app.py

import gradio as gr
import jax
import numpy as np
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from PIL import Image
from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
import cv2

# load control net and stable diffusion v1-5
controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
    "Nahrawy/controlnet-VIDIT-FAID", dtype=jnp.bfloat16, revision="615ba4a457b95a0eba813bcc8caf842c03a4f7bd"
)
pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.bfloat16
)

def create_key(seed=0):
    return jax.random.PRNGKey(seed)

def process_mask(image):
    mask = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    mask = cv2.resize(mask,(512,512))
    return mask



def infer(prompts, negative_prompts, image):
    params["controlnet"] = controlnet_params
    
    num_samples = 1 #jax.device_count()
    rng = create_key(0)
    rng = jax.random.split(rng, jax.device_count())
    im = process_mask(image)
    mask = Image.fromarray(im)
    
    prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
    negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples)
    processed_image = pipe.prepare_image_inputs([mask] * num_samples)
    
    p_params = replicate(params)
    prompt_ids = shard(prompt_ids)
    negative_prompt_ids = shard(negative_prompt_ids)
    processed_image = shard(processed_image)
    print(processed_image[0].shape)
    output = pipe(
        prompt_ids=prompt_ids,
        image=processed_image,
        params=p_params,
        prng_seed=rng,
        num_inference_steps=50,
        neg_prompt_ids=negative_prompt_ids,
        jit=True,
    ).images
    
    output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
    return output_images

e_images = ['examples/0.png',
          'examples/1.png'
          'examples/2.png']
e_prompts = ['a dog in the middle of the road, shadow on the ground,light direction north-east',
           'a skyscraper in the middle of an intersection, shadow on the ground, light direction east',
           'a red rural house, light temperature 5500, shadow on the ground, light direction south-west']
e_negative_prompts = ['monochromatic, unrealistic, bad looking, full of glitches'*3]
examples = []
for image, prompt, negative_prompt in zip(e_images, e_prompts, e_negative_prompts):
  examples.append([prompt, negative_prompt, image])

with gr.Blocks() as demo:
    gr.Markdown(title)
    prompts = gr.Textbox(label='prompts')
    negative_prompts = gr.Textbox(label='negative_prompts')
    with gr.Row():
      with gr.Column():
        in_image = gr.Image(label="Depth Map Conditioning")
      with gr.Column():
        out_image = gr.Image(label="Generated Image")
    with gr.Row():
        btn = gr.Button("Run")
        gr.Examples(examples=examples,
                   inputs=[prompts,negative_prompts, in_image],
                   outputs=out_image,
                   cache_examples=True)
    btn.click(fn=infer, inputs=[prompts,negative_prompts, in_image] , outputs=out_image)
    
demo.launch()