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 from icecream import ic import matplotlib.pyplot as plt import sys import matplotlib matplotlib.use('Agg') model = create_model('./models/cldm_v15.yaml').cpu() model.load_state_dict(load_state_dict('./farfetch_controlnet.ckpt', location='cuda')) model = model.cuda() ddim_sampler = DDIMSampler(model) sys.path.append("..") from segment_anything import sam_model_registry, SamPredictor def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta): with torch.no_grad(): img = resize_image(HWC3(input_image), image_resolution) H, W, C = img.shape detected_map = np.zeros_like(img, dtype=np.uint8) detected_map[np.min(img, axis=2) < 127] = 255 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)] ic((x_samples[0])) ic(results) return [255 - detected_map] + results def segment_anything(input_image, model_type="vit_h", device="cuda"): """ 处理图像，应用SAM模型，生成并保存处理后的图像。 参数: - input_image: 输入图像的numpy数组。 - sam_checkpoint: SAM模型的路径。 - model_type: 模型类型，默认为"vit_h"。 - device: 运行设备，默认为"cuda"。 """ for i in input_image: ic(type(i)) ic(i) sam_checkpoint="./sam_vit_h_4b8939.pth" # 添加路径以便可以从相对目录导入SAM相关模块 sys.path.append("..") from segment_anything import sam_model_registry, SamPredictor # 确保输入图像为RGB格式 image_path=input_image[-1]['name'] image = cv2.imread(image_path) input_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) if input_image.shape[2] == 3: image = input_image else: raise ValueError("Input image must be in RGB format.") # 加载SAM模型 sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) sam.to(device=device) # 预测器配置 predictor = SamPredictor(sam) predictor.set_image(image) # 输入点和标签 input_point = np.array([[280, 280], [220, 220]]) input_label = np.array([1, 1]) # 预测 masks, _, _ = predictor.predict( point_coords=input_point, point_labels=input_label, multimask_output=False, ) # 生成并处理掩码 segmentation_mask = masks[0] binary_mask = np.where(segmentation_mask > 0.5, 1, 0) # 创建白色背景，并将掩码应用于图像 white_background = np.ones_like(image) * 255 binary_mask = cv2.GaussianBlur(binary_mask.astype(np.float32), (15, 15), 0) new_image = white_background * (1 - binary_mask[..., np.newaxis]) + image * binary_mask[..., np.newaxis] ic(new_image) # plt.imshow(new_image.astype(np.uint8)) # plt.axis('off') # plt.savefig('pic3.png') new_image = new_image.clip(0, 255).astype(np.uint8) # sam_list= {'data': 'https://5710d7c97de8b56005.gradio.live/file=/tmp/gradio/7c98a3c16d9ac06d68f6caac66b61705fc214b9a/image.png', # 'is_file': True, # 'name': '/tmp/gradio/7c98a3c16d9ac06d68f6caac66b61705fc214b9a/image.png'} return [new_image] # # 显示和保存图像 block = gr.Blocks().queue() with block: with gr.Row(): gr.Markdown("## Control Stable Diffusion with farfetch") 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") sam_button=gr.Button("Sam") 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) 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='longbody, 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') with gr.Row(): sam_output= gr.Gallery(label='sam_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, strength, scale, seed, eta] run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) sam_button.click(fn=segment_anything,inputs=[result_gallery],outputs=[sam_output]) block.launch(server_name='0.0.0.0',share=True)