Cleaning up the code

app.py

@@ -25,15 +25,13 @@ MAX_IMAGE_SIZE = 2048  # not used anymore
 
 # Bind the custom method
 # pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
-python.model_loading()
+# python.model_loading()
 
 
 @spaces.GPU()
 def infer(prompt, seed=42, randomize_seed=False, aspect_ratio="4:3 landscape 1152x896", lora_weight="lora_weight_rank_32_alpha_32.safetensors",
           guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
-    # Default height + width
-    width, height = 1024, 1024
-    
+
     # Randomize seed if requested
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -46,8 +44,6 @@ def infer(prompt, seed=42, randomize_seed=False, aspect_ratio="4:3 landscape 115
     torch.cuda.empty_cache()
     image, seed = python.generate_image(
         prompt,
-        height,
-        width,
         guidance_scale,
         aspect_ratio,
         seed,

python.py

@@ -131,16 +131,16 @@ import_custom_nodes()
 # add_extra_model_paths()
 dualcliploader = NODE_CLASS_MAPPINGS["DualCLIPLoader"]()
 dualcliploader_11 = dualcliploader.load_clip(
-clip_name1="t5xxl_fp16.safetensors",
-clip_name2="clip_l.safetensors",
-type="flux",
-device="default",
+    clip_name1="t5xxl_fp16.safetensors",
+    clip_name2="clip_l.safetensors",
+    type="flux",
+    device="default",
 )
 
 cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
 cliptextencode_6 = cliptextencode.encode(
-text="Photo on a small glass panel. Color. Photo of trees with a body of water in the front and moutain in the background.",
-clip=get_value_at_index(dualcliploader_11, 0),
+    text="Photo on a small glass panel. Color. Photo of trees with a body of water in the front and moutain in the background.",
+    clip=get_value_at_index(dualcliploader_11, 0),
 )
 
 vaeloader = NODE_CLASS_MAPPINGS["VAELoader"]()
@@ -148,7 +148,7 @@ vaeloader_10 = vaeloader.load_vae(vae_name="ae.safetensors")
 
 unetloader = NODE_CLASS_MAPPINGS["UNETLoader"]()
 unetloader_12 = unetloader.load_unet(
-unet_name="flux1-dev.safetensors", weight_dtype="default"
+    unet_name="flux1-dev.safetensors", weight_dtype="default"
 )
 
 ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]()
@@ -159,19 +159,19 @@ randomnoise_25 = randomnoise.get_noise(noise_seed='42')
 
 loraloadermodelonly = NODE_CLASS_MAPPINGS["LoraLoaderModelOnly"]()
 loraloadermodelonly_72 = loraloadermodelonly.load_lora_model_only(
-lora_name='lora_weight_rank_32_alpha_32.safetensors',
-strength_model=1,
-model=get_value_at_index(unetloader_12, 0),
+    lora_name='lora_weight_rank_32_alpha_32.safetensors',
+    strength_model=1,
+    model=get_value_at_index(unetloader_12, 0),
 )
 
 cr_sdxl_aspect_ratio = NODE_CLASS_MAPPINGS["CR SDXL Aspect Ratio"]()
 cr_sdxl_aspect_ratio_85 = cr_sdxl_aspect_ratio.Aspect_Ratio(
-width=1024,
-height=1024,
-aspect_ratio="4:3 landscape 1152x896",
-swap_dimensions="Off",
-upscale_factor=1.5,
-batch_size=1,
+    width=1024,
+    height=1024,
+    aspect_ratio="4:3 landscape 1152x896",
+    swap_dimensions="Off",
+    upscale_factor=1.5,
+    batch_size=1,
 )
 
 modelsamplingflux = NODE_CLASS_MAPPINGS["ModelSamplingFlux"]()
@@ -182,27 +182,25 @@ samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]()
 vaedecode = NODE_CLASS_MAPPINGS["VAEDecode"]()
 saveimage = NODE_CLASS_MAPPINGS["SaveImage"]()
 
-def model_loading():
-    model_loaders = [dualcliploader_11, vaeloader_10, unetloader_12, loraloadermodelonly_72]
-    valid_models = [
-    getattr(loader[0], 'patcher', loader[0]) 
-    for loader in model_loaders
-    if not isinstance(loader[0], dict) and not isinstance(getattr(loader[0], 'patcher', None), dict)
-]
-    #Load the models
-    # model_management.load_models_gpu(valid_models)
+# def model_loading():
+#     model_loaders = [dualcliploader_11, vaeloader_10, unetloader_12, loraloadermodelonly_72]
+#     valid_models = [
+#     getattr(loader[0], 'patcher', loader[0]) 
+#     for loader in model_loaders
+#     if not isinstance(loader[0], dict) and not isinstance(getattr(loader[0], 'patcher', None), dict)
+# ]
+#     #Load the models
+#     # model_management.load_models_gpu(valid_models)
     
 
 def generate_image(prompt,
-        height,
-        width,
         guidance_scale,
         aspect_ratio,
         seed,
         num_inference_steps,
         lora_weight,
         ):
-    print(seed)
+    # print(seed)
     cliptextencode = NODE_CLASS_MAPPINGS["CLIPTextEncode"]()
     cliptextencode_6 = cliptextencode.encode(
         text=prompt,