Add I2V for causal forcing model.

comfy/k_diffusion/sampling.py

@@ -1859,6 +1859,23 @@ def sample_ar_video(model, x, sigmas, extra_args=None, callback=None, disable=No
     output = torch.zeros_like(x)
     s_in = x.new_ones([x.shape[0]])
     current_start_frame = 0
+
+    # I2V: seed KV cache with the initial image latent before the denoising loop
+    initial_latent = ar_config.get("initial_latent", None)
+    if initial_latent is not None:
+        initial_latent = inner_model.process_latent_in(initial_latent).to(device=device, dtype=model_dtype)
+        n_init = initial_latent.shape[2]
+        output[:, :, :n_init] = initial_latent
+
+        ar_state = {"start_frame": 0, "kv_caches": kv_caches, "crossattn_caches": crossattn_caches}
+        transformer_options["ar_state"] = ar_state
+        zero_sigma = sigmas.new_zeros([1])
+        _ = model(initial_latent, zero_sigma * s_in, **extra_args)
+
+        current_start_frame = n_init
+        remaining = lat_t - n_init
+        num_blocks = -(-remaining // num_frame_per_block)
+
     num_sigma_steps = len(sigmas) - 1
     total_real_steps = num_blocks * num_sigma_steps
     step_count = 0

comfy_extras/nodes_ar_video.py

@@ -2,6 +2,7 @@
 ComfyUI nodes for autoregressive video generation (Causal Forcing, Self-Forcing, etc.).
   - EmptyARVideoLatent: create 5D [B, C, T, H, W] video latent tensors
   - SamplerARVideo: SAMPLER for the block-by-block autoregressive denoising loop
+  - ARVideoI2V: image-to-video conditioning for AR models (seeds KV cache with start image)
 """
 
 import torch
@@ -9,6 +10,7 @@ from typing_extensions import override
 
 import comfy.model_management
 import comfy.samplers
+import comfy.utils
 from comfy_api.latest import ComfyExtension, io
 
 
@@ -71,12 +73,62 @@ class SamplerARVideo(io.ComfyNode):
         return io.NodeOutput(comfy.samplers.ksampler("ar_video", extra_options))
 
 
+class ARVideoI2V(io.ComfyNode):
+    """Image-to-video setup for AR video models (Causal Forcing, Self-Forcing).
+
+    VAE-encodes the start image and stores it in the model's transformer_options
+    so that sample_ar_video can seed the KV cache before denoising.
+    Uses the same T2V model checkpoint -- no separate I2V architecture needed.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ARVideoI2V",
+            category="conditioning/video_models",
+            inputs=[
+                io.Model.Input("model"),
+                io.Vae.Input("vae"),
+                io.Image.Input("start_image"),
+                io.Int.Input("width", default=832, min=16, max=8192, step=16),
+                io.Int.Input("height", default=480, min=16, max=8192, step=16),
+                io.Int.Input("length", default=81, min=1, max=1024, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=64),
+            ],
+            outputs=[
+                io.Model.Output(display_name="MODEL"),
+                io.Latent.Output(display_name="LATENT"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, vae, start_image, width, height, length, batch_size) -> io.NodeOutput:
+        start_image = comfy.utils.common_upscale(
+            start_image[:1].movedim(-1, 1), width, height, "bilinear", "center"
+        ).movedim(1, -1)
+
+        initial_latent = vae.encode(start_image[:, :, :, :3])
+
+        m = model.clone()
+        to = m.model_options.setdefault("transformer_options", {})
+        ar_cfg = to.setdefault("ar_config", {})
+        ar_cfg["initial_latent"] = initial_latent
+
+        lat_t = ((length - 1) // 4) + 1
+        latent = torch.zeros(
+            [batch_size, 16, lat_t, height // 8, width // 8],
+            device=comfy.model_management.intermediate_device(),
+        )
+        return io.NodeOutput(m, {"samples": latent})
+
+
 class ARVideoExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
         return [
             EmptyARVideoLatent,
             SamplerARVideo,
+            ARVideoI2V,
         ]