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257 lines
10 KiB
257 lines
10 KiB
import os
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import uuid
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import numpy as np
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import torch
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from diffusers import (
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EulerAncestralDiscreteScheduler,
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StableDiffusionInpaintPipeline,
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StableDiffusionInstructPix2PixPipeline,
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StableDiffusionPipeline,
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)
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from PIL import Image
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from transformers import (
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BlipForConditionalGeneration,
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BlipForQuestionAnswering,
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BlipProcessor,
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CLIPSegForImageSegmentation,
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CLIPSegProcessor,
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)
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from swarms.prompts.prebuild.multi_modal_prompts import IMAGE_PROMPT
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from swarms.tools.tool import tool
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from swarms.utils.logger import logger
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from swarms.utils.main import BaseHandler, get_new_image_name
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class MaskFormer:
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def __init__(self, device):
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print("Initializing MaskFormer to %s" % device)
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self.device = device
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self.processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
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self.model = CLIPSegForImageSegmentation.from_pretrained(
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"CIDAS/clipseg-rd64-refined"
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).to(device)
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def inference(self, image_path, text):
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threshold = 0.5
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min_area = 0.02
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padding = 20
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original_image = Image.open(image_path)
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image = original_image.resize((512, 512))
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inputs = self.processor(
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text=text, images=image, padding="max_length", return_tensors="pt"
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).to(self.device)
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with torch.no_grad():
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outputs = self.model(**inputs)
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mask = torch.sigmoid(outputs[0]).squeeze().cpu().numpy() > threshold
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area_ratio = len(np.argwhere(mask)) / (mask.shape[0] * mask.shape[1])
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if area_ratio < min_area:
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return None
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true_indices = np.argwhere(mask)
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mask_array = np.zeros_like(mask, dtype=bool)
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for idx in true_indices:
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padded_slice = tuple(
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slice(max(0, i - padding), i + padding + 1) for i in idx
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)
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mask_array[padded_slice] = True
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visual_mask = (mask_array * 255).astype(np.uint8)
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image_mask = Image.fromarray(visual_mask)
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return image_mask.resize(original_image.size)
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class ImageEditing:
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def __init__(self, device):
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print("Initializing ImageEditing to %s" % device)
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self.device = device
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self.mask_former = MaskFormer(device=self.device)
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self.revision = "fp16" if "cuda" in device else None
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self.torch_dtype = torch.float16 if "cuda" in device else torch.float32
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self.inpaint = StableDiffusionInpaintPipeline.from_pretrained(
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"runwayml/stable-diffusion-inpainting",
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revision=self.revision,
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torch_dtype=self.torch_dtype,
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).to(device)
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@tool(
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name="Remove Something From The Photo",
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description="useful when you want to remove and object or something from the photo "
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"from its description or location. "
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"The input to this tool should be a comma separated string of two, "
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"representing the image_path and the object need to be removed. ",
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)
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def inference_remove(self, inputs):
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image_path, to_be_removed_txt = inputs.split(",")
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return self.inference_replace(f"{image_path},{to_be_removed_txt},background")
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@tool(
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name="Replace Something From The Photo",
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description="useful when you want to replace an object from the object description or "
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"location with another object from its description. "
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"The input to this tool should be a comma separated string of three, "
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"representing the image_path, the object to be replaced, the object to be replaced with ",
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)
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def inference_replace(self, inputs):
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image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",")
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original_image = Image.open(image_path)
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original_size = original_image.size
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mask_image = self.mask_former.inference(image_path, to_be_replaced_txt)
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updated_image = self.inpaint(
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prompt=replace_with_txt,
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image=original_image.resize((512, 512)),
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mask_image=mask_image.resize((512, 512)),
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).images[0]
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updated_image_path = get_new_image_name(
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image_path, func_name="replace-something"
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)
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updated_image = updated_image.resize(original_size)
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updated_image.save(updated_image_path)
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logger.debug(
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f"\nProcessed ImageEditing, Input Image: {image_path}, Replace {to_be_replaced_txt} to {replace_with_txt}, "
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f"Output Image: {updated_image_path}"
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)
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return updated_image_path
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class InstructPix2Pix:
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def __init__(self, device):
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print("Initializing InstructPix2Pix to %s" % device)
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self.device = device
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self.torch_dtype = torch.float16 if "cuda" in device else torch.float32
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self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
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"timbrooks/instruct-pix2pix",
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safety_checker=None,
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torch_dtype=self.torch_dtype,
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).to(device)
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self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
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self.pipe.scheduler.config
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)
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@tool(
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name="Instruct Image Using Text",
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description="useful when you want to the style of the image to be like the text. "
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"like: make it look like a painting. or make it like a robot. "
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"The input to this tool should be a comma separated string of two, "
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"representing the image_path and the text. ",
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)
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def inference(self, inputs):
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"""Change style of image."""
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logger.debug("===> Starting InstructPix2Pix Inference")
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image_path, text = inputs.split(",")[0], ",".join(inputs.split(",")[1:])
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original_image = Image.open(image_path)
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image = self.pipe(
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text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2
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).images[0]
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updated_image_path = get_new_image_name(image_path, func_name="pix2pix")
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image.save(updated_image_path)
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logger.debug(
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f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text: {text}, "
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f"Output Image: {updated_image_path}"
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)
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return updated_image_path
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class Text2Image:
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def __init__(self, device):
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print("Initializing Text2Image to %s" % device)
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self.device = device
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self.torch_dtype = torch.float16 if "cuda" in device else torch.float32
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self.pipe = StableDiffusionPipeline.from_pretrained(
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"runwayml/stable-diffusion-v1-5", torch_dtype=self.torch_dtype
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)
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self.pipe.to(device)
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self.a_prompt = "best quality, extremely detailed"
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self.n_prompt = (
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"longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, "
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"fewer digits, cropped, worst quality, low quality"
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)
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@tool(
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name="Generate Image From User Input Text",
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description="useful when you want to generate an image from a user input text and save it to a file. "
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"like: generate an image of an object or something, or generate an image that includes some objects. "
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"The input to this tool should be a string, representing the text used to generate image. ",
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)
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def inference(self, text):
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image_filename = os.path.join("image", str(uuid.uuid4())[0:8] + ".png")
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prompt = text + ", " + self.a_prompt
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image = self.pipe(prompt, negative_prompt=self.n_prompt).images[0]
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image.save(image_filename)
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logger.debug(
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f"\nProcessed Text2Image, Input Text: {text}, Output Image: {image_filename}"
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)
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return image_filename
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class VisualQuestionAnswering:
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def __init__(self, device):
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print("Initializing VisualQuestionAnswering to %s" % device)
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self.torch_dtype = torch.float16 if "cuda" in device else torch.float32
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self.device = device
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self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
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self.model = BlipForQuestionAnswering.from_pretrained(
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"Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype
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).to(self.device)
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@tool(
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name="Answer Question About The Image",
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description="useful when you need an answer for a question based on an image. "
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"like: what is the background color of the last image, how many cats in this figure, what is in this figure. "
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"The input to this tool should be a comma separated string of two, representing the image_path and the question",
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)
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def inference(self, inputs):
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image_path, question = inputs.split(",")
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raw_image = Image.open(image_path).convert("RGB")
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inputs = self.processor(raw_image, question, return_tensors="pt").to(
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self.device, self.torch_dtype
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)
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out = self.model.generate(**inputs)
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answer = self.processor.decode(out[0], skip_special_tokens=True)
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logger.debug(
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f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input Question: {question}, "
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f"Output Answer: {answer}"
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)
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return answer
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class ImageCaptioning(BaseHandler):
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def __init__(self, device):
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print("Initializing ImageCaptioning to %s" % device)
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self.device = device
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self.torch_dtype = torch.float16 if "cuda" in device else torch.float32
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self.processor = BlipProcessor.from_pretrained(
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"Salesforce/blip-image-captioning-base"
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)
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self.model = BlipForConditionalGeneration.from_pretrained(
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"Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype
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).to(self.device)
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def handle(self, filename: str):
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img = Image.open(filename)
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width, height = img.size
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ratio = min(512 / width, 512 / height)
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width_new, height_new = (round(width * ratio), round(height * ratio))
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img = img.resize((width_new, height_new))
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img = img.convert("RGB")
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img.save(filename, "PNG")
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print(f"Resize image form {width}x{height} to {width_new}x{height_new}")
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inputs = self.processor(Image.open(filename), return_tensors="pt").to(
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self.device, self.torch_dtype
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)
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out = self.model.generate(**inputs)
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description = self.processor.decode(out[0], skip_special_tokens=True)
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print(
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f"\nProcessed ImageCaptioning, Input Image: {filename}, Output Text: {description}"
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)
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return IMAGE_PROMPT.format(filename=filename, description=description)
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