second prompt

app.py

@@ -3,7 +3,7 @@ import numpy as np
 
 import spaces
 import random
-from image_gen_aux import DepthPreprocessor
+# from image_gen_aux import DepthPreprocessor
 from PIL import Image
 import torch
 from torchvision import transforms
@@ -19,8 +19,8 @@ pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", tor
 pipe.load_lora_weights("alvdansen/flux-koda")
 pipe.enable_lora()
 
-vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae")
-processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
+# vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae")
+# processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
 
 preprocess = transforms.Compose(
     [
@@ -80,7 +80,7 @@ def calculate_optimal_dimensions(image: Image.Image):
     return width, height
 
 @spaces.GPU(durations=300)
-def infer(edit_images, prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+def infer(edit_images, prompt, prompt_2, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
     # pipe.enable_xformers_memory_efficient_attention()
 
     image = edit_images["background"]
@@ -89,10 +89,11 @@ def infer(edit_images, prompt, seed=42, randomize_seed=False, width=1024, height
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    controlImage = processor(image)
+    # controlImage = processor(image)
     image = pipe(
-        mask_image_latent=vae.encode(controlImage),
+        # mask_image_latent=vae.encode(controlImage),
         prompt=prompt,
+        prompt_2=prompt_2,
         image=image,
         mask_image=mask,
         height=height,
@@ -146,6 +147,13 @@ with gr.Blocks(css=css) as demo:
                     placeholder="Enter your prompt",
                     container=False,
                 )
+                prompt = gr.Text(
+                    label="Prompt2",
+                    show_label=False,
+                    max_lines=2,
+                    placeholder="Enter your second prompt",
+                    container=False,
+                )
                 run_button = gr.Button("Run")
 
             result = gr.Image(label="Result", show_label=False)