app.py

import jax
import jax.numpy as jnp
from flax import jax_utils
from flax.training.common_utils import shard
from PIL import Image
from argparse import Namespace
import gradio as gr

import numpy as np
import mediapipe as mp
from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
import cv2

from diffusers import (
    FlaxControlNetModel,
    FlaxStableDiffusionControlNetPipeline,
)


# mediapipe annotation
MARGIN = 10  # pixels
FONT_SIZE = 1
FONT_THICKNESS = 1
HANDEDNESS_TEXT_COLOR = (88, 205, 54) # vibrant green

def draw_landmarks_on_image(rgb_image, detection_result):
  hand_landmarks_list = detection_result.hand_landmarks
  handedness_list = detection_result.handedness
  annotated_image = np.zeros_like(rgb_image)

  # Loop through the detected hands to visualize.
  for idx in range(len(hand_landmarks_list)):
    hand_landmarks = hand_landmarks_list[idx]
    handedness = handedness_list[idx]

    # Draw the hand landmarks.
    hand_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
    hand_landmarks_proto.landmark.extend([
      landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in hand_landmarks
    ])
    solutions.drawing_utils.draw_landmarks(
      annotated_image,
      hand_landmarks_proto,
      solutions.hands.HAND_CONNECTIONS,
      solutions.drawing_styles.get_default_hand_landmarks_style(),
      solutions.drawing_styles.get_default_hand_connections_style())

  return annotated_image

def generate_annotation(img):
    """img(input): numpy array
       annotated_image(output): numpy array
    """
    # STEP 2: Create an HandLandmarker object.
    base_options = python.BaseOptions(model_asset_path='hand_landmarker.task')
    options = vision.HandLandmarkerOptions(base_options=base_options,
                                        num_hands=2)
    detector = vision.HandLandmarker.create_from_options(options)

    # STEP 3: Load the input image.
    image = mp.Image(
        image_format=mp.ImageFormat.SRGB, data=img)

    # STEP 4: Detect hand landmarks from the input image.
    detection_result = detector.detect(image)

    # STEP 5: Process the classification result. In this case, visualize it.
    annotated_image = draw_landmarks_on_image(image.numpy_view(), detection_result)
    return annotated_image

args = Namespace(
    pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5",
    revision="non-ema",
    from_pt=True,
    controlnet_model_name_or_path="Vincent-luo/controlnet-hands",
    controlnet_revision=None,
    controlnet_from_pt=False,
)

controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
    args.controlnet_model_name_or_path,
    revision=args.controlnet_revision,
    from_pt=args.controlnet_from_pt,
    dtype=jnp.bfloat16,
)

pipeline, pipeline_params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
    args.pretrained_model_name_or_path,
    # tokenizer=tokenizer,
    controlnet=controlnet,
    safety_checker=None,
    dtype=jnp.bfloat16,
    revision=args.revision,
    from_pt=args.from_pt,
)


pipeline_params["controlnet"] = controlnet_params
pipeline_params = jax_utils.replicate(pipeline_params)

rng = jax.random.PRNGKey(0)
num_samples = jax.device_count()
prng_seed = jax.random.split(rng, jax.device_count())


def infer(prompt, negative_prompt, image):
    prompts = num_samples * [prompt]
    prompt_ids = pipeline.prepare_text_inputs(prompts)
    prompt_ids = shard(prompt_ids)

    annotated_image = generate_annotation(image)
    validation_image = Image.fromarray(annotated_image).convert("RGB")
    processed_image = pipeline.prepare_image_inputs(num_samples * [validation_image])
    processed_image = shard(processed_image)

    negative_prompt_ids = pipeline.prepare_text_inputs([negative_prompt] * num_samples)
    negative_prompt_ids = shard(negative_prompt_ids)

    images = pipeline(
        prompt_ids=prompt_ids,
        image=processed_image,
        params=pipeline_params,
        prng_seed=prng_seed,
        num_inference_steps=50,
        neg_prompt_ids=negative_prompt_ids,
        jit=True,
    ).images


    images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])

    results = [i for i in images]
    return [annotated_image] + results


with gr.Blocks(theme='gradio/soft') as demo:
    gr.Markdown("## Stable Diffusion with Hand Control")

    with gr.Column():
        prompt_input = gr.Textbox(label="Prompt")
        negative_prompt = gr.Textbox(label="Negative Prompt")
        input_image = gr.Image(label="Input Image")
        output_image = gr.Gallery(label='Output Image', show_label=False, elem_id="gallery").style(grid=3, height='auto')
        submit_btn = gr.Button(value = "Submit")
        inputs = [prompt_input, negative_prompt, input_image]
        submit_btn.click(fn=infer, inputs=inputs, outputs=[output_image])

demo.launch()