Support for 6gb cards

README.md

@@ -24,8 +24,9 @@ c. Create a write token/Copy an existing Token key and enter in the cmd window.
 5. You can find output files in .\\data\\out\\%Timestamp% folder. Jpeg contain commentaries in metadata with some settings used while generating.
 		
 ## Requirements
-* 11GB of free space.
-* Nvidia card with 8GB+ video memory.
-	* Currently min dalle can run in 6gb. Testing purpose
-## Additional info
-Tested on RTX3070. One picture was making 12 - 14 seconds.
+* 10GB of free space.
+* Nvidia card with 6GB+ video memory.
+
+## Tests
+RTX3070:	 	12 - 14 seconds per picture.
+RTX2060_Laptop:	21 - 25 seconds per picture.

scripts/stable_diff.py

@@ -20,9 +20,9 @@ from diffusers import (
 from diffusers import StableDiffusionImg2ImgPipeline 
 
 t = torch.cuda.get_device_properties(0).total_memory
-if t <=8500000000:
-    print("Not enough GPU memory to generate pictures")
-else:
+if t<=6400000000:
+    print("Running with less than 6gb memory. Working is not garanted")
+
 file1 = open("prompt.txt","r+")
 text = file1.read()
 print(text)
@@ -43,7 +43,8 @@ else:
     torch_dtype=torch.float16,
     use_auth_token=True
 ).to(device)
-    if t <= 10500000000:
+if t <= 11000000000:
+    print("Less then 11gb video memory. Running with attention slicing.")
     pipe.enable_attention_slicing()
         
 def dummy_checker(images, **kwargs):
@@ -85,7 +86,7 @@ else:
 
 counterr=0
 allwork=0
-    directory="./data/out/" + strftime("%Y-%m-%d_%H-%M-%S", gmtime()) + "/"
+directory="./data/out/" + strftime("%Y-%m-%d_%H-%M-%S") + "/"
 if not os.path.exists(directory):
     os.makedirs(directory)
 with open(directory+"prompt.txt", 'w') as f: