Update app.py

app.py

@@ -322,22 +322,22 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
         ):
             yield img
 
-def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
-    pipe_i2i.to("cuda")
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    image_input = load_image(image_input_path)
-    final_image = pipe_i2i(
-        prompt=prompt_mash,
-        image=image_input,
-        strength=image_strength,
-        num_inference_steps=steps,
-        guidance_scale=cfg_scale,
-        width=width,
-        height=height,
-        generator=generator,
-        joint_attention_kwargs={"scale": 1.0},
-        output_type="pil",
-    ).images[0]
+#def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
+#    pipe_i2i.to("cuda")
+#    generator = torch.Generator(device="cuda").manual_seed(seed)
+#    image_input = load_image(image_input_path)
+#    final_image = pipe_i2i(
+#        prompt=prompt_mash,
+#        image=image_input,
+#        strength=image_strength,
+#        num_inference_steps=steps,
+#        guidance_scale=cfg_scale,
+#        width=width,
+#        height=height,
+#        generator=generator,
+#        joint_attention_kwargs={"scale": 1.0},
+#        output_type="pil",
+#    ).images[0]
     return final_image
 
 @spaces.GPU(duration=75)
@@ -362,7 +362,7 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
     # Unload previous LoRA weights
     with calculateDuration("Unloading LoRA"):
         pipe.unload_lora_weights()
-        pipe_i2i.unload_lora_weights()
+#        pipe_i2i.unload_lora_weights()
         
     print(pipe.get_active_adapters())
     # Load LoRA weights with respective scales
@@ -377,32 +377,32 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
             lora_path = lora['repo']
             weight_name = lora.get("weights")
             print(f"Lora Path: {lora_path}")
-            pipe_to_use = pipe_i2i if image_input is not None else pipe
-            pipe_to_use.load_lora_weights(
-                lora_path, 
-                weight_name=weight_name if weight_name else None,
-                low_cpu_mem_usage=True,
-                adapter_name=lora_name
-            )
-            if image_input is not None: pipe_i2i = pipe_to_use
-            else: pipe = pipe_to_use
+#            pipe_to_use = pipe_i2i if image_input is not None else pipe
+#            pipe_to_use.load_lora_weights(
+#                lora_path, 
+#                weight_name=weight_name if weight_name else None,
+#                low_cpu_mem_usage=True,
+#                adapter_name=lora_name
+#            )
+#            if image_input is not None: pipe_i2i = pipe_to_use
+#            else: pipe = pipe_to_use
         print("Loaded LoRAs:", lora_names)
         print("Adapter weights:", lora_weights)
-        if image_input is not None:
-            pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
-        else:
-            pipe.set_adapters(lora_names, adapter_weights=lora_weights)
-    print(pipe.get_active_adapters())
+#        if image_input is not None:
+#            pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
+#        else:
+#            pipe.set_adapters(lora_names, adapter_weights=lora_weights)
+#    print(pipe.get_active_adapters())
     # Set random seed for reproducibility
     with calculateDuration("Randomizing seed"):
         if randomize_seed:
             seed = random.randint(0, MAX_SEED)
 
 # Generate image
-    if image_input is not None:
-        final_image = generate_image_to_image(prompt_mash, steps, cfg_scale, width, height, seed)
-        yield final_image, seed, gr.update(visible=False)
-    else:
+#    if image_input is not None:
+#        final_image = generate_image_to_image(prompt_mash, steps, cfg_scale, width, height, seed)
+#        yield final_image, seed, gr.update(visible=False)
+#    else:
         image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
 # Consume the generator to get the final image
         final_image = None