ldm_patched/modules/utils.py

# 1st edit https://github.com/comfyanonymous/ComfyUI
# 2nd edit by Forge


import logging
import math
import struct

import numpy as np
import safetensors.torch
import torch
from PIL import Image
from tqdm import tqdm

import modules.safe

logger = logging.getLogger(__name__)


def load_torch_file(ckpt, safe_load=False, device=None):
    if device is None:
        device = torch.device("cpu")
    if ckpt.lower().endswith(".safetensors"):
        sd = safetensors.torch.load_file(ckpt, device=device.type)
    else:
        if safe_load:
            if not "weights_only" in torch.load.__code__.co_varnames:
                logger.debug("torch.load doesn't support weights_only on this version")
                safe_load = False
        if safe_load:
            pl_sd = torch.load(ckpt, map_location=device, weights_only=True)
        else:
            pl_sd = torch.load(
                ckpt,
                map_location=device,
                pickle_module=modules.safe,
            )
        if "global_step" in pl_sd:
            print(f"Global Step: {pl_sd['global_step']}")
        if "state_dict" in pl_sd:
            sd = pl_sd["state_dict"]
        else:
            sd = pl_sd
    return sd


def calculate_parameters(sd, prefix=""):
    params = 0
    for k in sd.keys():
        if k.startswith(prefix):
            params += sd[k].nelement()
    return params


def state_dict_key_replace(state_dict, keys_to_replace):
    for x in keys_to_replace:
        if x in state_dict:
            state_dict[keys_to_replace[x]] = state_dict.pop(x)
    return state_dict


def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False):
    if filter_keys:
        out = {}
    else:
        out = state_dict
    for rp in replace_prefix:
        replace = list(
            map(
                lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp) :])),
                filter(lambda a: a.startswith(rp), state_dict.keys()),
            )
        )
        for x in replace:
            w = state_dict.pop(x[0])
            out[x[1]] = w
    return out


def transformers_convert(sd, prefix_from, prefix_to, number):
    keys_to_replace = {
        "{}positional_embedding": "{}embeddings.position_embedding.weight",
        "{}token_embedding.weight": "{}embeddings.token_embedding.weight",
        "{}ln_final.weight": "{}final_layer_norm.weight",
        "{}ln_final.bias": "{}final_layer_norm.bias",
    }

    for k in keys_to_replace:
        x = k.format(prefix_from)
        if x in sd:
            sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x)

    resblock_to_replace = {
        "ln_1": "layer_norm1",
        "ln_2": "layer_norm2",
        "mlp.c_fc": "mlp.fc1",
        "mlp.c_proj": "mlp.fc2",
        "attn.out_proj": "self_attn.out_proj",
    }

    for resblock in range(number):
        for x in resblock_to_replace:
            for y in ["weight", "bias"]:
                k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y)
                k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y)
                if k in sd:
                    sd[k_to] = sd.pop(k)

        for y in ["weight", "bias"]:
            k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y)
            if k_from in sd:
                weights = sd.pop(k_from)
                shape_from = weights.shape[0] // 3
                for x in range(3):
                    p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
                    k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y)
                    sd[k_to] = weights[shape_from * x : shape_from * (x + 1)]
    return sd


UNET_MAP_ATTENTIONS = {
    "proj_in.weight",
    "proj_in.bias",
    "proj_out.weight",
    "proj_out.bias",
    "norm.weight",
    "norm.bias",
}

TRANSFORMER_BLOCKS = {
    "norm1.weight",
    "norm1.bias",
    "norm2.weight",
    "norm2.bias",
    "norm3.weight",
    "norm3.bias",
    "attn1.to_q.weight",
    "attn1.to_k.weight",
    "attn1.to_v.weight",
    "attn1.to_out.0.weight",
    "attn1.to_out.0.bias",
    "attn2.to_q.weight",
    "attn2.to_k.weight",
    "attn2.to_v.weight",
    "attn2.to_out.0.weight",
    "attn2.to_out.0.bias",
    "ff.net.0.proj.weight",
    "ff.net.0.proj.bias",
    "ff.net.2.weight",
    "ff.net.2.bias",
}

UNET_MAP_RESNET = {
    "in_layers.2.weight": "conv1.weight",
    "in_layers.2.bias": "conv1.bias",
    "emb_layers.1.weight": "time_emb_proj.weight",
    "emb_layers.1.bias": "time_emb_proj.bias",
    "out_layers.3.weight": "conv2.weight",
    "out_layers.3.bias": "conv2.bias",
    "skip_connection.weight": "conv_shortcut.weight",
    "skip_connection.bias": "conv_shortcut.bias",
    "in_layers.0.weight": "norm1.weight",
    "in_layers.0.bias": "norm1.bias",
    "out_layers.0.weight": "norm2.weight",
    "out_layers.0.bias": "norm2.bias",
}

UNET_MAP_BASIC = {
    ("label_emb.0.0.weight", "class_embedding.linear_1.weight"),
    ("label_emb.0.0.bias", "class_embedding.linear_1.bias"),
    ("label_emb.0.2.weight", "class_embedding.linear_2.weight"),
    ("label_emb.0.2.bias", "class_embedding.linear_2.bias"),
    ("label_emb.0.0.weight", "add_embedding.linear_1.weight"),
    ("label_emb.0.0.bias", "add_embedding.linear_1.bias"),
    ("label_emb.0.2.weight", "add_embedding.linear_2.weight"),
    ("label_emb.0.2.bias", "add_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
}


def unet_to_diffusers(unet_config):
    num_res_blocks = unet_config["num_res_blocks"]
    channel_mult = unet_config["channel_mult"]
    transformer_depth = unet_config["transformer_depth"][:]
    transformer_depth_output = unet_config["transformer_depth_output"][:]
    num_blocks = len(channel_mult)

    transformers_mid = unet_config.get("transformer_depth_middle", None)

    diffusers_unet_map = {}
    for x in range(num_blocks):
        n = 1 + (num_res_blocks[x] + 1) * x
        for i in range(num_res_blocks[x]):
            for b in UNET_MAP_RESNET:
                diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b)
            num_transformers = transformer_depth.pop(0)
            if num_transformers > 0:
                for b in UNET_MAP_ATTENTIONS:
                    diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b)
                for t in range(num_transformers):
                    for b in TRANSFORMER_BLOCKS:
                        diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
            n += 1
        for k in ["weight", "bias"]:
            diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k)

    i = 0
    for b in UNET_MAP_ATTENTIONS:
        diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b)
    for t in range(transformers_mid):
        for b in TRANSFORMER_BLOCKS:
            diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b)

    for i, n in enumerate([0, 2]):
        for b in UNET_MAP_RESNET:
            diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b)

    num_res_blocks = list(reversed(num_res_blocks))
    for x in range(num_blocks):
        n = (num_res_blocks[x] + 1) * x
        l = num_res_blocks[x] + 1
        for i in range(l):
            c = 0
            for b in UNET_MAP_RESNET:
                diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b)
            c += 1
            num_transformers = transformer_depth_output.pop()
            if num_transformers > 0:
                c += 1
                for b in UNET_MAP_ATTENTIONS:
                    diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b)
                for t in range(num_transformers):
                    for b in TRANSFORMER_BLOCKS:
                        diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
            if i == l - 1:
                for k in ["weight", "bias"]:
                    diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k)
            n += 1

    for k in UNET_MAP_BASIC:
        diffusers_unet_map[k[1]] = k[0]

    return diffusers_unet_map


def repeat_to_batch_size(tensor, batch_size):
    if tensor.shape[0] > batch_size:
        return tensor[:batch_size]
    elif tensor.shape[0] < batch_size:
        return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size]
    return tensor


def resize_to_batch_size(tensor, batch_size):
    in_batch_size = tensor.shape[0]
    if in_batch_size == batch_size:
        return tensor

    if batch_size <= 1:
        return tensor[:batch_size]

    output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device)
    if batch_size < in_batch_size:
        scale = (in_batch_size - 1) / (batch_size - 1)
        for i in range(batch_size):
            output[i] = tensor[min(round(i * scale), in_batch_size - 1)]
    else:
        scale = in_batch_size / batch_size
        for i in range(batch_size):
            output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)]

    return output


def convert_sd_to(state_dict, dtype):
    keys = list(state_dict.keys())
    for k in keys:
        state_dict[k] = state_dict[k].to(dtype)
    return state_dict


def safetensors_header(safetensors_path, max_size=100 * 1024 * 1024):
    with open(safetensors_path, "rb") as f:
        header = f.read(8)
        length_of_header = struct.unpack("<Q", header)[0]
        if length_of_header > max_size:
            return None
        return f.read(length_of_header)


def set_attr(obj, attr, value):
    attrs = attr.split(".")
    for name in attrs[:-1]:
        obj = getattr(obj, name)
    prev = getattr(obj, attrs[-1])
    setattr(obj, attrs[-1], torch.nn.Parameter(value, requires_grad=False))
    del prev


def set_attr_raw(obj, attr, value):
    attrs = attr.split(".")
    for name in attrs[:-1]:
        obj = getattr(obj, name)
    setattr(obj, attrs[-1], value)


def copy_to_param(obj, attr, value):
    # inplace update tensor instead of replacing it
    attrs = attr.split(".")
    for name in attrs[:-1]:
        obj = getattr(obj, name)
    prev = getattr(obj, attrs[-1])
    prev.data.copy_(value)


def get_attr(obj, attr):
    attrs = attr.split(".")
    for name in attrs:
        obj = getattr(obj, name)
    return obj


def bislerp(samples, width, height):
    def slerp(b1, b2, r):
        """slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC"""

        c = b1.shape[-1]

        # norms
        b1_norms = torch.norm(b1, dim=-1, keepdim=True)
        b2_norms = torch.norm(b2, dim=-1, keepdim=True)

        # normalize
        b1_normalized = b1 / b1_norms
        b2_normalized = b2 / b2_norms

        # zero when norms are zero
        b1_normalized[b1_norms.expand(-1, c) == 0.0] = 0.0
        b2_normalized[b2_norms.expand(-1, c) == 0.0] = 0.0

        # slerp
        dot = (b1_normalized * b2_normalized).sum(1)
        omega = torch.acos(dot)
        so = torch.sin(omega)

        # technically not mathematically correct, but more pleasing?
        res = (torch.sin((1.0 - r.squeeze(1)) * omega) / so).unsqueeze(1) * b1_normalized + (torch.sin(r.squeeze(1) * omega) / so).unsqueeze(1) * b2_normalized
        res *= (b1_norms * (1.0 - r) + b2_norms * r).expand(-1, c)

        # edge cases for same or polar opposites
        res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5]
        res[dot < 1e-5 - 1] = (b1 * (1.0 - r) + b2 * r)[dot < 1e-5 - 1]
        return res

    def generate_bilinear_data(length_old, length_new, device):
        coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1, 1, 1, -1))
        coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear")
        ratios = coords_1 - coords_1.floor()
        coords_1 = coords_1.to(torch.int64)

        coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1, 1, 1, -1)) + 1
        coords_2[:, :, :, -1] -= 1
        coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
        coords_2 = coords_2.to(torch.int64)
        return ratios, coords_1, coords_2

    orig_dtype = samples.dtype
    samples = samples.float()
    n, c, h, w = samples.shape
    h_new, w_new = (height, width)

    # linear w
    ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device)
    coords_1 = coords_1.expand((n, c, h, -1))
    coords_2 = coords_2.expand((n, c, h, -1))
    ratios = ratios.expand((n, 1, h, -1))

    pass_1 = samples.gather(-1, coords_1).movedim(1, -1).reshape((-1, c))
    pass_2 = samples.gather(-1, coords_2).movedim(1, -1).reshape((-1, c))
    ratios = ratios.movedim(1, -1).reshape((-1, 1))

    result = slerp(pass_1, pass_2, ratios)
    result = result.reshape(n, h, w_new, c).movedim(-1, 1)

    # linear h
    ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device)
    coords_1 = coords_1.reshape((1, 1, -1, 1)).expand((n, c, -1, w_new))
    coords_2 = coords_2.reshape((1, 1, -1, 1)).expand((n, c, -1, w_new))
    ratios = ratios.reshape((1, 1, -1, 1)).expand((n, 1, -1, w_new))

    pass_1 = result.gather(-2, coords_1).movedim(1, -1).reshape((-1, c))
    pass_2 = result.gather(-2, coords_2).movedim(1, -1).reshape((-1, c))
    ratios = ratios.movedim(1, -1).reshape((-1, 1))

    result = slerp(pass_1, pass_2, ratios)
    result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
    return result.to(orig_dtype)


def lanczos(samples, width, height):
    images = [Image.fromarray(np.clip(255.0 * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]
    images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images]
    images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images]
    result = torch.stack(images)
    return result.to(samples.device, samples.dtype)


def common_upscale(samples, width, height, upscale_method, crop):
    if crop == "center":
        old_width = samples.shape[3]
        old_height = samples.shape[2]
        old_aspect = old_width / old_height
        new_aspect = width / height
        x = 0
        y = 0
        if old_aspect > new_aspect:
            x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
        elif old_aspect < new_aspect:
            y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
        s = samples[:, :, y : old_height - y, x : old_width - x]
    else:
        s = samples

    if upscale_method == "bislerp":
        return bislerp(s, width, height)
    elif upscale_method == "lanczos":
        return lanczos(s, width, height)
    else:
        return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)


def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
    return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))


@torch.inference_mode()
def tiled_scale(
    samples,
    function,
    tile_x=64,
    tile_y=64,
    overlap=8,
    upscale_amount=4,
    out_channels=3,
    output_device="cpu",
    pbar=None,
):
    output = torch.empty(
        (
            samples.shape[0],
            out_channels,
            round(samples.shape[2] * upscale_amount),
            round(samples.shape[3] * upscale_amount),
        ),
        device=output_device,
    )
    for b in range(samples.shape[0]):
        s = samples[b : b + 1]
        out = torch.zeros(
            (
                s.shape[0],
                out_channels,
                round(s.shape[2] * upscale_amount),
                round(s.shape[3] * upscale_amount),
            ),
            device=output_device,
        )
        out_div = torch.zeros(
            (
                s.shape[0],
                out_channels,
                round(s.shape[2] * upscale_amount),
                round(s.shape[3] * upscale_amount),
            ),
            device=output_device,
        )
        for y in range(0, s.shape[2], tile_y - overlap):
            for x in range(0, s.shape[3], tile_x - overlap):
                x = max(0, min(s.shape[-1] - overlap, x))
                y = max(0, min(s.shape[-2] - overlap, y))
                s_in = s[:, :, y : y + tile_y, x : x + tile_x]

                ps = function(s_in).to(output_device)
                mask = torch.ones_like(ps)
                feather = round(overlap * upscale_amount)
                for t in range(feather):
                    mask[:, :, t : 1 + t, :] *= (1.0 / feather) * (t + 1)
                    mask[:, :, mask.shape[2] - 1 - t : mask.shape[2] - t, :] *= (1.0 / feather) * (t + 1)
                    mask[:, :, :, t : 1 + t] *= (1.0 / feather) * (t + 1)
                    mask[:, :, :, mask.shape[3] - 1 - t : mask.shape[3] - t] *= (1.0 / feather) * (t + 1)
                out[
                    :,
                    :,
                    round(y * upscale_amount) : round((y + tile_y) * upscale_amount),
                    round(x * upscale_amount) : round((x + tile_x) * upscale_amount),
                ] += (
                    ps * mask
                )
                out_div[
                    :,
                    :,
                    round(y * upscale_amount) : round((y + tile_y) * upscale_amount),
                    round(x * upscale_amount) : round((x + tile_x) * upscale_amount),
                ] += mask
                if pbar is not None:
                    pbar.update(1)

        output[b : b + 1] = out / out_div
    return output


PROGRESS_BAR_HOOK = None


class ProgressBar:
    def __init__(self, total, title=None):
        global PROGRESS_BAR_HOOK
        self.total = total
        self.current = 0
        self.hook = PROGRESS_BAR_HOOK
        self.tqdm = tqdm(total=total, desc=title)

    def update_absolute(self, value, total=None, preview=None):
        if total is not None:
            self.total = total
        if value > self.total:
            value = self.total
        inc = value - self.current
        self.tqdm.update(inc)
        self.current = value
        if self.hook is not None:
            self.hook(self.current, self.total, preview)
        if self.current >= self.total:
            self.tqdm.close()

    def update(self, value):
        self.update_absolute(self.current + value)