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import torch
import numpy as np
from tqdm import tqdm
from omegaconf import OmegaConf
import safetensors
import os
import einops
import cv2
from PIL import Image, ImageFilter, ImageOps
from utils.io_utils import resize_pad2divisior
import os
from utils.io_utils import submit_request, img2b64
import json
# Debug by Francis
# from ldm.util import instantiate_from_config
# from ldm.models.diffusion.ddpm import LatentDiffusion
# from ldm.models.diffusion.ddim import DDIMSampler
# from ldm.modules.diffusionmodules.util import noise_like
import io
import base64
from requests.auth import HTTPBasicAuth
# Debug by Francis
# def create_model(config_path):
# config = OmegaConf.load(config_path)
# model = instantiate_from_config(config.model).cpu()
# return model
#
# def get_state_dict(d):
# return d.get('state_dict', d)
#
# def load_state_dict(ckpt_path, location='cpu'):
# _, extension = os.path.splitext(ckpt_path)
# if extension.lower() == ".safetensors":
# import safetensors.torch
# state_dict = safetensors.torch.load_file(ckpt_path, device=location)
# else:
# state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location)))
# state_dict = get_state_dict(state_dict)
# return state_dict
#
#
# def load_ldm_sd(model, path) :
# if path.endswith('.safetensor') :
# sd = safetensors.torch.load_file(path)
# else :
# sd = load_state_dict(path)
# model.load_state_dict(sd, strict = False)
#
# def fill_mask_input(image, mask):
# """fills masked regions with colors from image using blur. Not extremely effective."""
#
# image_mod = Image.new('RGBA', (image.width, image.height))
#
# image_masked = Image.new('RGBa', (image.width, image.height))
# image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L')))
#
# image_masked = image_masked.convert('RGBa')
#
# for radius, repeats in [(256, 1), (64, 1), (16, 2), (4, 4), (2, 2), (0, 1)]:
# blurred = image_masked.filter(ImageFilter.GaussianBlur(radius)).convert('RGBA')
# for _ in range(repeats):
# image_mod.alpha_composite(blurred)
#
# return image_mod.convert("RGB")
#
#
# def get_inpainting_image_condition(model, image, mask) :
# conditioning_mask = np.array(mask.convert("L"))
# conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
# conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
# conditioning_mask = torch.round(conditioning_mask)
# conditioning_mask = conditioning_mask.to(device=image.device, dtype=image.dtype)
# conditioning_image = torch.lerp(
# image,
# image * (1.0 - conditioning_mask),
# 1
# )
# conditioning_image = model.get_first_stage_encoding(model.encode_first_stage(conditioning_image))
# conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=conditioning_image.shape[-2:])
# conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
# image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
# return image_conditioning
#
#
# class GuidedLDM(LatentDiffusion):
# def __init__(self, *args, **kwargs):
# super().__init__(*args, **kwargs)
#
# @torch.no_grad()
# def img2img_inpaint(
# self,
# image: Image.Image,
# c_text: str,
# uc_text: str,
# mask: Image.Image,
# ddim_steps = 50,
# mask_blur: int = 0,
# use_cuda: bool = True,
# **kwargs) -> Image.Image :
# ddim_sampler = GuidedDDIMSample(self)
# if use_cuda :
# self.cond_stage_model.cuda()
# self.first_stage_model.cuda()
# c_text = self.get_learned_conditioning([c_text])
# uc_text = self.get_learned_conditioning([uc_text])
# cond = {"c_crossattn": [c_text]}
# uc_cond = {"c_crossattn": [uc_text]}
#
# if use_cuda :
# device = torch.device('cuda:0')
# else :
# device = torch.device('cpu')
#
# image_mask = mask
# image_mask = image_mask.convert('L')
# image_mask = image_mask.filter(ImageFilter.GaussianBlur(mask_blur))
# latent_mask = image_mask
# # image = fill_mask_input(image, latent_mask)
# # image.save('image_fill.png')
# image = np.array(image).astype(np.float32) / 127.5 - 1.0
# image = np.moveaxis(image, 2, 0)
# image = torch.from_numpy(image).to(device)[None]
# init_latent = self.get_first_stage_encoding(self.encode_first_stage(image))
# init_mask = latent_mask
# latmask = init_mask.convert('RGB').resize((init_latent.shape[3], init_latent.shape[2]))
# latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
# latmask = latmask[0]
# latmask = np.around(latmask)
# latmask = np.tile(latmask[None], (4, 1, 1))
# nmask = torch.asarray(latmask).to(init_latent.device).float()
# init_latent = (1 - nmask) * init_latent + nmask * torch.randn_like(init_latent)
#
# denoising_strength = 1
# if self.model.conditioning_key == 'hybrid' :
# image_cdt = get_inpainting_image_condition(self, image, image_mask)
# cond["c_concat"] = [image_cdt]
# uc_cond["c_concat"] = [image_cdt]
#
# steps = ddim_steps
# t_enc = int(min(denoising_strength, 0.999) * steps)
# eta = 0
#
# noise = torch.randn_like(init_latent)
# ddim_sampler.make_schedule(ddim_num_steps=steps, ddim_eta=eta, ddim_discretize="uniform", verbose=False)
# x1 = ddim_sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * int(init_latent.shape[0])).to(device), noise=noise)
#
# if use_cuda :
# self.cond_stage_model.cpu()
# self.first_stage_model.cpu()
#
# if use_cuda :
# self.model.cuda()
# decoded = ddim_sampler.decode(x1, cond,t_enc,init_latent=init_latent,nmask=nmask,unconditional_guidance_scale=7,unconditional_conditioning=uc_cond)
# if use_cuda :
# self.model.cpu()
#
# if mask is not None :
# decoded = init_latent * (1 - nmask) + decoded * nmask
#
# if use_cuda :
# self.first_stage_model.cuda()
# with torch.cuda.amp.autocast(enabled=False):
# x_samples = self.decode_first_stage(decoded.to(torch.float32))
# if use_cuda :
# self.first_stage_model.cpu()
# return torch.clip(x_samples, -1, 1)
#
#
#
# class GuidedDDIMSample(DDIMSampler) :
# def __init__(self, *args, **kwargs):
# super().__init__(*args, **kwargs)
#
# @torch.no_grad()
# def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
# temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
# unconditional_guidance_scale=1., unconditional_conditioning=None,
# dynamic_threshold=None):
# b, *_, device = *x.shape, x.device
#
# if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
# model_output = self.model.apply_model(x, t, c)
# else:
# x_in = torch.cat([x] * 2)
# t_in = torch.cat([t] * 2)
# if isinstance(c, dict):
# assert isinstance(unconditional_conditioning, dict)
# c_in = dict()
# for k in c:
# if isinstance(c[k], list):
# c_in[k] = [torch.cat([
# unconditional_conditioning[k][i],
# c[k][i]]) for i in range(len(c[k]))]
# else:
# c_in[k] = torch.cat([
# unconditional_conditioning[k],
# c[k]])
# elif isinstance(c, list):
# c_in = list()
# assert isinstance(unconditional_conditioning, list)
# for i in range(len(c)):
# c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
# else:
# c_in = torch.cat([unconditional_conditioning, c])
# model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
# model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
#
# e_t = model_output
#
# alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
# alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
# sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
# sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
# # select parameters corresponding to the currently considered timestep
# a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
# a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
# sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
# sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
#
# # current prediction for x_0
# pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
#
# # direction pointing to x_t
# dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
# noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
# if noise_dropout > 0.:
# noise = torch.nn.functional.dropout(noise, p=noise_dropout)
# x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
# return x_prev, pred_x0
#
# @torch.no_grad()
# def decode(self, x_latent, cond, t_start, init_latent=None, nmask=None, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
# use_original_steps=False, callback=None):
#
# timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
# total_steps = len(timesteps)
# timesteps = timesteps[:t_start]
#
# time_range = np.flip(timesteps)
# total_steps = timesteps.shape[0]
# print(f"Running Guided DDIM Sampling with {len(timesteps)} timesteps, t_start={t_start}")
# iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
# x_dec = x_latent
# for i, step in enumerate(iterator):
# p = (i + (total_steps - t_start) + 1) / (total_steps)
# index = total_steps - i - 1
# ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
# if nmask is not None :
# noised_input = self.model.q_sample(init_latent.to(x_latent.device), ts.to(x_latent.device))
# x_dec = (1 - nmask) * noised_input + nmask * x_dec
# x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
# unconditional_guidance_scale=unconditional_guidance_scale,
# unconditional_conditioning=unconditional_conditioning)
# if callback: callback(i)
# return x_dec
#
#
# def ldm_inpaint(model, img, mask, inpaint_size=720, pos_prompt='', neg_prompt = '', use_cuda=True):
# img_original = np.copy(img)
# im_h, im_w = img.shape[:2]
# img_resized, (pad_h, pad_w) = resize_pad2divisior(img, inpaint_size)
#
# mask_original = np.copy(mask)
# mask_original[mask_original < 127] = 0
# mask_original[mask_original >= 127] = 1
# mask_original = mask_original[:, :, None]
# mask, _ = resize_pad2divisior(mask, inpaint_size)
#
# # cv2.imwrite('img_resized.png', img_resized)
# # cv2.imwrite('mask_resized.png', mask)
#
#
# if use_cuda :
# with torch.autocast(enabled = True, device_type = 'cuda') :
# img = model.img2img_inpaint(
# image = Image.fromarray(img_resized),
# c_text = pos_prompt,
# uc_text = neg_prompt,
# mask = Image.fromarray(mask),
# use_cuda = True
# )
# else :
# img = model.img2img_inpaint(
# image = Image.fromarray(img_resized),
# c_text = pos_prompt,
# uc_text = neg_prompt,
# mask = Image.fromarray(mask),
# use_cuda = False
# )
#
# img_inpainted = (einops.rearrange(img, '1 c h w -> h w c').cpu().numpy() * 127.5 + 127.5).astype(np.uint8)
# if pad_h != 0:
# img_inpainted = img_inpainted[:-pad_h]
# if pad_w != 0:
# img_inpainted = img_inpainted[:, :-pad_w]
#
#
# if img_inpainted.shape[0] != im_h or img_inpainted.shape[1] != im_w:
# img_inpainted = cv2.resize(img_inpainted, (im_w, im_h), interpolation = cv2.INTER_LINEAR)
# ans = img_inpainted * mask_original + img_original * (1 - mask_original)
# ans = img_inpainted
# return ans
import requests
from PIL import Image
def ldm_inpaint_webui(
img, mask, resolution: int, url: str, prompt: str = '', neg_prompt: str = '',
**inpaint_ldm_options):
if isinstance(img, np.ndarray):
img = Image.fromarray(img)
im_h, im_w = img.height, img.width
if img.height > img.width:
W = resolution
H = (img.height / img.width * resolution) // 32 * 32
H = int(H)
else:
H = resolution
W = (img.width / img.height * resolution) // 32 * 32
W = int(W)
auth = None
if 'username' in inpaint_ldm_options:
username = inpaint_ldm_options.pop('username')
password = inpaint_ldm_options.pop('password')
auth = HTTPBasicAuth(username, password)
img_b64 = img2b64(img)
mask_b64 = img2b64(mask)
data = {
"init_images": [img_b64],
"mask": mask_b64,
"prompt": prompt,
"negative_prompt": neg_prompt,
"width": W,
"height": H,
**inpaint_ldm_options,
}
data = json.dumps(data)
response = submit_request(url, data, auth=auth)
inpainted_b64 = response.json()['images'][0]
inpainted = Image.open(io.BytesIO(base64.b64decode(inpainted_b64)))
if inpainted.height != im_h or inpainted.width != im_w:
inpainted = inpainted.resize((im_w, im_h), resample=Image.Resampling.LANCZOS)
inpainted = np.array(inpainted)
return inpainted
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