Update app.py

app.py

@@ -389,6 +389,82 @@ def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
         f.write(f"Model VAE: sdxl-vae-bf16\n")
         f.write(f"To cuda and bfloat \n")
     return filename
+
+import spaces
+import torch.nn.functional as F
+from sageattention import sageattn
+import random
+import uuid
+#import gradio as gr
+import numpy as np
+from PIL import Image
+
+import diffusers
+#from diffusers import AutoencoderKL, StableDiffusionXLPipeline
+#from diffusers import EulerAncestralDiscreteScheduler
+#from typing import Tuple
+#import paramiko
+import datetime
+#import cyper
+from image_gen_aux import UpscaleWithModel
+#import torch
+import time 
+import gc
+
+
+@spaces.GPU(duration=40)
+def generate_30c(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    style_selection: str = "",
+    width: int = 768,
+    height: int = 768,
+    guidance_scale: float = 4,
+    num_inference_steps: int = 125,
+    sage: bool = False,
+    use_resolution_binning: bool = True, 
+    progress=gr.Progress(track_tqdm=True)
+):
+    if sage==True:
+        F.scaled_dot_product_attention = sageattn
+    if sage==False:
+        F.scaled_dot_product_attention = F.scaled_dot_product_attention
+    seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device='cuda').manual_seed(seed)
+    options = {
+        "prompt": [prompt],
+        "negative_prompt": [negative_prompt],
+        "negative_prompt_2": [neg_prompt_2],
+        "width": width,
+        "height": height,
+        "guidance_scale": guidance_scale,
+        "num_inference_steps": num_inference_steps,
+        "generator": generator,
+        "output_type": "pil",
+        "callback_on_step_end": pyx.scheduler_swap_callback,
+    }
+    if use_resolution_binning:
+        options["use_resolution_binning"] = True
+    images = []
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = pyx.uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
+    pyx.upload_to_ftp(filename) 
+    batch_options = options.copy()
+    rv_image = pipe(**batch_options).images[0]
+    sd_image_path = f"rv_C_{timestamp}.png"
+    rv_image.save(sd_image_path,optimize=False,compress_level=0)
+    pyx.upload_to_ftp(sd_image_path)    
+    torch.set_float32_matmul_precision("medium")
+    with torch.no_grad():
+        upscale = upscaler(rv_image, tiling=True, tile_width=256, tile_height=256)
+    downscale1 = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
+    downscale_path = f"rv50_upscale_{timestamp}.png"
+    downscale1.save(downscale_path,optimize=False,compress_level=0)
+    pyx.upload_to_ftp(downscale_path) 
+    unique_name = str(uuid.uuid4()) + ".png"  
+    os.symlink(sd_image_path, unique_name)  
+    return [unique_name]
     
 '''
 pyx = cyper.inline(code, fast_indexing=True, directives=dict(boundscheck=False, wraparound=False, language_level=3))
@@ -583,6 +659,7 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
             placeholder="Enter your prompt",
             container=False,
         )
+        run_button_30c = gr.Button("Run 30 Seconds", scale=0)
         run_button_30 = gr.Button("Run 30 Seconds", scale=0)
         run_button_60 = gr.Button("Run 60 Seconds", scale=0)
         run_button_90 = gr.Button("Run 90 Seconds", scale=0)
@@ -662,6 +739,26 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
         api_name=False,
     )
     
+    gr.on(
+        triggers=[
+            run_button_30c.click,
+        ],
+      #  api_name="generate",  # Add this line
+        fn=generate_30c,
+        inputs=[
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            style_selection,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            sage,
+        ],
+        outputs=[result],
+    )
+    
     gr.on(
         triggers=[
             run_button_30.click,