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.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+video/anthony.wav filter=lfs diff=lfs merge=lfs -text
+video/ComfyUI__00002-audio.mp4 filter=lfs diff=lfs merge=lfs -text
+video/not_today.wav filter=lfs diff=lfs merge=lfs -text
+video/scream_mono.wav filter=lfs diff=lfs merge=lfs -text
+video/Triumph_of_the_Iron.mp3 filter=lfs diff=lfs merge=lfs -text

Bernini-R-S2V-int8-convrot/wan2.2_bernini_r_high_noise_int8_convrot_s2v.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1a71c3cb2a3ded330086c2211f0525b93e9436fd0114096e662e8f43f28dd7a9
+size 19025569194

Bernini-R-S2V-int8-convrot/wan2.2_bernini_r_low_noise_int8_convrot_s2v.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c3dd9f648df805ec1dd7ad7ae1866864ed2574f72ab4817d7fc21c135f58e84
+size 19025569194

ComfyUI-WanBerniniS2V/__init__.py

@@ -0,0 +1,7 @@
+from .model_patch import apply_wan_s2v_bernini_model_patch
+
+apply_wan_s2v_bernini_model_patch()
+
+from .nodes import comfy_entrypoint
+
+__all__ = ["comfy_entrypoint"]

ComfyUI-WanBerniniS2V/model_patch.py

@@ -0,0 +1,122 @@
+import inspect
+import logging
+
+import torch
+
+import comfy.model_management
+
+
+def _append_context_latents(self, x, kwargs):
+    context_latents = kwargs.get("context_latents", None)
+    if context_latents is None:
+        return x
+    for lat in context_latents:
+        cl = self.patch_embedding(lat.float().to(x.device)).to(x.dtype).flatten(2).transpose(1, 2)
+        x = torch.cat([x, cl], dim=1)
+    return x
+
+
+def apply_wan_s2v_bernini_model_patch():
+    from comfy.ldm.wan.model import WanModel_S2V
+
+    try:
+        source = inspect.getsource(WanModel_S2V.forward_orig)
+    except (OSError, TypeError):
+        source = ""
+    if "context_latents" in source:
+        return False
+
+    if getattr(WanModel_S2V.forward_orig, "__wan_bernini_s2v_patch__", False):
+        return True
+
+    original = WanModel_S2V.forward_orig
+
+    def forward_orig(
+        self,
+        x,
+        t,
+        context,
+        audio_embed=None,
+        reference_latent=None,
+        control_video=None,
+        reference_motion=None,
+        clip_fea=None,
+        freqs=None,
+        transformer_options={},
+        **kwargs,
+    ):
+        if audio_embed is not None:
+            num_embeds = x.shape[-3] * 4
+            audio_emb_global, audio_emb = self.casual_audio_encoder(audio_embed[:, :, :, :num_embeds])
+        else:
+            audio_emb = None
+            audio_emb_global = None
+
+        bs, _, time, height, width = x.shape
+        x = self.patch_embedding(x.float()).to(x.dtype)
+        if control_video is not None:
+            x = x + self.cond_encoder(control_video)
+
+        if t.ndim == 1:
+            t = t.unsqueeze(1).repeat(1, x.shape[2])
+
+        grid_sizes = x.shape[2:]
+        x = x.flatten(2).transpose(1, 2)
+        seq_len = x.size(1)
+
+        cond_mask_weight = comfy.model_management.cast_to(self.trainable_cond_mask.weight, dtype=x.dtype, device=x.device).unsqueeze(1).unsqueeze(1)
+        x = x + cond_mask_weight[0]
+        x = _append_context_latents(self, x, kwargs)
+
+        if reference_latent is not None:
+            ref = self.patch_embedding(reference_latent.float()).to(x.dtype)
+            ref = ref.flatten(2).transpose(1, 2)
+            freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=max(30, time + 9), device=x.device, dtype=x.dtype)
+            ref = ref + cond_mask_weight[1]
+            x = torch.cat([x, ref], dim=1)
+            freqs = torch.cat([freqs, freqs_ref], dim=1)
+            t = torch.cat([t, torch.zeros((t.shape[0], reference_latent.shape[-3]), device=t.device, dtype=t.dtype)], dim=1)
+
+        if reference_motion is not None:
+            motion_encoded, freqs_motion = self.frame_packer(reference_motion, self)
+            motion_encoded = motion_encoded + cond_mask_weight[2]
+            x = torch.cat([x, motion_encoded], dim=1)
+            freqs = torch.cat([freqs, freqs_motion], dim=1)
+            t = torch.repeat_interleave(t, 2, dim=1)
+            t = torch.cat([t, torch.zeros((t.shape[0], 3), device=t.device, dtype=t.dtype)], dim=1)
+
+        from comfy.ldm.wan.model import sinusoidal_embedding_1d
+
+        e = self.time_embedding(
+            sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
+        e = e.reshape(t.shape[0], -1, e.shape[-1])
+        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
+
+        context = self.text_embedding(context)
+
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        transformer_options["total_blocks"] = len(self.blocks)
+        transformer_options["block_type"] = "double"
+        for i, block in enumerate(self.blocks):
+            transformer_options["block_index"] = i
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], transformer_options=args["transformer_options"])
+                    return out
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(x, e=e0, freqs=freqs, context=context, transformer_options=transformer_options)
+            if audio_emb is not None:
+                x = self.audio_injector(x, i, audio_emb, audio_emb_global, seq_len)
+        x = self.head(x, e)
+        x = self.unpatchify(x, grid_sizes)
+        return x
+
+    forward_orig.__wan_bernini_s2v_patch__ = True
+    forward_orig.__wan_bernini_s2v_original__ = original
+    WanModel_S2V.forward_orig = forward_orig
+    logging.info("ComfyUI-WanBerniniS2V: patched WanModel_S2V.forward_orig for Bernini context_latents")
+    return True

ComfyUI-WanBerniniS2V/nodes.py

@@ -0,0 +1,92 @@
+import torch
+from typing_extensions import override
+
+import comfy.model_management
+import comfy.utils
+import node_helpers
+from comfy_api.latest import ComfyExtension, io
+
+from .wan_audio import apply_wan_s2v_audio_conditioning
+
+
+def _resize_long_edge(image, max_size, stride=16):
+    h, w = image.shape[1], image.shape[2]
+    scale = min(max_size / max(h, w), 1.0)
+    nh = max(stride, round(h * scale / stride) * stride)
+    nw = max(stride, round(w * scale / stride) * stride)
+    return comfy.utils.common_upscale(image[:, :, :, :3].movedim(-1, 1), nw, nh, "area", "disabled").movedim(1, -1)
+
+
+class BerniniS2VConditioning(io.ComfyNode):
+    """Bernini in-context conditioning plus Wan 2.2 S2V audio."""
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="BerniniS2VConditioning",
+            display_name="Bernini S2V Conditioning",
+            category="model/conditioning/bernini",
+            description="Bernini in-context video/image conditioning with optional S2V audio. Requires a Wan2.2 S2V grafted diffusion model.",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                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=8192, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.AudioEncoderOutput.Input("audio_encoder_output", optional=True),
+                io.Image.Input("source_video", optional=True),
+                io.Image.Input("reference_video", optional=True),
+                io.Autogrow.Input("reference_images", optional=True,
+                    template=io.Autogrow.TemplatePrefix(
+                        input=io.Image.Input("reference_image"),
+                        prefix="reference_image_", min=0, max=8)),
+                io.Int.Input("ref_max_size", default=848, min=16, max=8192, step=16, optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, positive, negative, vae, width, height, length, batch_size, audio_encoder_output=None, source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
+        positive, negative, _ = apply_wan_s2v_audio_conditioning(positive, negative, length, audio_encoder_output=audio_encoder_output)
+
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+
+        context = []
+        if source_video is not None:
+            vid = comfy.utils.common_upscale(source_video[:length, :, :, :3].movedim(-1, 1), width, height, "area", "center").movedim(1, -1)
+            context.append(vae.encode(vid[:, :, :, :3]))
+
+        if reference_video is not None:
+            ref_vid = _resize_long_edge(reference_video[:length], ref_max_size)
+            context.append(vae.encode(ref_vid[:, :, :, :3]))
+
+        if reference_images:
+            for name in sorted(reference_images):
+                imgs = reference_images[name]
+                if imgs is None:
+                    continue
+                for i in range(imgs.shape[0]):
+                    img = _resize_long_edge(imgs[i:i + 1], ref_max_size)
+                    context.append(vae.encode(img[:, :, :, :3]))
+
+        if context:
+            positive = node_helpers.conditioning_set_values(positive, {"context_latents": context})
+            negative = node_helpers.conditioning_set_values(negative, {"context_latents": context})
+
+        return io.NodeOutput(positive, negative, {"samples": latent})
+
+
+class WanBerniniS2VExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [BerniniS2VConditioning]
+
+
+async def comfy_entrypoint() -> WanBerniniS2VExtension:
+    return WanBerniniS2VExtension()

ComfyUI-WanBerniniS2V/wan_audio.py

@@ -0,0 +1,29 @@
+import torch
+
+import node_helpers
+from comfy_extras.nodes_wan import get_audio_embed_bucket_fps, linear_interpolation
+
+
+def apply_wan_s2v_audio_conditioning(positive, negative, length, audio_encoder_output=None, frame_offset=0):
+    if audio_encoder_output is None:
+        return positive, negative, frame_offset
+
+    latent_t = ((length - 1) // 4) + 1
+    feat = torch.cat(audio_encoder_output["encoded_audio_all_layers"])
+    video_rate = 30
+    fps = 16
+    feat = linear_interpolation(feat, input_fps=50, output_fps=video_rate)
+    batch_frames = latent_t * 4
+    audio_embed_bucket, _ = get_audio_embed_bucket_fps(feat, fps=fps, batch_frames=batch_frames, m=0, video_rate=video_rate)
+    audio_embed_bucket = audio_embed_bucket.unsqueeze(0)
+    if len(audio_embed_bucket.shape) == 3:
+        audio_embed_bucket = audio_embed_bucket.permute(0, 2, 1)
+    elif len(audio_embed_bucket.shape) == 4:
+        audio_embed_bucket = audio_embed_bucket.permute(0, 2, 3, 1)
+
+    audio_embed_bucket = audio_embed_bucket[:, :, :, frame_offset:frame_offset + batch_frames]
+    if audio_embed_bucket.shape[3] > 0:
+        positive = node_helpers.conditioning_set_values(positive, {"audio_embed": audio_embed_bucket})
+        negative = node_helpers.conditioning_set_values(negative, {"audio_embed": audio_embed_bucket * 0.0})
+        frame_offset += batch_frames
+    return positive, negative, frame_offset

Workflow/Bernini-R-S2V_Workflow.json

@@ -0,0 +1,2174 @@
+{
+  "id": "ea7ba43e-ab56-40d4-a298-423f5bfe4462",
+  "revision": 0,
+  "last_node_id": 252,
+  "last_link_id": 664,
+  "nodes": [
+    {
+      "id": 80,
+      "type": "VAEDecode",
+      "pos": [
+        1210,
+        170
+      ],
+      "size": [
+        210,
+        50
+      ],
+      "flags": {},
+      "order": 28,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 533
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 285
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            580
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.54",
+        "Node name for S&R": "VAEDecode"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 57,
+      "type": "AudioEncoderLoader",
+      "pos": [
+        -80,
+        850
+      ],
+      "size": [
+        390,
+        70
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "AUDIO_ENCODER",
+          "type": "AUDIO_ENCODER",
+          "links": [
+            120
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.54",
+        "Node name for S&R": "AudioEncoderLoader",
+        "models": [
+          {
+            "name": "wav2vec2_large_english_fp16.safetensors",
+            "url": "https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/audio_encoders/wav2vec2_large_english_fp16.safetensors",
+            "directory": "audio_encoders"
+          }
+        ]
+      },
+      "widgets_values": [
+        "wav2vec2_large_english_fp16.safetensors"
+      ]
+    },
+    {
+      "id": 235,
+      "type": "Reroute",
+      "pos": [
+        360,
+        760
+      ],
+      "size": [
+        75,
+        26
+      ],
+      "flags": {},
+      "order": 23,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "",
+          "type": "*",
+          "link": 656
+        }
+      ],
+      "outputs": [
+        {
+          "name": "",
+          "type": "AUDIO",
+          "links": [
+            622,
+            630
+          ]
+        }
+      ],
+      "properties": {
+        "showOutputText": false,
+        "horizontal": false
+      },
+      "color": "#323",
+      "bgcolor": "#535"
+    },
+    {
+      "id": 238,
+      "type": "ComfyMathExpression",
+      "pos": [
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+        1120
+      ],
+      "size": [
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+      ],
+      "flags": {
+        "collapsed": true
+      },
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+      "mode": 0,
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+        {
+          "label": "a",
+          "name": "values.a",
+          "type": "FLOAT,INT,BOOLEAN",
+          "link": 627
+        },
+        {
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+          "name": "values.b",
+          "shape": 7,
+          "type": "FLOAT,INT,BOOLEAN",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "FLOAT",
+          "type": "FLOAT",
+          "links": null
+        },
+        {
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+          "type": "INT",
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+            653
+          ]
+        },
+        {
+          "name": "BOOL",
+          "type": "BOOLEAN",
+          "links": null
+        }
+      ],
+      "title": "16 F/S",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.27.0",
+        "Node name for S&R": "ComfyMathExpression"
+      },
+      "widgets_values": [
+        "a * 16"
+      ]
+    },
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+      "type": "CLIPLoader",
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+          "type": "CLIP",
+          "slot_index": 0,
+          "links": [
+            74,
+            75
+          ]
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+        "ver": "0.3.54",
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+        "models": [
+          {
+            "name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+            "url": "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+            "directory": "text_encoders"
+          }
+        ]
+      },
+      "widgets_values": [
+        "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+        "wan",
+        "default"
+      ]
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+      "type": "RIFEInterpolation",
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+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 580
+        }
+      ],
+      "outputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "links": [
+            629
+          ]
+        }
+      ],
+      "properties": {
+        "aux_id": "GACLove/ComfyUI-VFI",
+        "ver": "6176a430f12cd16003f4664c1e3c6af8e96cc3c6",
+        "Node name for S&R": "RIFEInterpolation"
+      },
+      "widgets_values": [
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+        24,
+        2,
+        "flownet.pkl",
+        4,
+        true
+      ]
+    },
+    {
+      "id": 107,
+      "type": "LoraLoaderModelOnly",
+      "pos": [
+        1140,
+        450
+      ],
+      "size": [
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+      ],
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+      "inputs": [
+        {
+          "name": "model",
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+        }
+      ],
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+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            363
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.54",
+        "Node name for S&R": "LoraLoaderModelOnly",
+        "models": [
+          {
+            "name": "wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors",
+            "url": "https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors",
+            "directory": "loras"
+          }
+        ]
+      },
+      "widgets_values": [
+        "Wan-Speed/Bernini-R/Bernini-R_LightX2V_high_noise.safetensors",
+        1
+      ]
+    },
+    {
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+      "type": "LoraLoaderModelOnly",
+      "pos": [
+        1560,
+        450
+      ],
+      "size": [
+        360,
+        90
+      ],
+      "flags": {},
+      "order": 16,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 586
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            581
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.54",
+        "Node name for S&R": "LoraLoaderModelOnly",
+        "models": [
+          {
+            "name": "wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors",
+            "url": "https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors",
+            "directory": "loras"
+          }
+        ]
+      },
+      "widgets_values": [
+        "Wan-Speed/Bernini-R/Bernini-R_LightX2V_low_noise.safetensors",
+        1
+      ]
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+      "type": "AudioEncoderEncode",
+      "pos": [
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+        850
+      ],
+      "size": [
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+        50
+      ],
+      "flags": {},
+      "order": 24,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "audio_encoder",
+          "type": "AUDIO_ENCODER",
+          "link": 120
+        },
+        {
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