diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..c18dd8d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+__pycache__/
diff --git a/nodes.py b/nodes.py
index 5c84abe..34e0b6a 100644
--- a/nodes.py
+++ b/nodes.py
@@ -8,6 +8,9 @@ from comfy.k_diffusion import sampling
 from comfy.samplers import KSAMPLER
 from tqdm.auto import trange
 
+from comfy import latent_formats
+latent_factors = torch.tensor(latent_formats.SD15().latent_rgb_factors)
+
 class LatentFFTAsImage:
     """Takes a latent as input , """
     @classmethod
@@ -33,6 +36,7 @@ def dump_image(tensor):
     if len(tensor.shape) == 3:
         tensor = tensor.unsqueeze(1).expand(-1,3,-1,-1)
     tensor = tensor.permute(0,2,3,1)[0]
+    tensor @= latent_factors
     h,w,c = tensor.shape
     b = np.clip(tensor.numpy() * 255, 0, 255).astype(np.uint8)
     Image.fromarray(b).save("test.png")
@@ -46,6 +50,8 @@ def sample_dynamic(model, x, sigmas, extra_args=None, callback=None, disable=Non
     hist = torch.zeros((n,h,w), device=x.device, dtype=x.dtype)
     kx, ky = torch.meshgrid(torch.linspace(-h/2,h/2,h),torch.linspace(-w/2,w/2,w), indexing="ij")
     base_mask = (kx*kx + ky*ky).sqrt().to(x.device)
+    kx, ky = torch.meshgrid(torch.linspace(-h/4,h/4,h//2),torch.linspace(-w/4,w/4,w//2), indexing="ij")
+    base_submask = (kx*kx + ky*ky).sqrt().to(x.device)
     cw = w-w//4+1
     ch = h-h//4+1
     kernel = torch.ones((1,1,h//4,w//4), dtype=x.dtype, device=x.device)
@@ -65,44 +71,8 @@ def sample_dynamic(model, x, sigmas, extra_args=None, callback=None, disable=Non
         filt = torch.fft.ifft2(mask*torch.fft.fft2(denoised)).real
         if prev_denoised is not None:
             ext = prev_denoised+filt-denoised
-            dist = (ext*ext).sum(1)/-dt
+            dist = (ext*ext).sum(1)/math.sqrt(-dt)
             hist+= dist
-            summed = torch.nn.functional.conv2d(dist, kernel, groups=1)
-            focal = torch.unravel_index(summed.view((n,ch*cw)).argmax(1), (ch,cw))
-            focal = torch.stack(focal).transpose(0,1)
-            sublocs = []
-            subinds = []
-            focalm = []
-            for j,ind in enumerate(focal):
-                focalm.append(summed[j, *ind])
-                if summed[j, *ind] > 1000:
-                    subx = x[j,:,ind[0]:ind[0]+h//4,ind[1]:ind[1]+w//4]
-                    #scale+noise subx
-                    sublocs.append(subx)
-                    subinds.append(j)
-            if len(sublocs) > 0:
-                early_terminate = True
-                subx = torch.stack(sublocs)
-                #subx = subx.repeat_interleave(2, dim=2).repeat_interleave(2, dim=3)
-                subx = interpolate(subx, size=(h//2,w//2), mode='bicubic')
-                subx += torch.randn_like(subx)*(1-math.sqrt(2))*sigma_hat*s_in
-                print("doing subrender")
-                subdn = model(subx, sigma_hat*s_in*math.sqrt(2), **extra_args)
-                #downscale
-                subdn = interpolate(subdn, size=(h//4,w//4), mode='bicubic')
-                #TODO: Does added noise need to be filtered out?
-                for j,dn in zip(subinds,subdn):
-                    subx = denoised[j,:,focal[j][0]:focal[j][0]+h//4,focal[j][1]:focal[j][1]+w//4]
-                    sub_weight = -1
-                    if sub_weight == -1:
-                        subx[:] = dn
-                    else:
-                        subx[:] += sub_weight*dn
-                        subx /= sub_weight+1
-            focal[:,0] += h//8
-            focal[:,1] += w//8
-            print(focal*8)
-            print(focalm)
         prev_denoised = denoised
         d = sampling.to_d(x, sigma_hat, denoised)
 
@@ -114,7 +84,39 @@ def sample_dynamic(model, x, sigmas, extra_args=None, callback=None, disable=Non
         if early_terminate:
             break
     summed = torch.nn.functional.conv2d(hist, kernel, groups=1)
-    dump_image(hist)
+    focal = torch.unravel_index(summed.view((n,ch*cw)).argmax(1), (ch,cw))
+    focal = torch.stack(focal).transpose(0,1)
+    sublocs = []
+    subinds = []
+    focalm = []
+    for j,ind in enumerate(focal):
+        focalm.append(summed[j, *ind])
+        if summed[j, *ind] > 1000:
+            subx = x[j,:,ind[0]:ind[0]+h//4,ind[1]:ind[1]+w//4]
+            sublocs.append(subx)
+            subinds.append(j)
+    if len(sublocs) > 0:
+        print("doing subrender")
+        subx = torch.stack(sublocs)
+        subx = interpolate(subx, size=(h//2,w//2), mode='bicubic')
+        subnoise = model.noise[:,:,:h//2,:w//2]
+        subnoise = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[len(sigmas)//2], subnoise, subx, 0)
+        subres = sampling.sample_euler(model, subx, sigmas[len(sigmas)//2:], extra_args, callback, disable, s_churn, s_tmin, s_tmax, s_noise)
+        #downscale
+        subres = interpolate(subres, size=(h//4,w//4))
+        #TODO: Does added noise need to be filtered out?
+        for j,dn in zip(subinds,subres):
+            subx = x[j,:,focal[j][0]:focal[j][0]+h//4,focal[j][1]:focal[j][1]+w//4]
+            sub_weight = -1
+            if sub_weight == -1:
+                subx[:] = dn
+            else:
+                subx[:] += sub_weight*dn
+                subx /= sub_weight+1
+    focal[:,0] += h//8
+    focal[:,1] += w//8
+    print(focal*8)
+    print(focalm)
     return x
 
 class DynamicSampler: