Update app.py

app.py

@@ -131,7 +131,7 @@ def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str
         negative = ""
     return p.replace("{prompt}", positive), n + negative
 
-def load_and_prepare_model(model_id):
+def load_and_prepare_model():
     #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None)
     vaeX = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None,use_safetensors=False)
     #vae = AutoencoderKL.from_pretrained('cross-attention/asymmetric-autoencoder-kl-x-2',use_safetensors=False)
@@ -231,11 +231,11 @@ def load_and_prepare_model(model_id):
     #pipe.to(device, torch.bfloat16)
     #del pipeX
     
-    pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/Fantasy_World_XL.safetensors", adapter_name="fantasy")
+    #pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/Fantasy_World_XL.safetensors", adapter_name="fantasy")
     pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/skin_texture_style_v4.safetensors", adapter_name="skin")
     pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/epicNewPhoto.safetensors", adapter_name="photo")
     #pipe.set_adapters(["skin", "photo", "fantasy"], adapter_weights=[0.5, 0.5, 0.5])
-    pipe.set_adapters(["skin", "photo", "fantasy"], adapter_weights=[1.0, 1.0, 1.0])
+    pipe.set_adapters(["skin", "photo"], adapter_weights=[1.0, 1.0])
 
     #sched = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", algorithm_type="dpmsolver++")
     #sched = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, beta_schedule="linear", algorithm_type="dpmsolver++")
@@ -243,8 +243,8 @@ def load_and_prepare_model(model_id):
     return pipe
     
 # Preload and compile both models
-models = {key: load_and_prepare_model(value) for key, value in MODEL_OPTIONS.items()}
-
+#models = {key: load_and_prepare_model(value) for key, value in MODEL_OPTIONS.items()}
+pipe = load_and_prepare_model()
 MAX_SEED = np.iinfo(np.int32).max
 
 neg_prompt_2 = " 'non-photorealistic':1.5, 'unrealistic skin','unattractive face':1.3, 'low quality':1.1, ('dull color scheme', 'dull colors', 'digital noise':1.2),'amateurish', 'poorly drawn face':1.3, 'poorly drawn', 'distorted face', 'low resolution', 'simplistic' "
@@ -311,8 +311,8 @@ def generate_30(
     #torch.backends.cudnn.benchmark = False
     #torch.cuda.empty_cache()
     #gc.collect()
-    global models
-    pipe = models[model_choice]
+    #global models
+    #pipe = models[model_choice]
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator(device='cuda').manual_seed(seed)
     #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
@@ -373,8 +373,8 @@ def generate_60(
     #torch.backends.cudnn.benchmark = True
     #torch.cuda.empty_cache()
     #gc.collect()
-    global models
-    pipe = models[model_choice]
+    #global models
+    #pipe = models[model_choice]
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator(device='cuda').manual_seed(seed)
     #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
@@ -435,8 +435,8 @@ def generate_90(
     #torch.backends.cudnn.benchmark = True
     #torch.cuda.empty_cache()
     #gc.collect()
-    global models
-    pipe = models[model_choice]
+    #global models
+    #pipe = models[model_choice]
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator(device='cuda').manual_seed(seed)
     #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)