diff --git a/.gitignore b/.gitignore
index 894a44cc..64c70f38 100644
--- a/.gitignore
+++ b/.gitignore
@@ -102,3 +102,5 @@ venv.bak/
 
 # mypy
 .mypy_cache/
+
+*.lmdb
diff --git a/README.md b/README.md
index 49659bb4..77681bb6 100755
--- a/README.md
+++ b/README.md
@@ -14,12 +14,7 @@ for FFHQ
 
 ## Sample
 
-![Sample of the model trained on CelebA](doc/sample.png)
-![Style mixing sample of the model trained on CelebA](doc/sample_mixing.png)
+![Sample of the model trained on FFHQ](doc/sample_ffhq_new.png)
+![Style mixing sample of the model trained on FFHQ](doc/sample_mixing_ffhq_new.png)
 
-I have mixed styles at 4^2 - 8^2 scale. I can't get samples as dramatic as samles in the original paper. I think my model too dependent on 4^2 scale features - it seems like that much of details determined in that scale, so little variations can be acquired after it.
-
-![Sample of the model trained on FFHQ](doc/sample_ffhq.png)
-![Style mixing sample of the model trained on FFHQ](doc/sample_mixing_ffhq.png)
-
-Trained high resolution model on FFHQ. I think result seems more interesting.
\ No newline at end of file
+512px sample from the generator trained on FFHQ.
\ No newline at end of file
diff --git a/doc/sample_ffhq_new.png b/doc/sample_ffhq_new.png
new file mode 100755
index 00000000..6364801e
Binary files /dev/null and b/doc/sample_ffhq_new.png differ
diff --git a/doc/sample_mixing_ffhq_new.png b/doc/sample_mixing_ffhq_new.png
new file mode 100755
index 00000000..51d76737
Binary files /dev/null and b/doc/sample_mixing_ffhq_new.png differ
diff --git a/generate.py b/generate.py
index 9da1ec83..82226b7a 100755
--- a/generate.py
+++ b/generate.py
@@ -4,66 +4,69 @@
 from model import StyledGenerator
 
 generator = StyledGenerator(512).cuda()
-generator.load_state_dict(torch.load('checkpoint/140000.model'))
+generator.load_state_dict(torch.load('checkpoint/180000.model'))
+generator.eval()
 
 mean_style = None
 
-step = 6
+step = 7
+alpha = 1
 
 shape = 4 * 2 ** step
 
-for i in range(10):
-    style = generator.mean_style(torch.randn(1024, 512).cuda())
+with torch.no_grad():
+    for i in range(10):
+        style = generator.mean_style(torch.randn(1024, 512).cuda())
 
-    if mean_style is None:
-        mean_style = style
+        if mean_style is None:
+            mean_style = style
 
-    else:
-        mean_style += style
+        else:
+            mean_style += style
 
-mean_style /= 10
+    mean_style /= 10
 
-image = generator(
-    torch.randn(50, 512).cuda(),
-    step=step,
-    alpha=1,
-    mean_style=mean_style,
-    style_weight=0.7,
-)
+    image = generator(
+        torch.randn(15, 512).cuda(),
+        step=step,
+        alpha=alpha,
+        mean_style=mean_style,
+        style_weight=0.7,
+    )
 
-utils.save_image(image, 'sample.png', nrow=10, normalize=True, range=(-1, 1))
+    utils.save_image(image, 'sample.png', nrow=5, normalize=True, range=(-1, 1))
 
-for j in range(20):
-    source_code = torch.randn(9, 512).cuda()
-    target_code = torch.randn(5, 512).cuda()
+    for j in range(20):
+        source_code = torch.randn(5, 512).cuda()
+        target_code = torch.randn(3, 512).cuda()
 
-    images = [torch.ones(1, 3, shape, shape).cuda() * -1]
+        images = [torch.ones(1, 3, shape, shape).cuda() * -1]
 
-    source_image = generator(
-        source_code, step=step, alpha=1, mean_style=mean_style, style_weight=0.7
-    )
-    target_image = generator(
-        target_code, step=step, alpha=1, mean_style=mean_style, style_weight=0.7
-    )
+        source_image = generator(
+            source_code, step=step, alpha=alpha, mean_style=mean_style, style_weight=0.7
+        )
+        target_image = generator(
+            target_code, step=step, alpha=alpha, mean_style=mean_style, style_weight=0.7
+        )
 
-    images.append(source_image)
+        images.append(source_image)
 
-    for i in range(5):
-        image = generator(
-            [target_code[i].unsqueeze(0).repeat(9, 1), source_code],
-            step=step,
-            alpha=1,
-            mean_style=mean_style,
-            style_weight=0.7,
-            mixing_range=(0, 1),
-        )
-        images.append(target_image[i].unsqueeze(0))
-        images.append(image)
+        for i in range(3):
+            image = generator(
+                [target_code[i].unsqueeze(0).repeat(5, 1), source_code],
+                step=step,
+                alpha=alpha,
+                mean_style=mean_style,
+                style_weight=0.7,
+                mixing_range=(0, 1),
+            )
+            images.append(target_image[i].unsqueeze(0))
+            images.append(image)
 
-    # print([i.shape for i in images])
+        # print([i.shape for i in images])
 
-    images = torch.cat(images, 0)
+        images = torch.cat(images, 0)
 
-    utils.save_image(
-        images, f'sample_mixing_{j}.png', nrow=10, normalize=True, range=(-1, 1)
-    )
+        utils.save_image(
+            images, f'sample_mixing_{j}.png', nrow=6, normalize=True, range=(-1, 1)
+        )