app.py

# This file is adapted from https://huggingface.co/spaces/diffusers/controlnet-canny/blob/main/app.py
# The original license file is LICENSE.ControlNet in this repo.
from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel, FlaxDPMSolverMultistepScheduler
from transformers import CLIPTokenizer, FlaxCLIPTextModel, set_seed
from flax.training.common_utils import shard
from flax.jax_utils import replicate    
from diffusers.utils import load_image
import jax.numpy as jnp
import jax
import cv2
from PIL import Image
import numpy as np
import gradio as gr

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

def load_controlnet(controlnet_version):
    controlnet, controlnet_params = FlaxControlNetModel.from_pretrained(
        "Baptlem/baptlem-controlnet",
        subfolder=controlnet_version,
        from_flax=True,
        dtype=jnp.float32,
    )
    return controlnet, controlnet_params


def load_sb_pipe(controlnet_version, sb_path="runwayml/stable-diffusion-v1-5"):
    controlnet, controlnet_params = load_controlnet(controlnet_version)

    scheduler, scheduler_params = FlaxDPMSolverMultistepScheduler.from_pretrained(
        sb_path,
        subfolder="scheduler"
    )
    
    pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
        sb_path,
        controlnet=controlnet, 
        revision="flax", 
        dtype=jnp.bfloat16
    )
        
    pipe.scheduler = scheduler
    params["controlnet"] = controlnet_params
    params["scheduler"] = scheduler_params
    return pipe, params  

    

controlnet_path = "Baptlem/baptlem-controlnet"
controlnet_version = "coyo-500k"

# Constants
low_threshold = 100
high_threshold = 200



# pipe.enable_xformers_memory_efficient_attention()
# pipe.enable_model_cpu_offload()
# pipe.enable_attention_slicing()
print("Loaded models...")
def pipe_inference(
    image,
    prompt,
    is_canny=False,
    num_samples=4,
    resolution=128,
    num_inference_steps=50,
    guidance_scale=7.5,
    model="coyo-500k",
    seed=0,
    negative_prompt="",
    ):
    print("Loading pipe")
    pipe, params = load_sb_pipe(model)
        
    if not isinstance(image, np.ndarray):
        image = np.array(image) 

    processed_image = resize_image(image, resolution) #-> PIL
        
    if not is_canny:
        resized_image, processed_image = preprocess_canny(processed_image, resolution)

    rng = create_key(seed)
    rng = jax.random.split(rng, jax.device_count())

    prompt_ids = pipe.prepare_text_inputs([prompt] * num_samples)
    negative_prompt_ids = pipe.prepare_text_inputs([negative_prompt] * num_samples)
    processed_image = pipe.prepare_image_inputs([processed_image] * num_samples)
        
    p_params = replicate(params)
    prompt_ids = shard(prompt_ids)
    negative_prompt_ids = shard(negative_prompt_ids)
    processed_image = shard(processed_image)
    print("Inference...")
    output = pipe(
        prompt_ids=prompt_ids,
        image=processed_image,
        params=p_params,
        prng_seed=rng,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        neg_prompt_ids=negative_prompt_ids,
        jit=True,
    ).images
    print("Finished inference...")
    # all_outputs = []
    # all_outputs.append(image)
    # if not is_canny:
    #     all_outputs.append(resized_image)
        
    # for image in output.images:
    #     all_outputs.append(image)

    all_outputs = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
    return all_outputs

def resize_image(image, resolution):  
    if not isinstance(image, np.ndarray):
        image = np.array(image) 
    h, w = image.shape[:2]
    ratio = w/h
    if ratio > 1 :
        resized_image = cv2.resize(image, (int(resolution*ratio), resolution), interpolation=cv2.INTER_NEAREST)
    elif ratio < 1 :
        resized_image = cv2.resize(image, (resolution, int(resolution/ratio)), interpolation=cv2.INTER_NEAREST)
    else:
        resized_image = cv2.resize(image, (resolution, resolution), interpolation=cv2.INTER_NEAREST)
    
    return Image.fromarray(resized_image)
    
    
def preprocess_canny(image, resolution=128):
    if not isinstance(image, np.ndarray):
        image = np.array(image) 
        
    processed_image = cv2.Canny(image, low_threshold, high_threshold)
    processed_image = processed_image[:, :, None]
    processed_image = np.concatenate([processed_image, processed_image, processed_image], axis=2)

    resized_image = Image.fromarray(image)
    processed_image = Image.fromarray(processed_image)
    return resized_image, processed_image


def create_demo(process, max_images=12, default_num_images=4):
    with gr.Blocks() as demo:
        with gr.Row():
            gr.Markdown('## Control Stable Diffusion with Canny Edge Maps')
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(source='upload', type='numpy')
                prompt = gr.Textbox(label='Prompt')
                run_button = gr.Button(label='Run')
                with gr.Accordion('Advanced options', open=False):
                    is_canny = gr.Checkbox(
                        label='Is canny', value=False)
                    num_samples = gr.Slider(label='Images',
                                            minimum=1,
                                            maximum=max_images,
                                            value=default_num_images,
                                            step=1)
                    """
                    canny_low_threshold = gr.Slider(
                        label='Canny low threshold',
                        minimum=1,
                        maximum=255,
                        value=100,
                        step=1)
                    canny_high_threshold = gr.Slider(
                        label='Canny high threshold',
                        minimum=1,
                        maximum=255,
                        value=200,
                        step=1)
                    """
                    resolution = gr.Slider(label='Resolution',
                                          minimum=128,
                                          maximum=128,
                                          value=128,
                                          step=1)
                    num_steps = gr.Slider(label='Steps',
                                          minimum=1,
                                          maximum=100,
                                          value=20,
                                          step=1)
                    guidance_scale = gr.Slider(label='Guidance Scale',
                                               minimum=0.1,
                                               maximum=30.0,
                                               value=7.5,
                                               step=0.1)
                    model = gr.Dropdown(choices=["coyo-500k", "bridge-2M", "coyo2M-bridge3M"],
                                        value="coyo-500k",
                                        label="Model used for inference", 
                                        info="Find every models at https://huggingface.co/Baptlem/baptlem-controlnet")
                    seed = gr.Slider(label='Seed',
                                     minimum=-1,
                                     maximum=2147483647,
                                     step=1,
                                     randomize=True)
                    n_prompt = gr.Textbox(
                        label='Negative Prompt',
                        value=
                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
                    )
            with gr.Column():
                result = gr.Gallery(label='Output',
                                    show_label=False,
                                    elem_id='gallery').style(grid=2,
                                                             height='auto')
        inputs = [
            input_image,
            prompt,
            is_canny,
            num_samples,
            resolution,
            #canny_low_threshold,
            #canny_high_threshold,
            num_steps,
            guidance_scale,
            model,
            seed,
            n_prompt,
        ]
        prompt.submit(fn=process, inputs=inputs, outputs=result)
        run_button.click(fn=process,
                         inputs=inputs,
                         outputs=result,
                         api_name='canny')
    
    return demo

if __name__ == '__main__':

    pipe_inference
    demo = create_demo(pipe_inference)
    demo.queue().launch()
    # gr.Interface(create_demo).launch()