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from diffusers import SD3Transformer2DModel
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from huggingface_hub import snapshot_download
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from accelerate import init_empty_weights
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from diffusers.models.model_loading_utils import load_model_dict_into_meta
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import safetensors.torch
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from huggingface_hub import upload_folder
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import glob
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import torch
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large_model_id = "stabilityai/stable-diffusion-3.5-large"
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turbo_model_id = "stabilityai/stable-diffusion-3.5-large-turbo"
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with init_empty_weights():
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config = SD3Transformer2DModel.load_config(large_model_id, subfolder="transformer")
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model = SD3Transformer2DModel.from_config(config)
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large_ckpt = snapshot_download(repo_id=large_model_id, allow_patterns="transformer/*")
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turbo_ckpt = snapshot_download(repo_id=turbo_model_id, allow_patterns="transformer/*")
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large_shards = sorted(glob.glob(f"{large_ckpt}/transformer/*.safetensors"))
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turbo_shards = sorted(glob.glob(f"{turbo_ckpt}/transformer/*.safetensors"))
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merged_state_dict = {}
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guidance_state_dict = {}
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for i in range(len((large_shards))):
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state_dict_large_temp = safetensors.torch.load_file(large_shards[i])
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state_dict_turbo_temp = safetensors.torch.load_file(turbo_shards[i])
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keys = list(state_dict_large_temp.keys())
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for k in keys:
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if "guidance" not in k:
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merged_state_dict[k] = (state_dict_large_temp.pop(k) + state_dict_turbo_temp.pop(k)) / 2
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else:
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guidance_state_dict[k] = state_dict_large_temp.pop(k)
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if len(state_dict_large_temp) > 0:
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raise ValueError(f"There should not be any residue but got: {list(state_dict_large_temp.keys())}.")
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if len(state_dict_turbo_temp) > 0:
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raise ValueError(f"There should not be any residue but got: {list(state_dict_turbo_temp.keys())}.")
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merged_state_dict.update(guidance_state_dict)
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load_model_dict_into_meta(model, merged_state_dict)
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model.to(torch.bfloat16).save_pretrained("transformer")
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upload_folder(
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repo_id="prithivMLmods/sd-3.5-merged",
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folder_path="transformer",
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path_in_repo="transformer",
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)
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