說時遲那時快，微軟第一時間釋出開源庫Visual ChatGPT，把 ChatGPT 的人工智慧AI能力和Stable Diffusion以及ControlNet進行了整合。常常被網際網路人掛在嘴邊的“賦能”一詞，幾乎已經變成了笑話，但這回，微軟玩了一次真真正正的AI“賦能”，徹底打通了人工智慧“閉環”。
配置Visual ChatGPT環境
老規矩，執行Git命令拉取Visual ChatGPT專案：
git clone https://github.com/microsoft/visual-chatgpt.git
進入專案目錄：
cd visual-chatgpt
確保本機的Python版本不低於Python3.10.9
隨後安裝依賴檔案：
pip3 install -r requirement.txt
這裡有幾個問題，一個是官方的Pytorch版本不是最新的，這裡推薦1.13.1：
pip3 install torch==1.13.1
另外langchain的版本也推薦最新的107版本。
pip3 install langchain==0.0.107
安裝好依賴之後，官方要求執行專案中的download.sh檔案：
bash download.sh
這個shell指令碼主要就是構建子專案ControlNet，同時下載所有的ControlNet模型，如果之前已經下載過相關模型，直接將模型檔案複製到專案目錄即可：
.
├── cldm_v15.yaml
├── cldm_v21.yaml
├── control_sd15_canny.pth
├── control_sd15_depth.pth
├── control_sd15_hed.pth
├── control_sd15_mlsd.pth
├── control_sd15_normal.pth
├── control_sd15_openpose.pth
├── control_sd15_scribble.pth
└── control_sd15_seg.pth
關於ControlNet，請移玉步至：登峰造極,師出造化,Pytorch人工智慧AI影像增強框架ControlNet繪畫實踐,基於Python3.10， 這裡不再贅述。
接著配置Openai的環境變數：
export OPENAI_API_KEY={你的openaik key}
如果是Windows使用者，遵循下列步驟，配置好OPENAI_API_KEY：
開啟“控制皮膚”，然後選擇“系統和安全”。
選擇“系統”，然後點選“高階系統設定”。
在“高階”選項卡下，點選“環境變數”。
在“使用者變數”或“系統變數”下，選擇要配置的變數，然後點選“編輯”。
在“變數值”欄位中，輸入要配置的值。
點選“確定”儲存更改。
至此，大體上環境就配置好了。
Visual ChatGPT部分程式碼修改：
和ControlNet一樣，Visual ChatGPT將執行方式寫死為cuda，這對於不支援cuda模式的電腦不太友好，比如蘋果M系列晶片的Mac系統，如果我們直接執行程式：
python3 visual_chatgpt.py
就會報這個錯誤：
AssertionError: Torch not compiled with CUDA enabled
這裡需要將visual-chatgpt.py檔案中寫死的cuda模式改寫為mps模式：
print("Initializing VisualChatGPT")
self.llm = OpenAI(temperature=0)
self.edit = ImageEditing(device="mps")
self.i2t = ImageCaptioning(device="mps")
self.t2i = T2I(device="mps")
關於MPS模式，請參照：聞其聲而知雅意,M1 Mac基於PyTorch(mps/cpu/cuda)的人工智慧AI本地語音識別庫Whisper(Python3.10) ，這裡不再贅述。
接著建立訓練圖片的資料夾：
mkdir image
隨後還可能觸發langchain庫的記憶體溢位問題，需要將這行程式碼遮蔽：
# self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500)
接著將記憶體緩衝區替換為儲存上下文邏輯：
self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt
self.agent.memory.save_context({"input": Human_prompt}, {"output": AI_prompt})
當我們以為萬事俱備只欠東風的時候，發現每次執行都會記憶體溢位，對此，官方給出瞭解釋：
Here we list the GPU memory usage of each visual foundation model, one can modify self.tools with fewer visual foundation models to save your GPU memory:
Foundation Model Memory Usage (MB)
ImageEditing 6667
ImageCaption 1755
T2I 6677
canny2image 5540
line2image 6679
hed2image 6679
scribble2image 6679
pose2image 6681
BLIPVQA 2709
seg2image 5540
depth2image 6677
normal2image 3974
InstructPix2Pix 2795
這就是載入了所有模型之後的視訊記憶體佔用，整整70個G的視訊記憶體佔用，這是給人玩的嗎？人們不禁要問。
沒辦法，只能另闢蹊徑，將非必要的模型載入程式碼進行遮蔽操作，一頓修改，修改後的完整程式碼：
import sys
import os
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, CLIPSegProcessor, CLIPSegForImageSegmentation
import torch
from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
import os
from langchain.agents.initialize import initialize_agent
from langchain.agents.tools import Tool
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.llms.openai import OpenAI
import re
import uuid
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
import numpy as np
from omegaconf import OmegaConf
from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
import cv2
import einops
from pytorch_lightning import seed_everything
import random
from ldm.util import instantiate_from_config
from ControlNet.cldm.model import create_model, load_state_dict
from ControlNet.cldm.ddim_hacked import DDIMSampler
from ControlNet.annotator.canny import CannyDetector
from ControlNet.annotator.mlsd import MLSDdetector
from ControlNet.annotator.util import HWC3, resize_image
from ControlNet.annotator.hed import HEDdetector, nms
from ControlNet.annotator.openpose import OpenposeDetector
from ControlNet.annotator.uniformer import UniformerDetector
from ControlNet.annotator.midas import MidasDetector
VISUAL_CHATGPT_PREFIX = """Visual ChatGPT is designed to be able to assist with a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. Visual ChatGPT is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
Visual ChatGPT is able to process and understand large amounts of text and images. As a language model, Visual ChatGPT can not directly read images, but it has a list of tools to finish different visual tasks. Each image will have a file name formed as "image/xxx.png", and Visual ChatGPT can invoke different tools to indirectly understand pictures. When talking about images, Visual ChatGPT is very strict to the file name and will never fabricate nonexistent files. When using tools to generate new image files, Visual ChatGPT is also known that the image may not be the same as the user's demand, and will use other visual question answering tools or description tools to observe the real image. Visual ChatGPT is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the image content and image file name. It will remember to provide the file name from the last tool observation, if a new image is generated.
Human may provide new figures to Visual ChatGPT with a description. The description helps Visual ChatGPT to understand this image, but Visual ChatGPT should use tools to finish following tasks, rather than directly imagine from the description.
Overall, Visual ChatGPT is a powerful visual dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics.
TOOLS:
------
Visual ChatGPT has access to the following tools:"""
VISUAL_CHATGPT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format:
Thought: Do I need to use a tool? Yes
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:
Thought: Do I need to use a tool? No
{ai_prefix}: [your response here]
"""
VISUAL_CHATGPT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if it does not exist.
You will remember to provide the image file name loyally if it's provided in the last tool observation.
Begin!
Previous conversation history:
{chat_history}
New input: {input}
Since Visual ChatGPT is a text language model, Visual ChatGPT must use tools to observe images rather than imagination.
The thoughts and observations are only visible for Visual ChatGPT, Visual ChatGPT should remember to repeat important information in the final response for Human.
Thought: Do I need to use a tool? {agent_scratchpad}"""
def cut_dialogue_history(history_memory, keep_last_n_words=500):
tokens = history_memory.split()
n_tokens = len(tokens)
print(f"hitory_memory:{history_memory}, n_tokens: {n_tokens}")
if n_tokens < keep_last_n_words:
return history_memory
else:
paragraphs = history_memory.split('\n')
last_n_tokens = n_tokens
while last_n_tokens >= keep_last_n_words:
last_n_tokens = last_n_tokens - len(paragraphs[0].split(' '))
paragraphs = paragraphs[1:]
return '\n' + '\n'.join(paragraphs)
def get_new_image_name(org_img_name, func_name="update"):
head_tail = os.path.split(org_img_name)
head = head_tail[0]
tail = head_tail[1]
name_split = tail.split('.')[0].split('_')
this_new_uuid = str(uuid.uuid4())[0:4]
if len(name_split) == 1:
most_org_file_name = name_split[0]
recent_prev_file_name = name_split[0]
new_file_name = '{}_{}_{}_{}.png'.format(this_new_uuid, func_name, recent_prev_file_name, most_org_file_name)
else:
assert len(name_split) == 4
most_org_file_name = name_split[3]
recent_prev_file_name = name_split[0]
new_file_name = '{}_{}_{}_{}.png'.format(this_new_uuid, func_name, recent_prev_file_name, most_org_file_name)
return os.path.join(head, new_file_name)
def create_model(config_path, device):
config = OmegaConf.load(config_path)
OmegaConf.update(config, "model.params.cond_stage_config.params.device", device)
model = instantiate_from_config(config.model).to('mps')
print(f'Loaded model config from [{config_path}]')
return model
class MaskFormer:
def __init__(self, device):
self.device = device
self.processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
self.model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined").to(device)
def inference(self, image_path, text):
threshold = 0.5
min_area = 0.02
padding = 20
original_image = Image.open(image_path)
image = original_image.resize((512, 512))
inputs = self.processor(text=text, images=image, padding="max_length", return_tensors="pt",).to(self.device)
with torch.no_grad():
outputs = self.model(**inputs)
mask = torch.sigmoid(outputs[0]).squeeze().cuda().numpy() > threshold
area_ratio = len(np.argwhere(mask)) / (mask.shape[0] * mask.shape[1])
if area_ratio < min_area:
return None
true_indices = np.argwhere(mask)
mask_array = np.zeros_like(mask, dtype=bool)
for idx in true_indices:
padded_slice = tuple(slice(max(0, i - padding), i + padding + 1) for i in idx)
mask_array[padded_slice] = True
visual_mask = (mask_array * 255).astype(np.uint8)
image_mask = Image.fromarray(visual_mask)
return image_mask.resize(image.size)
class ImageEditing:
def __init__(self, device):
print("Initializing StableDiffusionInpaint to %s" % device)
self.device = device
self.mask_former = MaskFormer(device=self.device)
self.inpainting = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting",).to(device)
def remove_part_of_image(self, input):
image_path, to_be_removed_txt = input.split(",")
print(f'remove_part_of_image: to_be_removed {to_be_removed_txt}')
return self.replace_part_of_image(f"{image_path},{to_be_removed_txt},background")
def replace_part_of_image(self, input):
image_path, to_be_replaced_txt, replace_with_txt = input.split(",")
print(f'replace_part_of_image: replace_with_txt {replace_with_txt}')
original_image = Image.open(image_path)
mask_image = self.mask_former.inference(image_path, to_be_replaced_txt)
updated_image = self.inpainting(prompt=replace_with_txt, image=original_image, mask_image=mask_image).images[0]
updated_image_path = get_new_image_name(image_path, func_name="replace-something")
updated_image.save(updated_image_path)
return updated_image_path
class Pix2Pix:
def __init__(self, device):
print("Initializing Pix2Pix to %s" % device)
self.device = device
self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("timbrooks/instruct-pix2pix", torch_dtype=torch.float16, safety_checker=None).to(device)
self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
def inference(self, inputs):
"""Change style of image."""
print("===>Starting Pix2Pix Inference")
image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
original_image = Image.open(image_path)
image = self.pipe(instruct_text,image=original_image,num_inference_steps=40,image_guidance_scale=1.2,).images[0]
updated_image_path = get_new_image_name(image_path, func_name="pix2pix")
image.save(updated_image_path)
return updated_image_path
class T2I:
def __init__(self, device):
print("Initializing T2I to %s" % device)
self.device = device
self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
self.text_refine_tokenizer = AutoTokenizer.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
self.text_refine_model = AutoModelForCausalLM.from_pretrained("Gustavosta/MagicPrompt-Stable-Diffusion")
self.text_refine_gpt2_pipe = pipeline("text-generation", model=self.text_refine_model, tokenizer=self.text_refine_tokenizer, device=self.device)
self.pipe.to(device)
def inference(self, text):
image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
refined_text = self.text_refine_gpt2_pipe(text)[0]["generated_text"]
print(f'{text} refined to {refined_text}')
image = self.pipe(refined_text).images[0]
image.save(image_filename)
print(f"Processed T2I.run, text: {text}, image_filename: {image_filename}")
return image_filename
class ImageCaptioning:
def __init__(self, device):
print("Initializing ImageCaptioning to %s" % device)
self.device = device
self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(self.device)
def inference(self, image_path):
inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device)
out = self.model.generate(**inputs)
captions = self.processor.decode(out[0], skip_special_tokens=True)
return captions
class image2canny:
def __init__(self):
print("Direct detect canny.")
self.detector = CannyDetector()
self.low_thresh = 100
self.high_thresh = 200
def inference(self, inputs):
print("===>Starting image2canny Inference")
image = Image.open(inputs)
image = np.array(image)
canny = self.detector(image, self.low_thresh, self.high_thresh)
canny = 255 - canny
image = Image.fromarray(canny)
updated_image_path = get_new_image_name(inputs, func_name="edge")
image.save(updated_image_path)
return updated_image_path
class canny2image:
def __init__(self, device):
print("Initialize the canny2image model.")
model = create_model('ControlNet/models/cldm_v15.yaml', device=device).to(device)
model.load_state_dict(load_state_dict('ControlNet/models/control_sd15_canny.pth', location='mps'))
self.model = model.to(device)
self.device = device
self.ddim_sampler = DDIMSampler(self.model)
self.ddim_steps = 20
self.image_resolution = 512
self.num_samples = 1
self.save_memory = False
self.strength = 1.0
self.guess_mode = False
self.scale = 9.0
self.seed = -1
self.a_prompt = 'best quality, extremely detailed'
self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
def inference(self, inputs):
print("===>Starting canny2image Inference")
image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
image = Image.open(image_path)
image = np.array(image)
image = 255 - image
prompt = instruct_text
img = resize_image(HWC3(image), self.image_resolution)
H, W, C = img.shape
control = torch.from_numpy(img.copy()).float().to(device=self.device) / 255.0
control = torch.stack([control for _ in range(self.num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
self.seed = random.randint(0, 65535)
seed_everything(self.seed)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
cond = {"c_concat": [control], "c_crossattn": [self.model.get_learned_conditioning([prompt + ', ' + self.a_prompt] * self.num_samples)]}
un_cond = {"c_concat": None if self.guess_mode else [control], "c_crossattn": [self.model.get_learned_conditioning([self.n_prompt] * self.num_samples)]}
shape = (4, H // 8, W // 8)
self.model.control_scales = [self.strength * (0.825 ** float(12 - i)) for i in range(13)] if self.guess_mode else ([self.strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
samples, intermediates = self.ddim_sampler.sample(self.ddim_steps, self.num_samples, shape, cond, verbose=False, eta=0., unconditional_guidance_scale=self.scale, unconditional_conditioning=un_cond)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
x_samples = self.model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cuda().numpy().clip(0, 255).astype(np.uint8)
updated_image_path = get_new_image_name(image_path, func_name="canny2image")
real_image = Image.fromarray(x_samples[0]) # get default the index0 image
real_image.save(updated_image_path)
return updated_image_path
class image2line:
def __init__(self):
print("Direct detect straight line...")
self.detector = MLSDdetector()
self.value_thresh = 0.1
self.dis_thresh = 0.1
self.resolution = 512
def inference(self, inputs):
print("===>Starting image2hough Inference")
image = Image.open(inputs)
image = np.array(image)
image = HWC3(image)
hough = self.detector(resize_image(image, self.resolution), self.value_thresh, self.dis_thresh)
updated_image_path = get_new_image_name(inputs, func_name="line-of")
hough = 255 - cv2.dilate(hough, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
image = Image.fromarray(hough)
image.save(updated_image_path)
return updated_image_path
class line2image:
def __init__(self, device):
print("Initialize the line2image model...")
model = create_model('ControlNet/models/cldm_v15.yaml', device=device).to(device)
model.load_state_dict(load_state_dict('ControlNet/models/control_sd15_mlsd.pth', location='mps'))
self.model = model.to(device)
self.device = device
self.ddim_sampler = DDIMSampler(self.model)
self.ddim_steps = 20
self.image_resolution = 512
self.num_samples = 1
self.save_memory = False
self.strength = 1.0
self.guess_mode = False
self.scale = 9.0
self.seed = -1
self.a_prompt = 'best quality, extremely detailed'
self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
def inference(self, inputs):
print("===>Starting line2image Inference")
image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
image = Image.open(image_path)
image = np.array(image)
image = 255 - image
prompt = instruct_text
img = resize_image(HWC3(image), self.image_resolution)
H, W, C = img.shape
img = cv2.resize(img, (W, H), interpolation=cv2.INTER_NEAREST)
control = torch.from_numpy(img.copy()).float().to(device=self.device) / 255.0
control = torch.stack([control for _ in range(self.num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
self.seed = random.randint(0, 65535)
seed_everything(self.seed)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
cond = {"c_concat": [control], "c_crossattn": [self.model.get_learned_conditioning([prompt + ', ' + self.a_prompt] * self.num_samples)]}
un_cond = {"c_concat": None if self.guess_mode else [control], "c_crossattn": [self.model.get_learned_conditioning([self.n_prompt] * self.num_samples)]}
shape = (4, H // 8, W // 8)
self.model.control_scales = [self.strength * (0.825 ** float(12 - i)) for i in range(13)] if self.guess_mode else ([self.strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
samples, intermediates = self.ddim_sampler.sample(self.ddim_steps, self.num_samples, shape, cond, verbose=False, eta=0., unconditional_guidance_scale=self.scale, unconditional_conditioning=un_cond)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
x_samples = self.model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).\
cuda().numpy().clip(0,255).astype(np.uint8)
updated_image_path = get_new_image_name(image_path, func_name="line2image")
real_image = Image.fromarray(x_samples[0]) # default the index0 image
real_image.save(updated_image_path)
return updated_image_path
class image2hed:
def __init__(self):
print("Direct detect soft HED boundary...")
self.detector = HEDdetector()
self.resolution = 512
def inference(self, inputs):
print("===>Starting image2hed Inference")
image = Image.open(inputs)
image = np.array(image)
image = HWC3(image)
hed = self.detector(resize_image(image, self.resolution))
updated_image_path = get_new_image_name(inputs, func_name="hed-boundary")
image = Image.fromarray(hed)
image.save(updated_image_path)
return updated_image_path
class hed2image:
def __init__(self, device):
print("Initialize the hed2image model...")
model = create_model('ControlNet/models/cldm_v15.yaml', device=device).to(device)
model.load_state_dict(load_state_dict('ControlNet/models/control_sd15_hed.pth', location='mps'))
self.model = model.to(device)
self.device = device
self.ddim_sampler = DDIMSampler(self.model)
self.ddim_steps = 20
self.image_resolution = 512
self.num_samples = 1
self.save_memory = False
self.strength = 1.0
self.guess_mode = False
self.scale = 9.0
self.seed = -1
self.a_prompt = 'best quality, extremely detailed'
self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
def inference(self, inputs):
print("===>Starting hed2image Inference")
image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
image = Image.open(image_path)
image = np.array(image)
prompt = instruct_text
img = resize_image(HWC3(image), self.image_resolution)
H, W, C = img.shape
img = cv2.resize(img, (W, H), interpolation=cv2.INTER_NEAREST)
control = torch.from_numpy(img.copy()).float().to(device=self.device) / 255.0
control = torch.stack([control for _ in range(self.num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
self.seed = random.randint(0, 65535)
seed_everything(self.seed)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
cond = {"c_concat": [control], "c_crossattn": [self.model.get_learned_conditioning([prompt + ', ' + self.a_prompt] * self.num_samples)]}
un_cond = {"c_concat": None if self.guess_mode else [control], "c_crossattn": [self.model.get_learned_conditioning([self.n_prompt] * self.num_samples)]}
shape = (4, H // 8, W // 8)
self.model.control_scales = [self.strength * (0.825 ** float(12 - i)) for i in range(13)] if self.guess_mode else ([self.strength] * 13)
samples, intermediates = self.ddim_sampler.sample(self.ddim_steps, self.num_samples, shape, cond, verbose=False, eta=0., unconditional_guidance_scale=self.scale, unconditional_conditioning=un_cond)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
x_samples = self.model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cuda().numpy().clip(0, 255).astype(np.uint8)
updated_image_path = get_new_image_name(image_path, func_name="hed2image")
real_image = Image.fromarray(x_samples[0]) # default the index0 image
real_image.save(updated_image_path)
return updated_image_path
class image2scribble:
def __init__(self):
print("Direct detect scribble.")
self.detector = HEDdetector()
self.resolution = 512
def inference(self, inputs):
print("===>Starting image2scribble Inference")
image = Image.open(inputs)
image = np.array(image)
image = HWC3(image)
detected_map = self.detector(resize_image(image, self.resolution))
detected_map = HWC3(detected_map)
image = resize_image(image, self.resolution)
H, W, C = image.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
detected_map = nms(detected_map, 127, 3.0)
detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
detected_map[detected_map > 4] = 255
detected_map[detected_map < 255] = 0
detected_map = 255 - detected_map
updated_image_path = get_new_image_name(inputs, func_name="scribble")
image = Image.fromarray(detected_map)
image.save(updated_image_path)
return updated_image_path
class scribble2image:
def __init__(self, device):
print("Initialize the scribble2image model...")
model = create_model('ControlNet/models/cldm_v15.yaml', device=device).to(device)
model.load_state_dict(load_state_dict('ControlNet/models/control_sd15_scribble.pth', location='mps'))
self.model = model.to(device)
self.device = device
self.ddim_sampler = DDIMSampler(self.model)
self.ddim_steps = 20
self.image_resolution = 512
self.num_samples = 1
self.save_memory = False
self.strength = 1.0
self.guess_mode = False
self.scale = 9.0
self.seed = -1
self.a_prompt = 'best quality, extremely detailed'
self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
def inference(self, inputs):
print("===>Starting scribble2image Inference")
print(f'sketch device {self.device}')
image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
image = Image.open(image_path)
image = np.array(image)
prompt = instruct_text
image = 255 - image
img = resize_image(HWC3(image), self.image_resolution)
H, W, C = img.shape
img = cv2.resize(img, (W, H), interpolation=cv2.INTER_NEAREST)
control = torch.from_numpy(img.copy()).float().to(device=self.device) / 255.0
control = torch.stack([control for _ in range(self.num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
self.seed = random.randint(0, 65535)
seed_everything(self.seed)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
cond = {"c_concat": [control], "c_crossattn": [self.model.get_learned_conditioning([prompt + ', ' + self.a_prompt] * self.num_samples)]}
un_cond = {"c_concat": None if self.guess_mode else [control], "c_crossattn": [self.model.get_learned_conditioning([self.n_prompt] * self.num_samples)]}
shape = (4, H // 8, W // 8)
self.model.control_scales = [self.strength * (0.825 ** float(12 - i)) for i in range(13)] if self.guess_mode else ([self.strength] * 13)
samples, intermediates = self.ddim_sampler.sample(self.ddim_steps, self.num_samples, shape, cond, verbose=False, eta=0., unconditional_guidance_scale=self.scale, unconditional_conditioning=un_cond)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
x_samples = self.model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cuda().numpy().clip(0, 255).astype(np.uint8)
updated_image_path = get_new_image_name(image_path, func_name="scribble2image")
real_image = Image.fromarray(x_samples[0]) # default the index0 image
real_image.save(updated_image_path)
return updated_image_path
class image2pose:
def __init__(self):
print("Direct human pose.")
self.detector = OpenposeDetector()
self.resolution = 512
def inference(self, inputs):
print("===>Starting image2pose Inference")
image = Image.open(inputs)
image = np.array(image)
image = HWC3(image)
detected_map, _ = self.detector(resize_image(image, self.resolution))
detected_map = HWC3(detected_map)
image = resize_image(image, self.resolution)
H, W, C = image.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
updated_image_path = get_new_image_name(inputs, func_name="human-pose")
image = Image.fromarray(detected_map)
image.save(updated_image_path)
return updated_image_path
class pose2image:
def __init__(self, device):
print("Initialize the pose2image model...")
model = create_model('ControlNet/models/cldm_v15.yaml', device=device).to(device)
model.load_state_dict(load_state_dict('ControlNet/models/control_sd15_openpose.pth', location='mps'))
self.model = model.to(device)
self.device = device
self.ddim_sampler = DDIMSampler(self.model)
self.ddim_steps = 20
self.image_resolution = 512
self.num_samples = 1
self.save_memory = False
self.strength = 1.0
self.guess_mode = False
self.scale = 9.0
self.seed = -1
self.a_prompt = 'best quality, extremely detailed'
self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
def inference(self, inputs):
print("===>Starting pose2image Inference")
image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
image = Image.open(image_path)
image = np.array(image)
prompt = instruct_text
img = resize_image(HWC3(image), self.image_resolution)
H, W, C = img.shape
img = cv2.resize(img, (W, H), interpolation=cv2.INTER_NEAREST)
control = torch.from_numpy(img.copy()).float().to(device=self.device) / 255.0
control = torch.stack([control for _ in range(self.num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
self.seed = random.randint(0, 65535)
seed_everything(self.seed)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
cond = {"c_concat": [control], "c_crossattn": [ self.model.get_learned_conditioning([prompt + ', ' + self.a_prompt] * self.num_samples)]}
un_cond = {"c_concat": None if self.guess_mode else [control], "c_crossattn": [self.model.get_learned_conditioning([self.n_prompt] * self.num_samples)]}
shape = (4, H // 8, W // 8)
self.model.control_scales = [self.strength * (0.825 ** float(12 - i)) for i in range(13)] if self.guess_mode else ([self.strength] * 13)
samples, intermediates = self.ddim_sampler.sample(self.ddim_steps, self.num_samples, shape, cond, verbose=False, eta=0., unconditional_guidance_scale=self.scale, unconditional_conditioning=un_cond)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
x_samples = self.model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cuda().numpy().clip(0, 255).astype(np.uint8)
updated_image_path = get_new_image_name(image_path, func_name="pose2image")
real_image = Image.fromarray(x_samples[0]) # default the index0 image
real_image.save(updated_image_path)
return updated_image_path
class image2seg:
def __init__(self):
print("Direct segmentations.")
self.detector = UniformerDetector()
self.resolution = 512
def inference(self, inputs):
print("===>Starting image2seg Inference")
image = Image.open(inputs)
image = np.array(image)
image = HWC3(image)
detected_map = self.detector(resize_image(image, self.resolution))
detected_map = HWC3(detected_map)
image = resize_image(image, self.resolution)
H, W, C = image.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
updated_image_path = get_new_image_name(inputs, func_name="segmentation")
image = Image.fromarray(detected_map)
image.save(updated_image_path)
return updated_image_path
class seg2image:
def __init__(self, device):
print("Initialize the seg2image model...")
model = create_model('ControlNet/models/cldm_v15.yaml', device=device).to(device)
model.load_state_dict(load_state_dict('ControlNet/models/control_sd15_seg.pth', location='mps'))
self.model = model.to(device)
self.device = device
self.ddim_sampler = DDIMSampler(self.model)
self.ddim_steps = 20
self.image_resolution = 512
self.num_samples = 1
self.save_memory = False
self.strength = 1.0
self.guess_mode = False
self.scale = 9.0
self.seed = -1
self.a_prompt = 'best quality, extremely detailed'
self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
def inference(self, inputs):
print("===>Starting seg2image Inference")
image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
image = Image.open(image_path)
image = np.array(image)
prompt = instruct_text
img = resize_image(HWC3(image), self.image_resolution)
H, W, C = img.shape
img = cv2.resize(img, (W, H), interpolation=cv2.INTER_NEAREST)
control = torch.from_numpy(img.copy()).float().to(device=self.device) / 255.0
control = torch.stack([control for _ in range(self.num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
self.seed = random.randint(0, 65535)
seed_everything(self.seed)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
cond = {"c_concat": [control], "c_crossattn": [self.model.get_learned_conditioning([prompt + ', ' + self.a_prompt] * self.num_samples)]}
un_cond = {"c_concat": None if self.guess_mode else [control], "c_crossattn": [self.model.get_learned_conditioning([self.n_prompt] * self.num_samples)]}
shape = (4, H // 8, W // 8)
self.model.control_scales = [self.strength * (0.825 ** float(12 - i)) for i in range(13)] if self.guess_mode else ([self.strength] * 13)
samples, intermediates = self.ddim_sampler.sample(self.ddim_steps, self.num_samples, shape, cond, verbose=False, eta=0., unconditional_guidance_scale=self.scale, unconditional_conditioning=un_cond)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
x_samples = self.model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cuda().numpy().clip(0, 255).astype(np.uint8)
updated_image_path = get_new_image_name(image_path, func_name="segment2image")
real_image = Image.fromarray(x_samples[0]) # default the index0 image
real_image.save(updated_image_path)
return updated_image_path
class image2depth:
def __init__(self):
print("Direct depth estimation.")
self.detector = MidasDetector()
self.resolution = 512
def inference(self, inputs):
print("===>Starting image2depth Inference")
image = Image.open(inputs)
image = np.array(image)
image = HWC3(image)
detected_map, _ = self.detector(resize_image(image, self.resolution))
detected_map = HWC3(detected_map)
image = resize_image(image, self.resolution)
H, W, C = image.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
updated_image_path = get_new_image_name(inputs, func_name="depth")
image = Image.fromarray(detected_map)
image.save(updated_image_path)
return updated_image_path
class depth2image:
def __init__(self, device):
print("Initialize depth2image model...")
model = create_model('ControlNet/models/cldm_v15.yaml', device=device).to(device)
model.load_state_dict(load_state_dict('ControlNet/models/control_sd15_depth.pth', location='mps'))
self.model = model.to(device)
self.device = device
self.ddim_sampler = DDIMSampler(self.model)
self.ddim_steps = 20
self.image_resolution = 512
self.num_samples = 1
self.save_memory = False
self.strength = 1.0
self.guess_mode = False
self.scale = 9.0
self.seed = -1
self.a_prompt = 'best quality, extremely detailed'
self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
def inference(self, inputs):
print("===>Starting depth2image Inference")
image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
image = Image.open(image_path)
image = np.array(image)
prompt = instruct_text
img = resize_image(HWC3(image), self.image_resolution)
H, W, C = img.shape
img = cv2.resize(img, (W, H), interpolation=cv2.INTER_NEAREST)
control = torch.from_numpy(img.copy()).float().to(device=self.device) / 255.0
control = torch.stack([control for _ in range(self.num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
self.seed = random.randint(0, 65535)
seed_everything(self.seed)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
cond = {"c_concat": [control], "c_crossattn": [ self.model.get_learned_conditioning([prompt + ', ' + self.a_prompt] * self.num_samples)]}
un_cond = {"c_concat": None if self.guess_mode else [control], "c_crossattn": [self.model.get_learned_conditioning([self.n_prompt] * self.num_samples)]}
shape = (4, H // 8, W // 8)
self.model.control_scales = [self.strength * (0.825 ** float(12 - i)) for i in range(13)] if self.guess_mode else ([self.strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
samples, intermediates = self.ddim_sampler.sample(self.ddim_steps, self.num_samples, shape, cond, verbose=False, eta=0., unconditional_guidance_scale=self.scale, unconditional_conditioning=un_cond)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
x_samples = self.model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cuda().numpy().clip(0, 255).astype(np.uint8)
updated_image_path = get_new_image_name(image_path, func_name="depth2image")
real_image = Image.fromarray(x_samples[0]) # default the index0 image
real_image.save(updated_image_path)
return updated_image_path
class image2normal:
def __init__(self):
print("Direct normal estimation.")
self.detector = MidasDetector()
self.resolution = 512
self.bg_threshold = 0.4
def inference(self, inputs):
print("===>Starting image2 normal Inference")
image = Image.open(inputs)
image = np.array(image)
image = HWC3(image)
_, detected_map = self.detector(resize_image(image, self.resolution), bg_th=self.bg_threshold)
detected_map = HWC3(detected_map)
image = resize_image(image, self.resolution)
H, W, C = image.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
updated_image_path = get_new_image_name(inputs, func_name="normal-map")
image = Image.fromarray(detected_map)
image.save(updated_image_path)
return updated_image_path
class normal2image:
def __init__(self, device):
print("Initialize normal2image model...")
model = create_model('ControlNet/models/cldm_v15.yaml', device=device).to(device)
model.load_state_dict(load_state_dict('ControlNet/models/control_sd15_normal.pth', location='mps'))
self.model = model.to(device)
self.device = device
self.ddim_sampler = DDIMSampler(self.model)
self.ddim_steps = 20
self.image_resolution = 512
self.num_samples = 1
self.save_memory = False
self.strength = 1.0
self.guess_mode = False
self.scale = 9.0
self.seed = -1
self.a_prompt = 'best quality, extremely detailed'
self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
def inference(self, inputs):
print("===>Starting normal2image Inference")
image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
image = Image.open(image_path)
image = np.array(image)
prompt = instruct_text
img = image[:, :, ::-1].copy()
img = resize_image(HWC3(img), self.image_resolution)
H, W, C = img.shape
img = cv2.resize(img, (W, H), interpolation=cv2.INTER_NEAREST)
control = torch.from_numpy(img.copy()).float().to(device=self.device) / 255.0
control = torch.stack([control for _ in range(self.num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
self.seed = random.randint(0, 65535)
seed_everything(self.seed)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
cond = {"c_concat": [control], "c_crossattn": [self.model.get_learned_conditioning([prompt + ', ' + self.a_prompt] * self.num_samples)]}
un_cond = {"c_concat": None if self.guess_mode else [control], "c_crossattn": [self.model.get_learned_conditioning([self.n_prompt] * self.num_samples)]}
shape = (4, H // 8, W // 8)
self.model.control_scales = [self.strength * (0.825 ** float(12 - i)) for i in range(13)] if self.guess_mode else ([self.strength] * 13)
samples, intermediates = self.ddim_sampler.sample(self.ddim_steps, self.num_samples, shape, cond, verbose=False, eta=0., unconditional_guidance_scale=self.scale, unconditional_conditioning=un_cond)
if self.save_memory:
self.model.low_vram_shift(is_diffusing=False)
x_samples = self.model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cuda().numpy().clip(0, 255).astype(np.uint8)
updated_image_path = get_new_image_name(image_path, func_name="normal2image")
real_image = Image.fromarray(x_samples[0]) # default the index0 image
real_image.save(updated_image_path)
return updated_image_path
class BLIPVQA:
def __init__(self, device):
print("Initializing BLIP VQA to %s" % device)
self.device = device
self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
self.model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to(self.device)
def get_answer_from_question_and_image(self, inputs):
image_path, question = inputs.split(",")
raw_image = Image.open(image_path).convert('RGB')
print(F'BLIPVQA :question :{question}')
inputs = self.processor(raw_image, question, return_tensors="pt").to(self.device)
out = self.model.generate(**inputs)
answer = self.processor.decode(out[0], skip_special_tokens=True)
return answer
class ConversationBot:
def __init__(self):
print("Initializing VisualChatGPT")
self.llm = OpenAI(temperature=0)
#self.edit = ImageEditing(device="mps")
self.i2t = ImageCaptioning(device="mps")
self.t2i = T2I(device="mps")
# self.image2canny = image2canny()
# self.canny2image = canny2image(device="mps")
# self.image2line = image2line()
# self.line2image = line2image(device="mps")
# self.image2hed = image2hed()
# self.hed2image = hed2image(device="mps")
# self.image2scribble = image2scribble()
# self.scribble2image = scribble2image(device="mps")
# self.image2pose = image2pose()
# self.pose2image = pose2image(device="mps")
# self.BLIPVQA = BLIPVQA(device="mps")
# self.image2seg = image2seg()
# self.seg2image = seg2image(device="mps")
# self.image2depth = image2depth()
# self.depth2image = depth2image(device="mps")
# self.image2normal = image2normal()
# self.normal2image = normal2image(device="mps")
#self.pix2pix = Pix2Pix(device="mps")
self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output')
self.tools = [
Tool(name="Get Photo Description", func=self.i2t.inference,
description="useful when you want to know what is inside the photo. receives image_path as input. "
"The input to this tool should be a string, representing the image_path. "),
Tool(name="Generate Image From User Input Text", func=self.t2i.inference,
description="useful when you want to generate an image from a user input text and save it to a file. like: generate an image of an object or something, or generate an image that includes some objects. "
"The input to this tool should be a string, representing the text used to generate image. "),
# Tool(name="Get Photo Description", func=self.i2t.inference,
# description="useful when you want to know what is inside the photo. receives image_path as input. "
# "The input to this tool should be a string, representing the image_path. "),
# Tool(name="Generate Image From User Input Text", func=self.t2i.inference,
# description="useful when you want to generate an image from a user input text and save it to a file. like: generate an image of an object or something, or generate an image that includes some objects. "
# "The input to this tool should be a string, representing the text used to generate image. "),
# Tool(name="Remove Something From The Photo", func=self.edit.remove_part_of_image,
# description="useful when you want to remove and object or something from the photo from its description or location. "
# "The input to this tool should be a comma seperated string of two, representing the image_path and the object need to be removed. "),
# Tool(name="Replace Something From The Photo", func=self.edit.replace_part_of_image,
# description="useful when you want to replace an object from the object description or location with another object from its description. "
# "The input to this tool should be a comma seperated string of three, representing the image_path, the object to be replaced, the object to be replaced with "),
# Tool(name="Instruct Image Using Text", func=self.pix2pix.inference,
# description="useful when you want to the style of the image to be like the text. like: make it look like a painting. or make it like a robot. "
# "The input to this tool should be a comma seperated string of two, representing the image_path and the text. "),
# Tool(name="Answer Question About The Image", func=self.BLIPVQA.get_answer_from_question_and_image,
# description="useful when you need an answer for a question based on an image. like: what is the background color of the last image, how many cats in this figure, what is in this figure. "
# "The input to this tool should be a comma seperated string of two, representing the image_path and the question"),
# Tool(name="Edge Detection On Image", func=self.image2canny.inference,
# description="useful when you want to detect the edge of the image. like: detect the edges of this image, or canny detection on image, or peform edge detection on this image, or detect the canny image of this image. "
# "The input to this tool should be a string, representing the image_path"),
# Tool(name="Generate Image Condition On Canny Image", func=self.canny2image.inference,
# description="useful when you want to generate a new real image from both the user desciption and a canny image. like: generate a real image of a object or something from this canny image, or generate a new real image of a object or something from this edge image. "
# "The input to this tool should be a comma seperated string of two, representing the image_path and the user description. "),
# Tool(name="Line Detection On Image", func=self.image2line.inference,
# description="useful when you want to detect the straight line of the image. like: detect the straight lines of this image, or straight line detection on image, or peform straight line detection on this image, or detect the straight line image of this image. "
# "The input to this tool should be a string, representing the image_path"),
# Tool(name="Generate Image Condition On Line Image", func=self.line2image.inference,
# description="useful when you want to generate a new real image from both the user desciption and a straight line image. like: generate a real image of a object or something from this straight line image, or generate a new real image of a object or something from this straight lines. "
# "The input to this tool should be a comma seperated string of two, representing the image_path and the user description. "),
# Tool(name="Hed Detection On Image", func=self.image2hed.inference,
# description="useful when you want to detect the soft hed boundary of the image. like: detect the soft hed boundary of this image, or hed boundary detection on image, or peform hed boundary detection on this image, or detect soft hed boundary image of this image. "
# "The input to this tool should be a string, representing the image_path"),
# Tool(name="Generate Image Condition On Soft Hed Boundary Image", func=self.hed2image.inference,
# description="useful when you want to generate a new real image from both the user desciption and a soft hed boundary image. like: generate a real image of a object or something from this soft hed boundary image, or generate a new real image of a object or something from this hed boundary. "
# "The input to this tool should be a comma seperated string of two, representing the image_path and the user description"),
# Tool(name="Segmentation On Image", func=self.image2seg.inference,
# description="useful when you want to detect segmentations of the image. like: segment this image, or generate segmentations on this image, or peform segmentation on this image. "
# "The input to this tool should be a string, representing the image_path"),
# Tool(name="Generate Image Condition On Segmentations", func=self.seg2image.inference,
# description="useful when you want to generate a new real image from both the user desciption and segmentations. like: generate a real image of a object or something from this segmentation image, or generate a new real image of a object or something from these segmentations. "
# "The input to this tool should be a comma seperated string of two, representing the image_path and the user description"),
# Tool(name="Predict Depth On Image", func=self.image2depth.inference,
# description="useful when you want to detect depth of the image. like: generate the depth from this image, or detect the depth map on this image, or predict the depth for this image. "
# "The input to this tool should be a string, representing the image_path"),
# Tool(name="Generate Image Condition On Depth", func=self.depth2image.inference,
# description="useful when you want to generate a new real image from both the user desciption and depth image. like: generate a real image of a object or something from this depth image, or generate a new real image of a object or something from the depth map. "
# "The input to this tool should be a comma seperated string of two, representing the image_path and the user description"),
# Tool(name="Predict Normal Map On Image", func=self.image2normal.inference,
# description="useful when you want to detect norm map of the image. like: generate normal map from this image, or predict normal map of this image. "
# "The input to this tool should be a string, representing the image_path"),
# Tool(name="Generate Image Condition On Normal Map", func=self.normal2image.inference,
# description="useful when you want to generate a new real image from both the user desciption and normal map. like: generate a real image of a object or something from this normal map, or generate a new real image of a object or something from the normal map. "
# "The input to this tool should be a comma seperated string of two, representing the image_path and the user description"),
# Tool(name="Sketch Detection On Image", func=self.image2scribble.inference,
# description="useful when you want to generate a scribble of the image. like: generate a scribble of this image, or generate a sketch from this image, detect the sketch from this image. "
# "The input to this tool should be a string, representing the image_path"),
# Tool(name="Generate Image Condition On Sketch Image", func=self.scribble2image.inference,
# description="useful when you want to generate a new real image from both the user desciption and a scribble image or a sketch image. "
# "The input to this tool should be a comma seperated string of two, representing the image_path and the user description"),
# Tool(name="Pose Detection On Image", func=self.image2pose.inference,
# description="useful when you want to detect the human pose of the image. like: generate human poses of this image, or generate a pose image from this image. "
# "The input to this tool should be a string, representing the image_path"),
# Tool(name="Generate Image Condition On Pose Image", func=self.pose2image.inference,
# description="useful when you want to generate a new real image from both the user desciption and a human pose image. like: generate a real image of a human from this human pose image, or generate a new real image of a human from this pose. "
# "The input to this tool should be a comma seperated string of two, representing the image_path and the user description")
]
self.agent = initialize_agent(
self.tools,
self.llm,
agent="conversational-react-description",
verbose=True,
memory=self.memory,
return_intermediate_steps=True,
agent_kwargs={'prefix': VISUAL_CHATGPT_PREFIX, 'format_instructions': VISUAL_CHATGPT_FORMAT_INSTRUCTIONS, 'suffix': VISUAL_CHATGPT_SUFFIX}, )
def run_text(self, text, state):
print("===============Running run_text =============")
print("Inputs:", text, state)
print("======>Previous memory:\n %s" % self.agent.memory)
#self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500)
res = self.agent({"input": text})
print("======>Current memory:\n %s" % self.agent.memory)
response = re.sub('(image/\S*png)', lambda m: f'})*{m.group(0)}*', res['output'])
state = state + [(text, response)]
print("Outputs:", state)
return state, state
def run_image(self, image, state, txt):
print("===============Running run_image =============")
print("Inputs:", image, state)
print("======>Previous memory:\n %s" % self.agent.memory)
image_filename = os.path.join('image', str(uuid.uuid4())[0:8] + ".png")
print("======>Auto Resize Image...")
img = Image.open(image.name)
width, height = img.size
ratio = min(512 / width, 512 / height)
width_new, height_new = (round(width * ratio), round(height * ratio))
img = img.resize((width_new, height_new))
img = img.convert('RGB')
img.save(image_filename, "PNG")
print(f"Resize image form {width}x{height} to {width_new}x{height_new}")
description = self.i2t.inference(image_filename)
Human_prompt = "\nHuman: provide a figure named {}. The description is: {}. This information helps you to understand this image, but you should use tools to finish following tasks, " \
"rather than directly imagine from my description. If you understand, say \"Received\". \n".format(image_filename, description)
AI_prompt = "Received. "
#self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt
self.agent.memory.buffer.save_context({"input": Human_prompt}, {"output": AI_prompt})
print("======>Current memory:\n %s" % self.agent.memory)
state = state + [(f"*{image_filename}*", AI_prompt)]
print("Outputs:", state)
return state, state, txt + ' ' + image_filename + ' '
if __name__ == '__main__':
bot = ConversationBot()
with gr.Blocks(css="#chatbot .overflow-y-auto{height:500px}") as demo:
chatbot = gr.Chatbot(elem_id="chatbot", label="Visual ChatGPT")
state = gr.State([])
with gr.Row():
with gr.Column(scale=0.7):
txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style(container=False)
with gr.Column(scale=0.15, min_width=0):
clear = gr.Button("Clear️")
with gr.Column(scale=0.15, min_width=0):
btn = gr.UploadButton("Upload", file_types=["image"])
txt.submit(bot.run_text, [txt, state], [chatbot, state])
txt.submit(lambda: "", None, txt)
btn.upload(bot.run_image, [btn, state, txt], [chatbot, state, txt])
clear.click(bot.memory.clear)
clear.click(lambda: [], None, chatbot)
clear.click(lambda: [], None, state)
demo.launch(server_name="0.0.0.0", server_port=7860)
注意，以上程式碼是修改了MPS模式、langchain庫bug以及遮蔽了多個模型後的修改版本。
執行Visual ChatGPT
折騰了大半天，終於可以無錯誤執行了：
python3 visual_chatgpt.py
程式返回：
➜ visual-chatgpt git:(main) ✗ python visual_chatgpt.py
Initializing VisualChatGPT
Initializing ImageCaptioning to mps
Initializing T2I to mps
/opt/homebrew/lib/python3.10/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.
warnings.warn(
Running on local URL: http://0.0.0.0:7860
程式設計的樂趣就在於，當你為了執行某個程式經歷了千難萬險，甚至瀕臨絕望的時候，突然，程式調通了，此時大腦皮層會大量分泌多巴胺（dopamine），那感覺，就像突然領悟了人生妙諦，又像是終於明白了天人化生、萬物滋長的要道，簡而言之，白日飛昇，快樂加倍，那種精神上的享受，絕對比玩電子遊戲或者享受美食更加的高階。
隨後訪問http://localhost:7860：
直接用中文開聊即可，不需要ControlNet那些令人厭煩的引導詞。
後臺程式邏輯：
Inputs: 給我一隻大金毛 []
======>Previous memory:
chat_memory=ChatMessageHistory(messages=[]) output_key='output' input_key=None return_messages=False human_prefix='Human' ai_prefix='AI' memory_key='chat_history'
> Entering new AgentExecutor chain...
Yes
Action: Generate Image From User Input Text
Action Input: A golden retrieverSetting `pad_token_id` to `eos_token_id`:50256 for open-end generation.
A golden retriever refined to A golden retriever,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
100%|█████████████████████████████████████████████████████████████████████████████████| 50/50 [00:47<00:00, 1.05it/s]
Processed T2I.run, text: A golden retriever, image_filename: image/865c561f.png
Observation: image/865c561f.png
Thought: Do I need to use a tool? No
AI: Here is a golden retriever for you: image/865c561f.png
> Finished chain.
======>Current memory:
chat_memory=ChatMessageHistory(messages=[HumanMessage(content='給我一隻大金毛', additional_kwargs={}), AIMessage(content='Here is a golden retriever for you: image/865c561f.png', additional_kwargs={})]) output_key='output' input_key=None return_messages=False human_prefix='Human' ai_prefix='AI' memory_key='chat_history'
Outputs: [('給我一隻大金毛', 'Here is a golden retriever for you: *image/865c561f.png*')]
透過觀察，我們可以得知，雖然是中文聊天，但其實ChatGPT會把中文翻譯為英文，將“給我一隻大金毛”翻譯為：“a golden retriever”。
隨後透過模型訓練生成圖片，再將聊天記錄新增到上下文列表中，關於ChatGPT的聊天上下文，請參照：重新定義價效比!人工智慧AI聊天ChatGPT新介面模型gpt-3.5-turbo閃電更新,成本降90%,Python3.10接入
當然，為了可以線下單機環境將Visual ChatGPT成功跑起來，所以遮蔽了多個ControlNet影像模型，因此有些圖片場景並不那麼盡如人意：
結語
有的時候，當我們稱讚一項技術的時候，我們會稱其為這樣或者那樣的行業標杆、教科書之類，但是對於ChatGPT來說，它已經超越了所謂的什麼標杆，或者說得更準確一些，它是標杆中的標杆，其他的所謂的類ChatGPT產品，別說望其項背了，就連ChatGPT的尾氣也聞不到，說白了，想碰瓷都不知道該怎麼碰，因為神明早已在ChatGPT的命格中寫下八個大字：前無古人，後無來者！最後，奉上修改後的專案程式碼，與眾鄉親同饗：github.com/zcxey2911/visual_chatgpt_mps_cut