!pip -q install -U diffusers transformers accelerate

import torch
from diffusers import DiffusionPipeline
import matplotlib.pyplot as plt

print("Loading model...")

model = DiffusionPipeline.from_pretrained(
    "segmind/tiny-sd",
    torch_dtype=torch.float32
)

print("Model loaded successfully!")
print("Generating image...")

image = model(
    "A futuristic smart manufacturing factory with robots",
    num_inference_steps=10
).images[0]

print("Image generated successfully!")

plt.imshow(image)
plt.axis("off")
plt.show()




!pip install -q diffusers transformers accelerate tensorflow

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from diffusers import DDPMPipeline

(x_train, _), _ = tf.keras.datasets.mnist.load_data()
image = x_train[0] / 255.0

noise = np.random.normal(0, 0.5, image.shape)
noisy_image = np.clip(image + noise, 0, 1)

model = DDPMPipeline.from_pretrained("1aurent/ddpm-mnist")

plt.figure(figsize=(8, 6))

plt.subplot(2, 3, 1)
plt.imshow(noisy_image, cmap="gray")
plt.title("Noisy Image")
plt.axis("off")

plt.subplot(2, 3, 2)
plt.imshow(image, cmap="gray")
plt.title("Denoised")
plt.axis("off")

plt.tight_layout()
plt.show()