Update controlnet/callable_functions.py

controlnet/callable_functions.py

@@ -10,7 +10,7 @@ from transformers import AutoProcessor, SiglipVisionModel
 
 
 
-def process_single_image(model,image_path, prompt, num_inference_steps, stylecode,image=None):
+def use_stylecode(model,image_path, prompt,negative_prompt, num_inference_steps, stylecode,image=None):
     # Load and preprocess image
     # Set up model components
     unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet", torch_dtype=torch.float16, device="cuda")
@@ -52,11 +52,12 @@ def process_single_image(model,image_path, prompt, num_inference_steps, stylecod
     # Run the image through the pipeline with the specified prompt
     output_images = pipe(
         prompt=prompt,
+        negative_prompt=negative_prompt,
         guidance_scale=3,
         #image=image,
         num_inference_steps=num_inference_steps,
         generator=generator,
-        controlnet_conditioning_scale=0.8,
+        controlnet_conditioning_scale=0.9,
         width=512,
         height=512,
         stylecode=stylecode,
@@ -88,7 +89,7 @@ def process_single_image_both_ways(model,image_path, prompt, num_inference_steps
         stylecodes_model=stylecodes_model,
         torch_dtype=torch.float16,
         device="cuda",
-        scheduler=noise_scheduler,
+        #scheduler=noise_scheduler,
         feature_extractor=None,
         safety_checker=None,
     )
@@ -110,7 +111,7 @@ def process_single_image_both_ways(model,image_path, prompt, num_inference_steps
         image=image,
         num_inference_steps=num_inference_steps,
         generator=generator,
-        controlnet_conditioning_scale=0.8,
+        controlnet_conditioning_scale=0.9,
         width=512,
         height=512,
         stylecode=None,