Upload extensions_built_in/diffusion_models/f_light/src/model.py with huggingface_hub

extensions_built_in/diffusion_models/f_light/src/model.py

@@ -0,0 +1,456 @@
+# originally from https://github.com/fal-ai/f-lite/blob/main/f_lite/model.py but modified slightly
+
+import math
+
+import torch
+import torch.nn.functional as F
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils.accelerate_utils import apply_forward_hook
+from einops import rearrange
+from peft import get_peft_model_state_dict, set_peft_model_state_dict
+from torch import nn
+
+
+def timestep_embedding(t, dim, max_period=10000):
+    half = dim // 2
+    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
+        device=t.device
+    )
+    args = t[:, None].float() * freqs[None]
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+
+    return embedding
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-6, trainable=False):
+        super().__init__()
+        self.eps = eps
+        if trainable:
+            self.weight = nn.Parameter(torch.ones(dim))
+        else:
+            self.weight = None
+
+    def forward(self, x):
+        x_dtype = x.dtype
+        x = x.float()
+        norm = torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+        if self.weight is not None:
+            return (x * norm * self.weight).to(dtype=x_dtype)
+        else:
+            return (x * norm).to(dtype=x_dtype)
+
+
+class QKNorm(nn.Module):
+    """Normalizing the query and the key independently, as Flux proposes"""
+
+    def __init__(self, dim, trainable=False):
+        super().__init__()
+        self.query_norm = RMSNorm(dim, trainable=trainable)
+        self.key_norm = RMSNorm(dim, trainable=trainable)
+
+    def forward(self, q, k):
+        q = self.query_norm(q)
+        k = self.key_norm(k)
+        return q, k
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=8,
+        qkv_bias=False,
+        is_self_attn=True,
+        cross_attn_input_size=None,
+        residual_v=False,
+        dynamic_softmax_temperature=False,
+    ):
+        super().__init__()
+        assert dim % num_heads == 0
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim**-0.5
+        self.is_self_attn = is_self_attn
+        self.residual_v = residual_v
+        self.dynamic_softmax_temperature = dynamic_softmax_temperature
+
+        if is_self_attn:
+            self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        else:
+            self.q = nn.Linear(dim, dim, bias=qkv_bias)
+            self.context_kv = nn.Linear(cross_attn_input_size, dim * 2, bias=qkv_bias)
+
+        self.proj = nn.Linear(dim, dim, bias=False)
+
+        if residual_v:
+            self.lambda_param = nn.Parameter(torch.tensor(0.5).reshape(1))
+
+        self.qk_norm = QKNorm(self.head_dim)
+
+    def forward(self, x, context=None, v_0=None, rope=None):
+        if self.is_self_attn:
+            qkv = self.qkv(x)
+            qkv = rearrange(qkv, "b l (k h d) -> k b h l d", k=3, h=self.num_heads)
+            q, k, v = qkv.unbind(0)
+
+            if self.residual_v and v_0 is not None:
+                v = self.lambda_param * v + (1 - self.lambda_param) * v_0
+
+            if rope is not None:
+                # print(q.shape, rope[0].shape, rope[1].shape)
+                q = apply_rotary_emb(q, rope[0], rope[1])
+                k = apply_rotary_emb(k, rope[0], rope[1])
+
+                # https://arxiv.org/abs/2306.08645
+                # https://arxiv.org/abs/2410.01104
+                # ratioonale is that if tokens get larger, categorical distribution get more uniform
+                # so you want to enlargen entropy.
+
+                token_length = q.shape[2]
+                if self.dynamic_softmax_temperature:
+                    ratio = math.sqrt(math.log(token_length) / math.log(1040.0))  # 1024 + 16
+                    k = k * ratio
+            q, k = self.qk_norm(q, k)
+
+        else:
+            q = rearrange(self.q(x), "b l (h d) -> b h l d", h=self.num_heads)
+            kv = rearrange(
+                self.context_kv(context),
+                "b l (k h d) -> k b h l d",
+                k=2,
+                h=self.num_heads,
+            )
+            k, v = kv.unbind(0)
+            q, k = self.qk_norm(q, k)
+
+        x = F.scaled_dot_product_attention(q, k, v)
+        x = rearrange(x, "b h l d -> b l (h d)")
+        x = self.proj(x)
+        return x, v if self.is_self_attn else None
+
+
+class DiTBlock(nn.Module):
+    def __init__(
+        self,
+        hidden_size,
+        cross_attn_input_size,
+        num_heads,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        residual_v=False,
+        dynamic_softmax_temperature=False,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.norm1 = RMSNorm(hidden_size, trainable=qkv_bias)
+        self.self_attn = Attention(
+            hidden_size,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            is_self_attn=True,
+            residual_v=residual_v,
+            dynamic_softmax_temperature=dynamic_softmax_temperature,
+        )
+
+        if cross_attn_input_size is not None:
+            self.norm2 = RMSNorm(hidden_size, trainable=qkv_bias)
+            self.cross_attn = Attention(
+                hidden_size,
+                num_heads=num_heads,
+                qkv_bias=qkv_bias,
+                is_self_attn=False,
+                cross_attn_input_size=cross_attn_input_size,
+                dynamic_softmax_temperature=dynamic_softmax_temperature,
+            )
+        else:
+            self.norm2 = None
+            self.cross_attn = None
+
+        self.norm3 = RMSNorm(hidden_size, trainable=qkv_bias)
+        mlp_hidden = int(hidden_size * mlp_ratio)
+        self.mlp = nn.Sequential(
+            nn.Linear(hidden_size, mlp_hidden),
+            nn.GELU(),
+            nn.Linear(mlp_hidden, hidden_size),
+        )
+
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 9 * hidden_size, bias=True))
+
+        self.adaLN_modulation[-1].weight.data.zero_()
+        self.adaLN_modulation[-1].bias.data.zero_()
+
+    # @torch.compile(mode='reduce-overhead')
+    def forward(self, x, context, c, v_0=None, rope=None):
+        (
+            shift_sa,
+            scale_sa,
+            gate_sa,
+            shift_ca,
+            scale_ca,
+            gate_ca,
+            shift_mlp,
+            scale_mlp,
+            gate_mlp,
+        ) = self.adaLN_modulation(c).chunk(9, dim=1)
+
+        scale_sa = scale_sa[:, None, :]
+        scale_ca = scale_ca[:, None, :]
+        scale_mlp = scale_mlp[:, None, :]
+
+        shift_sa = shift_sa[:, None, :]
+        shift_ca = shift_ca[:, None, :]
+        shift_mlp = shift_mlp[:, None, :]
+
+        gate_sa = gate_sa[:, None, :]
+        gate_ca = gate_ca[:, None, :]
+        gate_mlp = gate_mlp[:, None, :]
+
+        norm_x = self.norm1(x.clone())
+        norm_x = norm_x * (1 + scale_sa) + shift_sa
+        attn_out, v = self.self_attn(norm_x, v_0=v_0, rope=rope)
+        x = x + attn_out * gate_sa
+
+        if self.norm2 is not None:
+            norm_x = self.norm2(x)
+            norm_x = norm_x * (1 + scale_ca) + shift_ca
+            x = x + self.cross_attn(norm_x, context)[0] * gate_ca
+
+        norm_x = self.norm3(x)
+        norm_x = norm_x * (1 + scale_mlp) + shift_mlp
+        x = x + self.mlp(norm_x) * gate_mlp
+
+        return x, v
+
+
+class PatchEmbed(nn.Module):
+    def __init__(self, patch_size=16, in_channels=3, embed_dim=768):
+        super().__init__()
+        self.patch_proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
+        self.patch_size = patch_size
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        x = self.patch_proj(x)
+        x = rearrange(x, "b c h w -> b (h w) c")
+        return x
+
+
+class TwoDimRotary(torch.nn.Module):
+    def __init__(self, dim, base=10000, h=256, w=256):
+        super().__init__()
+        self.inv_freq = torch.FloatTensor([1.0 / (base ** (i / dim)) for i in range(0, dim, 2)])
+        self.h = h
+        self.w = w
+
+        t_h = torch.arange(h, dtype=torch.float32)
+        t_w = torch.arange(w, dtype=torch.float32)
+
+        freqs_h = torch.outer(t_h, self.inv_freq).unsqueeze(1)  # h, 1, d / 2
+        freqs_w = torch.outer(t_w, self.inv_freq).unsqueeze(0)  # 1, w, d / 2
+        freqs_h = freqs_h.repeat(1, w, 1)  # h, w, d / 2
+        freqs_w = freqs_w.repeat(h, 1, 1)  # h, w, d / 2
+        freqs_hw = torch.cat([freqs_h, freqs_w], 2)  # h, w, d
+
+        self.register_buffer("freqs_hw_cos", freqs_hw.cos())
+        self.register_buffer("freqs_hw_sin", freqs_hw.sin())
+
+    def forward(self, x, height_width=None, extend_with_register_tokens=0):
+        if height_width is not None:
+            this_h, this_w = height_width
+        else:
+            this_hw = x.shape[1]
+            this_h, this_w = int(this_hw**0.5), int(this_hw**0.5)
+
+        cos = self.freqs_hw_cos[0 : this_h, 0 : this_w]
+        sin = self.freqs_hw_sin[0 : this_h, 0 : this_w]
+
+        cos = cos.clone().reshape(this_h * this_w, -1)
+        sin = sin.clone().reshape(this_h * this_w, -1)
+
+        # append N of zero-attn tokens
+        if extend_with_register_tokens > 0:
+            cos = torch.cat(
+                [
+                    torch.ones(extend_with_register_tokens, cos.shape[1]).to(cos.device),
+                    cos,
+                ],
+                0,
+            )
+            sin = torch.cat(
+                [
+                    torch.zeros(extend_with_register_tokens, sin.shape[1]).to(sin.device),
+                    sin,
+                ],
+                0,
+            )
+
+        return cos[None, None, :, :], sin[None, None, :, :]  # [1, 1, T + N, Attn-dim]
+
+
+def apply_rotary_emb(x, cos, sin):
+    orig_dtype = x.dtype
+    x = x.to(dtype=torch.float32)
+    assert x.ndim == 4  # multihead attention
+    d = x.shape[3] // 2
+    x1 = x[..., :d]
+    x2 = x[..., d:]
+    y1 = x1 * cos + x2 * sin
+    y2 = x1 * (-sin) + x2 * cos
+    return torch.cat([y1, y2], 3).to(dtype=orig_dtype)
+
+
+class DiT(ModelMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):  # type: ignore[misc]
+    _supports_gradient_checkpointing = True
+    
+    @register_to_config
+    def __init__(
+        self,
+        in_channels=4,
+        patch_size=2,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4.0,
+        cross_attn_input_size=128,
+        residual_v=False,
+        train_bias_and_rms=True,
+        use_rope=True,
+        gradient_checkpoint=False,
+        dynamic_softmax_temperature=False,
+        rope_base=10000,
+    ):
+        super().__init__()
+
+        self.patch_embed = PatchEmbed(patch_size, in_channels, hidden_size)
+
+        if use_rope:
+            self.rope = TwoDimRotary(hidden_size // (2 * num_heads), base=rope_base, h=512, w=512)
+        else:
+            self.positional_embedding = nn.Parameter(torch.zeros(1, 2048, hidden_size))
+
+        self.register_tokens = nn.Parameter(torch.randn(1, 16, hidden_size))
+
+        self.time_embed = nn.Sequential(
+            nn.Linear(hidden_size, 4 * hidden_size),
+            nn.SiLU(),
+            nn.Linear(4 * hidden_size, hidden_size),
+        )
+
+        self.blocks = nn.ModuleList(
+            [
+                DiTBlock(
+                    hidden_size=hidden_size,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    cross_attn_input_size=cross_attn_input_size,
+                    residual_v=residual_v,
+                    qkv_bias=train_bias_and_rms,
+                    dynamic_softmax_temperature=dynamic_softmax_temperature,
+                )
+                for _ in range(depth)
+            ]
+        )
+
+        self.final_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
+
+        self.final_norm = RMSNorm(hidden_size, trainable=train_bias_and_rms)
+        self.final_proj = nn.Linear(hidden_size, patch_size * patch_size * in_channels)
+        nn.init.zeros_(self.final_modulation[-1].weight)
+        nn.init.zeros_(self.final_modulation[-1].bias)
+        nn.init.zeros_(self.final_proj.weight)
+        nn.init.zeros_(self.final_proj.bias)
+        self.paramstatus = {}
+        for n, p in self.named_parameters():
+            self.paramstatus[n] = {
+                "shape": p.shape,
+                "requires_grad": p.requires_grad,
+            }
+        self.gradient_checkpointing = False
+
+    def save_lora_weights(self, save_directory):
+        """Save LoRA weights to a file"""
+        lora_state_dict = get_peft_model_state_dict(self)
+        torch.save(lora_state_dict, f"{save_directory}/lora_weights.pt")
+
+    def load_lora_weights(self, load_directory):
+        """Load LoRA weights from a file"""
+        lora_state_dict = torch.load(f"{load_directory}/lora_weights.pt")
+        set_peft_model_state_dict(self, lora_state_dict)
+
+    @apply_forward_hook
+    def forward(self, x, context, timesteps):
+        b, c, h, w = x.shape
+        x = self.patch_embed(x)  # b, T, d
+
+        x = torch.cat([self.register_tokens.repeat(b, 1, 1), x], 1)  # b, T + N, d
+
+        if self.config.use_rope:
+            cos, sin = self.rope(
+                x,
+                extend_with_register_tokens=16,
+                height_width=(h // self.config.patch_size, w // self.config.patch_size),
+            )
+        else:
+            x = x + self.positional_embedding.repeat(b, 1, 1)[:, : x.shape[1], :]
+            cos, sin = None, None
+
+        t_emb = timestep_embedding(timesteps * 1000, self.config.hidden_size).to(x.device, dtype=x.dtype)
+        t_emb = self.time_embed(t_emb)
+
+        v_0 = None
+
+        for _idx, block in enumerate(self.blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                x, v = self._gradient_checkpointing_func(
+                    block,
+                    x,
+                    context,
+                    t_emb,
+                    v_0,
+                    (cos, sin)
+                )
+            else:
+                x, v = block(x, context, t_emb, v_0, (cos, sin))
+            if v_0 is None:
+                v_0 = v
+
+        x = x[:, 16:, :]
+        final_shift, final_scale = self.final_modulation(t_emb).chunk(2, dim=1)
+        x = self.final_norm(x)
+        x = x * (1 + final_scale[:, None, :]) + final_shift[:, None, :]
+        x = self.final_proj(x)
+
+        x = rearrange(
+            x,
+            "b (h w) (p1 p2 c) -> b c (h p1) (w p2)",
+            h=h // self.config.patch_size,
+            w=w // self.config.patch_size,
+            p1=self.config.patch_size,
+            p2=self.config.patch_size,
+        )
+        return x
+
+
+if __name__ == "__main__":
+    model = DiT(
+        in_channels=4,
+        patch_size=2,
+        hidden_size=1152,
+        depth=28,
+        num_heads=16,
+        mlp_ratio=4.0,
+        cross_attn_input_size=128,
+        residual_v=False,
+        train_bias_and_rms=True,
+        use_rope=True,
+    ).cuda()
+    print(
+        model(
+            torch.randn(1, 4, 64, 64).cuda(),
+            torch.randn(1, 37, 128).cuda(),
+            torch.tensor([1.0]).cuda(),
+        )
+    )