Add Ideogram-4 diffusion model architecture support#5
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A quantized GGMLTensor is backed by packed uint8 storage, so weight.dtype returns torch.uint8. Models that do x.to(weight.dtype) before a linear (Ideogram-4, Gemma-4, Qwen3.5, CogVideo, some VAEs) cast activations to uint8, corrupting them and breaking F.linear on MPS, which requires floating-point inputs. GGMLTensor.dtype now returns bfloat16 for quantized tensors; F16/F32 and unquantized tensors keep their real storage dtype. A loader-set compute_dtype takes precedence. Dequant paths read storage via .data and are unaffected. Regression-tested loading WAN 2.2 I2V A14B Q8_0 (1095 tensors) on MPS.
ComfyUI core already ships Ideogram-4 (comfy/ldm/ideogram4/, model_detection.py, supported_models.py). This adds the GGUF detection layer so Ideogram-4 GGUF files load via UnetLoaderGGUF instead of failing with "Unknown model architecture!". - tools/convert.py: ModelIdeogram4, detected by the combination of embed_image_indicator.weight (the discriminator core uses), input_proj.weight and layers.0.attention.qkv.weight - loader.py: "ideogram4" added to IMG_ARCH_LIST No tensor remapping is needed (GGUF tensor names already match core) and the quants in circulation (BF16/Q8_0/IQ4_NL) are already supported.
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city96#455