Update app.py

app.py

@@ -1,4 +1,27 @@
 from __future__ import annotations
+import os
+import zipfile
+from os.path import basename
+
+import numpy as np
+from PIL import Image
+import matplotlib
+
+from pipeline import Model_GAN, noise
+
+matplotlib.use('Agg')
+from math import floor
+from io import BytesIO
+import base64
+import tempfile
+
+from keras.layers import Conv2D, LeakyReLU, BatchNormalization, Dense, AveragePooling2D, GaussianNoise
+from keras.layers import Reshape, UpSampling2D, Activation, Dropout, Flatten, Conv2DTranspose
+from keras.models import model_from_json, Sequential
+from keras.optimizers import Adam
+
+import cv2
+
 
 import math
 import random
@@ -8,12 +31,9 @@ import torch
 from PIL import Image, ImageOps
 from diffusers import StableDiffusionPipeline
 
-
 help_text = """
-
 """
 
-
 example_instructions = [
     "A river"
 ]
@@ -23,15 +43,16 @@ model_id = "dimentox/heightmapstyle"
 
 def main():
     pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)
-    #example_image = Image.open("imgs/example.jpg").convert("RGB")
+
+    # example_image = Image.open("imgs/example.jpg").convert("RGB")
 
     def load_example(
-        steps: int,
-        randomize_seed: bool,
-        seed: int,
-        randomize_cfg: bool,
-        text_cfg_scale: float,
-        image_cfg_scale: float,
+            steps: int,
+            randomize_seed: bool,
+            seed: int,
+            randomize_cfg: bool,
+            text_cfg_scale: float,
+            image_cfg_scale: float,
     ):
         example_instruction = random.choice(example_instructions)
         return [example_instruction] + generate(
@@ -45,27 +66,27 @@ def main():
         )
 
     def generate(
-        instruction: str,
-        steps: int,
-        randomize_seed: bool,
-        seed: int,
-        randomize_cfg: bool,
-        text_cfg_scale: float,
-        image_cfg_scale: float,
+            instruction: str,
+            steps: int,
+            randomize_seed: bool,
+            seed: int,
+            randomize_cfg: bool,
+            text_cfg_scale: float,
+            image_cfg_scale: float,
     ):
         seed = random.randint(0, 100000) if randomize_seed else seed
         text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
         image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
 
-        width, height = input_image.size
-        factor = 512 / max(width, height)
-        factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
-        width = int((width * factor) // 64) * 64
-        height = int((height * factor) // 64) * 64
-        input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
+        # width, height = input_image.size
+        # factor = 512 / max(width, height)
+        # factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
+        # width = int((width * factor) // 64) * 64
+        # height = int((height * factor) // 64) * 64
+        # input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
 
         if instruction == "":
-            return [input_image, seed]
+            return [seed]
 
         generator = torch.manual_seed(seed)
         edited_image = pipe(
@@ -80,7 +101,7 @@ def main():
 
     with gr.Blocks() as demo:
         gr.HTML("""
-        
+
         """)
         with gr.Row():
             with gr.Column(scale=1, min_width=100):
@@ -92,7 +113,6 @@ def main():
             with gr.Column(scale=3):
                 instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
 
-
         with gr.Row():
             steps = gr.Number(value=50, precision=0, label="Steps", interactive=True)
             randomize_seed = gr.Radio(
@@ -125,7 +145,7 @@ def main():
                 text_cfg_scale,
                 image_cfg_scale,
             ],
-            outputs=[ instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
+            outputs=[instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
         )
         generate_button.click(
             fn=generate,
@@ -154,12 +174,12 @@ def main():
 if __name__ == "__main__":
     main()
 
-
-
 import gradio as gr
+
 gr.Examples(
-        [["heightmapsstyle", "a lake with a river"], ["heightmapsstyle","greyscale", "a river running though flat planes"]],
-        [txt, txt_2],
-        cache_examples=True,
-    )
-gr.load().launch()
+    [["heightmapsstyle", "a lake with a river"],
+     ["heightmapsstyle", "greyscale", "a river running though flat planes"]],
+    [txt, txt_2],
+    cache_examples=True,
+)
+gr.load().launch()