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| import contextlib | |
| import torch | |
| import k_diffusion | |
| from modules.models.sd3.sd3_impls import BaseModel, SDVAE, SD3LatentFormat | |
| from modules.models.sd3.sd3_cond import SD3Cond | |
| from modules import shared, devices | |
| class SD3Denoiser(k_diffusion.external.DiscreteSchedule): | |
| def __init__(self, inner_model, sigmas): | |
| super().__init__(sigmas, quantize=shared.opts.enable_quantization) | |
| self.inner_model = inner_model | |
| def forward(self, input, sigma, **kwargs): | |
| return self.inner_model.apply_model(input, sigma, **kwargs) | |
| class SD3Inferencer(torch.nn.Module): | |
| def __init__(self, state_dict, shift=3, use_ema=False): | |
| super().__init__() | |
| self.shift = shift | |
| with torch.no_grad(): | |
| self.model = BaseModel(shift=shift, state_dict=state_dict, prefix="model.diffusion_model.", device="cpu", dtype=devices.dtype) | |
| self.first_stage_model = SDVAE(device="cpu", dtype=devices.dtype_vae) | |
| self.first_stage_model.dtype = self.model.diffusion_model.dtype | |
| self.alphas_cumprod = 1 / (self.model.model_sampling.sigmas ** 2 + 1) | |
| self.text_encoders = SD3Cond() | |
| self.cond_stage_key = 'txt' | |
| self.parameterization = "eps" | |
| self.model.conditioning_key = "crossattn" | |
| self.latent_format = SD3LatentFormat() | |
| self.latent_channels = 16 | |
| def cond_stage_model(self): | |
| return self.text_encoders | |
| def before_load_weights(self, state_dict): | |
| self.cond_stage_model.before_load_weights(state_dict) | |
| def ema_scope(self): | |
| return contextlib.nullcontext() | |
| def get_learned_conditioning(self, batch: list[str]): | |
| return self.cond_stage_model(batch) | |
| def apply_model(self, x, t, cond): | |
| return self.model(x, t, c_crossattn=cond['crossattn'], y=cond['vector']) | |
| def decode_first_stage(self, latent): | |
| latent = self.latent_format.process_out(latent) | |
| return self.first_stage_model.decode(latent) | |
| def encode_first_stage(self, image): | |
| latent = self.first_stage_model.encode(image) | |
| return self.latent_format.process_in(latent) | |
| def get_first_stage_encoding(self, x): | |
| return x | |
| def create_denoiser(self): | |
| return SD3Denoiser(self, self.model.model_sampling.sigmas) | |
| def medvram_fields(self): | |
| return [ | |
| (self, 'first_stage_model'), | |
| (self, 'text_encoders'), | |
| (self, 'model'), | |
| ] | |
| def add_noise_to_latent(self, x, noise, amount): | |
| return x * (1 - amount) + noise * amount | |
| def fix_dimensions(self, width, height): | |
| return width // 16 * 16, height // 16 * 16 | |
| def diffusers_weight_mapping(self): | |
| for i in range(self.model.depth): | |
| yield f"transformer.transformer_blocks.{i}.attn.to_q", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_q_proj" | |
| yield f"transformer.transformer_blocks.{i}.attn.to_k", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_k_proj" | |
| yield f"transformer.transformer_blocks.{i}.attn.to_v", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_v_proj" | |
| yield f"transformer.transformer_blocks.{i}.attn.to_out.0", f"diffusion_model_joint_blocks_{i}_x_block_attn_proj" | |
| yield f"transformer.transformer_blocks.{i}.attn.add_q_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_q_proj" | |
| yield f"transformer.transformer_blocks.{i}.attn.add_k_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_k_proj" | |
| yield f"transformer.transformer_blocks.{i}.attn.add_v_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_v_proj" | |
| yield f"transformer.transformer_blocks.{i}.attn.add_out_proj.0", f"diffusion_model_joint_blocks_{i}_context_block_attn_proj" | |