merge changes

readme.md

@@ -1,3 +1,13 @@
-# Welcome to Banodoco
+# Welcome to Banodoco v. 0.5
 
-Coming soon.
+Banodoco v. 0.5 is intended to act as a demo of one approach to building a creative tool on top of open-source AI models - empowering users to create videos via creative interpolation using [Steerable Motion](https://github.com/banodoco/steerable-motion) to fill in the gaps between generated keyframes:
+
+<img src="sample_assets/sample_images/main_example.gif">
+
+## Access the web app or contribute to the project
+
+The web app is currently in beta. If you want access it, please reach out to POM in our [Discord](https://discord.com/invite/8Wx9dFu5tP) with some examples of stuff you've made before. If you'd like to contribute, please also get in touch with examples of previous projects.
+
+## Coming soon - local inference
+
+We're working on a local inference version of Banodoco, which will be available soon. This will allow you to run the app locally on your own machine, without needing to pay for cloud compute.

ui_components/widgets/animation_style_element.py

@@ -65,7 +65,7 @@ def animation_style_element(shot_uuid):
         footer1, _ = st.columns([2, 1])
         with footer1:
             interpolation_style = 'ease-in-out'
-            motion_scale = st_memory.slider("Motion scale:", min_value=0.0, max_value=2.0, value=1.0, step=0.1, key="motion_scale")
+            motion_scale = st_memory.slider("Motion scale:", min_value=0.0, max_value=2.0, value=1.0, step=0.01, key="motion_scale")
 
         st.markdown("***")
         if st.button("Reset to default settings", key="reset_animation_style"):
@@ -288,6 +288,7 @@ def plot_weights(weights_list, frame_numbers_list, frame_names):
         relative_ipadapter_strength = st_memory.slider("How much would you like to influence the style?", min_value=0.0, max_value=5.0, value=1.1, step=0.1, key="ip_adapter_strength")
         relative_ipadapter_influence = st_memory.slider("For how long would you like to influence the style?", min_value=0.0, max_value=5.0, value=1.1, step=0.1, key="ip_adapter_influence")
         soft_scaled_cn_weights_multipler = st_memory.slider("How much would you like to scale the CN weights?", min_value=0.0, max_value=10.0, value=0.85, step=0.1, key="soft_scaled_cn_weights_multiple_video")
+        append_to_prompt = st_memory.text_input("What would you like to append to the prompts?", key="append_to_prompt")
 
     normalise_speed = True
 
@@ -338,8 +339,9 @@ def plot_weights(weights_list, frame_numbers_list, frame_names):
                     if timing.primary_image and timing.primary_image.location:
                         b = timing.primary_image.inference_params
                         prompt = b['prompt'] if b else ""
+                        prompt += append_to_prompt  # Appending the text to each prompt
                         frame_prompt = f"{idx * linear_frame_distribution_value}_" + prompt
-                        positive_prompt +=  ":" + frame_prompt if positive_prompt else frame_prompt
+                        positive_prompt += ":" + frame_prompt if positive_prompt else frame_prompt
                     else:
                         st.error("Please generate primary images")
                         time.sleep(0.7)
@@ -540,7 +542,7 @@ def update_interpolation_settings(values=None, timing_list=None):
     }
 
     for idx in range(0, len(timing_list)):
-        default_values[f'dynamic_frame_distribution_values_{idx}'] = (idx ) * 16
+        default_values[f'dynamic_frame_distribution_values_{idx}'] = (idx) * 16
         default_values[f'dynamic_key_frame_influence_values_{idx}'] = 1.0
         default_values[f'dynamic_cn_strength_values_{idx}'] = (0.0,0.7)