Pure external backend

app.py

@@ -17,9 +17,9 @@ model_id = "CompVis/stable-diffusion-v1-4"
 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
+#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)
@@ -37,30 +37,30 @@ def infer(prompt):
             raise gr.Error("Unsafe content found. Please try again with different prompts.")
         
     #generator = torch.Generator(device=device).manual_seed(seed)
-    print("Is GPU busy? ", is_gpu_busy)
+    #print("Is GPU busy? ", is_gpu_busy)
     images = []
-    if(not is_gpu_busy):
-        is_gpu_busy = True
-        images_list = pipe(
-            [prompt] * samples,
-            num_inference_steps=steps,
-            guidance_scale=scale,
+    #if(not is_gpu_busy):
+    #    is_gpu_busy = True
+    #    images_list = pipe(
+    #        [prompt] * samples,
+    #        num_inference_steps=steps,
+    #        guidance_scale=scale,
             #generator=generator,
-        )
-        is_gpu_busy = False
-        safe_image = Image.open(r"unsafe.png")
-        for i, image in enumerate(images_list["sample"]):
-            if(images_list["nsfw_content_detected"][i]):
-                images.append(safe_image)
-            else:
-                images.append(image)
-    else:
-        url = os.getenv('JAX_BACKEND_URL')
-        payload = {'prompt': prompt}
-        images_request = requests.post(url, json = payload)
-        for image in images_request.json()["images"]:
-            image_decoded = Image.open(BytesIO(base64.b64decode(image)))
-            images.append(image_decoded)
+    #    )
+    #    is_gpu_busy = False
+    #    safe_image = Image.open(r"unsafe.png")
+    #    for i, image in enumerate(images_list["sample"]):
+     #       if(images_list["nsfw_content_detected"][i]):
+     #           images.append(safe_image)
+     #       else:
+     #           images.append(image)
+    #else:
+    url = os.getenv('JAX_BACKEND_URL')
+    payload = {'prompt': prompt}
+    images_request = requests.post(url, json = payload)
+    for image in images_request.json()["images"]:
+        image_decoded = Image.open(BytesIO(base64.b64decode(image)))
+        images.append(image_decoded)
     
     
     return images, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
@@ -372,4 +372,4 @@ Despite how impressive being able to turn text into image is, beware to the fact
            """
         )
 
-block.queue(max_size=25, concurrency_count=2).launch()
+block.queue(max_size=50, concurrency_count=40).launch()