Update app.py

app.py

@@ -11,7 +11,7 @@ from diffusers import AutoPipelineForImage2Image
 from diffusers.utils import make_image_grid, load_image
 import uuid
 
-base_url=f'https://omnibus-top-20-img-img.hf.space/file='
+base_url=f'https://omnibus-top-20-img-img-tint.hf.space/file='
 loaded_model=[]
 for i,model in enumerate(models):
     try:
@@ -21,8 +21,8 @@ for i,model in enumerate(models):
         pass
 print (loaded_model)
 
-pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None, variant="fp16", use_safetensors=True).to("cpu")
-pipeline.unet = torch.compile(pipeline.unet)
+#pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None, variant="fp16", use_safetensors=True).to("cpu")
+#pipeline.unet = torch.compile(pipeline.unet)
 
 grid_wide=10
 
@@ -63,6 +63,76 @@ def get_concat_v_cut(in1, in2):
 def load_model(model_drop):
     pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float32, use_safetensors=True)
 
+
+
+
+def run_dif_color(out_prompt,im_path,model_drop,tint,im_height,im_width):
+    p_seed=""
+    out_box=[]
+    out_html=""
+    for i,ea in enumerate(im_path.root):
+        print(f'root::{im_path.root[i]}')
+        #print(f'ea:: {ea}')        
+        #print(f'impath:: {im_path.path}')
+        url = base_url+im_path.root[i].image.path
+        myimg = cv2.imread(im_path.root[i].image.path)
+        avg_color_per_row = numpy.average(myimg, axis=0)
+        avg_color = numpy.average(avg_color_per_row, axis=0)
+        #print(avg_color)
+        
+        #h=color.lstrip('#')
+        #h = input('Enter hex: ').lstrip('#')
+        #print('RGB =', tuple(int(h[i:i+2], 16) for i in (0, 2, 4)))
+        #color=tuple(int(h[i:i+2], 16) for i in (0, 2, 4))
+        r,g,b= avg_color
+        
+        color = (int(r),int(g),int(b))
+    
+        
+        print (color)
+
+        #for i,ea in enumerate(loaded_model):
+    
+    
+    
+        #for i in range(int(cnt)):
+        rand=random.randint(1,500)
+        for i in range(rand):
+            p_seed+=" "
+        try:
+            #model=gr.load(f'models/{model[int(model_drop)]}')        
+            model=loaded_model[int(model_drop)]
+            out_img=model(out_prompt+p_seed)
+            print(out_img)
+
+            raw=Image.open(out_img)
+            raw=raw.convert('RGB')
+
+            colorize = RGBTransform().mix_with(color,factor=float(tint)).applied_to(raw)    
+            
+            out_box.append(colorize)           
+        except Exception as e:
+            print(e)
+            out_html=str(e)
+            pass
+            
+        yield out_box,out_html
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
 def run_dif(prompt,im_path,model_drop,cnt,strength,guidance,infer,im_height,im_width):
     uid=uuid.uuid4()
     print(f'im_path:: {im_path}')
@@ -251,7 +321,8 @@ with gr.Blocks(css=css) as app:
             strength=gr.Slider(label="Strength",minimum=0,maximum=1,step=0.1,value=0.2)
             guidance=gr.Slider(label="Guidance",minimum=0,maximum=10,step=0.1,value=8.0)
             infer=gr.Slider(label="Inference Steps",minimum=0,maximum=50,step=1,value=10)
-            
+            tint = gr.Slider(label="Tint Strength", minimum=0, maximum=1, step=0.01, value=0.30)
+
             with gr.Row():
                 btn=gr.Button()
                 stop_btn=gr.Button("Stop")
@@ -271,6 +342,9 @@ with gr.Blocks(css=css) as app:
     
     im_list=gr.Textbox(visible=False)    
     im_btn.click(load_im,inp_im,[outp,im_list,im_height,im_width])
-    go_btn = btn.click(run_dif,[inp,outp,model_drop,cnt,strength,guidance,infer,im_height,im_width],[fin,out_html])
+
+    go_btn=btn.click(run_dif_color,[inp,outp,model_drop,tint,im_height,im_width],[fingal,out_html])
+    
+    #go_btn = btn.click(run_dif_color,[inp,outp,model_drop,cnt,strength,guidance,infer,im_height,im_width],[fin,out_html])
     stop_btn.click(None,None,None,cancels=[go_btn])
 app.queue().launch()