Update app.py

app.py

@@ -236,21 +236,24 @@ def inference(source_prompt, target_prompt, source_guidance_scale=1, guidance_sc
     img = img.resize((int(img.width * ratio), int(img.height * ratio)))
 
     # create the CAC controller.
-    assert cross_attention_control in ['Replace', 'Refine', "None"]
-    if cross_attention_control == "replace":
+    if cross_attention_control == "Replace":
         controller = AttentionReplace([source_prompt, target_prompt],
                                       num_inference_steps,
                                       cross_replace_steps=cross_replace_steps,
                                       self_replace_steps=self_replace_steps,
                                       )
         ptp_utils.register_attention_control(pipe, controller)
-    elif cross_attention_control == "refine":
+    elif cross_attention_control == "Refine":
         controller = AttentionRefine([source_prompt, target_prompt],
                                      num_inference_steps,
                                      cross_replace_steps=cross_replace_steps,
                                      self_replace_steps=self_replace_steps,
                                      )
         ptp_utils.register_attention_control(pipe, controller)
+    elif cross_attention_control == "None":
+        pass
+    else:
+        raise ValueError("Unknown cross_attention_control: {}".format(cross_attention_control))
 
     results = pipe(prompt=target_prompt,
                    source_prompt=source_prompt,
@@ -286,7 +289,24 @@ with gr.Blocks(css=css) as demo:
                 CycleDiffusion (<a href="https://arxiv.org/abs/2210.05559">📄 Paper link</a> | <a href="https://huggingface.co/docs/diffusers/main/en/api/pipelines/cycle_diffusion">🧨 Pipeline doc</a>) is an image-to-image translation method that supports stochastic samplers for diffusion models. <br>
                 It also supports Cross Attention Control (<a href="https://arxiv.org/abs/2208.01626">📄 Paper link</a>), which is a technique to transfer the attention map from the source prompt to the target prompt. <br>
               </p>
-              <p>You can skip the queue in the colab: <a href="https://colab.research.google.com/gist/ChenWu98/0aa4fe7be80f6b45d3d055df9f14353a/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p>
+              <p>
+                <b>How to use:</b> <br>
+                1. Upload an image. <br>
+                2. Enter the source and target prompts. <br>
+                3. Select the source guidance scale (for "encoding") and the target guidance scale (for "decoding"). <br>
+                4. Select the strength (smaller strength means better content preservation). <br>
+                5 (optional). Configurate Cross Attention Control options (e.g., CAC type, cross replace steps, self replace steps). <br>
+                6 (optional). Configurate other options (e.g., image size, inference steps, random seed). <br>
+                7. Click the "Edit" button. <br>
+              </p>
+              <p>
+                <b>Notes:</b> <br>
+                1. CycleDiffusion is likely to fail when drastic changes are intended (e.g., changing a large black car to red). <br>
+                2. The value of strength can be set larger when CAC is used. <br>
+                3. If CAC type is "Replace", the source and target prompts should differ in only one token; otherwise, an error will be raised. <br>
+                4. If CAC type is "Refine", the source prompt be a subsequence of the target prompt; otherwise, an error will be raised. <br>
+              </p>
+              <p>You can skip the queue using Colab: <a href="https://colab.research.google.com/gist/ChenWu98/0aa4fe7be80f6b45d3d055df9f14353a/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p>
                Running on <b>{device_print}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
               </p>
             </div>
@@ -317,17 +337,6 @@ with gr.Blocks(css=css) as demo:
                         strength = gr.Slider(label="Strength", value=0.7, minimum=0.5, maximum=1, step=0.01)
                     with gr.Row():
                         generate1 = gr.Button(value="Edit")
-            with gr.Tab("Basic options"):
-                with gr.Group():
-                    with gr.Row():
-                        num_inference_steps = gr.Slider(label="Number of inference steps", value=100, minimum=25, maximum=500, step=1)
-                        width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
-                        height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
-                    with gr.Row():
-                        seed = gr.Slider(0, 2147483647, label='Seed', value=0, step=1)
-                    with gr.Row():
-                        generate2 = gr.Button(value="Edit")
 
             with gr.Tab("CAC options"):
                 with gr.Group():
@@ -337,6 +346,18 @@ with gr.Blocks(css=css) as demo:
                         # If not "None", the following two parameters will be used.
                         cross_replace_steps = gr.Slider(label="Cross replace steps", value=0.8, minimum=0.0, maximum=1, step=0.01)
                         self_replace_steps = gr.Slider(label="Self replace steps", value=0.4, minimum=0.0, maximum=1, step=0.01)
+                    with gr.Row():
+                        generate2 = gr.Button(value="Edit")
+
+            with gr.Tab("Other options"):
+                with gr.Group():
+                    with gr.Row():
+                        num_inference_steps = gr.Slider(label="Number of inference steps", value=100, minimum=25, maximum=500, step=1)
+                        width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
+                        height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
+
+                    with gr.Row():
+                        seed = gr.Slider(0, 2147483647, label='Seed', value=0, step=1)
                     with gr.Row():
                         generate3 = gr.Button(value="Edit")