cache

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -2,4 +2,6 @@
 .idea
 
 venv
-.venv
+.venv
+
+gradio_cached_examples

app.py

@@ -7,7 +7,10 @@ torch.backends.cuda.matmul.allow_tf32 = True
 controlnet = ControlNetModel.from_pretrained("ioclab/control_v1p_sd15_brightness", torch_dtype=torch.float16, use_safetensors=True)
 
 pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16,
+    "runwayml/stable-diffusion-v1-5",
+    controlnet=controlnet,
+    torch_dtype=torch.float16,
+    safety_checker=None,
 )
 
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
@@ -16,7 +19,15 @@ pipe.enable_xformers_memory_efficient_attention()
 pipe.enable_model_cpu_offload()
 pipe.enable_attention_slicing()
 
-def infer(prompt, negative_prompt, conditioning_image, num_inference_steps, size, guidance_scale, seed):
+def infer(
+        prompt,
+        negative_prompt,
+        conditioning_image,
+        num_inference_steps=30,
+        size=768,
+        guidance_scale=7.0,
+        seed=-1,
+):
 
     conditioning_image = Image.fromarray(conditioning_image)
     conditioning_image = conditioning_image.convert('L')
@@ -109,12 +120,16 @@ with gr.Blocks() as demo:
         examples=[
             ["a village in the mountains", "monochrome", "./conditioning_images/conditioning_image_1.jpg"],
             ["three people walking in an alleyway with hats and pants", "monochrome", "./conditioning_images/conditioning_image_2.jpg"],
-            ["an anime character, natural skin", "monochrome", "./conditioning_images/conditioning_image_3.jpg"],
-            ["white object standing on colorful ground", "monochrome", "./conditioning_images/conditioning_image_4.jpg"],
+            ["an anime character, natural skin", "monochrome, blue skin, grayscale", "./conditioning_images/conditioning_image_3.jpg"],
+            ["white object standing on colorful metal ground, soft_light, cray", "monochrome, grayscale", "./conditioning_images/conditioning_image_4.jpg"],
+            ["a man in a black suit", "monochrome", "./conditioning_images/conditioning_image_5.jpg"],
         ],
         inputs=[
             prompt, negative_prompt, conditioning_image
         ],
+        outputs=output,
+        fn=infer,
+        cache_examples=True,
     )
     gr.Markdown(
         """