diffusers/scripts/convert_dance_diffusion_to_diffusers.py at main · feisan/diffusers · GitHub

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#!/usr/bin/env python3

import argparse

import math

import os

from copy import deepcopy

import torch

from audio_diffusion.models import DiffusionAttnUnet1D

from diffusion import sampling

from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel

MODELS_MAP = {

"gwf-440k": {

"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",

"sample_rate": 48000,

"sample_size": 65536,

"jmann-small-190k": {

"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",

"sample_rate": 48000,

"sample_size": 65536,

"jmann-large-580k": {

"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",

"sample_rate": 48000,

"sample_size": 131072,

"maestro-uncond-150k": {

"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",

"sample_rate": 16000,

"sample_size": 65536,

"unlocked-uncond-250k": {

"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",

"sample_rate": 16000,

"sample_size": 65536,

"honk-140k": {

"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",

"sample_rate": 16000,

"sample_size": 65536,

}

def alpha_sigma_to_t(alpha, sigma):

"""Returns a timestep, given the scaling factors for the clean image and for

the noise."""

return torch.atan2(sigma, alpha) / math.pi * 2

def get_crash_schedule(t):

sigma = torch.sin(t * math.pi / 2) ** 2

alpha = (1 - sigma**2) ** 0.5

return alpha_sigma_to_t(alpha, sigma)

class Object(object):

pass

class DiffusionUncond(nn.Module):

def __init__(self, global_args):

super().__init__()

self.diffusion = DiffusionAttnUnet1D(global_args, n_attn_layers=4)

self.diffusion_ema = deepcopy(self.diffusion)

self.rng = torch.quasirandom.SobolEngine(1, scramble=True)

def download(model_name):

url = MODELS_MAP[model_name]["url"]

os.system(f"wget {url} ./")

return f"./{model_name}.ckpt"

DOWN_NUM_TO_LAYER = {

"1": "resnets.0",

"2": "attentions.0",

"3": "resnets.1",

"4": "attentions.1",

"5": "resnets.2",

"6": "attentions.2",

}

UP_NUM_TO_LAYER = {

"8": "resnets.0",

"9": "attentions.0",

"10": "resnets.1",

"11": "attentions.1",

"12": "resnets.2",

"13": "attentions.2",

}

MID_NUM_TO_LAYER = {

"1": "resnets.0",

"2": "attentions.0",

"3": "resnets.1",

"4": "attentions.1",

"5": "resnets.2",

"6": "attentions.2",

"8": "resnets.3",

"9": "attentions.3",

"10": "resnets.4",

"11": "attentions.4",

"12": "resnets.5",

"13": "attentions.5",

}

DEPTH_0_TO_LAYER = {

"0": "resnets.0",

"1": "resnets.1",

"2": "resnets.2",

"4": "resnets.0",

"5": "resnets.1",

"6": "resnets.2",

}

RES_CONV_MAP = {

"skip": "conv_skip",

"main.0": "conv_1",

"main.1": "group_norm_1",

"main.3": "conv_2",

"main.4": "group_norm_2",

}

ATTN_MAP = {

"norm": "group_norm",

"qkv_proj": ["query", "key", "value"],

"out_proj": ["proj_attn"],

}

def convert_resconv_naming(name):

if name.startswith("skip"):

return name.replace("skip", RES_CONV_MAP["skip"])

# name has to be of format main.{digit}

if not name.startswith("main."):

raise ValueError(f"ResConvBlock error with {name}")

return name.replace(name[:6], RES_CONV_MAP[name[:6]])

def convert_attn_naming(name):

for key, value in ATTN_MAP.items():

if name.startswith(key) and not isinstance(value, list):

return name.replace(key, value)

elif name.startswith(key):

return [name.replace(key, v) for v in value]

raise ValueError(f"Attn error with {name}")

def rename(input_string, max_depth=13):

string = input_string

if string.split(".")[0] == "timestep_embed":

return string.replace("timestep_embed", "time_proj")

depth = 0

if string.startswith("net.3."):

depth += 1

string = string[6:]

elif string.startswith("net."):

string = string[4:]

while string.startswith("main.7."):

depth += 1

string = string[7:]

if string.startswith("main."):

string = string[5:]

# mid block

if string[:2].isdigit():

layer_num = string[:2]

string_left = string[2:]

else:

layer_num = string[0]

string_left = string[1:]

if depth == max_depth:

new_layer = MID_NUM_TO_LAYER[layer_num]

prefix = "mid_block"

elif depth > 0 and int(layer_num) < 7:

new_layer = DOWN_NUM_TO_LAYER[layer_num]

prefix = f"down_blocks.{depth}"

elif depth > 0 and int(layer_num) > 7:

new_layer = UP_NUM_TO_LAYER[layer_num]

prefix = f"up_blocks.{max_depth - depth - 1}"

elif depth == 0:

new_layer = DEPTH_0_TO_LAYER[layer_num]

prefix = f"up_blocks.{max_depth - 1}" if int(layer_num) > 3 else "down_blocks.0"

if not string_left.startswith("."):

raise ValueError(f"Naming error with {input_string} and string_left: {string_left}.")

string_left = string_left[1:]

if "resnets" in new_layer:

string_left = convert_resconv_naming(string_left)

elif "attentions" in new_layer:

new_string_left = convert_attn_naming(string_left)

string_left = new_string_left

if not isinstance(string_left, list):

new_string = prefix + "." + new_layer + "." + string_left

else:

new_string = [prefix + "." + new_layer + "." + s for s in string_left]

return new_string

def rename_orig_weights(state_dict):

new_state_dict = {}

for k, v in state_dict.items():

if k.endswith("kernel"):

# up- and downsample layers, don't have trainable weights

continue

new_k = rename(k)

# check if we need to transform from Conv => Linear for attention

if isinstance(new_k, list):

new_state_dict = transform_conv_attns(new_state_dict, new_k, v)

else:

new_state_dict[new_k] = v

return new_state_dict

def transform_conv_attns(new_state_dict, new_k, v):

if len(new_k) == 1:

if len(v.shape) == 3:

# weight

new_state_dict[new_k[0]] = v[:, :, 0]

else:

# bias

new_state_dict[new_k[0]] = v

else:

# qkv matrices

trippled_shape = v.shape[0]

single_shape = trippled_shape // 3

for i in range(3):

if len(v.shape) == 3:

new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape, :, 0]

else:

new_state_dict[new_k[i]] = v[i * single_shape : (i + 1) * single_shape]

return new_state_dict

def main(args):

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model_name = args.model_path.split("/")[-1].split(".")[0]

if not os.path.isfile(args.model_path):

assert (

model_name == args.model_path

), f"Make sure to provide one of the official model names {MODELS_MAP.keys()}"

args.model_path = download(model_name)

sample_rate = MODELS_MAP[model_name]["sample_rate"]

sample_size = MODELS_MAP[model_name]["sample_size"]

config = Object()

config.sample_size = sample_size

config.sample_rate = sample_rate

config.latent_dim = 0

diffusers_model = UNet1DModel(sample_size=sample_size, sample_rate=sample_rate)

diffusers_state_dict = diffusers_model.state_dict()

orig_model = DiffusionUncond(config)

orig_model.load_state_dict(torch.load(args.model_path, map_location=device)["state_dict"])

orig_model = orig_model.diffusion_ema.eval()

orig_model_state_dict = orig_model.state_dict()

renamed_state_dict = rename_orig_weights(orig_model_state_dict)

renamed_minus_diffusers = set(renamed_state_dict.keys()) - set(diffusers_state_dict.keys())

diffusers_minus_renamed = set(diffusers_state_dict.keys()) - set(renamed_state_dict.keys())

assert len(renamed_minus_diffusers) == 0, f"Problem with {renamed_minus_diffusers}"

assert all(k.endswith("kernel") for k in list(diffusers_minus_renamed)), f"Problem with {diffusers_minus_renamed}"

for key, value in renamed_state_dict.items():

assert (

diffusers_state_dict[key].squeeze().shape == value.squeeze().shape

), f"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"

if key == "time_proj.weight":

value = value.squeeze()

diffusers_state_dict[key] = value

diffusers_model.load_state_dict(diffusers_state_dict)

steps = 100

seed = 33

diffusers_scheduler = IPNDMScheduler(num_train_timesteps=steps)

generator = torch.manual_seed(seed)

noise = torch.randn([1, 2, config.sample_size], generator=generator).to(device)

t = torch.linspace(1, 0, steps + 1, device=device)[:-1]

step_list = get_crash_schedule(t)

pipe = DanceDiffusionPipeline(unet=diffusers_model, scheduler=diffusers_scheduler)

generator = torch.manual_seed(33)

audio = pipe(num_inference_steps=steps, generator=generator).audios

generated = sampling.iplms_sample(orig_model, noise, step_list, {})

generated = generated.clamp(-1, 1)

diff_sum = (generated - audio).abs().sum()

diff_max = (generated - audio).abs().max()

if args.save:

pipe.save_pretrained(args.checkpoint_path)

print("Diff sum", diff_sum)

print("Diff max", diff_max)

assert diff_max < 1e-3, f"Diff max: {diff_max} is too much :-/"

print(f"Conversion for {model_name} successful!")

if __name__ == "__main__":

parser = argparse.ArgumentParser()

parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")

parser.add_argument(

"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."

)

parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")

args = parser.parse_args()

main(args)

diffusers/scripts/convert_dance_diffusion_to_diffusers.py at main · feisan/diffusers · GitHub

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