fix img2img

modules/models/sd3/sd3_model.py

@@ -73,3 +73,6 @@ class SD3Inferencer(torch.nn.Module):
             (self, 'cond_stage_model'),
             (self, 'model'),
         ]
+
+    def add_noise_to_latent(self, x, noise, amount):
+        return x * (1 - amount) + noise * amount

modules/processing.py

@@ -1737,10 +1737,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             latmask = latmask[0]
             if self.mask_round:
                 latmask = np.around(latmask)
-            latmask = np.tile(latmask[None], (4, 1, 1))
+            latmask = np.tile(latmask[None], (self.init_latent.shape[1], 1, 1))
 
-            self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
-            self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)
+            self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(devices.dtype)
+            self.nmask = torch.asarray(latmask).to(shared.device).type(devices.dtype)
 
             # this needs to be fixed to be done in sample() using actual seeds for batches
             if self.inpainting_fill == 2:

modules/sd_samplers_kdiffusion.py

@@ -133,6 +133,9 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
         sigmas = self.get_sigmas(p, steps)
         sigma_sched = sigmas[steps - t_enc - 1:]
 
+        if hasattr(shared.sd_model, 'add_noise_to_latent'):
+            xi = shared.sd_model.add_noise_to_latent(x, noise, sigma_sched[0])
+        else:
             xi = x + noise * sigma_sched[0]
 
         if opts.img2img_extra_noise > 0: