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import contextlib |
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from modules.shared import opts |
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import torch |
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if opts.sd_sampling == "A1111": |
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import k_diffusion |
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elif opts.sd_sampling == "ldm patched (Comfy)": |
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import ldm_patched.k_diffusion as k_diffusion |
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from modules.models.sd3.sd3_impls import BaseModel, SDVAE, SD3LatentFormat |
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from modules.models.sd3.sd3_cond import SD3Cond |
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from modules import shared, devices |
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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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self.text_encoders = SD3Cond() |
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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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@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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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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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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def get_first_stage_encoding(self, x): |
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return x |
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def create_denoiser(self): |
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return SD3Denoiser(self, self.model.model_sampling.sigmas) |
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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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(self, 'text_encoders'), |
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(self, 'model'), |
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] |
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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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def fix_dimensions(self, width, height): |
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return width // 16 * 16, height // 16 * 16 |
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def diffusers_weight_mapping(self): |
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for i in range(self.model.depth): |
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yield f"transformer.transformer_blocks.{i}.attn.to_q", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_q_proj" |
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yield f"transformer.transformer_blocks.{i}.attn.to_k", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_k_proj" |
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yield f"transformer.transformer_blocks.{i}.attn.to_v", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_v_proj" |
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yield f"transformer.transformer_blocks.{i}.attn.to_out.0", f"diffusion_model_joint_blocks_{i}_x_block_attn_proj" |
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yield f"transformer.transformer_blocks.{i}.attn.add_q_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_q_proj" |
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yield f"transformer.transformer_blocks.{i}.attn.add_k_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_k_proj" |
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yield f"transformer.transformer_blocks.{i}.attn.add_v_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_v_proj" |
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yield f"transformer.transformer_blocks.{i}.attn.add_out_proj.0", f"diffusion_model_joint_blocks_{i}_context_block_attn_proj" |
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