168 lines
5.2 KiB
Python
168 lines
5.2 KiB
Python
# import numpy as np
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# import torch
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#
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# from comfy.model_management import get_torch_device
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# from comfy_extras.nodes_model_advanced import rescale_zero_terminal_snr_sigmas, \
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# ModelSamplingDiscreteLCM, LCM
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# from nodes import KSampler
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#
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# from coreml_suite.nodes import CoreMLSampler
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#
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# class CoreMLSamplerLCM(CoreMLSampler):
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# def sample(
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# self,
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# model_patcher,
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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,
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# denoise=1.0,
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# callback=None,
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# disable_pbar=False,
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# **kwargs
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# ):
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# positive[0][1]["control_apply_to_uncond"] = False
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#
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# latent = latent_image["samples"].to(get_torch_device())
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#
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# batch_size = latent.shape[0]
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# dtype = latent.dtype
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# device = get_torch_device()
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#
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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=dtype
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# )
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#
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# model_options = {
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# "model_function_wrapper": model_function_wrapper(w_embedding),
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# "sampler_cfg_function": lambda x: x["cond"].to(device),
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# }
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# model_patcher.model_options |= model_options
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# self.prepare_timesteps(denoise, device, steps)
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# all_sigmas, sigmas = self.get_sigmas(steps, denoise)
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#
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# model_patcher.model.model_sampling.set_sigmas(all_sigmas)
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# sigma_to_timestep = {
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# s.item(): t for s, t in zip(sigmas, self.scheduler.timesteps)
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# }
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# model_patcher.model.model_sampling.timestep = lambda x: sigma_to_timestep[
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# x[0].item()
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# ].expand(1)
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#
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# noise_mask = latent_image.get("noise_mask")
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# batch_inds = latent_image.get("batch_index")
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# noise = prepare_noise(latent, seed, batch_inds)
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#
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# sampler = samplers.ksampler("ddpm")()
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# samples = sample_custom(
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# model_patcher,
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# noise,
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# cfg,
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# sampler,
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# sigmas,
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# positive,
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# (),
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# latent,
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# noise_mask,
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# callback,
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# disable_pbar,
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# seed,
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# )
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# model_patcher = lcm_patch(model_patcher)
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#
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# return KSampler.sample(
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# self,
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# model_patcher,
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# seed,
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# steps,
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# cfg,
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# "lcm",
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# "sgm_uniform",
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# positive,
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# None,
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# latent_image,
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# denoise,
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# )
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#
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# def get_sigmas(self, steps, denoise):
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# alphas_cumprod = self.scheduler.alphas_cumprod
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# sigmas = np.asarray(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5)
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# skipping_step = len(sigmas) // steps
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# s = sigmas[::-skipping_step][:steps]
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# if len(s) == steps:
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# s = np.append(s, 0.0).astype(np.float32)
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# sigmas = torch.from_numpy(sigmas).to(get_torch_device())
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# return (sigmas, torch.from_numpy(s.copy()).to(get_torch_device()))
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#
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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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#
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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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#
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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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#
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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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#
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#
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# def model_function_wrapper(w_embedding):
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# def wrapper(model_function, params):
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# x = params["input"]
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# t = params["timestep"]
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# c = params["c"]
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#
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# context = c.get("c_crossattn")
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#
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# if context is None:
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# return torch.zeros_like(x)
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#
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# return model_function(x, t, **c, timestep_cond=w_embedding)
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#
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# return wrapper
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#
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#
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# def lcm_patch(model):
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# m = model.clone()
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# sampling_type = LCM
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# sampling_base = ModelSamplingDiscreteLCM
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#
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# class ModelSamplingAdvanced(sampling_base, sampling_type):
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# pass
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#
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# model_sampling = ModelSamplingAdvanced()
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# model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas))
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#
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# m.add_object_patch("model_sampling", model_sampling)
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#
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# return m
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