178 lines
6.5 KiB
Python
178 lines
6.5 KiB
Python
import os
|
|
|
|
import numpy as np
|
|
import torch
|
|
from diffusers.utils.torch_utils import randn_tensor
|
|
from tqdm import tqdm
|
|
|
|
import latent_preview
|
|
from comfy import model_base
|
|
from comfy.model_management import get_torch_device
|
|
from comfy.model_patcher import ModelPatcher
|
|
from comfy.sample import prepare_sampling
|
|
from comfy.samplers import sampling_function
|
|
from coreml_suite.lcm.lcm_scheduler import LCMScheduler
|
|
from coreml_suite.logger import logger
|
|
from coreml_suite.models import CoreMLModelWrapper
|
|
from coreml_suite.nodes import CoreMLSampler
|
|
from coreml_suite.config import get_model_config
|
|
|
|
|
|
class CoreMLSamplerLCM(CoreMLSampler):
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
old_required = CoreMLSampler.INPUT_TYPES()["required"].copy()
|
|
old_required["steps"][1]["default"] = 4
|
|
old_required.pop("negative")
|
|
old_required.pop("sampler_name")
|
|
old_required.pop("scheduler")
|
|
new_required = {"coreml_model": ("COREML_UNET",)}
|
|
return {
|
|
"required": new_required | old_required,
|
|
"optional": {"latent_image": ("LATENT",)},
|
|
}
|
|
|
|
CATEGORY = "Core ML Suite"
|
|
|
|
def __init__(self):
|
|
self.scheduler = LCMScheduler.from_pretrained(
|
|
os.path.join(os.path.dirname(__file__), "scheduler_config.json")
|
|
)
|
|
|
|
def sample(
|
|
self,
|
|
coreml_model,
|
|
seed,
|
|
steps,
|
|
cfg,
|
|
positive,
|
|
latent_image=None,
|
|
denoise=1.0,
|
|
**kwargs,
|
|
):
|
|
model_config = get_model_config()
|
|
wrapped_model = CoreMLModelWrapper(coreml_model)
|
|
model = model_base.BaseModel(model_config, device=get_torch_device())
|
|
model.diffusion_model = wrapped_model
|
|
model_patcher = ModelPatcher(model, get_torch_device(), None)
|
|
|
|
if latent_image is None:
|
|
logger.warning("No latent image provided, using empty tensor.")
|
|
expected = coreml_model.expected_inputs["sample"]["shape"]
|
|
latent_image = {"samples": torch.zeros(*expected).to(get_torch_device())}
|
|
|
|
callback = latent_preview.prepare_callback(model_patcher, steps, None)
|
|
torch.manual_seed(seed)
|
|
|
|
model, positive, _, _, _ = prepare_sampling(
|
|
model_patcher, latent_image["samples"].shape, positive, (), None
|
|
)
|
|
|
|
return self._sample(
|
|
model_patcher, steps, cfg, positive, latent_image, denoise, callback
|
|
)
|
|
|
|
def _sample(
|
|
self, model, steps, cfg, positive, latent_image, denoise, callback=None
|
|
):
|
|
device = get_torch_device()
|
|
batch_size = latent_image["samples"].shape[0]
|
|
|
|
# prompt_embeds = self.prepare_prompt_embeds(batch_size, positive)
|
|
|
|
timesteps = self.prepare_timesteps(denoise, device, steps)
|
|
|
|
latents = self.prepare_latents(latent_image, device)
|
|
|
|
w = torch.tensor(cfg).repeat(batch_size)
|
|
w_embedding = self.get_w_embedding(w, embedding_dim=256).to(
|
|
device=device, dtype=latents.dtype
|
|
)
|
|
|
|
# LCM MultiStep Sampling Loop:
|
|
iterator = tqdm(timesteps, desc="Core ML LCM Sampler", total=steps)
|
|
for i, t in enumerate(iterator):
|
|
ts = torch.full((batch_size,), t, device=device, dtype=torch.float16)
|
|
|
|
model_options = {
|
|
"transformer_options": {"timestep_cond": w_embedding},
|
|
"sampler_cfg_function": lambda x: x["cond"].to(device),
|
|
}
|
|
model_pred = sampling_function(
|
|
model.apply_model, latents, ts, None, positive, denoise, model_options
|
|
)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents, denoised = self.scheduler.step(
|
|
model_pred, i, t, latents, return_dict=False
|
|
)
|
|
|
|
if callback:
|
|
callback(i, denoised.float(), latents, steps)
|
|
|
|
return ({"samples": denoised / 0.1825},)
|
|
|
|
def prepare_prompt_embeds(self, batch_size, positive):
|
|
bs_embed, seq_len, _ = positive.shape
|
|
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
|
prompt_embeds = positive.repeat(1, batch_size, 1)
|
|
prompt_embeds = prompt_embeds.view(bs_embed * batch_size, seq_len, -1)
|
|
return prompt_embeds
|
|
|
|
def prepare_timesteps(self, denoise, device, steps):
|
|
lcm_origin_steps = 50
|
|
self.scheduler.num_inference_steps = steps
|
|
c = self.scheduler.config.num_train_timesteps // lcm_origin_steps
|
|
lcm_origin_timesteps = (
|
|
np.asarray(list(range(1, int(lcm_origin_steps * denoise) + 1))) * c - 1
|
|
)
|
|
skipping_step = len(lcm_origin_timesteps) // steps
|
|
timesteps = lcm_origin_timesteps[::-skipping_step][:steps]
|
|
timesteps = torch.from_numpy(timesteps.copy()).to(device)
|
|
self.scheduler.timesteps = timesteps
|
|
timesteps = self.scheduler.timesteps
|
|
return timesteps
|
|
|
|
def prepare_latents(self, latent_image, device):
|
|
latent = latent_image["samples"].to(device) * 0.1825
|
|
latent = latent.to(torch.float16)
|
|
|
|
if not torch.any(latent):
|
|
latents = torch.randn(latent.shape, dtype=torch.float16).to(device)
|
|
latents *= self.scheduler.init_noise_sigma
|
|
return latents
|
|
|
|
batch_size = latent.shape[0]
|
|
|
|
burned = randn_tensor(latent.shape, device=device, dtype=torch.float16)
|
|
noise = randn_tensor(latent.shape, device=device, dtype=torch.float16)
|
|
|
|
latent_timestep = self.scheduler.timesteps[:1].repeat(batch_size)
|
|
latents = self.scheduler.add_noise(latent, noise, latent_timestep)
|
|
|
|
return latents
|
|
|
|
def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
|
|
"""
|
|
see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
|
|
Args:
|
|
timesteps: torch.Tensor: generate embedding vectors at these timesteps
|
|
embedding_dim: int: dimension of the embeddings to generate
|
|
dtype: data type of the generated embeddings
|
|
|
|
Returns:
|
|
embedding vectors with shape `(len(timesteps), embedding_dim)`
|
|
"""
|
|
assert len(w.shape) == 1
|
|
w = w * 1000.0
|
|
|
|
half_dim = embedding_dim // 2
|
|
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
|
|
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
|
|
emb = w.to(dtype)[:, None] * emb[None, :]
|
|
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
|
|
if embedding_dim % 2 == 1: # zero pad
|
|
emb = torch.nn.functional.pad(emb, (0, 1))
|
|
assert emb.shape == (w.shape[0], embedding_dim)
|
|
return emb
|