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visual_chatgpt / visual_foundation_models.py

LanHarmony

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ee54eed about 1 year ago

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	from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionInstructPix2PixPipeline
	from diffusers import EulerAncestralDiscreteScheduler
	from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
	from controlnet_aux import OpenposeDetector, MLSDdetector, HEDdetector

	from transformers import AutoModelForCausalLM, AutoTokenizer, CLIPSegProcessor, CLIPSegForImageSegmentation
	from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
	from transformers import AutoImageProcessor, UperNetForSemanticSegmentation

	import os
	import random
	import torch
	import cv2
	import uuid
	from PIL import Image
	import numpy as np
	from pytorch_lightning import seed_everything

	def prompts(name, description):
	def decorator(func):
	func.name = name
	func.description = description
	return func

	return decorator

	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)


	class MaskFormer:
	def __init__(self, device):
	print("Initializing MaskFormer to %s" % 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().cpu().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(original_image.size)


	class ImageEditing:
	def __init__(self, device):
	print("Initializing ImageEditing to %s" % device)
	self.device = device
	self.mask_former = MaskFormer(device=self.device)
	self.inpaint = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", revision="fp16", torch_dtype=torch.float16).to(device)

	@prompts(name="Remove Something From The Photo",
	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. ")
	def inference_remove(self, inputs):
	image_path, to_be_removed_txt = inputs.split(",")
	return self.inference_replace(f"{image_path},{to_be_removed_txt},background")

	@prompts(name="Replace Something From The Photo",
	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 ")
	def inference_replace(self, inputs):
	image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",")
	original_image = Image.open(image_path)
	original_size = original_image.size
	mask_image = self.mask_former.inference(image_path, to_be_replaced_txt)
	updated_image = self.inpaint(prompt=replace_with_txt, image=original_image.resize((512, 512)),
	mask_image=mask_image.resize((512, 512))).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="replace-something")
	updated_image = updated_image.resize(original_size)
	updated_image.save(updated_image_path)
	print(
	f"\nProcessed ImageEditing, Input Image: {image_path}, Replace {to_be_replaced_txt} to {replace_with_txt}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class InstructPix2Pix:
	def __init__(self, device):
	print("Initializing InstructPix2Pix 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)

	@prompts(name="Instruct Image Using Text",
	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. ")
	def inference(self, inputs):
	"""Change style of image."""
	print("===>Starting InstructPix2Pix Inference")
	image_path, text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	original_image = Image.open(image_path)
	image = self.pipe(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)
	print(f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text: {text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Text2Image:
	def __init__(self, device):
	print("Initializing Text2Image 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)

	@prompts(name="Generate Image From User Input Text",
	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. ")
	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"]
	image = self.pipe(refined_text).images[0]
	image.save(image_filename)
	print(
	f"\nProcessed Text2Image, Input Text: {text}, Refined Text: {refined_text}, Output Image: {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", torch_dtype=torch.float16).to(self.device)

	@prompts(name="Get Photo Description",
	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. ")
	def inference(self, image_path):
	inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device, torch.float16)
	out = self.model.generate(**inputs)
	captions = self.processor.decode(out[0], skip_special_tokens=True)
	print(f"\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text: {captions}")
	return captions


	class Image2Canny:
	def __init__(self, device):
	print("Initializing Image2Canny")
	self.low_threshold = 100
	self.high_threshold = 200

	@prompts(name="Edge Detection On Image",
	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 perform 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")
	def inference(self, inputs):
	image = Image.open(inputs)
	image = np.array(image)
	canny = cv2.Canny(image, self.low_threshold, self.high_threshold)
	canny = canny[:, :, None]
	canny = np.concatenate([canny, canny, canny], axis=2)
	canny = Image.fromarray(canny)
	updated_image_path = get_new_image_name(inputs, func_name="edge")
	canny.save(updated_image_path)
	print(f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text: {updated_image_path}")
	return updated_image_path


	class CannyText2Image:
	def __init__(self, device):
	print("Initializing CannyText2Image to %s" % device)
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-canny", torch_dtype=torch.float16)
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None, torch_dtype=torch.float16)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	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'

	@prompts(name="Generate Image Condition On Canny Image",
	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. ")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = instruct_text + ', ' + self.a_prompt
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="canny2image")
	image.save(updated_image_path)
	print(f"\nProcessed CannyText2Image, Input Canny: {image_path}, Input Text: {instruct_text}, "
	f"Output Text: {updated_image_path}")
	return updated_image_path


	class Image2Line:
	def __init__(self, device):
	print("Initializing Image2Line")
	self.detector = MLSDdetector.from_pretrained('lllyasviel/ControlNet')

	@prompts(name="Line Detection On Image",
	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")
	def inference(self, inputs):
	image = Image.open(inputs)
	mlsd = self.detector(image)
	updated_image_path = get_new_image_name(inputs, func_name="line-of")
	mlsd.save(updated_image_path)
	print(f"\nProcessed Image2Line, Input Image: {inputs}, Output Line: {updated_image_path}")
	return updated_image_path


	class LineText2Image:
	def __init__(self, device):
	print("Initializing LineText2Image to %s" % device)
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-mlsd")
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None
	)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	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'

	@prompts(name="Generate Image Condition On Line Image",
	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. ")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = instruct_text + ', ' + self.a_prompt
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="line2image")
	image.save(updated_image_path)
	print(f"\nProcessed LineText2Image, Input Line: {image_path}, Input Text: {instruct_text}, "
	f"Output Text: {updated_image_path}")
	return updated_image_path


	class Image2Hed:
	def __init__(self, device):
	print("Initializing Image2Hed")
	self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet')

	@prompts(name="Hed Detection On Image",
	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")
	def inference(self, inputs):
	image = Image.open(inputs)
	hed = self.detector(image)
	updated_image_path = get_new_image_name(inputs, func_name="hed-boundary")
	hed.save(updated_image_path)
	print(f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed: {updated_image_path}")
	return updated_image_path


	class HedText2Image:
	def __init__(self, device):
	print("Initializing HedText2Image to %s" % device)
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-hed")
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None
	)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	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'

	@prompts(name="Generate Image Condition On Soft Hed Boundary Image",
	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")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = instruct_text + ', ' + self.a_prompt
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="hed2image")
	image.save(updated_image_path)
	print(f"\nProcessed HedText2Image, Input Hed: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Image2Scribble:
	def __init__(self, device):
	print("Initializing Image2Scribble")
	self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet')

	@prompts(name="Sketch Detection On Image",
	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")
	def inference(self, inputs):
	image = Image.open(inputs)
	scribble = self.detector(image, scribble=True)
	updated_image_path = get_new_image_name(inputs, func_name="scribble")
	scribble.save(updated_image_path)
	print(f"\nProcessed Image2Scribble, Input Image: {inputs}, Output Scribble: {updated_image_path}")
	return updated_image_path


	class ScribbleText2Image:
	def __init__(self, device):
	print("Initializing ScribbleText2Image to %s" % device)
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-scribble")
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None
	)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	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'

	@prompts(name="Generate Image Condition On Sketch Image",
	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")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = instruct_text + ', ' + self.a_prompt
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="scribble2image")
	image.save(updated_image_path)
	print(f"\nProcessed ScribbleText2Image, Input Scribble: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Image2Pose:
	def __init__(self, device):
	print("Initializing Image2Pose")
	self.detector = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')

	@prompts(name="Pose Detection On Image",
	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")
	def inference(self, inputs):
	image = Image.open(inputs)
	pose = self.detector(image)
	updated_image_path = get_new_image_name(inputs, func_name="human-pose")
	pose.save(updated_image_path)
	print(f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose: {updated_image_path}")
	return updated_image_path


	class PoseText2Image:
	def __init__(self, device):
	print("Initializing PoseText2Image to %s" % device)
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-openpose")
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	self.num_inference_steps = 20
	self.seed = -1
	self.unconditional_guidance_scale = 9.0
	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'

	@prompts(name="Generate Image Condition On Pose Image",
	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")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = instruct_text + ', ' + self.a_prompt
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="pose2image")
	image.save(updated_image_path)
	print(f"\nProcessed PoseText2Image, Input Pose: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Image2Seg:
	def __init__(self, device):
	print("Initializing Image2Seg")
	self.image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
	self.image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
	self.ade_palette = [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
	[4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
	[230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
	[150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
	[143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
	[0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
	[255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
	[255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
	[255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
	[224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
	[255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
	[6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
	[140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
	[255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
	[255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
	[11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
	[0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
	[255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
	[0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
	[173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
	[255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
	[255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
	[255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
	[0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
	[0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
	[143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
	[8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
	[255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
	[92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
	[163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
	[255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
	[255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
	[10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
	[255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
	[41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
	[71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
	[184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
	[102, 255, 0], [92, 0, 255]]

	@prompts(name="Segmentation On Image",
	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")
	def inference(self, inputs):
	image = Image.open(inputs)
	pixel_values = self.image_processor(image, return_tensors="pt").pixel_values
	with torch.no_grad():
	outputs = self.image_segmentor(pixel_values)
	seg = self.image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
	color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
	palette = np.array(self.ade_palette)
	for label, color in enumerate(palette):
	color_seg[seg == label, :] = color
	color_seg = color_seg.astype(np.uint8)
	segmentation = Image.fromarray(color_seg)
	updated_image_path = get_new_image_name(inputs, func_name="segmentation")
	segmentation.save(updated_image_path)
	print(f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose: {updated_image_path}")
	return updated_image_path


	class SegText2Image:
	def __init__(self, device):
	print("Initializing SegText2Image to %s" % device)
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-seg")
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	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'

	@prompts(name="Generate Image Condition On Segmentations",
	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")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = instruct_text + ', ' + self.a_prompt
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="segment2image")
	image.save(updated_image_path)
	print(f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Image2Depth:
	def __init__(self, device):
	print("Initializing Image2Depth")
	self.depth_estimator = pipeline('depth-estimation')

	@prompts(name="Predict Depth On Image",
	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")
	def inference(self, inputs):
	image = Image.open(inputs)
	depth = self.depth_estimator(image)['depth']
	depth = np.array(depth)
	depth = depth[:, :, None]
	depth = np.concatenate([depth, depth, depth], axis=2)
	depth = Image.fromarray(depth)
	updated_image_path = get_new_image_name(inputs, func_name="depth")
	depth.save(updated_image_path)
	print(f"\nProcessed Image2Depth, Input Image: {inputs}, Output Depth: {updated_image_path}")
	return updated_image_path


	class DepthText2Image:
	def __init__(self, device):
	print("Initializing DepthText2Image to %s" % device)
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-depth")
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	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'

	@prompts(name="Generate Image Condition On Depth",
	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")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = instruct_text + ', ' + self.a_prompt
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="depth2image")
	image.save(updated_image_path)
	print(f"\nProcessed DepthText2Image, Input Depth: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class Image2Normal:
	def __init__(self, device):
	print("Initializing Image2Normal")
	self.depth_estimator = pipeline("depth-estimation", model="Intel/dpt-hybrid-midas")
	self.bg_threhold = 0.4

	@prompts(name="Predict Normal Map On Image",
	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")
	def inference(self, inputs):
	image = Image.open(inputs)
	original_size = image.size
	image = self.depth_estimator(image)['predicted_depth'][0]
	image = image.numpy()
	image_depth = image.copy()
	image_depth -= np.min(image_depth)
	image_depth /= np.max(image_depth)
	x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)
	x[image_depth < self.bg_threhold] = 0
	y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)
	y[image_depth < self.bg_threhold] = 0
	z = np.ones_like(x) * np.pi * 2.0
	image = np.stack([x, y, z], axis=2)
	image /= np.sum(image 2.0, axis=2, keepdims=True) 0.5
	image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
	image = Image.fromarray(image)
	image = image.resize(original_size)
	updated_image_path = get_new_image_name(inputs, func_name="normal-map")
	image.save(updated_image_path)
	print(f"\nProcessed Image2Normal, Input Image: {inputs}, Output Depth: {updated_image_path}")
	return updated_image_path


	class NormalText2Image:
	def __init__(self, device):
	print("Initializing NormalText2Image to %s" % device)
	self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-normal")
	self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
	"runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None)
	self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
	self.pipe.to(device)
	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'

	@prompts(name="Generate Image Condition On Normal Map",
	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")
	def inference(self, inputs):
	image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
	image = Image.open(image_path)
	self.seed = random.randint(0, 65535)
	seed_everything(self.seed)
	prompt = instruct_text + ', ' + self.a_prompt
	image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
	guidance_scale=9.0).images[0]
	updated_image_path = get_new_image_name(image_path, func_name="normal2image")
	image.save(updated_image_path)
	print(f"\nProcessed NormalText2Image, Input Normal: {image_path}, Input Text: {instruct_text}, "
	f"Output Image: {updated_image_path}")
	return updated_image_path


	class VisualQuestionAnswering:
	def __init__(self, device):
	print("Initializing VisualQuestionAnswering to %s" % device)
	self.device = device
	self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
	self.model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base", torch_dtype=torch.float16).to(self.device)

	@prompts(name="Answer Question About The 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")
	def inference(self, inputs):
	image_path, question = inputs.split(",")
	raw_image = Image.open(image_path).convert('RGB')
	inputs = self.processor(raw_image, question, return_tensors="pt").to(self.device, torch.float16)
	out = self.model.generate(**inputs)
	answer = self.processor.decode(out[0], skip_special_tokens=True)
	print(f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input Question: {question}, "
	f"Output Answer: {answer}")
	return answer