load_model

app.py

@@ -1,192 +1,138 @@
 import spaces
-import numpy as np
+from diffusers import ControlNetModel
+from diffusers import StableDiffusionXLControlNetPipeline
+from diffusers import EulerAncestralDiscreteScheduler
 from PIL import Image
-import gradio as gr
-import open3d as o3d
-import trimesh
-from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, EulerAncestralDiscreteScheduler
 import torch
-from collections import Counter
-import random
-
-pipe = None
-device = None
-torch_dtype = None
-
-def load_model():
-  global pipe, device, torch_dtype
-  device = "cuda" if torch.cuda.is_available() else "cpu"
-  torch_dtype = torch.float16 if device == "cuda" else torch.float32
-
-  pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
-      "yeq6x/animagine_position_map",
-      controlnet=ControlNetModel.from_pretrained("yeq6x/Image2PositionColor_v3"),
-  ).to(device)
-  pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-  
-  return pipe
-
-def convert_pil_to_opencv(pil_image):
-  return np.array(pil_image)
-
-def inv_func(y,
-  c = -712.380100,
-  a = 137.375240,
-  b = 192.435866):
-  return (np.exp((y - c) / a) - np.exp(-c/a)) / 964.8468371292845
-
-def create_point_cloud(img1, img2):
-  if img1.shape != img2.shape:
-    raise ValueError("Both images must have the same dimensions.")
-
-  h, w, _ = img1.shape
-  points = []
-  colors = []
-  for y in range(h):
-    for x in range(w):
-      # ピクセル位置 (x, y) のRGBをXYZとして取得
-      r, g, b = img1[y, x]
-      r = inv_func(r) * 0.9
-      g = inv_func(g) / 1.7 * 0.6
-      b = inv_func(b)
-      r *= 150
-      g *= 150
-      b *= 150
-      points.append([g, b, r])  # X, Y, Z
-      # 対応するピクセル位置の画像2の色を取得
-      colors.append(img2[y, x] / 255.0)  # 色は0〜1にスケール
-
-  return np.array(points), np.array(colors)
-
-def point_cloud_to_glb(points, colors):
-  # Open3Dでポイントクラウドを作成
-  pc = o3d.geometry.PointCloud()
-  pc.points = o3d.utility.Vector3dVector(points)
-  pc.colors = o3d.utility.Vector3dVector(colors)
-  
-  # 一時的にPLY形式で保存
-  temp_ply_file = "temp_output.ply"
-  o3d.io.write_point_cloud(temp_ply_file, pc)
-  
-  # PLYをGLBに変換
-  mesh = trimesh.load(temp_ply_file)
-  glb_file = "output.glb"
-  mesh.export(glb_file)
-
-  return glb_file
-
-def visualize_3d(image1, image2):
-  print("Processing...")
-  # PIL画像をOpenCV形式に変換
-  img1 = convert_pil_to_opencv(image1)
-  img2 = convert_pil_to_opencv(image2)
-
-  # ポイントクラウド生成
-  points, colors = create_point_cloud(img1, img2)
-
-  # GLB形式に変換
-  glb_file = point_cloud_to_glb(points, colors)
-
-  return glb_file
-
-def scale_image(original_image):
-  aspect_ratio = original_image.width / original_image.height
-
-  if original_image.width > original_image.height:
-    new_width = 1024
-    new_height = round(new_width / aspect_ratio)
-  else:
-    new_height = 1024
-    new_width = round(new_height * aspect_ratio)
-
-  resized_original = original_image.resize((new_width, new_height), Image.LANCZOS)
-
-  return resized_original
-
-def get_edge_mode_color(img, edge_width=10):
-  # 外周の10ピクセル領域を取得
-  left = img.crop((0, 0, edge_width, img.height))  # 左端
-  right = img.crop((img.width - edge_width, 0, img.width, img.height))  # 右端
-  top = img.crop((0, 0, img.width, edge_width))  # 上端
-  bottom = img.crop((0, img.height - edge_width, img.width, img.height))  # 下端
-
-  # 各領域のピクセルデータを取得して結合
-  colors = list(left.getdata()) + list(right.getdata()) + list(top.getdata()) + list(bottom.getdata())
-
-  # 最頻値（mode）を計算
-  mode_color = Counter(colors).most_common(1)[0][0]  # 最も頻繁に出現する色を取得
-
-  return mode_color
-
-def paste_image(resized_img):
-  # 外周10pxの最頻値を背景色に設定
-  mode_color = get_edge_mode_color(resized_img, edge_width=10)
-  mode_background = Image.new("RGBA", (1024, 1024), mode_color)
-  mode_background = mode_background.convert('RGB')
-
-  x = (1024 - resized_img.width) // 2
-  y = (1024 - resized_img.height) // 2
-  mode_background.paste(resized_img, (x, y))
-
-  return mode_background
+import numpy as np
+import cv2
+import gradio as gr
+from torchvision import transforms 
+from controlnet_aux import OpenposeDetector
+
+ratios_map =  {
+    0.5:{"width":704,"height":1408},
+    0.57:{"width":768,"height":1344},
+    0.68:{"width":832,"height":1216},
+    0.72:{"width":832,"height":1152},
+    0.78:{"width":896,"height":1152},
+    0.82:{"width":896,"height":1088},
+    0.88:{"width":960,"height":1088},
+    0.94:{"width":960,"height":1024},
+    1.00:{"width":1024,"height":1024},
+    1.13:{"width":1088,"height":960},
+    1.21:{"width":1088,"height":896},
+    1.29:{"width":1152,"height":896},
+    1.38:{"width":1152,"height":832},
+    1.46:{"width":1216,"height":832},
+    1.67:{"width":1280,"height":768},
+    1.75:{"width":1344,"height":768},
+    2.00:{"width":1408,"height":704}
+}
+ratios = np.array(list(ratios_map.keys()))
+
+
+openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
+
+controlnet = ControlNetModel.from_pretrained(
+    "briaai/BRIA-2.3-ControlNet-Pose",
+    torch_dtype=torch.float16
+).to('cuda')
+
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "briaai/BRIA-2.3",
+    controlnet=controlnet,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+    offload_state_dict=True,
+).to('cuda').to(torch.float16)
+
+pipe.scheduler = EulerAncestralDiscreteScheduler(
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    num_train_timesteps=1000,
+    steps_offset=1
+)
+# pipe.enable_freeu(b1=1.1, b2=1.1, s1=0.5, s2=0.7)
+# pipe.enable_xformers_memory_efficient_attention()
+pipe.force_zeros_for_empty_prompt = False
+
+def get_size(init_image):
+    w,h=init_image.size
+    curr_ratio = w/h
+    ind = np.argmin(np.abs(curr_ratio-ratios))
+    ratio = ratios[ind]
+    chosen_ratio  = ratios_map[ratio]
+    w,h = chosen_ratio['width'], chosen_ratio['height']
+    return w,h
+
+def resize_image(image):
+    image = image.convert('RGB')
+    w,h = get_size(image)
+    resized_image = image.resize((w, h))
+    return resized_image
+    
+def resize_image_old(image):
+    image = image.convert('RGB')
+    current_size = image.size
+    if current_size[0] > current_size[1]:
+        center_cropped_image = transforms.functional.center_crop(image, (current_size[1], current_size[1]))
+    else:
+        center_cropped_image = transforms.functional.center_crop(image, (current_size[0], current_size[0]))
+    resized_image = transforms.functional.resize(center_cropped_image, (1024, 1024))
+    return resized_image
 
-def outpaint_image(image):
-  if type(image) == type(None):
-    return None
-  resized_img = scale_image(image)
-  image = paste_image(resized_img)
-  
-  return image
 
 @spaces.GPU
-def predict_image(cond_image, prompt, negative_prompt):
-  print("predict position map")
-  global pipe
-  generator = torch.Generator()
-  generator.manual_seed(random.randint(0, 2147483647))
-  image = pipe(
-      prompt,
-      prompt,
-      cond_image,
-      negative_prompt=negative_prompt,
-      width=1024,
-      height=1024,
-      guidance_scale=8,
-      num_inference_steps=20,
-      generator=generator,
-      guess_mode = True,
-      controlnet_conditioning_scale = 0.6,
-  ).images[0]
-  
-  return image
-
-load_model()
+def generate_(prompt, negative_prompt, pose_image, input_image, num_steps, controlnet_conditioning_scale, seed):
+    generator = torch.Generator("cuda").manual_seed(seed)    
+    images = pipe(
+    prompt, negative_prompt=negative_prompt, image=pose_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+    generator=generator, height=input_image.size[1], width=input_image.size[0],
+    ).images 
+    return images
 
-# Gradioアプリケーション
-with gr.Blocks() as demo:
-  gr.Markdown("## Position Map Visualizer")
+@spaces.GPU
+def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
+    
+    # resize input_image to 1024x1024
+    input_image = resize_image(input_image)
+    
+    pose_image = openpose(input_image, include_body=True, include_hand=True, include_face=True)
   
-  with gr.Row():
-    with gr.Column():
-      with gr.Row():
-        img1 = gr.Image(type="pil", label="color Image", height=300)
-        img2 = gr.Image(type="pil", label="map Image", height=300)
-      prompt = gr.Textbox("position map, 1girl, white background", label="Prompt")
-      negative_prompt = gr.Textbox("lowres, bad anatomy, bad hands, bad feet, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry", label="Negative Prompt")
-      predict_map_btn = gr.Button("Predict Position Map")
-      visualize_3d_btn = gr.Button("Generate 3D Point Cloud")
-    with gr.Column():
-      reconstruction_output = gr.Model3D(label="3D Viewer", height=600)
-      gr.Examples(
-          examples=[
-          ["resources/source/000006.png", "resources/target/000006.png"],
-          ["resources/source/006420.png", "resources/target/006420.png"],
-      ],
-          inputs=[img1, img2]
-      )
-
-  img1.input(outpaint_image, inputs=img1, outputs=img1)
-  predict_map_btn.click(predict_image, inputs=[img1, prompt, negative_prompt], outputs=img2)
-  visualize_3d_btn.click(visualize_3d, inputs=[img2, img1], outputs=reconstruction_output)
-
-demo.launch()
+    images = generate_(prompt, negative_prompt, pose_image, input_image, num_steps, controlnet_conditioning_scale, seed)
+
+    return [pose_image,images[0]]
+    
+block = gr.Blocks().queue()
+
+with block:
+    gr.Markdown("## BRIA 2.3 ControlNet Pose")
+    gr.HTML('''
+      <p style="margin-bottom: 10px; font-size: 94%">
+        This is a demo for ControlNet Pose that using
+        <a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">BRIA 2.3 text-to-image model</a> as backbone. 
+        Trained on licensed data, BRIA 2.3 provide full legal liability coverage for copyright and privacy infringement.
+      </p>
+    ''')
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
+            prompt = gr.Textbox(label="Prompt")
+            negative_prompt = gr.Textbox(label="Negative prompt", value="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers")
+            num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
+            controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
+            seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
+            run_button = gr.Button(value="Run")
+                        
+        with gr.Column():
+            with gr.Row():
+                pose_image_output = gr.Image(label="Pose Image", type="pil", interactive=False)
+                generated_image_output = gr.Image(label="Generated Image", type="pil", interactive=False)
+
+    ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
+    run_button.click(fn=process, inputs=ips, outputs=[pose_image_output, generated_image_output])
+
+
+block.launch(debug = True)