|
226 | 226 | "cosmos-2.0-v2w-14B": {"pretrained_model_name_or_path": "nvidia/Cosmos-Predict2-14B-Video2World"}, |
227 | 227 | "z-image-turbo": {"pretrained_model_name_or_path": "Tongyi-MAI/Z-Image-Turbo"}, |
228 | 228 | "z-image-turbo-controlnet": {"pretrained_model_name_or_path": "hlky/Z-Image-Turbo-Fun-Controlnet-Union"}, |
229 | | - "z-image-turbo-controlnet-2.x": {"pretrained_model_name_or_path": "hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.1"}, |
| 229 | + "z-image-turbo-controlnet-2.0": {"pretrained_model_name_or_path": "hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.0"}, |
| 230 | + "z-image-turbo-controlnet-2.1": {"pretrained_model_name_or_path": "hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.1"}, |
230 | 231 | } |
231 | 232 |
|
232 | 233 | # Use to configure model sample size when original config is provided |
@@ -784,7 +785,13 @@ def infer_diffusers_model_type(checkpoint): |
784 | 785 | raise ValueError(f"Unexpected x_embedder shape: {x_embedder_shape} when loading Cosmos 2.0 model.") |
785 | 786 |
|
786 | 787 | elif CHECKPOINT_KEY_NAMES["z-image-turbo-controlnet-2.x"] in checkpoint: |
787 | | - model_type = "z-image-turbo-controlnet-2.x" |
| 788 | + before_proj_weight = checkpoint.get("control_noise_refiner.0.before_proj.weight", None) |
| 789 | + if before_proj_weight is None: |
| 790 | + model_type = "z-image-turbo-controlnet-2.0" |
| 791 | + elif before_proj_weight is not None and torch.all(before_proj_weight == 0.0): |
| 792 | + model_type = "z-image-turbo-controlnet-2.0" |
| 793 | + else: |
| 794 | + model_type = "z-image-turbo-controlnet-2.1" |
788 | 795 |
|
789 | 796 | elif CHECKPOINT_KEY_NAMES["z-image-turbo-controlnet"] in checkpoint: |
790 | 797 | model_type = "z-image-turbo-controlnet" |
|
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