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96eabf2
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Parent(s):
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Browse files- animatediff/models/__pycache__/attention.cpython-310.pyc +0 -0
- animatediff/models/__pycache__/motion_module.cpython-310.pyc +0 -0
- animatediff/models/__pycache__/unet.cpython-310.pyc +0 -0
- animatediff/utils/__pycache__/convert_from_ckpt.cpython-310.pyc +0 -0
- animatediff/utils/convert_from_ckpt.py +4 -2
- handler.py +3 -3
animatediff/models/__pycache__/attention.cpython-310.pyc
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animatediff/models/__pycache__/motion_module.cpython-310.pyc
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Binary files a/animatediff/models/__pycache__/motion_module.cpython-310.pyc and b/animatediff/models/__pycache__/motion_module.cpython-310.pyc differ
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animatediff/models/__pycache__/unet.cpython-310.pyc
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Binary files a/animatediff/models/__pycache__/unet.cpython-310.pyc and b/animatediff/models/__pycache__/unet.cpython-310.pyc differ
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animatediff/utils/__pycache__/convert_from_ckpt.cpython-310.pyc
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Binary files a/animatediff/utils/__pycache__/convert_from_ckpt.cpython-310.pyc and b/animatediff/utils/__pycache__/convert_from_ckpt.cpython-310.pyc differ
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animatediff/utils/convert_from_ckpt.py
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@@ -632,7 +632,8 @@ def convert_ldm_vae_checkpoint(checkpoint, config):
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oldKey = {"old": "key", "new": "to_k"}
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oldQuery = {"old": "query", "new": "to_q"}
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oldValue = {"old": "value", "new": "to_v"}
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-
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conv_attn_to_linear(new_checkpoint)
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for i in range(num_up_blocks):
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@@ -668,7 +669,8 @@ def convert_ldm_vae_checkpoint(checkpoint, config):
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oldKey = {"old": "key", "new": "to_k"}
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oldQuery = {"old": "query", "new": "to_q"}
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oldValue = {"old": "value", "new": "to_v"}
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-
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conv_attn_to_linear(new_checkpoint)
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return new_checkpoint
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oldKey = {"old": "key", "new": "to_k"}
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oldQuery = {"old": "query", "new": "to_q"}
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oldValue = {"old": "value", "new": "to_v"}
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oldOut = {"old": "proj_attn", "new": "to_out"}
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assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path, oldKey, oldQuery, oldValue, oldOut], config=config)
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conv_attn_to_linear(new_checkpoint)
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for i in range(num_up_blocks):
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oldKey = {"old": "key", "new": "to_k"}
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oldQuery = {"old": "query", "new": "to_q"}
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oldValue = {"old": "value", "new": "to_v"}
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oldOut = {"old": "proj_attn", "new": "to_out"}
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assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path, oldKey, oldQuery, oldValue, oldOut], config=config)
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conv_attn_to_linear(new_checkpoint)
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return new_checkpoint
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handler.py
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@@ -66,7 +66,7 @@ class EndpointHandler():
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self.pipeline = AnimationPipeline(
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vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
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scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs.DDIMScheduler))
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)
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# huggingface download motion module from bluestarburst/AnimateDiff-SceneFusion/models/Motion_Module/mm_sd_v15.ckpt
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@@ -130,7 +130,7 @@ class EndpointHandler():
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self.pipeline = convert_lora(self.pipeline, state_dict)
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# self.pipeline = convert_lora(self.pipeline, state_dict, alpha=model_config.lora_alpha)
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self.pipeline
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def __call__(self, data : Any):
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"""
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# This function will be called during inference time.
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self.pipeline = AnimationPipeline(
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vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
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scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs.DDIMScheduler))
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)
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# huggingface download motion module from bluestarburst/AnimateDiff-SceneFusion/models/Motion_Module/mm_sd_v15.ckpt
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self.pipeline = convert_lora(self.pipeline, state_dict)
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# self.pipeline = convert_lora(self.pipeline, state_dict, alpha=model_config.lora_alpha)
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self.pipeline
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def __call__(self, data : Any):
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"""
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# This function will be called during inference time.
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new_handler = EndpointHandler()
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