gradio_show.py

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)