chores: rebase commits

extern/ldm_zero123/modules/encoders/modules.py

@@ -0,0 +1,712 @@
+from functools import partial
+
+import clip
+import kornia
+import numpy as np
+import torch
+import torch.nn as nn
+
+from extern.ldm_zero123.modules.x_transformer import (  # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
+    Encoder,
+    TransformerWrapper,
+)
+from extern.ldm_zero123.util import default
+
+
+class AbstractEncoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def encode(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+class IdentityEncoder(AbstractEncoder):
+    def encode(self, x):
+        return x
+
+
+class FaceClipEncoder(AbstractEncoder):
+    def __init__(self, augment=True, retreival_key=None):
+        super().__init__()
+        self.encoder = FrozenCLIPImageEmbedder()
+        self.augment = augment
+        self.retreival_key = retreival_key
+
+    def forward(self, img):
+        encodings = []
+        with torch.no_grad():
+            x_offset = 125
+            if self.retreival_key:
+                # Assumes retrieved image are packed into the second half of channels
+                face = img[:, 3:, 190:440, x_offset : (512 - x_offset)]
+                other = img[:, :3, ...].clone()
+            else:
+                face = img[:, :, 190:440, x_offset : (512 - x_offset)]
+                other = img.clone()
+
+            if self.augment:
+                face = K.RandomHorizontalFlip()(face)
+
+            other[:, :, 190:440, x_offset : (512 - x_offset)] *= 0
+            encodings = [
+                self.encoder.encode(face),
+                self.encoder.encode(other),
+            ]
+
+        return torch.cat(encodings, dim=1)
+
+    def encode(self, img):
+        if isinstance(img, list):
+            # Uncondition
+            return torch.zeros(
+                (1, 2, 768), device=self.encoder.model.visual.conv1.weight.device
+            )
+
+        return self(img)
+
+
+class FaceIdClipEncoder(AbstractEncoder):
+    def __init__(self):
+        super().__init__()
+        self.encoder = FrozenCLIPImageEmbedder()
+        for p in self.encoder.parameters():
+            p.requires_grad = False
+        self.id = FrozenFaceEncoder(
+            "/home/jpinkney/code/stable-diffusion/model_ir_se50.pth", augment=True
+        )
+
+    def forward(self, img):
+        encodings = []
+        with torch.no_grad():
+            face = kornia.geometry.resize(
+                img, (256, 256), interpolation="bilinear", align_corners=True
+            )
+
+            other = img.clone()
+            other[:, :, 184:452, 122:396] *= 0
+            encodings = [
+                self.id.encode(face),
+                self.encoder.encode(other),
+            ]
+
+        return torch.cat(encodings, dim=1)
+
+    def encode(self, img):
+        if isinstance(img, list):
+            # Uncondition
+            return torch.zeros(
+                (1, 2, 768), device=self.encoder.model.visual.conv1.weight.device
+            )
+
+        return self(img)
+
+
+class ClassEmbedder(nn.Module):
+    def __init__(self, embed_dim, n_classes=1000, key="class"):
+        super().__init__()
+        self.key = key
+        self.embedding = nn.Embedding(n_classes, embed_dim)
+
+    def forward(self, batch, key=None):
+        if key is None:
+            key = self.key
+        # this is for use in crossattn
+        c = batch[key][:, None]
+        c = self.embedding(c)
+        return c
+
+
+class TransformerEmbedder(AbstractEncoder):
+    """Some transformer encoder layers"""
+
+    def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"):
+        super().__init__()
+        self.device = device
+        self.transformer = TransformerWrapper(
+            num_tokens=vocab_size,
+            max_seq_len=max_seq_len,
+            attn_layers=Encoder(dim=n_embed, depth=n_layer),
+        )
+
+    def forward(self, tokens):
+        tokens = tokens.to(self.device)  # meh
+        z = self.transformer(tokens, return_embeddings=True)
+        return z
+
+    def encode(self, x):
+        return self(x)
+
+
+class BERTTokenizer(AbstractEncoder):
+    """Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
+
+    def __init__(self, device="cuda", vq_interface=True, max_length=77):
+        super().__init__()
+        from transformers import BertTokenizerFast  # TODO: add to reuquirements
+
+        self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
+        self.device = device
+        self.vq_interface = vq_interface
+        self.max_length = max_length
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        tokens = batch_encoding["input_ids"].to(self.device)
+        return tokens
+
+    @torch.no_grad()
+    def encode(self, text):
+        tokens = self(text)
+        if not self.vq_interface:
+            return tokens
+        return None, None, [None, None, tokens]
+
+    def decode(self, text):
+        return text
+
+
+class BERTEmbedder(AbstractEncoder):
+    """Uses the BERT tokenizr model and add some transformer encoder layers"""
+
+    def __init__(
+        self,
+        n_embed,
+        n_layer,
+        vocab_size=30522,
+        max_seq_len=77,
+        device="cuda",
+        use_tokenizer=True,
+        embedding_dropout=0.0,
+    ):
+        super().__init__()
+        self.use_tknz_fn = use_tokenizer
+        if self.use_tknz_fn:
+            self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
+        self.device = device
+        self.transformer = TransformerWrapper(
+            num_tokens=vocab_size,
+            max_seq_len=max_seq_len,
+            attn_layers=Encoder(dim=n_embed, depth=n_layer),
+            emb_dropout=embedding_dropout,
+        )
+
+    def forward(self, text):
+        if self.use_tknz_fn:
+            tokens = self.tknz_fn(text)  # .to(self.device)
+        else:
+            tokens = text
+        z = self.transformer(tokens, return_embeddings=True)
+        return z
+
+    def encode(self, text):
+        # output of length 77
+        return self(text)
+
+
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+class FrozenT5Embedder(AbstractEncoder):
+    """Uses the T5 transformer encoder for text"""
+
+    def __init__(
+        self, version="google/t5-v1_1-large", device="cuda", max_length=77
+    ):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+        super().__init__()
+        self.tokenizer = T5Tokenizer.from_pretrained(version)
+        self.transformer = T5EncoderModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length  # TODO: typical value?
+        self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+        # self.train = disabled_train
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.transformer(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+import kornia.augmentation as K
+
+from extern.ldm_zero123.thirdp.psp.id_loss import IDFeatures
+
+
+class FrozenFaceEncoder(AbstractEncoder):
+    def __init__(self, model_path, augment=False):
+        super().__init__()
+        self.loss_fn = IDFeatures(model_path)
+        # face encoder is frozen
+        for p in self.loss_fn.parameters():
+            p.requires_grad = False
+        # Mapper is trainable
+        self.mapper = torch.nn.Linear(512, 768)
+        p = 0.25
+        if augment:
+            self.augment = K.AugmentationSequential(
+                K.RandomHorizontalFlip(p=0.5),
+                K.RandomEqualize(p=p),
+                # K.RandomPlanckianJitter(p=p),
+                # K.RandomPlasmaBrightness(p=p),
+                # K.RandomPlasmaContrast(p=p),
+                # K.ColorJiggle(0.02, 0.2, 0.2, p=p),
+            )
+        else:
+            self.augment = False
+
+    def forward(self, img):
+        if isinstance(img, list):
+            # Uncondition
+            return torch.zeros((1, 1, 768), device=self.mapper.weight.device)
+
+        if self.augment is not None:
+            # Transforms require 0-1
+            img = self.augment((img + 1) / 2)
+            img = 2 * img - 1
+
+        feat = self.loss_fn(img, crop=True)
+        feat = self.mapper(feat.unsqueeze(1))
+        return feat
+
+    def encode(self, img):
+        return self(img)
+
+
+class FrozenCLIPEmbedder(AbstractEncoder):
+    """Uses the CLIP transformer encoder for text (from huggingface)"""
+
+    def __init__(
+        self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77
+    ):  # clip-vit-base-patch32
+        super().__init__()
+        self.tokenizer = CLIPTokenizer.from_pretrained(version)
+        self.transformer = CLIPTextModel.from_pretrained(version)
+        self.device = device
+        self.max_length = max_length  # TODO: typical value?
+        self.freeze()
+
+    def freeze(self):
+        self.transformer = self.transformer.eval()
+        # self.train = disabled_train
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, text):
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=True,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+        tokens = batch_encoding["input_ids"].to(self.device)
+        outputs = self.transformer(input_ids=tokens)
+
+        z = outputs.last_hidden_state
+        return z
+
+    def encode(self, text):
+        return self(text)
+
+
+import torch.nn.functional as F
+from transformers import CLIPVisionModel
+
+
+class ClipImageProjector(AbstractEncoder):
+    """
+    Uses the CLIP image encoder.
+    """
+
+    def __init__(
+        self, version="openai/clip-vit-large-patch14", max_length=77
+    ):  # clip-vit-base-patch32
+        super().__init__()
+        self.model = CLIPVisionModel.from_pretrained(version)
+        self.model.train()
+        self.max_length = max_length  # TODO: typical value?
+        self.antialias = True
+        self.mapper = torch.nn.Linear(1024, 768)
+        self.register_buffer(
+            "mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False
+        )
+        self.register_buffer(
+            "std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False
+        )
+        null_cond = self.get_null_cond(version, max_length)
+        self.register_buffer("null_cond", null_cond)
+
+    @torch.no_grad()
+    def get_null_cond(self, version, max_length):
+        device = self.mean.device
+        embedder = FrozenCLIPEmbedder(
+            version=version, device=device, max_length=max_length
+        )
+        null_cond = embedder([""])
+        return null_cond
+
+    def preprocess(self, x):
+        # Expects inputs in the range -1, 1
+        x = kornia.geometry.resize(
+            x,
+            (224, 224),
+            interpolation="bicubic",
+            align_corners=True,
+            antialias=self.antialias,
+        )
+        x = (x + 1.0) / 2.0
+        # renormalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def forward(self, x):
+        if isinstance(x, list):
+            return self.null_cond
+        # x is assumed to be in range [-1,1]
+        x = self.preprocess(x)
+        outputs = self.model(pixel_values=x)
+        last_hidden_state = outputs.last_hidden_state
+        last_hidden_state = self.mapper(last_hidden_state)
+        return F.pad(
+            last_hidden_state,
+            [0, 0, 0, self.max_length - last_hidden_state.shape[1], 0, 0],
+        )
+
+    def encode(self, im):
+        return self(im)
+
+
+class ProjectedFrozenCLIPEmbedder(AbstractEncoder):
+    def __init__(
+        self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77
+    ):  # clip-vit-base-patch32
+        super().__init__()
+        self.embedder = FrozenCLIPEmbedder(
+            version=version, device=device, max_length=max_length
+        )
+        self.projection = torch.nn.Linear(768, 768)
+
+    def forward(self, text):
+        z = self.embedder(text)
+        return self.projection(z)
+
+    def encode(self, text):
+        return self(text)
+
+
+class FrozenCLIPImageEmbedder(AbstractEncoder):
+    """
+    Uses the CLIP image encoder.
+    Not actually frozen... If you want that set cond_stage_trainable=False in cfg
+    """
+
+    def __init__(
+        self,
+        model="ViT-L/14",
+        jit=False,
+        device="cpu",
+        antialias=False,
+    ):
+        super().__init__()
+        self.model, _ = clip.load(name=model, device=device, jit=jit, download_root=None)
+        # We don't use the text part so delete it
+        del self.model.transformer
+        self.antialias = antialias
+        self.register_buffer(
+            "mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False
+        )
+        self.register_buffer(
+            "std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False
+        )
+
+    def preprocess(self, x):
+        # Expects inputs in the range -1, 1
+        x = kornia.geometry.resize(
+            x,
+            (224, 224),
+            interpolation="bicubic",
+            align_corners=True,
+            antialias=self.antialias,
+        )
+        x = (x + 1.0) / 2.0
+        # renormalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def forward(self, x):
+        # x is assumed to be in range [-1,1]
+        if isinstance(x, list):
+            # [""] denotes condition dropout for ucg
+            device = self.model.visual.conv1.weight.device
+            return torch.zeros(1, 768, device=device)
+        return self.model.encode_image(self.preprocess(x)).float()
+
+    def encode(self, im):
+        return self(im).unsqueeze(1)
+
+
+import random
+
+from torchvision import transforms
+
+
+class FrozenCLIPImageMutliEmbedder(AbstractEncoder):
+    """
+    Uses the CLIP image encoder.
+    Not actually frozen... If you want that set cond_stage_trainable=False in cfg
+    """
+
+    def __init__(
+        self,
+        model="ViT-L/14",
+        jit=False,
+        device="cpu",
+        antialias=True,
+        max_crops=5,
+    ):
+        super().__init__()
+        self.model, _ = clip.load(name=model, device=device, jit=jit)
+        # We don't use the text part so delete it
+        del self.model.transformer
+        self.antialias = antialias
+        self.register_buffer(
+            "mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False
+        )
+        self.register_buffer(
+            "std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False
+        )
+        self.max_crops = max_crops
+
+    def preprocess(self, x):
+        # Expects inputs in the range -1, 1
+        randcrop = transforms.RandomResizedCrop(224, scale=(0.085, 1.0), ratio=(1, 1))
+        max_crops = self.max_crops
+        patches = []
+        crops = [randcrop(x) for _ in range(max_crops)]
+        patches.extend(crops)
+        x = torch.cat(patches, dim=0)
+        x = (x + 1.0) / 2.0
+        # renormalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def forward(self, x):
+        # x is assumed to be in range [-1,1]
+        if isinstance(x, list):
+            # [""] denotes condition dropout for ucg
+            device = self.model.visual.conv1.weight.device
+            return torch.zeros(1, self.max_crops, 768, device=device)
+        batch_tokens = []
+        for im in x:
+            patches = self.preprocess(im.unsqueeze(0))
+            tokens = self.model.encode_image(patches).float()
+            for t in tokens:
+                if random.random() < 0.1:
+                    t *= 0
+            batch_tokens.append(tokens.unsqueeze(0))
+
+        return torch.cat(batch_tokens, dim=0)
+
+    def encode(self, im):
+        return self(im)
+
+
+class SpatialRescaler(nn.Module):
+    def __init__(
+        self,
+        n_stages=1,
+        method="bilinear",
+        multiplier=0.5,
+        in_channels=3,
+        out_channels=None,
+        bias=False,
+    ):
+        super().__init__()
+        self.n_stages = n_stages
+        assert self.n_stages >= 0
+        assert method in [
+            "nearest",
+            "linear",
+            "bilinear",
+            "trilinear",
+            "bicubic",
+            "area",
+        ]
+        self.multiplier = multiplier
+        self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
+        self.remap_output = out_channels is not None
+        if self.remap_output:
+            print(
+                f"Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing."
+            )
+            self.channel_mapper = nn.Conv2d(in_channels, out_channels, 1, bias=bias)
+
+    def forward(self, x):
+        for stage in range(self.n_stages):
+            x = self.interpolator(x, scale_factor=self.multiplier)
+
+        if self.remap_output:
+            x = self.channel_mapper(x)
+        return x
+
+    def encode(self, x):
+        return self(x)
+
+
+from extern.ldm_zero123.modules.diffusionmodules.util import (
+    extract_into_tensor,
+    make_beta_schedule,
+    noise_like,
+)
+from extern.ldm_zero123.util import instantiate_from_config
+
+
+class LowScaleEncoder(nn.Module):
+    def __init__(
+        self,
+        model_config,
+        linear_start,
+        linear_end,
+        timesteps=1000,
+        max_noise_level=250,
+        output_size=64,
+        scale_factor=1.0,
+    ):
+        super().__init__()
+        self.max_noise_level = max_noise_level
+        self.model = instantiate_from_config(model_config)
+        self.augmentation_schedule = self.register_schedule(
+            timesteps=timesteps, linear_start=linear_start, linear_end=linear_end
+        )
+        self.out_size = output_size
+        self.scale_factor = scale_factor
+
+    def register_schedule(
+        self,
+        beta_schedule="linear",
+        timesteps=1000,
+        linear_start=1e-4,
+        linear_end=2e-2,
+        cosine_s=8e-3,
+    ):
+        betas = make_beta_schedule(
+            beta_schedule,
+            timesteps,
+            linear_start=linear_start,
+            linear_end=linear_end,
+            cosine_s=cosine_s,
+        )
+        alphas = 1.0 - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
+
+        (timesteps,) = betas.shape
+        self.num_timesteps = int(timesteps)
+        self.linear_start = linear_start
+        self.linear_end = linear_end
+        assert (
+            alphas_cumprod.shape[0] == self.num_timesteps
+        ), "alphas have to be defined for each timestep"
+
+        to_torch = partial(torch.tensor, dtype=torch.float32)
+
+        self.register_buffer("betas", to_torch(betas))
+        self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
+        self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
+        self.register_buffer(
+            "sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
+        )
+        self.register_buffer(
+            "log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
+        )
+        self.register_buffer(
+            "sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
+        )
+        self.register_buffer(
+            "sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
+        )
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (
+            extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+            + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
+            * noise
+        )
+
+    def forward(self, x):
+        z = self.model.encode(x).sample()
+        z = z * self.scale_factor
+        noise_level = torch.randint(
+            0, self.max_noise_level, (x.shape[0],), device=x.device
+        ).long()
+        z = self.q_sample(z, noise_level)
+        if self.out_size is not None:
+            z = torch.nn.functional.interpolate(
+                z, size=self.out_size, mode="nearest"
+            )  # TODO: experiment with mode
+        # z = z.repeat_interleave(2, -2).repeat_interleave(2, -1)
+        return z, noise_level
+
+    def decode(self, z):
+        z = z / self.scale_factor
+        return self.model.decode(z)
+
+
+if __name__ == "__main__":
+    from extern.ldm_zero123.util import count_params
+
+    sentences = [
+        "a hedgehog drinking a whiskey",
+        "der mond ist aufgegangen",
+        "Ein Satz mit vielen Sonderzeichen: äöü ß ?! : 'xx-y/@s'",
+    ]
+    model = FrozenT5Embedder(version="google/t5-v1_1-xl").cuda()
+    count_params(model, True)
+    z = model(sentences)
+    print(z.shape)
+
+    model = FrozenCLIPEmbedder().cuda()
+    count_params(model, True)
+    z = model(sentences)
+    print(z.shape)
+
+    print("done.")