add glide repo

app.py

@@ -1,4 +1,6 @@
 
+import os
+os.system('pip install -e .')
 import gradio as gr
 
 import base64

glide_text2im/__init__.py

@@ -0,0 +1,3 @@
+"""
+A codebase for performing model inference with a text-conditional diffusion model.
+"""

glide_text2im/clip/attention.py

@@ -0,0 +1,179 @@
+import math
+from abc import ABC, abstractmethod
+from itertools import product
+from typing import Any, Optional
+
+import attr
+import numpy as np
+import torch
+
+
+@attr.s
+class AttentionMask(ABC):
+    query_context_size: int = attr.ib(validator=lambda i, a, x: x >= 1)  # type: ignore
+    key_context_size: int = attr.ib(validator=lambda i, a, x: x >= 1)  # type: ignore
+    block_size: int = attr.ib(validator=lambda i, a, x: x >= 1)  # type: ignore
+    n_head: int = attr.ib(validator=lambda i, a, x: x >= 1)  # type: ignore
+    is_head_specific: bool = attr.ib(default=False)
+    n_query_pad: int = attr.ib(default=0)
+    n_key_pad: int = attr.ib(default=0)
+
+    def __attrs_post_init__(self) -> None:
+        if self.query_context_size % self.block_size != 0:
+            raise ValueError()
+        if self.key_context_size % self.block_size != 0:
+            raise ValueError()
+        if self.n_query_pad >= self.query_context_size:
+            raise ValueError()
+        if self.n_key_pad >= self.key_context_size:
+            raise ValueError()
+
+        self.n_query_block = self.query_context_size // self.block_size
+        self.n_key_block = self.key_context_size // self.block_size
+        self.first_pad_query_block_idx = self.n_query_block - int(
+            math.ceil(self.n_query_pad / self.block_size)
+        )
+        self.first_pad_key_block_idx = self.n_key_block - int(
+            math.ceil(self.n_key_pad / self.block_size)
+        )
+
+    def _make_global_layout(self) -> None:
+        if not self.is_head_specific:
+            m = np.ones([self.n_query_block, self.n_key_block], dtype=np.bool)
+            r = product(*[range(n) for n in m.shape])
+
+            for qb, kb in r:
+                m[qb, kb] = np.any(self.block_layout(None, 0, qb, kb, 0))
+        else:
+            m = np.ones([self.n_head, self.n_query_block, self.n_key_block], dtype=np.bool)
+            r = product(*[range(n) for n in m.shape])
+
+            for h, qb, kb in r:
+                m[h, qb, kb] = np.any(self.block_layout(None, h, qb, kb, 0))
+
+        self.global_layout = m
+
+    @abstractmethod
+    def _block_layout(
+        self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int
+    ) -> np.ndarray:
+        raise NotImplementedError()
+
+    def block_layout(
+        self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int
+    ) -> np.ndarray:
+        """
+        `query_idx`, `key_idx` are block-level, zero-based indices.
+        """
+
+        m = np.ones([self.block_size, self.block_size], dtype=np.bool)
+
+        if query_idx >= self.first_pad_query_block_idx:
+            n_pad = min(
+                self.block_size,
+                (query_idx + 1) * self.block_size - (self.query_context_size - self.n_query_pad),
+            )
+            assert n_pad > 0
+            m[self.block_size - n_pad :] = False
+        if key_idx >= self.first_pad_key_block_idx:
+            n_pad = min(
+                self.block_size,
+                (key_idx + 1) * self.block_size - (self.key_context_size - self.n_key_pad),
+            )
+            assert n_pad > 0
+            m[:, self.block_size - n_pad :] = False
+
+        return m & self._block_layout(blk_shape, head_idx, query_idx, key_idx, blk_idx)
+
+
+@attr.s
+class DenseAttentionMask(AttentionMask):
+    def __attrs_post_init__(self) -> None:
+        super().__attrs_post_init__()
+
+        self.global_layout = np.ones([self.n_query_block, self.n_key_block], dtype=np.bool)
+        n_zero_query_blocks = self.n_query_pad // self.block_size
+        n_zero_key_blocks = self.n_key_pad // self.block_size
+        self.global_layout[self.n_query_block - n_zero_query_blocks :] = False
+        self.global_layout[:, self.n_key_block - n_zero_key_blocks :] = False
+
+    def _block_layout(
+        self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int
+    ) -> np.ndarray:
+        return np.ones([self.block_size, self.block_size], dtype=np.bool)
+
+
+@attr.s
+class DenseCausalAttentionMask(AttentionMask):
+    def __attrs_post_init__(self) -> None:
+        super().__attrs_post_init__()
+
+        self.global_layout = np.tril(np.ones([self.n_query_block, self.n_key_block], dtype=np.bool))
+        n_zero_query_blocks = self.n_query_pad // self.block_size
+        n_zero_key_blocks = self.n_key_pad // self.block_size
+        self.global_layout[self.n_query_block - n_zero_query_blocks :] = False
+        self.global_layout[:, self.n_key_block - n_zero_key_blocks :] = False
+
+    def _block_layout(
+        self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int
+    ) -> np.ndarray:
+        if query_idx > key_idx:
+            return np.ones(2 * [self.block_size], dtype=np.bool)
+        elif query_idx < key_idx:
+            return np.zeros(2 * [self.block_size], dtype=np.bool)
+        else:
+            return np.tril(np.ones(2 * [self.block_size], dtype=np.bool))
+
+
+@attr.s(eq=False, repr=False)
+class AttentionInfo:
+    n_heads: int = attr.ib()
+    ctx_blks_q: int = attr.ib()
+    ctx_blks_k: int = attr.ib()
+    block_size: int = attr.ib()
+    pytorch_attn_bias: Optional[torch.Tensor] = attr.ib()
+
+
+def to_attention_info(d: AttentionMask) -> AttentionInfo:
+    return AttentionInfo(
+        n_heads=d.n_head,
+        ctx_blks_q=d.n_query_block,
+        ctx_blks_k=d.n_key_block,
+        block_size=d.block_size,
+        pytorch_attn_bias=None,
+    )
+
+
+def make_full_layout(d: AttentionMask) -> np.ndarray:
+    """
+    Returns the `context_size x context_size` layout matrix described by `d`. If the layout is dependent on the index of
+    the attention head, a `attention_head x context_size x context_size` layout matrix is returned instead.
+    """
+
+    if not d.is_head_specific:
+        u = np.reshape(d.global_layout, [d.n_query_block, d.n_key_block, 1, 1])
+        r = product(range(d.n_query_block), range(d.n_key_block))
+        v = np.array([d.block_layout(None, 0, i, j, 0) for i, j in r])
+        v = np.reshape(v, [d.n_query_block, d.n_key_block, d.block_size, d.block_size])
+
+        w = u * v
+        w = np.transpose(w, [0, 2, 1, 3])
+        w = np.reshape(w, [d.query_context_size, d.key_context_size])
+        return w
+    else:
+        if len(d.global_layout.shape) == 2:
+            u = np.reshape(d.global_layout, [1, d.n_query_block, d.n_key_block, 1, 1])
+            u = np.tile(u, [d.n_head, 1, 1, 1, 1])
+        elif len(d.global_layout.shape) == 3:
+            u = np.reshape(d.global_layout, [d.n_head, d.n_query_block, d.n_key_block, 1, 1])
+        else:
+            raise RuntimeError()
+
+        s = product(range(d.n_head), range(d.n_query_block), range(d.n_key_block))
+        v = np.array([d.block_layout(None, i, j, k, 0) for i, j, k in s])
+        v = np.reshape(v, [d.n_head, d.n_query_block, d.n_key_block, d.block_size, d.block_size])
+
+        w = u * v
+        w = np.transpose(w, [0, 1, 3, 2, 4])
+        w = np.reshape(w, [d.n_head, d.query_context_size, d.key_context_size])
+        return w

glide_text2im/clip/config.yaml

@@ -0,0 +1,18 @@
+logit_scale: 100.0
+
+# Diffusion settings
+beta_schedule: "squaredcos_cap_v2"
+n_timesteps: 1000
+
+# Architecture settings
+image_size: 64
+patch_size: 4
+n_vocab: 65536
+max_text_len: 77
+n_embd: 512
+n_head_state_text: 64
+n_head_text: 8
+n_xf_blocks_text: 12
+n_head_state_image: 64
+n_head_image: 12
+n_xf_blocks_image: 12

glide_text2im/clip/encoders.py

@@ -0,0 +1,497 @@
+import math
+from collections import OrderedDict
+from typing import List, Optional, Tuple, cast
+
+import attr
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .attention import (
+    AttentionInfo,
+    DenseAttentionMask,
+    DenseCausalAttentionMask,
+    make_full_layout,
+    to_attention_info,
+)
+from .utils import Affine, LayerNorm, zero_key_bias_grad
+
+# Constants used in the original CLIP implementation.
+image_channel_means = [122.77093945, 116.74601272, 104.09373519]
+image_channel_stds = [68.50053285, 66.63215831, 70.32316309]
+
+
+@attr.s(eq=False, repr=False)
+class TextEmbedding(nn.Module):
+    n_vocab: int = attr.ib()
+    n_context: int = attr.ib()
+    n_state: int = attr.ib()
+    device: torch.device = attr.ib(default=torch.device("cuda"))
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__()
+
+        w_voc = torch.empty((self.n_vocab, self.n_state), dtype=torch.float32, device=self.device)
+        w_pos = torch.empty((self.n_context, self.n_state), dtype=torch.float32, device=self.device)
+
+        with torch.no_grad():
+            w_voc.normal_(std=0.02)
+            w_pos.normal_(std=0.01)
+
+        self.w_voc = nn.Parameter(w_voc)
+        self.w_pos = nn.Parameter(w_pos)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if len(x.shape) != 2:
+            raise ValueError()
+
+        return F.embedding(x, self.w_voc) + self.w_pos[None, :, :]
+
+
+@attr.s(eq=False, repr=False)
+class ImageEmbedding(nn.Module):
+    image_size: int = attr.ib()
+    patch_size: int = attr.ib()
+    n_state: int = attr.ib()
+    n_timestep: int = attr.ib(default=0)
+    device: torch.device = attr.ib(default=torch.device("cuda"))
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__()
+
+        if self.image_size % self.patch_size != 0:
+            raise ValueError()
+
+        n_patch = self.image_size // self.patch_size
+        patch_proj = torch.empty(
+            (self.n_state, 3) + 2 * (self.patch_size,), dtype=torch.float32, device=self.device
+        )
+        w_pos = torch.empty(
+            (1 + n_patch ** 2, self.n_state), dtype=torch.float32, device=self.device
+        )
+
+        with torch.no_grad():
+            if self.n_timestep == 0:
+                pred_state = torch.empty((self.n_state,), dtype=torch.float32, device=self.device)
+                pred_state.normal_(std=1 / np.sqrt(self.n_state))
+                self.pred_state = nn.Parameter(pred_state)
+            else:
+                w_t = torch.empty(
+                    (self.n_timestep, self.n_state), dtype=torch.float32, device=self.device
+                )
+                w_t.normal_(std=1 / np.sqrt(self.n_state))
+                self.w_t = nn.Parameter(w_t)
+
+            patch_proj.normal_(std=np.sqrt(2 / (self.n_state * self.patch_size ** 2)))
+            w_pos.normal_(std=1 / np.sqrt(self.n_state))
+
+        self.patch_proj = nn.Parameter(patch_proj)
+        self.w_pos = nn.Parameter(w_pos)
+
+        self.channel_means = torch.tensor(
+            image_channel_means, dtype=torch.float32, device=self.device
+        )[None, :, None, None]
+        self.channel_stds = torch.tensor(
+            image_channel_stds, dtype=torch.float32, device=self.device
+        )[None, :, None, None]
+        self.ln = LayerNorm(self.n_state, eps=1e-5, device=self.device)
+
+    def forward(self, x: torch.Tensor, t: Optional[torch.Tensor] = None) -> torch.Tensor:
+        if len(x.shape) != 4:
+            raise ValueError("input should be 4d")
+        if x.shape[1] != 3:
+            raise ValueError("input should have 3 channels")
+        if not (x.shape[2] == self.image_size and x.shape[3] == self.image_size):
+            raise ValueError(f"input is not {self.image_size} x {self.image_size}")
+
+        if (self.n_timestep == 0 and t is not None) or (self.n_timestep != 0 and t is None):
+            raise ValueError()
+        if self.n_timestep != 0:
+            assert t is not None
+            if len(t.shape) != 1:
+                raise ValueError()
+            if t.shape[0] != x.shape[0]:
+                raise ValueError()
+
+        x = (x - self.channel_means) / self.channel_stds
+        x = F.conv2d(x, self.patch_proj, stride=self.patch_size)
+        x = x.reshape(x.shape[0], self.n_state, (self.image_size // self.patch_size) ** 2).permute(
+            0, 2, 1
+        )
+
+        sot = (
+            self.pred_state[None, None].expand(x.shape[0], -1, -1)
+            if self.n_timestep == 0
+            else F.embedding(cast(torch.Tensor, t), self.w_t)[:, None]
+        )
+        x = torch.cat((sot, x), dim=1) + self.w_pos[None]
+        return self.ln(x)
+
+
+@attr.s(eq=False, repr=False)
+class AttentionResblock(nn.Module):
+    n_state: int = attr.ib()
+    n_resblocks: int = attr.ib()
+    attn_fn: AttentionInfo = attr.ib()
+    device: torch.device = attr.ib(default=torch.device("cuda"))
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__()
+
+        self.n_head_state = self.n_state // self.attn_fn.n_heads
+        self.qk_scale = 1 / np.sqrt(self.n_head_state)
+
+        self.ln = LayerNorm(self.n_state, eps=1e-5, device=self.device)
+        self.f_q = Affine(
+            self.n_state,
+            self.n_state,
+            std=1 / math.sqrt(self.n_state),
+            use_bias=True,
+            bias_filter_fn=zero_key_bias_grad,
+            device=self.device,
+        )
+        self.f_k = Affine(
+            self.n_state,
+            self.n_state,
+            std=1 / math.sqrt(self.n_state),
+            use_bias=False,
+            bias_filter_fn=zero_key_bias_grad,
+            device=self.device,
+        )
+        self.f_v = Affine(
+            self.n_state,
+            self.n_state,
+            std=1 / math.sqrt(self.n_state),
+            use_bias=True,
+            bias_filter_fn=zero_key_bias_grad,
+            device=self.device,
+        )
+        self.f_c = Affine(
+            self.n_state,
+            self.n_state,
+            use_bias=True,
+            std=1 / np.sqrt(self.n_state * self.n_resblocks ** 2),
+            device=self.device,
+        )  # XXX
+
+    def forward(self, m: torch.Tensor) -> torch.Tensor:
+        n_context = m.shape[1]
+        n_query_pad = self.attn_fn.ctx_blks_q * self.attn_fn.block_size - n_context
+        n_key_pad = self.attn_fn.ctx_blks_k * self.attn_fn.block_size - n_context
+        assert n_query_pad >= 0
+        assert n_key_pad >= 0
+
+        r = m
+        r = self.ln(r)
+        q, k, v = self.f_q(r), self.f_k(r), self.f_v(r)
+
+        if n_query_pad != 0:
+            q = F.pad(q, (0, 0, 0, n_query_pad))
+
+        if n_key_pad != 0:
+            k = F.pad(k, (0, 0, 0, n_key_pad))
+            v = F.pad(v, (0, 0, 0, n_key_pad))
+
+        q = q.view([q.shape[0], -1, self.attn_fn.n_heads, self.n_head_state]).permute((0, 2, 1, 3))
+        k = k.view([k.shape[0], -1, self.attn_fn.n_heads, self.n_head_state]).permute((0, 2, 1, 3))
+        v = v.view([v.shape[0], -1, self.attn_fn.n_heads, self.n_head_state]).permute((0, 2, 1, 3))
+        w = torch.einsum(
+            "bhcd,bhkd->bhck", q * math.sqrt(self.qk_scale), k * math.sqrt(self.qk_scale)
+        )
+
+        if hasattr(self.attn_fn, "pytorch_attn_bias"):
+            bias = self.attn_fn.pytorch_attn_bias
+            assert len(bias.shape) in {2, 3}
+
+            if len(bias.shape) == 2:
+                w = torch.softmax(w + self.attn_fn.pytorch_attn_bias[None, None], dim=-1)
+            elif len(bias.shape) == 3:
+                w = torch.softmax(w + self.attn_fn.pytorch_attn_bias[None], dim=-1)
+        else:
+            w = torch.softmax(w, dim=-1)
+
+        r = torch.einsum("bhck,bhkd->bhcd", w, v)
+        r = r.permute((0, 2, 1, 3)).reshape((r.shape[0], -1, self.n_state))
+
+        if n_query_pad != 0:
+            r = r[:, :-n_query_pad]
+
+        assert r.shape[1] == n_context
+
+        r = self.f_c(r)
+        return m + r
+
+
+@attr.s(eq=False, repr=False)
+class FullyConnectedResblock(nn.Module):
+    """
+    Not imported from other files because we retain Alec's original inits.
+    """
+
+    n_state: int = attr.ib()
+    n_resblocks: int = attr.ib()
+    device: torch.device = attr.ib(default=torch.device("cuda"))
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__()
+
+        self.ln = LayerNorm(self.n_state, eps=1e-5, device=self.device)
+        self.f_1 = Affine(
+            self.n_state,
+            4 * self.n_state,
+            use_bias=True,
+            std=np.sqrt(2 / (4 * self.n_state)),
+            device=self.device,
+        )
+        self.f_2 = Affine(
+            4 * self.n_state,
+            self.n_state,
+            use_bias=True,
+            std=1 / np.sqrt(self.n_state * self.n_resblocks ** 2),
+            device=self.device,
+        )  # XXX
+
+    def forward(self, m: torch.Tensor) -> torch.Tensor:
+        r = m
+        r = self.ln(r)
+
+        r = self.f_2(F.gelu(self.f_1(r)))
+        return m + r
+
+
+@attr.s(eq=False, repr=False)
+class TransformerBlock(nn.Module):
+    n_state: int = attr.ib()
+    n_resblocks: int = attr.ib()
+    attn_fn: AttentionInfo = attr.ib()
+    device: torch.device = attr.ib(default=torch.device("cuda"))
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__()
+
+        self.f_attn = AttentionResblock(
+            self.n_state,
+            self.n_resblocks,
+            self.attn_fn,
+            self.device,
+        )
+        self.f_mlp = FullyConnectedResblock(self.n_state, self.n_resblocks, self.device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.f_mlp(self.f_attn(x))
+
+
+@attr.s(eq=False, repr=False)
+class TextFeatureExtractor(nn.Module):
+    n_state: int = attr.ib()
+    n_embd: int = attr.ib()
+    device: torch.device = attr.ib(default=torch.device("cuda"))
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__()
+
+        self.ln = LayerNorm(self.n_state, eps=1e-5, device=self.device)
+        self.f = Affine(self.n_state, self.n_embd, use_bias=False, device=self.device)
+
+    def forward(
+        self, text: torch.Tensor, text_len: torch.Tensor, return_probe_features: bool = False
+    ) -> torch.Tensor:
+        if len(text.shape) != 3:
+            raise ValueError("expected text to be 3d")
+        if len(text_len.shape) != 1:
+            raise ValueError("expected text length to be 1d")
+        if text.shape[0] != text_len.shape[0]:
+            raise ValueError("text and text_len have inconsistent batch dimensions")
+
+        index = (text_len - 1)[:, None, None].expand(-1, 1, text.shape[2])
+        x = torch.gather(text, dim=1, index=index)
+        assert list(x.shape) == [text.shape[0], 1, text.shape[2]]
+
+        if return_probe_features:
+            return x[:, 0]
+
+        x = self.ln(x)
+        return self.f(x[:, 0])
+
+
+@attr.s(eq=False, repr=False)
+class ImageFeatureExtractor(nn.Module):
+    n_state: int = attr.ib()
+    n_embd: int = attr.ib()
+    device: torch.device = attr.ib(default=torch.device("cuda"))
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__()
+
+        self.ln = LayerNorm(self.n_state, eps=1e-5, device=self.device)
+        self.f = Affine(self.n_state, self.n_embd, use_bias=False, device=self.device)
+
+    def forward(self, x: torch.Tensor, return_probe_features: bool = False) -> torch.Tensor:
+        if return_probe_features:
+            return x[:, 0]
+
+        x = self.ln(x[:, :1])
+        return self.f(x[:, 0])
+
+
+@attr.s(eq=False, repr=False)
+class TextEncoder(nn.Module):
+    n_bpe_vocab: int = attr.ib()
+    max_text_len: int = attr.ib()
+    n_embd: int = attr.ib()
+    n_head: int = attr.ib()
+    n_xf_blocks: int = attr.ib()
+    n_head_state: int = attr.ib(default=64)
+    device: torch.device = attr.ib(default=torch.device("cuda"))
+    block_size: int = attr.ib(init=False, default=32)
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__()
+
+        self.n_state = self.n_head * self.n_head_state
+        n_rounded_context = self.block_size * int(math.ceil(self.max_text_len / self.block_size))
+        n_pad = n_rounded_context - self.max_text_len
+
+        args = (
+            n_rounded_context,
+            n_rounded_context,
+            self.block_size,
+            self.n_head,
+            False,
+            n_pad,
+            n_pad,
+        )
+        mask = DenseCausalAttentionMask(*args)
+        attn_fn = to_attention_info(mask)
+
+        m = 1 - make_full_layout(mask).astype(np.float32)
+        m[m == 1] = -1e10
+        attn_fn.pytorch_attn_bias = torch.from_numpy(m).to(self.device)
+
+        blocks: List[Tuple[str, nn.Module]] = [
+            (
+                "input",
+                TextEmbedding(
+                    self.n_bpe_vocab, self.max_text_len, self.n_state, device=self.device
+                ),
+            )
+        ]
+
+        for i in range(self.n_xf_blocks):
+            blocks.append(
+                (
+                    f"block_{i}",
+                    TransformerBlock(self.n_state, 2 * self.n_xf_blocks, attn_fn, self.device),
+                )
+            )
+
+        blocks.append(
+            ("output", TextFeatureExtractor(self.n_state, self.n_embd, device=self.device))
+        )
+
+        self.blocks = nn.ModuleDict(OrderedDict(blocks))
+
+    def forward(
+        self,
+        text: torch.Tensor,
+        text_len: torch.Tensor,
+        return_probe_features: bool = False,
+    ) -> torch.Tensor:
+
+        n_batch = text.shape[0]
+        h = self.blocks["input"](text)
+
+        for i in range(self.n_xf_blocks):
+            h = self.blocks[f"block_{i}"](h)
+
+        h = self.blocks["output"](h, text_len, return_probe_features=return_probe_features)
+
+        assert list(h.shape) == [
+            n_batch,
+            self.n_embd if not return_probe_features else self.n_state,
+        ]
+        return h
+
+
+@attr.s(eq=False, repr=False)
+class ImageEncoder(nn.Module):
+    image_size: int = attr.ib()
+    patch_size: int = attr.ib()
+    n_embd: int = attr.ib()
+    n_head: int = attr.ib()
+    n_xf_blocks: int = attr.ib()
+    n_head_state: int = attr.ib(default=64)
+    n_timestep: int = attr.ib(default=0)
+    device: torch.device = attr.ib(default=torch.device("cuda"))
+    block_size: int = attr.ib(init=False, default=32)
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__()
+
+        self.n_state = self.n_head * self.n_head_state
+        self.n_context = 1 + (self.image_size // self.patch_size) ** 2
+        n_rounded_context = self.block_size * int(math.ceil(self.n_context / self.block_size))
+        n_pad = n_rounded_context - self.n_context
+
+        args = (
+            n_rounded_context,
+            n_rounded_context,
+            self.block_size,
+            self.n_head,
+            False,
+            n_pad,
+            n_pad,
+        )
+        mask = DenseAttentionMask(*args)
+        attn_fn = to_attention_info(mask)
+
+        m = 1 - make_full_layout(mask).astype(np.float32)
+        m[m == 1] = -1e10
+        attn_fn.pytorch_attn_bias = torch.from_numpy(m).to(self.device)
+
+        blocks: List[Tuple[str, nn.Module]] = [
+            (
+                "input",
+                ImageEmbedding(
+                    self.image_size,
+                    self.patch_size,
+                    self.n_state,
+                    n_timestep=self.n_timestep,
+                    device=self.device,
+                ),
+            )
+        ]
+
+        for i in range(self.n_xf_blocks):
+            blocks.append(
+                (
+                    f"block_{i}",
+                    TransformerBlock(self.n_state, 2 * self.n_xf_blocks, attn_fn, self.device),
+                )
+            )
+
+        blocks.append(("output", ImageFeatureExtractor(self.n_state, self.n_embd, self.device)))
+
+        self.blocks = nn.ModuleDict(OrderedDict(blocks))
+
+    def forward(
+        self,
+        image: torch.Tensor,
+        timesteps: Optional[torch.Tensor] = None,
+        return_probe_features: bool = False,
+    ) -> torch.Tensor:
+        n_batch = image.shape[0]
+        h = self.blocks["input"](image, t=timesteps)
+
+        for i in range(self.n_xf_blocks):
+            h = self.blocks[f"block_{i}"](h)
+
+        h = self.blocks["output"](h, return_probe_features=return_probe_features)
+
+        assert list(h.shape) == [
+            n_batch,
+            self.n_embd if not return_probe_features else self.n_state,
+        ]
+
+        return h

glide_text2im/clip/model_creation.py

@@ -0,0 +1,117 @@
+import os
+from functools import lru_cache
+from typing import Any, Callable, Dict, List, Optional, Tuple
+
+import attr
+import numpy as np
+import torch
+import torch.nn as nn
+import yaml
+from glide_text2im.tokenizer.simple_tokenizer import SimpleTokenizer
+
+from .encoders import ImageEncoder, TextEncoder
+
+
+@lru_cache()
+def default_config_path() -> str:
+    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "config.yaml")
+
+
+@attr.s
+class CLIPModel:
+    config: Dict[str, Any] = attr.ib()
+    text_encoder: nn.Module = attr.ib()
+    image_encoder: nn.Module = attr.ib()
+    logit_scale: torch.Tensor = attr.ib()
+    device: torch.device = attr.ib()
+    tokenizer: SimpleTokenizer = attr.ib()
+
+    def encode_prompts(self, prompts: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:
+        tokens = []
+        lens = []
+        for prompt in prompts:
+            sub_tokens, sub_len = self.tokenizer.padded_tokens_and_len(
+                self.tokenizer.encode(prompt), self.text_encoder.max_text_len
+            )
+            tokens.append(sub_tokens)
+            lens.append(sub_len)
+        return (
+            torch.tensor(tokens).to(dtype=torch.long, device=self.device),
+            torch.tensor(lens).to(dtype=torch.long, device=self.device),
+        )
+
+    def text_embeddings(self, prompts: List[str]) -> torch.Tensor:
+        tokens, lens = self.encode_prompts(prompts)
+        z_t = self.text_encoder(tokens, lens)
+        return z_t / (torch.linalg.norm(z_t, dim=-1, keepdim=True) + 1e-12)
+
+    def image_embeddings(self, images: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
+        z_i = self.image_encoder((images + 1) * 127.5, t)
+        return z_i / (torch.linalg.norm(z_i, dim=-1, keepdim=True) + 1e-12)
+
+    def cond_fn(self, prompts: List[str], grad_scale: float) -> Callable[..., torch.Tensor]:
+        with torch.no_grad():
+            z_t = self.text_embeddings(prompts)
+
+        def cond_fn(x, t, grad_scale=grad_scale, **kwargs):
+            with torch.enable_grad():
+                x_var = x.detach().requires_grad_(True)
+                z_i = self.image_embeddings(x_var, t)
+                loss = torch.exp(self.logit_scale) * (z_t * z_i).sum()
+                grad = torch.autograd.grad(loss, x_var)[0].detach()
+            return grad * grad_scale
+
+        return cond_fn
+
+
+def create_clip_model(
+    config_path: Optional[str] = None,
+    device: Optional[torch.device] = None,
+    tokenizer: Optional[SimpleTokenizer] = None,
+) -> CLIPModel:
+    if config_path is None:
+        config_path = default_config_path()
+    if device is None:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    if tokenizer is None:
+        tokenizer = SimpleTokenizer()
+
+    with open(config_path, "r") as f:
+        config = yaml.load(f, Loader=yaml.SafeLoader)
+
+    text_encoder = TextEncoder(
+        n_bpe_vocab=config["n_vocab"],
+        max_text_len=config["max_text_len"],
+        n_embd=config["n_embd"],
+        n_head=config["n_head_text"],
+        n_xf_blocks=config["n_xf_blocks_text"],
+        n_head_state=config["n_head_state_text"],
+        device=device,
+    )
+
+    image_encoder = ImageEncoder(
+        image_size=config["image_size"],
+        patch_size=config["patch_size"],
+        n_embd=config["n_embd"],
+        n_head=config["n_head_image"],
+        n_xf_blocks=config["n_xf_blocks_image"],
+        n_head_state=config["n_head_state_image"],
+        n_timestep=config["n_timesteps"],
+        device=device,
+    )
+
+    logit_scale = torch.tensor(
+        np.log(config["logit_scale"]),
+        dtype=torch.float32,
+        device=device,
+        requires_grad=False,
+    )
+
+    return CLIPModel(
+        config=config,
+        text_encoder=text_encoder,
+        image_encoder=image_encoder,
+        logit_scale=logit_scale,
+        device=device,
+        tokenizer=tokenizer,
+    )

glide_text2im/clip/utils.py

@@ -0,0 +1,97 @@
+import math
+from typing import Callable, Optional
+
+import attr
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+FilterFn = Callable[[torch.Tensor], torch.Tensor]
+
+
+class ZeroKeyBiasGrad(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x):
+        return x
+
+    @staticmethod
+    def backward(ctx, output_grad):
+        output_grad = output_grad.clone()
+        output_grad.chunk(3)[1].zero_()
+        return output_grad
+
+
+def zero_key_bias_grad(x: torch.Tensor) -> torch.Tensor:
+    return ZeroKeyBiasGrad.apply(x)
+
+
+@attr.s(eq=False, repr=False)
+class LayerNorm(nn.Module):
+    n_state: int = attr.ib()
+    eps: float = attr.ib(default=1e-6)
+    device: torch.device = attr.ib(default=torch.device("cuda"))
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__()
+        self.g = nn.Parameter(torch.ones((self.n_state,), dtype=torch.float32, device=self.device))
+        self.b = nn.Parameter(torch.zeros((self.n_state,), dtype=torch.float32, device=self.device))
+        self.g.weight_decay_level = "disable"  # type: ignore
+        self.b.weight_decay_level = "disable"  # type: ignore
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return F.layer_norm(
+            x.type(torch.float32), torch.Size((self.n_state,)), self.g, self.b, self.eps
+        )
+
+
+@attr.s(eq=False, repr=False)
+class Affine(nn.Module):
+    n_in: int = attr.ib()
+    n_out: int = attr.ib()
+    use_bias: bool = attr.ib(default=True)
+    use_admnet_init: bool = attr.ib(default=False)
+    std: Optional[float] = attr.ib(default=None)
+    extra_init_scale: Optional[float] = attr.ib(default=None)
+    bias_filter_fn: FilterFn = attr.ib(default=lambda x: x)
+    device: torch.device = attr.ib(default=torch.device("cuda"))
+
+    def __attrs_post_init__(self) -> None:
+        super().__init__()
+
+        if not self.use_admnet_init:
+            self.std = self.std if self.std is not None else math.sqrt(2 / (self.n_in + self.n_out))
+            self.std = (
+                self.std if self.extra_init_scale is None else self.std * self.extra_init_scale
+            )
+
+            w = torch.empty((self.n_out, self.n_in), dtype=torch.float32, device=self.device)
+            self.w = nn.Parameter(w)
+
+            if self.use_bias:
+                self.b = nn.Parameter(
+                    torch.zeros((self.n_out,), dtype=torch.float32, device=self.device)
+                )
+                self.b.weight_decay_level = "disable"  # type: ignore
+        else:
+            if self.extra_init_scale is not None:
+                raise ValueError("extra_init_scale incompatible with admnet init")
+
+            w = torch.empty((self.n_out, self.n_in), dtype=torch.float32, device=self.device)
+
+            if self.use_bias:
+                b = torch.empty((self.n_out,), dtype=torch.float32, device=self.device)
+
+            self.w = nn.Parameter(w)
+
+            if self.use_bias:
+                self.b = nn.Parameter(b)
+                self.b.weight_decay_level = "disable"  # type: ignore
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        w = self.w if self.w.dtype == x.dtype else self.w.to(x.dtype)
+        b = (
+            self.bias_filter_fn(self.b if self.b.dtype == x.dtype else self.b.to(x.dtype))
+            if self.use_bias
+            else None
+        )
+        return F.linear(x, w, b)

glide_text2im/download.py

@@ -0,0 +1,71 @@
+import os
+from functools import lru_cache
+from typing import Dict, Optional
+
+import requests
+import torch as th
+from filelock import FileLock
+from tqdm.auto import tqdm
+
+MODEL_PATHS = {
+    "base": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/base.pt",
+    "upsample": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/upsample.pt",
+    "base-inpaint": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/base_inpaint.pt",
+    "upsample-inpaint": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/upsample_inpaint.pt",
+    "clip/image-enc": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/clip_image_enc.pt",
+    "clip/text-enc": "https://openaipublic.blob.core.windows.net/diffusion/dec-2021/clip_text_enc.pt",
+}
+
+
+@lru_cache()
+def default_cache_dir() -> str:
+    return os.path.join(os.path.abspath(os.getcwd()), "glide_model_cache")
+
+
+def fetch_file_cached(
+    url: str, progress: bool = True, cache_dir: Optional[str] = None, chunk_size: int = 4096
+) -> str:
+    """
+    Download the file at the given URL into a local file and return the path.
+
+    If cache_dir is specified, it will be used to download the files.
+    Otherwise, default_cache_dir() is used.
+    """
+    if cache_dir is None:
+        cache_dir = default_cache_dir()
+    os.makedirs(cache_dir, exist_ok=True)
+    response = requests.get(url, stream=True)
+    size = int(response.headers.get("content-length", "0"))
+    local_path = os.path.join(cache_dir, url.split("/")[-1])
+    with FileLock(local_path + ".lock"):
+        if os.path.exists(local_path):
+            return local_path
+        if progress:
+            pbar = tqdm(total=size, unit="iB", unit_scale=True)
+        tmp_path = local_path + ".tmp"
+        with open(tmp_path, "wb") as f:
+            for chunk in response.iter_content(chunk_size):
+                if progress:
+                    pbar.update(len(chunk))
+                f.write(chunk)
+        os.rename(tmp_path, local_path)
+        if progress:
+            pbar.close()
+        return local_path
+
+
+def load_checkpoint(
+    checkpoint_name: str,
+    device: th.device,
+    progress: bool = True,
+    cache_dir: Optional[str] = None,
+    chunk_size: int = 4096,
+) -> Dict[str, th.Tensor]:
+    if checkpoint_name not in MODEL_PATHS:
+        raise ValueError(
+            f"Unknown checkpoint name {checkpoint_name}. Known names are: {MODEL_PATHS.keys()}."
+        )
+    path = fetch_file_cached(
+        MODEL_PATHS[checkpoint_name], progress=progress, cache_dir=cache_dir, chunk_size=chunk_size
+    )
+    return th.load(path, map_location=device)

glide_text2im/fp16_util.py

@@ -0,0 +1,25 @@
+"""
+Helpers to inference with 16-bit precision.
+"""
+
+import torch.nn as nn
+
+
+def convert_module_to_f16(l):
+    """
+    Convert primitive modules to float16.
+    """
+    if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
+        l.weight.data = l.weight.data.half()
+        if l.bias is not None:
+            l.bias.data = l.bias.data.half()
+
+
+def convert_module_to_f32(l):
+    """
+    Convert primitive modules to float32, undoing convert_module_to_f16().
+    """
+    if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
+        l.weight.data = l.weight.data.float()
+        if l.bias is not None:
+            l.bias.data = l.bias.data.float()

glide_text2im/gaussian_diffusion.py

@@ -0,0 +1,639 @@
+"""
+Simplified from https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/gaussian_diffusion.py.
+"""
+
+import math
+
+import numpy as np
+import torch as th
+
+
+def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_frac):
+    betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
+    warmup_time = int(num_diffusion_timesteps * warmup_frac)
+    betas[:warmup_time] = np.linspace(beta_start, beta_end, warmup_time, dtype=np.float64)
+    return betas
+
+
+def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps):
+    """
+    This is the deprecated API for creating beta schedules.
+
+    See get_named_beta_schedule() for the new library of schedules.
+    """
+    if beta_schedule == "quad":
+        betas = (
+            np.linspace(
+                beta_start ** 0.5,
+                beta_end ** 0.5,
+                num_diffusion_timesteps,
+                dtype=np.float64,
+            )
+            ** 2
+        )
+    elif beta_schedule == "linear":
+        betas = np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64)
+    elif beta_schedule == "warmup10":
+        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.1)
+    elif beta_schedule == "warmup50":
+        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.5)
+    elif beta_schedule == "const":
+        betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
+    elif beta_schedule == "jsd":  # 1/T, 1/(T-1), 1/(T-2), ..., 1
+        betas = 1.0 / np.linspace(
+            num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64
+        )
+    else:
+        raise NotImplementedError(beta_schedule)
+    assert betas.shape == (num_diffusion_timesteps,)
+    return betas
+
+
+def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
+    """
+    Get a pre-defined beta schedule for the given name.
+
+    The beta schedule library consists of beta schedules which remain similar
+    in the limit of num_diffusion_timesteps.
+    Beta schedules may be added, but should not be removed or changed once
+    they are committed to maintain backwards compatibility.
+    """
+    if schedule_name == "linear":
+        # Linear schedule from Ho et al, extended to work for any number of
+        # diffusion steps.
+        scale = 1000 / num_diffusion_timesteps
+        return get_beta_schedule(
+            "linear",
+            beta_start=scale * 0.0001,
+            beta_end=scale * 0.02,
+            num_diffusion_timesteps=num_diffusion_timesteps,
+        )
+    elif schedule_name == "squaredcos_cap_v2":
+        return betas_for_alpha_bar(
+            num_diffusion_timesteps,
+            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+        )
+    else:
+        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+
+class GaussianDiffusion:
+    """
+    Utilities for training and sampling diffusion models.
+
+    Original ported from this codebase:
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
+
+    :param betas: a 1-D numpy array of betas for each diffusion timestep,
+                  starting at T and going to 1.
+    """
+
+    def __init__(
+        self,
+        *,
+        betas,
+    ):
+        # Use float64 for accuracy.
+        betas = np.array(betas, dtype=np.float64)
+        self.betas = betas
+        assert len(betas.shape) == 1, "betas must be 1-D"
+        assert (betas > 0).all() and (betas <= 1).all()
+
+        self.num_timesteps = int(betas.shape[0])
+
+        alphas = 1.0 - betas
+        self.alphas_cumprod = np.cumprod(alphas, axis=0)
+        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
+        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
+        assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
+        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
+        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
+        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
+        self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        self.posterior_variance = (
+            betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+        )
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = np.log(
+            np.append(self.posterior_variance[1], self.posterior_variance[1:])
+        )
+        self.posterior_mean_coef1 = (
+            betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+        )
+        self.posterior_mean_coef2 = (
+            (1.0 - self.alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - self.alphas_cumprod)
+        )
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = _extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data for a given number of diffusion steps.
+
+        In other words, sample from q(x_t | x_0).
+
+        :param x_start: the initial data batch.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :param noise: if specified, the split-out normal noise.
+        :return: A noisy version of x_start.
+        """
+        if noise is None:
+            noise = th.randn_like(x_start)
+        assert noise.shape == x_start.shape
+        return (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+            + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
+        )
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior:
+
+            q(x_{t-1} | x_t, x_0)
+
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+            + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = _extract_into_tensor(
+            self.posterior_log_variance_clipped, t, x_t.shape
+        )
+        assert (
+            posterior_mean.shape[0]
+            == posterior_variance.shape[0]
+            == posterior_log_variance_clipped.shape[0]
+            == x_start.shape[0]
+        )
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None):
+        """
+        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
+        the initial x, x_0.
+
+        :param model: the model, which takes a signal and a batch of timesteps
+                      as input.
+        :param x: the [N x C x ...] tensor at time t.
+        :param t: a 1-D Tensor of timesteps.
+        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample. Applies before
+            clip_denoised.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict with the following keys:
+                 - 'mean': the model mean output.
+                 - 'variance': the model variance output.
+                 - 'log_variance': the log of 'variance'.
+                 - 'pred_xstart': the prediction for x_0.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+
+        B, C = x.shape[:2]
+        assert t.shape == (B,)
+        model_output = model(x, t, **model_kwargs)
+        if isinstance(model_output, tuple):
+            model_output, extra = model_output
+        else:
+            extra = None
+
+        assert model_output.shape == (B, C * 2, *x.shape[2:])
+        model_output, model_var_values = th.split(model_output, C, dim=1)
+        min_log = _extract_into_tensor(self.posterior_log_variance_clipped, t, x.shape)
+        max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
+        # The model_var_values is [-1, 1] for [min_var, max_var].
+        frac = (model_var_values + 1) / 2
+        model_log_variance = frac * max_log + (1 - frac) * min_log
+        model_variance = th.exp(model_log_variance)
+
+        def process_xstart(x):
+            if denoised_fn is not None:
+                x = denoised_fn(x)
+            if clip_denoised:
+                return x.clamp(-1, 1)
+            return x
+
+        pred_xstart = process_xstart(self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output))
+        model_mean, _, _ = self.q_posterior_mean_variance(x_start=pred_xstart, x_t=x, t=t)
+
+        assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
+        return {
+            "mean": model_mean,
+            "variance": model_variance,
+            "log_variance": model_log_variance,
+            "pred_xstart": pred_xstart,
+            "extra": extra,
+        }
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
+        )
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute the mean for the previous step, given a function cond_fn that
+        computes the gradient of a conditional log probability with respect to
+        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
+        condition on y.
+
+        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
+        """
+        gradient = cond_fn(x, t, **model_kwargs)
+        new_mean = p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
+        return new_mean
+
+    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute what the p_mean_variance output would have been, should the
+        model's score function be conditioned by cond_fn.
+
+        See condition_mean() for details on cond_fn.
+
+        Unlike condition_mean(), this instead uses the conditioning strategy
+        from Song et al (2020).
+        """
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+
+        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
+        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, t, **model_kwargs)
+
+        out = p_mean_var.copy()
+        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
+        out["mean"], _, _ = self.q_posterior_mean_variance(x_start=out["pred_xstart"], x_t=x, t=t)
+        return out
+
+    def p_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+    ):
+        """
+        Sample x_{t-1} from the model at the given timestep.
+
+        :param model: the model to sample from.
+        :param x: the current tensor at x_{t-1}.
+        :param t: the value of t, starting at 0 for the first diffusion step.
+        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict containing the following keys:
+                 - 'sample': a random sample from the model.
+                 - 'pred_xstart': a prediction of x_0.
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        noise = th.randn_like(x)
+        nonzero_mask = (
+            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        )  # no noise when t == 0
+        if cond_fn is not None:
+            out["mean"] = self.condition_mean(cond_fn, out, x, t, model_kwargs=model_kwargs)
+        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def p_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model.
+
+        :param model: the model module.
+        :param shape: the shape of the samples, (N, C, H, W).
+        :param noise: if specified, the noise from the encoder to sample.
+                      Should be of the same shape as `shape`.
+        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param device: if specified, the device to create the samples on.
+                       If not specified, use a model parameter's device.
+        :param progress: if True, show a tqdm progress bar.
+        :return: a non-differentiable batch of samples.
+        """
+        final = None
+        for sample in self.p_sample_loop_progressive(
+            model,
+            shape,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+        ):
+            final = sample
+        return final["sample"]
+
+    def p_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model and yield intermediate samples from
+        each timestep of diffusion.
+
+        Arguments are the same as p_sample_loop().
+        Returns a generator over dicts, where each dict is the return value of
+        p_sample().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.p_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                )
+                yield out
+                img = out["sample"]
+
+    def ddim_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t-1} from the model using DDIM.
+
+        Same usage as p_sample().
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
+
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
+
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
+        sigma = (
+            eta
+            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
+            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
+        )
+        # Equation 12.
+        noise = th.randn_like(x)
+        mean_pred = (
+            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
+            + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
+        )
+        nonzero_mask = (
+            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        )  # no noise when t == 0
+        sample = mean_pred + nonzero_mask * sigma * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_reverse_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t+1} from the model using DDIM reverse ODE.
+        """
+        assert eta == 0.0, "Reverse ODE only for deterministic path"
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
+            - out["pred_xstart"]
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
+        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
+
+        # Equation 12. reversed
+        mean_pred = out["pred_xstart"] * th.sqrt(alpha_bar_next) + th.sqrt(1 - alpha_bar_next) * eps
+
+        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Generate samples from the model using DDIM.
+
+        Same usage as p_sample_loop().
+        """
+        final = None
+        for sample in self.ddim_sample_loop_progressive(
+            model,
+            shape,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+            eta=eta,
+        ):
+            final = sample
+        return final["sample"]
+
+    def ddim_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Use DDIM to sample from the model and yield intermediate samples from
+        each timestep of DDIM.
+
+        Same usage as p_sample_loop_progressive().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.ddim_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                    eta=eta,
+                )
+                yield out
+                img = out["sample"]
+
+
+def _extract_into_tensor(arr, timesteps, broadcast_shape):
+    """
+    Extract values from a 1-D numpy array for a batch of indices.
+
+    :param arr: the 1-D numpy array.
+    :param timesteps: a tensor of indices into the array to extract.
+    :param broadcast_shape: a larger shape of K dimensions with the batch
+                            dimension equal to the length of timesteps.
+    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
+    """
+    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+    while len(res.shape) < len(broadcast_shape):
+        res = res[..., None]
+    return res + th.zeros(broadcast_shape, device=timesteps.device)

glide_text2im/model_creation.py

@@ -0,0 +1,195 @@
+from glide_text2im.gaussian_diffusion import get_named_beta_schedule
+from glide_text2im.respace import SpacedDiffusion, space_timesteps
+from glide_text2im.text2im_model import (
+    InpaintText2ImUNet,
+    SuperResInpaintText2ImUnet,
+    SuperResText2ImUNet,
+    Text2ImUNet,
+)
+from glide_text2im.tokenizer.bpe import get_encoder
+
+
+def model_and_diffusion_defaults():
+    return dict(
+        image_size=64,
+        num_channels=192,
+        num_res_blocks=3,
+        channel_mult="",
+        num_heads=1,
+        num_head_channels=64,
+        num_heads_upsample=-1,
+        attention_resolutions="32,16,8",
+        dropout=0.1,
+        text_ctx=128,
+        xf_width=512,
+        xf_layers=16,
+        xf_heads=8,
+        xf_final_ln=True,
+        xf_padding=True,
+        diffusion_steps=1000,
+        noise_schedule="squaredcos_cap_v2",
+        timestep_respacing="",
+        use_scale_shift_norm=True,
+        resblock_updown=True,
+        use_fp16=True,
+        cache_text_emb=False,
+        inpaint=False,
+        super_res=False,
+    )
+
+
+def model_and_diffusion_defaults_upsampler():
+    result = model_and_diffusion_defaults()
+    result.update(
+        dict(
+            image_size=256,
+            num_res_blocks=2,
+            noise_schedule="linear",
+            super_res=True,
+        )
+    )
+    return result
+
+
+def create_model_and_diffusion(
+    image_size,
+    num_channels,
+    num_res_blocks,
+    channel_mult,
+    num_heads,
+    num_head_channels,
+    num_heads_upsample,
+    attention_resolutions,
+    dropout,
+    text_ctx,
+    xf_width,
+    xf_layers,
+    xf_heads,
+    xf_final_ln,
+    xf_padding,
+    diffusion_steps,
+    noise_schedule,
+    timestep_respacing,
+    use_scale_shift_norm,
+    resblock_updown,
+    use_fp16,
+    cache_text_emb,
+    inpaint,
+    super_res,
+):
+    model = create_model(
+        image_size,
+        num_channels,
+        num_res_blocks,
+        channel_mult=channel_mult,
+        attention_resolutions=attention_resolutions,
+        num_heads=num_heads,
+        num_head_channels=num_head_channels,
+        num_heads_upsample=num_heads_upsample,
+        use_scale_shift_norm=use_scale_shift_norm,
+        dropout=dropout,
+        text_ctx=text_ctx,
+        xf_width=xf_width,
+        xf_layers=xf_layers,
+        xf_heads=xf_heads,
+        xf_final_ln=xf_final_ln,
+        xf_padding=xf_padding,
+        resblock_updown=resblock_updown,
+        use_fp16=use_fp16,
+        cache_text_emb=cache_text_emb,
+        inpaint=inpaint,
+        super_res=super_res,
+    )
+    diffusion = create_gaussian_diffusion(
+        steps=diffusion_steps,
+        noise_schedule=noise_schedule,
+        timestep_respacing=timestep_respacing,
+    )
+    return model, diffusion
+
+
+def create_model(
+    image_size,
+    num_channels,
+    num_res_blocks,
+    channel_mult,
+    attention_resolutions,
+    num_heads,
+    num_head_channels,
+    num_heads_upsample,
+    use_scale_shift_norm,
+    dropout,
+    text_ctx,
+    xf_width,
+    xf_layers,
+    xf_heads,
+    xf_final_ln,
+    xf_padding,
+    resblock_updown,
+    use_fp16,
+    cache_text_emb,
+    inpaint,
+    super_res,
+):
+    if channel_mult == "":
+        if image_size == 256:
+            channel_mult = (1, 1, 2, 2, 4, 4)
+        elif image_size == 128:
+            channel_mult = (1, 1, 2, 3, 4)
+        elif image_size == 64:
+            channel_mult = (1, 2, 3, 4)
+        else:
+            raise ValueError(f"unsupported image size: {image_size}")
+    else:
+        channel_mult = tuple(int(ch_mult) for ch_mult in channel_mult.split(","))
+        assert 2 ** (len(channel_mult) + 2) == image_size
+
+    attention_ds = []
+    for res in attention_resolutions.split(","):
+        attention_ds.append(image_size // int(res))
+
+    if inpaint and super_res:
+        model_cls = SuperResInpaintText2ImUnet
+    elif inpaint:
+        model_cls = InpaintText2ImUNet
+    elif super_res:
+        model_cls = SuperResText2ImUNet
+    else:
+        model_cls = Text2ImUNet
+    return model_cls(
+        text_ctx=text_ctx,
+        xf_width=xf_width,
+        xf_layers=xf_layers,
+        xf_heads=xf_heads,
+        xf_final_ln=xf_final_ln,
+        tokenizer=get_encoder(),
+        xf_padding=xf_padding,
+        in_channels=3,
+        model_channels=num_channels,
+        out_channels=6,
+        num_res_blocks=num_res_blocks,
+        attention_resolutions=tuple(attention_ds),
+        dropout=dropout,
+        channel_mult=channel_mult,
+        use_fp16=use_fp16,
+        num_heads=num_heads,
+        num_head_channels=num_head_channels,
+        num_heads_upsample=num_heads_upsample,
+        use_scale_shift_norm=use_scale_shift_norm,
+        resblock_updown=resblock_updown,
+        cache_text_emb=cache_text_emb,
+    )
+
+
+def create_gaussian_diffusion(
+    steps,
+    noise_schedule,
+    timestep_respacing,
+):
+    betas = get_named_beta_schedule(noise_schedule, steps)
+    if not timestep_respacing:
+        timestep_respacing = [steps]
+    return SpacedDiffusion(
+        use_timesteps=space_timesteps(steps, timestep_respacing),
+        betas=betas,
+    )

glide_text2im/nn.py

@@ -0,0 +1,105 @@
+"""
+Various utilities for neural networks.
+"""
+
+import math
+
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class GroupNorm32(nn.GroupNorm):
+    def __init__(self, num_groups, num_channels, swish, eps=1e-5):
+        super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps)
+        self.swish = swish
+
+    def forward(self, x):
+        y = super().forward(x.float()).to(x.dtype)
+        if self.swish == 1.0:
+            y = F.silu(y)
+        elif self.swish:
+            y = y * F.sigmoid(y * float(self.swish))
+        return y
+
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def normalization(channels, swish=0.0):
+    """
+    Make a standard normalization layer, with an optional swish activation.
+
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)
+
+
+def timestep_embedding(timesteps, dim, max_period=10000):
+    """
+    Create sinusoidal timestep embeddings.
+
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    half = dim // 2
+    freqs = th.exp(
+        -math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half
+    ).to(device=timesteps.device)
+    args = timesteps[:, None].float() * freqs[None]
+    embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1)
+    return embedding

glide_text2im/respace.py

@@ -0,0 +1,117 @@
+"""
+Utilities for changing sampling schedules of a trained model.
+
+Simplified from: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/respace.py
+"""
+
+import numpy as np
+import torch as th
+
+from .gaussian_diffusion import GaussianDiffusion
+
+
+def space_timesteps(num_timesteps, section_counts):
+    """
+    Create a list of timesteps to use from an original diffusion process,
+    given the number of timesteps we want to take from equally-sized portions
+    of the original process.
+
+    For example, if there's 300 timesteps and the section counts are [10,15,20]
+    then the first 100 timesteps are strided to be 10 timesteps, the second 100
+    are strided to be 15 timesteps, and the final 100 are strided to be 20.
+
+    :param num_timesteps: the number of diffusion steps in the original
+                          process to divide up.
+    :param section_counts: either a list of numbers, or a string containing
+                           comma-separated numbers, indicating the step count
+                           per section. As a special case, use "ddimN" where N
+                           is a number of steps to use the striding from the
+                           DDIM paper.
+    :return: a set of diffusion steps from the original process to use.
+    """
+    if isinstance(section_counts, str):
+        if section_counts.startswith("ddim"):
+            desired_count = int(section_counts[len("ddim") :])
+            for i in range(1, num_timesteps):
+                if len(range(0, num_timesteps, i)) == desired_count:
+                    return set(range(0, num_timesteps, i))
+            raise ValueError(f"cannot create exactly {num_timesteps} steps with an integer stride")
+        elif section_counts == "fast27":
+            steps = space_timesteps(num_timesteps, "10,10,3,2,2")
+            # Help reduce DDIM artifacts from noisiest timesteps.
+            steps.remove(num_timesteps - 1)
+            steps.add(num_timesteps - 3)
+            return steps
+        section_counts = [int(x) for x in section_counts.split(",")]
+    size_per = num_timesteps // len(section_counts)
+    extra = num_timesteps % len(section_counts)
+    start_idx = 0
+    all_steps = []
+    for i, section_count in enumerate(section_counts):
+        size = size_per + (1 if i < extra else 0)
+        if size < section_count:
+            raise ValueError(f"cannot divide section of {size} steps into {section_count}")
+        if section_count <= 1:
+            frac_stride = 1
+        else:
+            frac_stride = (size - 1) / (section_count - 1)
+        cur_idx = 0.0
+        taken_steps = []
+        for _ in range(section_count):
+            taken_steps.append(start_idx + round(cur_idx))
+            cur_idx += frac_stride
+        all_steps += taken_steps
+        start_idx += size
+    return set(all_steps)
+
+
+class SpacedDiffusion(GaussianDiffusion):
+    """
+    A diffusion process which can skip steps in a base diffusion process.
+
+    :param use_timesteps: a collection (sequence or set) of timesteps from the
+                          original diffusion process to retain.
+    :param kwargs: the kwargs to create the base diffusion process.
+    """
+
+    def __init__(self, use_timesteps, **kwargs):
+        self.use_timesteps = set(use_timesteps)
+        self.timestep_map = []
+        self.original_num_steps = len(kwargs["betas"])
+
+        base_diffusion = GaussianDiffusion(**kwargs)  # pylint: disable=missing-kwoa
+        last_alpha_cumprod = 1.0
+        new_betas = []
+        for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
+            if i in self.use_timesteps:
+                new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
+                last_alpha_cumprod = alpha_cumprod
+                self.timestep_map.append(i)
+        kwargs["betas"] = np.array(new_betas)
+        super().__init__(**kwargs)
+
+    def p_mean_variance(self, model, *args, **kwargs):
+        return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
+
+    def condition_mean(self, cond_fn, *args, **kwargs):
+        return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def condition_score(self, cond_fn, *args, **kwargs):
+        return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def _wrap_model(self, model):
+        if isinstance(model, _WrappedModel):
+            return model
+        return _WrappedModel(model, self.timestep_map, self.original_num_steps)
+
+
+class _WrappedModel:
+    def __init__(self, model, timestep_map, original_num_steps):
+        self.model = model
+        self.timestep_map = timestep_map
+        self.original_num_steps = original_num_steps
+
+    def __call__(self, x, ts, **kwargs):
+        map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
+        new_ts = map_tensor[ts]
+        return self.model(x, new_ts, **kwargs)

glide_text2im/text2im_model.py

@@ -0,0 +1,233 @@
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .nn import timestep_embedding
+from .unet import UNetModel
+from .xf import LayerNorm, Transformer, convert_module_to_f16
+
+
+class Text2ImUNet(UNetModel):
+    """
+    A UNetModel that conditions on text with an encoding transformer.
+
+    Expects an extra kwarg `tokens` of text.
+
+    :param text_ctx: number of text tokens to expect.
+    :param xf_width: width of the transformer.
+    :param xf_layers: depth of the transformer.
+    :param xf_heads: heads in the transformer.
+    :param xf_final_ln: use a LayerNorm after the output layer.
+    :param tokenizer: the text tokenizer for sampling/vocab size.
+    """
+
+    def __init__(
+        self,
+        text_ctx,
+        xf_width,
+        xf_layers,
+        xf_heads,
+        xf_final_ln,
+        tokenizer,
+        *args,
+        cache_text_emb=False,
+        xf_ar=0.0,
+        xf_padding=False,
+        share_unemb=False,
+        **kwargs,
+    ):
+        self.text_ctx = text_ctx
+        self.xf_width = xf_width
+        self.xf_ar = xf_ar
+        self.xf_padding = xf_padding
+        self.tokenizer = tokenizer
+
+        if not xf_width:
+            super().__init__(*args, **kwargs, encoder_channels=None)
+        else:
+            super().__init__(*args, **kwargs, encoder_channels=xf_width)
+        if self.xf_width:
+            self.transformer = Transformer(
+                text_ctx,
+                xf_width,
+                xf_layers,
+                xf_heads,
+            )
+            if xf_final_ln:
+                self.final_ln = LayerNorm(xf_width)
+            else:
+                self.final_ln = None
+
+            self.token_embedding = nn.Embedding(self.tokenizer.n_vocab, xf_width)
+            self.positional_embedding = nn.Parameter(th.empty(text_ctx, xf_width, dtype=th.float32))
+            self.transformer_proj = nn.Linear(xf_width, self.model_channels * 4)
+
+            if self.xf_padding:
+                self.padding_embedding = nn.Parameter(
+                    th.empty(text_ctx, xf_width, dtype=th.float32)
+                )
+            if self.xf_ar:
+                self.unemb = nn.Linear(xf_width, self.tokenizer.n_vocab)
+                if share_unemb:
+                    self.unemb.weight = self.token_embedding.weight
+
+        self.cache_text_emb = cache_text_emb
+        self.cache = None
+
+    def convert_to_fp16(self):
+        super().convert_to_fp16()
+        if self.xf_width:
+            self.transformer.apply(convert_module_to_f16)
+            self.transformer_proj.to(th.float16)
+            self.token_embedding.to(th.float16)
+            self.positional_embedding.to(th.float16)
+            if self.xf_padding:
+                self.padding_embedding.to(th.float16)
+            if self.xf_ar:
+                self.unemb.to(th.float16)
+
+    def get_text_emb(self, tokens, mask):
+        assert tokens is not None
+
+        if self.cache_text_emb and self.cache is not None:
+            assert (
+                tokens == self.cache["tokens"]
+            ).all(), f"Tokens {tokens.cpu().numpy().tolist()} do not match cache {self.cache['tokens'].cpu().numpy().tolist()}"
+            return self.cache
+
+        xf_in = self.token_embedding(tokens.long())
+        xf_in = xf_in + self.positional_embedding[None]
+        if self.xf_padding:
+            assert mask is not None
+            xf_in = th.where(mask[..., None], xf_in, self.padding_embedding[None])
+        xf_out = self.transformer(xf_in.to(self.dtype))
+        if self.final_ln is not None:
+            xf_out = self.final_ln(xf_out)
+        xf_proj = self.transformer_proj(xf_out[:, -1])
+        xf_out = xf_out.permute(0, 2, 1)  # NLC -> NCL
+
+        outputs = dict(xf_proj=xf_proj, xf_out=xf_out)
+
+        if self.cache_text_emb:
+            self.cache = dict(
+                tokens=tokens,
+                xf_proj=xf_proj.detach(),
+                xf_out=xf_out.detach() if xf_out is not None else None,
+            )
+
+        return outputs
+
+    def del_cache(self):
+        self.cache = None
+
+    def forward(self, x, timesteps, tokens=None, mask=None):
+        hs = []
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+        if self.xf_width:
+            text_outputs = self.get_text_emb(tokens, mask)
+            xf_proj, xf_out = text_outputs["xf_proj"], text_outputs["xf_out"]
+            emb = emb + xf_proj.to(emb)
+        else:
+            xf_out = None
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb, xf_out)
+            hs.append(h)
+        h = self.middle_block(h, emb, xf_out)
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, xf_out)
+        h = h.type(x.dtype)
+        h = self.out(h)
+        return h
+
+
+class SuperResText2ImUNet(Text2ImUNet):
+    """
+    A text2im model that performs super-resolution.
+    Expects an extra kwarg `low_res` to condition on a low-resolution image.
+    """
+
+    def __init__(self, *args, **kwargs):
+        if "in_channels" in kwargs:
+            kwargs = dict(kwargs)
+            kwargs["in_channels"] = kwargs["in_channels"] * 2
+        else:
+            # Curse you, Python. Or really, just curse positional arguments :|.
+            args = list(args)
+            args[1] = args[1] * 2
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x, timesteps, low_res=None, **kwargs):
+        _, _, new_height, new_width = x.shape
+        upsampled = F.interpolate(
+            low_res, (new_height, new_width), mode="bilinear", align_corners=False
+        )
+        x = th.cat([x, upsampled], dim=1)
+        return super().forward(x, timesteps, **kwargs)
+
+
+class InpaintText2ImUNet(Text2ImUNet):
+    """
+    A text2im model which can perform inpainting.
+    """
+
+    def __init__(self, *args, **kwargs):
+        if "in_channels" in kwargs:
+            kwargs = dict(kwargs)
+            kwargs["in_channels"] = kwargs["in_channels"] * 2 + 1
+        else:
+            # Curse you, Python. Or really, just curse positional arguments :|.
+            args = list(args)
+            args[1] = args[1] * 2 + 1
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x, timesteps, inpaint_image=None, inpaint_mask=None, **kwargs):
+        if inpaint_image is None:
+            inpaint_image = th.zeros_like(x)
+        if inpaint_mask is None:
+            inpaint_mask = th.zeros_like(x[:, :1])
+        return super().forward(
+            th.cat([x, inpaint_image * inpaint_mask, inpaint_mask], dim=1),
+            timesteps,
+            **kwargs,
+        )
+
+
+class SuperResInpaintText2ImUnet(Text2ImUNet):
+    """
+    A text2im model which can perform both upsampling and inpainting.
+    """
+
+    def __init__(self, *args, **kwargs):
+        if "in_channels" in kwargs:
+            kwargs = dict(kwargs)
+            kwargs["in_channels"] = kwargs["in_channels"] * 3 + 1
+        else:
+            # Curse you, Python. Or really, just curse positional arguments :|.
+            args = list(args)
+            args[1] = args[1] * 3 + 1
+        super().__init__(*args, **kwargs)
+
+    def forward(
+        self,
+        x,
+        timesteps,
+        inpaint_image=None,
+        inpaint_mask=None,
+        low_res=None,
+        **kwargs,
+    ):
+        if inpaint_image is None:
+            inpaint_image = th.zeros_like(x)
+        if inpaint_mask is None:
+            inpaint_mask = th.zeros_like(x[:, :1])
+        _, _, new_height, new_width = x.shape
+        upsampled = F.interpolate(
+            low_res, (new_height, new_width), mode="bilinear", align_corners=False
+        )
+        return super().forward(
+            th.cat([x, inpaint_image * inpaint_mask, inpaint_mask, upsampled], dim=1),
+            timesteps,
+            **kwargs,
+        )

glide_text2im/tokenizer/bpe.py

@@ -0,0 +1,151 @@
+"""
+Byte pair encoding utilities adapted from:
+https://github.com/openai/gpt-2/blob/master/src/encoder.py
+"""
+
+import gzip
+import json
+import os
+from functools import lru_cache
+from typing import List, Tuple
+
+import regex as re
+
+
+@lru_cache()
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    bs = (
+        list(range(ord("!"), ord("~") + 1))
+        + list(range(ord("¡"), ord("¬") + 1))
+        + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2 ** 8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2 ** 8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+    """Return set of symbol pairs in a word.
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+class Encoder:
+    def __init__(self, encoder, bpe_merges, errors="replace"):
+        self.encoder = encoder
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.errors = errors  # how to handle errors in decoding
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+        self.cache = {}
+
+        # Should haved added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
+        self.pat = re.compile(
+            r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
+        )
+
+    @property
+    def n_vocab(self) -> int:
+        return len(self.encoder)
+
+    @property
+    def end_token(self) -> int:
+        return self.n_vocab - 1
+
+    def padded_tokens_and_mask(
+        self, tokens: List[int], text_ctx: int
+    ) -> Tuple[List[int], List[bool]]:
+        tokens = tokens[:text_ctx]
+        padding = text_ctx - len(tokens)
+        padded_tokens = tokens + [self.end_token] * padding
+        mask = [True] * len(tokens) + [False] * padding
+        return padded_tokens, mask
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token
+
+        while True:
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                    new_word.extend(word[i:j])
+                    i = j
+                except:  # pylint: disable=bare-except
+                    new_word.extend(word[i:])
+                    break
+
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = " ".join(word)
+        self.cache[token] = word
+        return word
+
+    def encode(self, text):
+        text = text.lower()
+        bpe_tokens = []
+        for token in re.findall(self.pat, text):
+            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
+            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" "))
+        return bpe_tokens
+
+    def decode(self, tokens):
+        text = "".join([self.decoder[token] for token in tokens])
+        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
+        return text
+
+
+def get_encoder():
+    root_dir = os.path.dirname(os.path.abspath(__file__))
+    with gzip.open(os.path.join(root_dir, "encoder.json.gz"), "r") as f:
+        encoder = json.load(f)
+    with gzip.open(os.path.join(root_dir, "vocab.bpe.gz"), "r") as f:
+        bpe_data = str(f.read(), "utf-8")
+    bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
+    return Encoder(
+        encoder=encoder,
+        bpe_merges=bpe_merges,
+    )

glide_text2im/tokenizer/bpe_simple_vocab_16e6.txt.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
+size 1356917

glide_text2im/tokenizer/encoder.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4debc1cf25180021b07744bc9f4488d53c7bf112c8ce5de8097c6a7518f4ec7c
+size 348346

glide_text2im/tokenizer/simple_tokenizer.py

@@ -0,0 +1,163 @@
+"""
+Copied from: https://github.com/openai/CLIP/blob/573315e83f07b53a61ff5098757e8fc885f1703e/clip/simple_tokenizer.py
+"""
+
+import gzip
+import html
+import os
+from functools import lru_cache
+from typing import List, Tuple
+
+import ftfy
+import regex as re
+
+
+@lru_cache()
+def default_bpe():
+    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")
+
+
+@lru_cache()
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    bs = (
+        list(range(ord("!"), ord("~") + 1))
+        + list(range(ord("¡"), ord("¬") + 1))
+        + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2 ** 8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2 ** 8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+    """Return set of symbol pairs in a word.
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r"\s+", " ", text)
+    text = text.strip()
+    return text
+
+
+class SimpleTokenizer(object):
+    def __init__(self, bpe_path: str = default_bpe()):
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        merges = gzip.open(bpe_path).read().decode("utf-8").split("\n")
+        merges = merges[1 : 49152 - 256 - 2 + 1]
+        merges = [tuple(merge.split()) for merge in merges]
+        vocab = list(bytes_to_unicode().values())
+        vocab = vocab + [v + "</w>" for v in vocab]
+        for merge in merges:
+            vocab.append("".join(merge))
+        vocab.extend(["<|startoftext|>", "<|endoftext|>"])
+        self.encoder = dict(zip(vocab, range(len(vocab))))
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.bpe_ranks = dict(zip(merges, range(len(merges))))
+        self.cache = {"<|startoftext|>": "<|startoftext|>", "<|endoftext|>": "<|endoftext|>"}
+        self.pat = re.compile(
+            r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
+            re.IGNORECASE,
+        )
+
+    @property
+    def start_token(self):
+        return self.encoder["<|startoftext|>"]
+
+    @property
+    def end_token(self):
+        return self.encoder["<|endoftext|>"]
+
+    def padded_tokens_and_len(self, tokens: List[int], text_ctx: int) -> Tuple[List[int], int]:
+        tokens = [self.start_token] + tokens[: text_ctx - 2] + [self.end_token]
+        text_len = len(tokens)
+        padding = text_ctx - len(tokens)
+        padded_tokens = tokens + [0] * padding
+        return padded_tokens, text_len
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token[:-1]) + (token[-1] + "</w>",)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token + "</w>"
+
+        while True:
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                    new_word.extend(word[i:j])
+                    i = j
+                except:  # pylint: disable=bare-except
+                    new_word.extend(word[i:])
+                    break
+
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = " ".join(word)
+        self.cache[token] = word
+        return word
+
+    def encode(self, text):
+        bpe_tokens = []
+        text = whitespace_clean(basic_clean(text)).lower()
+        for token in re.findall(self.pat, text):
+            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
+            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" "))
+        return bpe_tokens
+
+    def decode(self, tokens):
+        text = "".join([self.decoder[token] for token in tokens])
+        text = (
+            bytearray([self.byte_decoder[c] for c in text])
+            .decode("utf-8", errors="replace")
+            .replace("</w>", " ")
+        )
+        return text

glide_text2im/tokenizer/vocab.bpe.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce239dd5a898827423fee00e3f7ab37de7900f247f2ba360753d860e8a46524d
+size 213544

glide_text2im/unet.py

@@ -0,0 +1,635 @@
+import math
+from abc import abstractmethod
+
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .fp16_util import convert_module_to_f16, convert_module_to_f32
+from .nn import avg_pool_nd, conv_nd, linear, normalization, timestep_embedding, zero_module
+
+
+class TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+
+    @abstractmethod
+    def forward(self, x, emb):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    """
+    A sequential module that passes timestep embeddings to the children that
+    support it as an extra input.
+    """
+
+    def forward(self, x, emb, encoder_out=None):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb)
+            elif isinstance(layer, AttentionBlock):
+                x = layer(x, encoder_out)
+            else:
+                x = layer(x)
+        return x
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.op = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=1)
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param use_checkpoint: if True, use gradient checkpointing on this module.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        use_checkpoint=False,
+        up=False,
+        down=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+
+        self.in_layers = nn.Sequential(
+            normalization(channels, swish=1.0),
+            nn.Identity(),
+            conv_nd(dims, channels, self.out_channels, 3, padding=1),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0),
+            nn.SiLU() if use_scale_shift_norm else nn.Identity(),
+            nn.Dropout(p=dropout),
+            zero_module(conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = th.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        encoder_channels=None,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels, swish=0.0)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        self.attention = QKVAttention(self.num_heads)
+
+        if encoder_channels is not None:
+            self.encoder_kv = conv_nd(1, encoder_channels, channels * 2, 1)
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x, encoder_out=None):
+        b, c, *spatial = x.shape
+        qkv = self.qkv(self.norm(x).view(b, c, -1))
+        if encoder_out is not None:
+            encoder_out = self.encoder_kv(encoder_out)
+            h = self.attention(qkv, encoder_out)
+        else:
+            h = self.attention(qkv)
+        h = self.proj_out(h)
+        return x + h.reshape(b, c, *spatial)
+
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv, encoder_kv=None):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        if encoder_kv is not None:
+            assert encoder_kv.shape[1] == self.n_heads * ch * 2
+            ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
+            k = th.cat([ek, k], dim=-1)
+            v = th.cat([ev, v], dim=-1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum(
+            "bct,bcs->bts", q * scale, k * scale
+        )  # More stable with f16 than dividing afterwards
+        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = th.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+
+class UNetModel(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        num_classes=None,
+        use_checkpoint=False,
+        use_fp16=False,
+        num_heads=1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        resblock_updown=False,
+        encoder_channels=None,
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.num_classes = num_classes
+        self.use_checkpoint = use_checkpoint
+        self.dtype = th.float16 if use_fp16 else th.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        if self.num_classes is not None:
+            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+
+        ch = input_ch = int(channel_mult[0] * model_channels)
+        self.input_blocks = nn.ModuleList(
+            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
+        )
+        self._feature_size = ch
+        input_block_chans = [ch]
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(mult * model_channels),
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(mult * model_channels)
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                            encoder_channels=encoder_channels,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+                encoder_channels=encoder_channels,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(num_res_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(model_channels * mult),
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(model_channels * mult)
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads_upsample,
+                            num_head_channels=num_head_channels,
+                            encoder_channels=encoder_channels,
+                        )
+                    )
+                if level and i == num_res_blocks:
+                    out_ch = ch
+                    layers.append(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch, swish=1.0),
+            nn.Identity(),
+            zero_module(conv_nd(dims, input_ch, out_channels, 3, padding=1)),
+        )
+        self.use_fp16 = use_fp16
+
+    def convert_to_fp16(self):
+        """
+        Convert the torso of the model to float16.
+        """
+        self.input_blocks.apply(convert_module_to_f16)
+        self.middle_block.apply(convert_module_to_f16)
+        self.output_blocks.apply(convert_module_to_f16)
+
+    def convert_to_fp32(self):
+        """
+        Convert the torso of the model to float32.
+        """
+        self.input_blocks.apply(convert_module_to_f32)
+        self.middle_block.apply(convert_module_to_f32)
+        self.output_blocks.apply(convert_module_to_f32)
+
+    def forward(self, x, timesteps, y=None):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert (y is not None) == (
+            self.num_classes is not None
+        ), "must specify y if and only if the model is class-conditional"
+
+        hs = []
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        if self.num_classes is not None:
+            assert y.shape == (x.shape[0],)
+            emb = emb + self.label_emb(y)
+
+        h = x.type(self.dtype)
+        for module in self.input_blocks:
+            h = module(h, emb)
+            hs.append(h)
+        h = self.middle_block(h, emb)
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb)
+        h = h.type(x.dtype)
+        return self.out(h)
+
+class SuperResUNetModel(UNetModel):
+    """
+    A UNetModel that performs super-resolution.
+
+    Expects an extra kwarg `low_res` to condition on a low-resolution image.
+    """
+
+    def __init__(self, *args, **kwargs):
+        if "in_channels" in kwargs:
+            kwargs = dict(kwargs)
+            kwargs["in_channels"] = kwargs["in_channels"] * 2
+        else:
+            # Curse you, Python. Or really, just curse positional arguments :|.
+            args = list(args)
+            args[1] = args[1] * 2
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x, timesteps, low_res=None, **kwargs):
+        _, _, new_height, new_width = x.shape
+        upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
+        x = th.cat([x, upsampled], dim=1)
+        return super().forward(x, timesteps, **kwargs)
+
+    
+class InpaintUNetModel(UNetModel):
+    """
+    A UNetModel which can perform inpainting.
+    """
+
+    def __init__(self, *args, **kwargs):
+        if "in_channels" in kwargs:
+            kwargs = dict(kwargs)
+            kwargs["in_channels"] = kwargs["in_channels"] * 2 + 1
+        else:
+            # Curse you, Python. Or really, just curse positional arguments :|.
+            args = list(args)
+            args[1] = args[1] * 2 + 1
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x, timesteps, inpaint_image=None, inpaint_mask=None, **kwargs):
+        if inpaint_image is None:
+            inpaint_image = th.zeros_like(x)
+        if inpaint_mask is None:
+            inpaint_mask = th.zeros_like(x[:, :1])
+        return super().forward(
+            th.cat([x, inpaint_image * inpaint_mask, inpaint_mask], dim=1),
+            timesteps,
+            **kwargs,
+        )
+
+
+class SuperResInpaintUNetModel(UNetModel):
+    """
+    A UNetModel which can perform both upsampling and inpainting.
+    """
+
+    def __init__(self, *args, **kwargs):
+        if "in_channels" in kwargs:
+            kwargs = dict(kwargs)
+            kwargs["in_channels"] = kwargs["in_channels"] * 3 + 1
+        else:
+            # Curse you, Python. Or really, just curse positional arguments :|.
+            args = list(args)
+            args[1] = args[1] * 3 + 1
+        super().__init__(*args, **kwargs)
+
+    def forward(
+        self,
+        x,
+        timesteps,
+        inpaint_image=None,
+        inpaint_mask=None,
+        low_res=None,
+        **kwargs,
+    ):
+        if inpaint_image is None:
+            inpaint_image = th.zeros_like(x)
+        if inpaint_mask is None:
+            inpaint_mask = th.zeros_like(x[:, :1])
+        _, _, new_height, new_width = x.shape
+        upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
+        return super().forward(
+            th.cat([x, inpaint_image * inpaint_mask, inpaint_mask, upsampled], dim=1),
+            timesteps,
+            **kwargs,
+        )

glide_text2im/xf.py

@@ -0,0 +1,130 @@
+"""
+Transformer implementation adapted from CLIP ViT:
+https://github.com/openai/CLIP/blob/4c0275784d6d9da97ca1f47eaaee31de1867da91/clip/model.py
+"""
+
+import math
+
+import torch as th
+import torch.nn as nn
+
+
+def convert_module_to_f16(l):
+    """
+    Convert primitive modules to float16.
+    """
+    if isinstance(l, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)):
+        l.weight.data = l.weight.data.half()
+        if l.bias is not None:
+            l.bias.data = l.bias.data.half()
+
+
+class LayerNorm(nn.LayerNorm):
+    """
+    Implementation that supports fp16 inputs but fp32 gains/biases.
+    """
+
+    def forward(self, x: th.Tensor):
+        return super().forward(x.float()).to(x.dtype)
+
+
+class MultiheadAttention(nn.Module):
+    def __init__(self, n_ctx, width, heads):
+        super().__init__()
+        self.n_ctx = n_ctx
+        self.width = width
+        self.heads = heads
+        self.c_qkv = nn.Linear(width, width * 3)
+        self.c_proj = nn.Linear(width, width)
+        self.attention = QKVMultiheadAttention(heads, n_ctx)
+
+    def forward(self, x):
+        x = self.c_qkv(x)
+        x = self.attention(x)
+        x = self.c_proj(x)
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, width):
+        super().__init__()
+        self.width = width
+        self.c_fc = nn.Linear(width, width * 4)
+        self.c_proj = nn.Linear(width * 4, width)
+        self.gelu = nn.GELU()
+
+    def forward(self, x):
+        return self.c_proj(self.gelu(self.c_fc(x)))
+
+
+class QKVMultiheadAttention(nn.Module):
+    def __init__(self, n_heads: int, n_ctx: int):
+        super().__init__()
+        self.n_heads = n_heads
+        self.n_ctx = n_ctx
+
+    def forward(self, qkv):
+        bs, n_ctx, width = qkv.shape
+        attn_ch = width // self.n_heads // 3
+        scale = 1 / math.sqrt(math.sqrt(attn_ch))
+        qkv = qkv.view(bs, n_ctx, self.n_heads, -1)
+        q, k, v = th.split(qkv, attn_ch, dim=-1)
+        weight = th.einsum(
+            "bthc,bshc->bhts", q * scale, k * scale
+        )  # More stable with f16 than dividing afterwards
+        wdtype = weight.dtype
+        weight = th.softmax(weight.float(), dim=-1).type(wdtype)
+        return th.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        n_ctx: int,
+        width: int,
+        heads: int,
+    ):
+        super().__init__()
+
+        self.attn = MultiheadAttention(
+            n_ctx,
+            width,
+            heads,
+        )
+        self.ln_1 = LayerNorm(width)
+        self.mlp = MLP(width)
+        self.ln_2 = LayerNorm(width)
+
+    def forward(self, x: th.Tensor):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        n_ctx: int,
+        width: int,
+        layers: int,
+        heads: int,
+    ):
+        super().__init__()
+        self.n_ctx = n_ctx
+        self.width = width
+        self.layers = layers
+        self.resblocks = nn.ModuleList(
+            [
+                ResidualAttentionBlock(
+                    n_ctx,
+                    width,
+                    heads,
+                )
+                for _ in range(layers)
+            ]
+        )
+
+    def forward(self, x: th.Tensor):
+        for block in self.resblocks:
+            x = block(x)
+        return x

model-card.md

@@ -0,0 +1,50 @@
+# Overview
+
+This card describes the diffusion model GLIDE (filtered) and noised CLIP model described in the paper [GLIDE: Towards
+Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://arxiv.org/abs/2112.10741)
+
+# Datasets
+
+GLIDE (filtered) was trained on a filtered version of a dataset comprised of several hundred million text-image pairs
+collected from the internet. We constructed a set of filters intended to remove all images of people, violent objects, and some
+and hate symbols (see Appendix F of the paper for details). The size of the dataset after filtering was approximately
+67M text-image pairs.
+
+Our noised CLIP model which was trained on the dataset described above, augmented with a filtered version of the dataset used
+to train the [original CLIP models](https://github.com/openai/clip). The total size of this augmented dataset is approximately 137M pairs.
+
+# Performance
+
+Qualitatively, we find that the generated images from GLIDE (filtered) often look semi-realistic, but the small size of the model hinders
+its ability to bind attributes to objects and perform compositional tasks. Because the dataset used to train GLIDE
+(filtered) has been preprocessed to remove images of people, this also limits its world knowledge, especially in regard
+to concepts that involve people.
+Finally, due to the dataset used to train GLIDE (filtered), the model has reduced capabilities to compose multiple objects in complex ways compared to models of a similar size trained on our internal dataset.
+
+We do not directly measure quantitative metrics for GLIDE (filtered). In particular, most of the evaluations we report for our other models are biased against GLIDE (filtered), since they use prompts that often require generations of people. Evaluating people-free models remains an open area of research.
+
+# Intended Use
+
+We release these models to help advance research in generative modeling. Due to the limitations and biases of GLIDE (filtered), we do not currently recommend it for commercial use.
+
+Functionally, these models are intended to be able to perform the following tasks for research purposes:
+ * Generate images from natural language prompts
+ * Iteratively edit and refine images using inpainting
+
+These models are explicitly not intended to generate images of people or other subjects we filtered for (see Appendix F of the paper for details).
+
+# Limitations
+
+Despite the dataset filtering applied before training, GLIDE (filtered) continues to exhibit biases that extend beyond those found in images of people.
+We explore some of these biases in our paper. For example:
+
+  * It produces different outputs when asked to generate toys for boys and toys for girls.
+  * It gravitates toward generating images of churches when asked to generate "a religious place",
+    and this bias is amplified by classifier-free guidance.
+  * It may have a greater propensity for generating hate symbols other than swastikas and confederate flags. Our filter
+    for hate symbols focused specifically on these two cases, as we found few relevant images of hate symbols in our
+    dataset. However, we also found that the model has diminished capabilities across a wider set of symbols.
+
+GLIDE (filtered) can fail to produce realistic outputs for complex prompts or for prompts that involve concepts that are
+not well-represented in its training data. While the data for the model was filtered to remove certain types of images,
+the data still exhibits biases toward Western-centric concepts.

notebooks/clip_guided.ipynb

@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "from IPython.display import display\n",
+    "import torch as th\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "from glide_text2im.clip.model_creation import create_clip_model\n",
+    "from glide_text2im.download import load_checkpoint\n",
+    "from glide_text2im.model_creation import (\n",
+    "    create_model_and_diffusion,\n",
+    "    model_and_diffusion_defaults,\n",
+    "    model_and_diffusion_defaults_upsampler,\n",
+    ")\n",
+    "from glide_text2im.tokenizer.simple_tokenizer import SimpleTokenizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This notebook supports both CPU and GPU.\n",
+    "# On CPU, generating one sample may take on the order of 20 minutes.\n",
+    "# On a GPU, it should be under a minute.\n",
+    "\n",
+    "has_cuda = th.cuda.is_available()\n",
+    "device = th.device('cpu' if not has_cuda else 'cuda')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create base model.\n",
+    "options = model_and_diffusion_defaults()\n",
+    "options['use_fp16'] = has_cuda\n",
+    "options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling\n",
+    "model, diffusion = create_model_and_diffusion(**options)\n",
+    "model.eval()\n",
+    "if has_cuda:\n",
+    "    model.convert_to_fp16()\n",
+    "model.to(device)\n",
+    "model.load_state_dict(load_checkpoint('base', device))\n",
+    "print('total base parameters', sum(x.numel() for x in model.parameters()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create upsampler model.\n",
+    "options_up = model_and_diffusion_defaults_upsampler()\n",
+    "options_up['use_fp16'] = has_cuda\n",
+    "options_up['timestep_respacing'] = 'fast27' # use 27 diffusion steps for very fast sampling\n",
+    "model_up, diffusion_up = create_model_and_diffusion(**options_up)\n",
+    "model_up.eval()\n",
+    "if has_cuda:\n",
+    "    model_up.convert_to_fp16()\n",
+    "model_up.to(device)\n",
+    "model_up.load_state_dict(load_checkpoint('upsample', device))\n",
+    "print('total upsampler parameters', sum(x.numel() for x in model_up.parameters()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create CLIP model.\n",
+    "clip_model = create_clip_model(device=device)\n",
+    "clip_model.image_encoder.load_state_dict(load_checkpoint('clip/image-enc', device))\n",
+    "clip_model.text_encoder.load_state_dict(load_checkpoint('clip/text-enc', device))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_images(batch: th.Tensor):\n",
+    "    \"\"\" Display a batch of images inline. \"\"\"\n",
+    "    scaled = ((batch + 1)*127.5).round().clamp(0,255).to(th.uint8).cpu()\n",
+    "    reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3])\n",
+    "    display(Image.fromarray(reshaped.numpy()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sampling parameters\n",
+    "prompt = \"an oil painting of a corgi\"\n",
+    "batch_size = 1\n",
+    "guidance_scale = 3.0\n",
+    "\n",
+    "# Tune this parameter to control the sharpness of 256x256 images.\n",
+    "# A value of 1.0 is sharper, but sometimes results in grainy artifacts.\n",
+    "upsample_temp = 0.997"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##############################\n",
+    "# Sample from the base model #\n",
+    "##############################\n",
+    "\n",
+    "# Create the text tokens to feed to the model.\n",
+    "tokens = model.tokenizer.encode(prompt)\n",
+    "tokens, mask = model.tokenizer.padded_tokens_and_mask(\n",
+    "    tokens, options['text_ctx']\n",
+    ")\n",
+    "\n",
+    "# Pack the tokens together into model kwargs.\n",
+    "model_kwargs = dict(\n",
+    "    tokens=th.tensor([tokens] * batch_size, device=device),\n",
+    "    mask=th.tensor([mask] * batch_size, dtype=th.bool, device=device),\n",
+    ")\n",
+    "\n",
+    "# Setup guidance function for CLIP model.\n",
+    "cond_fn = clip_model.cond_fn([prompt] * batch_size, guidance_scale)\n",
+    "\n",
+    "# Sample from the base model.\n",
+    "model.del_cache()\n",
+    "samples = diffusion.p_sample_loop(\n",
+    "    model,\n",
+    "    (batch_size, 3, options[\"image_size\"], options[\"image_size\"]),\n",
+    "    device=device,\n",
+    "    clip_denoised=True,\n",
+    "    progress=True,\n",
+    "    model_kwargs=model_kwargs,\n",
+    "    cond_fn=cond_fn,\n",
+    ")\n",
+    "model.del_cache()\n",
+    "\n",
+    "# Show the output\n",
+    "show_images(samples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##############################\n",
+    "# Upsample the 64x64 samples #\n",
+    "##############################\n",
+    "\n",
+    "tokens = model_up.tokenizer.encode(prompt)\n",
+    "tokens, mask = model_up.tokenizer.padded_tokens_and_mask(\n",
+    "    tokens, options_up['text_ctx']\n",
+    ")\n",
+    "\n",
+    "# Create the model conditioning dict.\n",
+    "model_kwargs = dict(\n",
+    "    # Low-res image to upsample.\n",
+    "    low_res=((samples+1)*127.5).round()/127.5 - 1,\n",
+    "\n",
+    "    # Text tokens\n",
+    "    tokens=th.tensor(\n",
+    "        [tokens] * batch_size, device=device\n",
+    "    ),\n",
+    "    mask=th.tensor(\n",
+    "        [mask] * batch_size,\n",
+    "        dtype=th.bool,\n",
+    "        device=device,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Sample from the base model.\n",
+    "model_up.del_cache()\n",
+    "up_shape = (batch_size, 3, options_up[\"image_size\"], options_up[\"image_size\"])\n",
+    "up_samples = diffusion_up.ddim_sample_loop(\n",
+    "    model_up,\n",
+    "    up_shape,\n",
+    "    noise=th.randn(up_shape, device=device) * upsample_temp,\n",
+    "    device=device,\n",
+    "    clip_denoised=True,\n",
+    "    progress=True,\n",
+    "    model_kwargs=model_kwargs,\n",
+    "    cond_fn=None,\n",
+    ")[:batch_size]\n",
+    "model_up.del_cache()\n",
+    "\n",
+    "# Show the output\n",
+    "show_images(up_samples)"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "e7d6e62d90e7e85f9a0faa7f0b1d576302d7ae6108e9fe361594f8e1c8b05781"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/inpaint.ipynb

@@ -0,0 +1,290 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Tuple\n",
+    "\n",
+    "from IPython.display import display\n",
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import torch as th\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "from glide_text2im.download import load_checkpoint\n",
+    "from glide_text2im.model_creation import (\n",
+    "    create_model_and_diffusion,\n",
+    "    model_and_diffusion_defaults,\n",
+    "    model_and_diffusion_defaults_upsampler\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This notebook supports both CPU and GPU.\n",
+    "# On CPU, generating one sample may take on the order of 20 minutes.\n",
+    "# On a GPU, it should be under a minute.\n",
+    "\n",
+    "has_cuda = th.cuda.is_available()\n",
+    "device = th.device('cpu' if not has_cuda else 'cuda')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create base model.\n",
+    "options = model_and_diffusion_defaults()\n",
+    "options['inpaint'] = True\n",
+    "options['use_fp16'] = has_cuda\n",
+    "options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling\n",
+    "model, diffusion = create_model_and_diffusion(**options)\n",
+    "model.eval()\n",
+    "if has_cuda:\n",
+    "    model.convert_to_fp16()\n",
+    "model.to(device)\n",
+    "model.load_state_dict(load_checkpoint('base-inpaint', device))\n",
+    "print('total base parameters', sum(x.numel() for x in model.parameters()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create upsampler model.\n",
+    "options_up = model_and_diffusion_defaults_upsampler()\n",
+    "options_up['inpaint'] = True\n",
+    "options_up['use_fp16'] = has_cuda\n",
+    "options_up['timestep_respacing'] = 'fast27' # use 27 diffusion steps for very fast sampling\n",
+    "model_up, diffusion_up = create_model_and_diffusion(**options_up)\n",
+    "model_up.eval()\n",
+    "if has_cuda:\n",
+    "    model_up.convert_to_fp16()\n",
+    "model_up.to(device)\n",
+    "model_up.load_state_dict(load_checkpoint('upsample-inpaint', device))\n",
+    "print('total upsampler parameters', sum(x.numel() for x in model_up.parameters()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_images(batch: th.Tensor):\n",
+    "    \"\"\" Display a batch of images inline. \"\"\"\n",
+    "    scaled = ((batch + 1)*127.5).round().clamp(0,255).to(th.uint8).cpu()\n",
+    "    reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3])\n",
+    "    display(Image.fromarray(reshaped.numpy()))\n",
+    "\n",
+    "def read_image(path: str, size: int = 256) -> Tuple[th.Tensor, th.Tensor]:\n",
+    "    pil_img = Image.open(path).convert('RGB')\n",
+    "    pil_img = pil_img.resize((size, size), resample=Image.BICUBIC)\n",
+    "    img = np.array(pil_img)\n",
+    "    return th.from_numpy(img)[None].permute(0, 3, 1, 2).float() / 127.5 - 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sampling parameters\n",
+    "prompt = \"a corgi in a field\"\n",
+    "batch_size = 1\n",
+    "guidance_scale = 5.0\n",
+    "\n",
+    "# Tune this parameter to control the sharpness of 256x256 images.\n",
+    "# A value of 1.0 is sharper, but sometimes results in grainy artifacts.\n",
+    "upsample_temp = 0.997\n",
+    "\n",
+    "# Source image we are inpainting\n",
+    "source_image_256 = read_image('grass.png', size=256)\n",
+    "source_image_64 = read_image('grass.png', size=64)\n",
+    "\n",
+    "# The mask should always be a boolean 64x64 mask, and then we\n",
+    "# can upsample it for the second stage.\n",
+    "source_mask_64 = th.ones_like(source_image_64)[:, :1]\n",
+    "source_mask_64[:, :, 20:] = 0\n",
+    "source_mask_256 = F.interpolate(source_mask_64, (256, 256), mode='nearest')\n",
+    "\n",
+    "# Visualize the image we are inpainting\n",
+    "show_images(source_image_256 * source_mask_256)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##############################\n",
+    "# Sample from the base model #\n",
+    "##############################\n",
+    "\n",
+    "# Create the text tokens to feed to the model.\n",
+    "tokens = model.tokenizer.encode(prompt)\n",
+    "tokens, mask = model.tokenizer.padded_tokens_and_mask(\n",
+    "    tokens, options['text_ctx']\n",
+    ")\n",
+    "\n",
+    "# Create the classifier-free guidance tokens (empty)\n",
+    "full_batch_size = batch_size * 2\n",
+    "uncond_tokens, uncond_mask = model.tokenizer.padded_tokens_and_mask(\n",
+    "    [], options['text_ctx']\n",
+    ")\n",
+    "\n",
+    "# Pack the tokens together into model kwargs.\n",
+    "model_kwargs = dict(\n",
+    "    tokens=th.tensor(\n",
+    "        [tokens] * batch_size + [uncond_tokens] * batch_size, device=device\n",
+    "    ),\n",
+    "    mask=th.tensor(\n",
+    "        [mask] * batch_size + [uncond_mask] * batch_size,\n",
+    "        dtype=th.bool,\n",
+    "        device=device,\n",
+    "    ),\n",
+    "\n",
+    "    # Masked inpainting image\n",
+    "    inpaint_image=(source_image_64 * source_mask_64).repeat(full_batch_size, 1, 1, 1).to(device),\n",
+    "    inpaint_mask=source_mask_64.repeat(full_batch_size, 1, 1, 1).to(device),\n",
+    ")\n",
+    "\n",
+    "# Create an classifier-free guidance sampling function\n",
+    "def model_fn(x_t, ts, **kwargs):\n",
+    "    half = x_t[: len(x_t) // 2]\n",
+    "    combined = th.cat([half, half], dim=0)\n",
+    "    model_out = model(combined, ts, **kwargs)\n",
+    "    eps, rest = model_out[:, :3], model_out[:, 3:]\n",
+    "    cond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim=0)\n",
+    "    half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)\n",
+    "    eps = th.cat([half_eps, half_eps], dim=0)\n",
+    "    return th.cat([eps, rest], dim=1)\n",
+    "\n",
+    "def denoised_fn(x_start):\n",
+    "    # Force the model to have the exact right x_start predictions\n",
+    "    # for the part of the image which is known.\n",
+    "    return (\n",
+    "        x_start * (1 - model_kwargs['inpaint_mask'])\n",
+    "        + model_kwargs['inpaint_image'] * model_kwargs['inpaint_mask']\n",
+    "    )\n",
+    "\n",
+    "# Sample from the base model.\n",
+    "model.del_cache()\n",
+    "samples = diffusion.p_sample_loop(\n",
+    "    model_fn,\n",
+    "    (full_batch_size, 3, options[\"image_size\"], options[\"image_size\"]),\n",
+    "    device=device,\n",
+    "    clip_denoised=True,\n",
+    "    progress=True,\n",
+    "    model_kwargs=model_kwargs,\n",
+    "    cond_fn=None,\n",
+    "    denoised_fn=denoised_fn,\n",
+    ")[:batch_size]\n",
+    "model.del_cache()\n",
+    "\n",
+    "# Show the output\n",
+    "show_images(samples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##############################\n",
+    "# Upsample the 64x64 samples #\n",
+    "##############################\n",
+    "\n",
+    "tokens = model_up.tokenizer.encode(prompt)\n",
+    "tokens, mask = model_up.tokenizer.padded_tokens_and_mask(\n",
+    "    tokens, options_up['text_ctx']\n",
+    ")\n",
+    "\n",
+    "# Create the model conditioning dict.\n",
+    "model_kwargs = dict(\n",
+    "    # Low-res image to upsample.\n",
+    "    low_res=((samples+1)*127.5).round()/127.5 - 1,\n",
+    "\n",
+    "    # Text tokens\n",
+    "    tokens=th.tensor(\n",
+    "        [tokens] * batch_size, device=device\n",
+    "    ),\n",
+    "    mask=th.tensor(\n",
+    "        [mask] * batch_size,\n",
+    "        dtype=th.bool,\n",
+    "        device=device,\n",
+    "    ),\n",
+    "\n",
+    "    # Masked inpainting image.\n",
+    "    inpaint_image=(source_image_256 * source_mask_256).repeat(batch_size, 1, 1, 1).to(device),\n",
+    "    inpaint_mask=source_mask_256.repeat(batch_size, 1, 1, 1).to(device),\n",
+    ")\n",
+    "\n",
+    "def denoised_fn(x_start):\n",
+    "    # Force the model to have the exact right x_start predictions\n",
+    "    # for the part of the image which is known.\n",
+    "    return (\n",
+    "        x_start * (1 - model_kwargs['inpaint_mask'])\n",
+    "        + model_kwargs['inpaint_image'] * model_kwargs['inpaint_mask']\n",
+    "    )\n",
+    "\n",
+    "# Sample from the base model.\n",
+    "model_up.del_cache()\n",
+    "up_shape = (batch_size, 3, options_up[\"image_size\"], options_up[\"image_size\"])\n",
+    "up_samples = diffusion_up.p_sample_loop(\n",
+    "    model_up,\n",
+    "    up_shape,\n",
+    "    noise=th.randn(up_shape, device=device) * upsample_temp,\n",
+    "    device=device,\n",
+    "    clip_denoised=True,\n",
+    "    progress=True,\n",
+    "    model_kwargs=model_kwargs,\n",
+    "    cond_fn=None,\n",
+    "    denoised_fn=denoised_fn,\n",
+    ")[:batch_size]\n",
+    "model_up.del_cache()\n",
+    "\n",
+    "# Show the output\n",
+    "show_images(up_samples)"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "e7d6e62d90e7e85f9a0faa7f0b1d576302d7ae6108e9fe361594f8e1c8b05781"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/text2im.ipynb

@@ -0,0 +1,239 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from PIL import Image\n",
+    "from IPython.display import display\n",
+    "import torch as th\n",
+    "\n",
+    "from glide_text2im.download import load_checkpoint\n",
+    "from glide_text2im.model_creation import (\n",
+    "    create_model_and_diffusion,\n",
+    "    model_and_diffusion_defaults,\n",
+    "    model_and_diffusion_defaults_upsampler\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This notebook supports both CPU and GPU.\n",
+    "# On CPU, generating one sample may take on the order of 20 minutes.\n",
+    "# On a GPU, it should be under a minute.\n",
+    "\n",
+    "has_cuda = th.cuda.is_available()\n",
+    "device = th.device('cpu' if not has_cuda else 'cuda')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create base model.\n",
+    "options = model_and_diffusion_defaults()\n",
+    "options['use_fp16'] = has_cuda\n",
+    "options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling\n",
+    "model, diffusion = create_model_and_diffusion(**options)\n",
+    "model.eval()\n",
+    "if has_cuda:\n",
+    "    model.convert_to_fp16()\n",
+    "model.to(device)\n",
+    "model.load_state_dict(load_checkpoint('base', device))\n",
+    "print('total base parameters', sum(x.numel() for x in model.parameters()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create upsampler model.\n",
+    "options_up = model_and_diffusion_defaults_upsampler()\n",
+    "options_up['use_fp16'] = has_cuda\n",
+    "options_up['timestep_respacing'] = 'fast27' # use 27 diffusion steps for very fast sampling\n",
+    "model_up, diffusion_up = create_model_and_diffusion(**options_up)\n",
+    "model_up.eval()\n",
+    "if has_cuda:\n",
+    "    model_up.convert_to_fp16()\n",
+    "model_up.to(device)\n",
+    "model_up.load_state_dict(load_checkpoint('upsample', device))\n",
+    "print('total upsampler parameters', sum(x.numel() for x in model_up.parameters()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_images(batch: th.Tensor):\n",
+    "    \"\"\" Display a batch of images inline. \"\"\"\n",
+    "    scaled = ((batch + 1)*127.5).round().clamp(0,255).to(th.uint8).cpu()\n",
+    "    reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3])\n",
+    "    display(Image.fromarray(reshaped.numpy()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sampling parameters\n",
+    "prompt = \"an oil painting of a corgi\"\n",
+    "batch_size = 1\n",
+    "guidance_scale = 3.0\n",
+    "\n",
+    "# Tune this parameter to control the sharpness of 256x256 images.\n",
+    "# A value of 1.0 is sharper, but sometimes results in grainy artifacts.\n",
+    "upsample_temp = 0.997"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##############################\n",
+    "# Sample from the base model #\n",
+    "##############################\n",
+    "\n",
+    "# Create the text tokens to feed to the model.\n",
+    "tokens = model.tokenizer.encode(prompt)\n",
+    "tokens, mask = model.tokenizer.padded_tokens_and_mask(\n",
+    "    tokens, options['text_ctx']\n",
+    ")\n",
+    "\n",
+    "# Create the classifier-free guidance tokens (empty)\n",
+    "full_batch_size = batch_size * 2\n",
+    "uncond_tokens, uncond_mask = model.tokenizer.padded_tokens_and_mask(\n",
+    "    [], options['text_ctx']\n",
+    ")\n",
+    "\n",
+    "# Pack the tokens together into model kwargs.\n",
+    "model_kwargs = dict(\n",
+    "    tokens=th.tensor(\n",
+    "        [tokens] * batch_size + [uncond_tokens] * batch_size, device=device\n",
+    "    ),\n",
+    "    mask=th.tensor(\n",
+    "        [mask] * batch_size + [uncond_mask] * batch_size,\n",
+    "        dtype=th.bool,\n",
+    "        device=device,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Create a classifier-free guidance sampling function\n",
+    "def model_fn(x_t, ts, **kwargs):\n",
+    "    half = x_t[: len(x_t) // 2]\n",
+    "    combined = th.cat([half, half], dim=0)\n",
+    "    model_out = model(combined, ts, **kwargs)\n",
+    "    eps, rest = model_out[:, :3], model_out[:, 3:]\n",
+    "    cond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim=0)\n",
+    "    half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)\n",
+    "    eps = th.cat([half_eps, half_eps], dim=0)\n",
+    "    return th.cat([eps, rest], dim=1)\n",
+    "\n",
+    "# Sample from the base model.\n",
+    "model.del_cache()\n",
+    "samples = diffusion.p_sample_loop(\n",
+    "    model_fn,\n",
+    "    (full_batch_size, 3, options[\"image_size\"], options[\"image_size\"]),\n",
+    "    device=device,\n",
+    "    clip_denoised=True,\n",
+    "    progress=True,\n",
+    "    model_kwargs=model_kwargs,\n",
+    "    cond_fn=None,\n",
+    ")[:batch_size]\n",
+    "model.del_cache()\n",
+    "\n",
+    "# Show the output\n",
+    "show_images(samples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##############################\n",
+    "# Upsample the 64x64 samples #\n",
+    "##############################\n",
+    "\n",
+    "tokens = model_up.tokenizer.encode(prompt)\n",
+    "tokens, mask = model_up.tokenizer.padded_tokens_and_mask(\n",
+    "    tokens, options_up['text_ctx']\n",
+    ")\n",
+    "\n",
+    "# Create the model conditioning dict.\n",
+    "model_kwargs = dict(\n",
+    "    # Low-res image to upsample.\n",
+    "    low_res=((samples+1)*127.5).round()/127.5 - 1,\n",
+    "\n",
+    "    # Text tokens\n",
+    "    tokens=th.tensor(\n",
+    "        [tokens] * batch_size, device=device\n",
+    "    ),\n",
+    "    mask=th.tensor(\n",
+    "        [mask] * batch_size,\n",
+    "        dtype=th.bool,\n",
+    "        device=device,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Sample from the base model.\n",
+    "model_up.del_cache()\n",
+    "up_shape = (batch_size, 3, options_up[\"image_size\"], options_up[\"image_size\"])\n",
+    "up_samples = diffusion_up.ddim_sample_loop(\n",
+    "    model_up,\n",
+    "    up_shape,\n",
+    "    noise=th.randn(up_shape, device=device) * upsample_temp,\n",
+    "    device=device,\n",
+    "    clip_denoised=True,\n",
+    "    progress=True,\n",
+    "    model_kwargs=model_kwargs,\n",
+    "    cond_fn=None,\n",
+    ")[:batch_size]\n",
+    "model_up.del_cache()\n",
+    "\n",
+    "# Show the output\n",
+    "show_images(up_samples)"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "e7d6e62d90e7e85f9a0faa7f0b1d576302d7ae6108e9fe361594f8e1c8b05781"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

setup.py

@@ -0,0 +1,15 @@
+from setuptools import setup
+
+setup(
+    name="glide-text2im",
+    packages=["glide_text2im"],
+    install_requires=[
+        "Pillow",
+        "attrs",
+        "torch",
+        "filelock",
+        "requests",
+        "tqdm",
+    ],
+    author="OpenAI",
+)