Update app.py

app.py

@@ -22,22 +22,22 @@ MAX_IMAGE_SIZE = 2048
 
 pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
 
-with open("lora_models.json", "r") as f:
-    lora_models = json.load(f)
+# with open("lora_models.json", "r") as f:
+#     lora_models = json.load(f)
 
-def download_model(model_name, model_path):
-    print(f"Downloading model: {model_name} from {model_path}")
-    try:
-        pipe.load_lora_weights(model_path)
-        print(f"Successfully downloaded model: {model_name}")
-    except Exception as e:
-        print(f"Failed to download model: {model_name}. Error: {e}")
+# def download_model(model_name, model_path):
+#     print(f"Downloading model: {model_name} from {model_path}")
+#     try:
+#         pipe.load_lora_weights(model_path)
+#         print(f"Successfully downloaded model: {model_name}")
+#     except Exception as e:
+#         print(f"Failed to download model: {model_name}. Error: {e}")
 
-# Iterate through the models and download each one
-for model_name, model_path in lora_models.items():
-    download_model(model_name, model_path)
+# # Iterate through the models and download each one
+# for model_name, model_path in lora_models.items():
+#     download_model(model_name, model_path)
 
-lora_models["None"] = None
+# lora_models["None"] = None
 
 # def calculate_optimal_dimensions(image: Image.Image):
 #     # Extract the original dimensions
@@ -77,13 +77,14 @@ lora_models["None"] = None
 #     return width, height
 
 @spaces.GPU(durations=300)
-def infer(edit_images, prompt, width, height, lora_model, strength, seed=42, randomize_seed=False, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+# def infer(edit_images, prompt, width, height, lora_model, strength, seed=42, randomize_seed=False, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+def infer(edit_images, prompt, width, height, strength, seed=42, randomize_seed=False, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
     # pipe.enable_xformers_memory_efficient_attention()
     gr.Info("Infering")
 
-    if lora_model != "None":
-        pipe.load_lora_weights(lora_models[lora_model])
-        pipe.enable_lora()
+    # if lora_model != "None":
+    #     pipe.load_lora_weights(lora_models[lora_model])
+    #     pipe.enable_lora()
 
     gr.Info("starting checks")
 
@@ -94,7 +95,6 @@ def infer(edit_images, prompt, width, height, lora_model, strength, seed=42, ran
         gr.Info("Please upload an image.")
         return None, None
 
-
     # width, height = calculate_optimal_dimensions(image)
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -211,11 +211,11 @@ with gr.Blocks(css=css) as demo:
                     container=False,
                 )
 
-                lora_model = gr.Dropdown(
-                    label="Select LoRA Model",
-                    choices=list(lora_models.keys()),
-                    value="None",
-                )
+                # lora_model = gr.Dropdown(
+                #     label="Select LoRA Model",
+                #     choices=list(lora_models.keys()),
+                #     value="None",
+                # )
 
                 run_button = gr.Button("Run")
 
@@ -282,7 +282,7 @@ with gr.Blocks(css=css) as demo:
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn = infer,
-        inputs = [edit_image, prompt, width, height, lora_model, strength, seed, randomize_seed, guidance_scale, num_inference_steps],
+        inputs = [edit_image, prompt, width, height, strength, seed, randomize_seed, guidance_scale, num_inference_steps],
         outputs = [result, seed]
     )