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Copy pathconvert_dance_diffusion_to_diffusers.py
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executable file
·339 lines (266 loc) · 10 KB
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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)
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