Update app.py

app.py

@@ -24,6 +24,7 @@ def generate(region1_concept,
                 region_neg_prompt,
                 seed,
                 randomize_seed,
+                alpha,
                 sketch_adaptor_weight,
                 keypose_adaptor_weight
                 ):
@@ -41,7 +42,7 @@ def generate(region1_concept,
         seed = random.randint(0, MAX_SEED)
 
     region1_concept, region2_concept = region1_concept.lower(), region2_concept.lower()
-    pretrained_model = merge(region1_concept, region2_concept)
+    pretrained_model = merge(region1_concept, region2_concept, alpha)
 
     with open('multi-concept/pose_data/pose.json') as f:
         d = json.load(f)
@@ -53,12 +54,12 @@ def generate(region1_concept,
     region1 = pose_image['region1']
     region2 = pose_image['region2']
 
-    region_pos_prompt = "high resolution, best quality, highly detailed, sharp focus, expressive, 8k uhd, detailed, sophisticated"
+    region_pos_prompt = "high resolution, best quality, highly detailed, sharp focus, sophisticated, Good anatomy, Clear facial features, Proportional body, Detailed clothing, Smooth textures"
     region1_prompt = f'<{region1_concept}1> <{region1_concept}2>, {region1_prompt}, {region_pos_prompt}'
     region2_prompt = f'<{region2_concept}1> <{region2_concept}2>, {region2_prompt}, {region_pos_prompt}'
     prompt_rewrite=f"{region1_prompt}-*-{region_neg_prompt}-*-{region1}|{region2_prompt}-*-{region_neg_prompt}-*-{region2}"
     print(prompt_rewrite)
-    prompt+=", Disney style photo, High resolution"
+    prompt+=", Disney style photo, High resolution, best quality, highly detailed, expressive,"
 
     result = infer(pretrained_model,
                   prompt,
@@ -73,13 +74,13 @@ def generate(region1_concept,
     
     return result
 
-def merge(concept1, concept2):
+def merge(concept1, concept2, alpha):
     device = "cuda" if torch.cuda.is_available() else "cpu"
     c1, c2 = sorted([concept1, concept2])
     assert c1!=c2
     merge_name = c1+'_'+c2
 
-    save_path = f'experiments/multi-concept/{merge_name}'
+    save_path = f'experiments/multi-concept/{merge_name}--{int(alpha*10)}'
 
     if os.path.isdir(save_path):
         print(f'{save_path} already exists. Collecting merged weights from existing weights...')
@@ -87,7 +88,7 @@ def merge(concept1, concept2):
     else:
         os.makedirs(save_path)
         json_path = os.path.join(save_path,'merge_config.json')
-        alpha = 1.8
+        # alpha = 1.8
         data = [
             {
                 "lora_path": f"experiments/single-concept/{c1}/models/edlora_model-latest.pth",
@@ -209,8 +210,8 @@ css="""
 with gr.Blocks(css=css) as demo:
     gr.Markdown(f"""
     # Orthogonal Adaptation
-    Describe your world with a **🪄 text prompt (global and local)** and choose two characters to merge. 
-    Select their **👯 poses (spatial conditions)** for regionally controllable sampling to generate a unique image using our model.
+    Describe your world with a ** [🪄 Text Prompts] **(global and regional prompts) and choose two characters to merge. 
+    Select their ** [ 👯 Poses ] **(spatial conditions) for regionally controllable sampling to generate a unique image using our model.
     Let your creativity run wild! (Currently running on : {power_device} )    
     """)
     
@@ -221,7 +222,7 @@ with gr.Blocks(css=css) as demo:
             # ### 🪄 Global and Region prompts
             # """)
             # with gr.Group():   
-            with gr.Tab('🪄 Global and Region prompts'):
+            with gr.Tab('🪄 Text Prompts'):
                 prompt = gr.Text(
                         label="ContextPrompt",
                         show_label=False,
@@ -289,7 +290,7 @@ with gr.Blocks(css=css) as demo:
             # ### 👯 Spatial Condition  
             # """)
             # with gr.Group():
-            with gr.Tab('👯 Spatial Condition '):
+            with gr.Tab('👯 Poses '):
                 gallery = gr.Gallery(label = "Select pose for characters",
                                     value = [obj[1]for obj in pose_image_list], 
                                     elem_id = [obj[0]for obj in pose_image_list],
@@ -326,6 +327,14 @@ with gr.Blocks(css=css) as demo:
                 )
                 
                 randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+                alpha = gr.Slider(
+                    label="Merge Weight",
+                    minimum=1.2,
+                    maximum=2.0,
+                    step=0.1,
+                    value=1.8,
+                )
                 
                 with gr.Row():
                     
@@ -367,6 +376,7 @@ with gr.Blocks(css=css) as demo:
                   randomize_seed,
                 #   sketch_condition,
                 #   keypose_condition,
+                  alpha,
                   sketch_adaptor_weight,
                   keypose_adaptor_weight
                   ],