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Code Issues Actions 13 Packages Projects Releases Wiki Activity

Small fixes

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This commit is contained in:
kijai 2026-03-04 01:30:40 +02:00
parent 7c93167ca7
commit 69c83a60db
2 changed files with 13 additions and 8 deletions
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3
comfy/supported_models.py
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@ -1746,6 +1746,9 @@ class RT_DETR_v4(supported_models_base.BASE):
out = model_base.RT_DETR_v4(self, device=device)
return out
def clip_target(self, state_dict={}):
return None
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, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4]
models += [SVD_img2vid]

18
comfy_extras/nodes_rtdetr.py
View File

@ -82,16 +82,18 @@ class DrawBBoxes(io.ComfyNode):
@classmethod
def execute(cls, bboxes, image=None) -> io.NodeOutput:
# Normalise bboxes to a list-of-lists (one list of detections per image).
# It may arrive as: a bare dict, a flat list of dicts, or a list of lists.
# Normalise to list[list[dict]], then fit to batch size B.
B = image.shape[0] if image is not None else 1
if isinstance(bboxes, dict):
bboxes = [[bboxes]] * B
elif not isinstance(bboxes, list) or len(bboxes) == 0:
bboxes = [[]] * B
elif not isinstance(bboxes[0], list):
# flat list of dicts: same detections for every image
bboxes = [bboxes] * B
bboxes = [[bboxes]]
elif not isinstance(bboxes, list) or not bboxes:
bboxes = [[]]
elif isinstance(bboxes[0], dict):
bboxes = [bboxes] # flat list → same detections for every image
if len(bboxes) == 1:
bboxes = bboxes * B
bboxes = (bboxes + [[]] * B)[:B]
if image is None:
B = len(bboxes)