diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index d95ad78196..759ba2dba4 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -10,8 +10,6 @@ from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.invoke.generator.omnibus import Omnibus
 from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from PIL import Image
-from ldm.invoke.devices import choose_autocast
-from ldm.invoke.image_util import InitImageResizer
 
 class Txt2Img2Img(Generator):
     def __init__(self, model, precision):
@@ -46,13 +44,16 @@ class Txt2Img2Img(Generator):
                     ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
             )
 
+            #x = self.get_noise(init_width, init_height)
+            x = x_T
+
             if self.free_gpu_mem and self.model.model.device != self.model.device:
                 self.model.model.to(self.model.device)
 
             samples, _ = sampler.sample(
                 batch_size                   = 1,
                 S                            = steps,
-                x_T                          = x_T,
+                x_T                          = x,
                 conditioning                 = c,
                 shape                        = shape,
                 verbose                      = False,
@@ -68,21 +69,11 @@ class Txt2Img2Img(Generator):
                  )
 
             # resizing
-
-            image = self.sample_to_image(samples)
-            image = InitImageResizer(image).resize(width, height)
-
-            image = np.array(image).astype(np.float32) / 255.0
-            image = image[None].transpose(0, 3, 1, 2)
-            image = torch.from_numpy(image)
-            image = 2.0 * image - 1.0
-            image = image.to(self.model.device)
-
-            scope = choose_autocast(self.precision)
-            with scope(self.model.device.type):
-                samples = self.model.get_first_stage_encoding(
-                    self.model.encode_first_stage(image)
-                ) # move back to latent space
+            samples = torch.nn.functional.interpolate(
+                samples,
+                size=(height // self.downsampling_factor, width // self.downsampling_factor),
+                mode="bilinear"
+            )
 
             t_enc = int(strength * steps)
             ddim_sampler = DDIMSampler(self.model, device=self.model.device)