diff --git a/aiserver.py b/aiserver.py
index 8da75fc5..8c027f89 100644
--- a/aiserver.py
+++ b/aiserver.py
@@ -9643,7 +9643,7 @@ def text2img_local(prompt: str) -> Optional[Image.Image]:
     logger.debug("Generating Image")
     from diffusers import StableDiffusionPipeline
     if koboldai_vars.image_pipeline is None:
-        pipe = tpool.execute(StableDiffusionPipeline.from_pretrained, "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, cache="models/stable-diffusion-v1-4").to("cuda")
+        pipe = tpool.execute(StableDiffusionPipeline.from_pretrained, "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, cache="functional_models/stable-diffusion").to("cuda")
     else:
         pipe = koboldai_vars.image_pipeline.to("cuda")
     logger.debug("time to load: {}".format(time.time() - start_time))
@@ -9886,14 +9886,14 @@ def summarize(text, max_length=100, min_length=30, unload=True):
     from transformers import pipeline as summary_pipeline
     start_time = time.time()
     if koboldai_vars.summarizer is None:
-        if os.path.exists("models/{}".format(args.summarizer_model.replace('/', '_'))):
-            koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained("models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
-            koboldai_vars.summarizer = AutoModelForSeq2SeqLM.from_pretrained("models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
+        if os.path.exists("functional_models/{}".format(args.summarizer_model.replace('/', '_'))):
+            koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained("functional_models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
+            koboldai_vars.summarizer = AutoModelForSeq2SeqLM.from_pretrained("functional_models/{}".format(args.summarizer_model.replace('/', '_')), cache_dir="cache")
         else:
             koboldai_vars.summary_tokenizer = AutoTokenizer.from_pretrained(args.summarizer_model, cache_dir="cache")
             koboldai_vars.summarizer = AutoModelForSeq2SeqLM.from_pretrained(args.summarizer_model, cache_dir="cache")
-            koboldai_vars.summary_tokenizer.save_pretrained("models/{}".format(args.summarizer_model.replace('/', '_')), max_shard_size="500MiB")
-            koboldai_vars.summarizer.save_pretrained("models/{}".format(args.summarizer_model.replace('/', '_')), max_shard_size="500MiB")
+            koboldai_vars.summary_tokenizer.save_pretrained("functional_models/{}".format(args.summarizer_model.replace('/', '_')), max_shard_size="500MiB")
+            koboldai_vars.summarizer.save_pretrained("functional_models/{}".format(args.summarizer_model.replace('/', '_')), max_shard_size="500MiB")
 
     #Try GPU accel
     if koboldai_vars.hascuda and torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_reserved(0) >= 1645778560:
diff --git a/functional_models/functional models go here.txt b/functional_models/functional models go here.txt
new file mode 100644
index 00000000..96991f5d
--- /dev/null
+++ b/functional_models/functional models go here.txt	
@@ -0,0 +1,2 @@
+Place the models extracted in their own subfolder.
+currently only for stable diffusion and summarization models
\ No newline at end of file
diff --git a/functional_models/stable-diffusion/put checkpoints here.txt b/functional_models/stable-diffusion/put checkpoints here.txt
new file mode 100644
index 00000000..7dfc13a6
--- /dev/null
+++ b/functional_models/stable-diffusion/put checkpoints here.txt	
@@ -0,0 +1 @@
+If you want to use local image generation, you have to download the full stable diffusion model and put all the files here
\ No newline at end of file