demo.py

import model_loader
import pipeline
from PIL import Image
from transformers import CLIPTokenizer
import torch

DEVICE = "cpu"

ALLOW_CUDA = True
ALLOW_MPS = False

if torch.cuda.is_available() and ALLOW_CUDA:
    DEVICE = "cuda"
elif (torch.has_mps or torch.backends.mps.is_available()) and ALLOW_MPS:
    DEVICE = "mps"
print(f"Using device: {DEVICE}")

tokenizer = CLIPTokenizer(r"../data/vocab.json", merges_file="../data/merges.txt")
model_file = "../data/v1-5-pruned-emaonly.ckpt"
models = model_loader.preload_models_from_standard_weights(model_file, DEVICE)


## TEXT TO IMAGE

prompt = "A playful dog running through a field of flowers, bathed in golden sunlight."
uncond_prompt = ""  # Also known as negative prompt
do_cfg = True
cfg_scale = 8  # min: 1, max: 14

## SAMPLER

sampler = "ddpm"
num_inference_steps = 50
seed = 42

output_image = pipeline.generate(
    prompt=prompt,
    uncond_prompt=uncond_prompt,
    input_image=None,  # No input image provided
    strength=0.5,      # Strength not needed for text-to-image
    do_cfg=do_cfg,
    cfg_scale=cfg_scale,
    sampler_name=sampler,
    n_inference_steps=num_inference_steps,
    seed=seed,
    models=models,
    device=DEVICE,
    idle_device="cpu",  # Idle device still set to CPU
    tokenizer=tokenizer,
)

# Save the output image
output_image_path = "output_image.png"
Image.fromarray(output_image).save(output_image_path)

print("Image saved successfully at:", output_image_path)