2024-06-15 23:04:31 -06:00
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import contextlib
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import torch
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import k_diffusion
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from modules.models.sd3.sd3_impls import BaseModel, SDVAE, SD3LatentFormat
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2024-06-26 14:22:00 -06:00
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from modules.models.sd3.sd3_cond import SD3Cond
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2024-06-15 23:04:31 -06:00
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2024-06-26 14:22:00 -06:00
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from modules import shared, devices
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2024-06-24 01:15:46 -06:00
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2024-06-15 23:04:31 -06:00
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class SD3Denoiser(k_diffusion.external.DiscreteSchedule):
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def __init__(self, inner_model, sigmas):
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super().__init__(sigmas, quantize=shared.opts.enable_quantization)
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self.inner_model = inner_model
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def forward(self, input, sigma, **kwargs):
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return self.inner_model.apply_model(input, sigma, **kwargs)
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class SD3Inferencer(torch.nn.Module):
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def __init__(self, state_dict, shift=3, use_ema=False):
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super().__init__()
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self.shift = shift
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with torch.no_grad():
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self.model = BaseModel(shift=shift, state_dict=state_dict, prefix="model.diffusion_model.", device="cpu", dtype=devices.dtype)
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self.first_stage_model = SDVAE(device="cpu", dtype=devices.dtype_vae)
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self.first_stage_model.dtype = self.model.diffusion_model.dtype
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self.alphas_cumprod = 1 / (self.model.model_sampling.sigmas ** 2 + 1)
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2024-06-28 15:38:52 -06:00
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self.text_encoders = SD3Cond()
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2024-06-15 23:04:31 -06:00
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self.cond_stage_key = 'txt'
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self.parameterization = "eps"
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self.model.conditioning_key = "crossattn"
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self.latent_format = SD3LatentFormat()
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self.latent_channels = 16
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2024-06-28 15:38:52 -06:00
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@property
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def cond_stage_model(self):
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return self.text_encoders
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def before_load_weights(self, state_dict):
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self.cond_stage_model.before_load_weights(state_dict)
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def ema_scope(self):
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return contextlib.nullcontext()
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def get_learned_conditioning(self, batch: list[str]):
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2024-06-28 02:15:34 -06:00
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return self.cond_stage_model(batch)
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def apply_model(self, x, t, cond):
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return self.model(x, t, c_crossattn=cond['crossattn'], y=cond['vector'])
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2024-06-15 23:04:31 -06:00
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def decode_first_stage(self, latent):
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latent = self.latent_format.process_out(latent)
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return self.first_stage_model.decode(latent)
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def encode_first_stage(self, image):
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latent = self.first_stage_model.encode(image)
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return self.latent_format.process_in(latent)
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2024-06-27 23:10:32 -06:00
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def get_first_stage_encoding(self, x):
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return x
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2024-06-15 23:04:31 -06:00
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def create_denoiser(self):
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return SD3Denoiser(self, self.model.model_sampling.sigmas)
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2024-06-24 01:15:46 -06:00
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def medvram_fields(self):
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return [
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(self, 'first_stage_model'),
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2024-06-29 22:06:28 -06:00
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(self, 'text_encoders'),
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(self, 'model'),
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]
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2024-06-28 00:23:41 -06:00
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def add_noise_to_latent(self, x, noise, amount):
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return x * (1 - amount) + noise * amount
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2024-06-28 09:06:49 -06:00
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def fix_dimensions(self, width, height):
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return width // 16 * 16, height // 16 * 16
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