One obsession text encoder

[xalteropsx]

https://civitai.com/models/2695493/one-obsessionanima

how can we load this model using diffuser it has textencoder and vae but
do we have to use CosmosTransformer3DModel ,
but i dont understand they have text encoder as well as vae and diffuser dont seems to have singlefile support ?

Please load the component before passing it in as an argument to `from_single_file`.

text_encoder = CLIPTextModel.from_pretrained('...')
pipe = StableDiffusionPipeline.from_single_file(<checkpoint path>, text_encoder=text_encoder)

[Arthur031221]

The component names in that checkpoint (qwen_3_06b_base.safetensors as text encoder, qwen_image_vae.safetensors as VAE) point to this being a Qwen-Image based checkpoint, not Cosmos, whatever base-model tag Civitai shows on the page. CosmosTransformer3DModel is the wrong class for it, that's a different model family entirely, and it's why StableDiffusionPipeline.from_single_file doesn't work either, that pipeline expects a UNet + CLIP text encoder, neither of which this checkpoint has.

I checked what's actually in diffusers 0.40.0 (pip install diffusers) for the Qwen-Image family. QwenImageTransformer2DModel is single-file loadable, it's listed in diffusers/loaders/single_file_model.py's SINGLE_FILE_LOADABLE_CLASSES with an identity checkpoint mapping (no key renaming needed):

from diffusers import QwenImageTransformer2DModel
transformer = QwenImageTransformer2DModel.from_single_file("Ob_animaV4.safetensors")  # works

AutoencoderKLQwenImage is not in that list, so calling .from_single_file() on it raises right away:

from diffusers import AutoencoderKLQwenImage
AutoencoderKLQwenImage.from_single_file("qwen_image_vae.safetensors")
# ValueError: FromOriginalModelMixin is currently only compatible with StableCascadeUNet,
# UNet2DConditionModel, AutoencoderKL, ... (QwenImageTransformer2DModel is in the list, this VAE isn't)

The workaround I actually got working is building the VAE from its published config and loading the safetensors weights into it directly, bypassing from_single_file entirely. I tested this against the official Qwen/Qwen-Image vae weights (same file name, qwen_image_vae.safetensors, so almost certainly the same architecture your checkpoint's VAE was exported from):

from diffusers import AutoencoderKLQwenImage
from safetensors.torch import load_file

cfg = AutoencoderKLQwenImage.load_config("Qwen/Qwen-Image", subfolder="vae")
vae = AutoencoderKLQwenImage.from_config(cfg)
result = vae.load_state_dict(load_file("qwen_image_vae.safetensors"))
print(result)  # <All keys matched successfully>

That loaded with zero missing and zero unexpected keys, strict load, no manual key renaming needed. If your qwen_image_vae.safetensors is byte-for-byte the stock Qwen-Image VAE (which the shared file name suggests), this same snippet should load it the same way.

QwenImagePipeline.from_single_file doesn't exist either (no such method), there's no single call that loads the whole pipeline from one file the way StableDiffusionPipeline.from_single_file does for SD1.5. You'd assemble it component by component:

pipe = QwenImagePipeline(transformer=transformer, vae=vae, text_encoder=..., tokenizer=..., scheduler=...)

The part I'd flag before you go further: QwenImagePipeline.__init__ expects text_encoder: Qwen2_5_VLForConditionalGeneration and tokenizer: Qwen2Tokenizer, that's the 7B Qwen2.5-VL used by the official Qwen-Image release. Your checkpoint ships qwen_3_06b_base, a much smaller plain Qwen3 model, not Qwen2.5-VL. If that's really a different, smaller text encoder swapped in by whoever built this checkpoint, QwenImagePipeline as shipped in diffusers won't accept it as a drop-in replacement, the class check and the VL-specific prompt encoding in encode_prompt are written against Qwen2.5-VL. You'd need to know the exact class that produced qwen_3_06b_base.safetensors and, most likely, write a small custom pipeline subclass that swaps in that text encoder and its own prompt encoding, rather than expecting from_pretrained/from_single_file on the stock QwenImagePipeline to accept it.