ruff updates

Signed-off-by: Vladimir Mandic <mandic00@live.com>

modules/processing_helpers.py

@@ -159,7 +159,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     # enables the generation of additional tensors with noise that the sampler will use during its processing.
     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
     # produce the same images as with two batches [100], [101].
-    if p is not None and p.sampler is not None and (len(seeds) > 1 and shared.opts.enable_batch_seeds or eta_noise_seed_delta > 0):
+    if p is not None and p.sampler is not None and ((len(seeds) > 1 and shared.opts.enable_batch_seeds) or (eta_noise_seed_delta > 0)):
         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
     else:
         sampler_noises = None
@@ -414,7 +414,7 @@ def resize_hires(p, latents): # input=latents output=pil if not latent_upscaler
     if latent_upscaler is not None:
         return torch.nn.functional.interpolate(latents, size=(p.hr_upscale_to_y // 8, p.hr_upscale_to_x // 8), mode=latent_upscaler["mode"], antialias=latent_upscaler["antialias"])
     first_pass_images = processing_vae.vae_decode(latents=latents, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.width, height=p.height)
-    if p.hr_upscale_to_x == 0 or p.hr_upscale_to_y == 0 and hasattr(p, 'init_hr'):
+    if p.hr_upscale_to_x == 0 or (p.hr_upscale_to_y == 0 and hasattr(p, 'init_hr')):
         shared.log.error('Hires: missing upscaling dimensions')
         return first_pass_images
     resized_images = []