ControlNet/gradio_paint-by-example.py

from share import *
import config

import einops
import gradio as gr
import numpy as np
import torch
import random
import cv2

from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler


model = create_model('models/cldm_v15-mask.yaml').cpu()
model.load_state_dict(load_state_dict('/tmp/paint-by-example-controlnet-full/controlnet-full-step=20999.ckpt', location='cuda'))
model = model.cuda()
ddim_sampler = DDIMSampler(model)

def process(ref_image, control_img, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta):
    with torch.no_grad():
        ref = cv2.resize(ref_image, (224,224))
        ref = torch.from_numpy(np.array(ref).astype(np.float32)).cuda() / 255.0
        ref = torch.stack([ref for _ in range(num_samples)], dim=0)
        ref = einops.rearrange(ref, 'b h w c -> b c h w').clone()
        
        control = cv2.resize(control_img, (image_resolution ,image_resolution))
        control = resize_image(HWC3(control), image_resolution)
        control = torch.from_numpy(np.array(control).astype(np.float32)).cuda() / 255.0
        control = torch.stack([control for _ in range(num_samples)], dim=0)
        control = einops.rearrange(control, 'b h w c -> b c h w').clone()
        B, C, H, W = control.shape
        
        
        if seed == -1:
            seed = random.randint(0, 65535)
        seed_everything(seed)

        if config.save_memory:
            model.low_vram_shift(is_diffusing=False)
        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning(ref)]}
        un_cond = {"c_concat": [control], "c_crossattn": [model.learnable_vector]}
        shape = (4, H // 8, W // 8)

        if config.save_memory:
            model.low_vram_shift(is_diffusing=True)
            
        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
                                                     shape, cond, verbose=False, eta=eta,
                                                     unconditional_guidance_scale=scale,
                                                     unconditional_conditioning=un_cond)

        if config.save_memory:
            model.low_vram_shift(is_diffusing=False)

        x_samples = model.decode_first_stage(samples)
        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)

        results = [x_samples[i] for i in range(num_samples)]
    return results

block = gr.Blocks().queue()
with block:
    with gr.Row():
        gr.Markdown("## Paint-by-Example + ControlNet")
    with gr.Row():
        with gr.Column():
            control_image = gr.Image(label="img mask", source='upload', type="numpy")
            ref_image = gr.Image(label="ref image", source='upload', type="numpy")
            run_button = gr.Button(label="Run")
            with gr.Accordion("Advanced options", open=False):
                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
                guess_mode = gr.Checkbox(label='Guess Mode', value=False)
                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
                eta = gr.Number(label="eta (DDIM)", value=0.0)
                
        with gr.Column():
            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
    ips = [ref_image, control_image, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta]
    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])


block.launch(debug=True, share=True)