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import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
from share import *
import config
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from PIL import Image
from torchvision import transforms
from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from cldm.model import create_model, load_state_dict
from models.ControlNet.ldm.models.diffusion.ddim import DDIMSampler
def init_model():
model = create_model(BASE_DIR+'/models/cldm_v15.yaml').cpu()
state_dict = load_state_dict(BASE_DIR+'/models/control_sd15_depth.pth')
model.load_state_dict(state_dict, strict=False)
# model.load_state_dict(state_dict)
model = model.cuda()
ddim_sampler = DDIMSampler(model)
return model, ddim_sampler
@torch.no_grad()
def process(model, ddim_sampler, input_image, prompt, a_prompt, n_prompt, num_samples,
ddim_steps, scale, seed, eta,
strength=1.0, detected_map=None, unknown_mask=None, save_memory=False, depth_pad=10):
"""
unknown mask has to be an array of shape (H, W) - should has values of (0, 255)
"""
with torch.no_grad():
H, W, C = input_image.shape
if seed == -1:
seed = random.randint(0, 65535)
seed_everything(seed)
if save_memory:
model.low_vram_shift(is_diffusing=False)
if save_memory:
model.low_vram_shift(is_diffusing=True)
# start from noising the input image
x0 = Image.fromarray(input_image).convert("RGB")
x0 = np.array(x0)
x0 = torch.from_numpy(x0).permute(2, 0, 1).float().to(model.device)
x0 = x0.unsqueeze(0).repeat(num_samples, 1, 1, 1)
x0 = (x0 / 127.5) - 1.0 # NOTE input image must be normalized to [-1, 1]
# encode input image
# NOTE ControlNet doesn't accept the raw input image
x0 = model.encode_first_stage(x0)
x0 = model.get_first_stage_encoding(x0).detach()
ddim_sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=eta, verbose=False, strength=strength)
ddim_steps = int(ddim_steps * strength) # actually DEPRECATED
# add noises to the maximum
ddim_steps_tensor = torch.full((x0.shape[0],), ddim_sampler.ddim_timesteps[-1]).to(model.device)
x_T = model.q_sample(x0, ddim_steps_tensor)
# control
if detected_map is None:
detected_map, _ = apply_midas(resize_image(input_image, H))
detected_map = HWC3(detected_map)
detected_map_resized = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
control = torch.from_numpy(detected_map_resized).float().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()
cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
un_cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
shape = (4, H // 8, W // 8)
# if unknown_mask is not None:
# # HACK
# # unknown_mask_dilate = cv2.dilate(unknown_mask, kernel=np.ones((5, 5), np.uint8), iterations=2)
# unknown_mask_dilate = cv2.dilate(unknown_mask, kernel=np.ones((5, 5), np.uint8), iterations=1)
# # depth_mask = np.zeros_like(detected_map[..., 0])
# # depth_mask[detected_map[..., 0] != 0] = 1 # 1 -> object, 0 -> background
# # reversed_depth_mask = 1 - depth_mask # 0 -> object, 1 -> background
# # diffusion_mask = reversed_depth_mask + unknown_mask
# # unknown_mask_dilate = cv2.dilate(diffusion_mask, kernel=np.ones((5, 5), np.uint8), iterations=2)
# unknown_mask_dilate = Image.fromarray(unknown_mask_dilate.astype(np.uint8)).convert("L")
# unknown_mask_dilate = unknown_mask_dilate.resize((H // 8, W // 8), Image.NEAREST)
# unknown_mask_dilate = transforms.ToTensor()(unknown_mask_dilate).to(model.device)
# unknown_mask_dilate = unknown_mask_dilate.repeat(4, 1, 1)
# # only contains 0 and 1
# assert set(torch.unique(unknown_mask_dilate).cpu().numpy().tolist()).issubset(set([0, 1]))
# else:
# unknown_mask_dilate = None
if unknown_mask is not None:
# # target: unknown region
# unknown_mask_image = np.copy(unknown_mask) # should be 0 - 255
# unknown_mask = unknown_mask.astype(np.float32)
# unknown_mask /= 255 # normalize it to 0 - 1
# target: unknown region + background
# HACK basically generate everything except known region
detected_map_image = Image.fromarray(detected_map.astype(np.uint8)).convert("L")
detected_map_np = np.array(detected_map_image)
background_mask = detected_map_np == depth_pad # bool
background_mask = background_mask.astype(np.float32) * 255 # 0 - 255
unknown_mask_image = unknown_mask + background_mask
# unknown_mask is still the unknown region
# will be used later to compose the generated region
unknown_mask = unknown_mask.astype(np.float32)
unknown_mask /= 255 # normalize it to 0 - 1
compose_flag = True
else:
detected_map_image = Image.fromarray(detected_map.astype(np.uint8)).convert("L")
detected_map_np = np.array(detected_map_image)
# # target: non-background region
# unknown_mask = (detected_map_np != depth_pad).astype(np.uint8)
# unknown_mask_image = (unknown_mask * 255.).astype(np.uint8)
# # Image.fromarray(unknown_mask_image).save("unknown.png")
# target: everything
unknown_mask = np.ones_like(detected_map_np)
unknown_mask_image = (unknown_mask * 255.).astype(np.uint8)
compose_flag = False
# HACK
# unknown_mask_dilate = np.copy(unknown_mask_image)
unknown_mask_dilate = cv2.dilate(unknown_mask_image, kernel=np.ones((5, 5), np.uint8), iterations=2)
unknown_mask_dilate = Image.fromarray(unknown_mask_dilate.astype(np.uint8)).convert("L")
unknown_mask_dilate = unknown_mask_dilate.resize((H // 8, W // 8), Image.NEAREST)
unknown_mask_dilate = transforms.ToTensor()(unknown_mask_dilate).to(model.device)
unknown_mask_dilate = unknown_mask_dilate.repeat(4, 1, 1)
# HACK make sure the mask only contains 0 and 1
try:
assert set(torch.unique(unknown_mask_dilate).cpu().numpy().tolist()).issubset(set([0, 1])), set(torch.unique(unknown_mask_dilate).cpu().numpy().tolist())
except AssertionError:
unknown_mask_dilate = torch.round(unknown_mask_dilate)
assert set(torch.unique(unknown_mask_dilate).cpu().numpy().tolist()).issubset(set([0, 1])), set(torch.unique(unknown_mask_dilate).cpu().numpy().tolist())
samples, intermediates = ddim_sampler.sample(
ddim_steps, num_samples,
shape, cond, x0=x0, x_T=x_T, mask=unknown_mask_dilate,
verbose=False, eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=un_cond
)
if 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 = []
for i in range(num_samples):
sample = x_samples[i]
# # HACK manually compose cropped generated region with the known region
# if compose_flag:
# # # unknown region + known region
# # input_image = Image.fromarray(input_image).convert("RGB")
# # input_image = np.array(input_image)
# # mask = np.repeat(unknown_mask[..., None], 3, axis=2)
# # new_sample = np.zeros_like(sample)
# # new_sample[mask == 1] = sample[mask == 1]
# # new_sample[mask == 0] = input_image[mask == 0]
# # sample = new_sample
# # non-background region
# detected_map_image = Image.fromarray(detected_map.astype(np.uint8)).convert("L")
# detected_map_np = np.array(detected_map_image)
# background_mask = (detected_map_np == depth_pad).astype(np.uint8)
# sample[background_mask == 1] = 255
results.append(sample)
return results
if __name__ == "__main__":
model, ddim_sampler = init_model()
block = gr.Blocks().queue()
with block:
with gr.Row():
gr.Markdown("## Control Stable Diffusion with Depth 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):
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=256)
detect_resolution = gr.Slider(label="Depth Resolution", minimum=128, maximum=1024, value=384, step=1)
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)
a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
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_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
ips = [model, ddim_sampler, input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, scale, seed, eta]
run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
block.launch(server_name='0.0.0.0')
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