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import gradio as gr
import jax
import jax.numpy as jnp
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from PIL import Image
from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
import cv2
title = "ControlNet for Cartoon-ifying"
description = "This is a demo on ControlNet for changing images of people into cartoons of different styles."
examples = [["./simpsons_human_1.jpg", "turn into a simpsons character", "./simpsons_animated_1.jpg"]]
# Constants
low_threshold = 100
high_threshold = 200
base_model_path = "runwayml/stable-diffusion-v1-5"
controlnet_path = "lmattingly/controlnet-uncanny-simpsons"
#controlnet_path = "JFoz/dog-cat-pose"
# Models
controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
controlnet_path, dtype=jnp.bfloat16
)
pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.bfloat16
)
def resize_image(im, max_size):
im_np = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
height, width = im_np.shape[:2]
scale_factor = max_size / max(height, width)
resized_np = cv2.resize(im_np, (int(width * scale_factor), int(height * scale_factor)))
#resized_im = Image.fromarray(resized_np)
return resized_np
def create_key(seed=0):
return jax.random.PRNGKey(seed)
def infer(prompts, image):
params["controlnet"] = controlnet_params
im = image
image = resize_image(im, 500)
num_samples = 1 #jax.device_count()
rng = create_key(0)
rng = jax.random.split(rng, jax.device_count())
#im = image
#image = Image.fromarray(im)
prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
processed_image = pipe.prepare_image_inputs([image] * num_samples)
p_params = replicate(params)
prompt_ids = shard(prompt_ids)
processed_image = shard(processed_image)
output = pipe(
prompt_ids=prompt_ids,
image=processed_image,
params=p_params,
prng_seed=rng,
num_inference_steps=5,
jit=True,
).images
output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
return output_images
gr.Interface(fn = infer, inputs = ["text", "image"], outputs = "gallery",
title = title, description = description, theme='gradio/soft',
examples=[["a simpsons cartoon character", "simpsons_human_1.jpg"]]
).launch()
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