restore ability of ksamplers to process -v variation options

- supersedes #977

ldm/dream/generator/img2img.py

@@ -49,6 +49,7 @@ class Img2Img(Generator):
                 img_callback = step_callback,
                 unconditional_guidance_scale=cfg_scale,
                 unconditional_conditioning=uc,
+                init_latent = self.init_latent,  # changes how noising is performed in ksampler
             )
 
             return self.sample_to_image(samples)

ldm/models/diffusion/ksampler.py

@@ -97,6 +97,7 @@ class KSampler(Sampler):
             rho=7.,
             device=self.device,
         )
+        self.sigmas = self.karras_sigmas
         
     # ALERT: We are completely overriding the sample() method in the base class, which
     # means that inpainting will not work. To get this to work we need to be able to
@@ -170,11 +171,16 @@ class KSampler(Sampler):
                 img_callback(k_callback_values['x'],k_callback_values['i'])
 
         # sigmas are set up in make_schedule - we take the last steps items
-        total_steps = len(self.karras_sigmas)
-        sigmas = self.karras_sigmas[-S-1:]
-        
+        total_steps = len(self.sigmas)
+        sigmas = self.sigmas[-S-1:]
+
+        # x_T is variation noise. When an init image is provided (in x0) we need to add
+        # more randomness to the starting image.
         if x_T is not None:
-            x = x_T + torch.randn([batch_size, *shape], device=self.device) * sigmas[0]
+            if x0 is not None:
+                x = x_T + torch.randn_like(x0, device=self.device) * sigmas[0]
+            else:
+                x = x_T * sigmas[0]
         else:
             x = torch.randn([batch_size, *shape], device=self.device) * sigmas[0]