Stable Video Diffusion

scripts/demo/discretization.py

@@ -0,0 +1,59 @@
+import torch
+
+from sgm.modules.diffusionmodules.discretizer import Discretization
+
+
+class Img2ImgDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(self, discretization: Discretization, strength: float = 1.0):
+        self.discretization = discretization
+        self.strength = strength
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
+        print("prune index:", max(int(self.strength * len(sigmas)), 1))
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas
+
+
+class Txt2NoisyDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 0.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(
+        self, discretization: Discretization, strength: float = 0.0, original_steps=None
+    ):
+        self.discretization = discretization
+        self.strength = strength
+        self.original_steps = original_steps
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        if self.original_steps is None:
+            steps = len(sigmas)
+        else:
+            steps = self.original_steps + 1
+        prune_index = max(min(int(self.strength * steps) - 1, steps - 1), 0)
+        sigmas = sigmas[prune_index:]
+        print("prune index:", prune_index)
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas

scripts/demo/sampling.py

@@ -253,7 +253,10 @@ if __name__ == "__main__":
     st.title("Stable Diffusion")
     version = st.selectbox("Model Version", list(VERSION2SPECS.keys()), 0)
     version_dict = VERSION2SPECS[version]
-    mode = st.radio("Mode", ("txt2img", "img2img"), 0)
+    if st.checkbox("Load Model"):
+        mode = st.radio("Mode", ("txt2img", "img2img"), 0)
+    else:
+        mode = "skip"
     st.write("__________________________")
 
     set_lowvram_mode(st.checkbox("Low vram mode", True))
@@ -269,10 +272,11 @@ if __name__ == "__main__":
 
     save_locally, save_path = init_save_locally(os.path.join(SAVE_PATH, version))
 
-    state = init_st(version_dict, load_filter=True)
-    if state["msg"]:
-        st.info(state["msg"])
-    model = state["model"]
+    if mode != "skip":
+        state = init_st(version_dict, load_filter=True)
+        if state["msg"]:
+            st.info(state["msg"])
+        model = state["model"]
 
     is_legacy = version_dict["is_legacy"]
 
@@ -333,6 +337,8 @@ if __name__ == "__main__":
             filter=state.get("filter"),
             stage2strength=stage2strength,
         )
+    elif mode == "skip":
+        out = None
     else:
         raise ValueError(f"unknown mode {mode}")
     if isinstance(out, (tuple, list)):

scripts/demo/streamlit_helpers.py

@@ -1,10 +1,15 @@
+import copy
 import math
 import os
-from typing import List, Union
+from glob import glob
+from typing import Dict, List, Optional, Tuple, Union
 
+import cv2
 import numpy as np
 import streamlit as st
 import torch
+import torch.nn as nn
+import torchvision.transforms as TT
 from einops import rearrange, repeat
 from imwatermark import WatermarkEncoder
 from omegaconf import ListConfig, OmegaConf
@@ -12,63 +17,22 @@ from PIL import Image
 from safetensors.torch import load_file as load_safetensors
 from torch import autocast
 from torchvision import transforms
-from torchvision.utils import make_grid
+from torchvision.utils import make_grid, save_image
 
-from scripts.util.detection.nsfw_and_watermark_dectection import DeepFloydDataFiltering
-from sgm.modules.diffusionmodules.sampling import (
-    DPMPP2MSampler,
-    DPMPP2SAncestralSampler,
-    EulerAncestralSampler,
-    EulerEDMSampler,
-    HeunEDMSampler,
-    LinearMultistepSampler,
-)
-from sgm.util import append_dims, instantiate_from_config
-
-
-class WatermarkEmbedder:
-    def __init__(self, watermark):
-        self.watermark = watermark
-        self.num_bits = len(WATERMARK_BITS)
-        self.encoder = WatermarkEncoder()
-        self.encoder.set_watermark("bits", self.watermark)
-
-    def __call__(self, image: torch.Tensor):
-        """
-        Adds a predefined watermark to the input image
-
-        Args:
-            image: ([N,] B, C, H, W) in range [0, 1]
-
-        Returns:
-            same as input but watermarked
-        """
-        # watermarking libary expects input as cv2 BGR format
-        squeeze = len(image.shape) == 4
-        if squeeze:
-            image = image[None, ...]
-        n = image.shape[0]
-        image_np = rearrange(
-            (255 * image).detach().cpu(), "n b c h w -> (n b) h w c"
-        ).numpy()[:, :, :, ::-1]
-        # torch (b, c, h, w) in [0, 1] -> numpy (b, h, w, c) [0, 255]
-        for k in range(image_np.shape[0]):
-            image_np[k] = self.encoder.encode(image_np[k], "dwtDct")
-        image = torch.from_numpy(
-            rearrange(image_np[:, :, :, ::-1], "(n b) h w c -> n b c h w", n=n)
-        ).to(image.device)
-        image = torch.clamp(image / 255, min=0.0, max=1.0)
-        if squeeze:
-            image = image[0]
-        return image
-
-
-# A fixed 48-bit message that was choosen at random
-# WATERMARK_MESSAGE = 0xB3EC907BB19E
-WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110
-# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
-WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
-embed_watemark = WatermarkEmbedder(WATERMARK_BITS)
+from scripts.demo.discretization import (Img2ImgDiscretizationWrapper,
+                                         Txt2NoisyDiscretizationWrapper)
+from scripts.util.detection.nsfw_and_watermark_dectection import \
+    DeepFloydDataFiltering
+from sgm.inference.helpers import embed_watermark
+from sgm.modules.diffusionmodules.guiders import (LinearPredictionGuider,
+                                                  VanillaCFG)
+from sgm.modules.diffusionmodules.sampling import (DPMPP2MSampler,
+                                                   DPMPP2SAncestralSampler,
+                                                   EulerAncestralSampler,
+                                                   EulerEDMSampler,
+                                                   HeunEDMSampler,
+                                                   LinearMultistepSampler)
+from sgm.util import append_dims, default, instantiate_from_config
 
 
 @st.cache_resource()
@@ -164,11 +128,12 @@ def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None):
     for key in keys:
         if key == "txt":
             if prompt is None:
-                prompt = st.text_input(
-                    "Prompt", "A professional photograph of an astronaut riding a pig"
-                )
+                prompt = "A professional photograph of an astronaut riding a pig"
             if negative_prompt is None:
-                negative_prompt = st.text_input("Negative prompt", "")
+                negative_prompt = ""
+
+            prompt = st.text_input("Prompt", prompt)
+            negative_prompt = st.text_input("Negative prompt", negative_prompt)
 
             value_dict["prompt"] = prompt
             value_dict["negative_prompt"] = negative_prompt
@@ -203,13 +168,35 @@ def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None):
             value_dict["target_width"] = init_dict["target_width"]
             value_dict["target_height"] = init_dict["target_height"]
 
+        if key in ["fps_id", "fps"]:
+            fps = st.number_input("fps", value=6, min_value=1)
+
+            value_dict["fps"] = fps
+            value_dict["fps_id"] = fps - 1
+
+        if key == "motion_bucket_id":
+            mb_id = st.number_input("motion bucket id", 0, 511, value=127)
+            value_dict["motion_bucket_id"] = mb_id
+
+        if key == "pool_image":
+            st.text("Image for pool conditioning")
+            image = load_img(
+                key="pool_image_input",
+                size=224,
+                center_crop=True,
+            )
+            if image is None:
+                st.info("Need an image here")
+                image = torch.zeros(1, 3, 224, 224)
+            value_dict["pool_image"] = image
+
     return value_dict
 
 
 def perform_save_locally(save_path, samples):
     os.makedirs(os.path.join(save_path), exist_ok=True)
     base_count = len(os.listdir(os.path.join(save_path)))
-    samples = embed_watemark(samples)
+    samples = embed_watermark(samples)
     for sample in samples:
         sample = 255.0 * rearrange(sample.cpu().numpy(), "c h w -> h w c")
         Image.fromarray(sample.astype(np.uint8)).save(
@@ -228,95 +215,99 @@ def init_save_locally(_dir, init_value: bool = False):
     return save_locally, save_path
 
 
-class Img2ImgDiscretizationWrapper:
-    """
-    wraps a discretizer, and prunes the sigmas
-    params:
-        strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
-    """
-
-    def __init__(self, discretization, strength: float = 1.0):
-        self.discretization = discretization
-        self.strength = strength
-        assert 0.0 <= self.strength <= 1.0
-
-    def __call__(self, *args, **kwargs):
-        # sigmas start large first, and decrease then
-        sigmas = self.discretization(*args, **kwargs)
-        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
-        sigmas = torch.flip(sigmas, (0,))
-        sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
-        print("prune index:", max(int(self.strength * len(sigmas)), 1))
-        sigmas = torch.flip(sigmas, (0,))
-        print(f"sigmas after pruning: ", sigmas)
-        return sigmas
-
-
-class Txt2NoisyDiscretizationWrapper:
-    """
-    wraps a discretizer, and prunes the sigmas
-    params:
-        strength: float between 0.0 and 1.0. 0.0 means full sampling (all sigmas are returned)
-    """
-
-    def __init__(self, discretization, strength: float = 0.0, original_steps=None):
-        self.discretization = discretization
-        self.strength = strength
-        self.original_steps = original_steps
-        assert 0.0 <= self.strength <= 1.0
-
-    def __call__(self, *args, **kwargs):
-        # sigmas start large first, and decrease then
-        sigmas = self.discretization(*args, **kwargs)
-        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
-        sigmas = torch.flip(sigmas, (0,))
-        if self.original_steps is None:
-            steps = len(sigmas)
-        else:
-            steps = self.original_steps + 1
-        prune_index = max(min(int(self.strength * steps) - 1, steps - 1), 0)
-        sigmas = sigmas[prune_index:]
-        print("prune index:", prune_index)
-        sigmas = torch.flip(sigmas, (0,))
-        print(f"sigmas after pruning: ", sigmas)
-        return sigmas
-
-
-def get_guider(key):
+def get_guider(options, key):
     guider = st.sidebar.selectbox(
         f"Discretization #{key}",
         [
             "VanillaCFG",
             "IdentityGuider",
+            "LinearPredictionGuider",
         ],
+        options.get("guider", 0),
     )
 
+    additional_guider_kwargs = options.pop("additional_guider_kwargs", {})
+
     if guider == "IdentityGuider":
         guider_config = {
             "target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
         }
     elif guider == "VanillaCFG":
-        scale = st.number_input(
-            f"cfg-scale #{key}", value=5.0, min_value=0.0, max_value=100.0
+        scale_schedule = st.sidebar.selectbox(
+            f"Scale schedule #{key}",
+            ["Identity", "Oscillating"],
         )
 
-        thresholder = st.sidebar.selectbox(
-            f"Thresholder #{key}",
-            [
-                "None",
-            ],
-        )
+        if scale_schedule == "Identity":
+            scale = st.number_input(
+                f"cfg-scale #{key}",
+                value=options.get("cfg", 5.0),
+                min_value=0.0,
+            )
 
-        if thresholder == "None":
-            dyn_thresh_config = {
-                "target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
+            scale_schedule_config = {
+                "target": "sgm.modules.diffusionmodules.guiders.IdentitySchedule",
+                "params": {"scale": scale},
+            }
+
+        elif scale_schedule == "Oscillating":
+            small_scale = st.number_input(
+                f"small cfg-scale #{key}",
+                value=4.0,
+                min_value=0.0,
+            )
+
+            large_scale = st.number_input(
+                f"large cfg-scale #{key}",
+                value=16.0,
+                min_value=0.0,
+            )
+
+            sigma_cutoff = st.number_input(
+                f"sigma cutoff #{key}",
+                value=1.0,
+                min_value=0.0,
+            )
+
+            scale_schedule_config = {
+                "target": "sgm.modules.diffusionmodules.guiders.OscillatingSchedule",
+                "params": {
+                    "small_scale": small_scale,
+                    "large_scale": large_scale,
+                    "sigma_cutoff": sigma_cutoff,
+                },
             }
         else:
             raise NotImplementedError
 
         guider_config = {
             "target": "sgm.modules.diffusionmodules.guiders.VanillaCFG",
-            "params": {"scale": scale, "dyn_thresh_config": dyn_thresh_config},
+            "params": {
+                "scale_schedule_config": scale_schedule_config,
+                **additional_guider_kwargs,
+            },
+        }
+    elif guider == "LinearPredictionGuider":
+        max_scale = st.number_input(
+            f"max-cfg-scale #{key}",
+            value=options.get("cfg", 1.5),
+            min_value=1.0,
+        )
+        min_scale = st.number_input(
+            f"min guidance scale",
+            value=options.get("min_cfg", 1.0),
+            min_value=1.0,
+            max_value=10.0,
+        )
+
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.LinearPredictionGuider",
+            "params": {
+                "max_scale": max_scale,
+                "min_scale": min_scale,
+                "num_frames": options["num_frames"],
+                **additional_guider_kwargs,
+            },
         }
     else:
         raise NotImplementedError
@@ -325,18 +316,21 @@ def get_guider(key):
 
 def init_sampling(
     key=1,
-    img2img_strength=1.0,
-    specify_num_samples=True,
-    stage2strength=None,
+    img2img_strength: Optional[float] = None,
+    specify_num_samples: bool = True,
+    stage2strength: Optional[float] = None,
+    options: Optional[Dict[str, int]] = None,
 ):
+    options = {} if options is None else options
+
     num_rows, num_cols = 1, 1
     if specify_num_samples:
         num_cols = st.number_input(
-            f"num cols #{key}", value=2, min_value=1, max_value=10
+            f"num cols #{key}", value=num_cols, min_value=1, max_value=10
         )
 
     steps = st.sidebar.number_input(
-        f"steps #{key}", value=40, min_value=1, max_value=1000
+        f"steps #{key}", value=options.get("num_steps", 40), min_value=1, max_value=1000
     )
     sampler = st.sidebar.selectbox(
         f"Sampler #{key}",
@@ -348,7 +342,7 @@ def init_sampling(
             "DPMPP2MSampler",
             "LinearMultistepSampler",
         ],
-        0,
+        options.get("sampler", 0),
     )
     discretization = st.sidebar.selectbox(
         f"Discretization #{key}",
@@ -356,14 +350,15 @@ def init_sampling(
             "LegacyDDPMDiscretization",
             "EDMDiscretization",
         ],
+        options.get("discretization", 0),
     )
 
-    discretization_config = get_discretization(discretization, key=key)
+    discretization_config = get_discretization(discretization, options=options, key=key)
 
-    guider_config = get_guider(key=key)
+    guider_config = get_guider(options=options, key=key)
 
     sampler = get_sampler(sampler, steps, discretization_config, guider_config, key=key)
-    if img2img_strength < 1.0:
+    if img2img_strength is not None:
         st.warning(
             f"Wrapping {sampler.__class__.__name__} with Img2ImgDiscretizationWrapper"
         )
@@ -377,15 +372,19 @@ def init_sampling(
     return sampler, num_rows, num_cols
 
 
-def get_discretization(discretization, key=1):
+def get_discretization(discretization, options, key=1):
     if discretization == "LegacyDDPMDiscretization":
         discretization_config = {
             "target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
         }
     elif discretization == "EDMDiscretization":
-        sigma_min = st.number_input(f"sigma_min #{key}", value=0.03)  # 0.0292
-        sigma_max = st.number_input(f"sigma_max #{key}", value=14.61)  # 14.6146
-        rho = st.number_input(f"rho #{key}", value=3.0)
+        sigma_min = st.number_input(
+            f"sigma_min #{key}", value=options.get("sigma_min", 0.03)
+        )  # 0.0292
+        sigma_max = st.number_input(
+            f"sigma_max #{key}", value=options.get("sigma_max", 14.61)
+        )  # 14.6146
+        rho = st.number_input(f"rho #{key}", value=options.get("rho", 3.0))
         discretization_config = {
             "target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
             "params": {
@@ -474,8 +473,8 @@ def get_sampler(sampler_name, steps, discretization_config, guider_config, key=1
     return sampler
 
 
-def get_interactive_image(key=None) -> Image.Image:
-    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"], key=key)
+def get_interactive_image() -> Image.Image:
+    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"])
     if image is not None:
         image = Image.open(image)
         if not image.mode == "RGB":
@@ -483,8 +482,12 @@ def get_interactive_image(key=None) -> Image.Image:
         return image
 
 
-def load_img(display=True, key=None):
-    image = get_interactive_image(key=key)
+def load_img(
+    display: bool = True,
+    size: Union[None, int, Tuple[int, int]] = None,
+    center_crop: bool = False,
+):
+    image = get_interactive_image()
     if image is None:
         return None
     if display:
@@ -492,12 +495,15 @@ def load_img(display=True, key=None):
     w, h = image.size
     print(f"loaded input image of size ({w}, {h})")
 
-    transform = transforms.Compose(
-        [
-            transforms.ToTensor(),
-            transforms.Lambda(lambda x: x * 2.0 - 1.0),
-        ]
-    )
+    transform = []
+    if size is not None:
+        transform.append(transforms.Resize(size))
+    if center_crop:
+        transform.append(transforms.CenterCrop(size))
+    transform.append(transforms.ToTensor())
+    transform.append(transforms.Lambda(lambda x: 2.0 * x - 1.0))
+
+    transform = transforms.Compose(transform)
     img = transform(image)[None, ...]
     st.text(f"input min/max/mean: {img.min():.3f}/{img.max():.3f}/{img.mean():.3f}")
     return img
@@ -518,15 +524,18 @@ def do_sample(
     W,
     C,
     F,
-    force_uc_zero_embeddings: List = None,
+    force_uc_zero_embeddings: Optional[List] = None,
+    force_cond_zero_embeddings: Optional[List] = None,
     batch2model_input: List = None,
     return_latents=False,
     filter=None,
+    T=None,
+    additional_batch_uc_fields=None,
+    decoding_t=None,
 ):
-    if force_uc_zero_embeddings is None:
-        force_uc_zero_embeddings = []
-    if batch2model_input is None:
-        batch2model_input = []
+    force_uc_zero_embeddings = default(force_uc_zero_embeddings, [])
+    batch2model_input = default(batch2model_input, [])
+    additional_batch_uc_fields = default(additional_batch_uc_fields, [])
 
     st.text("Sampling")
 
@@ -535,24 +544,25 @@ def do_sample(
     with torch.no_grad():
         with precision_scope("cuda"):
             with model.ema_scope():
-                num_samples = [num_samples]
+                if T is not None:
+                    num_samples = [num_samples, T]
+                else:
+                    num_samples = [num_samples]
+
                 load_model(model.conditioner)
                 batch, batch_uc = get_batch(
                     get_unique_embedder_keys_from_conditioner(model.conditioner),
                     value_dict,
                     num_samples,
+                    T=T,
+                    additional_batch_uc_fields=additional_batch_uc_fields,
                 )
-                for key in batch:
-                    if isinstance(batch[key], torch.Tensor):
-                        print(key, batch[key].shape)
-                    elif isinstance(batch[key], list):
-                        print(key, [len(l) for l in batch[key]])
-                    else:
-                        print(key, batch[key])
+
                 c, uc = model.conditioner.get_unconditional_conditioning(
                     batch,
                     batch_uc=batch_uc,
                     force_uc_zero_embeddings=force_uc_zero_embeddings,
+                    force_cond_zero_embeddings=force_cond_zero_embeddings,
                 )
                 unload_model(model.conditioner)
 
@@ -561,10 +571,29 @@ def do_sample(
                         c[k], uc[k] = map(
                             lambda y: y[k][: math.prod(num_samples)].to("cuda"), (c, uc)
                         )
+                    if k in ["crossattn", "concat"] and T is not None:
+                        uc[k] = repeat(uc[k], "b ... -> b t ...", t=T)
+                        uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=T)
+                        c[k] = repeat(c[k], "b ... -> b t ...", t=T)
+                        c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=T)
 
                 additional_model_inputs = {}
                 for k in batch2model_input:
-                    additional_model_inputs[k] = batch[k]
+                    if k == "image_only_indicator":
+                        assert T is not None
+
+                        if isinstance(
+                            sampler.guider, (VanillaCFG, LinearPredictionGuider)
+                        ):
+                            additional_model_inputs[k] = torch.zeros(
+                                num_samples[0] * 2, num_samples[1]
+                            ).to("cuda")
+                        else:
+                            additional_model_inputs[k] = torch.zeros(num_samples).to(
+                                "cuda"
+                            )
+                    else:
+                        additional_model_inputs[k] = batch[k]
 
                 shape = (math.prod(num_samples), C, H // F, W // F)
                 randn = torch.randn(shape).to("cuda")
@@ -581,6 +610,9 @@ def do_sample(
                 unload_model(model.denoiser)
 
                 load_model(model.first_stage_model)
+                model.en_and_decode_n_samples_a_time = (
+                    decoding_t  # Decode n frames at a time
+                )
                 samples_x = model.decode_first_stage(samples_z)
                 samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
                 unload_model(model.first_stage_model)
@@ -588,16 +620,32 @@ def do_sample(
                 if filter is not None:
                     samples = filter(samples)
 
-                grid = torch.stack([samples])
-                grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
-                outputs.image(grid.cpu().numpy())
+                if T is None:
+                    grid = torch.stack([samples])
+                    grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
+                    outputs.image(grid.cpu().numpy())
+                else:
+                    as_vids = rearrange(samples, "(b t) c h w -> b t c h w", t=T)
+                    for i, vid in enumerate(as_vids):
+                        grid = rearrange(make_grid(vid, nrow=4), "c h w -> h w c")
+                        st.image(
+                            grid.cpu().numpy(),
+                            f"Sample #{i} as image",
+                        )
 
                 if return_latents:
                     return samples, samples_z
                 return samples
 
 
-def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
+def get_batch(
+    keys,
+    value_dict: dict,
+    N: Union[List, ListConfig],
+    device: str = "cuda",
+    T: int = None,
+    additional_batch_uc_fields: List[str] = [],
+):
     # Hardcoded demo setups; might undergo some changes in the future
 
     batch = {}
@@ -605,21 +653,15 @@ def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
 
     for key in keys:
         if key == "txt":
-            batch["txt"] = (
-                np.repeat([value_dict["prompt"]], repeats=math.prod(N))
-                .reshape(N)
-                .tolist()
-            )
-            batch_uc["txt"] = (
-                np.repeat([value_dict["negative_prompt"]], repeats=math.prod(N))
-                .reshape(N)
-                .tolist()
-            )
+            batch["txt"] = [value_dict["prompt"]] * math.prod(N)
+
+            batch_uc["txt"] = [value_dict["negative_prompt"]] * math.prod(N)
+
         elif key == "original_size_as_tuple":
             batch["original_size_as_tuple"] = (
                 torch.tensor([value_dict["orig_height"], value_dict["orig_width"]])
                 .to(device)
-                .repeat(*N, 1)
+                .repeat(math.prod(N), 1)
             )
         elif key == "crop_coords_top_left":
             batch["crop_coords_top_left"] = (
@@ -627,30 +669,67 @@ def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
                     [value_dict["crop_coords_top"], value_dict["crop_coords_left"]]
                 )
                 .to(device)
-                .repeat(*N, 1)
+                .repeat(math.prod(N), 1)
             )
         elif key == "aesthetic_score":
             batch["aesthetic_score"] = (
-                torch.tensor([value_dict["aesthetic_score"]]).to(device).repeat(*N, 1)
+                torch.tensor([value_dict["aesthetic_score"]])
+                .to(device)
+                .repeat(math.prod(N), 1)
             )
             batch_uc["aesthetic_score"] = (
                 torch.tensor([value_dict["negative_aesthetic_score"]])
                 .to(device)
-                .repeat(*N, 1)
+                .repeat(math.prod(N), 1)
             )
 
         elif key == "target_size_as_tuple":
             batch["target_size_as_tuple"] = (
                 torch.tensor([value_dict["target_height"], value_dict["target_width"]])
                 .to(device)
-                .repeat(*N, 1)
+                .repeat(math.prod(N), 1)
+            )
+        elif key == "fps":
+            batch[key] = (
+                torch.tensor([value_dict["fps"]]).to(device).repeat(math.prod(N))
+            )
+        elif key == "fps_id":
+            batch[key] = (
+                torch.tensor([value_dict["fps_id"]]).to(device).repeat(math.prod(N))
+            )
+        elif key == "motion_bucket_id":
+            batch[key] = (
+                torch.tensor([value_dict["motion_bucket_id"]])
+                .to(device)
+                .repeat(math.prod(N))
+            )
+        elif key == "pool_image":
+            batch[key] = repeat(value_dict[key], "1 ... -> b ...", b=math.prod(N)).to(
+                device, dtype=torch.half
+            )
+        elif key == "cond_aug":
+            batch[key] = repeat(
+                torch.tensor([value_dict["cond_aug"]]).to("cuda"),
+                "1 -> b",
+                b=math.prod(N),
+            )
+        elif key == "cond_frames":
+            batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0])
+        elif key == "cond_frames_without_noise":
+            batch[key] = repeat(
+                value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0]
             )
         else:
             batch[key] = value_dict[key]
 
+    if T is not None:
+        batch["num_video_frames"] = T
+
     for key in batch.keys():
         if key not in batch_uc and isinstance(batch[key], torch.Tensor):
             batch_uc[key] = torch.clone(batch[key])
+        elif key in additional_batch_uc_fields and key not in batch_uc:
+            batch_uc[key] = copy.copy(batch[key])
     return batch, batch_uc
 
 
@@ -661,7 +740,8 @@ def do_img2img(
     sampler,
     value_dict,
     num_samples,
-    force_uc_zero_embeddings=[],
+    force_uc_zero_embeddings: Optional[List] = None,
+    force_cond_zero_embeddings: Optional[List] = None,
     additional_kwargs={},
     offset_noise_level: int = 0.0,
     return_latents=False,
@@ -686,6 +766,7 @@ def do_img2img(
                     batch,
                     batch_uc=batch_uc,
                     force_uc_zero_embeddings=force_uc_zero_embeddings,
+                    force_cond_zero_embeddings=force_cond_zero_embeddings,
                 )
                 unload_model(model.conditioner)
                 for k in c:
@@ -736,9 +817,112 @@ def do_img2img(
                 if filter is not None:
                     samples = filter(samples)
 
-                grid = embed_watemark(torch.stack([samples]))
                 grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
                 outputs.image(grid.cpu().numpy())
                 if return_latents:
                     return samples, samples_z
                 return samples
+
+
+def get_resizing_factor(
+    desired_shape: Tuple[int, int], current_shape: Tuple[int, int]
+) -> float:
+    r_bound = desired_shape[1] / desired_shape[0]
+    aspect_r = current_shape[1] / current_shape[0]
+    if r_bound >= 1.0:
+        if aspect_r >= r_bound:
+            factor = min(desired_shape) / min(current_shape)
+        else:
+            if aspect_r < 1.0:
+                factor = max(desired_shape) / min(current_shape)
+            else:
+                factor = max(desired_shape) / max(current_shape)
+    else:
+        if aspect_r <= r_bound:
+            factor = min(desired_shape) / min(current_shape)
+        else:
+            if aspect_r > 1:
+                factor = max(desired_shape) / min(current_shape)
+            else:
+                factor = max(desired_shape) / max(current_shape)
+
+    return factor
+
+
+def get_interactive_image(key=None) -> Image.Image:
+    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_for_prediction(
+    W: int, H: int, display=True, key=None, device="cuda"
+) -> torch.Tensor:
+    image = get_interactive_image(key=key)
+    if image is None:
+        return None
+    if display:
+        st.image(image)
+    w, h = image.size
+
+    image = np.array(image).transpose(2, 0, 1)
+    image = torch.from_numpy(image).to(dtype=torch.float32) / 255.0
+    image = image.unsqueeze(0)
+
+    rfs = get_resizing_factor((H, W), (h, w))
+    resize_size = [int(np.ceil(rfs * s)) for s in (h, w)]
+    top = (resize_size[0] - H) // 2
+    left = (resize_size[1] - W) // 2
+
+    image = torch.nn.functional.interpolate(
+        image, resize_size, mode="area", antialias=False
+    )
+    image = TT.functional.crop(image, top=top, left=left, height=H, width=W)
+
+    if display:
+        numpy_img = np.transpose(image[0].numpy(), (1, 2, 0))
+        pil_image = Image.fromarray((numpy_img * 255).astype(np.uint8))
+        st.image(pil_image)
+    return image.to(device) * 2.0 - 1.0
+
+
+def save_video_as_grid_and_mp4(
+    video_batch: torch.Tensor, save_path: str, T: int, fps: int = 5
+):
+    os.makedirs(save_path, exist_ok=True)
+    base_count = len(glob(os.path.join(save_path, "*.mp4")))
+
+    video_batch = rearrange(video_batch, "(b t) c h w -> b t c h w", t=T)
+    video_batch = embed_watermark(video_batch)
+    for vid in video_batch:
+        save_image(vid, fp=os.path.join(save_path, f"{base_count:06d}.png"), nrow=4)
+
+        video_path = os.path.join(save_path, f"{base_count:06d}.mp4")
+
+        writer = cv2.VideoWriter(
+            video_path,
+            cv2.VideoWriter_fourcc(*"MP4V"),
+            fps,
+            (vid.shape[-1], vid.shape[-2]),
+        )
+
+        vid = (
+            (rearrange(vid, "t c h w -> t h w c") * 255).cpu().numpy().astype(np.uint8)
+        )
+        for frame in vid:
+            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+            writer.write(frame)
+
+        writer.release()
+
+        video_path_h264 = video_path[:-4] + "_h264.mp4"
+        os.system(f"ffmpeg -i {video_path} -c:v libx264 {video_path_h264}")
+
+        with open(video_path_h264, "rb") as f:
+            video_bytes = f.read()
+        st.video(video_bytes)
+
+        base_count += 1

scripts/demo/video_sampling.py

@@ -0,0 +1,200 @@
+import os
+
+from pytorch_lightning import seed_everything
+
+from scripts.demo.streamlit_helpers import *
+
+SAVE_PATH = "outputs/demo/vid/"
+
+VERSION2SPECS = {
+    "svd": {
+        "T": 14,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd.yaml",
+        "ckpt": "checkpoints/svd.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 2.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 25,
+        },
+    },
+    "svd_image_decoder": {
+        "T": 14,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd_image_decoder.yaml",
+        "ckpt": "checkpoints/svd_image_decoder.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 2.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 25,
+        },
+    },
+    "svd_xt": {
+        "T": 25,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd.yaml",
+        "ckpt": "checkpoints/svd_xt.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 3.0,
+            "min_cfg": 1.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 30,
+            "decoding_t": 14,
+        },
+    },
+    "svd_xt_image_decoder": {
+        "T": 25,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd_image_decoder.yaml",
+        "ckpt": "checkpoints/svd_xt_image_decoder.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 3.0,
+            "min_cfg": 1.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 30,
+            "decoding_t": 14,
+        },
+    },
+}
+
+
+if __name__ == "__main__":
+    st.title("Stable Video Diffusion")
+    version = st.selectbox(
+        "Model Version",
+        [k for k in VERSION2SPECS.keys()],
+        0,
+    )
+    version_dict = VERSION2SPECS[version]
+    if st.checkbox("Load Model"):
+        mode = "img2vid"
+    else:
+        mode = "skip"
+
+    H = st.sidebar.number_input(
+        "H", value=version_dict["H"], min_value=64, max_value=2048
+    )
+    W = st.sidebar.number_input(
+        "W", value=version_dict["W"], min_value=64, max_value=2048
+    )
+    T = st.sidebar.number_input(
+        "T", value=version_dict["T"], min_value=0, max_value=128
+    )
+    C = version_dict["C"]
+    F = version_dict["f"]
+    options = version_dict["options"]
+
+    if mode != "skip":
+        state = init_st(version_dict, load_filter=True)
+        if state["msg"]:
+            st.info(state["msg"])
+        model = state["model"]
+
+        ukeys = set(
+            get_unique_embedder_keys_from_conditioner(state["model"].conditioner)
+        )
+
+        value_dict = init_embedder_options(
+            ukeys,
+            {},
+        )
+
+        value_dict["image_only_indicator"] = 0
+
+        if mode == "img2vid":
+            img = load_img_for_prediction(W, H)
+            cond_aug = st.number_input(
+                "Conditioning augmentation:", value=0.02, min_value=0.0
+            )
+            value_dict["cond_frames_without_noise"] = img
+            value_dict["cond_frames"] = img + cond_aug * torch.randn_like(img)
+            value_dict["cond_aug"] = cond_aug
+
+        seed = st.sidebar.number_input(
+            "seed", value=23, min_value=0, max_value=int(1e9)
+        )
+        seed_everything(seed)
+
+        save_locally, save_path = init_save_locally(
+            os.path.join(SAVE_PATH, version), init_value=True
+        )
+
+        options["num_frames"] = T
+
+        sampler, num_rows, num_cols = init_sampling(options=options)
+        num_samples = num_rows * num_cols
+
+        decoding_t = st.number_input(
+            "Decode t frames at a time (set small if you are low on VRAM)",
+            value=options.get("decoding_t", T),
+            min_value=1,
+            max_value=int(1e9),
+        )
+
+        if st.checkbox("Overwrite fps in mp4 generator", False):
+            saving_fps = st.number_input(
+                f"saving video at fps:", value=value_dict["fps"], min_value=1
+            )
+        else:
+            saving_fps = value_dict["fps"]
+
+        if st.button("Sample"):
+            out = do_sample(
+                model,
+                sampler,
+                value_dict,
+                num_samples,
+                H,
+                W,
+                C,
+                F,
+                T=T,
+                batch2model_input=["num_video_frames", "image_only_indicator"],
+                force_uc_zero_embeddings=options.get("force_uc_zero_embeddings", None),
+                force_cond_zero_embeddings=options.get(
+                    "force_cond_zero_embeddings", None
+                ),
+                return_latents=False,
+                decoding_t=decoding_t,
+            )
+
+            if isinstance(out, (tuple, list)):
+                samples, samples_z = out
+            else:
+                samples = out
+                samples_z = None
+
+            if save_locally:
+                save_video_as_grid_and_mp4(samples, save_path, T, fps=saving_fps)