Fix and enforce no trailing whitespace.

api_server/services/terminal_service.py

@@ -28,7 +28,7 @@ def update_size(self):
 
         if columns != self.cols:
             self.cols = columns
-            changed = True 
+            changed = True
 
         if lines != self.rows:
             self.rows = lines

app/custom_node_manager.py

@@ -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):

comfy/extra_samplers/uni_pc.py

@@ -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 @@ def __init__(
         max_val=1.,
         variant='bh1',
     ):
-        """Construct a UniPC. 
+        """Construct a UniPC.
 
         We support both data_prediction and noise_prediction.
         """
@@ -372,7 +372,7 @@ def __init__(
 
     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 @@ def data_prediction_fn(self, x, t):
 
     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 @@ def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type
 
     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 @@ def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order
         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 @@ def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order,
             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)

comfy/ldm/cascade/stage_b.py

@@ -138,7 +138,7 @@ def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):
     #     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 @@ def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):
     #                 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)

comfy/ldm/cascade/stage_c.py

@@ -142,7 +142,7 @@ def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):
     #     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 @@ def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):
     #                 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)

comfy/ldm/flux/model.py

@@ -168,7 +168,7 @@ def block_wrap(args):
                 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:

comfy/ldm/hydit/attn_layers.py

@@ -159,7 +159,7 @@ def forward(self, x, y, freqs_cis_img=None):
 
         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)
 

comfy/model_base.py

@@ -787,7 +787,7 @@ def extra_conds(self, **kwargs):
         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

comfy/model_detection.py

@@ -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]

comfy/utils.py

@@ -727,7 +727,7 @@ def slerp(b1, b2, r):
         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
 

comfy_extras/nodes_gits.py

@@ -162,7 +162,7 @@ def loglinear_interp(t_steps, num_steps):
         [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 @@ def loglinear_interp(t_steps, num_steps):
         [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],

comfy_extras/nodes_perpneg.py

@@ -73,7 +73,7 @@ def predict_noise(self, x, timestep, model_options={}, seed=None):
             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

execution.py

@@ -62,7 +62,7 @@ def get(self, node_id):
 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 process_inputs(inputs, index=None, input_is_list=False):
         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

nodes.py

@@ -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 @@ def __init__(self):
     @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 @@ def __init__(self):
     @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."})

ruff.toml

@@ -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.

tests-unit/folder_paths_test/filter_by_content_types_test.py

@@ -6,8 +6,8 @@
 @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']
     }
 

tests-unit/utils/extra_config_test.py

@@ -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

tests/compare/test_quality.py

@@ -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 @@ def test_dir_has_all_matching_metadata(self, fname, test_file_names, args_pytest
         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 @@ def find_file_match(self, baseline_file: str, file_paths: List[str]):
 
         # 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]

tests/inference/test_execution.py

@@ -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"]),