update

app.py

@@ -121,24 +121,47 @@ def preprocess_map(map):
     return map
 
 
-def inference(image, map, gs, prompt, negative_prompt):
+def inference(
+    image,
+    map,
+    guidance_scale,
+    prompt,
+    negative_prompt,
+    steps
+):
     validate_inputs(image, map)
     image = preprocess_image(image)
     map = preprocess_map(map)
-    base_cuda = base.to(DEVICE)
-    edited_images = base_cuda(prompt=prompt, original_image=image, image=image, strength=1, guidance_scale=gs,
-                         num_images_per_prompt=1,
-                         negative_prompt=negative_prompt,
-                         map=map,
-                         num_inference_steps=NUM_INFERENCE_STEPS, denoising_end=0.8, output_type="latent").images
-    base_cuda=None
-    refiner_cuda = refiner.to(DEVICE)
-    edited_images = refiner_cuda(prompt=prompt, original_image=image, image=edited_images, strength=1, guidance_scale=7.5,
-                            num_images_per_prompt=1,
-                            negative_prompt=negative_prompt,
-                            map=map,
-                            num_inference_steps=NUM_INFERENCE_STEPS, denoising_start=0.8).images[0]
-    refiner_cuda=None
+    base_device = base.to(DEVICE)
+    edited_images = base_device(
+        prompt=prompt,
+        original_image=image,
+        image=image,
+        strength=1,
+        guidance_scale=guidance_scale,
+        num_images_per_prompt=1,
+        negative_prompt=negative_prompt,
+        map=map,
+        num_inference_steps=steps,
+        denoising_start=denoising_start,
+        denoising_end=denoising_end,
+        output_type="latent"
+    ).images
+    base_device=None
+    refiner_device = refiner.to(DEVICE)
+    edited_images = refiner_cuda(
+        prompt=prompt,
+        original_image=image,
+        image=edited_images,
+        strength=1,
+        guidance_scale=guidance_scale,
+        num_images_per_prompt=1,
+        negative_prompt=negative_prompt,
+        map=map,
+        num_inference_steps=steps,
+        denoising_start=denoising_start
+    ).images[0]
+    refiner_device=None
     return edited_images
 
 
@@ -149,11 +172,11 @@ def validate_inputs(image, map):
         raise gr.Error("Missing map")
 
 
-def run(image, gs, prompt, neg_prompt):
+def run(image, gs, prompt, neg_prompt, steps, denoising_start, denoising_end):
     # first run 
     [(original_image, colored_depth), name, raw_depth] = depthify(image)
     print(f"original_image={original_image} colored_depth={colored_depth}, name={name}, raw_depth={raw_depth}")
-    return raw_depth, inference(original_image, raw_depth, gs, prompt, neg_prompt)
+    return raw_depth, inference(original_image, raw_depth, gs, prompt, neg_prompt, steps, denoising_start, denoising_end)
 
 with gr.Blocks() as demo:
     with gr.Row():
@@ -162,11 +185,14 @@ with gr.Blocks() as demo:
                 input_image = gr.Image(label="Input Image", type="pil")
 #                change_map = gr.Image(label="Change Map", type="pil")
             gs = gr.Slider(0, 28, value=7.5, label="Guidance Scale")
+            steps = gr.Number(value=50, label="Steps")
+            denoising_start = gr.Slider(0, 1, value=0.8, label="Denoising Start")
+            denoising_end = gr.Slider(0, 1, value=0.8, label="Denoising End")
             prompt = gr.Textbox(label="Prompt")
             neg_prompt = gr.Textbox(label="Negative Prompt")
             with gr.Row():
 #                clr_btn=gr.ClearButton(components=[input_image, change_map, gs, prompt, neg_prompt])
-                clr_btn=gr.ClearButton(components=[input_image, gs, prompt, neg_prompt])
+                clr_btn=gr.ClearButton(components=[input_image, gs, prompt, neg_prompt, steps, denoising_start, denoising_end])
                 run_btn = gr.Button("Run",variant="primary")
 
         change_map = gr.Image(label="Change Map")
@@ -174,7 +200,7 @@ with gr.Blocks() as demo:
     run_btn.click(
       run,
       #inference,
-      inputs=[input_image, gs, prompt, neg_prompt],
+      inputs=[input_image, gs, prompt, neg_prompt, steps, denoising_start, denoising_end],
       outputs=[change_map, output]
     )
     clr_btn.add(output)