Update app.py

app.py

@@ -24,6 +24,7 @@ def infer(
     width,
     height,
     guidance_scale,
+    lora_scale,
     num_inference_steps,
     progress=gr.Progress(track_tqdm=True),
 ):
@@ -42,8 +43,9 @@ def infer(
     if model == "Ramzes":
         pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch_dtype)
         pipe.unet = PeftModel.from_pretrained(pipe.unet, "Bordoglor/Ramzes_adapter_sd_v1.5", subfolder="unet")
-        #pipe.load_lora_weights("Bordoglor/Ramzes_adapter_sd_v1.5", subfolder="unet")
         pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, "Bordoglor/Ramzes_adapter_sd_v1.5", subfolder="text_encoder")
+        pipe.unet.load_state_dict({k: lora_scale*v for k, v in pipe.unet.state_dict().items()})
+        pipe.text_encoder.load_state_dict({k: lora_scale*v for k, v in pipe.text_encoder.state_dict().items()})
     else:     
         pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch_dtype)
     pipe = pipe.to(device)
@@ -138,6 +140,14 @@ with gr.Blocks(css=css) as demo:
                     value=7.0,  # Replace with defaults that work for your model
                 )
 
+                lora_scale = gr.Slider(
+                    label="LoRA scale",
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.05,
+                    value=0.9
+                )
+
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -158,6 +168,7 @@ with gr.Blocks(css=css) as demo:
             width,
             height,
             guidance_scale,
+            lora_scale,
             num_inference_steps,
         ],
         outputs=[result, seed],