diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index 93fa12016..98155276d 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -2379,6 +2379,9 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
         {
             printf("\nLLAMA EVAL returned nonzero: %d\n",er);
         }
+        tmp = {1};
+        llama_kv_self_clear(llama_ctx_v4);
+        er = llama_decode(llama_ctx_v4, llama_batch_get_one(tmp.data(), tmp.size()));
         return ModelLoadResult::SUCCESS;
     }
     else if (file_format == FileFormat::RWKV_1 || file_format==FileFormat::RWKV_2)
diff --git a/klite.embd b/klite.embd
index 4e1369113..5fbd1e950 100644
--- a/klite.embd
+++ b/klite.embd
@@ -6142,7 +6142,10 @@ Current version indicated by LITEVER below.
 			"prompt": prompt,
 			"n": 1,
 			"size": "1024x1024",
-			"response_format":"b64_json",
+		}
+		if(localsettings.saved_dalle_model!="gpt-image-1")
+		{
+			dalle_payload["response_format"] = "b64_json";
 		}
 
 		//remove all null fields
@@ -6164,17 +6167,17 @@ Current version indicated by LITEVER below.
 			console.log(resp);
 			if(resp.data && resp.data.length>0)
 			{
-				onImagesDone(resp.data[0].b64_json);
+				onImagesDone(resp.data[0].b64_json,null);
 			}
 			else
 			{
 				console.log("Generation Error!");
-				onImagesDone(null);
+				onImagesDone(null,JSON.stringify(resp));
 			}
 
 		}).catch((error) => {
 			console.log("Generation Error: " + error);
-			onImagesDone(null);
+			onImagesDone(null,null);
 		});
 
 	}
@@ -15595,7 +15598,7 @@ Current version indicated by LITEVER below.
 				image_db[imgid] = { done: false, queue: "Generating", result: "", prompt:sentence, poll_category:0 };
 				image_db[imgid].aspect = 0;
 				image_db[imgid].imsource = 0; //0=generated,1=uploaded
-				generate_dalle_image(genimg_payload,(outputimg)=>{
+				generate_dalle_image(genimg_payload,(outputimg,outputerr)=>{
 					if(outputimg)
 					{
 						//console.log(outputimg);
@@ -15607,7 +15610,7 @@ Current version indicated by LITEVER below.
 						}, true, imgres);
 					}else{
 						image_db[imgid].queue = "Failed";
-						msgbox("Image Generation Failed!\n\nPlease make sure your OpenAI key is set correctly and you are allowed to use DALL-E.\n");
+						msgbox(`Image Generation Failed!\n\n${outputerr?(outputerr+"\n\n"):""}Please make sure your OpenAI key is set correctly and you are allowed to use DALL-E.\n`);
 					}
 				});
 			}
diff --git a/koboldcpp.py b/koboldcpp.py
index 5535cea16..4d7493db5 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -994,7 +994,7 @@ def autoset_gpu_layers(ctxsize, sdquanted, bbs, qkv_level): #shitty algo to dete
                     fattn_discount = 1.0/(3.2 if qkv_level==2 else (1.6 if qkv_level==1 else 1.0))
                     mem1 = layers*(4 if bbs <= 512 else (bbs/128))*headkvlen*cs*fattn_discount*4*1.45
                     mem2 = layers*headcount*headkvlen*cs*fattn_discount*4*1.15
-                    ratio = (mem - reservedmem - mem1) / (fsize + mem2)
+                    ratio = max(ratio,(mem - reservedmem - mem1) / (fsize + mem2))
                 layerlimit = min(int(ratio*layers), (layers + 3))
         layerlimit = (0 if layerlimit<=2 else layerlimit)
         return layerlimit
@@ -4325,7 +4325,7 @@ def show_gui():
     quick_gpuname_label = ctk.CTkLabel(quick_tab, text="")
     quick_gpuname_label.grid(row=3, column=1, padx=75, sticky="W")
     quick_gpuname_label.configure(text_color="#ffff00")
-    quick_gpu_layers_entry,quick_gpu_layers_label = makelabelentry(quick_tab,"GPU Layers:", gpulayers_var, 6, 50,tooltip="How many layers to offload onto the GPU.\nVRAM intensive, usage increases with model and context size.\nRequires some trial and error to find the best fit value.\n\nCommon values for total layers, accuracy not guaranteed.\n\nLlama/Mistral 7b/8b: 33\nSolar 10.7b/11b: 49\nLlama 13b: 41\nLlama 20b(stack): 63\nLlama/Yi 34b: 61\nMixtral 8x7b: 33\nLlama 70b: 81")
+    quick_gpu_layers_entry,quick_gpu_layers_label = makelabelentry(quick_tab,"GPU Layers:", gpulayers_var, 6, 50,tooltip="How many layers to offload onto the GPU.\nUsage varies based on model type and increases with model and context size.\nRequires some trial and error to find the best fit value.\n\nNote: The auto estimation is often inaccurate! Please set layers yourself for best results!")
     quick_layercounter_label = ctk.CTkLabel(quick_tab, text="")
     quick_layercounter_label.grid(row=6, column=1, padx=75, sticky="W")
     quick_layercounter_label.configure(text_color="#ffff00")