update main to disable super-res due to api change

main.py

@@ -3,8 +3,7 @@ import torch
 import pickle
 from torchvision.utils import save_image
 import numpy as np
-from diffusers import StableDiffusionUpscalePipeline
-with open('../concept_checkpoints/augceleba_4838.pkl', 'rb') as f:
+with open('./augceleba_8064.pkl', 'rb') as f:
     G = pickle.load(f)['G_ema'].cpu().float()  # torch.nn.Module
 
 
@@ -44,26 +43,19 @@ cchoices = [
 import requests
 from PIL import Image
 from io import BytesIO
-from diffusers import LDMSuperResolutionPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model_id = "CompVis/ldm-super-resolution-4x-openimages"
 
 # load model and scheduler
-pipeline = LDMSuperResolutionPipeline.from_pretrained(model_id)
-pipeline = pipeline.to(device)
-model_id = "stabilityai/stable-diffusion-x4-upscaler"
-pipeline = StableDiffusionUpscalePipeline.from_pretrained(
-    model_id, variant="fp32", torch_dtype=torch.float32
-)
 # let's download an  image
 
 
-def super_res(low_res_img):
+def super_res(low_res_img, num_steps):
     # run pipeline in inference (sample random noise and denoise)
-    #upscaled_image = pipeline(low_res_img, num_inference_steps=10, eta=1).images[0]
-    upscaled_image = pipeline(prompt="a sharp image of human face", image=low_res_img, num_inference_steps=10).images[0]
+    upscaled_image = pipeline(low_res_img, num_inference_steps=num_steps, eta=1).images[0]
+    #upscaled_image = text_pipeline(prompt="a sharp image of human face", image=low_res_img, num_inference_steps=75).images[0]
     return upscaled_image
 
 
@@ -82,7 +74,7 @@ def generate(seed, *checkboxes):
         elif i == 4:
             checkboxes_vector[cchoices.index('Wavy Hair')] = checkboxes[i]
         elif i == 5:
-            checkboxes_vector[cchoices.index('Young')] = checkboxes[i]
+            checkboxes_vector[cchoices.index('Young')] = checkboxes[i] * 2
         elif i == 6:
             checkboxes_vector[cchoices.index('Male')] = checkboxes[i]
         elif i == 9:
@@ -90,13 +82,13 @@ def generate(seed, *checkboxes):
         elif i == 10:
             checkboxes_vector[cchoices.index('Chubby')] = checkboxes[i]
         elif i == 11:
-            checkboxes_vector[cchoices.index('Eyeglasses')] = checkboxes[i]
+            checkboxes_vector[cchoices.index('Eyeglasses')] = checkboxes[i] * 2
         elif i == 12:
             checkboxes_vector[cchoices.index('Pale Skin')] = checkboxes[i]
         elif i == 13:
             checkboxes_vector[cchoices.index('Smiling')] = checkboxes[i]
         elif i == 14:
-            checkboxes_vector[cchoices.index('Wearing Hat')] = checkboxes[i] * 1.5
+            checkboxes_vector[cchoices.index('Wearing Hat')] = checkboxes[i] * 2
 
 
     is_young = checkboxes[5]
@@ -105,10 +97,10 @@ def generate(seed, *checkboxes):
     is_goatee = checkboxes[7]
     is_mustache = checkboxes[8]
 
-    checkboxes_vector[12] = is_mustache * 1.5
-    checkboxes_vector[13] = is_mustache * 1.5
-    checkboxes_vector[14] = is_goatee *1.5
-    checkboxes_vector[15] = is_goatee*1.5
+    checkboxes_vector[12] = is_mustache * 2
+    checkboxes_vector[13] = is_mustache * 2
+    checkboxes_vector[14] = is_goatee *2
+    checkboxes_vector[15] = is_goatee*2
 
     checkboxes_vector[16] = is_bald
     checkboxes_vector[17] = is_bald
@@ -122,48 +114,54 @@ def generate(seed, *checkboxes):
     m = checkboxes_vector.view(1, 20)
     ws = G.mapping(z, m, truncation_psi=0.5)
     img = (G.synthesis(ws, force_fp32=True).clip(-1,1)+1)/2
-    up_img = np.array(super_res(img))
-    print(img.min(), img.max(), up_img.min(), up_img.max(), ' >>>>>>image sis zee')
-    #return img[0].permute(1, 2, 0).numpy()
-    return up_img
+    if False:
+        up_img = np.array(super_res(img*2-1, upscale_steps))
+        return up_img
+    else:
+        return img[0].permute(1, 2, 0).numpy()
 
 
 # Create the interface using gr.Blocks
 with gr.Blocks() as demo:
     with gr.Row():
-        sliders = [
-            gr.Slider(label='Bald', minimum=0, maximum=1, step=0.01),
-            gr.Slider(label='Black Hair', minimum=0, maximum=1, step=0.01),
-            gr.Slider(label='Blond Hair', minimum=0, maximum=1, step=0.01),
-            gr.Slider(label='Straight Hair', minimum=0, maximum=1, step=0.01),
-            gr.Slider(label='Wavy Hair', minimum=0, maximum=1, step=0.01),
-        ]
+        slider1 = gr.Slider(label='Not Bald          <-------------->  Bald', minimum=0, maximum=1, step=0.01)
+        slider2 = gr.Slider(label='No Black Hair     <-------->  Black Hair', minimum=0, maximum=1, step=0.01)
+        slider3 = gr.Slider(label='No Blond Hair     <-------->  Blond Hair', minimum=0, maximum=1, step=0.01)
+        slider4 = gr.Slider(label='No Straight Hair  <----->  Straight Hair', minimum=0, maximum=1, step=0.01)
+        slider5 = gr.Slider(label='No Wavy Hair      <------->  Wavy Hair', minimum=0, maximum=1, step=0.01)
+        sliders = [ slider1, slider2, slider3, slider4, slider5]
 
     with gr.Row():
-        sliders += [gr.Slider(label='Young', minimum=0, maximum=1, step=0.01)]
-        sliders += [gr.Slider(label='Male', minimum=0, maximum=1, step=0.01)]
+        sliders += [gr.Slider(label='Old            <--------------> Young', minimum=0, maximum=1, step=0.01)]
+        sliders += [gr.Slider(label='Female         <--------------> Male', minimum=0, maximum=1, step=0.01)]
 
     with gr.Row():
-        sliders += [gr.Slider(label='Goatee', minimum=0, maximum=1, step=0.01)]
-        sliders += [gr.Slider(label='Mustache', minimum=0, maximum=1, step=0.01)]
+        sliders += [gr.Slider(label='No Goatee         <--------------> Goatee', minimum=0, maximum=1, step=0.01)]
+        sliders += [gr.Slider(label='No Mustache       <--------------> Mustache', minimum=0, maximum=1, step=0.01)]
 
     with gr.Row():
         sliders += [
-            gr.Slider(label='Big Nose', minimum=0, maximum=1, step=0.01),
-            gr.Slider(label='Chubby', minimum=0, maximum=1, step=0.01),
-            gr.Slider(label='Eyeglasses', minimum=0, maximum=1, step=0.01),
-            gr.Slider(label='Pale Skin', minimum=0, maximum=1, step=0.01),
-            gr.Slider(label='Smiling', minimum=0, maximum=1, step=0.01),
-            gr.Slider(label='Wearing Hat', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Small Nose     <-------> Big Nose', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Slim     <-------->  Chubby', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='No Eyeglasses     <--------> Eyeglasses', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Tan Skin     <-------> Pale Skin', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='Not Smiling      <---------> Smiling', minimum=0, maximum=1, step=0.01),
+            gr.Slider(label='No Hat   <---------> Wearing Hat', minimum=0, maximum=1, step=0.01),
         ]
 
-    seed_input = gr.Number(label="Seed")
+    seed_input = gr.Number(label="Seed", value=6)
+    upscale_funcs = []
+    #with gr.Row():
+    #    upscale_funcs = [gr.Checkbox(label="Upscale 4x")]
+    #    upscale_funcs += [gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=10)]
     generate_button = gr.Button("Generate")
 
     output_image = gr.Image(label="Generated Image")
+    for slider in sliders:
+        slider.change(fn=generate, inputs=[seed_input] + sliders, outputs=output_image)
 
     # Set the action for the button
-    generate_button.click(fn=generate, inputs=[seed_input] + sliders, outputs=output_image)
+    generate_button.click(fn=generate, inputs=[seed_input] +sliders, outputs=output_image)
 
 # Launch the demo
 demo.launch()