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import nodes
from .ldm.util import instantiate_from_config
from .ldm.models.diffusion.ddim import DDIMSampler
from .ldm.util import ismap
from PIL import Image
from einops import rearrange, repeat
import torch, torchvision
import time
from omegaconf import OmegaConf
import numpy as np
from os import path
import warnings
from comfy import model_management
import comfy
from PIL import ImageOps
warnings.filterwarnings("ignore", category=UserWarning)
class LDSR():
def __init__(self, modelPath=None, model=None, torchdevice=model_management.get_torch_device(), on_progress=None, yamlPath=path.join(path.dirname(__file__), "config.yaml")):
self.modelPath = modelPath
self.model = model
self.yamlPath = yamlPath
self.torchdevice = torchdevice
self.progress_hook = on_progress if on_progress else None
@staticmethod
def normalize_image(image):
w, h = image.size
# ensure (min length > 128)
if h < w and h < 128:
scale_ratio = 128 / h
h = 128
w = int(scale_ratio * w)
elif w < 128:
scale_ratio = 128 / w
w = 128
h = int(scale_ratio * h)
resample = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
image = image.resize((w, h), resample=resample)
# ensure (multiply of 64)
w_pad = 64 - w % 64
h_pad = 64 - h % 64
padded_image = Image.new("RGB", (w + w_pad, h + h_pad), color="black")
padded_image.paste(image, (0, 0))
return padded_image, w_pad, h_pad
@staticmethod
def remove_padding(prev_pil, image, w_pad, h_pad):
if w_pad == 0 and h_pad == 0:
return image
w1, h1 = prev_pil.size
h2, w2, _ = image.size()
scale_ratio = h2 / h1
w_pad = float.__ceil__(w_pad * scale_ratio)
h_pad = float.__ceil__(h_pad * scale_ratio)
return image[:h2-h_pad, :w2-w_pad, :]
@staticmethod
def load_model_from_path(modelPath, device=model_management.get_torch_device(), yamlPath=path.join(path.dirname(__file__), "config.yaml")):
print(f"Loading model from {modelPath}")
pl_sd = torch.load(modelPath, map_location="cpu")
sd = pl_sd["state_dict"]
config = OmegaConf.load(yamlPath)
model = instantiate_from_config(config.model)
model.load_state_dict(sd, strict=False)
model.to(device)
model.eval()
return {"model": model}
def load_model_from_config(self):
if self.model is None:
self.model = LDSR.load_model_from_path(self.modelPath, self.torchdevice)
else:
self.model['model'].to(self.torchdevice)
return self.model
def progress_callback(self, i):
if self.progress_hook:
self.progress_hook(i)
def run(self, model, image, task, custom_steps, eta, resize_enabled=False, classifier_ckpt=None, global_step=None):
def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, eta=1.0, swap_mode=False,
masked=False,
invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000,
resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0.,
corrector=None,
corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,
ddim_use_x0_pred=False):
log = dict()
z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
return_first_stage_outputs=True,
force_c_encode=not (hasattr(model, 'split_input_params')
and model.cond_stage_key == 'coordinates_bbox'),
return_original_cond=True)
log_every_t = 1 if save_intermediate_vid else None
if custom_shape is not None:
z = torch.randn(custom_shape)
# print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
z0 = None
log["input"] = x
log["reconstruction"] = xrec
if ismap(xc):
log["original_conditioning"] = model.to_rgb(xc)
if hasattr(model, 'cond_stage_key'):
log[model.cond_stage_key] = model.to_rgb(xc)
else:
log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_model:
log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
if model.cond_stage_key == 'class_label':
log[model.cond_stage_key] = xc[model.cond_stage_key]
with model.ema_scope("Plotting"):
t0 = time.time()
img_cb = None
sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
eta=eta,
callback=self.progress_callback,
quantize_x0=quantize_x0, img_callback=img_cb, mask=None, x0=z0,
temperature=temperature, noise_dropout=noise_dropout,
score_corrector=corrector, corrector_kwargs=corrector_kwargs,
x_T=x_T, log_every_t=log_every_t)
t1 = time.time()
if ddim_use_x0_pred:
sample = intermediates['pred_x0'][-1]
x_sample = model.decode_first_stage(sample)
try:
x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
log["sample_noquant"] = x_sample_noquant
log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
except:
pass
log["sample"] = x_sample
log["time"] = t1 - t0
return log
def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
mask=None, x0=None, quantize_x0=False, img_callback=None,
temperature=1., noise_dropout=0., score_corrector=None,
corrector_kwargs=None, x_T=None, log_every_t=None
):
ddim = DDIMSampler(model)
bs = shape[0] # dont know where this comes from but wayne
shape = shape[1:] # cut batch dim
print(f"Sampling with eta = {eta}; steps: {steps}")
samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond,
callback=callback,
normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
mask=mask, x0=x0, temperature=temperature, verbose=False,
score_corrector=score_corrector,
corrector_kwargs=corrector_kwargs, x_T=x_T)
return samples, intermediates
# global stride
def get_cond(mode, img):
example = dict()
if mode == "superresolution":
up_f = 4
# visualize_cond_img(selected_path)
c = img.convert('RGB')
c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
antialias=True)
c_up = rearrange(c_up, '1 c h w -> 1 h w c')
c = rearrange(c, '1 c h w -> 1 h w c')
c = 2. * c - 1.
c = c.to(self.torchdevice)
example["LR_image"] = c
example["image"] = c_up
return example
example = get_cond(task, image)
save_intermediate_vid = False
n_runs = 1
masked = False
guider = None
ckwargs = None
mode = 'ddim'
ddim_use_x0_pred = False
temperature = 1.
eta = eta
make_progrow = True
custom_shape = None
height, width = example["image"].shape[1:3]
split_input = height >= 128 and width >= 128
if split_input:
ks = 128
stride = 64
vqf = 4 #
model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
"vqf": vqf,
"patch_distributed_vq": True,
"tie_braker": False,
"clip_max_weight": 0.5,
"clip_min_weight": 0.01,
"clip_max_tie_weight": 0.5,
"clip_min_tie_weight": 0.01}
else:
if hasattr(model, "split_input_params"):
delattr(model, "split_input_params")
invert_mask = False
x_T = None
for n in range(n_runs):
if custom_shape is not None:
x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
x_T = repeat(x_T, '1 c h w -> b c h w', b=custom_shape[0])
logs = make_convolutional_sample(example, model,
mode=mode, custom_steps=custom_steps,
eta=eta, swap_mode=False, masked=masked,
invert_mask=invert_mask, quantize_x0=False,
custom_schedule=None, decode_interval=10,
resize_enabled=resize_enabled, custom_shape=custom_shape,
temperature=temperature, noise_dropout=0.,
corrector=guider, corrector_kwargs=ckwargs, x_T=x_T,
save_intermediate_vid=save_intermediate_vid,
make_progrow=make_progrow, ddim_use_x0_pred=ddim_use_x0_pred
)
return logs
@torch.no_grad()
def superResolution(self, image, ddimSteps=100, preDownScale='None', postDownScale='None', downsampleMethod="Lanczos"):
diffMode = 'superresolution'
model = self.load_model_from_config()
# Run settings
diffusion_steps = int(ddimSteps) # @param [25, 50, 100, 250, 500, 1000]
eta = 1.0 # @param {type: 'raw'}
stride = 0 # not working atm
# ####Scaling options:
# Downsampling to 256px first will often improve the final image and runs faster.
# You can improve sharpness without upscaling by upscaling and then downsampling to the original size (i.e. Super Resolution)
pre_downsample = preDownScale # @param ['None', '1/2', '1/4']
post_downsample = postDownScale # @param ['None', 'Original Size', '1/2', '1/4']
# Nearest gives sharper results, but may look more pixellated. Lancoz is much higher quality, but result may be less crisp.
downsample_method = downsampleMethod # @param ['Nearest', 'Lanczos']
i = 255. * image.cpu().numpy()
im_og = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
width_og, height_og = im_og.size
# Downsample Pre
if pre_downsample == '1/2':
downsample_rate = 2
elif pre_downsample == '1/4':
downsample_rate = 4
else:
downsample_rate = 1
width_downsampled_pre = width_og // downsample_rate
height_downsampled_pre = height_og // downsample_rate
if downsample_rate != 1:
print(f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
im_og, w_pad, h_pad = LDSR.normalize_image(im_og)
logs = self.run(model["model"], im_og, diffMode, diffusion_steps, eta)
sample = logs["sample"]
sample = sample.detach().cpu()
sample = torch.clamp(sample, -1., 1.)
sample = (sample + 1.) / 2. * 255
sample = sample.numpy().astype(np.uint8)
sample = np.transpose(sample, (0, 2, 3, 1))
a = Image.fromarray(sample[0])
# Downsample Post
if post_downsample == '1/2':
downsample_rate = 2
elif post_downsample == '1/4':
downsample_rate = 4
else:
downsample_rate = 1
width, height = a.size
width_downsampled_post = width // downsample_rate
height_downsampled_post = height // downsample_rate
if downsample_method == 'Lanczos':
aliasing = Image.LANCZOS
else:
aliasing = Image.NEAREST
if downsample_rate != 1:
print(f'Downsampling from [{width}, {height}] to [{width_downsampled_post}, {height_downsampled_post}]')
a = a.resize((width_downsampled_post, height_downsampled_post), aliasing)
elif post_downsample == 'Original Size':
print(f'Downsampling from [{width}, {height}] to Original Size [{width_og}, {height_og}]')
a = a.resize((width_og+w_pad, height_og+h_pad), aliasing)
out = np.array(a).astype(np.float32) / 255.0
# Finalize
result_image = torch.from_numpy(out)
result_image = LDSR.remove_padding(im_og, result_image, w_pad, h_pad)
model['model'].cpu()
result_image.cpu()
return result_image
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