mirror of
https://github.com/ml-explore/mlx-examples.git
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137 lines
4.1 KiB
Python
137 lines
4.1 KiB
Python
# Copyright © 2024 Apple Inc.
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from dataclasses import dataclass
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from typing import Optional
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import mlx.core as mx
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import mlx.nn as nn
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from .layers import (
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DoubleStreamBlock,
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EmbedND,
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LastLayer,
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MLPEmbedder,
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SingleStreamBlock,
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timestep_embedding,
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)
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@dataclass
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class FluxParams:
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in_channels: int
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vec_in_dim: int
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context_in_dim: int
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hidden_size: int
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mlp_ratio: float
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num_heads: int
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depth: int
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depth_single_blocks: int
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axes_dim: list[int]
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theta: int
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qkv_bias: bool
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guidance_embed: bool
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class Flux(nn.Module):
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def __init__(self, params: FluxParams):
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super().__init__()
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self.params = params
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self.in_channels = params.in_channels
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self.out_channels = self.in_channels
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if params.hidden_size % params.num_heads != 0:
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raise ValueError(
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f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
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)
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pe_dim = params.hidden_size // params.num_heads
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if sum(params.axes_dim) != pe_dim:
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raise ValueError(
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f"Got {params.axes_dim} but expected positional dim {pe_dim}"
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)
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self.hidden_size = params.hidden_size
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self.num_heads = params.num_heads
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self.pe_embedder = EmbedND(
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dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim
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)
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self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
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self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
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self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
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self.guidance_in = (
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MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
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if params.guidance_embed
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else nn.Identity()
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)
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self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
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self.double_blocks = [
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DoubleStreamBlock(
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self.hidden_size,
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self.num_heads,
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mlp_ratio=params.mlp_ratio,
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qkv_bias=params.qkv_bias,
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)
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for _ in range(params.depth)
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]
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self.single_blocks = [
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SingleStreamBlock(
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self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio
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)
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for _ in range(params.depth_single_blocks)
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]
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self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
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def sanitize(self, weights):
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new_weights = {}
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for k, w in weights.items():
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if k.startswith("model.diffusion_model."):
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k = k[22:]
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if k.endswith(".scale"):
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k = k[:-6] + ".weight"
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for seq in ["img_mlp", "txt_mlp", "adaLN_modulation"]:
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if f".{seq}." in k:
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k = k.replace(f".{seq}.", f".{seq}.layers.")
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break
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new_weights[k] = w
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return new_weights
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def __call__(
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self,
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img: mx.array,
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img_ids: mx.array,
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txt: mx.array,
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txt_ids: mx.array,
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timesteps: mx.array,
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y: mx.array,
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guidance: Optional[mx.array] = None,
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) -> mx.array:
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if img.ndim != 3 or txt.ndim != 3:
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raise ValueError("Input img and txt tensors must have 3 dimensions.")
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img = self.img_in(img)
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vec = self.time_in(timestep_embedding(timesteps, 256))
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if self.params.guidance_embed:
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if guidance is None:
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raise ValueError(
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"Didn't get guidance strength for guidance distilled model."
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)
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vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
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vec = vec + self.vector_in(y)
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txt = self.txt_in(txt)
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ids = mx.concatenate([txt_ids, img_ids], axis=1)
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pe = self.pe_embedder(ids).astype(img.dtype)
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for block in self.double_blocks:
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img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
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img = mx.concatenate([txt, img], axis=1)
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for block in self.single_blocks:
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img = block(img, vec=vec, pe=pe)
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img = img[:, txt.shape[1] :, ...]
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img = self.final_layer(img, vec)
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return img
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