Allow checkpoint merger to merge pix2pix models in the same way that it currently supports inpainting models.
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@ -132,6 +132,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
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tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None
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result_is_inpainting_model = False
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result_is_pix2pix_model = False
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if theta_func2:
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shared.state.textinfo = f"Loading B"
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@ -186,13 +187,17 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
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if a.shape[1] == 4 and b.shape[1] == 9:
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raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.")
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assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
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theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
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result_is_inpainting_model = True
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if a.shape[1] == 8 and b.shape[1] == 4:#If we have an InstructPix2Pix model...
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print("Detected possible merge of instruct model with non-instruct model.")
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theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)#Merge only the vectors the models have in common. Otherwise we get an error due to dimension mismatch.
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result_is_pix2pix_model = True
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else:
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assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
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theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
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result_is_inpainting_model = True
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else:
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theta_0[key] = theta_func2(a, b, multiplier)
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theta_0[key] = to_half(theta_0[key], save_as_half)
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shared.state.sampling_step += 1
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@ -226,6 +231,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
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filename = filename_generator() if custom_name == '' else custom_name
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filename += ".inpainting" if result_is_inpainting_model else ""
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filename += ".pix2pix" if result_is_pix2pix_model else ""
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filename += "." + checkpoint_format
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output_modelname = os.path.join(ckpt_dir, filename)
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