Update app.py

app.py

@@ -5,7 +5,6 @@ import torch
 
 controlnet = ControlNetModel.from_pretrained("ioclab/control_v1p_sd15_brightness", torch_dtype=torch.float32, use_safetensors=True)
 
-
 pipe = StableDiffusionControlNetPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float32,
 )
@@ -16,19 +15,22 @@ pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 pipe.enable_model_cpu_offload()
 
 
-def infer(prompt, negative_prompt, num_inference_steps, conditioning_image):
-    # conditioning_image = Image.open(conditioning_image)
+def infer(prompt, negative_prompt, conditioning_image, num_inference_steps, size, guidance_scale, seed):
+
     conditioning_image = Image.fromarray(conditioning_image)
-    generator = torch.Generator(device="cpu").manual_seed(1500)
+    conditioning_image = conditioning_image.convert('L')
+
+    generator = torch.Generator(device="cpu").manual_seed(seed)
 
     output_image = pipe(
         prompt,
         conditioning_image,
-        height=512,
-        width=512,
+        height=size,
+        width=size,
         num_inference_steps=num_inference_steps,
         generator=generator,
         negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
         controlnet_conditioning_scale=1.0,
     ).images[0]
 
@@ -50,14 +52,36 @@ with gr.Blocks() as demo:
             negative_prompt = gr.Textbox(
                 label="Negative Prompt",
             )
-            num_inference_steps = gr.Slider(
-                10, 40, 20,
-                step=1,
-                label="Steps",
-            )
             conditioning_image = gr.Image(
                 label="Conditioning Image",
             )
+            with gr.Accordion('Advanced options', open=False):
+                with gr.Row():
+                    num_inference_steps = gr.Slider(
+                        10, 40, 20,
+                        step=1,
+                        label="Steps",
+                    )
+                    size = gr.Slider(
+                        256, 768, 512,
+                        step=128,
+                        label="Size",
+                    )
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label='Guidance Scale',
+                        minimum=0.1,
+                        maximum=30.0,
+                        value=7.0,
+                        step=0.1
+                    )
+                    seed = gr.Slider(
+                        label='Seed',
+                        minimum=-1,
+                        maximum=2147483647,
+                        step=1,
+                        randomize=True
+                    )
             submit_btn = gr.Button(
                 value="Submit",
                 variant="primary"
@@ -70,7 +94,7 @@ with gr.Blocks() as demo:
     submit_btn.click(
         fn=infer,
         inputs=[
-            prompt, negative_prompt, num_inference_steps, conditioning_image
+            prompt, negative_prompt, conditioning_image, num_inference_steps, size, guidance_scale, seed
         ],
         outputs=output
     )