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Remove unnecessary module.

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Talmaj Marinc 2026-02-27 21:01:01 +01:00
parent 8e668d9e67
commit fe515cf62f
1 changed files with 0 additions and 109 deletions
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109
comfy_extras/nodes_longcat_image.py
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@ -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()