Enabling xformers.

app.py

@@ -6,13 +6,19 @@ import random
 from PIL import Image
 import os
 import time
-from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
+from diffusers import (
+    StableDiffusionInstructPix2PixPipeline,
+    EulerAncestralDiscreteScheduler,
+)
 
-#Loading from Diffusers Library
+# Loading from Diffusers Library
 model_id = "timbrooks/instruct-pix2pix"
-pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16") #, safety_checker=None)
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+    model_id, torch_dtype=torch.float16, revision="fp16"
+)  # , safety_checker=None)
 pipe.to("cuda")
-pipe.enable_attention_slicing()
+pipe.enable_xformers_memory_efficient_attention()
+pipe.unet.to(memory_format=torch.channels_last)
 
 counter = 0
 
@@ -39,49 +45,85 @@ be to the input. This pipeline requires a value of at least `1`. It's possible y
     * Cropping the image so the face takes up a larger portion of the frame.
 """
 
-def chat(image_in, in_steps, in_guidance_scale, in_img_guidance_scale, image_hid, img_name, counter_out, prompt, history, progress=gr.Progress(track_tqdm=True)):
+
+def chat(
+    image_in,
+    in_steps,
+    in_guidance_scale,
+    in_img_guidance_scale,
+    image_hid,
+    img_name,
+    counter_out,
+    prompt,
+    history,
+    progress=gr.Progress(track_tqdm=True),
+):
     progress(0, desc="Starting...")
-    #if message == "revert": --to add revert functionality later
+    # if message == "revert": --to add revert functionality later
     if counter_out > 0:
-      edited_image = pipe(prompt, image=image_hid, num_inference_steps=int(in_steps), guidance_scale=float(in_guidance_scale), image_guidance_scale=float(in_img_guidance_scale)).images[0]
-      if os.path.exists(img_name):
-        os.remove(img_name)
-      temp_img_name = img_name[:-4]+str(int(time.time()))+'.png' 
-      # Create a file-like object
-      with open(temp_img_name, "wb") as fp:
-        # Save the image to the file-like object
-        edited_image.save(fp)
-      #Get the name of the saved image
-      saved_image_name = fp.name
-      #edited_image.save(temp_img_name) #, overwrite=True)
-      counter_out += 1
+        edited_image = pipe(
+            prompt,
+            image=image_hid,
+            num_inference_steps=int(in_steps),
+            guidance_scale=float(in_guidance_scale),
+            image_guidance_scale=float(in_img_guidance_scale),
+        ).images[0]
+        if os.path.exists(img_name):
+            os.remove(img_name)
+        temp_img_name = img_name[:-4] + str(int(time.time())) + ".png"
+        # Create a file-like object
+        with open(temp_img_name, "wb") as fp:
+            # Save the image to the file-like object
+            edited_image.save(fp)
+        # Get the name of the saved image
+        saved_image_name = fp.name
+        # edited_image.save(temp_img_name) #, overwrite=True)
+        counter_out += 1
     else:
-      seed = random.randint(0, 1000000)
-      img_name = f"./edited_image_{seed}.png"
-      edited_image = pipe(prompt, image=image_in, num_inference_steps=int(in_steps), guidance_scale=float(in_guidance_scale), image_guidance_scale=float(in_img_guidance_scale)).images[0]
-      if os.path.exists(img_name):
-        os.remove(img_name)
-      with open(img_name, "wb") as fp:
-        # Save the image to the file-like object
-        edited_image.save(fp)
-      #Get the name of the saved image
-      saved_image_name2 = fp.name
+        seed = random.randint(0, 1000000)
+        img_name = f"./edited_image_{seed}.png"
+        edited_image = pipe(
+            prompt,
+            image=image_in,
+            num_inference_steps=int(in_steps),
+            guidance_scale=float(in_guidance_scale),
+            image_guidance_scale=float(in_img_guidance_scale),
+        ).images[0]
+        if os.path.exists(img_name):
+            os.remove(img_name)
+        with open(img_name, "wb") as fp:
+            # Save the image to the file-like object
+            edited_image.save(fp)
+        # Get the name of the saved image
+        saved_image_name2 = fp.name
     history = history or []
-    #Resizing (or not) the image for better display and adding supportive sample text
-    add_text_list = ["There you go", "Enjoy your image!", "Nice work! Wonder what you gonna do next!", "Way to go!", "Does this work for you?", "Something like this?"]
+    # Resizing (or not) the image for better display and adding supportive sample text
+    add_text_list = [
+        "There you go",
+        "Enjoy your image!",
+        "Nice work! Wonder what you gonna do next!",
+        "Way to go!",
+        "Does this work for you?",
+        "Something like this?",
+    ]
     if counter_out > 0:
-        response = random.choice(add_text_list) + '<img src="/file=' + saved_image_name + '">'  
+        response = (
+            random.choice(add_text_list) + '<img src="/file=' + saved_image_name + '">'
+        )
         history.append((prompt, response))
         return history, history, edited_image, temp_img_name, counter_out
     else:
-        response = random.choice(add_text_list) + '<img src="/file=' + saved_image_name2 + '">'   #IMG_NAME
+        response = (
+            random.choice(add_text_list) + '<img src="/file=' + saved_image_name2 + '">'
+        )  # IMG_NAME
         history.append((prompt, response))
         counter_out += 1
         return history, history, edited_image, img_name, counter_out
-        
+
 
 with gr.Blocks() as demo:
-    gr.Markdown("""<h1><center> Chat Interface with InstructPix2Pix: Give Image Editing Instructions</h1></center>
+    gr.Markdown(
+        """<h1><center> Chat Interface with InstructPix2Pix: Give Image Editing Instructions</h1></center>
     <p>For faster inference without waiting in the queue, you may duplicate the space and upgrade to GPU in settings.<br/>
     <a href="https://huggingface.co/spaces/ysharma/InstructPix2Pix_Chatbot?duplicate=true">
     <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
@@ -89,24 +131,49 @@ with gr.Blocks() as demo:
     Any attempts to generate inappropriate or NSFW images will result in the display of a black screen 
     as a precautionary measure for the protection of all users. We appreciate your cooperation in 
     maintaining a safe and appropriate environment for all members of our community.**
-    <p/>""")
+    <p/>"""
+    )
     with gr.Row():
-      with gr.Column():
-        image_in = gr.Image(type='pil', label="Original Image")
-        text_in = gr.Textbox()
-        state_in = gr.State()
-        b1 = gr.Button('Edit the image!')
-        with gr.Accordion("Advance settings for Training and Inference", open=False):
-          gr.Markdown("Advance settings for - Number of Inference steps, Guidanace scale, and Image guidance scale.")
-          in_steps = gr.Number(label="Enter the number of Inference steps", value = 20)
-          in_guidance_scale = gr.Slider(1,10, step=0.5, label="Set Guidance scale", value=7.5)
-          in_img_guidance_scale = gr.Slider(1,10, step=0.5, label="Set Image Guidance scale", value=1.5)
-          image_hid = gr.Image(type='pil', visible=False)
-          img_name_temp_out = gr.Textbox(visible=False)
-          counter_out = gr.Number(visible=False, value=0, precision=0)
-      chatbot = gr.Chatbot() 
-    b1.click(chat,[image_in, in_steps, in_guidance_scale, in_img_guidance_scale, image_hid, img_name_temp_out,counter_out,  text_in, state_in], [chatbot, state_in, image_hid, img_name_temp_out, counter_out]) #, queue=True)
+        with gr.Column():
+            image_in = gr.Image(type="pil", label="Original Image")
+            text_in = gr.Textbox()
+            state_in = gr.State()
+            b1 = gr.Button("Edit the image!")
+            with gr.Accordion(
+                "Advance settings for Training and Inference", open=False
+            ):
+                gr.Markdown(
+                    "Advance settings for - Number of Inference steps, Guidanace scale, and Image guidance scale."
+                )
+                in_steps = gr.Number(
+                    label="Enter the number of Inference steps", value=20
+                )
+                in_guidance_scale = gr.Slider(
+                    1, 10, step=0.5, label="Set Guidance scale", value=7.5
+                )
+                in_img_guidance_scale = gr.Slider(
+                    1, 10, step=0.5, label="Set Image Guidance scale", value=1.5
+                )
+                image_hid = gr.Image(type="pil", visible=False)
+                img_name_temp_out = gr.Textbox(visible=False)
+                counter_out = gr.Number(visible=False, value=0, precision=0)
+        chatbot = gr.Chatbot()
+    b1.click(
+        chat,
+        [
+            image_in,
+            in_steps,
+            in_guidance_scale,
+            in_img_guidance_scale,
+            image_hid,
+            img_name_temp_out,
+            counter_out,
+            text_in,
+            state_in,
+        ],
+        [chatbot, state_in, image_hid, img_name_temp_out, counter_out],
+    )  # , queue=True)
     gr.Markdown(help_text)
-    
+
 demo.queue(concurrency_count=10)
-demo.launch(debug=True, width="80%", height=2000)
+demo.launch(debug=True, width="80%", height=2000, share=True)

requirements.txt

@@ -1,4 +1,5 @@
 git+https://github.com/huggingface/diffusers.git
 transformers
 accelerate
-safetensors
+safetensors
+https://huggingface.co/datasets/Narsil/test/resolve/main/xformers-0.0.14.dev0-cp38-cp38-linux_x86_64.whl