Python 3.13.0 : Predicted XAU/USD with torch.

Testing the torch python package

import torch
import torch.nn as nn
import numpy as np

data = np.array([
    [1800.5, 1810.0, 1795.0, 1000, 1805.2],
    [1805.2, 1815.0, 1800.0, 1200, 1812.8],
    [1812.8, 1820.0, 1808.0, 1100, 1810.5],
    [1810.5, 1818.0, 1805.0, 1300, 1825.0],
    [1825.0, 1830.0, 1815.0, 1400, 1820.3],
    [1820.3, 1828.0, 1810.0, 1250, 1835.7]
])

X, y = torch.tensor(data[:, :4], dtype=torch.float32), torch.tensor(data[:, 4], dtype=torch.float32)
model = nn.Sequential(nn.Linear(4, 6), nn.ReLU(), nn.Linear(6, 4), nn.ReLU(), nn.Linear(4, 1))
optimizer = torch.optim.Adam(model.parameters())
loss_fn = nn.MSELoss()
for _ in range(3000):
    optimizer.zero_grad()
    y_pred = model(X).squeeze()
    loss = loss_fn(y_pred, y)
    loss.backward()
    optimizer.step()
prediction = model(torch.tensor([[1830.0, 1840.0, 1825.0, 1150]], dtype=torch.float32))
print("Predicted XAU/USD closing price:", round(prediction.item(), 2))

The result is :

python torch_001.py
Predicted XAU/USD closing price: 1819.57

Python 3.11.11 : Colab simple image to video with stabilityai - part 052.

Today, a simple test with artificial intelligence and stabilityai/stable-video-diffusion-img2vid-xt.

The result is not very good, but I believe the source code can be improved ...

pipe = StableVideoDiffusionPipeline.from_pretrained(
    'stabilityai/stable-video-diffusion-img2vid-xt',
    torch_dtype=torch.float16,
    variant='fp16'
)

pipe.enable_model_cpu_offload()

You can find the source code on my colab GitHUb projects - catafest_069.

Python 3.11.11 : Colab fixed catafest_0059 with NVIDIA models - part 051.

Fixed the colab notebook for test models from NVIDIA for waveglow and tacotron2.

Python 3.10.12 : Simple test with diffusers to create texture.

Today I tested on colab a simple python script to generate a texture using: diffusers, torch and gradio.

I upload the notebook on my colab repo from GitHub.

The script is simple and works good:

import gradio as gr
from diffusers import StableDiffusionPipeline
import torch

model_id = "dream-textures/texture-diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

def generate_image(prompt):
    image = pipe(prompt).images[0]
    image.save("result.png")
    return image

iface = gr.Interface(
    fn=generate_image,
    inputs="text",
    outputs="image",
    title="Stable Diffusion Image Generator",
    description="Introduceți un prompt pentru a genera o imagine folosind Stable Diffusion."
)

iface.launch()

The result for the pbr winter terrain is this image: