125 lines
3.4 KiB
Python
125 lines
3.4 KiB
Python
from ..session import post
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from io import BytesIO
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from PIL import Image
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import numpy as np
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import torch
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class TextToImage:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"endpoint": ("STRING", {}),
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"prompt": ("STRING", {"multiline": True}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "inference"
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CATEGORY = "HF_Inference/Image"
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TITLE = "HF Image TextToImage"
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def inference(self, endpoint, prompt):
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payload = {"inputs": prompt}
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response = post(endpoint, json=payload)
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result = BytesIO(response.content)
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image = Image.open(result)
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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mask = torch.zeros((64, 64), device="cpu")
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return (image, mask)
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class Classification:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"endpoint": ("STRING", {}),
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"image": ("IMAGE",),
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}
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}
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RETURN_TYPES = ("STRING",)
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FUNCTION = "inference"
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CATEGORY = "HF_Inference/Image"
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TITLE = "HF Image Classification"
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def inference(self, endpoint, image):
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response = post(endpoint, data=image)
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result = response.json()
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return result
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class ObjectDetection:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"endpoint": ("STRING", {}),
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"image": ("IMAGE",),
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}
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}
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RETURN_TYPES = ("STRING",)
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FUNCTION = "inference"
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CATEGORY = "HF_Inference/Image"
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TITLE = "HF Image Object Detection"
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def inference(self, endpoint, image):
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response = post(endpoint, data=image)
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result = response.json()
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return {"ui": {"text": result}}
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from base64 import b64decode
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class Segmentation:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"endpoint": ("STRING", {}),
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"images": ("IMAGE",),
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}
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}
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RETURN_TYPES = ()
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FUNCTION = "inference"
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OUTPUT_NODE = True
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CATEGORY = "HF_Inference/Image"
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TITLE = "HF Image Segmentation"
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def inference(self, endpoint, images):
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for image in images:
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image_bytes = BytesIO()
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pil_img = Image.fromarray(
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np.clip(
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image.cpu().numpy() * 255.0,
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0,
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255,
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).astype(np.uint8)
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)
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pil_img.save(image_bytes, format='png')
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image_bytes.seek(0)
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image_bytes = image_bytes.read()
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print(len(image_bytes))
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response = post(endpoint, data=image_bytes)
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result = response.json()
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for item in result:
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label = item['label']
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mask_data = item['mask']
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mask_img = Image.open(BytesIO(b64decode(mask_data)))
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pil_img.paste(mask_img, (0, 0), mask_img)
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pil_img = np.array(pil_img).astype(np.float32) / 255.0
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pil_img = torch.from_numpy(pil_img)[None,]
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return {"ui": {"images": [pil_img, ]}}
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NODE_CLASS_MAPPINGS = {
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"Classification": Classification,
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"ObjectDetection": ObjectDetection,
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"Segmentation": Segmentation,
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"TextToImage": TextToImage,
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} |