apply lint

api/onnx_web/chain/tile.py

@@ -101,7 +101,7 @@ def make_tile_mask(
 
     # sort gradient points
     p1 = adj_tile
-    p2 = (tile - adj_tile)
+    p2 = tile - adj_tile
     points = [0, min(p1, p2), max(p1, p2), tile]
 
     # build gradients

api/onnx_web/convert/diffusion/diffusion.py

@@ -36,7 +36,14 @@ from ...diffusers.pipelines.upscale import OnnxStableDiffusionUpscalePipeline
 from ...diffusers.version_safe_diffusers import AttnProcessor
 from ...models.cnet import UNet2DConditionModel_CNet
 from ...utils import run_gc
-from ..utils import RESOLVE_FORMATS, ConversionContext, check_ext, is_torch_2_0, load_tensor, onnx_export
+from ..utils import (
+    RESOLVE_FORMATS,
+    ConversionContext,
+    check_ext,
+    is_torch_2_0,
+    load_tensor,
+    onnx_export,
+)
 from .checkpoint import convert_extract_checkpoint
 
 logger = getLogger(__name__)

api/onnx_web/diffusers/pipelines/panorama.py

@@ -619,7 +619,14 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
 
                 # get the latents corresponding to the current view coordinates
                 latents_for_region = latents[:, :, h_start:h_end, w_start:w_end]
-                logger.trace("region latent shape: [:,:,%s:%s,%s:%s] -> %s", h_start, h_end, w_start, w_end, latents_for_region.shape)
+                logger.trace(
+                    "region latent shape: [:,:,%s:%s,%s:%s] -> %s",
+                    h_start,
+                    h_end,
+                    w_start,
+                    w_end,
+                    latents_for_region.shape,
+                )
 
                 # expand the latents if we are doing classifier free guidance
                 latent_region_input = (
@@ -660,14 +667,19 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
                 latents_region_denoised = scheduler_output.prev_sample.numpy()
 
                 if feather > 0.0:
-                    mask = make_tile_mask((h_end - h_start, w_end - w_start), self.window, feather)
+                    mask = make_tile_mask(
+                        (h_end - h_start, w_end - w_start), self.window, feather
+                    )
+                    mask = np.expand_dims(mask, axis=0)
                     mask = np.repeat(mask, 4, axis=0)
                     mask = np.expand_dims(mask, axis=0)
                 else:
                     mask = 1
 
                 if weight >= 10.0:
-                    value[:, :, h_start:h_end, w_start:w_end] = latents_region_denoised * mask
+                    value[:, :, h_start:h_end, w_start:w_end] = (
+                        latents_region_denoised * mask
+                    )
                     count[:, :, h_start:h_end, w_start:w_end] = mask
                 else:
                     value[:, :, h_start:h_end, w_start:w_end] += (

api/onnx_web/diffusers/pipelines/panorama_xl.py

@@ -464,7 +464,14 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
 
                 # get the latents corresponding to the current view coordinates
                 latents_for_region = latents[:, :, h_start:h_end, w_start:w_end]
-                logger.trace("region latent shape: [:,:,%s:%s,%s:%s] -> %s", h_start, h_end, w_start, w_end, latents_for_region.shape)
+                logger.trace(
+                    "region latent shape: [:,:,%s:%s,%s:%s] -> %s",
+                    h_start,
+                    h_end,
+                    w_start,
+                    w_end,
+                    latents_for_region.shape,
+                )
 
                 # expand the latents if we are doing classifier free guidance
                 latent_region_input = (
@@ -514,14 +521,19 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
                 latents_region_denoised = scheduler_output.prev_sample.numpy()
 
                 if feather > 0.0:
-                    mask = make_tile_mask((h_end - h_start, w_end - w_start), self.window, feather)
+                    mask = make_tile_mask(
+                        (h_end - h_start, w_end - w_start), self.window, feather
+                    )
+                    mask = np.expand_dims(mask, axis=0)
                     mask = np.repeat(mask, 4, axis=0)
                     mask = np.expand_dims(mask, axis=0)
                 else:
                     mask = 1
 
                 if weight >= 10.0:
-                    value[:, :, h_start:h_end, w_start:w_end] = latents_region_denoised * mask
+                    value[:, :, h_start:h_end, w_start:w_end] = (
+                        latents_region_denoised * mask
+                    )
                     count[:, :, h_start:h_end, w_start:w_end] = mask
                 else:
                     value[:, :, h_start:h_end, w_start:w_end] += (

api/onnx_web/diffusers/utils.py

@@ -3,7 +3,7 @@ from copy import deepcopy
 from logging import getLogger
 from math import ceil
 from re import Pattern, compile
-from typing import Dict, List, Literal, Optional, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import numpy as np
 import torch
@@ -21,7 +21,9 @@ CLIP_TOKEN = compile(r"\<clip:([-\w]+):(\d+)\>")
 INVERSION_TOKEN = compile(r"\<inversion:([^:\>]+):(-?[\.|\d]+)\>")
 LORA_TOKEN = compile(r"\<lora:([^:\>]+):(-?[\.|\d]+)\>")
 WILDCARD_TOKEN = compile(r"__([-/\\\w]+)__")
-REGION_TOKEN = compile(r"\<region:(\d+):(\d+):(\d+):(\d+):(\d+):(-?[\.|\d]+):([^\>]+)\>")
+REGION_TOKEN = compile(
+    r"\<region:(\d+):(\d+):(\d+):(\d+):(\d+):(-?[\.|\d]+):([^\>]+)\>"
+)
 
 INTERVAL_RANGE = compile(r"(\w+)-{(\d+),(\d+)(?:,(\d+))?}")
 ALTERNATIVE_RANGE = compile(r"\(([^\)]+)\)")
@@ -460,7 +462,15 @@ Region = Tuple[int, int, int, int, float, str]
 
 def parse_region_group(group) -> Region:
     top, left, bottom, right, weight, feather, prompt = group
-    return (int(top), int(left), int(bottom), int(right), float(weight), float(feather), prompt)
+    return (
+        int(top),
+        int(left),
+        int(bottom),
+        int(right),
+        float(weight),
+        float(feather),
+        prompt,
+    )
 
 
 def parse_regions(prompt: str) -> Tuple[str, List[Region]]: