ruff fixes

scripts/img2imgalt.py

@@ -12,7 +12,7 @@
 import k_diffusion as K
 
 # Debugging notes - the original method apply_model is being called for sd1.5 is in modules.sd_hijack_utils and is ldm.models.diffusion.ddpm.LatentDiffusion
-# For sdxl - OpenAIWrapper will be called, which will call the underlying diffusion_model 
+# For sdxl - OpenAIWrapper will be called, which will call the underlying diffusion_model
 
 
 def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
@@ -47,9 +47,9 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
 
         c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
         t = dnw.sigma_to_t(sigma_in)
-        
+
         if shared.sd_model.is_sdxl:
-            eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )            
+            eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )
         else:
             eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
 
@@ -104,7 +104,7 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
         x_in = torch.cat([x] * 2)
         sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
 
-        
+
         if shared.sd_model.is_sdxl:
             cond_tensor = cond['crossattn']
             uncond_tensor = uncond['crossattn']
@@ -122,9 +122,9 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
         else:
             t = dnw.sigma_to_t(sigma_in)
 
-        
+
         if shared.sd_model.is_sdxl:
-            eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )            
+            eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )
         else:
             eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)