Performance upgrades

app.py

@@ -13,6 +13,8 @@ device = "cuda"
 #If you are running this code locally, you need to either do a 'huggingface-cli login` or paste your User Access Token from here https://huggingface.co/settings/tokens into the use_auth_token field below. 
 pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True, revision="fp16", torch_dtype=torch.float16)
 pipe = pipe.to(device)
+torch.backends.cudnn.benchmark = True
+
 #When running locally, you won`t have access to this, so you can remove this part
 word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True)
 word_list = word_list_dataset["train"]['text']
@@ -25,14 +27,12 @@ def infer(prompt, samples, steps, scale, seed):
         
     generator = torch.Generator(device=device).manual_seed(seed)
     
-    #If you are running locally with CPU, you can remove the `with autocast("cuda")`
-    with autocast("cuda"):
-        images_list = pipe(
-            [prompt] * samples,
-            num_inference_steps=steps,
-            guidance_scale=scale,
-            generator=generator,
-        )
+    images_list = pipe(
+        [prompt] * samples,
+        num_inference_steps=steps,
+        guidance_scale=scale,
+        generator=generator,
+    )
     images = []
     safe_image = Image.open(r"unsafe.png")
     for i, image in enumerate(images_list["sample"]):

requirements.txt

@@ -1,4 +1,4 @@
-diffusers
+-e git+https://github.com/Narsil/diffusers.git@6a4d2ef1e514a25ff5b511cafa1f06b039f0909b#egg=diffusers
 transformers
 nvidia-ml-py3
 ftfy