performance

app.py

@@ -2,27 +2,33 @@ from PIL import Image
 import gradio as gr
 from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
 import torch
+torch.backends.cuda.matmul.allow_tf32 = True
 
-controlnet = ControlNetModel.from_pretrained("ioclab/control_v1p_sd15_brightness", torch_dtype=torch.float32, use_safetensors=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.float32,
+    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16,
 )
 
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 
-# pipe.enable_xformers_memory_efficient_attention()
+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):
 
     conditioning_image = Image.fromarray(conditioning_image)
     conditioning_image = conditioning_image.convert('L')
 
-    generator = torch.Generator(device="cpu").manual_seed(seed)
+    g_cpu = torch.Generator()
+
+    if seed == -1:
+        generator = g_cpu.manual_seed(g_cpu.seed())
+    else:
+        generator = g_cpu.manual_seed(seed)
 
-    output_image = pipe(
+    output_images = pipe(
         prompt,
         conditioning_image,
         height=size,
@@ -32,9 +38,9 @@ def infer(prompt, negative_prompt, conditioning_image, num_inference_steps, size
         negative_prompt=negative_prompt,
         guidance_scale=guidance_scale,
         controlnet_conditioning_scale=1.0,
-    ).images[0]
+    ).images
 
-    return output_image
+    return output_images
 
 with gr.Blocks() as demo:
     gr.Markdown(
@@ -72,22 +78,23 @@ with gr.Blocks() as demo:
                         label='Guidance Scale',
                         minimum=0.1,
                         maximum=30.0,
-                        value=9.0,
+                        value=7.0,
                         step=0.1
                     )
                     seed = gr.Slider(
                         label='Seed',
+                        value=-1,
                         minimum=-1,
                         maximum=2147483647,
                         step=1,
-                        randomize=True
+                        # randomize=True
                     )
             submit_btn = gr.Button(
                 value="Submit",
                 variant="primary"
             )
         with gr.Column(min_width=300):
-            output = gr.Image(
+            output = gr.Gallery(
                 label="Result",
             )
 
@@ -102,8 +109,8 @@ with gr.Blocks() as demo:
         examples=[
             ["a painting of 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 with blue hair", "monochrome", "./conditioning_images/conditioning_image_3.jpg"],
-            ["white object standing on colored ground", "monochrome", "./conditioning_images/conditioning_image_4.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"],
         ],
         inputs=[
             prompt, negative_prompt, conditioning_image

requirements.txt

@@ -3,4 +3,5 @@ accelerate
 diffusers
 transformers
 torch
+xformers
 safetensors