video works

comfy/ldm/seedvr/model.py

@@ -16,10 +16,6 @@ from torch.nn.modules.utils import _triple
 from torch import nn
 import math
 import logging
-try:
-    from flash_attn import flash_attn_varlen_func
-except:
-    logging.warning("Best results will be achieved with flash attention enabled for SeedVR2")
 
 class Cache:
     def __init__(self, disable=False, prefix="", cache=None):
@@ -1299,6 +1295,9 @@ class NaDiT(nn.Module):
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
         conditions = kwargs.get("condition")
+        b, tc, h, w = x.shape
+        x = x.view(b, 16, -1, h, w)
+        conditions = conditions.view(b, 17, -1, h, w)
         x = x.movedim(1, -1)
         conditions = conditions.movedim(1, -1)
 
@@ -1375,11 +1374,11 @@ class NaDiT(nn.Module):
         vid, vid_shape = self.vid_out(vid, vid_shape, cache, vid_shape_before_patchify = vid_shape_before_patchify)
         vid = unflatten(vid, vid_shape)
         out =  torch.stack(vid)
+        out = out.movedim(-1, 1)
+        out = rearrange(out, "b c t h w -> b (c t) h w")
         try:
             pos, neg = out.chunk(2)
             out = torch.cat([neg, pos])
-            out = out.movedim(-1, 1)
             return out
         except:
-            out = out.movedim(-1, 1)
             return out

comfy/ldm/seedvr/vae.py

@@ -9,6 +9,7 @@ from torch import Tensor
 import comfy.model_management
 from comfy.ldm.seedvr.model import safe_pad_operation
 from comfy.ldm.modules.attention import optimized_attention
+from comfy_extras.nodes_seedvr import tiled_vae
 
 class DiagonalGaussianDistribution(object):
     def __init__(self, parameters: torch.Tensor, deterministic: bool = False):
@@ -1450,7 +1451,7 @@ class VideoAutoencoderKL(nn.Module):
 
         return posterior
 
-    def decode(
+    def decode_(
         self, z: torch.Tensor, return_dict: bool = True
     ):
         decoded = self.slicing_decode(z)
@@ -1541,10 +1542,11 @@ class VideoAutoencoderKLWrapper(VideoAutoencoderKL):
         x = self.decode(z).sample
         return x, z, p
 
-    def encode(self, x, orig_dims):
+    def encode(self, x, orig_dims=None):
         # we need to keep a reference to the image/video so we later can do a colour fix later
-        self.original_image_video = x
+        #self.original_image_video = x
-        self.img_dims = orig_dims
+        if orig_dims is not None:
+            self.img_dims = orig_dims
         if x.ndim == 4:
             x = x.unsqueeze(2)
         x = x.to(next(self.parameters()).dtype)
@@ -1554,6 +1556,8 @@ class VideoAutoencoderKLWrapper(VideoAutoencoderKL):
         return z, p
 
     def decode(self, z: torch.FloatTensor):
+        b, tc, h, w = z.shape
+        z = z.view(b, 16, -1, h, w)
         z = z.movedim(1, -1)
         latent = z.unsqueeze(0)
         scale = 0.9152
@@ -1567,12 +1571,31 @@ class VideoAutoencoderKLWrapper(VideoAutoencoderKL):
 
         target_device = comfy.model_management.get_torch_device()
         self.decoder.to(target_device)
-        x = super().decode(latent).squeeze(2)
+        x = tiled_vae(latent, self, **self.tiled_args, encode=False).squeeze(2)
+        #x = super().decode(latent).squeeze(2)
+
+        input = rearrange(self.original_image_video, "b c t h w -> (b t) c h w")
+        if x.ndim == 4:
+            x = x.unsqueeze(0)
+
+        # in case of padded frames
+        t = input.size(0)
+        x = x[:, :, :t]
+
+        x = rearrange(x, "b c t h w -> (b t) c h w")
 
-        input = rearrange(self.original_image_video[0], "c t h w -> t c h w")
         x = wavelet_reconstruction(x, input)
+        x = x.unsqueeze(0)
         o_h, o_w = self.img_dims
         x = x[..., :o_h, :o_w]
+        x = rearrange(x, "b t c h w -> b c t h w")
+
+        # ensure even dims for save video
+        h, w = x.shape[-2:]
+        w2 = w - (w % 2)
+        h2 = h - (h % 2)
+        x = x[..., :h2, :w2]
+
         return x
 
     def set_memory_limit(self, conv_max_mem: Optional[float], norm_max_mem: Optional[float]):

comfy_extras/nodes_seedvr.py

@@ -4,12 +4,146 @@ import torch
 import math
 from einops import rearrange
 
+import gc
 import comfy.model_management
+from comfy.utils import ProgressBar
+
 import torch.nn.functional as F
 from torchvision.transforms import functional as TVF
 from torchvision.transforms import Lambda, Normalize
 from torchvision.transforms.functional import InterpolationMode
 
+@torch.inference_mode()
+def tiled_vae(x, vae_model, tile_size=(512, 512), tile_overlap=(64, 64), temporal_size=16, temporal_overlap=4, encode=True):
+    
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    if x.ndim != 5:
+        x = x.unsqueeze(2)
+
+    b, c, d, h, w = x.shape
+    
+    sf_s = getattr(vae_model, "spatial_downsample_factor", 8)
+    sf_t = getattr(vae_model, "temporal_downsample_factor", 4)
+
+    if encode:
+        ti_h, ti_w = tile_size
+        ov_h, ov_w = tile_overlap
+        ti_t = temporal_size
+        ov_t = temporal_overlap
+        
+        target_d = (d + sf_t - 1) // sf_t
+        target_h = (h + sf_s - 1) // sf_s
+        target_w = (w + sf_s - 1) // sf_s
+    else:
+        ti_h = max(1, tile_size[0] // sf_s)
+        ti_w = max(1, tile_size[1] // sf_s)
+        ov_h = max(0, tile_overlap[0] // sf_s)
+        ov_w = max(0, tile_overlap[1] // sf_s)
+        ti_t = max(1, temporal_size // sf_t)
+        ov_t = max(0, temporal_overlap // sf_t)
+
+        target_d = d * sf_t
+        target_h = h * sf_s
+        target_w = w * sf_s
+
+    stride_t = max(1, ti_t - ov_t)
+    stride_h = max(1, ti_h - ov_h)
+    stride_w = max(1, ti_w - ov_w)
+
+    storage_device = torch.device("cpu")
+    result = None
+    count = None
+    
+    ramp_cache = {}
+    def get_ramp(steps):
+        if steps not in ramp_cache:
+            t = torch.linspace(0, 1, steps=steps, device=storage_device, dtype=torch.float32)
+            ramp_cache[steps] = 0.5 - 0.5 * torch.cos(t * torch.pi)
+        return ramp_cache[steps]
+
+    bar = ProgressBar(d // stride_t)
+    for t_idx in range(0, d, stride_t):
+        t_end = min(t_idx + ti_t, d)
+        
+        for y_idx in range(0, h, stride_h):
+            y_end = min(y_idx + ti_h, h)
+            
+            for x_idx in range(0, w, stride_w):
+                x_end = min(x_idx + ti_w, w)
+
+                tile_x = x[:, :, t_idx:t_end, y_idx:y_end, x_idx:x_end]
+
+                if encode:
+                    tile_out = vae_model.encode(tile_x)[0]
+                else:
+                    tile_out = vae_model.decode_(tile_x)
+
+                if tile_out.ndim == 4:
+                    tile_out = tile_out.unsqueeze(2)
+
+                tile_out = tile_out.to(storage_device).float()
+
+                if result is None:
+                    b_out, c_out = tile_out.shape[0], tile_out.shape[1]
+                    result = torch.zeros((b_out, c_out, target_d, target_h, target_w), device=storage_device, dtype=torch.float32)
+                    count = torch.zeros((1, 1, target_d, target_h, target_w), device=storage_device, dtype=torch.float32)
+
+                if encode:
+                    ts, te = t_idx // sf_t, (t_idx // sf_t) + tile_out.shape[2]
+                    ys, ye = y_idx // sf_s, (y_idx // sf_s) + tile_out.shape[3]
+                    xs, xe = x_idx // sf_s, (x_idx // sf_s) + tile_out.shape[4]
+                    
+                    cur_ov_t = max(0, min(ov_t // sf_t, tile_out.shape[2] // 2))
+                    cur_ov_h = max(0, min(ov_h // sf_s, tile_out.shape[3] // 2))
+                    cur_ov_w = max(0, min(ov_w // sf_s, tile_out.shape[4] // 2))
+                else:
+                    ts, te = t_idx * sf_t, (t_idx * sf_t) + tile_out.shape[2]
+                    ys, ye = y_idx * sf_s, (y_idx * sf_s) + tile_out.shape[3]
+                    xs, xe = x_idx * sf_s, (x_idx * sf_s) + tile_out.shape[4]
+
+                    cur_ov_t = max(0, min(ov_t, tile_out.shape[2] // 2))
+                    cur_ov_h = max(0, min(ov_h, tile_out.shape[3] // 2))
+                    cur_ov_w = max(0, min(ov_w, tile_out.shape[4] // 2))
+
+                w_t = torch.ones((tile_out.shape[2],), device=storage_device)
+                w_h = torch.ones((tile_out.shape[3],), device=storage_device)
+                w_w = torch.ones((tile_out.shape[4],), device=storage_device)
+
+                if cur_ov_t > 0:
+                    r = get_ramp(cur_ov_t)
+                    if t_idx > 0: w_t[:cur_ov_t] = r
+                    if t_end < d: w_t[-cur_ov_t:] = 1.0 - r
+                
+                if cur_ov_h > 0:
+                    r = get_ramp(cur_ov_h)
+                    if y_idx > 0: w_h[:cur_ov_h] = r
+                    if y_end < h: w_h[-cur_ov_h:] = 1.0 - r
+
+                if cur_ov_w > 0:
+                    r = get_ramp(cur_ov_w)
+                    if x_idx > 0: w_w[:cur_ov_w] = r
+                    if x_end < w: w_w[-cur_ov_w:] = 1.0 - r
+
+                final_weight = w_t.view(1,1,-1,1,1) * w_h.view(1,1,1,-1,1) * w_w.view(1,1,1,1,-1)
+
+                tile_out.mul_(final_weight)
+                result[:, :, ts:te, ys:ye, xs:xe] += tile_out
+                count[:, :, ts:te, ys:ye, xs:xe] += final_weight
+                
+                del tile_out, final_weight, tile_x, w_t, w_h, w_w
+        bar.update(1)
+    result.div_(count.clamp(min=1e-6))
+    
+    if result.device != x.device:
+        result = result.to(x.device).to(x.dtype)
+
+    if x.shape[2] == 1 and sf_t == 1:
+        result = result.squeeze(2)
+
+    return result
+
 def expand_dims(tensor, ndim):
     shape = tensor.shape + (1,) * (ndim - tensor.ndim)
     return tensor.reshape(shape)
@@ -115,7 +249,11 @@ class SeedVR2InputProcessing(io.ComfyNode):
                 io.Image.Input("images"),
                 io.Vae.Input("vae"),
                 io.Int.Input("resolution_height", default = 1280, min = 120), # //
-                io.Int.Input("resolution_width", default = 720, min = 120) # just non-zero value
+                io.Int.Input("resolution_width", default = 720, min = 120), # just non-zero value
+                io.Int.Input("spatial_tile_size", default = 512, min = -1),
+                io.Int.Input("temporal_tile_size", default = 8, min = -1),
+                io.Int.Input("spatial_overlap", default = 64, min = -1),
+                io.Int.Input("temporal_overlap", default = 8, min = -1),
             ],
             outputs = [
                 io.Latent.Output("vae_conditioning")
@@ -123,7 +261,7 @@ class SeedVR2InputProcessing(io.ComfyNode):
         )
     
     @classmethod
-    def execute(cls, images, vae, resolution_height, resolution_width):
+    def execute(cls, images, vae, resolution_height, resolution_width, spatial_tile_size, temporal_tile_size, spatial_overlap, temporal_overlap):
         device = vae.patcher.load_device
 
         offload_device = comfy.model_management.intermediate_device()
@@ -155,8 +293,15 @@ class SeedVR2InputProcessing(io.ComfyNode):
         images = rearrange(images, "b t c h w -> b c t h w")
         images = images.to(device)
         vae_model = vae_model.to(device)
-        latent = vae_model.encode(images, [o_h, o_w])[0]
+        vae_model.original_image_video = images
+
+        args = {"tile_size": (spatial_tile_size, spatial_tile_size), "tile_overlap": (spatial_overlap, spatial_overlap),
+                "temporal_size":temporal_tile_size, "temporal_overlap": temporal_overlap}
+        vae_model.tiled_args = args
+        latent = tiled_vae(images, vae_model, encode=True, **args)
+
         vae_model = vae_model.to(offload_device)
+        vae_model.img_dims = [o_h, o_w]
 
         latent = latent.unsqueeze(2) if latent.ndim == 4 else latent
         latent = rearrange(latent, "b c ... -> b ... c")
@@ -213,6 +358,9 @@ class SeedVR2Conditioning(io.ComfyNode):
         else:
             pos_cond = F.pad(pos_cond, (0, 0, 0, diff))
 
+        noises = rearrange(noises, "b c t h w -> b (c t) h w")
+        condition = rearrange(condition, "b c t h w -> b (c t) h w")
+
         negative = [[neg_cond.unsqueeze(0), {"condition": condition}]]
         positive = [[pos_cond.unsqueeze(0), {"condition": condition}]]