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from flax.jax_utils import replicate
from jax import pmap
from flax.training.common_utils import shard
import jax
import jax.numpy as jnp
from pathlib import Path
from PIL import Image
import numpy as np
from diffusers import FlaxStableDiffusionPipeline
import os
if 'TPU_NAME' in os.environ:
import requests
if 'TPU_DRIVER_MODE' not in globals():
url = 'http:' + os.environ['TPU_NAME'].split(':')[1] + ':8475/requestversion/tpu_driver_nightly'
resp = requests.post(url)
TPU_DRIVER_MODE = 1
from jax.config import config
config.FLAGS.jax_xla_backend = "tpu_driver"
config.FLAGS.jax_backend_target = os.environ['TPU_NAME']
print('Registered TPU:', config.FLAGS.jax_backend_target)
else:
print('No TPU detected. Can be changed under "Runtime/Change runtime type".')
import jax
jax.local_devices()
num_devices = jax.device_count()
device_type = jax.devices()[0].device_kind
print(f"Found {num_devices} JAX devices of type {device_type}.")
def sd2_inference(pipeline, prompts, params, seed = 42, num_inference_steps = 50 ):
prng_seed = jax.random.PRNGKey(seed)
prompt_ids = pipeline.prepare_inputs(prompts)
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)
images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images
images = images.reshape((images.shape[0] * images.shape[1], ) + images.shape[-3:])
images = pipeline.numpy_to_pil(images)
return images
def image_grid(imgs, rows, cols, down_sample = 1 ):
w,h = imgs[0].size
grid = Image.new('RGB', size=(cols*w, rows*h))
for i, img in enumerate(imgs): grid.paste(img, box=(i%cols*w, i//cols*h))
grid = grid.resize( (grid.size[0]//down_sample, grid.size[1]//down_sample) )
return grid
HF_ACCESS_TOKEN = os.environ["HFAUTH"]
# Load Model
# - Reference: https://github.com/huggingface/diffusers/blob/main/README.md
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
use_auth_token = HF_ACCESS_TOKEN,
revision="bf16",
dtype=jnp.bfloat16,
) |