Merge pull request #215 from Stability-AI/unclip_prerelease

Add unCLIP finetunes

README.md

@@ -8,12 +8,14 @@ new checkpoints. The following list provides an overview of all currently availa
 
 ## News
 
+
 **March 24, 2023**
 
 *Stable UnCLIP 2.1*
 
 - New stable diffusion finetune (_Stable unCLIP 2.1_, [HuggingFace](https://huggingface.co/stabilityai/)) at 768x768 resolution,  based on SD2.1-768. This model allows for image variations and mixing operations as described in [*Hierarchical Text-Conditional Image Generation with CLIP Latents*](https://arxiv.org/abs/2204.06125), and, thanks to its modularity, can be combined with other models such as [KARLO](https://github.com/kakaobrain/karlo). Comes in two variants: [*Stable unCLIP-L*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-l.ckpt) and [*Stable unCLIP-H*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-h.ckpt), which are conditioned on CLIP ViT-L and ViT-H image embeddings, respectively. Instructions are available [here](doc/UNCLIP.MD).
 
+
 **December 7, 2022**
 
 *Version 2.1*

checkpoints/checkpoints.txt

@@ -0,0 +1 @@
+Put unCLIP checkpoints here.

configs/karlo/decoder_900M_vit_l.yaml

@@ -0,0 +1,37 @@
+model:
+  type: t2i-decoder
+  diffusion_sampler: uniform
+  hparams:
+    image_size: 64
+    num_channels: 320
+    num_res_blocks: 3
+    channel_mult: ''
+    attention_resolutions: 32,16,8
+    num_heads: -1
+    num_head_channels: 64
+    num_heads_upsample: -1
+    use_scale_shift_norm: true
+    dropout: 0.1
+    clip_dim: 768
+    clip_emb_mult: 4
+    text_ctx: 77
+    xf_width: 1536
+    xf_layers: 0
+    xf_heads: 0
+    xf_final_ln: false
+    resblock_updown: true
+    learn_sigma: true
+    text_drop: 0.3
+    clip_emb_type: image
+    clip_emb_drop: 0.1
+    use_plm: true
+
+diffusion:
+  steps: 1000
+  learn_sigma: true
+  sigma_small: false
+  noise_schedule: squaredcos_cap_v2
+  use_kl: false
+  predict_xstart: false
+  rescale_learned_sigmas: true
+  timestep_respacing: ''

configs/karlo/improved_sr_64_256_1.4B.yaml

@@ -0,0 +1,27 @@
+model:
+  type: improved_sr_64_256
+  diffusion_sampler: uniform
+  hparams:
+    channels: 320
+    depth: 3
+    channels_multiple:
+    - 1
+    - 2
+    - 3
+    - 4
+    dropout: 0.0
+
+diffusion:
+  steps: 1000
+  learn_sigma: false
+  sigma_small: true
+  noise_schedule: squaredcos_cap_v2
+  use_kl: false
+  predict_xstart: false
+  rescale_learned_sigmas: true
+  timestep_respacing: '7'
+
+
+sampling:
+  timestep_respacing: '7' # fix
+  clip_denoise: true

configs/karlo/prior_1B_vit_l.yaml

@@ -0,0 +1,21 @@
+model:
+  type: prior
+  diffusion_sampler: uniform
+  hparams:
+    text_ctx: 77
+    xf_width: 2048
+    xf_layers: 20
+    xf_heads: 32
+    xf_final_ln: true
+    text_drop: 0.2
+    clip_dim: 768
+
+diffusion:
+  steps: 1000
+  learn_sigma: false
+  sigma_small: true
+  noise_schedule: squaredcos_cap_v2
+  use_kl: false
+  predict_xstart: true
+  rescale_learned_sigmas: false
+  timestep_respacing: ''

configs/stable-diffusion/v2-1-stable-unclip-h-inference.yaml

@@ -0,0 +1,80 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.ImageEmbeddingConditionedLatentDiffusion
+  params:
+    embedding_dropout: 0.25
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.0120
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 96
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn-adm
+    scale_factor: 0.18215
+    monitor: val/loss_simple_ema
+    use_ema: False
+
+    embedder_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+
+    noise_aug_config:
+      target: ldm.modules.encoders.modules.CLIPEmbeddingNoiseAugmentation
+      params:
+        timestep_dim: 1024
+        noise_schedule_config:
+          timesteps: 1000
+          beta_schedule: squaredcos_cap_v2
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        num_classes: "sequential"
+        adm_in_channels: 2048
+        use_checkpoint: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+            - 1
+            - 2
+            - 4
+            - 4
+          num_res_blocks: 2
+          attn_resolutions: [ ]
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"

configs/stable-diffusion/v2-1-stable-unclip-l-inference.yaml

@@ -0,0 +1,83 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.ImageEmbeddingConditionedLatentDiffusion
+  params:
+    embedding_dropout: 0.25
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.0120
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 96
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn-adm
+    scale_factor: 0.18215
+    monitor: val/loss_simple_ema
+    use_ema: False
+
+    embedder_config:
+      target: ldm.modules.encoders.modules.ClipImageEmbedder
+      params:
+        model: "ViT-L/14"
+
+    noise_aug_config:
+      target: ldm.modules.encoders.modules.CLIPEmbeddingNoiseAugmentation
+      params:
+        clip_stats_path: "checkpoints/karlo_models/ViT-L-14_stats.th"
+        timestep_dim: 768
+        noise_schedule_config:
+          timesteps: 1000
+          beta_schedule: squaredcos_cap_v2
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        num_classes: "sequential"
+        adm_in_channels: 1536
+        use_checkpoint: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+            - 1
+            - 2
+            - 4
+            - 4
+          num_res_blocks: 2
+          attn_resolutions: [ ]
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"

ldm/models/diffusion/ddpm.py

@@ -1799,3 +1799,75 @@ class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
         log = super().log_images(*args, **kwargs)
         log["lr"] = rearrange(args[0]["lr"], 'b h w c -> b c h w')
         return log
+
+
+class ImageEmbeddingConditionedLatentDiffusion(LatentDiffusion):
+    def __init__(self, embedder_config, embedding_key="jpg", embedding_dropout=0.5,
+                 freeze_embedder=True, noise_aug_config=None, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.embed_key = embedding_key
+        self.embedding_dropout = embedding_dropout
+        self._init_embedder(embedder_config, freeze_embedder)
+        self._init_noise_aug(noise_aug_config)
+
+    def _init_embedder(self, config, freeze=True):
+        embedder = instantiate_from_config(config)
+        if freeze:
+            self.embedder = embedder.eval()
+            self.embedder.train = disabled_train
+            for param in self.embedder.parameters():
+                param.requires_grad = False
+
+    def _init_noise_aug(self, config):
+        if config is not None:
+            # use the KARLO schedule for noise augmentation on CLIP image embeddings
+            noise_augmentor = instantiate_from_config(config)
+            assert isinstance(noise_augmentor, nn.Module)
+            noise_augmentor = noise_augmentor.eval()
+            noise_augmentor.train = disabled_train
+            self.noise_augmentor = noise_augmentor
+        else:
+            self.noise_augmentor = None
+
+    def get_input(self, batch, k, cond_key=None, bs=None, **kwargs):
+        outputs = LatentDiffusion.get_input(self, batch, k, bs=bs, **kwargs)
+        z, c = outputs[0], outputs[1]
+        img = batch[self.embed_key][:bs]
+        img = rearrange(img, 'b h w c -> b c h w')
+        c_adm = self.embedder(img)
+        if self.noise_augmentor is not None:
+            c_adm, noise_level_emb = self.noise_augmentor(c_adm)
+            # assume this gives embeddings of noise levels
+            c_adm = torch.cat((c_adm, noise_level_emb), 1)
+        if self.training:
+            c_adm = torch.bernoulli((1. - self.embedding_dropout) * torch.ones(c_adm.shape[0],
+                                                                               device=c_adm.device)[:, None]) * c_adm
+        all_conds = {"c_crossattn": [c], "c_adm": c_adm}
+        noutputs = [z, all_conds]
+        noutputs.extend(outputs[2:])
+        return noutputs
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, **kwargs):
+        log = dict()
+        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True,
+                                           return_original_cond=True)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        assert self.model.conditioning_key is not None
+        assert self.cond_stage_key in ["caption", "txt"]
+        xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+        log["conditioning"] = xc
+        uc = self.get_unconditional_conditioning(N, kwargs.get('unconditional_guidance_label', ''))
+        unconditional_guidance_scale = kwargs.get('unconditional_guidance_scale', 5.)
+
+        uc_ = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
+        ema_scope = self.ema_scope if kwargs.get('use_ema_scope', True) else nullcontext
+        with ema_scope(f"Sampling"):
+            samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=True,
+                                             ddim_steps=kwargs.get('ddim_steps', 50), eta=kwargs.get('ddim_eta', 0.),
+                                             unconditional_guidance_scale=unconditional_guidance_scale,
+                                             unconditional_conditioning=uc_, )
+            x_samples_cfg = self.decode_first_stage(samples_cfg)
+            log[f"samplescfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+        return log

ldm/models/diffusion/dpm_solver/dpm_solver.py

@@ -307,6 +307,15 @@ def model_wrapper(
             else:
                 x_in = torch.cat([x] * 2)
                 t_in = torch.cat([t_continuous] * 2)
+                if isinstance(condition, dict):
+                    assert isinstance(unconditional_condition, dict)
+                    c_in = dict()
+                    for k in condition:
+                        if isinstance(condition[k], list):
+                            c_in[k] = [torch.cat([unconditional_condition[k][i], condition[k][i]]) for i in range(len(condition[k]))]
+                        else:
+                            c_in[k] = torch.cat([unconditional_condition[k], condition[k]])
+                else:
                     c_in = torch.cat([unconditional_condition, condition])
                 noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
                 return noise_uncond + guidance_scale * (noise - noise_uncond)

ldm/models/diffusion/dpm_solver/sampler.py

@@ -3,7 +3,6 @@ import torch
 
 from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
 
-
 MODEL_TYPES = {
     "eps": "noise",
     "v": "v"
@@ -51,10 +50,18 @@ class DPMSolverSampler(object):
                ):
         if conditioning is not None:
             if isinstance(conditioning, dict):
-                cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+                ctmp = conditioning[list(conditioning.keys())[0]]
+                while isinstance(ctmp, list): ctmp = ctmp[0]
+                if isinstance(ctmp, torch.Tensor):
+                    cbs = ctmp.shape[0]
                     if cbs != batch_size:
                         print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+            elif isinstance(conditioning, list):
+                for ctmp in conditioning:
+                    if ctmp.shape[0] != batch_size:
+                        print(f"Warning: Got {ctmp.shape[0]} conditionings but batch-size is {batch_size}")
             else:
+                if isinstance(conditioning, torch.Tensor):
                     if conditioning.shape[0] != batch_size:
                         print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
 
@@ -83,6 +90,7 @@ class DPMSolverSampler(object):
         )
 
         dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
-        x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
+        x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2,
+                              lower_order_final=True)
 
         return x.to(device), None

ldm/modules/diffusionmodules/openaimodel.py

@@ -409,6 +409,15 @@ class QKVAttention(nn.Module):
         return count_flops_attn(model, _x, y)
 
 
+class Timestep(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, t):
+        return timestep_embedding(t, self.dim)
+
+
 class UNetModel(nn.Module):
     """
     The full UNet model with attention and timestep embedding.
@@ -470,6 +479,7 @@ class UNetModel(nn.Module):
         num_attention_blocks=None,
         disable_middle_self_attn=False,
         use_linear_in_transformer=False,
+        adm_in_channels=None,
     ):
         super().__init__()
         if use_spatial_transformer:
@@ -538,6 +548,15 @@ class UNetModel(nn.Module):
             elif self.num_classes == "continuous":
                 print("setting up linear c_adm embedding layer")
                 self.label_emb = nn.Linear(1, time_embed_dim)
+            elif self.num_classes == "sequential":
+                assert adm_in_channels is not None
+                self.label_emb = nn.Sequential(
+                    nn.Sequential(
+                        linear(adm_in_channels, time_embed_dim),
+                        nn.SiLU(),
+                        linear(time_embed_dim, time_embed_dim),
+                    )
+                )
             else:
                 raise ValueError()
 

ldm/modules/diffusionmodules/util.py

@@ -34,6 +34,13 @@ def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2,
         betas = 1 - alphas[1:] / alphas[:-1]
         betas = np.clip(betas, a_min=0, a_max=0.999)
 
+    elif schedule == "squaredcos_cap_v2":  # used for karlo prior
+        # return early
+        return betas_for_alpha_bar(
+            n_timestep,
+            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+        )
+
     elif schedule == "sqrt_linear":
         betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
     elif schedule == "sqrt":
@@ -218,6 +225,7 @@ class GroupNorm32(nn.GroupNorm):
     def forward(self, x):
         return super().forward(x.float()).type(x.dtype)
 
+
 def conv_nd(dims, *args, **kwargs):
     """
     Create a 1D, 2D, or 3D convolution module.

ldm/modules/encoders/modules.py

@@ -1,11 +1,12 @@
 import torch
 import torch.nn as nn
+import kornia
 from torch.utils.checkpoint import checkpoint
 
 from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
 
 import open_clip
-from ldm.util import default, count_params
+from ldm.util import default, count_params, autocast
 
 
 class AbstractEncoder(nn.Module):
@@ -37,7 +38,7 @@ class ClassEmbedder(nn.Module):
         c = batch[key][:, None]
         if self.ucg_rate > 0. and not disable_dropout:
             mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
-            c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1)
+            c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1)
             c = c.long()
         c = self.embedding(c)
         return c
@@ -57,7 +58,9 @@ def disabled_train(self, mode=True):
 
 class FrozenT5Embedder(AbstractEncoder):
     """Uses the T5 transformer encoder for text"""
-    def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True):  # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+
+    def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77,
+                 freeze=True):  # 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)
@@ -68,7 +71,7 @@ class FrozenT5Embedder(AbstractEncoder):
 
     def freeze(self):
         self.transformer = self.transformer.eval()
-        #self.train = disabled_train
+        # self.train = disabled_train
         for param in self.parameters():
             param.requires_grad = False
 
@@ -92,6 +95,7 @@ class FrozenCLIPEmbedder(AbstractEncoder):
         "pooled",
         "hidden"
     ]
+
     def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
                  freeze=True, layer="last", layer_idx=None):  # clip-vit-base-patch32
         super().__init__()
@@ -110,7 +114,7 @@ class FrozenCLIPEmbedder(AbstractEncoder):
 
     def freeze(self):
         self.transformer = self.transformer.eval()
-        #self.train = disabled_train
+        # self.train = disabled_train
         for param in self.parameters():
             param.requires_grad = False
 
@@ -118,7 +122,7 @@ class FrozenCLIPEmbedder(AbstractEncoder):
         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, output_hidden_states=self.layer=="hidden")
+        outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden")
         if self.layer == "last":
             z = outputs.last_hidden_state
         elif self.layer == "pooled":
@@ -131,15 +135,54 @@ class FrozenCLIPEmbedder(AbstractEncoder):
         return self(text)
 
 
+class ClipImageEmbedder(nn.Module):
+    def __init__(
+            self,
+            model,
+            jit=False,
+            device='cuda' if torch.cuda.is_available() else 'cpu',
+            antialias=True,
+            ucg_rate=0.
+    ):
+        super().__init__()
+        from clip import load as load_clip
+        self.model, _ = load_clip(name=model, device=device, jit=jit)
+
+        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.ucg_rate = ucg_rate
+
+    def preprocess(self, x):
+        # normalize to [0,1]
+        x = kornia.geometry.resize(x, (224, 224),
+                                   interpolation='bicubic', align_corners=True,
+                                   antialias=self.antialias)
+        x = (x + 1.) / 2.
+        # re-normalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def forward(self, x, no_dropout=False):
+        # x is assumed to be in range [-1,1]
+        out = self.model.encode_image(self.preprocess(x))
+        out = out.to(x.dtype)
+        if self.ucg_rate > 0. and not no_dropout:
+            out = torch.bernoulli((1. - self.ucg_rate) * torch.ones(out.shape[0], device=out.device))[:, None] * out
+        return out
+
+
 class FrozenOpenCLIPEmbedder(AbstractEncoder):
     """
     Uses the OpenCLIP transformer encoder for text
     """
     LAYERS = [
-        #"pooled",
+        # "pooled",
         "last",
         "penultimate"
     ]
+
     def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
                  freeze=True, layer="last"):
         super().__init__()
@@ -179,7 +222,7 @@ class FrozenOpenCLIPEmbedder(AbstractEncoder):
         x = self.model.ln_final(x)
         return x
 
-    def text_transformer_forward(self, x: torch.Tensor, attn_mask = None):
+    def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
         for i, r in enumerate(self.model.transformer.resblocks):
             if i == len(self.model.transformer.resblocks) - self.layer_idx:
                 break
@@ -193,14 +236,73 @@ class FrozenOpenCLIPEmbedder(AbstractEncoder):
         return self(text)
 
 
+class FrozenOpenCLIPImageEmbedder(AbstractEncoder):
+    """
+    Uses the OpenCLIP vision transformer encoder for images
+    """
+
+    def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
+                 freeze=True, layer="pooled", antialias=True, ucg_rate=0.):
+        super().__init__()
+        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'),
+                                                            pretrained=version, )
+        del model.transformer
+        self.model = model
+
+        self.device = device
+        self.max_length = max_length
+        if freeze:
+            self.freeze()
+        self.layer = layer
+        if self.layer == "penultimate":
+            raise NotImplementedError()
+            self.layer_idx = 1
+
+        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.ucg_rate = ucg_rate
+
+    def preprocess(self, x):
+        # normalize to [0,1]
+        x = kornia.geometry.resize(x, (224, 224),
+                                   interpolation='bicubic', align_corners=True,
+                                   antialias=self.antialias)
+        x = (x + 1.) / 2.
+        # renormalize according to clip
+        x = kornia.enhance.normalize(x, self.mean, self.std)
+        return x
+
+    def freeze(self):
+        self.model = self.model.eval()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    @autocast
+    def forward(self, image, no_dropout=False):
+        z = self.encode_with_vision_transformer(image)
+        if self.ucg_rate > 0. and not no_dropout:
+            z = torch.bernoulli((1. - self.ucg_rate) * torch.ones(z.shape[0], device=z.device))[:, None] * z
+        return z
+
+    def encode_with_vision_transformer(self, img):
+        img = self.preprocess(img)
+        x = self.model.visual(img)
+        return x
+
+    def encode(self, text):
+        return self(text)
+
+
 class FrozenCLIPT5Encoder(AbstractEncoder):
     def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
                  clip_max_length=77, t5_max_length=77):
         super().__init__()
         self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
         self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
-        print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
-              f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.")
+        print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
+              f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params.")
 
     def encode(self, text):
         return self(text)
@@ -211,3 +313,38 @@ class FrozenCLIPT5Encoder(AbstractEncoder):
         return [clip_z, t5_z]
 
 
+from ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
+from ldm.modules.diffusionmodules.openaimodel import Timestep
+
+
+class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
+    def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs):
+        super().__init__(*args, **kwargs)
+        if clip_stats_path is None:
+            clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim)
+        else:
+            clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu")
+        self.register_buffer("data_mean", clip_mean[None, :], persistent=False)
+        self.register_buffer("data_std", clip_std[None, :], persistent=False)
+        self.time_embed = Timestep(timestep_dim)
+
+    def scale(self, x):
+        # re-normalize to centered mean and unit variance
+        x = (x - self.data_mean) * 1. / self.data_std
+        return x
+
+    def unscale(self, x):
+        # back to original data stats
+        x = (x * self.data_std) + self.data_mean
+        return x
+
+    def forward(self, x, noise_level=None):
+        if noise_level is None:
+            noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
+        else:
+            assert isinstance(noise_level, torch.Tensor)
+        x = self.scale(x)
+        z = self.q_sample(x, noise_level)
+        z = self.unscale(z)
+        noise_level = self.time_embed(noise_level)
+        return z, noise_level

ldm/modules/karlo/diffusers_pipeline.py

@@ -0,0 +1,512 @@
+# Copyright 2022 Kakao Brain 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.
+
+import inspect
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch.nn import functional as F
+
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+from transformers.models.clip.modeling_clip import CLIPTextModelOutput
+
+from ...models import PriorTransformer, UNet2DConditionModel, UNet2DModel
+from ...pipelines import DiffusionPipeline, ImagePipelineOutput
+from ...schedulers import UnCLIPScheduler
+from ...utils import is_accelerate_available, logging, randn_tensor
+from .text_proj import UnCLIPTextProjModel
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class UnCLIPPipeline(DiffusionPipeline):
+    """
+    Pipeline for text-to-image generation using unCLIP
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+    Args:
+        text_encoder ([`CLIPTextModelWithProjection`]):
+            Frozen text-encoder.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        prior ([`PriorTransformer`]):
+            The canonincal unCLIP prior to approximate the image embedding from the text embedding.
+        text_proj ([`UnCLIPTextProjModel`]):
+            Utility class to prepare and combine the embeddings before they are passed to the decoder.
+        decoder ([`UNet2DConditionModel`]):
+            The decoder to invert the image embedding into an image.
+        super_res_first ([`UNet2DModel`]):
+            Super resolution unet. Used in all but the last step of the super resolution diffusion process.
+        super_res_last ([`UNet2DModel`]):
+            Super resolution unet. Used in the last step of the super resolution diffusion process.
+        prior_scheduler ([`UnCLIPScheduler`]):
+            Scheduler used in the prior denoising process. Just a modified DDPMScheduler.
+        decoder_scheduler ([`UnCLIPScheduler`]):
+            Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.
+        super_res_scheduler ([`UnCLIPScheduler`]):
+            Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler.
+    """
+
+    prior: PriorTransformer
+    decoder: UNet2DConditionModel
+    text_proj: UnCLIPTextProjModel
+    text_encoder: CLIPTextModelWithProjection
+    tokenizer: CLIPTokenizer
+    super_res_first: UNet2DModel
+    super_res_last: UNet2DModel
+
+    prior_scheduler: UnCLIPScheduler
+    decoder_scheduler: UnCLIPScheduler
+    super_res_scheduler: UnCLIPScheduler
+
+    def __init__(
+        self,
+        prior: PriorTransformer,
+        decoder: UNet2DConditionModel,
+        text_encoder: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        text_proj: UnCLIPTextProjModel,
+        super_res_first: UNet2DModel,
+        super_res_last: UNet2DModel,
+        prior_scheduler: UnCLIPScheduler,
+        decoder_scheduler: UnCLIPScheduler,
+        super_res_scheduler: UnCLIPScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            prior=prior,
+            decoder=decoder,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            text_proj=text_proj,
+            super_res_first=super_res_first,
+            super_res_last=super_res_last,
+            prior_scheduler=prior_scheduler,
+            decoder_scheduler=decoder_scheduler,
+            super_res_scheduler=super_res_scheduler,
+        )
+
+    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+
+        latents = latents * scheduler.init_noise_sigma
+        return latents
+
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
+        text_attention_mask: Optional[torch.Tensor] = None,
+    ):
+        if text_model_output is None:
+            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
+            text_mask = text_inputs.attention_mask.bool().to(device)
+
+            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_encoder_output = self.text_encoder(text_input_ids.to(device))
+
+            text_embeddings = text_encoder_output.text_embeds
+            text_encoder_hidden_states = text_encoder_output.last_hidden_state
+
+        else:
+            batch_size = text_model_output[0].shape[0]
+            text_embeddings, text_encoder_hidden_states = text_model_output[0], text_model_output[1]
+            text_mask = text_attention_mask
+
+        text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
+        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
+
+        if do_classifier_free_guidance:
+            uncond_tokens = [""] * batch_size
+
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            uncond_text_mask = uncond_input.attention_mask.bool().to(device)
+            uncond_embeddings_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
+
+            uncond_embeddings = uncond_embeddings_text_encoder_output.text_embeds
+            uncond_text_encoder_hidden_states = uncond_embeddings_text_encoder_output.last_hidden_state
+
+            # 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)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len)
+
+            seq_len = uncond_text_encoder_hidden_states.shape[1]
+            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
+            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
+                batch_size * num_images_per_prompt, seq_len, -1
+            )
+            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
+
+            # done duplicates
+
+            # 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])
+            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
+
+            text_mask = torch.cat([uncond_text_mask, text_mask])
+
+        return text_embeddings, text_encoder_hidden_states, text_mask
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
+        models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
+        when their specific submodule has its `forward` method called.
+        """
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        # TODO: self.prior.post_process_latents is not covered by the offload hooks, so it fails if added to the list
+        models = [
+            self.decoder,
+            self.text_proj,
+            self.text_encoder,
+            self.super_res_first,
+            self.super_res_last,
+        ]
+        for cpu_offloaded_model in models:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @property
+    def _execution_device(self):
+        r"""
+        Returns the device on which the pipeline's models will be executed. After calling
+        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
+        hooks.
+        """
+        if self.device != torch.device("meta") or not hasattr(self.decoder, "_hf_hook"):
+            return self.device
+        for module in self.decoder.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: int = 1,
+        prior_num_inference_steps: int = 25,
+        decoder_num_inference_steps: int = 25,
+        super_res_num_inference_steps: int = 7,
+        generator: Optional[torch.Generator] = None,
+        prior_latents: Optional[torch.FloatTensor] = None,
+        decoder_latents: Optional[torch.FloatTensor] = None,
+        super_res_latents: Optional[torch.FloatTensor] = None,
+        text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
+        text_attention_mask: Optional[torch.Tensor] = None,
+        prior_guidance_scale: float = 4.0,
+        decoder_guidance_scale: float = 8.0,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ):
+        """
+        Function invoked when calling the pipeline for generation.
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation. This can only be left undefined if
+                `text_model_output` and `text_attention_mask` is passed.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            prior_num_inference_steps (`int`, *optional*, defaults to 25):
+                The number of denoising steps for the prior. More denoising steps usually lead to a higher quality
+                image at the expense of slower inference.
+            decoder_num_inference_steps (`int`, *optional*, defaults to 25):
+                The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
+                image at the expense of slower inference.
+            super_res_num_inference_steps (`int`, *optional*, defaults to 7):
+                The number of denoising steps for super resolution. More denoising steps usually lead to a higher
+                quality image at the expense of slower inference.
+            generator (`torch.Generator`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            prior_latents (`torch.FloatTensor` of shape (batch size, embeddings dimension), *optional*):
+                Pre-generated noisy latents to be used as inputs for the prior.
+            decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*):
+                Pre-generated noisy latents to be used as inputs for the decoder.
+            super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*):
+                Pre-generated noisy latents to be used as inputs for the decoder.
+            prior_guidance_scale (`float`, *optional*, defaults to 4.0):
+                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.
+            decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
+                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.
+            text_model_output (`CLIPTextModelOutput`, *optional*):
+                Pre-defined CLIPTextModel outputs that can be derived from the text encoder. Pre-defined text outputs
+                can be passed for tasks like text embedding interpolations. Make sure to also pass
+                `text_attention_mask` in this case. `prompt` can the be left to `None`.
+            text_attention_mask (`torch.Tensor`, *optional*):
+                Pre-defined CLIP text attention mask that can be derived from the tokenizer. Pre-defined text attention
+                masks are necessary when passing `text_model_output`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
+        """
+        if prompt is not None:
+            if isinstance(prompt, str):
+                batch_size = 1
+            elif isinstance(prompt, list):
+                batch_size = len(prompt)
+            else:
+                raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        else:
+            batch_size = text_model_output[0].shape[0]
+
+        device = self._execution_device
+
+        batch_size = batch_size * num_images_per_prompt
+
+        do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
+
+        text_embeddings, text_encoder_hidden_states, text_mask = self._encode_prompt(
+            prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output, text_attention_mask
+        )
+
+        # prior
+
+        self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)
+        prior_timesteps_tensor = self.prior_scheduler.timesteps
+
+        embedding_dim = self.prior.config.embedding_dim
+
+        prior_latents = self.prepare_latents(
+            (batch_size, embedding_dim),
+            text_embeddings.dtype,
+            device,
+            generator,
+            prior_latents,
+            self.prior_scheduler,
+        )
+
+        for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents
+
+            predicted_image_embedding = self.prior(
+                latent_model_input,
+                timestep=t,
+                proj_embedding=text_embeddings,
+                encoder_hidden_states=text_encoder_hidden_states,
+                attention_mask=text_mask,
+            ).predicted_image_embedding
+
+            if do_classifier_free_guidance:
+                predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
+                predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
+                    predicted_image_embedding_text - predicted_image_embedding_uncond
+                )
+
+            if i + 1 == prior_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = prior_timesteps_tensor[i + 1]
+
+            prior_latents = self.prior_scheduler.step(
+                predicted_image_embedding,
+                timestep=t,
+                sample=prior_latents,
+                generator=generator,
+                prev_timestep=prev_timestep,
+            ).prev_sample
+
+        prior_latents = self.prior.post_process_latents(prior_latents)
+
+        image_embeddings = prior_latents
+
+        # done prior
+
+        # decoder
+
+        text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
+            image_embeddings=image_embeddings,
+            text_embeddings=text_embeddings,
+            text_encoder_hidden_states=text_encoder_hidden_states,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+        )
+
+        decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)
+
+        self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)
+        decoder_timesteps_tensor = self.decoder_scheduler.timesteps
+
+        num_channels_latents = self.decoder.in_channels
+        height = self.decoder.sample_size
+        width = self.decoder.sample_size
+
+        decoder_latents = self.prepare_latents(
+            (batch_size, num_channels_latents, height, width),
+            text_encoder_hidden_states.dtype,
+            device,
+            generator,
+            decoder_latents,
+            self.decoder_scheduler,
+        )
+
+        for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
+
+            noise_pred = self.decoder(
+                sample=latent_model_input,
+                timestep=t,
+                encoder_hidden_states=text_encoder_hidden_states,
+                class_labels=additive_clip_time_embeddings,
+                attention_mask=decoder_text_mask,
+            ).sample
+
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)
+                noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)
+                noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
+                noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
+
+            if i + 1 == decoder_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = decoder_timesteps_tensor[i + 1]
+
+            # compute the previous noisy sample x_t -> x_t-1
+            decoder_latents = self.decoder_scheduler.step(
+                noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
+            ).prev_sample
+
+        decoder_latents = decoder_latents.clamp(-1, 1)
+
+        image_small = decoder_latents
+
+        # done decoder
+
+        # super res
+
+        self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)
+        super_res_timesteps_tensor = self.super_res_scheduler.timesteps
+
+        channels = self.super_res_first.in_channels // 2
+        height = self.super_res_first.sample_size
+        width = self.super_res_first.sample_size
+
+        super_res_latents = self.prepare_latents(
+            (batch_size, channels, height, width),
+            image_small.dtype,
+            device,
+            generator,
+            super_res_latents,
+            self.super_res_scheduler,
+        )
+
+        interpolate_antialias = {}
+        if "antialias" in inspect.signature(F.interpolate).parameters:
+            interpolate_antialias["antialias"] = True
+
+        image_upscaled = F.interpolate(
+            image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
+        )
+
+        for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
+            # no classifier free guidance
+
+            if i == super_res_timesteps_tensor.shape[0] - 1:
+                unet = self.super_res_last
+            else:
+                unet = self.super_res_first
+
+            latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)
+
+            noise_pred = unet(
+                sample=latent_model_input,
+                timestep=t,
+            ).sample
+
+            if i + 1 == super_res_timesteps_tensor.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = super_res_timesteps_tensor[i + 1]
+
+            # compute the previous noisy sample x_t -> x_t-1
+            super_res_latents = self.super_res_scheduler.step(
+                noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
+            ).prev_sample
+
+        image = super_res_latents
+        # done super res
+
+        # post processing
+
+        image = image * 0.5 + 0.5
+        image = image.clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)

ldm/modules/karlo/kakao/models/clip.py

@@ -0,0 +1,182 @@
+# ------------------------------------------------------------------------------------
+# Karlo-v1.0.alpha
+# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
+# ------------------------------------------------------------------------------------
+# ------------------------------------------------------------------------------------
+# Adapted from OpenAI's CLIP (https://github.com/openai/CLIP/)
+# ------------------------------------------------------------------------------------
+
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import clip
+
+from clip.model import CLIP, convert_weights
+from clip.simple_tokenizer import SimpleTokenizer, default_bpe
+
+
+"""===== Monkey-Patching original CLIP for JIT compile ====="""
+
+
+class LayerNorm(nn.LayerNorm):
+    """Subclass torch's LayerNorm to handle fp16."""
+
+    def forward(self, x: torch.Tensor):
+        orig_type = x.dtype
+        ret = F.layer_norm(
+            x.type(torch.float32),
+            self.normalized_shape,
+            self.weight,
+            self.bias,
+            self.eps,
+        )
+        return ret.type(orig_type)
+
+
+clip.model.LayerNorm = LayerNorm
+delattr(clip.model.CLIP, "forward")
+
+"""===== End of Monkey-Patching ====="""
+
+
+class CustomizedCLIP(CLIP):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    @torch.jit.export
+    def encode_image(self, image):
+        return self.visual(image)
+
+    @torch.jit.export
+    def encode_text(self, text):
+        # re-define this function to return unpooled text features
+
+        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
+
+        x = x + self.positional_embedding.type(self.dtype)
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.transformer(x)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+        x = self.ln_final(x).type(self.dtype)
+
+        x_seq = x
+        # x.shape = [batch_size, n_ctx, transformer.width]
+        # take features from the eot embedding (eot_token is the highest number in each sequence)
+        x_out = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
+
+        return x_out, x_seq
+
+    @torch.jit.ignore
+    def forward(self, image, text):
+        super().forward(image, text)
+
+    @classmethod
+    def load_from_checkpoint(cls, ckpt_path: str):
+        state_dict = torch.load(ckpt_path, map_location="cpu").state_dict()
+
+        vit = "visual.proj" in state_dict
+        if vit:
+            vision_width = state_dict["visual.conv1.weight"].shape[0]
+            vision_layers = len(
+                [
+                    k
+                    for k in state_dict.keys()
+                    if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")
+                ]
+            )
+            vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
+            grid_size = round(
+                (state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5
+            )
+            image_resolution = vision_patch_size * grid_size
+        else:
+            counts: list = [
+                len(
+                    set(
+                        k.split(".")[2]
+                        for k in state_dict
+                        if k.startswith(f"visual.layer{b}")
+                    )
+                )
+                for b in [1, 2, 3, 4]
+            ]
+            vision_layers = tuple(counts)
+            vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
+            output_width = round(
+                (state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5
+            )
+            vision_patch_size = None
+            assert (
+                output_width**2 + 1
+                == state_dict["visual.attnpool.positional_embedding"].shape[0]
+            )
+            image_resolution = output_width * 32
+
+        embed_dim = state_dict["text_projection"].shape[1]
+        context_length = state_dict["positional_embedding"].shape[0]
+        vocab_size = state_dict["token_embedding.weight"].shape[0]
+        transformer_width = state_dict["ln_final.weight"].shape[0]
+        transformer_heads = transformer_width // 64
+        transformer_layers = len(
+            set(
+                k.split(".")[2]
+                for k in state_dict
+                if k.startswith("transformer.resblocks")
+            )
+        )
+
+        model = cls(
+            embed_dim,
+            image_resolution,
+            vision_layers,
+            vision_width,
+            vision_patch_size,
+            context_length,
+            vocab_size,
+            transformer_width,
+            transformer_heads,
+            transformer_layers,
+        )
+
+        for key in ["input_resolution", "context_length", "vocab_size"]:
+            if key in state_dict:
+                del state_dict[key]
+
+        convert_weights(model)
+        model.load_state_dict(state_dict)
+        model.eval()
+        model.float()
+        return model
+
+
+class CustomizedTokenizer(SimpleTokenizer):
+    def __init__(self):
+        super().__init__(bpe_path=default_bpe())
+
+        self.sot_token = self.encoder["<|startoftext|>"]
+        self.eot_token = self.encoder["<|endoftext|>"]
+
+    def padded_tokens_and_mask(self, texts, text_ctx):
+        assert isinstance(texts, list) and all(
+            isinstance(elem, str) for elem in texts
+        ), "texts should be a list of strings"
+
+        all_tokens = [
+            [self.sot_token] + self.encode(text) + [self.eot_token] for text in texts
+        ]
+
+        mask = [
+            [True] * min(text_ctx, len(tokens))
+            + [False] * max(text_ctx - len(tokens), 0)
+            for tokens in all_tokens
+        ]
+        mask = torch.tensor(mask, dtype=torch.bool)
+        result = torch.zeros(len(all_tokens), text_ctx, dtype=torch.int)
+        for i, tokens in enumerate(all_tokens):
+            if len(tokens) > text_ctx:
+                tokens = tokens[:text_ctx]
+                tokens[-1] = self.eot_token
+            result[i, : len(tokens)] = torch.tensor(tokens)
+
+        return result, mask

ldm/modules/karlo/kakao/models/decoder_model.py

@@ -0,0 +1,193 @@
+# ------------------------------------------------------------------------------------
+# Karlo-v1.0.alpha
+# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
+# ------------------------------------------------------------------------------------
+
+import copy
+import torch
+
+from ldm.modules.karlo.kakao.modules import create_gaussian_diffusion
+from ldm.modules.karlo.kakao.modules.unet import PLMImUNet
+
+
+class Text2ImProgressiveModel(torch.nn.Module):
+    """
+    A decoder that generates 64x64px images based on the text prompt.
+
+    :param config: yaml config to define the decoder.
+    :param tokenizer: tokenizer used in clip.
+    """
+
+    def __init__(
+        self,
+        config,
+        tokenizer,
+    ):
+        super().__init__()
+
+        self._conf = config
+        self._model_conf = config.model.hparams
+        self._diffusion_kwargs = dict(
+            steps=config.diffusion.steps,
+            learn_sigma=config.diffusion.learn_sigma,
+            sigma_small=config.diffusion.sigma_small,
+            noise_schedule=config.diffusion.noise_schedule,
+            use_kl=config.diffusion.use_kl,
+            predict_xstart=config.diffusion.predict_xstart,
+            rescale_learned_sigmas=config.diffusion.rescale_learned_sigmas,
+            timestep_respacing=config.diffusion.timestep_respacing,
+        )
+        self._tokenizer = tokenizer
+
+        self.model = self.create_plm_dec_model()
+
+        cf_token, cf_mask = self.set_cf_text_tensor()
+        self.register_buffer("cf_token", cf_token, persistent=False)
+        self.register_buffer("cf_mask", cf_mask, persistent=False)
+
+    @classmethod
+    def load_from_checkpoint(cls, config, tokenizer, ckpt_path, strict: bool = True):
+        ckpt = torch.load(ckpt_path, map_location="cpu")["state_dict"]
+
+        model = cls(config, tokenizer)
+        model.load_state_dict(ckpt, strict=strict)
+        return model
+
+    def create_plm_dec_model(self):
+        image_size = self._model_conf.image_size
+        if self._model_conf.channel_mult == "":
+            if image_size == 256:
+                channel_mult = (1, 1, 2, 2, 4, 4)
+            elif image_size == 128:
+                channel_mult = (1, 1, 2, 3, 4)
+            elif image_size == 64:
+                channel_mult = (1, 2, 3, 4)
+            else:
+                raise ValueError(f"unsupported image size: {image_size}")
+        else:
+            channel_mult = tuple(
+                int(ch_mult) for ch_mult in self._model_conf.channel_mult.split(",")
+            )
+            assert 2 ** (len(channel_mult) + 2) == image_size
+
+        attention_ds = []
+        for res in self._model_conf.attention_resolutions.split(","):
+            attention_ds.append(image_size // int(res))
+
+        return PLMImUNet(
+            text_ctx=self._model_conf.text_ctx,
+            xf_width=self._model_conf.xf_width,
+            in_channels=3,
+            model_channels=self._model_conf.num_channels,
+            out_channels=6 if self._model_conf.learn_sigma else 3,
+            num_res_blocks=self._model_conf.num_res_blocks,
+            attention_resolutions=tuple(attention_ds),
+            dropout=self._model_conf.dropout,
+            channel_mult=channel_mult,
+            num_heads=self._model_conf.num_heads,
+            num_head_channels=self._model_conf.num_head_channels,
+            num_heads_upsample=self._model_conf.num_heads_upsample,
+            use_scale_shift_norm=self._model_conf.use_scale_shift_norm,
+            resblock_updown=self._model_conf.resblock_updown,
+            clip_dim=self._model_conf.clip_dim,
+            clip_emb_mult=self._model_conf.clip_emb_mult,
+            clip_emb_type=self._model_conf.clip_emb_type,
+            clip_emb_drop=self._model_conf.clip_emb_drop,
+        )
+
+    def set_cf_text_tensor(self):
+        return self._tokenizer.padded_tokens_and_mask([""], self.model.text_ctx)
+
+    def get_sample_fn(self, timestep_respacing):
+        use_ddim = timestep_respacing.startswith(("ddim", "fast"))
+
+        diffusion_kwargs = copy.deepcopy(self._diffusion_kwargs)
+        diffusion_kwargs.update(timestep_respacing=timestep_respacing)
+        diffusion = create_gaussian_diffusion(**diffusion_kwargs)
+        sample_fn = (
+            diffusion.ddim_sample_loop_progressive
+            if use_ddim
+            else diffusion.p_sample_loop_progressive
+        )
+
+        return sample_fn
+
+    def forward(
+        self,
+        txt_feat,
+        txt_feat_seq,
+        tok,
+        mask,
+        img_feat=None,
+        cf_guidance_scales=None,
+        timestep_respacing=None,
+    ):
+        # cfg should be enabled in inference
+        assert cf_guidance_scales is not None and all(cf_guidance_scales > 0.0)
+        assert img_feat is not None
+
+        bsz = txt_feat.shape[0]
+        img_sz = self._model_conf.image_size
+
+        def guided_model_fn(x_t, ts, **kwargs):
+            half = x_t[: len(x_t) // 2]
+            combined = torch.cat([half, half], dim=0)
+            model_out = self.model(combined, ts, **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 + cf_guidance_scales.view(-1, 1, 1, 1) * (
+                cond_eps - uncond_eps
+            )
+            eps = torch.cat([half_eps, half_eps], dim=0)
+            return torch.cat([eps, rest], dim=1)
+
+        cf_feat = self.model.cf_param.unsqueeze(0)
+        cf_feat = cf_feat.expand(bsz // 2, -1)
+        feat = torch.cat([img_feat, cf_feat.to(txt_feat.device)], dim=0)
+
+        cond = {
+            "y": feat,
+            "txt_feat": txt_feat,
+            "txt_feat_seq": txt_feat_seq,
+            "mask": mask,
+        }
+        sample_fn = self.get_sample_fn(timestep_respacing)
+        sample_outputs = sample_fn(
+            guided_model_fn,
+            (bsz, 3, img_sz, img_sz),
+            noise=None,
+            device=txt_feat.device,
+            clip_denoised=True,
+            model_kwargs=cond,
+        )
+
+        for out in sample_outputs:
+            sample = out["sample"]
+            yield sample if cf_guidance_scales is None else sample[
+                : sample.shape[0] // 2
+            ]
+
+
+class Text2ImModel(Text2ImProgressiveModel):
+    def forward(
+        self,
+        txt_feat,
+        txt_feat_seq,
+        tok,
+        mask,
+        img_feat=None,
+        cf_guidance_scales=None,
+        timestep_respacing=None,
+    ):
+        last_out = None
+        for out in super().forward(
+            txt_feat,
+            txt_feat_seq,
+            tok,
+            mask,
+            img_feat,
+            cf_guidance_scales,
+            timestep_respacing,
+        ):
+            last_out = out
+        return last_out

ldm/modules/karlo/kakao/models/prior_model.py

@@ -0,0 +1,138 @@
+# ------------------------------------------------------------------------------------
+# Karlo-v1.0.alpha
+# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
+# ------------------------------------------------------------------------------------
+
+import copy
+import torch
+
+from ldm.modules.karlo.kakao.modules import create_gaussian_diffusion
+from ldm.modules.karlo.kakao.modules.xf import PriorTransformer
+
+
+class PriorDiffusionModel(torch.nn.Module):
+    """
+    A prior that generates clip image feature based on the text prompt.
+
+    :param config: yaml config to define the decoder.
+    :param tokenizer: tokenizer used in clip.
+    :param clip_mean: mean to normalize the clip image feature (zero-mean, unit variance).
+    :param clip_std: std to noramlize the clip image feature (zero-mean, unit variance).
+    """
+
+    def __init__(self, config, tokenizer, clip_mean, clip_std):
+        super().__init__()
+
+        self._conf = config
+        self._model_conf = config.model.hparams
+        self._diffusion_kwargs = dict(
+            steps=config.diffusion.steps,
+            learn_sigma=config.diffusion.learn_sigma,
+            sigma_small=config.diffusion.sigma_small,
+            noise_schedule=config.diffusion.noise_schedule,
+            use_kl=config.diffusion.use_kl,
+            predict_xstart=config.diffusion.predict_xstart,
+            rescale_learned_sigmas=config.diffusion.rescale_learned_sigmas,
+            timestep_respacing=config.diffusion.timestep_respacing,
+        )
+        self._tokenizer = tokenizer
+
+        self.register_buffer("clip_mean", clip_mean[None, :], persistent=False)
+        self.register_buffer("clip_std", clip_std[None, :], persistent=False)
+
+        causal_mask = self.get_causal_mask()
+        self.register_buffer("causal_mask", causal_mask, persistent=False)
+
+        self.model = PriorTransformer(
+            text_ctx=self._model_conf.text_ctx,
+            xf_width=self._model_conf.xf_width,
+            xf_layers=self._model_conf.xf_layers,
+            xf_heads=self._model_conf.xf_heads,
+            xf_final_ln=self._model_conf.xf_final_ln,
+            clip_dim=self._model_conf.clip_dim,
+        )
+
+        cf_token, cf_mask = self.set_cf_text_tensor()
+        self.register_buffer("cf_token", cf_token, persistent=False)
+        self.register_buffer("cf_mask", cf_mask, persistent=False)
+
+    @classmethod
+    def load_from_checkpoint(
+        cls, config, tokenizer, clip_mean, clip_std, ckpt_path, strict: bool = True
+    ):
+        ckpt = torch.load(ckpt_path, map_location="cpu")["state_dict"]
+
+        model = cls(config, tokenizer, clip_mean, clip_std)
+        model.load_state_dict(ckpt, strict=strict)
+        return model
+
+    def set_cf_text_tensor(self):
+        return self._tokenizer.padded_tokens_and_mask([""], self.model.text_ctx)
+
+    def get_sample_fn(self, timestep_respacing):
+        use_ddim = timestep_respacing.startswith(("ddim", "fast"))
+
+        diffusion_kwargs = copy.deepcopy(self._diffusion_kwargs)
+        diffusion_kwargs.update(timestep_respacing=timestep_respacing)
+        diffusion = create_gaussian_diffusion(**diffusion_kwargs)
+        sample_fn = diffusion.ddim_sample_loop if use_ddim else diffusion.p_sample_loop
+
+        return sample_fn
+
+    def get_causal_mask(self):
+        seq_len = self._model_conf.text_ctx + 4
+        mask = torch.empty(seq_len, seq_len)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)
+        mask = mask[None, ...]
+        return mask
+
+    def forward(
+        self,
+        txt_feat,
+        txt_feat_seq,
+        mask,
+        cf_guidance_scales=None,
+        timestep_respacing=None,
+        denoised_fn=True,
+    ):
+        # cfg should be enabled in inference
+        assert cf_guidance_scales is not None and all(cf_guidance_scales > 0.0)
+
+        bsz_ = txt_feat.shape[0]
+        bsz = bsz_ // 2
+
+        def guided_model_fn(x_t, ts, **kwargs):
+            half = x_t[: len(x_t) // 2]
+            combined = torch.cat([half, half], dim=0)
+            model_out = self.model(combined, ts, **kwargs)
+            eps, rest = (
+                model_out[:, : int(x_t.shape[1])],
+                model_out[:, int(x_t.shape[1]) :],
+            )
+            cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+            half_eps = uncond_eps + cf_guidance_scales.view(-1, 1) * (
+                cond_eps - uncond_eps
+            )
+            eps = torch.cat([half_eps, half_eps], dim=0)
+            return torch.cat([eps, rest], dim=1)
+
+        cond = {
+            "text_emb": txt_feat,
+            "text_enc": txt_feat_seq,
+            "mask": mask,
+            "causal_mask": self.causal_mask,
+        }
+        sample_fn = self.get_sample_fn(timestep_respacing)
+        sample = sample_fn(
+            guided_model_fn,
+            (bsz_, self.model.clip_dim),
+            noise=None,
+            device=txt_feat.device,
+            clip_denoised=False,
+            denoised_fn=lambda x: torch.clamp(x, -10, 10),
+            model_kwargs=cond,
+        )
+        sample = (sample * self.clip_std) + self.clip_mean
+
+        return sample[:bsz]

ldm/modules/karlo/kakao/models/sr_256_1k.py

@@ -0,0 +1,10 @@
+# ------------------------------------------------------------------------------------
+# Karlo-v1.0.alpha
+# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
+# ------------------------------------------------------------------------------------
+
+from ldm.modules.karlo.kakao.models.sr_64_256 import SupRes64to256Progressive
+
+
+class SupRes256to1kProgressive(SupRes64to256Progressive):
+    pass  # no difference currently

ldm/modules/karlo/kakao/models/sr_64_256.py

@@ -0,0 +1,88 @@
+# ------------------------------------------------------------------------------------
+# Karlo-v1.0.alpha
+# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
+# ------------------------------------------------------------------------------------
+
+import copy
+import torch
+
+from ldm.modules.karlo.kakao.modules.unet import SuperResUNetModel
+from ldm.modules.karlo.kakao.modules import create_gaussian_diffusion
+
+
+class ImprovedSupRes64to256ProgressiveModel(torch.nn.Module):
+    """
+    ImprovedSR model fine-tunes the pretrained DDPM-based SR model by using adversarial and perceptual losses.
+    In specific, the low-resolution sample is iteratively recovered by 6 steps with the frozen pretrained SR model.
+    In the following additional one step, a seperate fine-tuned model recovers high-frequency details.
+    This approach greatly improves the fidelity of images of 256x256px, even with small number of reverse steps.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+
+        self._config = config
+        self._diffusion_kwargs = dict(
+            steps=config.diffusion.steps,
+            learn_sigma=config.diffusion.learn_sigma,
+            sigma_small=config.diffusion.sigma_small,
+            noise_schedule=config.diffusion.noise_schedule,
+            use_kl=config.diffusion.use_kl,
+            predict_xstart=config.diffusion.predict_xstart,
+            rescale_learned_sigmas=config.diffusion.rescale_learned_sigmas,
+        )
+
+        self.model_first_steps = SuperResUNetModel(
+            in_channels=3,  # auto-changed to 6 inside the model
+            model_channels=config.model.hparams.channels,
+            out_channels=3,
+            num_res_blocks=config.model.hparams.depth,
+            attention_resolutions=(),  # no attention
+            dropout=config.model.hparams.dropout,
+            channel_mult=config.model.hparams.channels_multiple,
+            resblock_updown=True,
+            use_middle_attention=False,
+        )
+        self.model_last_step = SuperResUNetModel(
+            in_channels=3,  # auto-changed to 6 inside the model
+            model_channels=config.model.hparams.channels,
+            out_channels=3,
+            num_res_blocks=config.model.hparams.depth,
+            attention_resolutions=(),  # no attention
+            dropout=config.model.hparams.dropout,
+            channel_mult=config.model.hparams.channels_multiple,
+            resblock_updown=True,
+            use_middle_attention=False,
+        )
+
+    @classmethod
+    def load_from_checkpoint(cls, config, ckpt_path, strict: bool = True):
+        ckpt = torch.load(ckpt_path, map_location="cpu")["state_dict"]
+
+        model = cls(config)
+        model.load_state_dict(ckpt, strict=strict)
+        return model
+
+    def get_sample_fn(self, timestep_respacing):
+        diffusion_kwargs = copy.deepcopy(self._diffusion_kwargs)
+        diffusion_kwargs.update(timestep_respacing=timestep_respacing)
+        diffusion = create_gaussian_diffusion(**diffusion_kwargs)
+        return diffusion.p_sample_loop_progressive_for_improved_sr
+
+    def forward(self, low_res, timestep_respacing="7", **kwargs):
+        assert (
+            timestep_respacing == "7"
+        ), "different respacing method may work, but no guaranteed"
+
+        sample_fn = self.get_sample_fn(timestep_respacing)
+        sample_outputs = sample_fn(
+            self.model_first_steps,
+            self.model_last_step,
+            shape=low_res.shape,
+            clip_denoised=True,
+            model_kwargs=dict(low_res=low_res),
+            **kwargs,
+        )
+        for x in sample_outputs:
+            sample = x["sample"]
+            yield sample

ldm/modules/karlo/kakao/modules/__init__.py

@@ -0,0 +1,49 @@
+# ------------------------------------------------------------------------------------
+# Adapted from Guided-Diffusion repo (https://github.com/openai/guided-diffusion)
+# ------------------------------------------------------------------------------------
+
+
+from .diffusion import gaussian_diffusion as gd
+from .diffusion.respace import (
+    SpacedDiffusion,
+    space_timesteps,
+)
+
+
+def create_gaussian_diffusion(
+    steps,
+    learn_sigma,
+    sigma_small,
+    noise_schedule,
+    use_kl,
+    predict_xstart,
+    rescale_learned_sigmas,
+    timestep_respacing,
+):
+    betas = gd.get_named_beta_schedule(noise_schedule, steps)
+    if use_kl:
+        loss_type = gd.LossType.RESCALED_KL
+    elif rescale_learned_sigmas:
+        loss_type = gd.LossType.RESCALED_MSE
+    else:
+        loss_type = gd.LossType.MSE
+    if not timestep_respacing:
+        timestep_respacing = [steps]
+
+    return SpacedDiffusion(
+        use_timesteps=space_timesteps(steps, timestep_respacing),
+        betas=betas,
+        model_mean_type=(
+            gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X
+        ),
+        model_var_type=(
+            (
+                gd.ModelVarType.FIXED_LARGE
+                if not sigma_small
+                else gd.ModelVarType.FIXED_SMALL
+            )
+            if not learn_sigma
+            else gd.ModelVarType.LEARNED_RANGE
+        ),
+        loss_type=loss_type,
+    )

ldm/modules/karlo/kakao/modules/diffusion/gaussian_diffusion.py

@@ -0,0 +1,828 @@
+# ------------------------------------------------------------------------------------
+# Adapted from Guided-Diffusion repo (https://github.com/openai/guided-diffusion)
+# ------------------------------------------------------------------------------------
+
+import enum
+import math
+
+import numpy as np
+import torch as th
+
+
+def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_frac):
+    betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
+    warmup_time = int(num_diffusion_timesteps * warmup_frac)
+    betas[:warmup_time] = np.linspace(
+        beta_start, beta_end, warmup_time, dtype=np.float64
+    )
+    return betas
+
+
+def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps):
+    """
+    This is the deprecated API for creating beta schedules.
+    See get_named_beta_schedule() for the new library of schedules.
+    """
+    if beta_schedule == "quad":
+        betas = (
+            np.linspace(
+                beta_start**0.5,
+                beta_end**0.5,
+                num_diffusion_timesteps,
+                dtype=np.float64,
+            )
+            ** 2
+        )
+    elif beta_schedule == "linear":
+        betas = np.linspace(
+            beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
+        )
+    elif beta_schedule == "warmup10":
+        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.1)
+    elif beta_schedule == "warmup50":
+        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.5)
+    elif beta_schedule == "const":
+        betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
+    elif beta_schedule == "jsd":  # 1/T, 1/(T-1), 1/(T-2), ..., 1
+        betas = 1.0 / np.linspace(
+            num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64
+        )
+    else:
+        raise NotImplementedError(beta_schedule)
+    assert betas.shape == (num_diffusion_timesteps,)
+    return betas
+
+
+def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
+    """
+    Get a pre-defined beta schedule for the given name.
+    The beta schedule library consists of beta schedules which remain similar
+    in the limit of num_diffusion_timesteps.
+    Beta schedules may be added, but should not be removed or changed once
+    they are committed to maintain backwards compatibility.
+    """
+    if schedule_name == "linear":
+        # Linear schedule from Ho et al, extended to work for any number of
+        # diffusion steps.
+        scale = 1000 / num_diffusion_timesteps
+        return get_beta_schedule(
+            "linear",
+            beta_start=scale * 0.0001,
+            beta_end=scale * 0.02,
+            num_diffusion_timesteps=num_diffusion_timesteps,
+        )
+    elif schedule_name == "squaredcos_cap_v2":
+        return betas_for_alpha_bar(
+            num_diffusion_timesteps,
+            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+        )
+    else:
+        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+
+class ModelMeanType(enum.Enum):
+    """
+    Which type of output the model predicts.
+    """
+
+    PREVIOUS_X = enum.auto()  # the model predicts x_{t-1}
+    START_X = enum.auto()  # the model predicts x_0
+    EPSILON = enum.auto()  # the model predicts epsilon
+
+
+class ModelVarType(enum.Enum):
+    """
+    What is used as the model's output variance.
+    The LEARNED_RANGE option has been added to allow the model to predict
+    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
+    """
+
+    LEARNED = enum.auto()
+    FIXED_SMALL = enum.auto()
+    FIXED_LARGE = enum.auto()
+    LEARNED_RANGE = enum.auto()
+
+
+class LossType(enum.Enum):
+    MSE = enum.auto()  # use raw MSE loss (and KL when learning variances)
+    RESCALED_MSE = (
+        enum.auto()
+    )  # use raw MSE loss (with RESCALED_KL when learning variances)
+    KL = enum.auto()  # use the variational lower-bound
+    RESCALED_KL = enum.auto()  # like KL, but rescale to estimate the full VLB
+
+    def is_vb(self):
+        return self == LossType.KL or self == LossType.RESCALED_KL
+
+
+class GaussianDiffusion(th.nn.Module):
+    """
+    Utilities for training and sampling diffusion models.
+    Original ported from this codebase:
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
+    :param betas: a 1-D numpy array of betas for each diffusion timestep,
+                  starting at T and going to 1.
+    """
+
+    def __init__(
+        self,
+        *,
+        betas,
+        model_mean_type,
+        model_var_type,
+        loss_type,
+    ):
+        super(GaussianDiffusion, self).__init__()
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        self.loss_type = loss_type
+
+        # Use float64 for accuracy.
+        betas = np.array(betas, dtype=np.float64)
+        assert len(betas.shape) == 1, "betas must be 1-D"
+        assert (betas > 0).all() and (betas <= 1).all()
+
+        self.num_timesteps = int(betas.shape[0])
+
+        alphas = 1.0 - betas
+        alphas_cumprod = np.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
+        alphas_cumprod_next = np.append(alphas_cumprod[1:], 0.0)
+        assert alphas_cumprod_prev.shape == (self.num_timesteps,)
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        sqrt_alphas_cumprod = np.sqrt(alphas_cumprod)
+        sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - alphas_cumprod)
+        log_one_minus_alphas_cumprod = np.log(1.0 - alphas_cumprod)
+        sqrt_recip_alphas_cumprod = np.sqrt(1.0 / alphas_cumprod)
+        sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / alphas_cumprod - 1)
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = (
+            betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
+        )
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        posterior_log_variance_clipped = np.log(
+            np.append(posterior_variance[1], posterior_variance[1:])
+        )
+        posterior_mean_coef1 = (
+            betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)
+        )
+        posterior_mean_coef2 = (
+            (1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
+        )
+
+        self.register_buffer("betas", th.from_numpy(betas), persistent=False)
+        self.register_buffer(
+            "alphas_cumprod", th.from_numpy(alphas_cumprod), persistent=False
+        )
+        self.register_buffer(
+            "alphas_cumprod_prev", th.from_numpy(alphas_cumprod_prev), persistent=False
+        )
+        self.register_buffer(
+            "alphas_cumprod_next", th.from_numpy(alphas_cumprod_next), persistent=False
+        )
+
+        self.register_buffer(
+            "sqrt_alphas_cumprod", th.from_numpy(sqrt_alphas_cumprod), persistent=False
+        )
+        self.register_buffer(
+            "sqrt_one_minus_alphas_cumprod",
+            th.from_numpy(sqrt_one_minus_alphas_cumprod),
+            persistent=False,
+        )
+        self.register_buffer(
+            "log_one_minus_alphas_cumprod",
+            th.from_numpy(log_one_minus_alphas_cumprod),
+            persistent=False,
+        )
+        self.register_buffer(
+            "sqrt_recip_alphas_cumprod",
+            th.from_numpy(sqrt_recip_alphas_cumprod),
+            persistent=False,
+        )
+        self.register_buffer(
+            "sqrt_recipm1_alphas_cumprod",
+            th.from_numpy(sqrt_recipm1_alphas_cumprod),
+            persistent=False,
+        )
+
+        self.register_buffer(
+            "posterior_variance", th.from_numpy(posterior_variance), persistent=False
+        )
+        self.register_buffer(
+            "posterior_log_variance_clipped",
+            th.from_numpy(posterior_log_variance_clipped),
+            persistent=False,
+        )
+        self.register_buffer(
+            "posterior_mean_coef1",
+            th.from_numpy(posterior_mean_coef1),
+            persistent=False,
+        )
+        self.register_buffer(
+            "posterior_mean_coef2",
+            th.from_numpy(posterior_mean_coef2),
+            persistent=False,
+        )
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+        )
+        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = _extract_into_tensor(
+            self.log_one_minus_alphas_cumprod, t, x_start.shape
+        )
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data for a given number of diffusion steps.
+        In other words, sample from q(x_t | x_0).
+        :param x_start: the initial data batch.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :param noise: if specified, the split-out normal noise.
+        :return: A noisy version of x_start.
+        """
+        if noise is None:
+            noise = th.randn_like(x_start)
+        assert noise.shape == x_start.shape
+        return (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+            + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
+            * noise
+        )
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior:
+            q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+            + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = _extract_into_tensor(
+            self.posterior_log_variance_clipped, t, x_t.shape
+        )
+        assert (
+            posterior_mean.shape[0]
+            == posterior_variance.shape[0]
+            == posterior_log_variance_clipped.shape[0]
+            == x_start.shape[0]
+        )
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        model_kwargs=None,
+        **ignore_kwargs,
+    ):
+        """
+        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
+        the initial x, x_0.
+        :param model: the model, which takes a signal and a batch of timesteps
+                      as input.
+        :param x: the [N x C x ...] tensor at time t.
+        :param t: a 1-D Tensor of timesteps.
+        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample. Applies before
+            clip_denoised.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict with the following keys:
+                 - 'mean': the model mean output.
+                 - 'variance': the model variance output.
+                 - 'log_variance': the log of 'variance'.
+                 - 'pred_xstart': the prediction for x_0.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+
+        B, C = x.shape[:2]
+        assert t.shape == (B,)
+        model_output = model(x, t, **model_kwargs)
+        if isinstance(model_output, tuple):
+            model_output, extra = model_output
+        else:
+            extra = None
+
+        if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
+            assert model_output.shape == (B, C * 2, *x.shape[2:])
+            model_output, model_var_values = th.split(model_output, C, dim=1)
+            if self.model_var_type == ModelVarType.LEARNED:
+                model_log_variance = model_var_values
+                model_variance = th.exp(model_log_variance)
+            else:
+                min_log = _extract_into_tensor(
+                    self.posterior_log_variance_clipped, t, x.shape
+                )
+                max_log = _extract_into_tensor(th.log(self.betas), t, x.shape)
+                # The model_var_values is [-1, 1] for [min_var, max_var].
+                frac = (model_var_values + 1) / 2
+                model_log_variance = frac * max_log + (1 - frac) * min_log
+                model_variance = th.exp(model_log_variance)
+        else:
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so
+                # to get a better decoder log likelihood.
+                ModelVarType.FIXED_LARGE: (
+                    th.cat([self.posterior_variance[1][None], self.betas[1:]]),
+                    th.log(th.cat([self.posterior_variance[1][None], self.betas[1:]])),
+                ),
+                ModelVarType.FIXED_SMALL: (
+                    self.posterior_variance,
+                    self.posterior_log_variance_clipped,
+                ),
+            }[self.model_var_type]
+            model_variance = _extract_into_tensor(model_variance, t, x.shape)
+            model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
+
+        def process_xstart(x):
+            if denoised_fn is not None:
+                x = denoised_fn(x)
+            if clip_denoised:
+                return x.clamp(-1, 1)
+            return x
+
+        if self.model_mean_type == ModelMeanType.PREVIOUS_X:
+            pred_xstart = process_xstart(
+                self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
+            )
+            model_mean = model_output
+        elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
+            if self.model_mean_type == ModelMeanType.START_X:
+                pred_xstart = process_xstart(model_output)
+            else:
+                pred_xstart = process_xstart(
+                    self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
+                )
+            model_mean, _, _ = self.q_posterior_mean_variance(
+                x_start=pred_xstart, x_t=x, t=t
+            )
+        else:
+            raise NotImplementedError(self.model_mean_type)
+
+        assert (
+            model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
+        )
+        return {
+            "mean": model_mean,
+            "variance": model_variance,
+            "log_variance": model_log_variance,
+            "pred_xstart": pred_xstart,
+        }
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
+        )
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - pred_xstart
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute the mean for the previous step, given a function cond_fn that
+        computes the gradient of a conditional log probability with respect to
+        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
+        condition on y.
+        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
+        """
+        gradient = cond_fn(x, t, **model_kwargs)
+        new_mean = (
+            p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
+        )
+        return new_mean
+
+    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute what the p_mean_variance output would have been, should the
+        model's score function be conditioned by cond_fn.
+        See condition_mean() for details on cond_fn.
+        Unlike condition_mean(), this instead uses the conditioning strategy
+        from Song et al (2020).
+        """
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+
+        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
+        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, t, **model_kwargs)
+
+        out = p_mean_var.copy()
+        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
+        out["mean"], _, _ = self.q_posterior_mean_variance(
+            x_start=out["pred_xstart"], x_t=x, t=t
+        )
+        return out
+
+    def p_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+    ):
+        """
+        Sample x_{t-1} from the model at the given timestep.
+        :param model: the model to sample from.
+        :param x: the current tensor at x_{t-1}.
+        :param t: the value of t, starting at 0 for the first diffusion step.
+        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict containing the following keys:
+                 - 'sample': a random sample from the model.
+                 - 'pred_xstart': a prediction of x_0.
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        noise = th.randn_like(x)
+        nonzero_mask = (
+            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        )  # no noise when t == 0
+        if cond_fn is not None:
+            out["mean"] = self.condition_mean(
+                cond_fn, out, x, t, model_kwargs=model_kwargs
+            )
+        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def p_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model.
+        :param model: the model module.
+        :param shape: the shape of the samples, (N, C, H, W).
+        :param noise: if specified, the noise from the encoder to sample.
+                      Should be of the same shape as `shape`.
+        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param device: if specified, the device to create the samples on.
+                       If not specified, use a model parameter's device.
+        :param progress: if True, show a tqdm progress bar.
+        :return: a non-differentiable batch of samples.
+        """
+        final = None
+        for sample in self.p_sample_loop_progressive(
+            model,
+            shape,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+        ):
+            final = sample
+        return final["sample"]
+
+    def p_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model and yield intermediate samples from
+        each timestep of diffusion.
+        Arguments are the same as p_sample_loop().
+        Returns a generator over dicts, where each dict is the return value of
+        p_sample().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for idx, i in enumerate(indices):
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.p_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                )
+                yield out
+                img = out["sample"]
+
+    def p_sample_loop_progressive_for_improved_sr(
+        self,
+        model,
+        model_aux,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Modified version of p_sample_loop_progressive for sampling from the improved sr model
+        """
+
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for idx, i in enumerate(indices):
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.p_sample(
+                    model_aux if len(indices) - 1 == idx else model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                )
+                yield out
+                img = out["sample"]
+
+    def ddim_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t-1} from the model using DDIM.
+        Same usage as p_sample().
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
+
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
+
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
+        sigma = (
+            eta
+            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
+            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
+        )
+        # Equation 12.
+        noise = th.randn_like(x)
+        mean_pred = (
+            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
+            + th.sqrt(1 - alpha_bar_prev - sigma**2) * eps
+        )
+        nonzero_mask = (
+            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        )  # no noise when t == 0
+        sample = mean_pred + nonzero_mask * sigma * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_reverse_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t+1} from the model using DDIM reverse ODE.
+        """
+        assert eta == 0.0, "Reverse ODE only for deterministic path"
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
+            - out["pred_xstart"]
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
+        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
+
+        # Equation 12. reversed
+        mean_pred = (
+            out["pred_xstart"] * th.sqrt(alpha_bar_next)
+            + th.sqrt(1 - alpha_bar_next) * eps
+        )
+
+        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Generate samples from the model using DDIM.
+        Same usage as p_sample_loop().
+        """
+        final = None
+        for sample in self.ddim_sample_loop_progressive(
+            model,
+            shape,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+            eta=eta,
+        ):
+            final = sample
+        return final["sample"]
+
+    def ddim_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Use DDIM to sample from the model and yield intermediate samples from
+        each timestep of DDIM.
+        Same usage as p_sample_loop_progressive().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.ddim_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                    eta=eta,
+                )
+                yield out
+                img = out["sample"]
+
+
+def _extract_into_tensor(arr, timesteps, broadcast_shape):
+    """
+    Extract values from a 1-D numpy array for a batch of indices.
+    :param arr: the 1-D numpy array.
+    :param timesteps: a tensor of indices into the array to extract.
+    :param broadcast_shape: a larger shape of K dimensions with the batch
+                            dimension equal to the length of timesteps.
+    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
+    """
+    res = arr.to(device=timesteps.device)[timesteps].float()
+    while len(res.shape) < len(broadcast_shape):
+        res = res[..., None]
+    return res + th.zeros(broadcast_shape, device=timesteps.device)

ldm/modules/karlo/kakao/modules/diffusion/respace.py

@@ -0,0 +1,112 @@
+# ------------------------------------------------------------------------------------
+# Adapted from Guided-Diffusion repo (https://github.com/openai/guided-diffusion)
+# ------------------------------------------------------------------------------------
+
+
+import torch as th
+
+from .gaussian_diffusion import GaussianDiffusion
+
+
+def space_timesteps(num_timesteps, section_counts):
+    """
+    Create a list of timesteps to use from an original diffusion process,
+    given the number of timesteps we want to take from equally-sized portions
+    of the original process.
+
+    For example, if there's 300 timesteps and the section counts are [10,15,20]
+    then the first 100 timesteps are strided to be 10 timesteps, the second 100
+    are strided to be 15 timesteps, and the final 100 are strided to be 20.
+
+    :param num_timesteps: the number of diffusion steps in the original
+                          process to divide up.
+    :param section_counts: either a list of numbers, or a string containing
+                           comma-separated numbers, indicating the step count
+                           per section. As a special case, use "ddimN" where N
+                           is a number of steps to use the striding from the
+                           DDIM paper.
+    :return: a set of diffusion steps from the original process to use.
+    """
+    if isinstance(section_counts, str):
+        if section_counts.startswith("ddim"):
+            desired_count = int(section_counts[len("ddim") :])
+            for i in range(1, num_timesteps):
+                if len(range(0, num_timesteps, i)) == desired_count:
+                    return set(range(0, num_timesteps, i))
+            raise ValueError(
+                f"cannot create exactly {num_timesteps} steps with an integer stride"
+            )
+        elif section_counts == "fast27":
+            steps = space_timesteps(num_timesteps, "10,10,3,2,2")
+            # Help reduce DDIM artifacts from noisiest timesteps.
+            steps.remove(num_timesteps - 1)
+            steps.add(num_timesteps - 3)
+            return steps
+        section_counts = [int(x) for x in section_counts.split(",")]
+    size_per = num_timesteps // len(section_counts)
+    extra = num_timesteps % len(section_counts)
+    start_idx = 0
+    all_steps = []
+    for i, section_count in enumerate(section_counts):
+        size = size_per + (1 if i < extra else 0)
+        if size < section_count:
+            raise ValueError(
+                f"cannot divide section of {size} steps into {section_count}"
+            )
+        if section_count <= 1:
+            frac_stride = 1
+        else:
+            frac_stride = (size - 1) / (section_count - 1)
+        cur_idx = 0.0
+        taken_steps = []
+        for _ in range(section_count):
+            taken_steps.append(start_idx + round(cur_idx))
+            cur_idx += frac_stride
+        all_steps += taken_steps
+        start_idx += size
+    return set(all_steps)
+
+
+class SpacedDiffusion(GaussianDiffusion):
+    """
+    A diffusion process which can skip steps in a base diffusion process.
+
+    :param use_timesteps: a collection (sequence or set) of timesteps from the
+                          original diffusion process to retain.
+    :param kwargs: the kwargs to create the base diffusion process.
+    """
+
+    def __init__(self, use_timesteps, **kwargs):
+        self.use_timesteps = set(use_timesteps)
+        self.original_num_steps = len(kwargs["betas"])
+
+        base_diffusion = GaussianDiffusion(**kwargs)  # pylint: disable=missing-kwoa
+        last_alpha_cumprod = 1.0
+        new_betas = []
+        timestep_map = []
+        for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
+            if i in self.use_timesteps:
+                new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
+                last_alpha_cumprod = alpha_cumprod
+                timestep_map.append(i)
+        kwargs["betas"] = th.tensor(new_betas).numpy()
+        super().__init__(**kwargs)
+        self.register_buffer("timestep_map", th.tensor(timestep_map), persistent=False)
+
+    def p_mean_variance(self, model, *args, **kwargs):
+        return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
+
+    def condition_mean(self, cond_fn, *args, **kwargs):
+        return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def condition_score(self, cond_fn, *args, **kwargs):
+        return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def _wrap_model(self, model):
+        def wrapped(x, ts, **kwargs):
+            ts_cpu = ts.detach().to("cpu")
+            return model(
+                x, self.timestep_map[ts_cpu].to(device=ts.device, dtype=ts.dtype), **kwargs
+            )
+
+        return wrapped

ldm/modules/karlo/kakao/modules/nn.py

@@ -0,0 +1,114 @@
+# ------------------------------------------------------------------------------------
+# Adapted from Guided-Diffusion repo (https://github.com/openai/guided-diffusion)
+# ------------------------------------------------------------------------------------
+
+import math
+
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class GroupNorm32(nn.GroupNorm):
+    def __init__(self, num_groups, num_channels, swish, eps=1e-5):
+        super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps)
+        self.swish = swish
+
+    def forward(self, x):
+        y = super().forward(x.float()).to(x.dtype)
+        if self.swish == 1.0:
+            y = F.silu(y)
+        elif self.swish:
+            y = y * F.sigmoid(y * float(self.swish))
+        return y
+
+
+def conv_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D convolution module.
+    """
+    if dims == 1:
+        return nn.Conv1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.Conv2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.Conv3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+    """
+    Create a linear module.
+    """
+    return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+    """
+    Create a 1D, 2D, or 3D average pooling module.
+    """
+    if dims == 1:
+        return nn.AvgPool1d(*args, **kwargs)
+    elif dims == 2:
+        return nn.AvgPool2d(*args, **kwargs)
+    elif dims == 3:
+        return nn.AvgPool3d(*args, **kwargs)
+    raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def scale_module(module, scale):
+    """
+    Scale the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().mul_(scale)
+    return module
+
+
+def normalization(channels, swish=0.0):
+    """
+    Make a standard normalization layer, with an optional swish activation.
+
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)
+
+
+def timestep_embedding(timesteps, dim, max_period=10000):
+    """
+    Create sinusoidal timestep embeddings.
+
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    half = dim // 2
+    freqs = th.exp(
+        -math.log(max_period)
+        * th.arange(start=0, end=half, dtype=th.float32, device=timesteps.device)
+        / half
+    )
+    args = timesteps[:, None].float() * freqs[None]
+    embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1)
+    return embedding
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))

ldm/modules/karlo/kakao/modules/resample.py

@@ -0,0 +1,68 @@
+# ------------------------------------------------------------------------------------
+# Modified from Guided-Diffusion (https://github.com/openai/guided-diffusion)
+# ------------------------------------------------------------------------------------
+
+from abc import abstractmethod
+
+import torch as th
+
+
+def create_named_schedule_sampler(name, diffusion):
+    """
+    Create a ScheduleSampler from a library of pre-defined samplers.
+
+    :param name: the name of the sampler.
+    :param diffusion: the diffusion object to sample for.
+    """
+    if name == "uniform":
+        return UniformSampler(diffusion)
+    else:
+        raise NotImplementedError(f"unknown schedule sampler: {name}")
+
+
+class ScheduleSampler(th.nn.Module):
+    """
+    A distribution over timesteps in the diffusion process, intended to reduce
+    variance of the objective.
+
+    By default, samplers perform unbiased importance sampling, in which the
+    objective's mean is unchanged.
+    However, subclasses may override sample() to change how the resampled
+    terms are reweighted, allowing for actual changes in the objective.
+    """
+
+    @abstractmethod
+    def weights(self):
+        """
+        Get a numpy array of weights, one per diffusion step.
+
+        The weights needn't be normalized, but must be positive.
+        """
+
+    def sample(self, batch_size, device):
+        """
+        Importance-sample timesteps for a batch.
+
+        :param batch_size: the number of timesteps.
+        :param device: the torch device to save to.
+        :return: a tuple (timesteps, weights):
+                 - timesteps: a tensor of timestep indices.
+                 - weights: a tensor of weights to scale the resulting losses.
+        """
+        w = self.weights()
+        p = w / th.sum(w)
+        indices = p.multinomial(batch_size, replacement=True)
+        weights = 1 / (len(p) * p[indices])
+        return indices, weights
+
+
+class UniformSampler(ScheduleSampler):
+    def __init__(self, diffusion):
+        super(UniformSampler, self).__init__()
+        self.diffusion = diffusion
+        self.register_buffer(
+            "_weights", th.ones([diffusion.num_timesteps]), persistent=False
+        )
+
+    def weights(self):
+        return self._weights

ldm/modules/karlo/kakao/modules/unet.py

@@ -0,0 +1,792 @@
+# ------------------------------------------------------------------------------------
+# Modified from Guided-Diffusion (https://github.com/openai/guided-diffusion)
+# ------------------------------------------------------------------------------------
+
+import math
+from abc import abstractmethod
+
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .nn import (
+    avg_pool_nd,
+    conv_nd,
+    linear,
+    normalization,
+    timestep_embedding,
+    zero_module,
+)
+from .xf import LayerNorm
+
+
+class TimestepBlock(nn.Module):
+    """
+    Any module where forward() takes timestep embeddings as a second argument.
+    """
+
+    @abstractmethod
+    def forward(self, x, emb):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    """
+    A sequential module that passes timestep embeddings to the children that
+    support it as an extra input.
+    """
+
+    def forward(self, x, emb, encoder_out=None, mask=None):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb)
+            elif isinstance(layer, AttentionBlock):
+                x = layer(x, encoder_out, mask=mask)
+            else:
+                x = layer(x)
+        return x
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 upsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        if use_conv:
+            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        if self.dims == 3:
+            x = F.interpolate(
+                x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+            )
+        else:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+                 downsampling occurs in the inner-two dimensions.
+    """
+
+    def __init__(self, channels, use_conv, dims=2, out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.dims = dims
+        stride = 2 if dims != 3 else (1, 2, 2)
+        if use_conv:
+            self.op = conv_nd(
+                dims, self.channels, self.out_channels, 3, stride=stride, padding=1
+            )
+        else:
+            assert self.channels == self.out_channels
+            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+    """
+    A residual block that can optionally change the number of channels.
+
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param use_checkpoint: if True, use gradient checkpointing on this module.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        use_checkpoint=False,
+        up=False,
+        down=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+        self.use_scale_shift_norm = use_scale_shift_norm
+
+        self.in_layers = nn.Sequential(
+            normalization(channels, swish=1.0),
+            nn.Identity(),
+            conv_nd(dims, channels, self.out_channels, 3, padding=1),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(
+                self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0
+            ),
+            nn.SiLU() if use_scale_shift_norm else nn.Identity(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = conv_nd(
+                dims, channels, self.out_channels, 3, padding=1
+            )
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb):
+        """
+        Apply the block to a Tensor, conditioned on a timestep embedding.
+
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        emb_out = self.emb_layers(emb)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = th.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class ResBlockNoTimeEmbedding(nn.Module):
+    """
+    A residual block without time embedding
+
+    :param channels: the number of input channels.
+    :param emb_channels: the number of timestep embedding channels.
+    :param dropout: the rate of dropout.
+    :param out_channels: if specified, the number of out channels.
+    :param use_conv: if True and out_channels is specified, use a spatial
+        convolution instead of a smaller 1x1 convolution to change the
+        channels in the skip connection.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param use_checkpoint: if True, use gradient checkpointing on this module.
+    :param up: if True, use this block for upsampling.
+    :param down: if True, use this block for downsampling.
+    """
+
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        dims=2,
+        use_checkpoint=False,
+        up=False,
+        down=False,
+        **kwargs,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_checkpoint = use_checkpoint
+
+        self.in_layers = nn.Sequential(
+            normalization(channels, swish=1.0),
+            nn.Identity(),
+            conv_nd(dims, channels, self.out_channels, 3, padding=1),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels, swish=1.0),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = conv_nd(
+                dims, channels, self.out_channels, 3, padding=1
+            )
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+    def forward(self, x, emb=None):
+        """
+        Apply the block to a Tensor, NOT conditioned on a timestep embedding.
+
+        :param x: an [N x C x ...] Tensor of features.
+        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert emb is None
+
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        encoder_channels=None,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels, swish=0.0)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        self.attention = QKVAttention(self.num_heads)
+
+        if encoder_channels is not None:
+            self.encoder_kv = conv_nd(1, encoder_channels, channels * 2, 1)
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x, encoder_out=None, mask=None):
+        b, c, *spatial = x.shape
+        qkv = self.qkv(self.norm(x).view(b, c, -1))
+        if encoder_out is not None:
+            encoder_out = self.encoder_kv(encoder_out)
+            h = self.attention(qkv, encoder_out, mask=mask)
+        else:
+            h = self.attention(qkv)
+        h = self.proj_out(h)
+        return x + h.reshape(b, c, *spatial)
+
+
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv, encoder_kv=None, mask=None):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        if encoder_kv is not None:
+            assert encoder_kv.shape[1] == self.n_heads * ch * 2
+            ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
+            k = th.cat([ek, k], dim=-1)
+            v = th.cat([ev, v], dim=-1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = th.einsum("bct,bcs->bts", q * scale, k * scale)
+        if mask is not None:
+            mask = F.pad(mask, (0, length), value=0.0)
+            mask = (
+                mask.unsqueeze(1)
+                .expand(-1, self.n_heads, -1)
+                .reshape(bs * self.n_heads, 1, -1)
+            )
+            weight = weight + mask
+        weight = th.softmax(weight, dim=-1)
+        a = th.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+
+class UNetModel(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+
+    :param in_channels: channels in the input Tensor.
+    :param model_channels: base channel count for the model.
+    :param out_channels: channels in the output Tensor.
+    :param num_res_blocks: number of residual blocks per downsample.
+    :param attention_resolutions: a collection of downsample rates at which
+        attention will take place. May be a set, list, or tuple.
+        For example, if this contains 4, then at 4x downsampling, attention
+        will be used.
+    :param dropout: the dropout probability.
+    :param channel_mult: channel multiplier for each level of the UNet.
+    :param conv_resample: if True, use learned convolutions for upsampling and
+        downsampling.
+    :param dims: determines if the signal is 1D, 2D, or 3D.
+    :param clip_dim: dimension of clip feature.
+    :param num_classes: if specified (as an int), then this model will be
+        class-conditional with `num_classes` classes.
+    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+    :param num_heads: the number of attention heads in each attention layer.
+    :param num_heads_channels: if specified, ignore num_heads and instead use
+                               a fixed channel width per attention head.
+    :param num_heads_upsample: works with num_heads to set a different number
+                               of heads for upsampling. Deprecated.
+    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+    :param resblock_updown: use residual blocks for up/downsampling.
+    :param encoder_channels: use to make the dimension of query and kv same in AttentionBlock.
+    :param use_time_embedding: use time embedding for condition.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        model_channels,
+        out_channels,
+        num_res_blocks,
+        attention_resolutions,
+        dropout=0,
+        channel_mult=(1, 2, 4, 8),
+        conv_resample=True,
+        dims=2,
+        clip_dim=None,
+        use_checkpoint=False,
+        num_heads=1,
+        num_head_channels=-1,
+        num_heads_upsample=-1,
+        use_scale_shift_norm=False,
+        use_middle_attention=True,
+        resblock_updown=False,
+        encoder_channels=None,
+        use_time_embedding=True,
+    ):
+        super().__init__()
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.num_res_blocks = num_res_blocks
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.clip_dim = clip_dim
+        self.use_checkpoint = use_checkpoint
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.use_middle_attention = use_middle_attention
+        self.use_time_embedding = use_time_embedding
+
+        if self.use_time_embedding:
+            time_embed_dim = model_channels * 4
+            self.time_embed = nn.Sequential(
+                linear(model_channels, time_embed_dim),
+                nn.SiLU(),
+                linear(time_embed_dim, time_embed_dim),
+            )
+
+            if self.clip_dim is not None:
+                self.clip_emb = nn.Linear(clip_dim, time_embed_dim)
+        else:
+            time_embed_dim = None
+
+        CustomResidualBlock = (
+            ResBlock if self.use_time_embedding else ResBlockNoTimeEmbedding
+        )
+        ch = input_ch = int(channel_mult[0] * model_channels)
+        self.input_blocks = nn.ModuleList(
+            [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
+        )
+        self._feature_size = ch
+        input_block_chans = [ch]
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for _ in range(num_res_blocks):
+                layers = [
+                    CustomResidualBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(mult * model_channels),
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(mult * model_channels)
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                            encoder_channels=encoder_channels,
+                        )
+                    )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        CustomResidualBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                ds *= 2
+                self._feature_size += ch
+
+        self.middle_block = TimestepEmbedSequential(
+            CustomResidualBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            *(
+                AttentionBlock(
+                    ch,
+                    use_checkpoint=use_checkpoint,
+                    num_heads=num_heads,
+                    num_head_channels=num_head_channels,
+                    encoder_channels=encoder_channels,
+                ),
+            )
+            if self.use_middle_attention
+            else tuple(),  # add AttentionBlock or not
+            CustomResidualBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, mult in list(enumerate(channel_mult))[::-1]:
+            for i in range(num_res_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    CustomResidualBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(model_channels * mult),
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = int(model_channels * mult)
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            use_checkpoint=use_checkpoint,
+                            num_heads=num_heads_upsample,
+                            num_head_channels=num_head_channels,
+                            encoder_channels=encoder_channels,
+                        )
+                    )
+                if level and i == num_res_blocks:
+                    out_ch = ch
+                    layers.append(
+                        CustomResidualBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            up=True,
+                        )
+                        if resblock_updown
+                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+                    )
+                    ds //= 2
+                self.output_blocks.append(TimestepEmbedSequential(*layers))
+                self._feature_size += ch
+
+        self.out = nn.Sequential(
+            normalization(ch, swish=1.0),
+            nn.Identity(),
+            zero_module(conv_nd(dims, input_ch, out_channels, 3, padding=1)),
+        )
+
+    def forward(self, x, timesteps, y=None):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param y: an [N] Tensor of labels, if class-conditional.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert (y is not None) == (
+            self.clip_dim is not None
+        ), "must specify y if and only if the model is clip-rep-conditional"
+
+        hs = []
+        if self.use_time_embedding:
+            emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+            if self.clip_dim is not None:
+                emb = emb + self.clip_emb(y)
+        else:
+            emb = None
+
+        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 = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb)
+
+        return self.out(h)
+
+
+class SuperResUNetModel(UNetModel):
+    """
+    A UNetModel that performs super-resolution.
+
+    Expects an extra kwarg `low_res` to condition on a low-resolution image.
+    Assumes that the shape of low-resolution and the input should be the same.
+    """
+
+    def __init__(self, *args, **kwargs):
+        if "in_channels" in kwargs:
+            kwargs = dict(kwargs)
+            kwargs["in_channels"] = kwargs["in_channels"] * 2
+        else:
+            # Curse you, Python. Or really, just curse positional arguments :|.
+            args = list(args)
+            args[1] = args[1] * 2
+        super().__init__(*args, **kwargs)
+
+    def forward(self, x, timesteps, low_res=None, **kwargs):
+        _, _, new_height, new_width = x.shape
+        assert new_height == low_res.shape[2] and new_width == low_res.shape[3]
+
+        x = th.cat([x, low_res], dim=1)
+        return super().forward(x, timesteps, **kwargs)
+
+
+class PLMImUNet(UNetModel):
+    """
+    A UNetModel that conditions on text with a pretrained text encoder in CLIP.
+
+    :param text_ctx: number of text tokens to expect.
+    :param xf_width: width of the transformer.
+    :param clip_emb_mult: #extra tokens by projecting clip text feature.
+    :param clip_emb_type: type of condition (here, we fix clip image feature).
+    :param clip_emb_drop: dropout rato of clip image feature for cfg.
+    """
+
+    def __init__(
+        self,
+        text_ctx,
+        xf_width,
+        *args,
+        clip_emb_mult=None,
+        clip_emb_type="image",
+        clip_emb_drop=0.0,
+        **kwargs,
+    ):
+        self.text_ctx = text_ctx
+        self.xf_width = xf_width
+        self.clip_emb_mult = clip_emb_mult
+        self.clip_emb_type = clip_emb_type
+        self.clip_emb_drop = clip_emb_drop
+
+        if not xf_width:
+            super().__init__(*args, **kwargs, encoder_channels=None)
+        else:
+            super().__init__(*args, **kwargs, encoder_channels=xf_width)
+
+        # Project text encoded feat seq from pre-trained text encoder in CLIP
+        self.text_seq_proj = nn.Sequential(
+            nn.Linear(self.clip_dim, xf_width),
+            LayerNorm(xf_width),
+        )
+        # Project CLIP text feat
+        self.text_feat_proj = nn.Linear(self.clip_dim, self.model_channels * 4)
+
+        assert clip_emb_mult is not None
+        assert clip_emb_type == "image"
+        assert self.clip_dim is not None, "CLIP representation dim should be specified"
+
+        self.clip_tok_proj = nn.Linear(
+            self.clip_dim, self.xf_width * self.clip_emb_mult
+        )
+        if self.clip_emb_drop > 0:
+            self.cf_param = nn.Parameter(th.empty(self.clip_dim, dtype=th.float32))
+
+    def proc_clip_emb_drop(self, feat):
+        if self.clip_emb_drop > 0:
+            bsz, feat_dim = feat.shape
+            assert (
+                feat_dim == self.clip_dim
+            ), f"CLIP input dim: {feat_dim}, model CLIP dim: {self.clip_dim}"
+            drop_idx = th.rand((bsz,), device=feat.device) < self.clip_emb_drop
+            feat = th.where(
+                drop_idx[..., None], self.cf_param[None].type_as(feat), feat
+            )
+        return feat
+
+    def forward(
+        self, x, timesteps, txt_feat=None, txt_feat_seq=None, mask=None, y=None
+    ):
+        bsz = x.shape[0]
+        hs = []
+        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+        emb = emb + self.clip_emb(y)
+
+        xf_out = self.text_seq_proj(txt_feat_seq)
+        xf_out = xf_out.permute(0, 2, 1)
+        emb = emb + self.text_feat_proj(txt_feat)
+        xf_out = th.cat(
+            [
+                self.clip_tok_proj(y).reshape(bsz, -1, self.clip_emb_mult),
+                xf_out,
+            ],
+            dim=2,
+        )
+        mask = F.pad(mask, (self.clip_emb_mult, 0), value=True)
+        mask = th.where(mask, 0.0, float("-inf"))
+
+        h = x
+        for module in self.input_blocks:
+            h = module(h, emb, xf_out, mask=mask)
+            hs.append(h)
+        h = self.middle_block(h, emb, xf_out, mask=mask)
+        for module in self.output_blocks:
+            h = th.cat([h, hs.pop()], dim=1)
+            h = module(h, emb, xf_out, mask=mask)
+        h = self.out(h)
+
+        return h

ldm/modules/karlo/kakao/modules/xf.py

@@ -0,0 +1,231 @@
+# ------------------------------------------------------------------------------------
+# Adapted from the repos below:
+# (a) Guided-Diffusion (https://github.com/openai/guided-diffusion)
+# (b) CLIP ViT (https://github.com/openai/CLIP/)
+# ------------------------------------------------------------------------------------
+
+import math
+
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .nn import timestep_embedding
+
+
+def convert_module_to_f16(param):
+    """
+    Convert primitive modules to float16.
+    """
+    if isinstance(param, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)):
+        param.weight.data = param.weight.data.half()
+        if param.bias is not None:
+            param.bias.data = param.bias.data.half()
+
+
+class LayerNorm(nn.LayerNorm):
+    """
+    Implementation that supports fp16 inputs but fp32 gains/biases.
+    """
+
+    def forward(self, x: th.Tensor):
+        return super().forward(x.float()).to(x.dtype)
+
+
+class MultiheadAttention(nn.Module):
+    def __init__(self, n_ctx, width, heads):
+        super().__init__()
+        self.n_ctx = n_ctx
+        self.width = width
+        self.heads = heads
+        self.c_qkv = nn.Linear(width, width * 3)
+        self.c_proj = nn.Linear(width, width)
+        self.attention = QKVMultiheadAttention(heads, n_ctx)
+
+    def forward(self, x, mask=None):
+        x = self.c_qkv(x)
+        x = self.attention(x, mask=mask)
+        x = self.c_proj(x)
+        return x
+
+
+class MLP(nn.Module):
+    def __init__(self, width):
+        super().__init__()
+        self.width = width
+        self.c_fc = nn.Linear(width, width * 4)
+        self.c_proj = nn.Linear(width * 4, width)
+        self.gelu = nn.GELU()
+
+    def forward(self, x):
+        return self.c_proj(self.gelu(self.c_fc(x)))
+
+
+class QKVMultiheadAttention(nn.Module):
+    def __init__(self, n_heads: int, n_ctx: int):
+        super().__init__()
+        self.n_heads = n_heads
+        self.n_ctx = n_ctx
+
+    def forward(self, qkv, mask=None):
+        bs, n_ctx, width = qkv.shape
+        attn_ch = width // self.n_heads // 3
+        scale = 1 / math.sqrt(math.sqrt(attn_ch))
+        qkv = qkv.view(bs, n_ctx, self.n_heads, -1)
+        q, k, v = th.split(qkv, attn_ch, dim=-1)
+        weight = th.einsum("bthc,bshc->bhts", q * scale, k * scale)
+        wdtype = weight.dtype
+        if mask is not None:
+            weight = weight + mask[:, None, ...]
+        weight = th.softmax(weight, dim=-1).type(wdtype)
+        return th.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        n_ctx: int,
+        width: int,
+        heads: int,
+    ):
+        super().__init__()
+
+        self.attn = MultiheadAttention(
+            n_ctx,
+            width,
+            heads,
+        )
+        self.ln_1 = LayerNorm(width)
+        self.mlp = MLP(width)
+        self.ln_2 = LayerNorm(width)
+
+    def forward(self, x, mask=None):
+        x = x + self.attn(self.ln_1(x), mask=mask)
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        n_ctx: int,
+        width: int,
+        layers: int,
+        heads: int,
+    ):
+        super().__init__()
+        self.n_ctx = n_ctx
+        self.width = width
+        self.layers = layers
+        self.resblocks = nn.ModuleList(
+            [
+                ResidualAttentionBlock(
+                    n_ctx,
+                    width,
+                    heads,
+                )
+                for _ in range(layers)
+            ]
+        )
+
+    def forward(self, x, mask=None):
+        for block in self.resblocks:
+            x = block(x, mask=mask)
+        return x
+
+
+class PriorTransformer(nn.Module):
+    """
+    A Causal Transformer that conditions on CLIP text embedding, text.
+
+    :param text_ctx: number of text tokens to expect.
+    :param xf_width: width of the transformer.
+    :param xf_layers: depth of the transformer.
+    :param xf_heads: heads in the transformer.
+    :param xf_final_ln: use a LayerNorm after the output layer.
+    :param clip_dim: dimension of clip feature.
+    """
+
+    def __init__(
+        self,
+        text_ctx,
+        xf_width,
+        xf_layers,
+        xf_heads,
+        xf_final_ln,
+        clip_dim,
+    ):
+        super().__init__()
+
+        self.text_ctx = text_ctx
+        self.xf_width = xf_width
+        self.xf_layers = xf_layers
+        self.xf_heads = xf_heads
+        self.clip_dim = clip_dim
+        self.ext_len = 4
+
+        self.time_embed = nn.Sequential(
+            nn.Linear(xf_width, xf_width),
+            nn.SiLU(),
+            nn.Linear(xf_width, xf_width),
+        )
+        self.text_enc_proj = nn.Linear(clip_dim, xf_width)
+        self.text_emb_proj = nn.Linear(clip_dim, xf_width)
+        self.clip_img_proj = nn.Linear(clip_dim, xf_width)
+        self.out_proj = nn.Linear(xf_width, clip_dim)
+        self.transformer = Transformer(
+            text_ctx + self.ext_len,
+            xf_width,
+            xf_layers,
+            xf_heads,
+        )
+        if xf_final_ln:
+            self.final_ln = LayerNorm(xf_width)
+        else:
+            self.final_ln = None
+
+        self.positional_embedding = nn.Parameter(
+            th.empty(1, text_ctx + self.ext_len, xf_width)
+        )
+        self.prd_emb = nn.Parameter(th.randn((1, 1, xf_width)))
+
+        nn.init.normal_(self.prd_emb, std=0.01)
+        nn.init.normal_(self.positional_embedding, std=0.01)
+
+    def forward(
+        self,
+        x,
+        timesteps,
+        text_emb=None,
+        text_enc=None,
+        mask=None,
+        causal_mask=None,
+    ):
+        bsz = x.shape[0]
+        mask = F.pad(mask, (0, self.ext_len), value=True)
+
+        t_emb = self.time_embed(timestep_embedding(timesteps, self.xf_width))
+        text_enc = self.text_enc_proj(text_enc)
+        text_emb = self.text_emb_proj(text_emb)
+        x = self.clip_img_proj(x)
+
+        input_seq = [
+            text_enc,
+            text_emb[:, None, :],
+            t_emb[:, None, :],
+            x[:, None, :],
+            self.prd_emb.to(x.dtype).expand(bsz, -1, -1),
+        ]
+        input = th.cat(input_seq, dim=1)
+        input = input + self.positional_embedding.to(input.dtype)
+
+        mask = th.where(mask, 0.0, float("-inf"))
+        mask = (mask[:, None, :] + causal_mask).to(input.dtype)
+
+        out = self.transformer(input, mask=mask)
+        if self.final_ln is not None:
+            out = self.final_ln(out)
+
+        out = self.out_proj(out[:, -1])
+
+        return out

ldm/modules/karlo/kakao/sampler.py

@@ -0,0 +1,272 @@
+# ------------------------------------------------------------------------------------
+# Karlo-v1.0.alpha
+# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
+
+# source: https://github.com/kakaobrain/karlo/blob/3c68a50a16d76b48a15c181d1c5a5e0879a90f85/karlo/sampler/t2i.py#L15
+# ------------------------------------------------------------------------------------
+
+from typing import Iterator
+
+import torch
+import torchvision.transforms.functional as TVF
+from torchvision.transforms import InterpolationMode
+
+from .template import BaseSampler, CKPT_PATH
+
+
+class T2ISampler(BaseSampler):
+    """
+    A sampler for text-to-image generation.
+    :param root_dir: directory for model checkpoints.
+    :param sampling_type: ["default", "fast"]
+    """
+
+    def __init__(
+            self,
+            root_dir: str,
+            sampling_type: str = "default",
+    ):
+        super().__init__(root_dir, sampling_type)
+
+    @classmethod
+    def from_pretrained(
+            cls,
+            root_dir: str,
+            clip_model_path: str,
+            clip_stat_path: str,
+            sampling_type: str = "default",
+    ):
+
+        model = cls(
+            root_dir=root_dir,
+            sampling_type=sampling_type,
+        )
+        model.load_clip(clip_model_path)
+        model.load_prior(
+            f"{CKPT_PATH['prior']}",
+            clip_stat_path=clip_stat_path,
+            prior_config="configs/karlo/prior_1B_vit_l.yaml"
+        )
+        model.load_decoder(f"{CKPT_PATH['decoder']}", decoder_config="configs/karlo/decoder_900M_vit_l.yaml")
+        model.load_sr_64_256(CKPT_PATH["sr_256"], sr_config="configs/karlo/improved_sr_64_256_1.4B.yaml")
+        return model
+
+    def preprocess(
+            self,
+            prompt: str,
+            bsz: int,
+    ):
+        """Setup prompts & cfg scales"""
+        prompts_batch = [prompt for _ in range(bsz)]
+
+        prior_cf_scales_batch = [self._prior_cf_scale] * len(prompts_batch)
+        prior_cf_scales_batch = torch.tensor(prior_cf_scales_batch, device="cuda")
+
+        decoder_cf_scales_batch = [self._decoder_cf_scale] * len(prompts_batch)
+        decoder_cf_scales_batch = torch.tensor(decoder_cf_scales_batch, device="cuda")
+
+        """ Get CLIP text feature """
+        clip_model = self._clip
+        tokenizer = self._tokenizer
+        max_txt_length = self._prior.model.text_ctx
+
+        tok, mask = tokenizer.padded_tokens_and_mask(prompts_batch, max_txt_length)
+        cf_token, cf_mask = tokenizer.padded_tokens_and_mask([""], max_txt_length)
+        if not (cf_token.shape == tok.shape):
+            cf_token = cf_token.expand(tok.shape[0], -1)
+            cf_mask = cf_mask.expand(tok.shape[0], -1)
+
+        tok = torch.cat([tok, cf_token], dim=0)
+        mask = torch.cat([mask, cf_mask], dim=0)
+
+        tok, mask = tok.to(device="cuda"), mask.to(device="cuda")
+        txt_feat, txt_feat_seq = clip_model.encode_text(tok)
+
+        return (
+            prompts_batch,
+            prior_cf_scales_batch,
+            decoder_cf_scales_batch,
+            txt_feat,
+            txt_feat_seq,
+            tok,
+            mask,
+        )
+
+    def __call__(
+            self,
+            prompt: str,
+            bsz: int,
+            progressive_mode=None,
+    ) -> Iterator[torch.Tensor]:
+        assert progressive_mode in ("loop", "stage", "final")
+        with torch.no_grad(), torch.cuda.amp.autocast():
+            (
+                prompts_batch,
+                prior_cf_scales_batch,
+                decoder_cf_scales_batch,
+                txt_feat,
+                txt_feat_seq,
+                tok,
+                mask,
+            ) = self.preprocess(
+                prompt,
+                bsz,
+            )
+
+            """ Transform CLIP text feature into image feature """
+            img_feat = self._prior(
+                txt_feat,
+                txt_feat_seq,
+                mask,
+                prior_cf_scales_batch,
+                timestep_respacing=self._prior_sm,
+            )
+
+            """ Generate 64x64px images """
+            images_64_outputs = self._decoder(
+                txt_feat,
+                txt_feat_seq,
+                tok,
+                mask,
+                img_feat,
+                cf_guidance_scales=decoder_cf_scales_batch,
+                timestep_respacing=self._decoder_sm,
+            )
+
+            images_64 = None
+            for k, out in enumerate(images_64_outputs):
+                images_64 = out
+                if progressive_mode == "loop":
+                    yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
+            if progressive_mode == "stage":
+                yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
+
+            images_64 = torch.clamp(images_64, -1, 1)
+
+            """ Upsample 64x64 to 256x256 """
+            images_256 = TVF.resize(
+                images_64,
+                [256, 256],
+                interpolation=InterpolationMode.BICUBIC,
+                antialias=True,
+            )
+            images_256_outputs = self._sr_64_256(
+                images_256, timestep_respacing=self._sr_sm
+            )
+
+            for k, out in enumerate(images_256_outputs):
+                images_256 = out
+                if progressive_mode == "loop":
+                    yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
+            if progressive_mode == "stage":
+                yield torch.clamp(out * 0.5 + 0.5, 0.0, 1.0)
+
+        yield torch.clamp(images_256 * 0.5 + 0.5, 0.0, 1.0)
+
+
+class PriorSampler(BaseSampler):
+    """
+    A sampler for text-to-image generation, but only the prior.
+    :param root_dir: directory for model checkpoints.
+    :param sampling_type: ["default", "fast"]
+    """
+
+    def __init__(
+            self,
+            root_dir: str,
+            sampling_type: str = "default",
+    ):
+        super().__init__(root_dir, sampling_type)
+
+    @classmethod
+    def from_pretrained(
+            cls,
+            root_dir: str,
+            clip_model_path: str,
+            clip_stat_path: str,
+            sampling_type: str = "default",
+    ):
+        model = cls(
+            root_dir=root_dir,
+            sampling_type=sampling_type,
+        )
+        model.load_clip(clip_model_path)
+        model.load_prior(
+            f"{CKPT_PATH['prior']}",
+            clip_stat_path=clip_stat_path,
+            prior_config="configs/karlo/prior_1B_vit_l.yaml"
+        )
+        return model
+
+    def preprocess(
+            self,
+            prompt: str,
+            bsz: int,
+    ):
+        """Setup prompts & cfg scales"""
+        prompts_batch = [prompt for _ in range(bsz)]
+
+        prior_cf_scales_batch = [self._prior_cf_scale] * len(prompts_batch)
+        prior_cf_scales_batch = torch.tensor(prior_cf_scales_batch, device="cuda")
+
+        decoder_cf_scales_batch = [self._decoder_cf_scale] * len(prompts_batch)
+        decoder_cf_scales_batch = torch.tensor(decoder_cf_scales_batch, device="cuda")
+
+        """ Get CLIP text feature """
+        clip_model = self._clip
+        tokenizer = self._tokenizer
+        max_txt_length = self._prior.model.text_ctx
+
+        tok, mask = tokenizer.padded_tokens_and_mask(prompts_batch, max_txt_length)
+        cf_token, cf_mask = tokenizer.padded_tokens_and_mask([""], max_txt_length)
+        if not (cf_token.shape == tok.shape):
+            cf_token = cf_token.expand(tok.shape[0], -1)
+            cf_mask = cf_mask.expand(tok.shape[0], -1)
+
+        tok = torch.cat([tok, cf_token], dim=0)
+        mask = torch.cat([mask, cf_mask], dim=0)
+
+        tok, mask = tok.to(device="cuda"), mask.to(device="cuda")
+        txt_feat, txt_feat_seq = clip_model.encode_text(tok)
+
+        return (
+            prompts_batch,
+            prior_cf_scales_batch,
+            decoder_cf_scales_batch,
+            txt_feat,
+            txt_feat_seq,
+            tok,
+            mask,
+        )
+
+    def __call__(
+            self,
+            prompt: str,
+            bsz: int,
+            progressive_mode=None,
+    ) -> Iterator[torch.Tensor]:
+        assert progressive_mode in ("loop", "stage", "final")
+        with torch.no_grad(), torch.cuda.amp.autocast():
+            (
+                prompts_batch,
+                prior_cf_scales_batch,
+                decoder_cf_scales_batch,
+                txt_feat,
+                txt_feat_seq,
+                tok,
+                mask,
+            ) = self.preprocess(
+                prompt,
+                bsz,
+            )
+
+            """ Transform CLIP text feature into image feature """
+            img_feat = self._prior(
+                txt_feat,
+                txt_feat_seq,
+                mask,
+                prior_cf_scales_batch,
+                timestep_respacing=self._prior_sm,
+            )
+
+            yield img_feat

ldm/modules/karlo/kakao/template.py

@@ -0,0 +1,141 @@
+# ------------------------------------------------------------------------------------
+# Karlo-v1.0.alpha
+# Copyright (c) 2022 KakaoBrain. All Rights Reserved.
+# ------------------------------------------------------------------------------------
+
+import os
+import logging
+import torch
+
+from omegaconf import OmegaConf
+
+from ldm.modules.karlo.kakao.models.clip import CustomizedCLIP, CustomizedTokenizer
+from ldm.modules.karlo.kakao.models.prior_model import PriorDiffusionModel
+from ldm.modules.karlo.kakao.models.decoder_model import Text2ImProgressiveModel
+from ldm.modules.karlo.kakao.models.sr_64_256 import ImprovedSupRes64to256ProgressiveModel
+
+
+SAMPLING_CONF = {
+    "default": {
+        "prior_sm": "25",
+        "prior_n_samples": 1,
+        "prior_cf_scale": 4.0,
+        "decoder_sm": "50",
+        "decoder_cf_scale": 8.0,
+        "sr_sm": "7",
+    },
+    "fast": {
+        "prior_sm": "25",
+        "prior_n_samples": 1,
+        "prior_cf_scale": 4.0,
+        "decoder_sm": "25",
+        "decoder_cf_scale": 8.0,
+        "sr_sm": "7",
+    },
+}
+
+CKPT_PATH = {
+    "prior": "prior-ckpt-step=01000000-of-01000000.ckpt",
+    "decoder": "decoder-ckpt-step=01000000-of-01000000.ckpt",
+    "sr_256": "improved-sr-ckpt-step=1.2M.ckpt",
+}
+
+
+class BaseSampler:
+    _PRIOR_CLASS = PriorDiffusionModel
+    _DECODER_CLASS = Text2ImProgressiveModel
+    _SR256_CLASS = ImprovedSupRes64to256ProgressiveModel
+
+    def __init__(
+        self,
+        root_dir: str,
+        sampling_type: str = "fast",
+    ):
+        self._root_dir = root_dir
+
+        sampling_type = SAMPLING_CONF[sampling_type]
+        self._prior_sm = sampling_type["prior_sm"]
+        self._prior_n_samples = sampling_type["prior_n_samples"]
+        self._prior_cf_scale = sampling_type["prior_cf_scale"]
+
+        assert self._prior_n_samples == 1
+
+        self._decoder_sm = sampling_type["decoder_sm"]
+        self._decoder_cf_scale = sampling_type["decoder_cf_scale"]
+
+        self._sr_sm = sampling_type["sr_sm"]
+
+    def __repr__(self):
+        line = ""
+        line += f"Prior, sampling method: {self._prior_sm}, cf_scale: {self._prior_cf_scale}\n"
+        line += f"Decoder, sampling method: {self._decoder_sm}, cf_scale: {self._decoder_cf_scale}\n"
+        line += f"SR(64->256), sampling method: {self._sr_sm}"
+
+        return line
+
+    def load_clip(self, clip_path: str):
+        clip = CustomizedCLIP.load_from_checkpoint(
+            os.path.join(self._root_dir, clip_path)
+        )
+        clip = torch.jit.script(clip)
+        clip.cuda()
+        clip.eval()
+
+        self._clip = clip
+        self._tokenizer = CustomizedTokenizer()
+
+    def load_prior(
+        self,
+        ckpt_path: str,
+        clip_stat_path: str,
+        prior_config: str = "configs/prior_1B_vit_l.yaml"
+    ):
+        logging.info(f"Loading prior: {ckpt_path}")
+
+        config = OmegaConf.load(prior_config)
+        clip_mean, clip_std = torch.load(
+            os.path.join(self._root_dir, clip_stat_path), map_location="cpu"
+        )
+
+        prior = self._PRIOR_CLASS.load_from_checkpoint(
+            config,
+            self._tokenizer,
+            clip_mean,
+            clip_std,
+            os.path.join(self._root_dir, ckpt_path),
+            strict=True,
+        )
+        prior.cuda()
+        prior.eval()
+        logging.info("done.")
+
+        self._prior = prior
+
+    def load_decoder(self, ckpt_path: str, decoder_config: str = "configs/decoder_900M_vit_l.yaml"):
+        logging.info(f"Loading decoder: {ckpt_path}")
+
+        config = OmegaConf.load(decoder_config)
+        decoder = self._DECODER_CLASS.load_from_checkpoint(
+            config,
+            self._tokenizer,
+            os.path.join(self._root_dir, ckpt_path),
+            strict=True,
+        )
+        decoder.cuda()
+        decoder.eval()
+        logging.info("done.")
+
+        self._decoder = decoder
+
+    def load_sr_64_256(self, ckpt_path: str, sr_config: str = "configs/improved_sr_64_256_1.4B.yaml"):
+        logging.info(f"Loading SR(64->256): {ckpt_path}")
+
+        config = OmegaConf.load(sr_config)
+        sr = self._SR256_CLASS.load_from_checkpoint(
+            config, os.path.join(self._root_dir, ckpt_path), strict=True
+        )
+        sr.cuda()
+        sr.eval()
+        logging.info("done.")
+
+        self._sr_64_256 = sr

ldm/util.py

@@ -8,6 +8,16 @@ from inspect import isfunction
 from PIL import Image, ImageDraw, ImageFont
 
 
+def autocast(f):
+    def do_autocast(*args, **kwargs):
+        with torch.cuda.amp.autocast(enabled=True,
+                                     dtype=torch.get_autocast_gpu_dtype(),
+                                     cache_enabled=torch.is_autocast_cache_enabled()):
+            return f(*args, **kwargs)
+
+    return do_autocast
+
+
 def log_txt_as_img(wh, xc, size=10):
     # wh a tuple of (width, height)
     # xc a list of captions to plot

modelcard.md

@@ -97,7 +97,7 @@ We currently provide the following checkpoints, for various versions:
 - `512-base-ema.ckpt`: Fine-tuned on `512-base-ema.ckpt` 2.0 with 220k extra steps taken, with `punsafe=0.98` on the same dataset.
 - `768-v-ema.ckpt`: Resumed from `768-v-ema.ckpt` 2.0 with an additional 55k steps on the same dataset (`punsafe=0.1`), and then fine-tuned for another 155k extra steps with `punsafe=0.98`.
 
-SD-unCLIP 2.1 is a finetuned version of Stable Diffusion 2.1, modified to accept (noisy) CLIP image embedding in addition to the text prompt, and can be used to create image variations ([Examples](../assets/stable-samples/stable-unclip/unclip-variations_noise.png)) or can be chained with text-to-image CLIP priors. The amount of noise added to the image embedding can be specified via the `noise_level` (0 means no noise, 1000 full noise).
+**SD-unCLIP 2.1** is a finetuned version of Stable Diffusion 2.1, modified to accept (noisy) CLIP image embedding in addition to the text prompt, and can be used to create image variations ([Examples](../assets/stable-samples/stable-unclip/unclip-variations_noise.png)) or can be chained with text-to-image CLIP priors. The amount of noise added to the image embedding can be specified via the `noise_level` (0 means no noise, 1000 full noise).
 
 ### Version 2.0
 

scripts/streamlit/stableunclip.py

@@ -0,0 +1,416 @@
+import importlib
+import streamlit as st
+import torch
+import cv2
+import numpy as np
+import PIL
+from omegaconf import OmegaConf
+from PIL import Image
+from tqdm import trange
+import io, os
+from torch import autocast
+from einops import rearrange, repeat
+from torchvision.utils import make_grid
+from pytorch_lightning import seed_everything
+from contextlib import nullcontext
+
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.plms import PLMSSampler
+from ldm.models.diffusion.dpm_solver import DPMSolverSampler
+
+torch.set_grad_enabled(False)
+
+PROMPTS_ROOT = "scripts/prompts/"
+SAVE_PATH = "outputs/demo/stable-unclip/"
+
+VERSION2SPECS = {
+    "Stable unCLIP-L": {"H": 768, "W": 768, "C": 4, "f": 8},
+    "Stable unOpenCLIP-H": {"H": 768, "W": 768, "C": 4, "f": 8},
+    "Full Karlo": {}
+}
+
+
+def get_obj_from_str(string, reload=False):
+    module, cls = string.rsplit(".", 1)
+    importlib.invalidate_caches()
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def instantiate_from_config(config):
+    if not "target" in config:
+        raise KeyError("Expected key `target` to instantiate.")
+    return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_interactive_image(key=None):
+    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"], key=key)
+    if image is not None:
+        image = Image.open(image)
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+        return image
+
+
+def load_img(display=True, key=None):
+    image = get_interactive_image(key=key)
+    if display:
+        st.image(image)
+    w, h = image.size
+    print(f"loaded input image of size ({w}, {h})")
+    w, h = map(lambda x: x - x % 64, (w, h))
+    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+    return 2. * image - 1.
+
+
+def get_init_img(batch_size=1, key=None):
+    init_image = load_img(key=key).cuda()
+    init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
+    return init_image
+
+
+def sample(
+        model,
+        prompt,
+        n_runs=3,
+        n_samples=2,
+        H=512,
+        W=512,
+        C=4,
+        f=8,
+        scale=10.0,
+        ddim_steps=50,
+        ddim_eta=0.0,
+        callback=None,
+        skip_single_save=False,
+        save_grid=True,
+        ucg_schedule=None,
+        negative_prompt="",
+        adm_cond=None,
+        adm_uc=None,
+        use_full_precision=False,
+        only_adm_cond=False
+):
+    batch_size = n_samples
+    precision_scope = autocast if not use_full_precision else nullcontext
+    # decoderscope = autocast if not use_full_precision else nullcontext
+    if use_full_precision: st.warning(f"Running {model.__class__.__name__} at full precision.")
+    if isinstance(prompt, str):
+        prompt = [prompt]
+    prompts = batch_size * prompt
+
+    outputs = st.empty()
+
+    with precision_scope("cuda"):
+        with model.ema_scope():
+            all_samples = list()
+            for n in trange(n_runs, desc="Sampling"):
+                shape = [C, H // f, W // f]
+                if not only_adm_cond:
+                    uc = None
+                    if scale != 1.0:
+                        uc = model.get_learned_conditioning(batch_size * [negative_prompt])
+                    if isinstance(prompts, tuple):
+                        prompts = list(prompts)
+                    c = model.get_learned_conditioning(prompts)
+
+                if adm_cond is not None:
+                    if adm_cond.shape[0] == 1:
+                        adm_cond = repeat(adm_cond, '1 ... -> b ...', b=batch_size)
+                    if adm_uc is None:
+                        st.warning("Not guiding via c_adm")
+                        adm_uc = adm_cond
+                    else:
+                        if adm_uc.shape[0] == 1:
+                            adm_uc = repeat(adm_uc, '1 ... -> b ...', b=batch_size)
+                    if not only_adm_cond:
+                        c = {"c_crossattn": [c], "c_adm": adm_cond}
+                        uc = {"c_crossattn": [uc], "c_adm": adm_uc}
+                    else:
+                        c = adm_cond
+                        uc = adm_uc
+                samples_ddim, _ = sampler.sample(S=ddim_steps,
+                                                 conditioning=c,
+                                                 batch_size=batch_size,
+                                                 shape=shape,
+                                                 verbose=False,
+                                                 unconditional_guidance_scale=scale,
+                                                 unconditional_conditioning=uc,
+                                                 eta=ddim_eta,
+                                                 x_T=None,
+                                                 callback=callback,
+                                                 ucg_schedule=ucg_schedule
+                                                 )
+                x_samples = model.decode_first_stage(samples_ddim)
+                x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
+
+                if not skip_single_save:
+                    base_count = len(os.listdir(os.path.join(SAVE_PATH, "samples")))
+                    for x_sample in x_samples:
+                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                        Image.fromarray(x_sample.astype(np.uint8)).save(
+                            os.path.join(SAVE_PATH, "samples", f"{base_count:09}.png"))
+                        base_count += 1
+
+                all_samples.append(x_samples)
+
+                # get grid of all samples
+                grid = torch.stack(all_samples, 0)
+                grid = rearrange(grid, 'n b c h w -> (n h) (b w) c')
+                outputs.image(grid.cpu().numpy())
+
+            # additionally, save grid
+            grid = Image.fromarray((255. * grid.cpu().numpy()).astype(np.uint8))
+            if save_grid:
+                grid_count = len(os.listdir(SAVE_PATH)) - 1
+                grid.save(os.path.join(SAVE_PATH, f'grid-{grid_count:06}.png'))
+
+    return x_samples
+
+
+def make_oscillating_guidance_schedule(num_steps, max_weight=15., min_weight=1.):
+    schedule = list()
+    for i in range(num_steps):
+        if float(i / num_steps) < 0.1:
+            schedule.append(max_weight)
+        elif i % 2 == 0:
+            schedule.append(min_weight)
+        else:
+            schedule.append(max_weight)
+    print(f"OSCILLATING GUIDANCE SCHEDULE: \n {schedule}")
+    return schedule
+
+
+def torch2np(x):
+    x = ((x + 1.0) * 127.5).clamp(0, 255).to(dtype=torch.uint8)
+    x = x.permute(0, 2, 3, 1).detach().cpu().numpy()
+    return x
+
+
+@st.cache(allow_output_mutation=True, suppress_st_warning=True)
+def init(version="Stable unCLIP-L", load_karlo_prior=False):
+    state = dict()
+    if not "model" in state:
+        if version == "Stable unCLIP-L":
+            config = "configs/stable-diffusion/v2-1-stable-unclip-l-inference.yaml"
+            ckpt = "checkpoints/sd21-unclip-l.ckpt"
+
+        elif version == "Stable unOpenCLIP-H":
+            config = "configs/stable-diffusion/v2-1-stable-unclip-h-inference.yaml"
+            ckpt = "checkpoints/sd21-unclip-h.ckpt"
+
+        elif version == "Full Karlo":
+            from ldm.modules.karlo.kakao.sampler import T2ISampler
+            st.info("Loading full KARLO..")
+            karlo = T2ISampler.from_pretrained(
+                root_dir="checkpoints/karlo_models",
+                clip_model_path="ViT-L-14.pt",
+                clip_stat_path="ViT-L-14_stats.th",
+                sampling_type="default",
+            )
+            state["karlo_prior"] = karlo
+            state["msg"] = "loaded full Karlo"
+            return state
+        else:
+            raise ValueError(f"version {version} unknown!")
+
+        config = OmegaConf.load(config)
+        model, msg = load_model_from_config(config, ckpt, vae_sd=None)
+        state["msg"] = msg
+
+        if load_karlo_prior:
+            from ldm.modules.karlo.kakao.sampler import PriorSampler
+            st.info("Loading KARLO CLIP prior...")
+            karlo_prior = PriorSampler.from_pretrained(
+                root_dir="checkpoints/karlo_models",
+                clip_model_path="ViT-L-14.pt",
+                clip_stat_path="ViT-L-14_stats.th",
+                sampling_type="default",
+            )
+            state["karlo_prior"] = karlo_prior
+        state["model"] = model
+        state["ckpt"] = ckpt
+        state["config"] = config
+    return state
+
+
+def load_model_from_config(config, ckpt, verbose=False, vae_sd=None):
+    print(f"Loading model from {ckpt}")
+    pl_sd = torch.load(ckpt, map_location="cpu")
+    msg = None
+    if "global_step" in pl_sd:
+        msg = f"This is global step {pl_sd['global_step']}. "
+    if "model_ema.num_updates" in pl_sd["state_dict"]:
+        msg += f"And we got {pl_sd['state_dict']['model_ema.num_updates']} EMA updates."
+    global_step = pl_sd.get("global_step", "?")
+    sd = pl_sd["state_dict"]
+    if vae_sd is not None:
+        for k in sd.keys():
+            if "first_stage" in k:
+                sd[k] = vae_sd[k[len("first_stage_model."):]]
+
+    model = instantiate_from_config(config.model)
+    m, u = model.load_state_dict(sd, strict=False)
+    if len(m) > 0 and verbose:
+        print("missing keys:")
+        print(m)
+    if len(u) > 0 and verbose:
+        print("unexpected keys:")
+        print(u)
+
+    model.cuda()
+    model.eval()
+    print(f"Loaded global step {global_step}")
+    return model, msg
+
+
+if __name__ == "__main__":
+    st.title("Stable unCLIP")
+    mode = "txt2img"
+    version = st.selectbox("Model Version", list(VERSION2SPECS.keys()), 0)
+    use_karlo_prior = version in ["Stable unCLIP-L"] and st.checkbox("Use KARLO prior", False)
+    state = init(version=version, load_karlo_prior=use_karlo_prior)
+    prompt = st.text_input("Prompt", "a professional photograph")
+    negative_prompt = st.text_input("Negative Prompt", "")
+    scale = st.number_input("cfg-scale", value=10., min_value=-100., max_value=100.)
+    number_rows = st.number_input("num rows", value=2, min_value=1, max_value=10)
+    number_cols = st.number_input("num cols", value=2, min_value=1, max_value=10)
+    steps = st.sidebar.number_input("steps", value=20, min_value=1, max_value=1000)
+    eta = st.sidebar.number_input("eta (DDIM)", value=0., min_value=0., max_value=1.)
+    force_full_precision = st.sidebar.checkbox("Force FP32", False)  # TODO: check if/where things break.
+    if version != "Full Karlo":
+        H = st.sidebar.number_input("H", value=VERSION2SPECS[version]["H"], min_value=64, max_value=2048)
+        W = st.sidebar.number_input("W", value=VERSION2SPECS[version]["W"], min_value=64, max_value=2048)
+        C = VERSION2SPECS[version]["C"]
+        f = VERSION2SPECS[version]["f"]
+
+    SAVE_PATH = os.path.join(SAVE_PATH, version)
+    os.makedirs(os.path.join(SAVE_PATH, "samples"), exist_ok=True)
+
+    seed = st.sidebar.number_input("seed", value=42, min_value=0, max_value=int(1e9))
+    seed_everything(seed)
+
+    ucg_schedule = None
+    sampler = st.sidebar.selectbox("Sampler", ["DDIM", "DPM"], 0)
+    if version == "Full Karlo":
+        pass
+    else:
+        if sampler == "DPM":
+            sampler = DPMSolverSampler(state["model"])
+        elif sampler == "DDIM":
+            sampler = DDIMSampler(state["model"])
+        else:
+            raise ValueError(f"unknown sampler {sampler}!")
+
+    adm_cond, adm_uc = None, None
+    if use_karlo_prior:
+        # uses the prior
+        karlo_sampler = state["karlo_prior"]
+        noise_level = None
+        if state["model"].noise_augmentor is not None:
+            noise_level = st.number_input("Noise Augmentation for CLIP embeddings", min_value=0,
+                                          max_value=state["model"].noise_augmentor.max_noise_level - 1, value=0)
+        with torch.no_grad():
+            karlo_prediction = iter(
+                karlo_sampler(
+                    prompt=prompt,
+                    bsz=number_cols,
+                    progressive_mode="final",
+                )
+            ).__next__()
+            adm_cond = karlo_prediction
+            if noise_level is not None:
+                c_adm, noise_level_emb = state["model"].noise_augmentor(adm_cond, noise_level=repeat(
+                    torch.tensor([noise_level]).to(state["model"].device), '1 -> b', b=number_cols))
+                adm_cond = torch.cat((c_adm, noise_level_emb), 1)
+            adm_uc = torch.zeros_like(adm_cond)
+    elif version == "Full Karlo":
+        pass
+    else:
+        num_inputs = st.number_input("Number of Input Images", 1)
+
+
+        def make_conditionings_from_input(num=1, key=None):
+            init_img = get_init_img(batch_size=number_cols, key=key)
+            with torch.no_grad():
+                adm_cond = state["model"].embedder(init_img)
+                weight = st.slider(f"Weight for Input {num}", min_value=-10., max_value=10., value=1.)
+                if state["model"].noise_augmentor is not None:
+                    noise_level = st.number_input(f"Noise Augmentation for CLIP embedding of input #{num}", min_value=0,
+                                                  max_value=state["model"].noise_augmentor.max_noise_level - 1,
+                                                  value=0, )
+                    c_adm, noise_level_emb = state["model"].noise_augmentor(adm_cond, noise_level=repeat(
+                        torch.tensor([noise_level]).to(state["model"].device), '1 -> b', b=number_cols))
+                    adm_cond = torch.cat((c_adm, noise_level_emb), 1) * weight
+                adm_uc = torch.zeros_like(adm_cond)
+            return adm_cond, adm_uc, weight
+
+
+        adm_inputs = list()
+        weights = list()
+        for n in range(num_inputs):
+            adm_cond, adm_uc, w = make_conditionings_from_input(num=n + 1, key=n)
+            weights.append(w)
+            adm_inputs.append(adm_cond)
+        adm_cond = torch.stack(adm_inputs).sum(0) / sum(weights)
+        if num_inputs > 1:
+            if st.checkbox("Apply Noise to Embedding Mix", True):
+                noise_level = st.number_input(f"Noise Augmentation for averaged CLIP embeddings", min_value=0,
+                                              max_value=state["model"].noise_augmentor.max_noise_level - 1, value=50, )
+                c_adm, noise_level_emb = state["model"].noise_augmentor(
+                    adm_cond[:, :state["model"].noise_augmentor.time_embed.dim],
+                    noise_level=repeat(
+                        torch.tensor([noise_level]).to(state["model"].device), '1 -> b', b=number_cols))
+                adm_cond = torch.cat((c_adm, noise_level_emb), 1)
+
+    if st.button("Sample"):
+        print("running prompt:", prompt)
+        st.text("Sampling")
+        t_progress = st.progress(0)
+        result = st.empty()
+
+
+        def t_callback(t):
+            t_progress.progress(min((t + 1) / steps, 1.))
+
+
+        if version == "Full Karlo":
+            outputs = st.empty()
+            karlo_sampler = state["karlo_prior"]
+            all_samples = list()
+            with torch.no_grad():
+                for _ in range(number_rows):
+                    karlo_prediction = iter(
+                        karlo_sampler(
+                            prompt=prompt,
+                            bsz=number_cols,
+                            progressive_mode="final",
+                        )
+                    ).__next__()
+                    all_samples.append(karlo_prediction)
+            grid = torch.stack(all_samples, 0)
+            grid = rearrange(grid, 'n b c h w -> (n h) (b w) c')
+            outputs.image(grid.cpu().numpy())
+
+        else:
+            samples = sample(
+                state["model"],
+                prompt,
+                n_runs=number_rows,
+                n_samples=number_cols,
+                H=H, W=W, C=C, f=f,
+                scale=scale,
+                ddim_steps=steps,
+                ddim_eta=eta,
+                callback=t_callback,
+                ucg_schedule=ucg_schedule,
+                negative_prompt=negative_prompt,
+                adm_cond=adm_cond, adm_uc=adm_uc,
+                use_full_precision=force_full_precision,
+                only_adm_cond=False
+            )