243 lines
8.2 KiB
Python
243 lines
8.2 KiB
Python
import os
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import numpy as np
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import torch
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from diffusers.utils.torch_utils import randn_tensor
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import comfy.utils
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import latent_preview
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from comfy.model_management import get_torch_device
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from comfy.model_patcher import ModelPatcher
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from coreml_suite.lcm.lcm_pipeline import LatentConsistencyModelPipeline
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from coreml_suite.lcm.lcm_scheduler import LCMScheduler
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from coreml_suite.logger import logger
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from coreml_suite.models import get_model_config, CoreMLModelWrapperLCM
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from coreml_suite.nodes import CoreMLSampler
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class CoreMLSamplerLCM_Simple:
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def __init__(self):
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self.scheduler = LCMScheduler.from_pretrained(
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os.path.join(os.path.dirname(__file__), "scheduler_config.json")
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)
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self.pipe = None
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"coreml_model": ("COREML_UNET",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
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"steps": ("INT", {"default": 4, "min": 1, "max": 10000}),
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"cfg": (
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"FLOAT",
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{
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"default": 8.0,
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"min": 0.0,
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"max": 100.0,
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"step": 0.5,
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"round": 0.01,
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},
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),
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"num_images": ("INT", {"default": 1, "min": 1, "max": 64}),
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"positive_prompt": ("STRING", {"multiline": True}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "sample"
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CATEGORY = "sampling"
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def sample(
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self,
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coreml_model,
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seed,
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steps,
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cfg,
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positive_prompt,
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num_images,
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):
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height = coreml_model.expected_inputs["sample"]["shape"][2] * 8
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width = coreml_model.expected_inputs["sample"]["shape"][3] * 8
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model_config = get_model_config()
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wrapped_model = CoreMLModelWrapperLCM(model_config, coreml_model)
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if self.pipe is None:
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self.pipe = LatentConsistencyModelPipeline.from_pretrained(
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pretrained_model_name_or_path="SimianLuo/LCM_Dreamshaper_v7",
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scheduler=self.scheduler,
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safety_checker=None,
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)
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self.pipe.to(torch_device=get_torch_device(), torch_dtype=torch.float16)
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coreml_unet = wrapped_model
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coreml_unet.config = self.pipe.unet.config
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self.pipe.unet = coreml_unet
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torch.manual_seed(seed)
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result = self.pipe(
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prompt=positive_prompt,
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width=width,
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height=height,
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guidance_scale=cfg,
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num_inference_steps=steps,
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num_images_per_prompt=num_images,
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lcm_origin_steps=50,
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output_type="np",
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).images
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images_tensor = torch.from_numpy(result)
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return (images_tensor,)
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class CoreMLSamplerLCM(CoreMLSampler):
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@classmethod
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def INPUT_TYPES(s):
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old_required = CoreMLSampler.INPUT_TYPES()["required"].copy()
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old_required.pop("negative")
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old_required.pop("sampler_name")
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old_required.pop("scheduler")
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new_required = {"coreml_model": ("COREML_UNET",)}
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return {
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"required": new_required | old_required,
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"optional": {"latent_image": ("LATENT",)},
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}
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CATEGORY = "Core ML Suite"
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def __init__(self):
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self.scheduler = LCMScheduler.from_pretrained(
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os.path.join(os.path.dirname(__file__), "scheduler_config.json")
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)
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def sample(
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self,
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coreml_model,
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seed,
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steps,
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cfg,
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positive,
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latent_image=None,
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denoise=1.0,
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**kwargs,
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):
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model_config = get_model_config()
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wrapped_model = CoreMLModelWrapperLCM(model_config, coreml_model)
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patched_model = ModelPatcher(wrapped_model, get_torch_device(), None)
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if latent_image is None:
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logger.warning("No latent image provided, using empty tensor.")
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expected = coreml_model.expected_inputs["sample"]["shape"]
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latent_image = {"samples": torch.zeros(*expected).to(get_torch_device())}
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positive = positive[0][0]
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torch.manual_seed(seed)
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return self._sample(patched_model, steps, cfg, positive, latent_image, denoise)
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def _sample(self, model, steps, cfg, positive, latent_image, denoise):
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device = get_torch_device()
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batch_size = latent_image["samples"].shape[0]
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# callback = latent_preview.prepare_callback(model, steps, None)
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prompt_embeds = self.prepare_prompt_embeds(batch_size, positive)
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timesteps = self.prepare_timesteps(denoise, device, steps)
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latents = self.prepare_latents(latent_image, device)
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w = torch.tensor(cfg).repeat(batch_size)
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w_embedding = self.get_w_embedding(w, embedding_dim=256).to(
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device=device, dtype=latents.dtype
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)
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# LCM MultiStep Sampling Loop:
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for i, t in enumerate(timesteps):
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ts = torch.full((batch_size,), t, device=device, dtype=torch.float16)
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model_pred = model.model(
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latents,
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ts,
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encoder_hidden_states=prompt_embeds,
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timestep_cond=w_embedding,
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)[0]
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# compute the previous noisy sample x_t -> x_t-1
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latents, denoised = self.scheduler.step(
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model_pred, i, t, latents, return_dict=False
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)
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denoised = denoised.to(get_torch_device())
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return ({"samples": denoised / 0.1825},)
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def prepare_prompt_embeds(self, batch_size, positive):
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bs_embed, seq_len, _ = positive.shape
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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prompt_embeds = positive.repeat(1, batch_size, 1)
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prompt_embeds = prompt_embeds.view(bs_embed * batch_size, seq_len, -1)
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return prompt_embeds
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def prepare_timesteps(self, denoise, device, steps):
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lcm_origin_steps = 50
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self.scheduler.num_inference_steps = steps
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c = self.scheduler.config.num_train_timesteps // lcm_origin_steps
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lcm_origin_timesteps = (
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np.asarray(list(range(1, int(lcm_origin_steps * denoise) + 1))) * c - 1
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)
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skipping_step = len(lcm_origin_timesteps) // steps
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timesteps = lcm_origin_timesteps[::-skipping_step][:steps]
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timesteps = torch.from_numpy(timesteps.copy()).to(device)
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self.scheduler.timesteps = timesteps
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timesteps = self.scheduler.timesteps
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return timesteps
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def prepare_latents(self, latent_image, device):
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latent = latent_image["samples"].to(device) * 0.1825
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latent = latent.to(torch.float16)
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if not torch.any(latent):
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latents = torch.randn(latent.shape, dtype=torch.float16).to(device)
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latents *= self.scheduler.init_noise_sigma
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return latents
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batch_size = latent.shape[0]
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burned = randn_tensor(latent.shape, device=device, dtype=torch.float16)
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noise = randn_tensor(latent.shape, device=device, dtype=torch.float16)
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latent_timestep = self.scheduler.timesteps[:1].repeat(batch_size)
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latents = self.scheduler.add_noise(latent, noise, latent_timestep)
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return latents
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def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
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"""
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see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
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Args:
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timesteps: torch.Tensor: generate embedding vectors at these timesteps
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embedding_dim: int: dimension of the embeddings to generate
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dtype: data type of the generated embeddings
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Returns:
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embedding vectors with shape `(len(timesteps), embedding_dim)`
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"""
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assert len(w.shape) == 1
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w = w * 1000.0
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half_dim = embedding_dim // 2
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emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
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emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
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emb = w.to(dtype)[:, None] * emb[None, :]
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emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
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if embedding_dim % 2 == 1: # zero pad
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emb = torch.nn.functional.pad(emb, (0, 1))
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assert emb.shape == (w.shape[0], embedding_dim)
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return emb
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