Merge dbf8f9dcf9 into 2e9d51680a

This logic was checking comfy_cast_weights, and going straight to
to the forward_comfy_cast_weights implementation without
attempting to downscale input to fp8 in the event comfy_cast_weights
is set.

The main reason comfy_cast_weights would be set would be for async
offload, which is not a good reason to nix FP8MM.

So instead, and together the underlying exclusions for FP8MM which
are:

* having a weight_function (usually LowVramPatch)
* force_cast_weights (compute dtype override)
* the weight is not Quantized
* the input is already quantized
* the model or layer has MM explictily disabled.

If you get past all of those exclusions, quantize the input tensor.
Then hand the new input, quantized or not off to
forward_comfy_cast_weights to handle it. If the weight is offloaded
but input is quantized you will get an offloaded MM8.

comfy/latent_formats.py

@@ -747,6 +747,10 @@ class ACEAudio(LatentFormat):
     latent_channels = 8
     latent_dimensions = 2
 
+class SeedVR2(LatentFormat):
+    latent_channels = 16
+    latent_dimensions = 16
+
 class ChromaRadiance(LatentFormat):
     latent_channels = 3
 

comfy/ldm/modules/attention.py

@@ -19,9 +19,15 @@ if model_management.xformers_enabled():
     import xformers.ops
 
 SAGE_ATTENTION_IS_AVAILABLE = False
+SAGE_ATTENTION_VAR_LENGTH_AVAILABLE = False
 try:
     from sageattention import sageattn
     SAGE_ATTENTION_IS_AVAILABLE = True
+    try:
+        from sageattention import sageattn_varlen
+        SAGE_ATTENTION_VAR_LENGTH_AVAILABLE = True
+    except:
+        pass
 except ImportError as e:
     if model_management.sage_attention_enabled():
         if e.name == "sageattention":
@@ -39,7 +45,7 @@ except ImportError:
 
 FLASH_ATTENTION_IS_AVAILABLE = False
 try:
-    from flash_attn import flash_attn_func
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
     FLASH_ATTENTION_IS_AVAILABLE = True
 except ImportError:
     if model_management.flash_attention_enabled():
@@ -87,7 +93,13 @@ def default(val, d):
         return val
     return d
 
-
+def var_attn_arg(kwargs):
+    cu_seqlens_q = kwargs.get("cu_seqlens_q", None)
+    cu_seqlens_k = kwargs.get("cu_seqlens_k", cu_seqlens_q)
+    max_seqlen_q = kwargs.get("max_seqlen_q", None)
+    max_seqlen_k = kwargs.get("max_seqlen_k", max_seqlen_q)
+    assert cu_seqlens_q is not None, "cu_seqlens_q shouldn't be None when var_length is True"
+    return cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k
 # feedforward
 class GEGLU(nn.Module):
     def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops):
@@ -412,13 +424,14 @@ except:
 
 @wrap_attn
 def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    var_length = kwargs.get("var_length", False)
     b = q.shape[0]
     dim_head = q.shape[-1]
     # check to make sure xformers isn't broken
     disabled_xformers = False
 
     if BROKEN_XFORMERS:
-        if b * heads > 65535:
+        if b * heads > 65535 and not var_length:
             disabled_xformers = True
 
     if not disabled_xformers:
@@ -426,9 +439,27 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
             disabled_xformers = True
 
     if disabled_xformers:
-        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs)
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, var_length=var_length, **kwargs)
 
-    if skip_reshape:
+    if var_length:
+        if not skip_reshape:
+            total_tokens, hidden_dim = q.shape
+            dim_head = hidden_dim // heads
+            q = q.view(1, total_tokens, heads, dim_head)
+            k = k.view(1, total_tokens, heads, dim_head)
+            v = v.view(1, total_tokens, heads, dim_head)
+        else:
+            if q.ndim == 3:
+                q = q.unsqueeze(0)
+            if k.ndim == 3:
+                k = k.unsqueeze(0)
+            if v.ndim == 3:
+                v = v.unsqueeze(0)
+            dim_head = q.shape[-1]
+
+        target_output_shape = (q.shape[1], -1)
+        b = 1
+    elif skip_reshape:
         # b h k d -> b k h d
         q, k, v = map(
             lambda t: t.permute(0, 2, 1, 3),
@@ -442,7 +473,11 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
             (q, k, v),
         )
 
-    if mask is not None:
+    if var_length:
+        cu_seqlens_q, _, _, _ = var_attn_arg(kwargs)
+        seq_lens = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
+        mask = xformers.ops.BlockDiagonalMask.from_seqlens(seq_lens_q=seq_lens, seq_lens_k=seq_lens)
+    elif mask is not None:
         # add a singleton batch dimension
         if mask.ndim == 2:
             mask = mask.unsqueeze(0)
@@ -464,6 +499,8 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
 
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
 
+    if var_length:
+        return out.reshape(*target_output_shape)
     if skip_output_reshape:
         out = out.permute(0, 2, 1, 3)
     else:
@@ -481,7 +518,28 @@ else:
 
 @wrap_attn
 def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
-    if skip_reshape:
+    var_length = kwargs.get("var_length", False)
+    if var_length:
+        cu_seqlens_q, cu_seqlens_k, _, _ = var_attn_arg(kwargs)
+        if not skip_reshape:
+            # assumes 2D q, k,v [total_tokens, embed_dim]
+            total_tokens, embed_dim = q.shape
+            head_dim = embed_dim // heads
+            q = q.view(total_tokens, heads, head_dim)
+            k = k.view(k.shape[0], heads, head_dim)
+            v = v.view(v.shape[0], heads, head_dim)
+
+        b = q.size(0)
+        dim_head = q.shape[-1]
+        q = torch.nested.nested_tensor_from_jagged(q, offsets=cu_seqlens_q.long())
+        k = torch.nested.nested_tensor_from_jagged(k, offsets=cu_seqlens_k.long())
+        v = torch.nested.nested_tensor_from_jagged(v, offsets=cu_seqlens_k.long())
+
+        mask = None
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+    elif skip_reshape:
         b, _, _, dim_head = q.shape
     else:
         b, _, dim_head = q.shape
@@ -499,8 +557,10 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
         if mask.ndim == 3:
             mask = mask.unsqueeze(1)
 
-    if SDP_BATCH_LIMIT >= b:
+    if SDP_BATCH_LIMIT >= b or var_length:
         out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+        if var_length:
+            return out.contiguous().transpose(1, 2).values()
         if not skip_output_reshape:
             out = (
                 out.transpose(1, 2).reshape(b, -1, heads * dim_head)
@@ -524,8 +584,19 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
 
 @wrap_attn
 def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    var_length = kwargs.get("var_length", False)
     exception_fallback = False
-    if skip_reshape:
+    if var_length:
+        if not skip_reshape:
+            total_tokens, hidden_dim = q.shape
+            dim_head = hidden_dim // heads
+            q, k, v = [t.view(total_tokens, heads, dim_head) for t in (q, k, v)]
+        b, _, dim_head = q.shape
+        # skips batched code
+        mask = None
+        tensor_layout = "VAR"
+        target_output_shape = (q.shape[0], -1)
+    elif skip_reshape:
         b, _, _, dim_head = q.shape
         tensor_layout = "HND"
     else:
@@ -546,7 +617,14 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
             mask = mask.unsqueeze(1)
 
     try:
-        out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
+        if var_length and not SAGE_ATTENTION_VAR_LENGTH_AVAILABLE:
+            raise ValueError("Sage Attention two is required to run variable length attention.")
+        elif var_length:
+            cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k = var_attn_arg(kwargs)
+            sm_scale = 1.0 / (q.shape[-1] ** 0.5)
+            out = sageattn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, is_causal=False, sm_scale=sm_scale)
+        else:
+            out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
     except Exception as e:
         logging.error("Error running sage attention: {}, using pytorch attention instead.".format(e))
         exception_fallback = True
@@ -556,7 +634,7 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
                 lambda t: t.transpose(1, 2),
                 (q, k, v),
             )
-        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs)
+        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, var_length=var_length, **kwargs)
 
     if tensor_layout == "HND":
         if not skip_output_reshape:
@@ -567,6 +645,8 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
         if skip_output_reshape:
             out = out.transpose(1, 2)
         else:
+            if var_length:
+                return out.view(*target_output_shape)
             out = out.reshape(b, -1, heads * dim_head)
     return out
 
@@ -678,6 +758,15 @@ except AttributeError as error:
 
 @wrap_attn
 def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    var_length = kwargs.get("var_length", False)
+    if var_length:
+        cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k = var_attn_arg(kwargs)
+        return flash_attn_varlen_func(
+            q=q, k=k, v=v,
+            cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k,
+            max_seqlen_q=max_seqlen_q, max_seqlen_k=max_seqlen_k,
+            dropout_p=0.0, softmax_scale=None, causal=False
+        )
     if skip_reshape:
         b, _, _, dim_head = q.shape
     else:

comfy/ldm/modules/diffusionmodules/model.py

@@ -13,13 +13,14 @@ if model_management.xformers_enabled_vae():
     import xformers
     import xformers.ops
 
+
 def torch_cat_if_needed(xl, dim):
     if len(xl) > 1:
         return torch.cat(xl, dim)
     else:
         return xl[0]
 
-def get_timestep_embedding(timesteps, embedding_dim):
+def get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos = False, downscale_freq_shift = 1):
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models:
     From Fairseq.
@@ -30,11 +31,13 @@ def get_timestep_embedding(timesteps, embedding_dim):
     assert len(timesteps.shape) == 1
 
     half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
+    emb = math.log(10000) / (half_dim - downscale_freq_shift)
     emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
     emb = emb.to(device=timesteps.device)
     emb = timesteps.float()[:, None] * emb[None, :]
     emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
     if embedding_dim % 2 == 1:  # zero pad
         emb = torch.nn.functional.pad(emb, (0,1,0,0))
     return emb

comfy/model_base.py

@@ -47,6 +47,8 @@ import comfy.ldm.chroma.model
 import comfy.ldm.chroma_radiance.model
 import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
+import comfy.ldm.seedvr.model
+
 import comfy.ldm.qwen_image.model
 import comfy.ldm.kandinsky5.model
 
@@ -815,6 +817,16 @@ class HunyuanDiT(BaseModel):
         out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
         return out
 
+class SeedVR2(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device, comfy.ldm.seedvr.model.NaDiT)
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        condition = kwargs.get("condition", None)
+        if condition is not None:
+            out["condition"] = comfy.conds.CONDRegular(condition)
+        return out
+
 class PixArt(BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.pixart.pixartms.PixArtMS)

comfy/model_detection.py

@@ -447,6 +447,28 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
 
         return dit_config
 
+    elif "{}blocks.36.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 7b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 3072
+        dit_config["heads"] = 24
+        dit_config["num_layers"] = 36
+        dit_config["norm_eps"] = 1e-5
+        dit_config["qk_rope"] = True
+        dit_config["mlp_type"] = "normal"
+        return dit_config
+    elif "{}blocks.31.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 3b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 2560
+        dit_config["heads"] = 20
+        dit_config["num_layers"] = 32
+        dit_config["norm_eps"] = 1.0e-05
+        dit_config["qk_rope"] = None
+        dit_config["mlp_type"] = "swiglu"
+        dit_config["vid_out_norm"] = True
+        return dit_config
+
     if '{}head.modulation'.format(key_prefix) in state_dict_keys:  # Wan 2.1
         dit_config = {}
         dit_config["image_model"] = "wan2.1"

comfy/model_patcher.py

@@ -718,6 +718,7 @@ class ModelPatcher:
                             continue
 
                 cast_weight = self.force_cast_weights
+                m.comfy_force_cast_weights = self.force_cast_weights
                 if lowvram_weight:
                     if hasattr(m, "comfy_cast_weights"):
                         m.weight_function = []
@@ -790,11 +791,12 @@ class ModelPatcher:
                 for param in params:
                     self.pin_weight_to_device("{}.{}".format(n, param))
 
+            usable_stat = "{:.2f} MB usable,".format(lowvram_model_memory / (1024 * 1024)) if lowvram_model_memory < 1e32 else ""
             if lowvram_counter > 0:
-                logging.info("loaded partially; {:.2f} MB usable, {:.2f} MB loaded, {:.2f} MB offloaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), offload_buffer / (1024 * 1024), patch_counter))
+                logging.info("loaded partially; {} {:.2f} MB loaded, {:.2f} MB offloaded, {:.2f} MB buffer reserved, lowvram patches: {}".format(usable_stat, mem_counter / (1024 * 1024), lowvram_mem_counter / (1024 * 1024), offload_buffer / (1024 * 1024), patch_counter))
                 self.model.model_lowvram = True
             else:
-                logging.info("loaded completely; {:.2f} MB usable, {:.2f} MB loaded, full load: {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
+                logging.info("loaded completely; {} {:.2f} MB loaded, full load: {}".format(usable_stat, mem_counter / (1024 * 1024), full_load))
                 self.model.model_lowvram = False
                 if full_load:
                     self.model.to(device_to)

comfy/ops.py

@@ -654,29 +654,29 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 run_every_op()
 
                 input_shape = input.shape
-                tensor_3d = input.ndim == 3
-
-                if self._full_precision_mm or self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
-                    return self.forward_comfy_cast_weights(input, *args, **kwargs)
+                reshaped_3d = False
 
                 if (getattr(self, 'layout_type', None) is not None and
-                    not isinstance(input, QuantizedTensor)):
+                    not isinstance(input, QuantizedTensor) and not self._full_precision_mm and
+                    not getattr(self, 'comfy_force_cast_weights', False) and
+                    len(self.weight_function) == 0 and len(self.bias_function) == 0):
 
                     # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
-                    if tensor_3d:
-                        input = input.reshape(-1, input_shape[2])
+                    input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input
 
-                    if input.ndim != 2:
-                        # Fall back to comfy_cast_weights for non-2D tensors
-                        return self.forward_comfy_cast_weights(input.reshape(input_shape), *args, **kwargs)
+                    # Fall back to non-quantized for non-2D tensors
+                    if input_reshaped.ndim == 2:
+                        reshaped_3d = input.ndim == 3
+                        # dtype is now implicit in the layout class
+                        scale = getattr(self, 'input_scale', None)
+                        if scale is not None:
+                            scale = comfy.model_management.cast_to_device(scale, input.device, None)
+                        input = QuantizedTensor.from_float(input_reshaped, self.layout_type, scale=scale)
 
-                    # dtype is now implicit in the layout class
-                    input = QuantizedTensor.from_float(input, self.layout_type, scale=getattr(self, 'input_scale', None))
-
-                output = self._forward(input, self.weight, self.bias)
+                output = self.forward_comfy_cast_weights(input)
 
                 # Reshape output back to 3D if input was 3D
-                if tensor_3d:
+                if reshaped_3d:
                     output = output.reshape((input_shape[0], input_shape[1], self.weight.shape[0]))
 
                 return output

comfy/quant_ops.py

@@ -19,6 +19,7 @@ try:
         cuda_version = tuple(map(int, str(torch.version.cuda).split('.')))
         if cuda_version < (13,):
             ck.registry.disable("cuda")
+            logging.warning("WARNING: You need pytorch with cu130 or higher to use optimized CUDA operations.")
 
     ck.registry.disable("triton")
     for k, v in ck.list_backends().items():

comfy/sd.py

@@ -16,6 +16,7 @@ import comfy.ldm.cosmos.vae
 import comfy.ldm.wan.vae
 import comfy.ldm.wan.vae2_2
 import comfy.ldm.hunyuan3d.vae
+import comfy.ldm.seedvr.vae
 import comfy.ldm.ace.vae.music_dcae_pipeline
 import comfy.ldm.hunyuan_video.vae
 import comfy.ldm.mmaudio.vae.autoencoder
@@ -218,7 +219,7 @@ class CLIP:
             if unprojected:
                 self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
-            self.load_model()
+            self.load_model(tokens)
             self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
             all_hooks.reset()
             self.patcher.patch_hooks(None)
@@ -266,7 +267,7 @@ class CLIP:
         if return_pooled == "unprojected":
             self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
-        self.load_model()
+        self.load_model(tokens)
         self.cond_stage_model.set_clip_options({"execution_device": self.patcher.load_device})
         o = self.cond_stage_model.encode_token_weights(tokens)
         cond, pooled = o[:2]
@@ -299,8 +300,11 @@ class CLIP:
             sd_clip[k] = sd_tokenizer[k]
         return sd_clip
 
-    def load_model(self):
-        model_management.load_model_gpu(self.patcher)
+    def load_model(self, tokens={}):
+        memory_used = 0
+        if hasattr(self.cond_stage_model, "memory_estimation_function"):
+            memory_used = self.cond_stage_model.memory_estimation_function(tokens, device=self.patcher.load_device)
+        model_management.load_models_gpu([self.patcher], memory_required=memory_used)
         return self.patcher
 
     def get_key_patches(self):
@@ -309,7 +313,10 @@ class CLIP:
 class VAE:
     def __init__(self, sd=None, device=None, config=None, dtype=None, metadata=None):
         if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
-            sd = diffusers_convert.convert_vae_state_dict(sd)
+            if (metadata is not None and metadata["keep_diffusers_format"] == "true"):
+                pass
+            else:
+                sd = diffusers_convert.convert_vae_state_dict(sd)
 
         if model_management.is_amd():
             VAE_KL_MEM_RATIO = 2.73
@@ -376,6 +383,17 @@ class VAE:
                 self.first_stage_model = StageC_coder()
                 self.downscale_ratio = 32
                 self.latent_channels = 16
+            elif "decoder.up_blocks.2.upsamplers.0.upscale_conv.weight" in sd: # seedvr2
+                self.first_stage_model = comfy.ldm.seedvr.vae.VideoAutoencoderKLWrapper()
+                self.memory_used_decode = lambda shape, dtype: (shape[1] * shape[2] * shape[3] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (max(shape[1], 5) * shape[2] * shape[3]) * model_management.dtype_size(dtype)
+                self.working_dtypes = [torch.bfloat16, torch.float32]
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
+                self.downscale_index_formula = (4, 8, 8)
+                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+                self.upscale_index_formula = (4, 8, 8)
+                self.process_input = lambda image: image
+                self.crop_input = False
             elif "decoder.conv_in.weight" in sd:
                 if sd['decoder.conv_in.weight'].shape[1] == 64:
                     ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True}
@@ -476,13 +494,14 @@ class VAE:
                 self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version, config=vae_config)
                 self.latent_channels = 128
                 self.latent_dim = 3
-                self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
-                self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (1200 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (80 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                 self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32)
                 self.upscale_index_formula = (8, 32, 32)
                 self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32)
                 self.downscale_index_formula = (8, 32, 32)
                 self.working_dtypes = [torch.bfloat16, torch.float32]
+
             elif "decoder.conv_in.conv.weight" in sd and sd['decoder.conv_in.conv.weight'].shape[1] == 32:
                 ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True}
                 ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]

comfy/supported_models.py

@@ -845,7 +845,7 @@ class LTXAV(LTXV):
 
     def __init__(self, unet_config):
         super().__init__(unet_config)
-        self.memory_usage_factor = 0.055  # TODO
+        self.memory_usage_factor = 0.061  # TODO
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.LTXAV(self, device=device)
@@ -1303,6 +1303,25 @@ class Chroma(supported_models_base.BASE):
         t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect))
 
+class SeedVR2(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "seedvr2"
+    }
+    latent_format = comfy.latent_formats.SeedVR2
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+    sampling_settings = {
+        "shift": 1.0,
+    }
+
+    def get_model(self, state_dict, prefix = "", device=None):
+        out = model_base.SeedVR2(self, device=device)
+        return out
+    def clip_target(self, state_dict={}):
+        return None
+
 class ChromaRadiance(Chroma):
     unet_config = {
         "image_model": "chroma_radiance",
@@ -1551,6 +1570,6 @@ class Kandinsky5Image(Kandinsky5):
         return supported_models_base.ClipTarget(comfy.text_encoders.kandinsky5.Kandinsky5TokenizerImage, comfy.text_encoders.kandinsky5.te(**hunyuan_detect))
 
 
-models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5]
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, SeedVR2]
 
 models += [SVD_img2vid]

comfy/text_encoders/lt.py

@@ -98,10 +98,13 @@ class LTXAVTEModel(torch.nn.Module):
 
         out, pooled, extra = self.gemma3_12b.encode_token_weights(token_weight_pairs)
         out_device = out.device
+        if comfy.model_management.should_use_bf16(self.execution_device):
+            out = out.to(device=self.execution_device, dtype=torch.bfloat16)
         out = out.movedim(1, -1).to(self.execution_device)
         out = 8.0 * (out - out.mean(dim=(1, 2), keepdim=True)) / (out.amax(dim=(1, 2), keepdim=True) - out.amin(dim=(1, 2), keepdim=True) + 1e-6)
         out = out.reshape((out.shape[0], out.shape[1], -1))
         out = self.text_embedding_projection(out)
+        out = out.float()
         out_vid = self.video_embeddings_connector(out)[0]
         out_audio = self.audio_embeddings_connector(out)[0]
         out = torch.concat((out_vid, out_audio), dim=-1)
@@ -118,6 +121,14 @@ class LTXAVTEModel(torch.nn.Module):
 
             return self.load_state_dict(sdo, strict=False)
 
+    def memory_estimation_function(self, token_weight_pairs, device=None):
+        constant = 6.0
+        if comfy.model_management.should_use_bf16(device):
+            constant /= 2.0
+
+        token_weight_pairs = token_weight_pairs.get("gemma3_12b", [])
+        num_tokens = sum(map(lambda a: len(a), token_weight_pairs))
+        return num_tokens * constant * 1024 * 1024
 
 def ltxav_te(dtype_llama=None, llama_quantization_metadata=None):
     class LTXAVTEModel_(LTXAVTEModel):

comfy_extras/nodes_lt_audio.py

@@ -185,6 +185,10 @@ class LTXAVTextEncoderLoader(io.ComfyNode):
                 io.Combo.Input(
                     "ckpt_name",
                     options=folder_paths.get_filename_list("checkpoints"),
+                ),
+                io.Combo.Input(
+                    "device",
+                    options=["default", "cpu"],
                 )
             ],
             outputs=[io.Clip.Output()],
@@ -197,7 +201,11 @@ class LTXAVTextEncoderLoader(io.ComfyNode):
         clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", text_encoder)
         clip_path2 = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
 
-        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
+        model_options = {}
+        if device == "cpu":
+            model_options["load_device"] = model_options["offload_device"] = torch.device("cpu")
+
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options)
         return io.NodeOutput(clip)
 
 

comfy_extras/nodes_seedvr.py

@@ -0,0 +1,465 @@
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+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, encode=True):
+
+    gc.collect()
+    torch.cuda.empty_cache()
+
+    x = x.to(next(vae_model.parameters()).dtype)
+    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
+        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)
+
+        target_d = d * sf_t
+        target_h = h * sf_s
+        target_w = w * sf_s
+
+    stride_h = max(1, ti_h - ov_h)
+    stride_w = max(1, ti_w - ov_w)
+
+    storage_device = vae_model.device
+    result = None
+    count = None
+
+    def run_temporal_chunks(spatial_tile):
+        chunk_results = []
+        t_dim_size = spatial_tile.shape[2]
+
+        if encode:
+            input_chunk = temporal_size
+        else:
+            input_chunk = max(1, temporal_size // sf_t)
+        for i in range(0, t_dim_size, input_chunk):
+            t_chunk = spatial_tile[:, :, i : i + input_chunk, :, :]
+            current_valid_len = t_chunk.shape[2]
+
+            pad_amount = 0
+            if current_valid_len < input_chunk:
+                pad_amount = input_chunk - current_valid_len
+
+                last_frame = t_chunk[:, :, -1:, :, :]
+                padding = last_frame.repeat(1, 1, pad_amount, 1, 1)
+
+                t_chunk = torch.cat([t_chunk, padding], dim=2)
+            t_chunk = t_chunk.contiguous()
+
+            if encode:
+                out = vae_model.encode(t_chunk)[0]
+            else:
+                out = vae_model.decode_(t_chunk)
+
+            if isinstance(out, (tuple, list)):
+                out = out[0]
+            if out.ndim == 4:
+                out = out.unsqueeze(2)
+
+            if pad_amount > 0:
+                if encode:
+                    expected_valid_out = (current_valid_len + sf_t - 1) // sf_t
+                    out = out[:, :, :expected_valid_out, :, :]
+
+                else:
+                    expected_valid_out = current_valid_len * sf_t
+                    out = out[:, :, :expected_valid_out, :, :]
+
+            chunk_results.append(out.to(storage_device))
+
+        return torch.cat(chunk_results, dim=2)
+
+    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]
+
+    total_tiles = len(range(0, h, stride_h)) * len(range(0, w, stride_w))
+    bar = ProgressBar(total_tiles)
+
+    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[:, :, :, y_idx:y_end, x_idx:x_end]
+
+            # Run VAE
+            tile_out = run_temporal_chunks(tile_x)
+
+            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, 1, target_h, target_w), device=storage_device, dtype=torch.float32)
+
+            if encode:
+                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_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:
+                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_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_h = torch.ones((tile_out.shape[3],), device=storage_device)
+            w_w = torch.ones((tile_out.shape[4],), device=storage_device)
+
+            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_h.view(1,1,1,-1,1) * w_w.view(1,1,1,1,-1)
+
+            valid_d = min(tile_out.shape[2], result.shape[2])
+            tile_out = tile_out[:, :, :valid_d, :, :]
+
+            tile_out.mul_(final_weight)
+
+            result[:, :, :valid_d, ys:ye, xs:xe] += tile_out
+            count[:, :, :, ys:ye, xs:xe] += final_weight
+
+            del tile_out, final_weight, tile_x, 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 pad_video_temporal(videos: torch.Tensor, count: int = 0, temporal_dim: int = 1, prepend: bool = False):
+    t = videos.size(temporal_dim)
+
+    if count == 0 and not prepend:
+        if t % 4 == 1:
+            return videos
+        count = ((t - 1) // 4 + 1) * 4 + 1 - t
+
+    if count <= 0:
+        return videos
+
+    def select(start, end):
+        return videos[start:end] if temporal_dim == 0 else videos[:, start:end]
+
+    if count >= t:
+        repeat_count = count - t + 1
+        last = select(-1, None)
+
+        if temporal_dim == 0:
+            repeated = last.repeat(repeat_count, 1, 1, 1)
+            reversed_frames = select(1, None).flip(temporal_dim) if t > 1 else last[:0]
+        else:
+            repeated = last.expand(-1, repeat_count, -1, -1).contiguous()
+            reversed_frames = select(1, None).flip(temporal_dim) if t > 1 else last[:, :0]
+
+        return torch.cat([repeated, reversed_frames, videos] if prepend else
+                        [videos, reversed_frames, repeated], dim=temporal_dim)
+
+    if prepend:
+        reversed_frames = select(1, count+1).flip(temporal_dim)
+    else:
+        reversed_frames = select(-count-1, -1).flip(temporal_dim)
+
+    return torch.cat([reversed_frames, videos] if prepend else
+                    [videos, reversed_frames], dim=temporal_dim)
+
+def clear_vae_memory(vae_model):
+    for module in vae_model.modules():
+        if hasattr(module, "memory"):
+            module.memory = None
+    gc.collect()
+    torch.cuda.empty_cache()
+
+def expand_dims(tensor, ndim):
+    shape = tensor.shape + (1,) * (ndim - tensor.ndim)
+    return tensor.reshape(shape)
+
+def get_conditions(latent, latent_blur):
+    t, h, w, c = latent.shape
+    cond = torch.ones([t, h, w, c + 1], device=latent.device, dtype=latent.dtype)
+    cond[:, ..., :-1] = latent_blur[:]
+    cond[:, ..., -1:] = 1.0
+    return cond
+
+def timestep_transform(timesteps, latents_shapes):
+    vt = 4
+    vs = 8
+    frames = (latents_shapes[:, 0] - 1) * vt + 1
+    heights = latents_shapes[:, 1] * vs
+    widths = latents_shapes[:, 2] * vs
+
+    # Compute shift factor.
+    def get_lin_function(x1, y1, x2, y2):
+        m = (y2 - y1) / (x2 - x1)
+        b = y1 - m * x1
+        return lambda x: m * x + b
+
+    img_shift_fn = get_lin_function(x1=256 * 256, y1=1.0, x2=1024 * 1024, y2=3.2)
+    vid_shift_fn = get_lin_function(x1=256 * 256 * 37, y1=1.0, x2=1280 * 720 * 145, y2=5.0)
+    shift = torch.where(
+        frames > 1,
+        vid_shift_fn(heights * widths * frames),
+        img_shift_fn(heights * widths),
+    ).to(timesteps.device)
+
+    # Shift timesteps.
+    T = 1000.0
+    timesteps = timesteps / T
+    timesteps = shift * timesteps / (1 + (shift - 1) * timesteps)
+    timesteps = timesteps * T
+    return timesteps
+
+def inter(x_0, x_T, t):
+    t = expand_dims(t, x_0.ndim)
+    T = 1000.0
+    B = lambda t: t / T
+    A = lambda t: 1 - (t / T)
+    return A(t) * x_0 + B(t) * x_T
+def area_resize(image, max_area):
+
+    height, width = image.shape[-2:]
+    scale = math.sqrt(max_area / (height * width))
+
+    resized_height, resized_width = round(height * scale), round(width * scale)
+
+    return TVF.resize(
+        image,
+        size=(resized_height, resized_width),
+        interpolation=InterpolationMode.BICUBIC,
+    )
+
+def div_pad(image, factor):
+
+    height_factor, width_factor = factor
+    height, width = image.shape[-2:]
+
+    pad_height = (height_factor - (height % height_factor)) % height_factor
+    pad_width = (width_factor - (width % width_factor)) % width_factor
+
+    if pad_height == 0 and pad_width == 0:
+        return image
+
+    if isinstance(image, torch.Tensor):
+        padding = (0, pad_width, 0, pad_height)
+        image = torch.nn.functional.pad(image, padding, mode='constant', value=0.0)
+
+    return image
+
+def cut_videos(videos):
+    t = videos.size(1)
+    if t == 1:
+        return videos
+    if t <= 4 :
+        padding = [videos[:, -1].unsqueeze(1)] * (4 - t + 1)
+        padding = torch.cat(padding, dim=1)
+        videos = torch.cat([videos, padding], dim=1)
+        return videos
+    if (t - 1) % (4) == 0:
+        return videos
+    else:
+        padding = [videos[:, -1].unsqueeze(1)] * (
+            4 - ((t - 1) % (4))
+        )
+        padding = torch.cat(padding, dim=1)
+        videos = torch.cat([videos, padding], dim=1)
+        assert (videos.size(1) - 1) % (4) == 0
+        return videos
+
+def side_resize(image, size):
+    antialias = not (isinstance(image, torch.Tensor) and image.device.type == 'mps')
+    resized = TVF.resize(image, size, InterpolationMode.BICUBIC, antialias=antialias)
+    return resized
+
+class SeedVR2InputProcessing(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id = "SeedVR2InputProcessing",
+            category="image/video",
+            inputs = [
+                io.Image.Input("images"),
+                io.Vae.Input("vae"),
+                io.Int.Input("resolution", default = 1280, min = 120), # just non-zero value
+                io.Int.Input("spatial_tile_size", default = 512, min = 1),
+                io.Int.Input("spatial_overlap", default = 64, min = 1),
+                io.Int.Input("temporal_tile_size", default=5, min=1, max=16384, step=4),
+                io.Boolean.Input("enable_tiling", default=False),
+            ],
+            outputs = [
+                io.Latent.Output("vae_conditioning")
+            ]
+        )
+
+    @classmethod
+    def execute(cls, images, vae, resolution, spatial_tile_size, temporal_tile_size, spatial_overlap, enable_tiling):
+
+        comfy.model_management.load_models_gpu([vae.patcher])
+        vae_model = vae.first_stage_model
+        scale = 0.9152
+        shift = 0
+        if images.dim() != 5: # add the t dim
+            images = images.unsqueeze(0)
+        images = images.permute(0, 1, 4, 2, 3)
+
+        b, t, c, h, w = images.shape
+        images = images.reshape(b * t, c, h, w)
+
+        clip = Lambda(lambda x: torch.clamp(x, 0.0, 1.0))
+        normalize = Normalize(0.5, 0.5)
+        images = side_resize(images, resolution)
+
+        images = clip(images)
+        o_h, o_w = images.shape[-2:]
+        images = div_pad(images, (16, 16))
+        images = normalize(images)
+        _, _, new_h, new_w = images.shape
+
+        images = images.reshape(b, t, c, new_h, new_w)
+        images = cut_videos(images)
+
+        images = rearrange(images, "b t c h w -> b c t h w")
+
+        # in case users a non-compatiable number for tiling
+        def make_divisible(val, divisor):
+            return max(divisor, round(val / divisor) * divisor)
+
+        spatial_tile_size = make_divisible(spatial_tile_size, 32)
+        spatial_overlap = make_divisible(spatial_overlap, 32)
+
+        if spatial_overlap >= spatial_tile_size:
+            spatial_overlap = max(0, spatial_tile_size - 8)
+
+        args = {"tile_size": (spatial_tile_size, spatial_tile_size), "tile_overlap": (spatial_overlap, spatial_overlap),
+                "temporal_size":temporal_tile_size}
+        if enable_tiling:
+            latent = tiled_vae(images, vae_model, encode=True, **args)
+        else:
+            latent = vae_model.encode(images, orig_dims = [o_h, o_w])[0]
+
+        clear_vae_memory(vae_model)
+        #images = images.to(offload_device)
+        #vae_model = vae_model.to(offload_device)
+
+        vae_model.img_dims = [o_h, o_w]
+        args["enable_tiling"] = enable_tiling
+        vae_model.tiled_args = args
+        vae_model.original_image_video = images
+
+        latent = latent.unsqueeze(2) if latent.ndim == 4 else latent
+        latent = rearrange(latent, "b c ... -> b ... c")
+
+        latent = (latent - shift) * scale
+
+        return io.NodeOutput({"samples": latent})
+
+class SeedVR2Conditioning(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SeedVR2Conditioning",
+            category="image/video",
+            inputs=[
+                io.Latent.Input("vae_conditioning"),
+                io.Model.Input("model"),
+            ],
+            outputs=[io.Conditioning.Output(display_name = "positive"),
+                     io.Conditioning.Output(display_name = "negative"),
+                     io.Latent.Output(display_name = "latent")],
+        )
+
+    @classmethod
+    def execute(cls, vae_conditioning, model) -> io.NodeOutput:
+
+        vae_conditioning = vae_conditioning["samples"]
+        device = vae_conditioning.device
+        model = model.model.diffusion_model
+        pos_cond = model.positive_conditioning
+        neg_cond = model.negative_conditioning
+
+        noises = torch.randn_like(vae_conditioning).to(device)
+        aug_noises =  torch.randn_like(vae_conditioning).to(device)
+        aug_noises = noises * 0.1 + aug_noises * 0.05
+        cond_noise_scale = 0.0
+        t = (
+            torch.tensor([1000.0])
+            * cond_noise_scale
+        ).to(device)
+        shape = torch.tensor(vae_conditioning.shape[1:]).to(device)[None] # avoid batch dim
+        t = timestep_transform(t, shape)
+        cond = inter(vae_conditioning, aug_noises, t)
+        condition = torch.stack([get_conditions(noise, c) for noise, c in zip(noises, cond)])
+        condition = condition.movedim(-1, 1)
+        noises = noises.movedim(-1, 1)
+
+        pos_shape = pos_cond.shape[0]
+        neg_shape = neg_cond.shape[0]
+        diff = abs(pos_shape - neg_shape)
+        if pos_shape > neg_shape:
+            neg_cond = F.pad(neg_cond, (0, 0, 0, diff))
+        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}]]
+
+        return io.NodeOutput(positive, negative, {"samples": noises})
+
+class SeedVRExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SeedVR2Conditioning,
+            SeedVR2InputProcessing
+        ]
+
+async def comfy_entrypoint() -> SeedVRExtension:
+    return SeedVRExtension()

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.8.0"
+__version__ = "0.8.2"

nodes.py

@@ -2358,6 +2358,7 @@ async def init_builtin_extra_nodes():
         "nodes_camera_trajectory.py",
         "nodes_edit_model.py",
         "nodes_tcfg.py",
+        "nodes_seedvr.py",
         "nodes_context_windows.py",
         "nodes_qwen.py",
         "nodes_chroma_radiance.py",

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.8.0"
+version = "0.8.2"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.10"

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.35.9
-comfyui-workflow-templates==0.7.67
+comfyui-workflow-templates==0.7.69
 comfyui-embedded-docs==0.3.1
 torch
 torchsde
@@ -21,7 +21,7 @@ psutil
 alembic
 SQLAlchemy
 av>=14.2.0
-comfy-kitchen>=0.2.3
+comfy-kitchen>=0.2.5
 
 #non essential dependencies:
 kornia>=0.7.1