app.py

from functools import partial

from PIL import Image
import numpy as np
import gradio as gr
import torch
import os
import fire
from omegaconf import OmegaConf

from ldm.models.diffusion.sync_dreamer import SyncDDIMSampler, SyncMultiviewDiffusion
from ldm.util import add_margin, instantiate_from_config
from sam_utils import sam_init, sam_out_nosave

from ldm.util import instantiate_from_config, prepare_inputs
import argparse
import cv2
from transformers import pipeline
from diffusers.utils import load_image, make_image_grid
from diffusers import UniPCMultistepScheduler
from pipeline_controlnet_sync import StableDiffusionControlNetPipeline
from controlnet_sync import ControlNetModelSync

_TITLE = '''ControlNet + SyncDreamer'''
_DESCRIPTION = '''
Given a single-view image and select a target azimuth, ControlNet + SyncDreamer is able to generate the target view

This HF app is modified from [SyncDreamer HF app](https://huggingface.co/spaces/liuyuan-pal/SyncDreamer). The difference is that I added ControlNet on top of SyncDreamer. 
'''
_USER_GUIDE0 = "Step1: Please upload an image in the block above (or choose an example shown in the left)."
_USER_GUIDE2 = "Step2: Please choose a **Elevation angle** and click **Run Generate**. The **Elevation angle** is the elevation of the input image. This costs about 30s."
_USER_GUIDE3 = "Generated multiview images are shown below! (You may adjust the **Crop size** and **Elevation angle** to get a better result!)"

others = '''**Step 1**. Select "Crop size" and click "Crop it". ==> The foreground object is centered and resized. </br>'''

deployed = True

if deployed:
    print(f"Is CUDA available: {torch.cuda.is_available()}")
    print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")


class BackgroundRemoval:
    def __init__(self, device='cuda'):
        from carvekit.api.high import HiInterface
        self.interface = HiInterface(
            object_type="object",  # Can be "object" or "hairs-like".
            batch_size_seg=5,
            batch_size_matting=1,
            device=device,
            seg_mask_size=640,  # Use 640 for Tracer B7 and 320 for U2Net
            matting_mask_size=2048,
            trimap_prob_threshold=231,
            trimap_dilation=30,
            trimap_erosion_iters=5,
            fp16=True,
        )

    @torch.no_grad()
    def __call__(self, image):
        # image: [H, W, 3] array in [0, 255].
        image = self.interface([image])[0]
        return image

def resize_inputs(image_input, crop_size):
    if image_input is None: return None
    alpha_np = np.asarray(image_input)[:, :, 3]
    coords = np.stack(np.nonzero(alpha_np), 1)[:, (1, 0)]
    min_x, min_y = np.min(coords, 0)
    max_x, max_y = np.max(coords, 0)
    ref_img_ = image_input.crop((min_x, min_y, max_x, max_y))
    h, w = ref_img_.height, ref_img_.width
    scale = crop_size / max(h, w)
    h_, w_ = int(scale * h), int(scale * w)
    ref_img_ = ref_img_.resize((w_, h_), resample=Image.BICUBIC)
    results = add_margin(ref_img_, size=256)
    return results

# def generate(model, sample_steps, batch_view_num, sample_num, cfg_scale, seed, image_input, elevation_input):
#     if deployed:
#         assert isinstance(model, SyncMultiviewDiffusion)
#         seed=int(seed)
#         torch.random.manual_seed(seed)
#         np.random.seed(seed)

#         # prepare data
#         image_input = np.asarray(image_input)
#         image_input = image_input.astype(np.float32) / 255.0
#         alpha_values = image_input[:,:, 3:]
#         image_input[:, :, :3] = alpha_values * image_input[:,:, :3] + 1 - alpha_values # white background
#         image_input = image_input[:, :, :3] * 2.0 - 1.0
#         image_input = torch.from_numpy(image_input.astype(np.float32))
#         elevation_input = torch.from_numpy(np.asarray([np.deg2rad(elevation_input)], np.float32))
#         data = {"input_image": image_input, "input_elevation": elevation_input}
#         for k, v in data.items():
#             if deployed:
#                 data[k] = v.unsqueeze(0).cuda()
#             else:
#                 data[k] = v.unsqueeze(0)
#             data[k] = torch.repeat_interleave(data[k], sample_num, dim=0)

#         if deployed:
#             sampler = SyncDDIMSampler(model, sample_steps)
#             x_sample = model.sample(sampler, data, cfg_scale, batch_view_num)
#         else:
#             x_sample = torch.zeros(sample_num, 16, 3, 256, 256)

#         B, N, _, H, W = x_sample.shape
#         x_sample = (torch.clamp(x_sample,max=1.0,min=-1.0) + 1) * 0.5
#         x_sample = x_sample.permute(0,1,3,4,2).cpu().numpy() * 255
#         x_sample = x_sample.astype(np.uint8)

#         results = []
#         for bi in range(B):
#             results.append(np.concatenate([x_sample[bi,ni] for ni in range(N)], 1))
#         results = np.concatenate(results, 0)
#         return Image.fromarray(results)
#     else:
#         return Image.fromarray(np.zeros([sample_num*256,16*256,3],np.uint8))

def generate(pipe, image_input, target_index):
    output = pipe(conditioning_image=image_input)
    return output[target_index]

def sam_predict(predictor, removal, raw_im):
    if raw_im is None: return None
    if deployed:
        raw_im.thumbnail([512, 512], Image.Resampling.LANCZOS)
        image_nobg = removal(raw_im.convert('RGB'))
        arr = np.asarray(image_nobg)[:, :, -1]
        x_nonzero = np.nonzero(arr.sum(axis=0))
        y_nonzero = np.nonzero(arr.sum(axis=1))
        x_min = int(x_nonzero[0].min())
        y_min = int(y_nonzero[0].min())
        x_max = int(x_nonzero[0].max())
        y_max = int(y_nonzero[0].max())
        # image_nobg.save('./nobg.png')

        image_nobg.thumbnail([512, 512], Image.Resampling.LANCZOS)
        image_sam = sam_out_nosave(predictor, image_nobg.convert("RGB"), (x_min, y_min, x_max, y_max))

        # imsave('./mask.png', np.asarray(image_sam)[:,:,3]*255)
        image_sam = np.asarray(image_sam, np.float32) / 255
        out_mask = image_sam[:, :, 3:]
        out_rgb = image_sam[:, :, :3] * out_mask + 1 - out_mask
        out_img = (np.concatenate([out_rgb, out_mask], 2) * 255).astype(np.uint8)

        image_sam = Image.fromarray(out_img, mode='RGBA')
        # image_sam.save('./output.png')
        torch.cuda.empty_cache()
        return image_sam
    else:
        return raw_im

def load_model(cfg,ckpt,strict=True):
    config = OmegaConf.load(cfg)
    model = instantiate_from_config(config.model)
    print(f'loading model from {ckpt} ...')
    ckpt = torch.load(ckpt,map_location='cuda')
    model.load_state_dict(ckpt['state_dict'],strict=strict)
    model = model.cuda().eval()
    return model

def run_demo():
    # # device = f"cuda:0" if torch.cuda.is_available() else "cpu"
    # # models = None # init_model(device, os.path.join(code_dir, ckpt))
    # cfg = 'configs/syncdreamer.yaml'
    # ckpt = 'ckpt/syncdreamer-pretrain.ckpt'
    # config = OmegaConf.load(cfg)
    # # model = None

    if deployed:
        controlnet = ControlNetModelSync.from_pretrained('controlnet_ckpt', torch_dtype=torch.float32, use_safetensors=True)
        cfg = 'configs/syncdreamer.yaml'
        dreamer = load_model(cfg, 'ckpt/syncdreamer-pretrain.ckpt', strict=True)

        controlnet.to('cuda', dtype=torch.float32)

        pipe = StableDiffusionControlNetPipeline.from_pretrained(
            controlnet=controlnet, dreamer=dreamer, torch_dtype=torch.float32, use_safetensors=True
        )
        pipe.to('cuda', dtype=torch.float32)

    # if deployed:
    #     model = instantiate_from_config(config.model)
    #     print(f'loading model from {ckpt} ...')
    #     ckpt = torch.load(ckpt,map_location='cpu')
    #     model.load_state_dict(ckpt['state_dict'], strict=True)
    #     model = model.cuda().eval()
    #     del ckpt
        mask_predictor = sam_init()
        removal = BackgroundRemoval()
    else:
        # model = None
        # mask_predictor = None
        # removal = None
        controlnet = None
        dreamer = None
        pipe = None

    # NOTE: Examples must match inputs
    examples_full = [
        ['hf_demo/examples/monkey.png',30,200],
        ['hf_demo/examples/cat.png',30,200],
        ['hf_demo/examples/crab.png',30,200],
        ['hf_demo/examples/elephant.png',30,200],
        ['hf_demo/examples/flower.png',0,200],
        ['hf_demo/examples/forest.png',30,200],
        ['hf_demo/examples/teapot.png',20,200],
        ['hf_demo/examples/basket.png',30,200],
    ]

    image_block = gr.Image(type='pil', image_mode='RGBA', height=256, label='Input image', tool=None, interactive=True)
    azimuth = gr.Slider(0, 360, 90, step=22.5, label='Target azimuth', interactive=True)
    crop_size = gr.Slider(120, 240, 200, step=10, label='Crop size', interactive=True)

    target_index = round(azimuth % 360 / 22.5)

    # Compose demo layout & data flow.
    with gr.Blocks(title=_TITLE, css="hf_demo/style.css") as demo:
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown('# ' + _TITLE)
            # with gr.Column(scale=0):
            #     gr.DuplicateButton(value='Duplicate Space for private use', elem_id='duplicate-button')
        gr.Markdown(_DESCRIPTION)

        with gr.Row(variant='panel'):
            with gr.Column(scale=1.2):
                gr.Examples(
                    examples=examples_full,  # NOTE: elements must match inputs list!
                    inputs=[image_block, azimuth, crop_size],
                    outputs=[image_block, azimuth, crop_size],
                    cache_examples=False,
                    label='Examples (click one of the images below to start)',
                    examples_per_page=5,
                )

            with gr.Column(scale=0.8):
                image_block.render()
                guide_text = gr.Markdown(_USER_GUIDE0, visible=True)
                fig0 = gr.Image(value=Image.open('assets/crop_size.jpg'), type='pil', image_mode='RGB', height=256, show_label=False, tool=None, interactive=False)


            with gr.Column(scale=0.8):
                sam_block = gr.Image(type='pil', image_mode='RGBA', label="SAM output", height=256, interactive=False)
                crop_size.render()
                # crop_btn = gr.Button('Crop it', variant='primary', interactive=True)
                fig1 = gr.Image(value=Image.open('assets/elevation.jpg'), type='pil', image_mode='RGB', height=256, show_label=False, tool=None, interactive=False)

            with gr.Column(scale=0.8):
                input_block = gr.Image(type='pil', image_mode='RGBA', label="Input to SyncDreamer", height=256, interactive=False)
                azimuth.render()
                with gr.Accordion('Advanced options', open=False):
                    cfg_scale = gr.Slider(1.0, 5.0, 2.0, step=0.1, label='Classifier free guidance', interactive=True)
                    sample_num = gr.Slider(1, 2, 1, step=1, label='Sample num', interactive=False, info='How many instance (16 images per instance)')
                    sample_steps = gr.Slider(10, 300, 50, step=10, label='Sample steps', interactive=False)
                    batch_view_num = gr.Slider(1, 16, 16, step=1, label='Batch num', interactive=True)
                    seed = gr.Number(6033, label='Random seed', interactive=True)
                run_btn = gr.Button('Run generation', variant='primary', interactive=True)


        output_block = gr.Image(type='pil', image_mode='RGB', label="Outputs of SyncDreamer", height=256, interactive=False)

        def update_guide2(text, im):
            if im is None:
                return _USER_GUIDE0
            else:
                return text
        update_guide = lambda GUIDE_TEXT: gr.update(value=GUIDE_TEXT)

        image_block.clear(fn=partial(update_guide, _USER_GUIDE0), outputs=[guide_text], queue=False)
        image_block.change(fn=partial(sam_predict, mask_predictor, removal), inputs=[image_block], outputs=[sam_block], queue=True) \
                   .success(fn=resize_inputs, inputs=[sam_block, crop_size], outputs=[input_block], queue=True)\
                   .success(fn=partial(update_guide2, _USER_GUIDE2), inputs=[image_block], outputs=[guide_text], queue=False)\

        crop_size.change(fn=resize_inputs, inputs=[sam_block, crop_size], outputs=[input_block], queue=True)\
                 .success(fn=partial(update_guide, _USER_GUIDE2), outputs=[guide_text], queue=False)
        # crop_btn.click(fn=resize_inputs, inputs=[sam_block, crop_size], outputs=[input_block], queue=False)\
        #                .success(fn=partial(update_guide, _USER_GUIDE2), outputs=[guide_text], queue=False)

        run_btn.click(partial(generate, pipe), inputs=[input_block, target_index], outputs=[output_block], queue=True)\
               .success(fn=partial(update_guide, _USER_GUIDE3), outputs=[guide_text], queue=False)

    demo.queue().launch(share=False, max_threads=80)  # auth=("admin", os.environ['PASSWD'])

if __name__=="__main__":
    fire.Fire(run_demo)