Merge branch 'main' of https://github.com/huggingface/diffusers into main

models/vision/glide/README.md

@@ -0,0 +1,4 @@
+# References
+
+[GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://arxiv.org/pdf/2112.10741.pdf)
+[Diffusion Models Beat GANs on Image Synthesis](https://arxiv.org/pdf/2105.05233.pdf)

models/vision/glide/convert_weights.py

@@ -1,23 +1,28 @@
-import argparse
-
 import torch
 from torch import nn
 
-from transformers import CLIPTextConfig, CLIPTextModel, GPT2Tokenizer
+from diffusers import ClassifierFreeGuidanceScheduler, CLIPTextModel, UNetGLIDEModel
+from modeling_glide import GLIDE
+from transformers import CLIPTextConfig, GPT2Tokenizer
+
 
 # wget https://openaipublic.blob.core.windows.net/diffusion/dec-2021/base.pt
 state_dict = torch.load("base.pt", map_location="cpu")
 state_dict = {k: nn.Parameter(v) for k, v in state_dict.items()}
+
+### Convert the text encoder
+
 config = CLIPTextConfig(
+    vocab_size=50257,
+    max_position_embeddings=128,
     hidden_size=512,
     intermediate_size=2048,
     num_hidden_layers=16,
     num_attention_heads=8,
-    max_position_embeddings=128
+    use_padding_embeddings=True,
 )
 model = CLIPTextModel(config).eval()
-tokenizer = GPT2Tokenizer("./glide-base/vocab.json", "./glide-base/merges.txt", pad_token="<|endoftext|>")
-tokenizer.save_pretrained("./glide-base")
+tokenizer = GPT2Tokenizer("./glide-base/tokenizer/vocab.json", "./glide-base/tokenizer/merges.txt", pad_token="<|endoftext|>")
 
 hf_encoder = model.text_model
 
@@ -30,15 +35,8 @@ hf_encoder.final_layer_norm.bias = state_dict["final_ln.bias"]
 
 for layer_idx in range(config.num_hidden_layers):
     hf_layer = hf_encoder.encoder.layers[layer_idx]
-    q_proj, k_proj, v_proj = state_dict[f"transformer.resblocks.{layer_idx}.attn.c_qkv.weight"].chunk(3, dim=0)
-    q_proj_bias, k_proj_bias, v_proj_bias = state_dict[f"transformer.resblocks.{layer_idx}.attn.c_qkv.bias"].chunk(3, dim=0)
-
-    hf_layer.self_attn.q_proj.weight.data = q_proj
-    hf_layer.self_attn.q_proj.bias.data = q_proj_bias
-    hf_layer.self_attn.k_proj.weight.data = k_proj
-    hf_layer.self_attn.k_proj.bias.data = k_proj_bias
-    hf_layer.self_attn.v_proj.weight.data = v_proj
-    hf_layer.self_attn.v_proj.bias.data = v_proj_bias
+    hf_layer.self_attn.qkv_proj.weight = state_dict[f"transformer.resblocks.{layer_idx}.attn.c_qkv.weight"]
+    hf_layer.self_attn.qkv_proj.bias = state_dict[f"transformer.resblocks.{layer_idx}.attn.c_qkv.bias"]
 
     hf_layer.self_attn.out_proj.weight = state_dict[f"transformer.resblocks.{layer_idx}.attn.c_proj.weight"]
     hf_layer.self_attn.out_proj.bias = state_dict[f"transformer.resblocks.{layer_idx}.attn.c_proj.bias"]
@@ -53,8 +51,28 @@ for layer_idx in range(config.num_hidden_layers):
     hf_layer.mlp.fc2.weight = state_dict[f"transformer.resblocks.{layer_idx}.mlp.c_proj.weight"]
     hf_layer.mlp.fc2.bias = state_dict[f"transformer.resblocks.{layer_idx}.mlp.c_proj.bias"]
 
-inputs = tokenizer(["an oil painting of a corgi", ""], padding="max_length", max_length=128, return_tensors="pt")
-with torch.no_grad():
-    outputs = model(**inputs)
+### Convert the UNet
 
-model.save_pretrained("./glide-base")
+unet_model = UNetGLIDEModel(
+    in_channels=3,
+    model_channels=192,
+    out_channels=6,
+    num_res_blocks=3,
+    attention_resolutions=(2, 4, 8),
+    dropout=0.1,
+    channel_mult=(1, 2, 3, 4),
+    num_heads=1,
+    num_head_channels=64,
+    num_heads_upsample=1,
+    use_scale_shift_norm=True,
+    resblock_updown=True,
+    transformer_dim=512,
+)
+
+unet_model.load_state_dict(state_dict, strict=False)
+
+scheduler = ClassifierFreeGuidanceScheduler(timesteps=1000, beta_schedule="squaredcos_cap_v2")
+
+glide = GLIDE(unet=unet_model, noise_scheduler=scheduler, text_encoder=model, tokenizer=tokenizer)
+
+glide.save_pretrained("./glide-base")

models/vision/glide/modeling_glide.py

@@ -14,46 +14,154 @@
 # limitations under the License.
 
 
-from diffusers import DiffusionPipeline
-from diffusers import UNetGLIDEModel
+import numpy as np
+import torch
 
 import tqdm
-import torch
+from diffusers import ClassifierFreeGuidanceScheduler, CLIPTextModel, DiffusionPipeline, UNetGLIDEModel
+from transformers import GPT2Tokenizer
+
+
+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 = torch.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+    while len(res.shape) < len(broadcast_shape):
+        res = res[..., None]
+    return res + torch.zeros(broadcast_shape, device=timesteps.device)
 
 
 class GLIDE(DiffusionPipeline):
-    def __init__(self, unet: UNetGLIDEModel, noise_scheduler):
+    def __init__(
+        self,
+        unet: UNetGLIDEModel,
+        noise_scheduler: ClassifierFreeGuidanceScheduler,
+        text_encoder: CLIPTextModel,
+        tokenizer: GPT2Tokenizer,
+    ):
         super().__init__()
-        self.register_modules(unet=unet, noise_scheduler=noise_scheduler)
+        self.register_modules(
+            unet=unet, noise_scheduler=noise_scheduler, text_encoder=text_encoder, tokenizer=tokenizer
+        )
 
-    def __call__(self, generator=None, torch_device=None):
+    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.noise_scheduler.posterior_mean_coef1, t, x_t.shape) * x_start
+            + _extract_into_tensor(self.noise_scheduler.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = _extract_into_tensor(self.noise_scheduler.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = _extract_into_tensor(
+            self.noise_scheduler.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, transformer_out, clip_denoised=True, 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 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, transformer_out)
+
+        assert model_output.shape == (B, C * 2, *x.shape[2:])
+        model_output, model_var_values = torch.split(model_output, C, dim=1)
+        min_log = _extract_into_tensor(self.noise_scheduler.posterior_log_variance_clipped, t, x.shape)
+        max_log = _extract_into_tensor(np.log(self.noise_scheduler.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 = torch.exp(model_log_variance)
+
+        pred_xstart = self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
+        if clip_denoised:
+            pred_xstart = pred_xstart.clamp(-1, 1)
+        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 model_mean, model_variance, model_log_variance, pred_xstart
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+            _extract_into_tensor(self.noise_scheduler.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - _extract_into_tensor(self.noise_scheduler.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
+        )
+
+    def __call__(self, prompt, generator=None, torch_device=None):
         torch_device = "cuda" if torch.cuda.is_available() else "cpu"
 
         self.unet.to(torch_device)
+        self.text_encoder.to(torch_device)
+
+        # Create a classifier-free guidance sampling function
+        guidance_scale = 3.0
+
+        def model_fn(x_t, ts, transformer_out, **kwargs):
+            half = x_t[: len(x_t) // 2]
+            combined = torch.cat([half, half], dim=0)
+            model_out = self.unet(combined, ts, transformer_out, **kwargs)
+            eps, rest = model_out[:, :3], model_out[:, 3:]
+            cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+            half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
+            eps = torch.cat([half_eps, half_eps], dim=0)
+            return torch.cat([eps, rest], dim=1)
+
         # 1. Sample gaussian noise
-        image = self.noise_scheduler.sample_noise((1, self.unet.in_channels, self.unet.resolution, self.unet.resolution), device=torch_device, generator=generator)
-        for t in tqdm.tqdm(reversed(range(len(self.noise_scheduler))), total=len(self.noise_scheduler)):
-            # i) define coefficients for time step t
-            clip_image_coeff = 1 / torch.sqrt(self.noise_scheduler.get_alpha_prod(t))
-            clip_noise_coeff = torch.sqrt(1 / self.noise_scheduler.get_alpha_prod(t) - 1)
-            image_coeff = (1 - self.noise_scheduler.get_alpha_prod(t - 1)) * torch.sqrt(self.noise_scheduler.get_alpha(t)) / (1 - self.noise_scheduler.get_alpha_prod(t))
-            clip_coeff = torch.sqrt(self.noise_scheduler.get_alpha_prod(t - 1)) * self.noise_scheduler.get_beta(t) / (1 - self.noise_scheduler.get_alpha_prod(t))
+        batch_size = 2  # second image is empty for classifier-free guidance
+        image = self.noise_scheduler.sample_noise(
+            (batch_size, self.unet.in_channels, 64, 64), device=torch_device, generator=generator
+        )
 
-            # ii) predict noise residual
-            with torch.no_grad():
-                noise_residual = self.unet(image, t)
+        # 2. Encode tokens
+        # an empty input is needed to guide the model away from (
+        inputs = self.tokenizer([prompt, ""], padding="max_length", max_length=128, return_tensors="pt")
+        input_ids = inputs["input_ids"].to(torch_device)
+        attention_mask = inputs["attention_mask"].to(torch_device)
+        transformer_out = self.text_encoder(input_ids, attention_mask).last_hidden_state
 
-            # iii) compute predicted image from residual
-            # See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
-            pred_mean = clip_image_coeff * image - clip_noise_coeff * noise_residual
-            pred_mean = torch.clamp(pred_mean, -1, 1)
-            prev_image = clip_coeff * pred_mean + image_coeff * image
-
-            # iv) sample variance
-            prev_variance = self.noise_scheduler.sample_variance(t, prev_image.shape, device=torch_device, generator=generator)
-
-            # v) sample  x_{t-1} ~ N(prev_image, prev_variance)
-            sampled_prev_image = prev_image + prev_variance
-            image = sampled_prev_image
+        num_timesteps = len(self.noise_scheduler)
+        for i in tqdm.tqdm(reversed(range(num_timesteps)), total=num_timesteps):
+            t = torch.tensor([i] * image.shape[0], device=torch_device)
+            mean, variance, log_variance, pred_xstart = self.p_mean_variance(model_fn, image, t, transformer_out)
+            noise = self.noise_scheduler.sample_noise(image.shape, device=torch_device, generator=generator)
+            nonzero_mask = (t != 0).float().view(-1, *([1] * (len(image.shape) - 1)))  # no noise when t == 0
+            image = mean + nonzero_mask * torch.exp(0.5 * log_variance) * noise
 
         return image

models/vision/glide/run_glide.py

@@ -1,17 +1,14 @@
 import torch
-from .modeling_glide import GLIDE
-from diffusers import UNetGLIDEModel, GaussianDDPMScheduler
+
+from modeling_glide import GLIDE
+
 
 generator = torch.Generator()
 generator = generator.manual_seed(0)
 
 # 1. Load models
+pipeline = GLIDE.from_pretrained("fusing/glide-base")
 
-scheduler = GaussianDDPMScheduler.from_config("fusing/glide-base")
-model = UNetGLIDEModel.from_pretrained("fusing/glide-base")
-
-pipeline = GLIDE(model, scheduler)
-
-img = pipeline(generator)
+img = pipeline("an oil painting of a corgi", generator)
 
 print(img)

src/diffusers/__init__.py

@@ -5,7 +5,10 @@
 __version__ = "0.0.1"
 
 from .modeling_utils import ModelMixin
+from .models.clip_text_transformer import CLIPTextModel
 from .models.unet import UNetModel
 from .models.unet_glide import UNetGLIDEModel
+from .models.unet_ldm import UNetLDMModel
 from .pipeline_utils import DiffusionPipeline
+from .schedulers.classifier_free_guidance import ClassifierFreeGuidanceScheduler
 from .schedulers.gaussian_ddpm import GaussianDDPMScheduler

src/diffusers/configuration_utils.py

@@ -89,7 +89,6 @@ class ConfigMixin:
 
         self.to_json_file(output_config_file)
         logger.info(f"ConfigMixinuration saved in {output_config_file}")
-    
 
     @classmethod
     def get_config_dict(
@@ -183,7 +182,7 @@ class ConfigMixin:
             logger.info(f"loading configuration file {config_file}")
         else:
             logger.info(f"loading configuration file {config_file} from cache at {resolved_config_file}")
-        
+
         return config_dict
 
     @classmethod
@@ -199,9 +198,8 @@ class ConfigMixin:
                 # use value from config dict
                 init_dict[key] = config_dict.pop(key)
 
-
         unused_kwargs = config_dict.update(kwargs)
-        
+
         passed_keys = set(init_dict.keys())
         if len(expected_keys - passed_keys) > 0:
             logger.warn(
@@ -212,9 +210,7 @@ class ConfigMixin:
 
     @classmethod
     def from_config(cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, **kwargs):
-        config_dict = cls.get_config_dict(
-            pretrained_model_name_or_path=pretrained_model_name_or_path, **kwargs
-        )
+        config_dict = cls.get_config_dict(pretrained_model_name_or_path=pretrained_model_name_or_path, **kwargs)
 
         init_dict, unused_kwargs = cls.extract_init_dict(config_dict, **kwargs)
 

src/diffusers/models/__init__.py

@@ -16,5 +16,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from .clip_text_transformer import CLIPTextModel
 from .unet import UNetModel
 from .unet_glide import UNetGLIDEModel
+from .unet_ldm import UNetLDMModel

src/diffusers/models/clip_text_transformer.py

@@ -0,0 +1,685 @@
+# coding=utf-8
+# Copyright 2021 The OpenAI Team Authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch CLIP model."""
+
+import math
+from dataclasses import dataclass
+from typing import Any, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from transformers import CLIPConfig, CLIPModel, CLIPTextConfig, CLIPVisionConfig
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    ModelOutput,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "openai/clip-vit-base-patch32"
+
+CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "openai/clip-vit-base-patch32",
+    # See all CLIP models at https://huggingface.co/models?filter=clip
+]
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+# contrastive loss function, adapted from
+# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
+def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
+    return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))
+
+
+def clip_loss(similarity: torch.Tensor) -> torch.Tensor:
+    caption_loss = contrastive_loss(similarity)
+    image_loss = contrastive_loss(similarity.T)
+    return (caption_loss + image_loss) / 2.0
+
+
+@dataclass
+class CLIPOutput(ModelOutput):
+    """
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
+            Contrastive loss for image-text similarity.
+        logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
+            The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
+            similarity scores.
+        logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
+            The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
+            similarity scores.
+        text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
+            The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPTextModel`].
+        image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
+            The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPVisionModel`].
+        text_model_output(`BaseModelOutputWithPooling`):
+            The output of the [`CLIPTextModel`].
+        vision_model_output(`BaseModelOutputWithPooling`):
+            The output of the [`CLIPVisionModel`].
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits_per_image: torch.FloatTensor = None
+    logits_per_text: torch.FloatTensor = None
+    text_embeds: torch.FloatTensor = None
+    image_embeds: torch.FloatTensor = None
+    text_model_output: BaseModelOutputWithPooling = None
+    vision_model_output: BaseModelOutputWithPooling = None
+
+    def to_tuple(self) -> Tuple[Any]:
+        return tuple(
+            self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
+            for k in self.keys()
+        )
+
+
+class CLIPVisionEmbeddings(nn.Module):
+    def __init__(self, config: CLIPVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+        self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)))
+
+    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+        batch_size = pixel_values.shape[0]
+        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+        embeddings = embeddings + self.position_embedding(self.position_ids)
+        return embeddings
+
+
+class CLIPTextEmbeddings(nn.Module):
+    def __init__(self, config: CLIPTextConfig):
+        super().__init__()
+        embed_dim = config.hidden_size
+
+        self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
+        self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
+        self.use_padding_embeddings = config.use_padding_embeddings
+        if self.use_padding_embeddings:
+            self.padding_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, :seq_length]
+
+        if inputs_embeds is None:
+            inputs_embeds = self.token_embedding(input_ids)
+
+        position_embeddings = self.position_embedding(position_ids)
+        embeddings = inputs_embeds + position_embeddings
+
+        if self.use_padding_embeddings and attention_mask is not None:
+            padding_embeddings = self.padding_embedding(position_ids)
+            embeddings = torch.where(attention_mask.bool().unsqueeze(-1), embeddings, padding_embeddings)
+
+        return embeddings
+
+
+class CLIPAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+                f" {self.num_heads})."
+            )
+        self.scale = 1 / math.sqrt(math.sqrt(self.head_dim))
+
+        self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3)
+        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        causal_attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """Input shape: Batch x Time x Channel"""
+
+        bsz, tgt_len, embed_dim = hidden_states.size()
+
+        qkv_states = self.qkv_proj(hidden_states)
+        qkv_states = qkv_states.view(bsz, tgt_len, self.num_heads, -1)
+        query_states, key_states, value_states = torch.split(qkv_states, self.head_dim, dim=-1)
+
+        attn_weights = torch.einsum("bthc,bshc->bhts", query_states * self.scale, key_states * self.scale)
+
+        wdtype = attn_weights.dtype
+        attn_weights = nn.functional.softmax(attn_weights.float(), dim=-1).type(wdtype)
+
+        attn_output = torch.einsum("bhts,bshc->bthc", attn_weights, value_states)
+        attn_output = attn_output.reshape(bsz, tgt_len, -1)
+
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+class CLIPMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class CLIPEncoderLayer(nn.Module):
+    def __init__(self, config: CLIPConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.self_attn = CLIPAttention(config)
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim)
+        self.mlp = CLIPMLP(config)
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        causal_attention_mask: torch.Tensor,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+                `(config.encoder_attention_heads,)`.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+        """
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states, attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            causal_attention_mask=causal_attention_mask,
+            output_attentions=output_attentions,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs
+
+
+class CLIPPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = CLIPConfig
+    base_model_prefix = "clip"
+    supports_gradient_checkpointing = True
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        factor = self.config.initializer_factor
+        if isinstance(module, CLIPTextEmbeddings):
+            module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
+            module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
+            if hasattr(module, "padding_embedding"):
+                module.padding_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
+        elif isinstance(module, CLIPVisionEmbeddings):
+            factor = self.config.initializer_factor
+            nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
+            nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
+            nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
+        elif isinstance(module, CLIPAttention):
+            factor = self.config.initializer_factor
+            in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
+            out_proj_std = (module.embed_dim**-0.5) * factor
+            nn.init.normal_(module.qkv_proj.weight, std=in_proj_std)
+            nn.init.normal_(module.out_proj.weight, std=out_proj_std)
+        elif isinstance(module, CLIPMLP):
+            factor = self.config.initializer_factor
+            in_proj_std = (
+                (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
+            )
+            fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
+            nn.init.normal_(module.fc1.weight, std=fc_std)
+            nn.init.normal_(module.fc2.weight, std=in_proj_std)
+        elif isinstance(module, CLIPModel):
+            nn.init.normal_(
+                module.text_projection.weight,
+                std=module.text_embed_dim**-0.5 * self.config.initializer_factor,
+            )
+            nn.init.normal_(
+                module.visual_projection.weight,
+                std=module.vision_embed_dim**-0.5 * self.config.initializer_factor,
+            )
+
+        if isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, CLIPEncoder):
+            module.gradient_checkpointing = value
+
+
+CLIP_START_DOCSTRING = r"""
+    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
+    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
+    behavior.
+
+    Parameters:
+        config ([`CLIPConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+CLIP_TEXT_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+CLIP_VISION_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
+            [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+CLIP_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
+            [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details.
+        return_loss (`bool`, *optional*):
+            Whether or not to return the contrastive loss.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+class CLIPEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`CLIPEncoderLayer`].
+
+    Args:
+        config: CLIPConfig
+    """
+
+    def __init__(self, config: CLIPConfig):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList([CLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        inputs_embeds,
+        attention_mask: Optional[torch.Tensor] = None,
+        causal_attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+                than the model's internal embedding lookup matrix.
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Causal mask for the text model. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        encoder_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+
+        hidden_states = inputs_embeds
+        for idx, encoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(encoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    causal_attention_mask,
+                )
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                    attention_mask,
+                    causal_attention_mask,
+                    output_attentions=output_attentions,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_attentions = all_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+        )
+
+
+class CLIPTextTransformer(nn.Module):
+    def __init__(self, config: CLIPTextConfig):
+        super().__init__()
+        self.config = config
+        embed_dim = config.hidden_size
+        self.embeddings = CLIPTextEmbeddings(config)
+        self.encoder = CLIPEncoder(config)
+        self.final_layer_norm = nn.LayerNorm(embed_dim)
+
+    @add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig)
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        r"""
+        Returns:
+
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is None:
+            raise ValueError("You have to specify either input_ids")
+
+        input_shape = input_ids.size()
+        input_ids = input_ids.view(-1, input_shape[-1])
+
+        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, attention_mask=attention_mask)
+
+        bsz, seq_len = input_shape
+        # CLIP's text model uses causal mask, prepare it here.
+        # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
+        causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len).to(hidden_states.device)
+
+        # expand attention_mask
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            attention_mask = _expand_mask(attention_mask, hidden_states.dtype)
+
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            attention_mask=None,
+            causal_attention_mask=None,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        last_hidden_state = encoder_outputs[0]
+        last_hidden_state = self.final_layer_norm(last_hidden_state)
+
+        # text_embeds.shape = [batch_size, sequence_length, transformer.width]
+        # take features from the eot embedding (eot_token is the highest number in each sequence)
+        pooled_output = last_hidden_state[torch.arange(last_hidden_state.shape[0]), input_ids.argmax(dim=-1)]
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+    def _build_causal_attention_mask(self, bsz, seq_len):
+        # lazily create causal attention mask, with full attention between the vision tokens
+        # pytorch uses additive attention mask; fill with -inf
+        mask = torch.empty(bsz, seq_len, seq_len)
+        mask.fill_(torch.tensor(float("-inf")))
+        mask.triu_(1)  # zero out the lower diagonal
+        mask = mask.unsqueeze(1)  # expand mask
+        return mask
+
+
+class CLIPTextModel(CLIPPreTrainedModel):
+    config_class = CLIPTextConfig
+
+    def __init__(self, config: CLIPTextConfig):
+        super().__init__(config)
+        self.text_model = CLIPTextTransformer(config)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.text_model.embeddings.token_embedding
+
+    def set_input_embeddings(self, value):
+        self.text_model.embeddings.token_embedding = value
+
+    @add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=CLIPTextConfig)
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        r"""
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from transformers import CLIPTokenizer, CLIPTextModel
+
+        >>> model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
+        >>> tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+
+        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
+
+        >>> outputs = model(**inputs)
+        >>> last_hidden_state = outputs.last_hidden_state
+        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
+        ```"""
+        return self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )

src/diffusers/models/unet_glide.py

@@ -435,7 +435,7 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
         num_heads_upsample=-1,
         use_scale_shift_norm=False,
         resblock_updown=False,
-        encoder_channels=None,
+        transformer_dim=512,
     ):
         super().__init__()
         self.register(
@@ -455,7 +455,7 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
             num_heads_upsample=num_heads_upsample,
             use_scale_shift_norm=use_scale_shift_norm,
             resblock_updown=resblock_updown,
-            encoder_channels=encoder_channels,
+            transformer_dim=transformer_dim,
         )
 
         if num_heads_upsample == -1:
@@ -470,7 +470,7 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
         self.channel_mult = channel_mult
         self.conv_resample = conv_resample
         self.use_checkpoint = use_checkpoint
-        self.dtype = torch.float16 if use_fp16 else torch.float32
+        # self.dtype = torch.float16 if use_fp16 else torch.float32
         self.num_heads = num_heads
         self.num_head_channels = num_head_channels
         self.num_heads_upsample = num_heads_upsample
@@ -482,6 +482,8 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
             linear(time_embed_dim, time_embed_dim),
         )
 
+        self.transformer_proj = nn.Linear(transformer_dim, self.model_channels * 4)
+
         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
@@ -508,7 +510,7 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
                             use_checkpoint=use_checkpoint,
                             num_heads=num_heads,
                             num_head_channels=num_head_channels,
-                            encoder_channels=encoder_channels,
+                            encoder_channels=transformer_dim,
                         )
                     )
                 self.input_blocks.append(TimestepEmbedSequential(*layers))
@@ -551,7 +553,7 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
                 use_checkpoint=use_checkpoint,
                 num_heads=num_heads,
                 num_head_channels=num_head_channels,
-                encoder_channels=encoder_channels,
+                encoder_channels=transformer_dim,
             ),
             ResBlock(
                 ch,
@@ -587,7 +589,7 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
                             use_checkpoint=use_checkpoint,
                             num_heads=num_heads_upsample,
                             num_head_channels=num_head_channels,
-                            encoder_channels=encoder_channels,
+                            encoder_channels=transformer_dim,
                         )
                     )
                 if level and i == num_res_blocks:
@@ -642,10 +644,6 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
         :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))
 
@@ -653,13 +651,15 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
         transformer_proj = self.transformer_proj(transformer_out[:, -1])
         transformer_out = transformer_out.permute(0, 2, 1)  # NLC -> NCL
 
-        h = x.type(self.dtype)
+        emb = emb + transformer_proj.to(emb)
+
+        h = x
         for module in self.input_blocks:
-            h = module(h, emb)
+            h = module(h, emb, transformer_out)
             hs.append(h)
-        h = self.middle_block(h, emb)
+        h = self.middle_block(h, emb, transformer_out)
         for module in self.output_blocks:
-            h = torch.cat([h, hs.pop()], dim=1)
-            h = module(h, emb)
-        h = h.type(x.dtype)
+            other = hs.pop()
+            h = torch.cat([h, other], dim=1)
+            h = module(h, emb, transformer_out)
         return self.out(h)

src/diffusers/models/unet_ldm.py

@@ -830,7 +830,7 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
         self.conv_resample = conv_resample
         self.num_classes = num_classes
         self.use_checkpoint = use_checkpoint
-        self.dtype = torch.float16 if use_fp16 else torch.float32
+        self.dtype_ = torch.float16 if use_fp16 else torch.float32
         self.num_heads = num_heads
         self.num_head_channels = num_head_channels
         self.num_heads_upsample = num_heads_upsample
@@ -1060,7 +1060,7 @@ class UNetLDMModel(ModelMixin, ConfigMixin):
             assert y.shape == (x.shape[0],)
             emb = emb + self.label_emb(y)
 
-        h = x.type(self.dtype)
+        h = x.type(self.dtype_)
         for module in self.input_blocks:
             h = module(h, emb, context)
             hs.append(h)

src/diffusers/pipeline_utils.py

@@ -17,6 +17,7 @@
 import importlib
 import os
 from typing import Optional, Union
+
 from huggingface_hub import snapshot_download
 
 # CHANGE to diffusers.utils
@@ -35,10 +36,12 @@ logger = logging.get_logger(__name__)
 LOADABLE_CLASSES = {
     "diffusers": {
         "ModelMixin": ["save_pretrained", "from_pretrained"],
+        "CLIPTextModel": ["save_pretrained", "from_pretrained"],  # TODO (Anton): move to transformers
         "GaussianDDPMScheduler": ["save_config", "from_config"],
+        "ClassifierFreeGuidanceScheduler": ["save_config", "from_config"],
     },
     "transformers": {
-        "ModelMixin": ["save_pretrained", "from_pretrained"],
+        "GPT2Tokenizer": ["save_pretrained", "from_pretrained"],
     },
 }
 
@@ -62,7 +65,7 @@ class DiffusionPipeline(ConfigMixin):
             # set models
             setattr(self, name, module)
 
-        register_dict = {"_module" : self.__module__.split(".")[-1] + ".py"}
+        register_dict = {"_module": self.__module__.split(".")[-1] + ".py"}
         self.register(**register_dict)
 
     def save_pretrained(self, save_directory: Union[str, os.PathLike]):

src/diffusers/schedulers/__init__.py

@@ -16,4 +16,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from .classifier_free_guidance import ClassifierFreeGuidanceScheduler
 from .gaussian_ddpm import GaussianDDPMScheduler

src/diffusers/schedulers/classifier_free_guidance.py

@@ -0,0 +1,97 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+
+import numpy as np
+import torch
+from torch import nn
+
+from ..configuration_utils import ConfigMixin
+
+
+SAMPLING_CONFIG_NAME = "scheduler_config.json"
+
+
+def linear_beta_schedule(timesteps, beta_start, beta_end):
+    return torch.linspace(beta_start, beta_end, timesteps, dtype=torch.float64)
+
+
+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, dtype=np.float64)
+
+
+class ClassifierFreeGuidanceScheduler(nn.Module, ConfigMixin):
+
+    config_name = SAMPLING_CONFIG_NAME
+
+    def __init__(
+        self,
+        timesteps=1000,
+        beta_schedule="squaredcos_cap_v2",
+    ):
+        super().__init__()
+        self.register(
+            timesteps=timesteps,
+            beta_schedule=beta_schedule,
+        )
+        self.num_timesteps = int(timesteps)
+
+        if beta_schedule == "squaredcos_cap_v2":
+            # GLIDE cosine schedule
+            self.betas = betas_for_alpha_bar(
+                timesteps,
+                lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+            )
+        else:
+            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+
+        alphas = 1.0 - self.betas
+        self.alphas_cumprod = np.cumprod(alphas, axis=0)
+        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        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 = self.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 = self.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 sample_noise(self, shape, device, generator=None):
+        # always sample on CPU to be deterministic
+        return torch.randn(shape, generator=generator).to(device)
+
+    def __len__(self):
+        return self.num_timesteps