add 2 scale

app.py

@@ -224,12 +224,24 @@ def split_image(im, rows, cols, should_square, should_quiet=False):
             n += 1
     return [img for img in images]
 
-def upscale_image(img, rows, cols, seed, prompt, negative_prompt, xformers, cpu_offload, attention_slicing, enable_custom_sliders=False, guidance=7, iterations=50):
-    model_id = "stabilityai/stable-diffusion-x4-upscaler"
-    try:
-        pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-    except:
-        pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16, local_files_only=True)
+def upscale_image(img, rows, up_factor, cols, seed, prompt, negative_prompt, xformers, cpu_offload, attention_slicing, enable_custom_sliders=False, guidance=7, iterations=50):
+    
+    if up_factor==2:
+        model_id = "stabilityai/sd-x2-latent-upscaler"
+        try:
+            pipeline = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+        except:
+            pipeline = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16, local_files_only=True)
+
+    if up_factor==4:
+        model_id = "stabilityai/stable-diffusion-x4-upscaler"
+
+        try:
+            pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+        except:
+            pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16, local_files_only=True)            
+
+
     pipeline = pipeline.to("cuda")
     if xformers:
         pipeline.enable_xformers_memory_efficient_attention()
@@ -306,26 +318,27 @@ def upscale_image(img, rows, cols, seed, prompt, negative_prompt, xformers, cpu_
     return final_img
 
     
-def upscale( image, prompt, negative_prompt, rows, guidance, iterations, xformers_input, cpu_offload_input, attention_slicing_input):
-    print("upscale", image, prompt, negative_prompt, rows, guidance, iterations, xformers_input, cpu_offload_input, attention_slicing_input)   
-    # return upscale1(image, prompt)
-    return upscale_image(image,
+def upscale( image, prompt, negative_prompt, rows, up_factor, guidance, iterations, xformers_input, cpu_offload_input, attention_slicing_input):
+    print("upscale", prompt, negative_prompt, rows, up_factor, guidance, iterations, xformers_input, cpu_offload_input, attention_slicing_input)   
+    return upscale_image(img=image, 
                          rows=rows,cols=rows,
+                         up_factor=up_factor,
                          seed=-1,
                          prompt=prompt,
-                         guidance=guidance,
-                         negative_prompt=negative_prompt,
-                         xformers=xformers_input,
-                         cpu_offload=cpu_offload_input,
+                         negative_prompt=negative_prompt, 
+                         enable_custom_sliders=True,
+                         xformers=xformers_input, 
+                         cpu_offload=cpu_offload_input, 
                          attention_slicing=attention_slicing_input,
+                         guidance=guidance,
                          iterations=iterations)
 
-modes = {
-    '1': '1',
-    'img2img': 'Image to Image',
-    'inpaint': 'Inpainting',
-    'upscale4x': 'Upscale 4x',
-}
+# modes = {
+#     '1': '1',
+#     'img2img': 'Image to Image',
+#     'inpaint': 'Inpainting',
+#     'upscale4x': 'Upscale 4x',
+# }
 
 
 
@@ -371,8 +384,9 @@ with gr.Blocks() as app:
                     gr.Textbox(label="prompt",value="empty room"),
                     gr.Textbox(label="negative prompt",value="jpeg artifacts, lowres, bad quality, watermark, text"),
                     gr.Number(value=2, label="Tile grid dimension amount (number of rows and columns) - X by X "),
+                    gr.Slider(2, 4, 2, step=2, label='Upscale 2 or 4'),
                     gr.Slider(2, 15, 7, step=1, label='Guidance Scale: How much the AI influences the Upscaling.'),
-                    gr.Slider(10, 100, 10, step=1, label='Number of Iterations'),
+                    gr.Slider(2, 100, 10, step=1, label='Number of Iterations'),
                     gr.Checkbox(value=True,label="Enable Xformers memory efficient attention"),                    
                     gr.Checkbox(value=True,label="Enable sequential CPU offload"),
                     gr.Checkbox(value=True,label="Enable attention slicing")
@@ -380,7 +394,7 @@ with gr.Blocks() as app:
                 outputs=gr.Image())
 
 
-# app.queue()
-app.launch(debug=True,share=True, height=768)
+app.queue()
+app.launch()
 
 # UP 1