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

models/vision/glide/convert_weights.py

 import torch
 from torch import nn
 
-from diffusers import ClassifierFreeGuidanceScheduler, CLIPTextModel, UNetGLIDEModel
+from diffusers import ClassifierFreeGuidanceScheduler, GlideDDIMScheduler, CLIPTextModel, GLIDETextToImageUNetModel, GLIDESuperResUNetModel
 from modeling_glide import GLIDE
 from transformers import CLIPTextConfig, GPT2Tokenizer
 
@@ -51,9 +51,9 @@ 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"]
 
-### Convert the UNet
+### Convert the Text-to-Image UNet
 
-unet_model = UNetGLIDEModel(
+text2im_model = GLIDETextToImageUNetModel(
     in_channels=3,
     model_channels=192,
     out_channels=6,
@@ -69,10 +69,38 @@ unet_model = UNetGLIDEModel(
     transformer_dim=512,
 )
 
-unet_model.load_state_dict(state_dict, strict=False)
+text2im_model.load_state_dict(state_dict, strict=False)
 
-scheduler = ClassifierFreeGuidanceScheduler(timesteps=1000, beta_schedule="squaredcos_cap_v2")
+text_scheduler = ClassifierFreeGuidanceScheduler(timesteps=1000, beta_schedule="squaredcos_cap_v2")
 
-glide = GLIDE(unet=unet_model, noise_scheduler=scheduler, text_encoder=model, tokenizer=tokenizer)
+### Convert the Super-Resolution UNet
+
+# wget https://openaipublic.blob.core.windows.net/diffusion/dec-2021/upsample.pt
+ups_state_dict = torch.load("upsample.pt", map_location="cpu")
+
+superres_model = GLIDESuperResUNetModel(
+    in_channels=6,
+    model_channels=192,
+    out_channels=6,
+    num_res_blocks=2,
+    attention_resolutions=(8, 16, 32),
+    dropout=0.1,
+    channel_mult=(1, 1, 2, 2, 4, 4),
+    num_heads=1,
+    num_head_channels=64,
+    num_heads_upsample=1,
+    use_scale_shift_norm=True,
+    resblock_updown=True,
+)
+
+superres_model.load_state_dict(ups_state_dict, strict=False)
+
+upscale_scheduler = GlideDDIMScheduler(timesteps=1000, beta_schedule="linear")
+
+glide = GLIDE(text_unet=text2im_model, text_noise_scheduler=text_scheduler, text_encoder=model, tokenizer=tokenizer,
+              upscale_unet=superres_model, upscale_noise_scheduler=upscale_scheduler)
 
 glide.save_pretrained("./glide-base")
+
+
+

models/vision/glide/modeling_glide.py

@@ -18,7 +18,7 @@ import numpy as np
 import torch
 
 import tqdm
-from diffusers import ClassifierFreeGuidanceScheduler, CLIPTextModel, DiffusionPipeline, UNetGLIDEModel
+from diffusers import ClassifierFreeGuidanceScheduler, GlideDDIMScheduler, CLIPTextModel, DiffusionPipeline, GLIDETextToImageUNetModel, GLIDESuperResUNetModel
 from transformers import GPT2Tokenizer
 
 
@@ -41,17 +41,20 @@ def _extract_into_tensor(arr, timesteps, broadcast_shape):
 class GLIDE(DiffusionPipeline):
     def __init__(
         self,
-        unet: UNetGLIDEModel,
-        noise_scheduler: ClassifierFreeGuidanceScheduler,
+        text_unet: GLIDETextToImageUNetModel,
+        text_noise_scheduler: ClassifierFreeGuidanceScheduler,
         text_encoder: CLIPTextModel,
         tokenizer: GPT2Tokenizer,
+        upscale_unet: GLIDESuperResUNetModel,
+        upscale_noise_scheduler: GlideDDIMScheduler
     ):
         super().__init__()
         self.register_modules(
-            unet=unet, noise_scheduler=noise_scheduler, text_encoder=text_encoder, tokenizer=tokenizer
+            text_unet=text_unet, text_noise_scheduler=text_noise_scheduler, text_encoder=text_encoder, tokenizer=tokenizer,
+            upscale_unet=upscale_unet, upscale_noise_scheduler=upscale_noise_scheduler
         )
 
-    def q_posterior_mean_variance(self, x_start, x_t, t):
+    def q_posterior_mean_variance(self, scheduler, x_start, x_t, t):
         """
         Compute the mean and variance of the diffusion posterior:
 
@@ -60,12 +63,12 @@ class GLIDE(DiffusionPipeline):
         """
         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
+            _extract_into_tensor(scheduler.posterior_mean_coef1, t, x_t.shape) * x_start
+            + _extract_into_tensor(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_variance = _extract_into_tensor(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
+            scheduler.posterior_log_variance_clipped, t, x_t.shape
         )
         assert (
             posterior_mean.shape[0]
@@ -75,7 +78,7 @@ class GLIDE(DiffusionPipeline):
         )
         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):
+    def p_mean_variance(self, model, scheduler, x, t, transformer_out=None, low_res=None, clip_denoised=True):
         """
         Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
         the initial x, x_0.
@@ -93,51 +96,60 @@ class GLIDE(DiffusionPipeline):
                  - '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,)
+        if transformer_out is None:
+            # super-res model
+            model_output = model(x, t, low_res)
+        else:
+            # text2image model
             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)
+        min_log = _extract_into_tensor(scheduler.posterior_log_variance_clipped, t, x.shape)
+        max_log = _extract_into_tensor(np.log(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)
+        pred_xstart = self._predict_xstart_from_eps(scheduler, 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)
+        model_mean, _, _ = self.q_posterior_mean_variance(scheduler, 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):
+    def _predict_xstart_from_eps(self, scheduler, 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
+            _extract_into_tensor(scheduler.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - _extract_into_tensor(scheduler.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
         )
 
+    def _predict_eps_from_xstart(self, scheduler, x_t, t, pred_xstart):
+        return (
+            _extract_into_tensor(scheduler.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart
+        ) / _extract_into_tensor(scheduler.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
     @torch.no_grad()
     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_unet.to(torch_device)
         self.text_encoder.to(torch_device)
+        self.upscale_unet.to(torch_device)
 
         # Create a classifier-free guidance sampling function
         guidance_scale = 3.0
 
-        def model_fn(x_t, ts, transformer_out, **kwargs):
+        def text_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)
+            model_out = self.text_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)
@@ -146,8 +158,8 @@ class GLIDE(DiffusionPipeline):
 
         # 1. Sample gaussian noise
         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
+        image = self.text_noise_scheduler.sample_noise(
+            (batch_size, self.text_unet.in_channels, 64, 64), device=torch_device, generator=generator
         )
 
         # 2. Encode tokens
@@ -157,14 +169,60 @@ class GLIDE(DiffusionPipeline):
         attention_mask = inputs["attention_mask"].to(torch_device)
         transformer_out = self.text_encoder(input_ids, attention_mask).last_hidden_state
 
-        num_timesteps = len(self.noise_scheduler)
+        # 3. Run the text2image generation step
+        num_timesteps = len(self.text_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)
+            mean, variance, log_variance, pred_xstart = self.p_mean_variance(
+                text_model_fn, self.text_noise_scheduler, image, t, transformer_out=transformer_out
+            )
+            noise = self.text_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
 
+        # 4. Run the upscaling step
+        batch_size = 1
+        image = image[:1]
+        low_res = ((image + 1) * 127.5).round() / 127.5 - 1
+        eta = 0.0
+
+        # Tune this parameter to control the sharpness of 256x256 images.
+        # A value of 1.0 is sharper, but sometimes results in grainy artifacts.
+        upsample_temp = 0.997
+
+        image = self.upscale_noise_scheduler.sample_noise(
+            (batch_size, 3, 256, 256), device=torch_device, generator=generator
+        ) * upsample_temp
+
+        num_timesteps = len(self.upscale_noise_scheduler)
+        for t in tqdm.tqdm(reversed(range(len(self.upscale_noise_scheduler))), total=len(self.upscale_noise_scheduler)):
+            # i) define coefficients for time step t
+            clipped_image_coeff = 1 / torch.sqrt(self.upscale_noise_scheduler.get_alpha_prod(t))
+            clipped_noise_coeff = torch.sqrt(1 / self.upscale_noise_scheduler.get_alpha_prod(t) - 1)
+            image_coeff = (1 - self.upscale_noise_scheduler.get_alpha_prod(t - 1)) * torch.sqrt(
+                self.upscale_noise_scheduler.get_alpha(t)) / (1 - self.upscale_noise_scheduler.get_alpha_prod(t))
+            clipped_coeff = torch.sqrt(self.upscale_noise_scheduler.get_alpha_prod(t - 1)) * self.upscale_noise_scheduler.get_beta(
+                t) / (1 - self.upscale_noise_scheduler.get_alpha_prod(t))
+
+            # ii) predict noise residual
+            time_input = torch.tensor([t] * image.shape[0], device=torch_device)
+            model_output = self.upscale_unet(image, time_input, low_res)
+            noise_residual, pred_variance = torch.split(model_output, 3, dim=1)
+
+            # iii) compute predicted image from residual
+            # See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
+            pred_mean = clipped_image_coeff * image - clipped_noise_coeff * noise_residual
+            pred_mean = torch.clamp(pred_mean, -1, 1)
+            prev_image = clipped_coeff * pred_mean + image_coeff * image
+
+            # iv) sample variance
+            prev_variance = self.upscale_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
+
         image = image[0].permute(1, 2, 0)
 
         return image

models/vision/glide/run_glide.py

 import torch
-
-from modeling_glide import GLIDE
-import matplotlib
-import matplotlib.pyplot as plt
-matplotlib.rcParams['interactive'] = True
-
+from diffusers import DiffusionPipeline
+import PIL.Image
 
 generator = torch.Generator()
 generator = generator.manual_seed(0)
 
-# 1. Load models
-pipeline = GLIDE.from_pretrained("fusing/glide-base")
+model_id = "fusing/glide-base"
+
+# load model and scheduler
+pipeline = DiffusionPipeline.from_pretrained(model_id)
+
+# run inference (text-conditioned denoising + upscaling)
+img = pipeline("a clip art of a hugging face", generator)
 
-img = pipeline("an oil painting of a corgi", generator)
+# process image to PIL
 img = ((img + 1)*127.5).round().clamp(0, 255).to(torch.uint8).cpu().numpy()
+image_pil = PIL.Image.fromarray(img)
 
-plt.figure(figsize=(8, 8))
-plt.imshow(img)
-plt.show()
+# save image
+image_pil.save("test.png")
\ No newline at end of file

src/diffusers/__init__.py

@@ -7,9 +7,10 @@ __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_glide import GLIDETextToImageUNetModel, GLIDESuperResUNetModel
 from .models.unet_ldm import UNetLDMModel
 from .models.vqvae import VQModel
 from .pipeline_utils import DiffusionPipeline
 from .schedulers.classifier_free_guidance import ClassifierFreeGuidanceScheduler
 from .schedulers.gaussian_ddpm import GaussianDDPMScheduler
+from .schedulers.glide_ddim import GlideDDIMScheduler

src/diffusers/models/__init__.py

@@ -18,6 +18,6 @@
 
 from .clip_text_transformer import CLIPTextModel
 from .unet import UNetModel
-from .unet_glide import UNetGLIDEModel
+from .unet_glide import GLIDETextToImageUNetModel, GLIDESuperResUNetModel
 from .unet_ldm import UNetLDMModel
 from .vqvae import VQModel
\ No newline at end of file

src/diffusers/models/unet_glide.py

@@ -388,7 +388,7 @@ class QKVAttention(nn.Module):
         return a.reshape(bs, -1, length)
 
 
-class UNetGLIDEModel(ModelMixin, ConfigMixin):
+class GLIDEUNetModel(ModelMixin, ConfigMixin):
     """
     The full UNet model with attention and timestep embedding.
 
@@ -419,11 +419,11 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
 
     def __init__(
         self,
-        in_channels,
-        model_channels,
-        out_channels,
-        num_res_blocks,
-        attention_resolutions,
+        in_channels=3,
+        model_channels=192,
+        out_channels=6,
+        num_res_blocks=3,
+        attention_resolutions=(2, 4, 8),
         dropout=0,
         channel_mult=(1, 2, 4, 8),
         conv_resample=True,
@@ -435,28 +435,9 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
         num_heads_upsample=-1,
         use_scale_shift_norm=False,
         resblock_updown=False,
-        transformer_dim=512,
+        transformer_dim=None,
     ):
         super().__init__()
-        self.register(
-            in_channels=in_channels,
-            model_channels=model_channels,
-            out_channels=out_channels,
-            num_res_blocks=num_res_blocks,
-            attention_resolutions=attention_resolutions,
-            dropout=dropout,
-            channel_mult=channel_mult,
-            conv_resample=conv_resample,
-            dims=dims,
-            use_checkpoint=use_checkpoint,
-            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,
-            transformer_dim=transformer_dim,
-        )
 
         if num_heads_upsample == -1:
             num_heads_upsample = num_heads
@@ -482,8 +463,6 @@ 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
@@ -635,7 +614,7 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
         self.middle_block.apply(convert_module_to_f32)
         self.output_blocks.apply(convert_module_to_f32)
 
-    def forward(self, x, timesteps, transformer_out):
+    def forward(self, x, timesteps):
         """
         Apply the model to an input batch.
 
@@ -644,6 +623,91 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
         :param y: an [N] Tensor of labels, if class-conditional.
         :return: an [N x C x ...] Tensor of outputs.
         """
+
+        hs = []
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        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 = torch.cat([h, hs.pop()], dim=1)
+            h = module(h, emb)
+        h = h.type(x.dtype)
+        return self.out(h)
+
+
+class GLIDETextToImageUNetModel(GLIDEUNetModel):
+    """
+    A UNetModel that performs super-resolution.
+
+    Expects an extra kwarg `low_res` to condition on a low-resolution image.
+    """
+
+    def __init__(
+             self,
+             in_channels=3,
+             model_channels=192,
+             out_channels=6,
+             num_res_blocks=3,
+             attention_resolutions=(2, 4, 8),
+             dropout=0,
+             channel_mult=(1, 2, 4, 8),
+             conv_resample=True,
+             dims=2,
+             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,
+             transformer_dim=512
+    ):
+        super().__init__(
+            in_channels=in_channels,
+            model_channels=model_channels,
+            out_channels=out_channels,
+            num_res_blocks=num_res_blocks,
+            attention_resolutions=attention_resolutions,
+            dropout=dropout,
+            channel_mult=channel_mult,
+            conv_resample=conv_resample,
+            dims=dims,
+            use_checkpoint=use_checkpoint,
+            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,
+            transformer_dim=transformer_dim
+        )
+        self.register(
+            in_channels=in_channels,
+            model_channels=model_channels,
+            out_channels=out_channels,
+            num_res_blocks=num_res_blocks,
+            attention_resolutions=attention_resolutions,
+            dropout=dropout,
+            channel_mult=channel_mult,
+            conv_resample=conv_resample,
+            dims=dims,
+            use_checkpoint=use_checkpoint,
+            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,
+            transformer_dim=transformer_dim
+        )
+
+        self.transformer_proj = nn.Linear(transformer_dim, self.model_channels * 4)
+
+    def forward(self, x, timesteps, transformer_out=None):
         hs = []
         emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
 
@@ -663,3 +727,86 @@ class UNetGLIDEModel(ModelMixin, ConfigMixin):
             h = torch.cat([h, other], dim=1)
             h = module(h, emb, transformer_out)
         return self.out(h)
+
+
+class GLIDESuperResUNetModel(GLIDEUNetModel):
+    """
+    A UNetModel that performs super-resolution.
+
+    Expects an extra kwarg `low_res` to condition on a low-resolution image.
+    """
+
+    def __init__(
+            self,
+            in_channels=3,
+            model_channels=192,
+            out_channels=6,
+            num_res_blocks=3,
+            attention_resolutions=(2, 4, 8),
+            dropout=0,
+            channel_mult=(1, 2, 4, 8),
+            conv_resample=True,
+            dims=2,
+            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,
+    ):
+        super().__init__(
+            in_channels=in_channels,
+            model_channels=model_channels,
+            out_channels=out_channels,
+            num_res_blocks=num_res_blocks,
+            attention_resolutions=attention_resolutions,
+            dropout=dropout,
+            channel_mult=channel_mult,
+            conv_resample=conv_resample,
+            dims=dims,
+            use_checkpoint=use_checkpoint,
+            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,
+        )
+        self.register(
+            in_channels=in_channels,
+            model_channels=model_channels,
+            out_channels=out_channels,
+            num_res_blocks=num_res_blocks,
+            attention_resolutions=attention_resolutions,
+            dropout=dropout,
+            channel_mult=channel_mult,
+            conv_resample=conv_resample,
+            dims=dims,
+            use_checkpoint=use_checkpoint,
+            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,
+        )
+
+    def forward(self, x, timesteps, low_res=None):
+        _, _, new_height, new_width = x.shape
+        upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
+        x = torch.cat([x, upsampled], dim=1)
+
+        hs = []
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+        h = x
+        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 = torch.cat([h, hs.pop()], dim=1)
+            h = module(h, emb)
+
+        return self.out(h)
\ No newline at end of file

src/diffusers/pipeline_utils.py

@@ -39,9 +39,10 @@ LOADABLE_CLASSES = {
         "CLIPTextModel": ["save_pretrained", "from_pretrained"],  # TODO (Anton): move to transformers
         "GaussianDDPMScheduler": ["save_config", "from_config"],
         "ClassifierFreeGuidanceScheduler": ["save_config", "from_config"],
+        "GlideDDIMScheduler": ["save_config", "from_config"],
     },
     "transformers": {
-        "GPT2Tokenizer": ["save_pretrained", "from_pretrained"],
+        "PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
     },
 }
 

src/diffusers/schedulers/__init__.py

@@ -18,3 +18,4 @@
 
 from .classifier_free_guidance import ClassifierFreeGuidanceScheduler
 from .gaussian_ddpm import GaussianDDPMScheduler
+from .glide_ddim import GlideDDIMScheduler

src/diffusers/schedulers/ddim.py → src/diffusers/schedulers/glide_ddim.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import torch
-import math
+import numpy as np
 from torch import nn
 
 from ..configuration_utils import ConfigMixin
@@ -22,36 +22,30 @@ from .schedulers_utils import linear_beta_schedule, betas_for_alpha_bar
 SAMPLING_CONFIG_NAME = "scheduler_config.json"
 
 
-class GaussianDDPMScheduler(nn.Module, ConfigMixin):
+class GlideDDIMScheduler(nn.Module, ConfigMixin):
 
     config_name = SAMPLING_CONFIG_NAME
 
     def __init__(
         self,
         timesteps=1000,
-        beta_start=0.0001,
-        beta_end=0.02,
         beta_schedule="linear",
-        variance_type="fixed_small",
+        variance_type="fixed_large"
     ):
         super().__init__()
         self.register(
             timesteps=timesteps,
-            beta_start=beta_start,
-            beta_end=beta_end,
             beta_schedule=beta_schedule,
-            variance_type=variance_type,
         )
         self.num_timesteps = int(timesteps)
 
         if beta_schedule == "linear":
+            # Linear schedule from Ho et al, extended to work for any number of
+            # diffusion steps.
+            scale = 1000 / self.num_timesteps
+            beta_start = scale * 0.0001
+            beta_end = scale * 0.02
             betas = linear_beta_schedule(timesteps, beta_start=beta_start, beta_end=beta_end)
-        elif beta_schedule == "squaredcos_cap_v2":
-            # GLIDE cosine schedule
-            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__}")