init

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
-#import torch
+import torch
 #from torch import autocast
-#from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusionPipeline
 from datasets import load_dataset
 from PIL import Image  
 #from io import BytesIO
@@ -10,19 +10,20 @@ import re
 import os
 import requests
 
+
 from share_btn import community_icon_html, loading_icon_html, share_js
 
 model_id = "CompVis/stable-diffusion-v1-4"
-device = "cuda"
+#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 = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=os.getenv('ACCESS_TOKEN'), torch_dtype=torch.float32)
 #pipe = pipe.to(device)
-#torch.backends.cudnn.benchmark = True
+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']
+##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']
 
 is_gpu_busy = False
 def infer(prompt):
@@ -30,38 +31,38 @@ def infer(prompt):
     samples = 4
     steps = 50
     scale = 7.5
-    #When running locally you can also remove this filter
-    for filter in word_list:
-        if re.search(rf"\b{filter}\b", prompt):
-            raise gr.Error("Unsafe content found. Please try again with different prompts.")
+    ##When running locally you can also remove this filter
+    #for filter in word_list:
+     #   if re.search(rf"\b{filter}\b", 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)
     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"]):
+        )
+        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)
+            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_b64 = (f"data:image/png;base64,{image}")
-        images.append(image_b64)
+    #url = os.getenv('JAX_BACKEND_URL')
+    #payload = {'prompt': prompt}
+    #images_request = requests.post(url, json = payload)
+    #for image in images_request.json()["images"]:
+     #   image_b64 = (f"data:image/jpeg;base64,{image}")
+      #  images.append(image_b64)
     
-    return images, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+    return images
     
     
 css = """
@@ -312,9 +313,9 @@ with block:
         with gr.Group(elem_id="container-advanced-btns"):
             advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
             with gr.Group(elem_id="share-btn-container"):
-                community_icon = gr.HTML(community_icon_html, visible=False)
-                loading_icon = gr.HTML(loading_icon_html, visible=False)
-                share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
+                community_icon = gr.HTML(community_icon_html)
+                loading_icon = gr.HTML(loading_icon_html)
+                share_button = gr.Button("Share to community", elem_id="share-btn")
 
         with gr.Row(elem_id="advanced-options"):
             gr.Markdown("Advanced settings are temporarily unavailable")
@@ -334,8 +335,8 @@ with block:
         ex = gr.Examples(examples=examples, fn=infer, inputs=text, outputs=[gallery, community_icon, loading_icon, share_button], cache_examples=False)
         ex.dataset.headers = [""]
 
-        text.submit(infer, inputs=text, outputs=[gallery, community_icon, loading_icon, share_button], postprocess=False)
-        btn.click(infer, inputs=text, outputs=[gallery, community_icon, loading_icon, share_button], postprocess=False)
+        text.submit(infer, inputs=text, outputs=[gallery], postprocess=False)
+        btn.click(infer, inputs=text, outputs=[gallery], postprocess=False)
         
         advanced_button.click(
             None,
@@ -368,4 +369,4 @@ Despite how impressive being able to turn text into image is, beware to the fact
            """
         )
 
-block.queue(max_size=5, concurrency_count=2).launch()
+block.queue(max_size=50, concurrency_count=20).launch()

requirements.txt

@@ -1,2 +1,9 @@
 python-dotenv
+mkl
+spacy
+ftfy
+torch
+transformers
+diffusers
+torchvision
 https://gradio-builds.s3.amazonaws.com/queue-disconnect/v3/gradio-3.4b2-py3-none-any.whl