vq diffusion classifier free sampling (#1294)

* vq diffusion classifier free sampling

* correct

* uP
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

scripts/convert_vq_diffusion_to_diffusers.py

@@ -39,8 +39,8 @@ import torch
 
 import yaml
 from accelerate import init_empty_weights, load_checkpoint_and_dispatch
-from diffusers import VQDiffusionPipeline, VQDiffusionScheduler, VQModel
-from diffusers.models.attention import Transformer2DModel
+from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
+from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
 from transformers import CLIPTextModel, CLIPTokenizer
 from yaml.loader import FullLoader
 
@@ -826,6 +826,20 @@ if __name__ == "__main__":
         transformer_model, checkpoint
     )
 
+    # classifier free sampling embeddings interlude
+
+    # The learned embeddings are stored on the transformer in the original VQ-diffusion. We store them on a separate
+    # model, so we pull them off the checkpoint before the checkpoint is deleted.
+
+    learnable_classifier_free_sampling_embeddings = diffusion_config.params.learnable_cf
+
+    if learnable_classifier_free_sampling_embeddings:
+        learned_classifier_free_sampling_embeddings_embeddings = checkpoint["transformer.empty_text_embed"]
+    else:
+        learned_classifier_free_sampling_embeddings_embeddings = None
+
+    # done classifier free sampling embeddings interlude
+
     with tempfile.NamedTemporaryFile() as diffusers_transformer_checkpoint_file:
         torch.save(diffusers_transformer_checkpoint, diffusers_transformer_checkpoint_file.name)
         del diffusers_transformer_checkpoint
@@ -871,6 +885,31 @@ if __name__ == "__main__":
 
     # done scheduler
 
+    # learned classifier free sampling embeddings
+
+    with init_empty_weights():
+        learned_classifier_free_sampling_embeddings_model = LearnedClassifierFreeSamplingEmbeddings(
+            learnable_classifier_free_sampling_embeddings,
+            hidden_size=text_encoder_model.config.hidden_size,
+            length=tokenizer_model.model_max_length,
+        )
+
+    learned_classifier_free_sampling_checkpoint = {
+        "embeddings": learned_classifier_free_sampling_embeddings_embeddings.float()
+    }
+
+    with tempfile.NamedTemporaryFile() as learned_classifier_free_sampling_checkpoint_file:
+        torch.save(learned_classifier_free_sampling_checkpoint, learned_classifier_free_sampling_checkpoint_file.name)
+        del learned_classifier_free_sampling_checkpoint
+        del learned_classifier_free_sampling_embeddings_embeddings
+        load_checkpoint_and_dispatch(
+            learned_classifier_free_sampling_embeddings_model,
+            learned_classifier_free_sampling_checkpoint_file.name,
+            device_map="auto",
+        )
+
+    # done learned classifier free sampling embeddings
+
     print(f"saving VQ diffusion model, path: {args.dump_path}")
 
     pipe = VQDiffusionPipeline(
@@ -878,6 +917,7 @@ if __name__ == "__main__":
         transformer=transformer_model,
         tokenizer=tokenizer_model,
         text_encoder=text_encoder_model,
+        learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings_model,
         scheduler=scheduler_model,
     )
     pipe.save_pretrained(args.dump_path)

src/diffusers/pipelines/vq_diffusion/__init__.py

-from .pipeline_vq_diffusion import VQDiffusionPipeline
+from ...utils import is_torch_available, is_transformers_available
+
+
+if is_transformers_available() and is_torch_available():
+    from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

src/diffusers/pipelines/vq_diffusion/pipeline_vq_diffusion.py

@@ -20,6 +20,8 @@ from diffusers import Transformer2DModel, VQModel
 from diffusers.schedulers.scheduling_vq_diffusion import VQDiffusionScheduler
 from transformers import CLIPTextModel, CLIPTokenizer
 
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...modeling_utils import ModelMixin
 from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 from ...utils import logging
 
@@ -27,6 +29,28 @@ from ...utils import logging
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
+class LearnedClassifierFreeSamplingEmbeddings(ModelMixin, ConfigMixin):
+    """
+    Utility class for storing learned text embeddings for classifier free sampling
+    """
+
+    @register_to_config
+    def __init__(self, learnable: bool, hidden_size: Optional[int] = None, length: Optional[int] = None):
+        super().__init__()
+
+        self.learnable = learnable
+
+        if self.learnable:
+            assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
+            assert length is not None, "learnable=True requires `length` to be set"
+
+            embeddings = torch.zeros(length, hidden_size)
+        else:
+            embeddings = None
+
+        self.embeddings = torch.nn.Parameter(embeddings)
+
+
 class VQDiffusionPipeline(DiffusionPipeline):
     r"""
     Pipeline for text-to-image generation using VQ Diffusion
@@ -55,6 +79,7 @@ class VQDiffusionPipeline(DiffusionPipeline):
     text_encoder: CLIPTextModel
     tokenizer: CLIPTokenizer
     transformer: Transformer2DModel
+    learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings
     scheduler: VQDiffusionScheduler
 
     def __init__(
@@ -64,6 +89,7 @@ class VQDiffusionPipeline(DiffusionPipeline):
         tokenizer: CLIPTokenizer,
         transformer: Transformer2DModel,
         scheduler: VQDiffusionScheduler,
+        learned_classifier_free_sampling_embeddings: LearnedClassifierFreeSamplingEmbeddings,
     ):
         super().__init__()
 
@@ -73,13 +99,78 @@ class VQDiffusionPipeline(DiffusionPipeline):
             text_encoder=text_encoder,
             tokenizer=tokenizer,
             scheduler=scheduler,
+            learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
+        )
+
+    def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance):
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        # get prompt text embeddings
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
         )
+        text_input_ids = text_inputs.input_ids
+
+        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
+            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
+        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
+
+        # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
+        # While CLIP does normalize the pooled output of the text transformer when combining
+        # the image and text embeddings, CLIP does not directly normalize the last hidden state.
+        #
+        # CLIP normalizing the pooled output.
+        # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
+        text_embeddings = text_embeddings / text_embeddings.norm(dim=-1, keepdim=True)
+
+        # duplicate text embeddings for each generation per prompt
+        text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
+
+        if do_classifier_free_guidance:
+            if self.learned_classifier_free_sampling_embeddings.learnable:
+                uncond_embeddings = self.learned_classifier_free_sampling_embeddings.embeddings
+                uncond_embeddings = uncond_embeddings.unsqueeze(0).repeat(batch_size, 1, 1)
+            else:
+                uncond_tokens = [""] * batch_size
+
+                max_length = text_input_ids.shape[-1]
+                uncond_input = self.tokenizer(
+                    uncond_tokens,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+                uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
+                # See comment for normalizing text embeddings
+                uncond_embeddings = uncond_embeddings / uncond_embeddings.norm(dim=-1, keepdim=True)
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
 
     @torch.no_grad()
     def __call__(
         self,
         prompt: Union[str, List[str]],
         num_inference_steps: int = 100,
+        guidance_scale: float = 5.0,
         truncation_rate: float = 1.0,
         num_images_per_prompt: int = 1,
         generator: Optional[torch.Generator] = None,
@@ -98,6 +189,12 @@ class VQDiffusionPipeline(DiffusionPipeline):
             num_inference_steps (`int`, *optional*, defaults to 100):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                 expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
             truncation_rate (`float`, *optional*, defaults to 1.0 (equivalent to no truncation)):
                 Used to "truncate" the predicted classes for x_0 such that the cumulative probability for a pixel is at
                 most `truncation_rate`. The lowest probabilities that would increase the cumulative probability above
@@ -137,6 +234,10 @@ class VQDiffusionPipeline(DiffusionPipeline):
 
         batch_size = batch_size * num_images_per_prompt
 
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        text_embeddings = self._encode_prompt(prompt, num_images_per_prompt, do_classifier_free_guidance)
+
         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
         ):
@@ -145,35 +246,6 @@ class VQDiffusionPipeline(DiffusionPipeline):
                 f" {type(callback_steps)}."
             )
 
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-
-        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
-            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
-
-        # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
-        # While CLIP does normalize the pooled output of the text transformer when combining
-        # the image and text embeddings, CLIP does not directly normalize the last hidden state.
-        #
-        # CLIP normalizing the pooled output.
-        # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
-        text_embeddings = text_embeddings / text_embeddings.norm(dim=-1, keepdim=True)
-
-        # duplicate text embeddings for each generation per prompt
-        text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
-
         # get the initial completely masked latents unless the user supplied it
 
         latents_shape = (batch_size, self.transformer.num_latent_pixels)
@@ -198,9 +270,19 @@ class VQDiffusionPipeline(DiffusionPipeline):
         sample = latents
 
         for i, t in enumerate(self.progress_bar(timesteps_tensor)):
+            # expand the sample if we are doing classifier free guidance
+            latent_model_input = torch.cat([sample] * 2) if do_classifier_free_guidance else sample
+
             # predict the un-noised image
             # model_output == `log_p_x_0`
-            model_output = self.transformer(sample, encoder_hidden_states=text_embeddings, timestep=t).sample
+            model_output = self.transformer(
+                latent_model_input, encoder_hidden_states=text_embeddings, timestep=t
+            ).sample
+
+            if do_classifier_free_guidance:
+                model_output_uncond, model_output_text = model_output.chunk(2)
+                model_output = model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
+                model_output -= torch.logsumexp(model_output, dim=1, keepdim=True)
 
             model_output = self.truncate(model_output, truncation_rate)
 

tests/pipelines/vq_diffusion/test_vq_diffusion.py

@@ -20,7 +20,8 @@ import numpy as np
 import torch
 
 from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
-from diffusers.utils import load_image, slow, torch_device
+from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
+from diffusers.utils import load_numpy, slow, torch_device
 from diffusers.utils.testing_utils import require_torch_gpu
 from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
@@ -45,6 +46,10 @@ class VQDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     def num_embeds_ada_norm(self):
         return 12
 
+    @property
+    def text_embedder_hidden_size(self):
+        return 32
+
     @property
     def dummy_vqvae(self):
         torch.manual_seed(0)
@@ -71,7 +76,7 @@ class VQDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         config = CLIPTextConfig(
             bos_token_id=0,
             eos_token_id=2,
-            hidden_size=32,
+            hidden_size=self.text_embedder_hidden_size,
             intermediate_size=37,
             layer_norm_eps=1e-05,
             num_attention_heads=4,
@@ -111,9 +116,15 @@ class VQDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         tokenizer = self.dummy_tokenizer
         transformer = self.dummy_transformer
         scheduler = VQDiffusionScheduler(self.num_embed)
+        learned_classifier_free_sampling_embeddings = LearnedClassifierFreeSamplingEmbeddings(learnable=False)
 
         pipe = VQDiffusionPipeline(
-            vqvae=vqvae, text_encoder=text_encoder, tokenizer=tokenizer, transformer=transformer, scheduler=scheduler
+            vqvae=vqvae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+            learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
         )
         pipe = pipe.to(device)
         pipe.set_progress_bar_config(disable=None)
@@ -139,6 +150,50 @@ class VQDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
 
+    def test_vq_diffusion_classifier_free_sampling(self):
+        device = "cpu"
+
+        vqvae = self.dummy_vqvae
+        text_encoder = self.dummy_text_encoder
+        tokenizer = self.dummy_tokenizer
+        transformer = self.dummy_transformer
+        scheduler = VQDiffusionScheduler(self.num_embed)
+        learned_classifier_free_sampling_embeddings = LearnedClassifierFreeSamplingEmbeddings(
+            learnable=True, hidden_size=self.text_embedder_hidden_size, length=tokenizer.model_max_length
+        )
+
+        pipe = VQDiffusionPipeline(
+            vqvae=vqvae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+            learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
+        )
+        pipe = pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        prompt = "teddy bear playing in the pool"
+
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = pipe([prompt], generator=generator, num_inference_steps=2, output_type="np")
+        image = output.images
+
+        generator = torch.Generator(device=device).manual_seed(0)
+        image_from_tuple = pipe(
+            [prompt], generator=generator, output_type="np", return_dict=False, num_inference_steps=2
+        )[0]
+
+        image_slice = image[0, -3:, -3:, -1]
+        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 24, 24, 3)
+
+        expected_slice = np.array([0.6647, 0.6531, 0.5303, 0.5891, 0.5726, 0.4439, 0.6304, 0.5564, 0.4912])
+
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
+
 
 @slow
 @require_torch_gpu
@@ -149,12 +204,11 @@ class VQDiffusionPipelineIntegrationTests(unittest.TestCase):
         gc.collect()
         torch.cuda.empty_cache()
 
-    def test_vq_diffusion(self):
-        expected_image = load_image(
+    def test_vq_diffusion_classifier_free_sampling(self):
+        expected_image = load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/vq_diffusion/teddy_bear_pool.png"
+            "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"
         )
-        expected_image = np.array(expected_image, dtype=np.float32) / 255.0
 
         pipeline = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq")
         pipeline = pipeline.to(torch_device)
@@ -163,7 +217,6 @@ class VQDiffusionPipelineIntegrationTests(unittest.TestCase):
         generator = torch.Generator(device=torch_device).manual_seed(0)
         output = pipeline(
             "teddy bear playing in the pool",
-            truncation_rate=0.86,
             num_images_per_prompt=1,
             generator=generator,
             output_type="np",