animation

ImageState.py

@@ -41,7 +41,7 @@ class ImageState:
         self.transform_history = []
         self.attn_mask = None
         self.prompt_optim = prompt_optimizer
-        self.state_id = "./" + str(uuid.uuid4())
+        self.state_id = "./img_history"
         print("NEW INSTANCE")
         print(self.state_id)
         self._load_vectors()
@@ -91,7 +91,6 @@ class ImageState:
     # def _get_current_vector_transforms(self):
     #     current_vector_transforms = (self.blue_eyes, self.lip_size, self.hair_gp, self.asian_transform, sum(self.current_prompt_transforms))
     #     return (self.blend_latent, current_vector_transforms)
-    # @cache
     def _get_mask(self, img, mask=None):
         if img and "mask" in img and img["mask"] is not None:
             attn_mask = torchvision.transforms.ToTensor()(img["mask"])
@@ -156,8 +155,7 @@ class ImageState:
         if path1 is None: path1 = path2
         if path2 is None: path2 = path1
         self.path1, self.path2 = path1, path2
-        # self.aligned_path1 = align_from_path(path1)
-        # self.aligned_path2 = align_from_path(path2)
+        clear_img_dir(self.state_id)
         return self.blend(blend_weight)
     @torch.no_grad()
     def blend(self, weight):

app.py

@@ -47,6 +47,7 @@ class StateWrapper:
     def apply_lip_vector(state, *args, **kwargs):
         return state, *state[0].apply_lip_vector(*args, **kwargs)
     def apply_prompts(state, *args, **kwargs):
+        print(state[1])
         for image in state[0].apply_prompts(*args, **kwargs):
             yield state, *image
     def apply_rb_vector(state, *args, **kwargs):
@@ -69,11 +70,16 @@ class StateWrapper:
         return state, *state[0].update_images(*args, **kwargs)
     def update_requant(state, *args, **kwargs):
         return state, *state[0].update_requant(*args, **kwargs)
-    
+def ret_id(id):
+    print(id)
+    return(id)
 with gr.Blocks(css="styles.css") as demo:
-    state = gr.State([ImageState(vqgan, promptoptim)])
+    id = gr.State(str(uuid.uuid4()))
+    state = gr.State([ImageState(vqgan, promptoptim), str(uuid.uuid4())])
     with gr.Row():
         with gr.Column(scale=1):
+            x = gr.Button(label="asd")
+            x.click(ret_id, inputs=id, outputs=id)
             blue_eyes = gr.Slider(
                 label="Blue Eyes",
                 minimum=-.8,
@@ -192,7 +198,7 @@ with gr.Blocks(css="styles.css") as demo:
                                             maximum=50,
                                             value=3,
                                             step=1,
-                                            label="Steps to run at the end of the optimization, optimizing only the masked perceptual loss. If the edit is changing the identity too much, this setting will run steps at the end that will 'pull' the image back towards the original identity")
+                                            label="Steps to run at the end of the optimization, optimizing only the masked perceptual loss. If the edit is changing the identity too much, this setting will run steps at the end that 'pull' the image back towards the original identity")
                     # discriminator_steps = gr.Slider(minimum=0,
                     #                         maximum=50,
                     #                         step=1,

configs.py

@@ -1,7 +1,7 @@
 import gradio as gr
 def set_small_local():
-    return (gr.Slider.update(value=25), gr.Slider.update(value=0.15), gr.Slider.update(value=1), gr.Slider.update(value=4))
+    return (gr.Slider.update(value=25), gr.Slider.update(value=0.15), gr.Slider.update(value=5), gr.Slider.update(value=4))
 def set_major_local():
-    return (gr.Slider.update(value=25), gr.Slider.update(value=0.2), gr.Slider.update(value=36.6), gr.Slider.update(value=6))
+    return (gr.Slider.update(value=25), gr.Slider.update(value=0.187), gr.Slider.update(value=36.6), gr.Slider.update(value=6))
 def set_major_global():
     return (gr.Slider.update(value=30), gr.Slider.update(value=0.1), gr.Slider.update(value=1), gr.Slider.update(value=1))