hanugm

import torch
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler

model_id = "stabilityai/stable-diffusion-2"

# Use the Euler scheduler here instead
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

tokenizer = pipe.tokenizer
text_encoder = pipe.text_encoder
print(tokenizer)
#print(text_encoder)

prompt = "cat"

# Step 1: Tokenize the prompt

Are Adversarial Attacks and Data Poisoning Distinct in the Context of Reinforcement Learning?

In the context of machine learning, adversarial attacks are typically associated with test time, where an attacker aims to fool a trained model with manipulated input data. On the other hand, data poisoning is generally linked to training time, where the training data is altered to corrupt the learning process.

However, in reinforcement learning (RL), the distinction seems less clear. For example, manipulating the observations received by an RL agent during training could be considered a form of …