Create app2.py

app2.py

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+from share import *
+import config
+
+import cv2
+import einops
+import gradio as gr
+import numpy as np
+import torch
+import random
+
+from pytorch_lightning import seed_everything
+from annotator.util import resize_image, HWC3
+from cldm.model import create_model, load_state_dict
+from cldm.ddim_hacked import DDIMSampler
+
+import dlib
+from PIL import Image, ImageDraw
+
+model = create_model('./models/cldm_v15.yaml').cpu()
+model.load_state_dict(load_state_dict('./models/control_sd15_landmarks.pth', location='cuda'))
+model = model.cuda()
+ddim_sampler = DDIMSampler(model)
+
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
+
+def draw_landmarks(image, landmarks, color="white", radius=2.5):
+    draw = ImageDraw.Draw(image)
+    for dot in landmarks:
+        x, y = dot
+        draw.ellipse((x-radius, y-radius, x+radius, y+radius), fill=color)
+
+def get_68landmarks_img(img):
+    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    faces = detector(gray)
+    landmarks = []
+    for face in faces:
+        shape = predictor(gray, face)
+        for i in range(68):
+            x = shape.part(i).x
+            y = shape.part(i).y
+            landmarks.append((x, y))
+    con_img = Image.new('RGB', (img.shape[1], img.shape[0]), color=(0, 0, 0))
+    draw_landmarks(con_img, landmarks)
+    con_img = np.array(con_img)
+    return con_img
+
+def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta):
+    with torch.no_grad():
+        img = resize_image(HWC3(input_image), image_resolution)
+        H, W, C = img.shape
+
+        if landmark_direct_mode:
+            detected_map = img
+        else:
+            detected_map = get_68landmarks_img(img)
+        detected_map = HWC3(detected_map)
+
+        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
+        control = torch.stack([control for _ in range(num_samples)], dim=0)
+        control = einops.rearrange(control, 'b h w c -> b c h w').clone()
+
+        if seed == -1:
+            seed = random.randint(0, 65535)
+        seed_everything(seed)
+
+        if config.save_memory:
+            model.low_vram_shift(is_diffusing=False)
+
+        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
+        un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
+        shape = (4, H // 8, W // 8)
+
+        if config.save_memory:
+            model.low_vram_shift(is_diffusing=True)
+
+        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
+        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
+                                                     shape, cond, verbose=False, eta=eta,
+                                                     unconditional_guidance_scale=scale,
+                                                     unconditional_conditioning=un_cond)
+
+        if config.save_memory:
+            model.low_vram_shift(is_diffusing=False)
+
+        x_samples = model.decode_first_stage(samples)
+        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
+
+        results = [x_samples[i] for i in range(num_samples)]
+    return [255 - detected_map] + results
+
+
+block = gr.Blocks().queue()
+with block:
+    with gr.Row():
+        gr.Markdown("## Control Stable Diffusion with Face Landmarks")
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(source='upload', type="numpy")
+            prompt = gr.Textbox(label="Prompt")
+            run_button = gr.Button(label="Run")
+            with gr.Accordion("Advanced options", open=False):
+                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
+                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
+                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
+                guess_mode = gr.Checkbox(label='Guess Mode', value=False)
+                landmark_direct_mode = gr.Checkbox(label='Input Landmark Directly', value=False)
+                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
+                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
+                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
+                eta = gr.Number(label="eta (DDIM)", value=0.0)
+                a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
+                n_prompt = gr.Textbox(label="Negative Prompt",
+                                      value='cartoon, disfigured, bad art, deformed, poorly drawn, extra limbs, weird colors, blurry, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
+        with gr.Column():
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
+    ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, landmark_direct_mode, strength, scale, seed, eta]
+    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
+
+
+block.launch(server_name='0.0.0.0')