Remove unnecessary module.

comfy_extras/nodes_longcat_image.py

@@ -1,109 +0,0 @@
-import logging
-
-import torch
-from typing_extensions import override
-from comfy_api.latest import ComfyExtension, io
-
-logger = logging.getLogger(__name__)
-
-
-class CLIPTextEncodeLongCatImage(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="CLIPTextEncodeLongCatImage",
-            display_name="CLIP Text Encode (LongCat-Image)",
-            category="advanced/conditioning/longcat",
-            description="Text encoding for LongCat-Image with character-level quoted text support. Wrap text in quotes for accurate text rendering.",
-            inputs=[
-                io.Clip.Input("clip"),
-                io.String.Input("text", multiline=True, dynamic_prompts=True),
-                io.Float.Input("guidance", default=4.0, min=0.0, max=100.0, step=0.1),
-            ],
-            outputs=[
-                io.Conditioning.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, clip, text, guidance) -> io.NodeOutput:
-        tokens = clip.tokenize(text)
-        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}))
-
-    encode = execute
-
-
-class CFGRenormLongCatImage(io.ComfyNode):
-    """Per-patch CFG renormalization matching HuggingFace's LongCat-Image pipeline.
-
-    After standard CFG combination, rescales the noise prediction at each 2x2 patch
-    so its norm doesn't exceed the conditional prediction's norm.
-    """
-
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="CFGRenormLongCatImage",
-            display_name="CFG Renorm (LongCat-Image)",
-            category="advanced/model/longcat",
-            description="Applies per-patch CFG renormalization used by the LongCat-Image pipeline. Connect between the model loader and the sampler.",
-            inputs=[
-                io.Model.Input("model"),
-            ],
-            outputs=[
-                io.Model.Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, model) -> io.NodeOutput:
-        def cfg_renorm_post(args):
-            denoised = args["denoised"]
-            cond_denoised = args["cond_denoised"]
-            x = args["input"]
-
-            B, C, H, W = denoised.shape
-            ps = 2
-
-            if H % ps != 0 or W % ps != 0:
-                logger.warning(f"CFG Renorm: incompatible shape {H}x{W}, skipping renorm")
-                return denoised
-
-            noise = x - denoised
-            noise_cond = x - cond_denoised
-
-            noise_packed = noise.reshape(B, C, H // ps, ps, W // ps, ps) \
-                               .permute(0, 2, 4, 1, 3, 5) \
-                               .reshape(B, -1, C * ps * ps)
-            cond_packed = noise_cond.reshape(B, C, H // ps, ps, W // ps, ps) \
-                                    .permute(0, 2, 4, 1, 3, 5) \
-                                    .reshape(B, -1, C * ps * ps)
-
-            noise_norm = torch.norm(noise_packed, dim=-1, keepdim=True)
-            cond_norm = torch.norm(cond_packed, dim=-1, keepdim=True)
-
-            scale = (cond_norm / (noise_norm + 1e-8)).clamp(min=0.0, max=1.0)
-
-            renormed = (noise_packed * scale) \
-                .reshape(B, H // ps, W // ps, C, ps, ps) \
-                .permute(0, 3, 1, 4, 2, 5) \
-                .reshape(B, C, H, W)
-
-            return x - renormed
-
-        m = model.clone()
-        m.set_model_sampler_post_cfg_function(cfg_renorm_post, disable_cfg1_optimization=True)
-        return io.NodeOutput(m)
-
-
-class LongCatImageExtension(ComfyExtension):
-    @override
-    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [
-            CLIPTextEncodeLongCatImage,
-            CFGRenormLongCatImage,
-        ]
-
-
-async def comfy_entrypoint() -> LongCatImageExtension:
-    return LongCatImageExtension()