 +32,7 @@ class TestCompareImageMetrics:
@fixture(scope="class")
def test_file_names(self, args_pytest):
test_dir = args_pytest['test_dir']
fnames = self.gather_file_basenames(test_dir)
fnames = self.gather_file_basenames(test_dir)
yield fnames
del fnames
@ -84,7 +84,7 @@ class TestCompareImageMetrics:
file_match = self.find_file_match(baseline_file_path, file_paths)
assert file_match is not None, f"Could not find a file in {args_pytest['test_dir']} with matching metadata to {baseline_file_path}"
# For a baseline image file, finds the corresponding file name in test_dir and
# For a baseline image file, finds the corresponding file name in test_dir and
# compares the images using the metrics in METRICS
@pytest.mark.parametrize("metric", METRICS.keys())
def test_pipeline_compare(
@ -181,7 +181,7 @@ class TestCompareImageMetrics:
# Find file match
# Reorder test_file_names so that the file with matching name is first
# This is an optimization because matching file names are more likely
# This is an optimization because matching file names are more likely
# to have matching metadata if they were generated with the same script
basename = os.path.basename(baseline_file)
file_path_basenames = [os.path.basename(f) for f in file_paths]

6
tests/inference/test_execution.py
View File

@ -40,8 +40,8 @@ class ComfyClient:
def __init__(self):
self.test_name = ""
def connect(self,
listen:str = '127.0.0.1',
def connect(self,
listen:str = '127.0.0.1',
port:Union[str,int] = 8188,
client_id: str = str(uuid.uuid4())
):
@ -125,7 +125,7 @@ class TestExecution:
def _server(self, args_pytest, request):
# Start server
pargs = [
'python','main.py',
'python','main.py',
'--output-directory', args_pytest["output_dir"],
'--listen', args_pytest["listen"],
'--port', str(args_pytest["port"]),

8
tests/inference/test_inference.py
View File

@ -23,7 +23,7 @@ These tests generate and save images through a range of parameters
"""
class ComfyGraph:
def __init__(self,
def __init__(self,
graph: dict,
sampler_nodes: list[str],
):
@ -59,8 +59,8 @@ class ComfyGraph:
class ComfyClient:
# From examples/websockets_api_example.py
def connect(self,
listen:str = '127.0.0.1',
def connect(self,
listen:str = '127.0.0.1',
port:Union[str,int] = 8188,
client_id: str = str(uuid.uuid4())
):
@ -152,7 +152,7 @@ class TestInference:
def _server(self, args_pytest):
# Start server
p = subprocess.Popen([
'python','main.py',
'python','main.py',
'--output-directory', args_pytest["output_dir"],
'--listen', args_pytest["listen"],
'--port', str(args_pytest["port"]),