diff --git a/.gitignore b/.gitignore
new file mode 100644
index 000000000..00c591c03
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,16 @@
+
+*.png
+*.pyc
+*.csv
+CodeFormer/
+src/clip/
+src/taming-transformers/
+latent_diffusion.egg-info/dependency_links.txt
+latent_diffusion.egg-info/PKG-INFO
+latent_diffusion.egg-info/requires.txt
+latent_diffusion.egg-info/SOURCES.txt
+latent_diffusion.egg-info/top_level.txt
+log.txt
+models/ldm/stable-diffusion-v1/model.ckpt
+.vscode/settings.json
+tqdm.txt
diff --git a/ldm/modules/diffusionmodules/model.py b/ldm/modules/diffusionmodules/model.py
index 8dcfce963..53bb4638e 100644
--- a/ldm/modules/diffusionmodules/model.py
+++ b/ldm/modules/diffusionmodules/model.py
@@ -184,7 +184,7 @@ def __init__(self, in_channels):
 
 
 @torch.jit.script
-def fused_t(x, c):
+def fused_t(x, c: int):
     return x * (int(c) ** (-0.5))
 
 
@@ -197,7 +197,7 @@ def fused_memory_opt(mem_reserved, mem_active, mem_free_cuda, b, h, w, c):
     # s3 = (b * c * h * w * 3) * 2  # zeros_like, empty_like, empty_strided
     # s = 2 * (s1 + s2 + s3)  # 2 because of small allocations which don't really matter
     s = 16 * b * ((h * w) ** 2) + 12 * b * c * h * w
-    s = (s // mem_free_total) + 1 if s > mem_free_total else torch.tensor(1)
+    s = int((s // mem_free_total) + 1) if s > mem_free_total else 1
     return s
 
 
diff --git a/optimizedSD/optimized_txt2img.py b/optimizedSD/optimized_txt2img.py
index 9776097d4..bd100116e 100644
--- a/optimizedSD/optimized_txt2img.py
+++ b/optimizedSD/optimized_txt2img.py
@@ -1,4 +1,5 @@
 import argparse
+from datetime import datetime
 import os
 import random
 import re
@@ -117,6 +118,7 @@ def get_image(opt, model, modelCS, modelFS, prompt=None, save=True, callback_fn=
         negative_prompt = ""
     with torch.no_grad():
         all_samples = list()
+        count_iter = 0
         for _ in trange(opt.n_iter, desc="Sampling"):
             for prompts in tqdm(data, desc="data"):
                 sample_path = os.path.join(opt.outpath, "_".join(re.split(":| ", prompts[0])))[:150]
@@ -147,7 +149,7 @@ def get_image(opt, model, modelCS, modelFS, prompt=None, save=True, callback_fn=
                         c = modelCS.get_learned_conditioning(prompts)
 
                     shape = [opt.num_images, opt.C, opt.height // opt.f, opt.width // opt.f]
-
+                    seed_at_begin = opt.seed
                     if opt.device != "cpu":
                         mem = torch.cuda.memory_allocated() / 1e6
                         modelCS.to("cpu")
@@ -190,6 +192,15 @@ def get_image(opt, model, modelCS, modelFS, prompt=None, save=True, callback_fn=
                         opt.seed += 1
                         base_count += 1
 
+                    count = 0
+                    for x_index in range(count_iter * batch_size, len(all_samples)):
+                        x_sample = 255. * rearrange(all_samples[x_index][0].numpy(), 'c h w -> h w c')
+                        now = datetime.now()
+                        dt_string = now.strftime("%d-%m-%Y %H_%M_%S ")
+                        Image.fromarray(x_sample.astype(np.uint8)).save(
+                                                            os.path.join(sample_path, f"{dt_string}s{seed_at_begin}d{opt.ddim_steps}-{str(count)}.png"))
+                        count += 1
+                        
                     if opt.device != "cpu":
                         mem = torch.cuda.memory_allocated() / 1e6
                         modelFS.to("cpu")
@@ -197,7 +208,7 @@ def get_image(opt, model, modelCS, modelFS, prompt=None, save=True, callback_fn=
                             time.sleep(1)
                     del samples_ddim
                     print("memory_final = ", torch.cuda.memory_allocated() / 1e6)
-
+            count_iter += 1
     toc = time.time()
 
     time_taken = (toc - tic) / 60.0
@@ -430,7 +441,10 @@ def get_image(opt, model, modelCS, modelFS, prompt=None, save=True, callback_fn=
     grid = torch.cat(all_samples, 0)
     grid = make_grid(grid, nrow=opt.n_iter)
     grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
+    
+    now = datetime.now()
+    dt_string = now.strftime(" %d-%m-%Y %H_%M_%S ")
     Image.fromarray(grid.astype(np.uint8)).save(
-        os.path.join(outpath + "/" + str(opt.prompt).replace("/", "")[:100] + f".{opt.format}")
+        os.path.join(outpath + "/" + str(opt.prompt).replace("/", "")[:100] + dt_string + f".{opt.format}")
     )
     print("exported to", outpath + "/" + str(opt.prompt).replace("/", "")[:100] + f".{opt.format}")