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86294d3c7f
* Fix typos in docs and comments * Apply style fixes --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
1081 lines
40 KiB
Python
1081 lines
40 KiB
Python
import argparse
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import tempfile
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import torch
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from accelerate import load_checkpoint_and_dispatch
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from diffusers.models.transformers.prior_transformer import PriorTransformer
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from diffusers.pipelines.shap_e import ShapERenderer
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"""
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Example - From the diffusers root directory:
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Download weights:
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```sh
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$ wget "https://openaipublic.azureedge.net/main/shap-e/text_cond.pt"
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```
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Convert the model:
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```sh
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$ python scripts/convert_shap_e_to_diffusers.py \
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--prior_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/text_cond.pt \
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--prior_image_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/image_cond.pt \
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--transmitter_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/transmitter.pt\
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--dump_path /home/yiyi_huggingface_co/model_repo/shap-e-img2img/shap_e_renderer\
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--debug renderer
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```
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"""
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# prior
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PRIOR_ORIGINAL_PREFIX = "wrapped"
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PRIOR_CONFIG = {
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"num_attention_heads": 16,
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"attention_head_dim": 1024 // 16,
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"num_layers": 24,
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"embedding_dim": 1024,
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"num_embeddings": 1024,
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"additional_embeddings": 0,
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"time_embed_act_fn": "gelu",
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"norm_in_type": "layer",
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"encoder_hid_proj_type": None,
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"added_emb_type": None,
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"time_embed_dim": 1024 * 4,
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"embedding_proj_dim": 768,
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"clip_embed_dim": 1024 * 2,
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}
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def prior_model_from_original_config():
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model = PriorTransformer(**PRIOR_CONFIG)
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return model
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def prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
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diffusers_checkpoint = {}
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# <original>.time_embed.c_fc -> <diffusers>.time_embedding.linear_1
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diffusers_checkpoint.update(
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{
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"time_embedding.linear_1.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_fc.weight"],
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"time_embedding.linear_1.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_fc.bias"],
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}
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)
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# <original>.time_embed.c_proj -> <diffusers>.time_embedding.linear_2
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diffusers_checkpoint.update(
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{
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"time_embedding.linear_2.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_proj.weight"],
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"time_embedding.linear_2.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_proj.bias"],
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}
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)
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# <original>.input_proj -> <diffusers>.proj_in
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diffusers_checkpoint.update(
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{
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"proj_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.input_proj.weight"],
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"proj_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.input_proj.bias"],
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}
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)
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# <original>.clip_emb -> <diffusers>.embedding_proj
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diffusers_checkpoint.update(
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{
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"embedding_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_embed.weight"],
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"embedding_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_embed.bias"],
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}
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)
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# <original>.pos_emb -> <diffusers>.positional_embedding
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diffusers_checkpoint.update({"positional_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.pos_emb"][None, :]})
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# <original>.ln_pre -> <diffusers>.norm_in
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diffusers_checkpoint.update(
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{
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"norm_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_pre.weight"],
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"norm_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_pre.bias"],
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}
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)
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# <original>.backbone.resblocks.<x> -> <diffusers>.transformer_blocks.<x>
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for idx in range(len(model.transformer_blocks)):
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diffusers_transformer_prefix = f"transformer_blocks.{idx}"
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original_transformer_prefix = f"{PRIOR_ORIGINAL_PREFIX}.backbone.resblocks.{idx}"
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# <original>.attn -> <diffusers>.attn1
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diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1"
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original_attention_prefix = f"{original_transformer_prefix}.attn"
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diffusers_checkpoint.update(
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prior_attention_to_diffusers(
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checkpoint,
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diffusers_attention_prefix=diffusers_attention_prefix,
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original_attention_prefix=original_attention_prefix,
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attention_head_dim=model.attention_head_dim,
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)
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)
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# <original>.mlp -> <diffusers>.ff
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diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff"
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original_ff_prefix = f"{original_transformer_prefix}.mlp"
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diffusers_checkpoint.update(
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prior_ff_to_diffusers(
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checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix
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)
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)
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# <original>.ln_1 -> <diffusers>.norm1
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diffusers_checkpoint.update(
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{
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f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[
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f"{original_transformer_prefix}.ln_1.weight"
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],
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f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"],
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}
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)
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# <original>.ln_2 -> <diffusers>.norm3
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diffusers_checkpoint.update(
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{
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f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[
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f"{original_transformer_prefix}.ln_2.weight"
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],
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f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"],
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}
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)
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# <original>.ln_post -> <diffusers>.norm_out
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diffusers_checkpoint.update(
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{
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"norm_out.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_post.weight"],
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"norm_out.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_post.bias"],
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}
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)
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# <original>.output_proj -> <diffusers>.proj_to_clip_embeddings
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diffusers_checkpoint.update(
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{
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"proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.output_proj.weight"],
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"proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.output_proj.bias"],
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}
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)
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return diffusers_checkpoint
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def prior_attention_to_diffusers(
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checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim
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):
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diffusers_checkpoint = {}
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# <original>.c_qkv -> <diffusers>.{to_q, to_k, to_v}
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[q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions(
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weight=checkpoint[f"{original_attention_prefix}.c_qkv.weight"],
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bias=checkpoint[f"{original_attention_prefix}.c_qkv.bias"],
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split=3,
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chunk_size=attention_head_dim,
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)
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diffusers_checkpoint.update(
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{
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f"{diffusers_attention_prefix}.to_q.weight": q_weight,
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f"{diffusers_attention_prefix}.to_q.bias": q_bias,
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f"{diffusers_attention_prefix}.to_k.weight": k_weight,
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f"{diffusers_attention_prefix}.to_k.bias": k_bias,
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f"{diffusers_attention_prefix}.to_v.weight": v_weight,
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f"{diffusers_attention_prefix}.to_v.bias": v_bias,
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}
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)
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# <original>.c_proj -> <diffusers>.to_out.0
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diffusers_checkpoint.update(
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{
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f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{original_attention_prefix}.c_proj.weight"],
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f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{original_attention_prefix}.c_proj.bias"],
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}
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)
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return diffusers_checkpoint
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def prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix):
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diffusers_checkpoint = {
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# <original>.c_fc -> <diffusers>.net.0.proj
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f"{diffusers_ff_prefix}.net.{0}.proj.weight": checkpoint[f"{original_ff_prefix}.c_fc.weight"],
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f"{diffusers_ff_prefix}.net.{0}.proj.bias": checkpoint[f"{original_ff_prefix}.c_fc.bias"],
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# <original>.c_proj -> <diffusers>.net.2
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f"{diffusers_ff_prefix}.net.{2}.weight": checkpoint[f"{original_ff_prefix}.c_proj.weight"],
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f"{diffusers_ff_prefix}.net.{2}.bias": checkpoint[f"{original_ff_prefix}.c_proj.bias"],
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}
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return diffusers_checkpoint
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# done prior
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# prior_image (only slightly different from prior)
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PRIOR_IMAGE_ORIGINAL_PREFIX = "wrapped"
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# Uses default arguments
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PRIOR_IMAGE_CONFIG = {
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"num_attention_heads": 8,
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"attention_head_dim": 1024 // 8,
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"num_layers": 24,
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"embedding_dim": 1024,
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"num_embeddings": 1024,
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"additional_embeddings": 0,
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"time_embed_act_fn": "gelu",
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"norm_in_type": "layer",
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"embedding_proj_norm_type": "layer",
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"encoder_hid_proj_type": None,
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"added_emb_type": None,
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"time_embed_dim": 1024 * 4,
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"embedding_proj_dim": 1024,
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"clip_embed_dim": 1024 * 2,
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}
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def prior_image_model_from_original_config():
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model = PriorTransformer(**PRIOR_IMAGE_CONFIG)
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return model
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def prior_image_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
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diffusers_checkpoint = {}
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# <original>.time_embed.c_fc -> <diffusers>.time_embedding.linear_1
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diffusers_checkpoint.update(
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{
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"time_embedding.linear_1.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_fc.weight"],
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"time_embedding.linear_1.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_fc.bias"],
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}
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)
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# <original>.time_embed.c_proj -> <diffusers>.time_embedding.linear_2
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diffusers_checkpoint.update(
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{
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"time_embedding.linear_2.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_proj.weight"],
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"time_embedding.linear_2.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_proj.bias"],
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}
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)
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# <original>.input_proj -> <diffusers>.proj_in
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diffusers_checkpoint.update(
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{
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"proj_in.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.input_proj.weight"],
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"proj_in.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.input_proj.bias"],
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}
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)
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# <original>.clip_embed.0 -> <diffusers>.embedding_proj_norm
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diffusers_checkpoint.update(
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{
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"embedding_proj_norm.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.0.weight"],
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"embedding_proj_norm.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.0.bias"],
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}
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)
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# <original>..clip_embed.1 -> <diffusers>.embedding_proj
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diffusers_checkpoint.update(
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{
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"embedding_proj.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.1.weight"],
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"embedding_proj.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.1.bias"],
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}
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)
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# <original>.pos_emb -> <diffusers>.positional_embedding
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diffusers_checkpoint.update(
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{"positional_embedding": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.pos_emb"][None, :]}
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)
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# <original>.ln_pre -> <diffusers>.norm_in
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diffusers_checkpoint.update(
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{
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"norm_in.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_pre.weight"],
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"norm_in.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_pre.bias"],
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}
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)
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# <original>.backbone.resblocks.<x> -> <diffusers>.transformer_blocks.<x>
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for idx in range(len(model.transformer_blocks)):
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diffusers_transformer_prefix = f"transformer_blocks.{idx}"
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original_transformer_prefix = f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.backbone.resblocks.{idx}"
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# <original>.attn -> <diffusers>.attn1
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diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1"
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original_attention_prefix = f"{original_transformer_prefix}.attn"
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diffusers_checkpoint.update(
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prior_attention_to_diffusers(
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checkpoint,
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diffusers_attention_prefix=diffusers_attention_prefix,
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original_attention_prefix=original_attention_prefix,
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attention_head_dim=model.attention_head_dim,
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)
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)
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# <original>.mlp -> <diffusers>.ff
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diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff"
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original_ff_prefix = f"{original_transformer_prefix}.mlp"
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diffusers_checkpoint.update(
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prior_ff_to_diffusers(
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checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix
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)
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)
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# <original>.ln_1 -> <diffusers>.norm1
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diffusers_checkpoint.update(
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{
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f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[
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f"{original_transformer_prefix}.ln_1.weight"
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],
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f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"],
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}
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)
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# <original>.ln_2 -> <diffusers>.norm3
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diffusers_checkpoint.update(
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{
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f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[
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f"{original_transformer_prefix}.ln_2.weight"
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],
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f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"],
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}
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)
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# <original>.ln_post -> <diffusers>.norm_out
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diffusers_checkpoint.update(
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{
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"norm_out.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_post.weight"],
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"norm_out.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_post.bias"],
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}
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)
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# <original>.output_proj -> <diffusers>.proj_to_clip_embeddings
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diffusers_checkpoint.update(
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{
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"proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.output_proj.weight"],
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"proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.output_proj.bias"],
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}
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)
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return diffusers_checkpoint
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# done prior_image
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# renderer
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## create the lookup table for marching cubes method used in MeshDecoder
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MC_TABLE = [
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[],
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[[0, 1, 0, 2, 0, 4]],
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[[1, 0, 1, 5, 1, 3]],
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[[0, 4, 1, 5, 0, 2], [1, 5, 1, 3, 0, 2]],
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[[2, 0, 2, 3, 2, 6]],
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[[0, 1, 2, 3, 0, 4], [2, 3, 2, 6, 0, 4]],
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[[1, 0, 1, 5, 1, 3], [2, 6, 0, 2, 3, 2]],
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[[3, 2, 2, 6, 3, 1], [3, 1, 2, 6, 1, 5], [1, 5, 2, 6, 0, 4]],
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[[3, 1, 3, 7, 3, 2]],
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[[0, 2, 0, 4, 0, 1], [3, 7, 2, 3, 1, 3]],
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[[1, 5, 3, 7, 1, 0], [3, 7, 3, 2, 1, 0]],
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[[2, 0, 0, 4, 2, 3], [2, 3, 0, 4, 3, 7], [3, 7, 0, 4, 1, 5]],
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[[2, 0, 3, 1, 2, 6], [3, 1, 3, 7, 2, 6]],
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[[1, 3, 3, 7, 1, 0], [1, 0, 3, 7, 0, 4], [0, 4, 3, 7, 2, 6]],
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[[0, 1, 1, 5, 0, 2], [0, 2, 1, 5, 2, 6], [2, 6, 1, 5, 3, 7]],
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[[0, 4, 1, 5, 3, 7], [0, 4, 3, 7, 2, 6]],
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[[4, 0, 4, 6, 4, 5]],
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[[0, 2, 4, 6, 0, 1], [4, 6, 4, 5, 0, 1]],
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[[1, 5, 1, 3, 1, 0], [4, 6, 5, 4, 0, 4]],
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[[5, 1, 1, 3, 5, 4], [5, 4, 1, 3, 4, 6], [4, 6, 1, 3, 0, 2]],
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[[2, 0, 2, 3, 2, 6], [4, 5, 0, 4, 6, 4]],
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[[6, 4, 4, 5, 6, 2], [6, 2, 4, 5, 2, 3], [2, 3, 4, 5, 0, 1]],
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[[2, 6, 2, 0, 3, 2], [1, 0, 1, 5, 3, 1], [6, 4, 5, 4, 0, 4]],
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[[1, 3, 5, 4, 1, 5], [1, 3, 4, 6, 5, 4], [1, 3, 3, 2, 4, 6], [3, 2, 2, 6, 4, 6]],
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[[3, 1, 3, 7, 3, 2], [6, 4, 5, 4, 0, 4]],
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[[4, 5, 0, 1, 4, 6], [0, 1, 0, 2, 4, 6], [7, 3, 2, 3, 1, 3]],
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[[3, 2, 1, 0, 3, 7], [1, 0, 1, 5, 3, 7], [6, 4, 5, 4, 0, 4]],
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[[3, 7, 3, 2, 1, 5], [3, 2, 6, 4, 1, 5], [1, 5, 6, 4, 5, 4], [3, 2, 2, 0, 6, 4]],
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[[3, 7, 2, 6, 3, 1], [2, 6, 2, 0, 3, 1], [5, 4, 0, 4, 6, 4]],
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[[1, 0, 1, 3, 5, 4], [1, 3, 2, 6, 5, 4], [1, 3, 3, 7, 2, 6], [5, 4, 2, 6, 4, 6]],
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[[0, 1, 1, 5, 0, 2], [0, 2, 1, 5, 2, 6], [2, 6, 1, 5, 3, 7], [4, 5, 0, 4, 4, 6]],
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[[6, 2, 4, 6, 4, 5], [4, 5, 5, 1, 6, 2], [6, 2, 5, 1, 7, 3]],
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[[5, 1, 5, 4, 5, 7]],
|
|
[[0, 1, 0, 2, 0, 4], [5, 7, 1, 5, 4, 5]],
|
|
[[1, 0, 5, 4, 1, 3], [5, 4, 5, 7, 1, 3]],
|
|
[[4, 5, 5, 7, 4, 0], [4, 0, 5, 7, 0, 2], [0, 2, 5, 7, 1, 3]],
|
|
[[2, 0, 2, 3, 2, 6], [7, 5, 1, 5, 4, 5]],
|
|
[[2, 6, 0, 4, 2, 3], [0, 4, 0, 1, 2, 3], [7, 5, 1, 5, 4, 5]],
|
|
[[5, 7, 1, 3, 5, 4], [1, 3, 1, 0, 5, 4], [6, 2, 0, 2, 3, 2]],
|
|
[[3, 1, 3, 2, 7, 5], [3, 2, 0, 4, 7, 5], [3, 2, 2, 6, 0, 4], [7, 5, 0, 4, 5, 4]],
|
|
[[3, 7, 3, 2, 3, 1], [5, 4, 7, 5, 1, 5]],
|
|
[[0, 4, 0, 1, 2, 0], [3, 1, 3, 7, 2, 3], [4, 5, 7, 5, 1, 5]],
|
|
[[7, 3, 3, 2, 7, 5], [7, 5, 3, 2, 5, 4], [5, 4, 3, 2, 1, 0]],
|
|
[[0, 4, 2, 3, 0, 2], [0, 4, 3, 7, 2, 3], [0, 4, 4, 5, 3, 7], [4, 5, 5, 7, 3, 7]],
|
|
[[2, 0, 3, 1, 2, 6], [3, 1, 3, 7, 2, 6], [4, 5, 7, 5, 1, 5]],
|
|
[[1, 3, 3, 7, 1, 0], [1, 0, 3, 7, 0, 4], [0, 4, 3, 7, 2, 6], [5, 7, 1, 5, 5, 4]],
|
|
[[2, 6, 2, 0, 3, 7], [2, 0, 4, 5, 3, 7], [3, 7, 4, 5, 7, 5], [2, 0, 0, 1, 4, 5]],
|
|
[[4, 0, 5, 4, 5, 7], [5, 7, 7, 3, 4, 0], [4, 0, 7, 3, 6, 2]],
|
|
[[4, 6, 5, 7, 4, 0], [5, 7, 5, 1, 4, 0]],
|
|
[[1, 0, 0, 2, 1, 5], [1, 5, 0, 2, 5, 7], [5, 7, 0, 2, 4, 6]],
|
|
[[0, 4, 4, 6, 0, 1], [0, 1, 4, 6, 1, 3], [1, 3, 4, 6, 5, 7]],
|
|
[[0, 2, 4, 6, 5, 7], [0, 2, 5, 7, 1, 3]],
|
|
[[5, 1, 4, 0, 5, 7], [4, 0, 4, 6, 5, 7], [3, 2, 6, 2, 0, 2]],
|
|
[[2, 3, 2, 6, 0, 1], [2, 6, 7, 5, 0, 1], [0, 1, 7, 5, 1, 5], [2, 6, 6, 4, 7, 5]],
|
|
[[0, 4, 4, 6, 0, 1], [0, 1, 4, 6, 1, 3], [1, 3, 4, 6, 5, 7], [2, 6, 0, 2, 2, 3]],
|
|
[[3, 1, 2, 3, 2, 6], [2, 6, 6, 4, 3, 1], [3, 1, 6, 4, 7, 5]],
|
|
[[4, 6, 5, 7, 4, 0], [5, 7, 5, 1, 4, 0], [2, 3, 1, 3, 7, 3]],
|
|
[[1, 0, 0, 2, 1, 5], [1, 5, 0, 2, 5, 7], [5, 7, 0, 2, 4, 6], [3, 2, 1, 3, 3, 7]],
|
|
[[0, 1, 0, 4, 2, 3], [0, 4, 5, 7, 2, 3], [0, 4, 4, 6, 5, 7], [2, 3, 5, 7, 3, 7]],
|
|
[[7, 5, 3, 7, 3, 2], [3, 2, 2, 0, 7, 5], [7, 5, 2, 0, 6, 4]],
|
|
[[0, 4, 4, 6, 5, 7], [0, 4, 5, 7, 1, 5], [0, 2, 1, 3, 3, 7], [3, 7, 2, 6, 0, 2]],
|
|
[
|
|
[3, 1, 7, 3, 6, 2],
|
|
[6, 2, 0, 1, 3, 1],
|
|
[6, 4, 0, 1, 6, 2],
|
|
[6, 4, 5, 1, 0, 1],
|
|
[6, 4, 7, 5, 5, 1],
|
|
],
|
|
[
|
|
[4, 0, 6, 4, 7, 5],
|
|
[7, 5, 1, 0, 4, 0],
|
|
[7, 3, 1, 0, 7, 5],
|
|
[7, 3, 2, 0, 1, 0],
|
|
[7, 3, 6, 2, 2, 0],
|
|
],
|
|
[[7, 3, 6, 2, 6, 4], [7, 5, 7, 3, 6, 4]],
|
|
[[6, 2, 6, 7, 6, 4]],
|
|
[[0, 4, 0, 1, 0, 2], [6, 7, 4, 6, 2, 6]],
|
|
[[1, 0, 1, 5, 1, 3], [7, 6, 4, 6, 2, 6]],
|
|
[[1, 3, 0, 2, 1, 5], [0, 2, 0, 4, 1, 5], [7, 6, 4, 6, 2, 6]],
|
|
[[2, 3, 6, 7, 2, 0], [6, 7, 6, 4, 2, 0]],
|
|
[[4, 0, 0, 1, 4, 6], [4, 6, 0, 1, 6, 7], [6, 7, 0, 1, 2, 3]],
|
|
[[6, 4, 2, 0, 6, 7], [2, 0, 2, 3, 6, 7], [5, 1, 3, 1, 0, 1]],
|
|
[[1, 5, 1, 3, 0, 4], [1, 3, 7, 6, 0, 4], [0, 4, 7, 6, 4, 6], [1, 3, 3, 2, 7, 6]],
|
|
[[3, 2, 3, 1, 3, 7], [6, 4, 2, 6, 7, 6]],
|
|
[[3, 7, 3, 2, 1, 3], [0, 2, 0, 4, 1, 0], [7, 6, 4, 6, 2, 6]],
|
|
[[1, 5, 3, 7, 1, 0], [3, 7, 3, 2, 1, 0], [4, 6, 2, 6, 7, 6]],
|
|
[[2, 0, 0, 4, 2, 3], [2, 3, 0, 4, 3, 7], [3, 7, 0, 4, 1, 5], [6, 4, 2, 6, 6, 7]],
|
|
[[7, 6, 6, 4, 7, 3], [7, 3, 6, 4, 3, 1], [3, 1, 6, 4, 2, 0]],
|
|
[[0, 1, 4, 6, 0, 4], [0, 1, 6, 7, 4, 6], [0, 1, 1, 3, 6, 7], [1, 3, 3, 7, 6, 7]],
|
|
[[0, 2, 0, 1, 4, 6], [0, 1, 3, 7, 4, 6], [0, 1, 1, 5, 3, 7], [4, 6, 3, 7, 6, 7]],
|
|
[[7, 3, 6, 7, 6, 4], [6, 4, 4, 0, 7, 3], [7, 3, 4, 0, 5, 1]],
|
|
[[4, 0, 6, 2, 4, 5], [6, 2, 6, 7, 4, 5]],
|
|
[[2, 6, 6, 7, 2, 0], [2, 0, 6, 7, 0, 1], [0, 1, 6, 7, 4, 5]],
|
|
[[6, 7, 4, 5, 6, 2], [4, 5, 4, 0, 6, 2], [3, 1, 0, 1, 5, 1]],
|
|
[[2, 0, 2, 6, 3, 1], [2, 6, 4, 5, 3, 1], [2, 6, 6, 7, 4, 5], [3, 1, 4, 5, 1, 5]],
|
|
[[0, 2, 2, 3, 0, 4], [0, 4, 2, 3, 4, 5], [4, 5, 2, 3, 6, 7]],
|
|
[[0, 1, 2, 3, 6, 7], [0, 1, 6, 7, 4, 5]],
|
|
[[0, 2, 2, 3, 0, 4], [0, 4, 2, 3, 4, 5], [4, 5, 2, 3, 6, 7], [1, 3, 0, 1, 1, 5]],
|
|
[[5, 4, 1, 5, 1, 3], [1, 3, 3, 2, 5, 4], [5, 4, 3, 2, 7, 6]],
|
|
[[4, 0, 6, 2, 4, 5], [6, 2, 6, 7, 4, 5], [1, 3, 7, 3, 2, 3]],
|
|
[[2, 6, 6, 7, 2, 0], [2, 0, 6, 7, 0, 1], [0, 1, 6, 7, 4, 5], [3, 7, 2, 3, 3, 1]],
|
|
[[0, 1, 1, 5, 3, 7], [0, 1, 3, 7, 2, 3], [0, 4, 2, 6, 6, 7], [6, 7, 4, 5, 0, 4]],
|
|
[
|
|
[6, 2, 7, 6, 5, 4],
|
|
[5, 4, 0, 2, 6, 2],
|
|
[5, 1, 0, 2, 5, 4],
|
|
[5, 1, 3, 2, 0, 2],
|
|
[5, 1, 7, 3, 3, 2],
|
|
],
|
|
[[3, 1, 3, 7, 2, 0], [3, 7, 5, 4, 2, 0], [2, 0, 5, 4, 0, 4], [3, 7, 7, 6, 5, 4]],
|
|
[[1, 0, 3, 1, 3, 7], [3, 7, 7, 6, 1, 0], [1, 0, 7, 6, 5, 4]],
|
|
[
|
|
[1, 0, 5, 1, 7, 3],
|
|
[7, 3, 2, 0, 1, 0],
|
|
[7, 6, 2, 0, 7, 3],
|
|
[7, 6, 4, 0, 2, 0],
|
|
[7, 6, 5, 4, 4, 0],
|
|
],
|
|
[[7, 6, 5, 4, 5, 1], [7, 3, 7, 6, 5, 1]],
|
|
[[5, 7, 5, 1, 5, 4], [6, 2, 7, 6, 4, 6]],
|
|
[[0, 2, 0, 4, 1, 0], [5, 4, 5, 7, 1, 5], [2, 6, 7, 6, 4, 6]],
|
|
[[1, 0, 5, 4, 1, 3], [5, 4, 5, 7, 1, 3], [2, 6, 7, 6, 4, 6]],
|
|
[[4, 5, 5, 7, 4, 0], [4, 0, 5, 7, 0, 2], [0, 2, 5, 7, 1, 3], [6, 7, 4, 6, 6, 2]],
|
|
[[2, 3, 6, 7, 2, 0], [6, 7, 6, 4, 2, 0], [1, 5, 4, 5, 7, 5]],
|
|
[[4, 0, 0, 1, 4, 6], [4, 6, 0, 1, 6, 7], [6, 7, 0, 1, 2, 3], [5, 1, 4, 5, 5, 7]],
|
|
[[0, 2, 2, 3, 6, 7], [0, 2, 6, 7, 4, 6], [0, 1, 4, 5, 5, 7], [5, 7, 1, 3, 0, 1]],
|
|
[
|
|
[5, 4, 7, 5, 3, 1],
|
|
[3, 1, 0, 4, 5, 4],
|
|
[3, 2, 0, 4, 3, 1],
|
|
[3, 2, 6, 4, 0, 4],
|
|
[3, 2, 7, 6, 6, 4],
|
|
],
|
|
[[5, 4, 5, 7, 1, 5], [3, 7, 3, 2, 1, 3], [4, 6, 2, 6, 7, 6]],
|
|
[[1, 0, 0, 2, 0, 4], [1, 5, 5, 4, 5, 7], [3, 2, 1, 3, 3, 7], [2, 6, 7, 6, 4, 6]],
|
|
[[7, 3, 3, 2, 7, 5], [7, 5, 3, 2, 5, 4], [5, 4, 3, 2, 1, 0], [6, 2, 7, 6, 6, 4]],
|
|
[
|
|
[0, 4, 2, 3, 0, 2],
|
|
[0, 4, 3, 7, 2, 3],
|
|
[0, 4, 4, 5, 3, 7],
|
|
[4, 5, 5, 7, 3, 7],
|
|
[6, 7, 4, 6, 2, 6],
|
|
],
|
|
[[7, 6, 6, 4, 7, 3], [7, 3, 6, 4, 3, 1], [3, 1, 6, 4, 2, 0], [5, 4, 7, 5, 5, 1]],
|
|
[
|
|
[0, 1, 4, 6, 0, 4],
|
|
[0, 1, 6, 7, 4, 6],
|
|
[0, 1, 1, 3, 6, 7],
|
|
[1, 3, 3, 7, 6, 7],
|
|
[5, 7, 1, 5, 4, 5],
|
|
],
|
|
[
|
|
[6, 7, 4, 6, 0, 2],
|
|
[0, 2, 3, 7, 6, 7],
|
|
[0, 1, 3, 7, 0, 2],
|
|
[0, 1, 5, 7, 3, 7],
|
|
[0, 1, 4, 5, 5, 7],
|
|
],
|
|
[[4, 0, 6, 7, 4, 6], [4, 0, 7, 3, 6, 7], [4, 0, 5, 7, 7, 3], [4, 5, 5, 7, 4, 0]],
|
|
[[7, 5, 5, 1, 7, 6], [7, 6, 5, 1, 6, 2], [6, 2, 5, 1, 4, 0]],
|
|
[[0, 2, 1, 5, 0, 1], [0, 2, 5, 7, 1, 5], [0, 2, 2, 6, 5, 7], [2, 6, 6, 7, 5, 7]],
|
|
[[1, 3, 1, 0, 5, 7], [1, 0, 2, 6, 5, 7], [5, 7, 2, 6, 7, 6], [1, 0, 0, 4, 2, 6]],
|
|
[[2, 0, 6, 2, 6, 7], [6, 7, 7, 5, 2, 0], [2, 0, 7, 5, 3, 1]],
|
|
[[0, 4, 0, 2, 1, 5], [0, 2, 6, 7, 1, 5], [0, 2, 2, 3, 6, 7], [1, 5, 6, 7, 5, 7]],
|
|
[[7, 6, 5, 7, 5, 1], [5, 1, 1, 0, 7, 6], [7, 6, 1, 0, 3, 2]],
|
|
[
|
|
[2, 0, 3, 2, 7, 6],
|
|
[7, 6, 4, 0, 2, 0],
|
|
[7, 5, 4, 0, 7, 6],
|
|
[7, 5, 1, 0, 4, 0],
|
|
[7, 5, 3, 1, 1, 0],
|
|
],
|
|
[[7, 5, 3, 1, 3, 2], [7, 6, 7, 5, 3, 2]],
|
|
[[7, 5, 5, 1, 7, 6], [7, 6, 5, 1, 6, 2], [6, 2, 5, 1, 4, 0], [3, 1, 7, 3, 3, 2]],
|
|
[
|
|
[0, 2, 1, 5, 0, 1],
|
|
[0, 2, 5, 7, 1, 5],
|
|
[0, 2, 2, 6, 5, 7],
|
|
[2, 6, 6, 7, 5, 7],
|
|
[3, 7, 2, 3, 1, 3],
|
|
],
|
|
[
|
|
[3, 7, 2, 3, 0, 1],
|
|
[0, 1, 5, 7, 3, 7],
|
|
[0, 4, 5, 7, 0, 1],
|
|
[0, 4, 6, 7, 5, 7],
|
|
[0, 4, 2, 6, 6, 7],
|
|
],
|
|
[[2, 0, 3, 7, 2, 3], [2, 0, 7, 5, 3, 7], [2, 0, 6, 7, 7, 5], [2, 6, 6, 7, 2, 0]],
|
|
[
|
|
[5, 7, 1, 5, 0, 4],
|
|
[0, 4, 6, 7, 5, 7],
|
|
[0, 2, 6, 7, 0, 4],
|
|
[0, 2, 3, 7, 6, 7],
|
|
[0, 2, 1, 3, 3, 7],
|
|
],
|
|
[[1, 0, 5, 7, 1, 5], [1, 0, 7, 6, 5, 7], [1, 0, 3, 7, 7, 6], [1, 3, 3, 7, 1, 0]],
|
|
[[0, 2, 0, 1, 0, 4], [3, 7, 6, 7, 5, 7]],
|
|
[[7, 5, 7, 3, 7, 6]],
|
|
[[7, 3, 7, 5, 7, 6]],
|
|
[[0, 1, 0, 2, 0, 4], [6, 7, 3, 7, 5, 7]],
|
|
[[1, 3, 1, 0, 1, 5], [7, 6, 3, 7, 5, 7]],
|
|
[[0, 4, 1, 5, 0, 2], [1, 5, 1, 3, 0, 2], [6, 7, 3, 7, 5, 7]],
|
|
[[2, 6, 2, 0, 2, 3], [7, 5, 6, 7, 3, 7]],
|
|
[[0, 1, 2, 3, 0, 4], [2, 3, 2, 6, 0, 4], [5, 7, 6, 7, 3, 7]],
|
|
[[1, 5, 1, 3, 0, 1], [2, 3, 2, 6, 0, 2], [5, 7, 6, 7, 3, 7]],
|
|
[[3, 2, 2, 6, 3, 1], [3, 1, 2, 6, 1, 5], [1, 5, 2, 6, 0, 4], [7, 6, 3, 7, 7, 5]],
|
|
[[3, 1, 7, 5, 3, 2], [7, 5, 7, 6, 3, 2]],
|
|
[[7, 6, 3, 2, 7, 5], [3, 2, 3, 1, 7, 5], [4, 0, 1, 0, 2, 0]],
|
|
[[5, 7, 7, 6, 5, 1], [5, 1, 7, 6, 1, 0], [1, 0, 7, 6, 3, 2]],
|
|
[[2, 3, 2, 0, 6, 7], [2, 0, 1, 5, 6, 7], [2, 0, 0, 4, 1, 5], [6, 7, 1, 5, 7, 5]],
|
|
[[6, 2, 2, 0, 6, 7], [6, 7, 2, 0, 7, 5], [7, 5, 2, 0, 3, 1]],
|
|
[[0, 4, 0, 1, 2, 6], [0, 1, 5, 7, 2, 6], [2, 6, 5, 7, 6, 7], [0, 1, 1, 3, 5, 7]],
|
|
[[1, 5, 0, 2, 1, 0], [1, 5, 2, 6, 0, 2], [1, 5, 5, 7, 2, 6], [5, 7, 7, 6, 2, 6]],
|
|
[[5, 1, 7, 5, 7, 6], [7, 6, 6, 2, 5, 1], [5, 1, 6, 2, 4, 0]],
|
|
[[4, 5, 4, 0, 4, 6], [7, 3, 5, 7, 6, 7]],
|
|
[[0, 2, 4, 6, 0, 1], [4, 6, 4, 5, 0, 1], [3, 7, 5, 7, 6, 7]],
|
|
[[4, 6, 4, 5, 0, 4], [1, 5, 1, 3, 0, 1], [6, 7, 3, 7, 5, 7]],
|
|
[[5, 1, 1, 3, 5, 4], [5, 4, 1, 3, 4, 6], [4, 6, 1, 3, 0, 2], [7, 3, 5, 7, 7, 6]],
|
|
[[2, 3, 2, 6, 0, 2], [4, 6, 4, 5, 0, 4], [3, 7, 5, 7, 6, 7]],
|
|
[[6, 4, 4, 5, 6, 2], [6, 2, 4, 5, 2, 3], [2, 3, 4, 5, 0, 1], [7, 5, 6, 7, 7, 3]],
|
|
[[0, 1, 1, 5, 1, 3], [0, 2, 2, 3, 2, 6], [4, 5, 0, 4, 4, 6], [5, 7, 6, 7, 3, 7]],
|
|
[
|
|
[1, 3, 5, 4, 1, 5],
|
|
[1, 3, 4, 6, 5, 4],
|
|
[1, 3, 3, 2, 4, 6],
|
|
[3, 2, 2, 6, 4, 6],
|
|
[7, 6, 3, 7, 5, 7],
|
|
],
|
|
[[3, 1, 7, 5, 3, 2], [7, 5, 7, 6, 3, 2], [0, 4, 6, 4, 5, 4]],
|
|
[[1, 0, 0, 2, 4, 6], [1, 0, 4, 6, 5, 4], [1, 3, 5, 7, 7, 6], [7, 6, 3, 2, 1, 3]],
|
|
[[5, 7, 7, 6, 5, 1], [5, 1, 7, 6, 1, 0], [1, 0, 7, 6, 3, 2], [4, 6, 5, 4, 4, 0]],
|
|
[
|
|
[7, 5, 6, 7, 2, 3],
|
|
[2, 3, 1, 5, 7, 5],
|
|
[2, 0, 1, 5, 2, 3],
|
|
[2, 0, 4, 5, 1, 5],
|
|
[2, 0, 6, 4, 4, 5],
|
|
],
|
|
[[6, 2, 2, 0, 6, 7], [6, 7, 2, 0, 7, 5], [7, 5, 2, 0, 3, 1], [4, 0, 6, 4, 4, 5]],
|
|
[
|
|
[4, 6, 5, 4, 1, 0],
|
|
[1, 0, 2, 6, 4, 6],
|
|
[1, 3, 2, 6, 1, 0],
|
|
[1, 3, 7, 6, 2, 6],
|
|
[1, 3, 5, 7, 7, 6],
|
|
],
|
|
[
|
|
[1, 5, 0, 2, 1, 0],
|
|
[1, 5, 2, 6, 0, 2],
|
|
[1, 5, 5, 7, 2, 6],
|
|
[5, 7, 7, 6, 2, 6],
|
|
[4, 6, 5, 4, 0, 4],
|
|
],
|
|
[[5, 1, 4, 6, 5, 4], [5, 1, 6, 2, 4, 6], [5, 1, 7, 6, 6, 2], [5, 7, 7, 6, 5, 1]],
|
|
[[5, 4, 7, 6, 5, 1], [7, 6, 7, 3, 5, 1]],
|
|
[[7, 3, 5, 1, 7, 6], [5, 1, 5, 4, 7, 6], [2, 0, 4, 0, 1, 0]],
|
|
[[3, 1, 1, 0, 3, 7], [3, 7, 1, 0, 7, 6], [7, 6, 1, 0, 5, 4]],
|
|
[[0, 2, 0, 4, 1, 3], [0, 4, 6, 7, 1, 3], [1, 3, 6, 7, 3, 7], [0, 4, 4, 5, 6, 7]],
|
|
[[5, 4, 7, 6, 5, 1], [7, 6, 7, 3, 5, 1], [0, 2, 3, 2, 6, 2]],
|
|
[[1, 5, 5, 4, 7, 6], [1, 5, 7, 6, 3, 7], [1, 0, 3, 2, 2, 6], [2, 6, 0, 4, 1, 0]],
|
|
[[3, 1, 1, 0, 3, 7], [3, 7, 1, 0, 7, 6], [7, 6, 1, 0, 5, 4], [2, 0, 3, 2, 2, 6]],
|
|
[
|
|
[2, 3, 6, 2, 4, 0],
|
|
[4, 0, 1, 3, 2, 3],
|
|
[4, 5, 1, 3, 4, 0],
|
|
[4, 5, 7, 3, 1, 3],
|
|
[4, 5, 6, 7, 7, 3],
|
|
],
|
|
[[1, 5, 5, 4, 1, 3], [1, 3, 5, 4, 3, 2], [3, 2, 5, 4, 7, 6]],
|
|
[[1, 5, 5, 4, 1, 3], [1, 3, 5, 4, 3, 2], [3, 2, 5, 4, 7, 6], [0, 4, 1, 0, 0, 2]],
|
|
[[1, 0, 5, 4, 7, 6], [1, 0, 7, 6, 3, 2]],
|
|
[[2, 3, 0, 2, 0, 4], [0, 4, 4, 5, 2, 3], [2, 3, 4, 5, 6, 7]],
|
|
[[1, 3, 1, 5, 0, 2], [1, 5, 7, 6, 0, 2], [1, 5, 5, 4, 7, 6], [0, 2, 7, 6, 2, 6]],
|
|
[
|
|
[5, 1, 4, 5, 6, 7],
|
|
[6, 7, 3, 1, 5, 1],
|
|
[6, 2, 3, 1, 6, 7],
|
|
[6, 2, 0, 1, 3, 1],
|
|
[6, 2, 4, 0, 0, 1],
|
|
],
|
|
[[6, 7, 2, 6, 2, 0], [2, 0, 0, 1, 6, 7], [6, 7, 0, 1, 4, 5]],
|
|
[[6, 2, 4, 0, 4, 5], [6, 7, 6, 2, 4, 5]],
|
|
[[6, 7, 7, 3, 6, 4], [6, 4, 7, 3, 4, 0], [4, 0, 7, 3, 5, 1]],
|
|
[[1, 5, 1, 0, 3, 7], [1, 0, 4, 6, 3, 7], [1, 0, 0, 2, 4, 6], [3, 7, 4, 6, 7, 6]],
|
|
[[1, 0, 3, 7, 1, 3], [1, 0, 7, 6, 3, 7], [1, 0, 0, 4, 7, 6], [0, 4, 4, 6, 7, 6]],
|
|
[[6, 4, 7, 6, 7, 3], [7, 3, 3, 1, 6, 4], [6, 4, 3, 1, 2, 0]],
|
|
[[6, 7, 7, 3, 6, 4], [6, 4, 7, 3, 4, 0], [4, 0, 7, 3, 5, 1], [2, 3, 6, 2, 2, 0]],
|
|
[
|
|
[7, 6, 3, 7, 1, 5],
|
|
[1, 5, 4, 6, 7, 6],
|
|
[1, 0, 4, 6, 1, 5],
|
|
[1, 0, 2, 6, 4, 6],
|
|
[1, 0, 3, 2, 2, 6],
|
|
],
|
|
[
|
|
[1, 0, 3, 7, 1, 3],
|
|
[1, 0, 7, 6, 3, 7],
|
|
[1, 0, 0, 4, 7, 6],
|
|
[0, 4, 4, 6, 7, 6],
|
|
[2, 6, 0, 2, 3, 2],
|
|
],
|
|
[[3, 1, 7, 6, 3, 7], [3, 1, 6, 4, 7, 6], [3, 1, 2, 6, 6, 4], [3, 2, 2, 6, 3, 1]],
|
|
[[3, 2, 3, 1, 7, 6], [3, 1, 0, 4, 7, 6], [7, 6, 0, 4, 6, 4], [3, 1, 1, 5, 0, 4]],
|
|
[
|
|
[0, 1, 2, 0, 6, 4],
|
|
[6, 4, 5, 1, 0, 1],
|
|
[6, 7, 5, 1, 6, 4],
|
|
[6, 7, 3, 1, 5, 1],
|
|
[6, 7, 2, 3, 3, 1],
|
|
],
|
|
[[0, 1, 4, 0, 4, 6], [4, 6, 6, 7, 0, 1], [0, 1, 6, 7, 2, 3]],
|
|
[[6, 7, 2, 3, 2, 0], [6, 4, 6, 7, 2, 0]],
|
|
[
|
|
[2, 6, 0, 2, 1, 3],
|
|
[1, 3, 7, 6, 2, 6],
|
|
[1, 5, 7, 6, 1, 3],
|
|
[1, 5, 4, 6, 7, 6],
|
|
[1, 5, 0, 4, 4, 6],
|
|
],
|
|
[[1, 5, 1, 0, 1, 3], [4, 6, 7, 6, 2, 6]],
|
|
[[0, 1, 2, 6, 0, 2], [0, 1, 6, 7, 2, 6], [0, 1, 4, 6, 6, 7], [0, 4, 4, 6, 0, 1]],
|
|
[[6, 7, 6, 2, 6, 4]],
|
|
[[6, 2, 7, 3, 6, 4], [7, 3, 7, 5, 6, 4]],
|
|
[[7, 5, 6, 4, 7, 3], [6, 4, 6, 2, 7, 3], [1, 0, 2, 0, 4, 0]],
|
|
[[6, 2, 7, 3, 6, 4], [7, 3, 7, 5, 6, 4], [0, 1, 5, 1, 3, 1]],
|
|
[[2, 0, 0, 4, 1, 5], [2, 0, 1, 5, 3, 1], [2, 6, 3, 7, 7, 5], [7, 5, 6, 4, 2, 6]],
|
|
[[3, 7, 7, 5, 3, 2], [3, 2, 7, 5, 2, 0], [2, 0, 7, 5, 6, 4]],
|
|
[[3, 2, 3, 7, 1, 0], [3, 7, 6, 4, 1, 0], [3, 7, 7, 5, 6, 4], [1, 0, 6, 4, 0, 4]],
|
|
[[3, 7, 7, 5, 3, 2], [3, 2, 7, 5, 2, 0], [2, 0, 7, 5, 6, 4], [1, 5, 3, 1, 1, 0]],
|
|
[
|
|
[7, 3, 5, 7, 4, 6],
|
|
[4, 6, 2, 3, 7, 3],
|
|
[4, 0, 2, 3, 4, 6],
|
|
[4, 0, 1, 3, 2, 3],
|
|
[4, 0, 5, 1, 1, 3],
|
|
],
|
|
[[2, 3, 3, 1, 2, 6], [2, 6, 3, 1, 6, 4], [6, 4, 3, 1, 7, 5]],
|
|
[[2, 3, 3, 1, 2, 6], [2, 6, 3, 1, 6, 4], [6, 4, 3, 1, 7, 5], [0, 1, 2, 0, 0, 4]],
|
|
[[1, 0, 1, 5, 3, 2], [1, 5, 4, 6, 3, 2], [3, 2, 4, 6, 2, 6], [1, 5, 5, 7, 4, 6]],
|
|
[
|
|
[0, 2, 4, 0, 5, 1],
|
|
[5, 1, 3, 2, 0, 2],
|
|
[5, 7, 3, 2, 5, 1],
|
|
[5, 7, 6, 2, 3, 2],
|
|
[5, 7, 4, 6, 6, 2],
|
|
],
|
|
[[2, 0, 3, 1, 7, 5], [2, 0, 7, 5, 6, 4]],
|
|
[[4, 6, 0, 4, 0, 1], [0, 1, 1, 3, 4, 6], [4, 6, 1, 3, 5, 7]],
|
|
[[0, 2, 1, 0, 1, 5], [1, 5, 5, 7, 0, 2], [0, 2, 5, 7, 4, 6]],
|
|
[[5, 7, 4, 6, 4, 0], [5, 1, 5, 7, 4, 0]],
|
|
[[5, 4, 4, 0, 5, 7], [5, 7, 4, 0, 7, 3], [7, 3, 4, 0, 6, 2]],
|
|
[[0, 1, 0, 2, 4, 5], [0, 2, 3, 7, 4, 5], [4, 5, 3, 7, 5, 7], [0, 2, 2, 6, 3, 7]],
|
|
[[5, 4, 4, 0, 5, 7], [5, 7, 4, 0, 7, 3], [7, 3, 4, 0, 6, 2], [1, 0, 5, 1, 1, 3]],
|
|
[
|
|
[1, 5, 3, 1, 2, 0],
|
|
[2, 0, 4, 5, 1, 5],
|
|
[2, 6, 4, 5, 2, 0],
|
|
[2, 6, 7, 5, 4, 5],
|
|
[2, 6, 3, 7, 7, 5],
|
|
],
|
|
[[2, 3, 0, 4, 2, 0], [2, 3, 4, 5, 0, 4], [2, 3, 3, 7, 4, 5], [3, 7, 7, 5, 4, 5]],
|
|
[[3, 2, 7, 3, 7, 5], [7, 5, 5, 4, 3, 2], [3, 2, 5, 4, 1, 0]],
|
|
[
|
|
[2, 3, 0, 4, 2, 0],
|
|
[2, 3, 4, 5, 0, 4],
|
|
[2, 3, 3, 7, 4, 5],
|
|
[3, 7, 7, 5, 4, 5],
|
|
[1, 5, 3, 1, 0, 1],
|
|
],
|
|
[[3, 2, 1, 5, 3, 1], [3, 2, 5, 4, 1, 5], [3, 2, 7, 5, 5, 4], [3, 7, 7, 5, 3, 2]],
|
|
[[2, 6, 2, 3, 0, 4], [2, 3, 7, 5, 0, 4], [2, 3, 3, 1, 7, 5], [0, 4, 7, 5, 4, 5]],
|
|
[
|
|
[3, 2, 1, 3, 5, 7],
|
|
[5, 7, 6, 2, 3, 2],
|
|
[5, 4, 6, 2, 5, 7],
|
|
[5, 4, 0, 2, 6, 2],
|
|
[5, 4, 1, 0, 0, 2],
|
|
],
|
|
[
|
|
[4, 5, 0, 4, 2, 6],
|
|
[2, 6, 7, 5, 4, 5],
|
|
[2, 3, 7, 5, 2, 6],
|
|
[2, 3, 1, 5, 7, 5],
|
|
[2, 3, 0, 1, 1, 5],
|
|
],
|
|
[[2, 3, 2, 0, 2, 6], [1, 5, 7, 5, 4, 5]],
|
|
[[5, 7, 4, 5, 4, 0], [4, 0, 0, 2, 5, 7], [5, 7, 0, 2, 1, 3]],
|
|
[[5, 4, 1, 0, 1, 3], [5, 7, 5, 4, 1, 3]],
|
|
[[0, 2, 4, 5, 0, 4], [0, 2, 5, 7, 4, 5], [0, 2, 1, 5, 5, 7], [0, 1, 1, 5, 0, 2]],
|
|
[[5, 4, 5, 1, 5, 7]],
|
|
[[4, 6, 6, 2, 4, 5], [4, 5, 6, 2, 5, 1], [5, 1, 6, 2, 7, 3]],
|
|
[[4, 6, 6, 2, 4, 5], [4, 5, 6, 2, 5, 1], [5, 1, 6, 2, 7, 3], [0, 2, 4, 0, 0, 1]],
|
|
[[3, 7, 3, 1, 2, 6], [3, 1, 5, 4, 2, 6], [3, 1, 1, 0, 5, 4], [2, 6, 5, 4, 6, 4]],
|
|
[
|
|
[6, 4, 2, 6, 3, 7],
|
|
[3, 7, 5, 4, 6, 4],
|
|
[3, 1, 5, 4, 3, 7],
|
|
[3, 1, 0, 4, 5, 4],
|
|
[3, 1, 2, 0, 0, 4],
|
|
],
|
|
[[2, 0, 2, 3, 6, 4], [2, 3, 1, 5, 6, 4], [6, 4, 1, 5, 4, 5], [2, 3, 3, 7, 1, 5]],
|
|
[
|
|
[0, 4, 1, 0, 3, 2],
|
|
[3, 2, 6, 4, 0, 4],
|
|
[3, 7, 6, 4, 3, 2],
|
|
[3, 7, 5, 4, 6, 4],
|
|
[3, 7, 1, 5, 5, 4],
|
|
],
|
|
[
|
|
[1, 3, 0, 1, 4, 5],
|
|
[4, 5, 7, 3, 1, 3],
|
|
[4, 6, 7, 3, 4, 5],
|
|
[4, 6, 2, 3, 7, 3],
|
|
[4, 6, 0, 2, 2, 3],
|
|
],
|
|
[[3, 7, 3, 1, 3, 2], [5, 4, 6, 4, 0, 4]],
|
|
[[3, 1, 2, 6, 3, 2], [3, 1, 6, 4, 2, 6], [3, 1, 1, 5, 6, 4], [1, 5, 5, 4, 6, 4]],
|
|
[
|
|
[3, 1, 2, 6, 3, 2],
|
|
[3, 1, 6, 4, 2, 6],
|
|
[3, 1, 1, 5, 6, 4],
|
|
[1, 5, 5, 4, 6, 4],
|
|
[0, 4, 1, 0, 2, 0],
|
|
],
|
|
[[4, 5, 6, 4, 6, 2], [6, 2, 2, 3, 4, 5], [4, 5, 2, 3, 0, 1]],
|
|
[[2, 3, 6, 4, 2, 6], [2, 3, 4, 5, 6, 4], [2, 3, 0, 4, 4, 5], [2, 0, 0, 4, 2, 3]],
|
|
[[1, 3, 5, 1, 5, 4], [5, 4, 4, 6, 1, 3], [1, 3, 4, 6, 0, 2]],
|
|
[[1, 3, 0, 4, 1, 0], [1, 3, 4, 6, 0, 4], [1, 3, 5, 4, 4, 6], [1, 5, 5, 4, 1, 3]],
|
|
[[4, 6, 0, 2, 0, 1], [4, 5, 4, 6, 0, 1]],
|
|
[[4, 6, 4, 0, 4, 5]],
|
|
[[4, 0, 6, 2, 7, 3], [4, 0, 7, 3, 5, 1]],
|
|
[[1, 5, 0, 1, 0, 2], [0, 2, 2, 6, 1, 5], [1, 5, 2, 6, 3, 7]],
|
|
[[3, 7, 1, 3, 1, 0], [1, 0, 0, 4, 3, 7], [3, 7, 0, 4, 2, 6]],
|
|
[[3, 1, 2, 0, 2, 6], [3, 7, 3, 1, 2, 6]],
|
|
[[0, 4, 2, 0, 2, 3], [2, 3, 3, 7, 0, 4], [0, 4, 3, 7, 1, 5]],
|
|
[[3, 7, 1, 5, 1, 0], [3, 2, 3, 7, 1, 0]],
|
|
[[0, 4, 1, 3, 0, 1], [0, 4, 3, 7, 1, 3], [0, 4, 2, 3, 3, 7], [0, 2, 2, 3, 0, 4]],
|
|
[[3, 7, 3, 1, 3, 2]],
|
|
[[2, 6, 3, 2, 3, 1], [3, 1, 1, 5, 2, 6], [2, 6, 1, 5, 0, 4]],
|
|
[[1, 5, 3, 2, 1, 3], [1, 5, 2, 6, 3, 2], [1, 5, 0, 2, 2, 6], [1, 0, 0, 2, 1, 5]],
|
|
[[2, 3, 0, 1, 0, 4], [2, 6, 2, 3, 0, 4]],
|
|
[[2, 3, 2, 0, 2, 6]],
|
|
[[1, 5, 0, 4, 0, 2], [1, 3, 1, 5, 0, 2]],
|
|
[[1, 5, 1, 0, 1, 3]],
|
|
[[0, 2, 0, 1, 0, 4]],
|
|
[],
|
|
]
|
|
|
|
|
|
def create_mc_lookup_table():
|
|
cases = torch.zeros(256, 5, 3, dtype=torch.long)
|
|
masks = torch.zeros(256, 5, dtype=torch.bool)
|
|
|
|
edge_to_index = {
|
|
(0, 1): 0,
|
|
(2, 3): 1,
|
|
(4, 5): 2,
|
|
(6, 7): 3,
|
|
(0, 2): 4,
|
|
(1, 3): 5,
|
|
(4, 6): 6,
|
|
(5, 7): 7,
|
|
(0, 4): 8,
|
|
(1, 5): 9,
|
|
(2, 6): 10,
|
|
(3, 7): 11,
|
|
}
|
|
|
|
for i, case in enumerate(MC_TABLE):
|
|
for j, tri in enumerate(case):
|
|
for k, (c1, c2) in enumerate(zip(tri[::2], tri[1::2])):
|
|
cases[i, j, k] = edge_to_index[(c1, c2) if c1 < c2 else (c2, c1)]
|
|
masks[i, j] = True
|
|
return cases, masks
|
|
|
|
|
|
RENDERER_CONFIG = {}
|
|
|
|
|
|
def renderer_model_from_original_config():
|
|
model = ShapERenderer(**RENDERER_CONFIG)
|
|
|
|
return model
|
|
|
|
|
|
RENDERER_MLP_ORIGINAL_PREFIX = "renderer.nerstf"
|
|
|
|
RENDERER_PARAMS_PROJ_ORIGINAL_PREFIX = "encoder.params_proj"
|
|
|
|
|
|
def renderer_model_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
|
|
diffusers_checkpoint = {}
|
|
diffusers_checkpoint.update(
|
|
{f"mlp.{k}": checkpoint[f"{RENDERER_MLP_ORIGINAL_PREFIX}.{k}"] for k in model.mlp.state_dict().keys()}
|
|
)
|
|
|
|
diffusers_checkpoint.update(
|
|
{
|
|
f"params_proj.{k}": checkpoint[f"{RENDERER_PARAMS_PROJ_ORIGINAL_PREFIX}.{k}"]
|
|
for k in model.params_proj.state_dict().keys()
|
|
}
|
|
)
|
|
|
|
diffusers_checkpoint.update({"void.background": model.state_dict()["void.background"]})
|
|
|
|
cases, masks = create_mc_lookup_table()
|
|
|
|
diffusers_checkpoint.update({"mesh_decoder.cases": cases})
|
|
diffusers_checkpoint.update({"mesh_decoder.masks": masks})
|
|
|
|
return diffusers_checkpoint
|
|
|
|
|
|
# done renderer
|
|
|
|
|
|
# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?)
|
|
def split_attentions(*, weight, bias, split, chunk_size):
|
|
weights = [None] * split
|
|
biases = [None] * split
|
|
|
|
weights_biases_idx = 0
|
|
|
|
for starting_row_index in range(0, weight.shape[0], chunk_size):
|
|
row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size)
|
|
|
|
weight_rows = weight[row_indices, :]
|
|
bias_rows = bias[row_indices]
|
|
|
|
if weights[weights_biases_idx] is None:
|
|
assert weights[weights_biases_idx] is None
|
|
weights[weights_biases_idx] = weight_rows
|
|
biases[weights_biases_idx] = bias_rows
|
|
else:
|
|
assert weights[weights_biases_idx] is not None
|
|
weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows])
|
|
biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows])
|
|
|
|
weights_biases_idx = (weights_biases_idx + 1) % split
|
|
|
|
return weights, biases
|
|
|
|
|
|
# done unet utils
|
|
|
|
|
|
# Driver functions
|
|
|
|
|
|
def prior(*, args, checkpoint_map_location):
|
|
print("loading prior")
|
|
|
|
prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location)
|
|
|
|
prior_model = prior_model_from_original_config()
|
|
|
|
prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint(prior_model, prior_checkpoint)
|
|
|
|
del prior_checkpoint
|
|
|
|
load_prior_checkpoint_to_model(prior_diffusers_checkpoint, prior_model)
|
|
|
|
print("done loading prior")
|
|
|
|
return prior_model
|
|
|
|
|
|
def prior_image(*, args, checkpoint_map_location):
|
|
print("loading prior_image")
|
|
|
|
print(f"load checkpoint from {args.prior_image_checkpoint_path}")
|
|
prior_checkpoint = torch.load(args.prior_image_checkpoint_path, map_location=checkpoint_map_location)
|
|
|
|
prior_model = prior_image_model_from_original_config()
|
|
|
|
prior_diffusers_checkpoint = prior_image_original_checkpoint_to_diffusers_checkpoint(prior_model, prior_checkpoint)
|
|
|
|
del prior_checkpoint
|
|
|
|
load_prior_checkpoint_to_model(prior_diffusers_checkpoint, prior_model)
|
|
|
|
print("done loading prior_image")
|
|
|
|
return prior_model
|
|
|
|
|
|
def renderer(*, args, checkpoint_map_location):
|
|
print(" loading renderer")
|
|
|
|
renderer_checkpoint = torch.load(args.transmitter_checkpoint_path, map_location=checkpoint_map_location)
|
|
|
|
renderer_model = renderer_model_from_original_config()
|
|
|
|
renderer_diffusers_checkpoint = renderer_model_original_checkpoint_to_diffusers_checkpoint(
|
|
renderer_model, renderer_checkpoint
|
|
)
|
|
|
|
del renderer_checkpoint
|
|
|
|
load_checkpoint_to_model(renderer_diffusers_checkpoint, renderer_model, strict=True)
|
|
|
|
print("done loading renderer")
|
|
|
|
return renderer_model
|
|
|
|
|
|
# prior model will expect clip_mean and clip_std, which are missing from the state_dict
|
|
PRIOR_EXPECTED_MISSING_KEYS = ["clip_mean", "clip_std"]
|
|
|
|
|
|
def load_prior_checkpoint_to_model(checkpoint, model):
|
|
with tempfile.NamedTemporaryFile() as file:
|
|
torch.save(checkpoint, file.name)
|
|
del checkpoint
|
|
missing_keys, unexpected_keys = model.load_state_dict(torch.load(file.name), strict=False)
|
|
missing_keys = list(set(missing_keys) - set(PRIOR_EXPECTED_MISSING_KEYS))
|
|
|
|
if len(unexpected_keys) > 0:
|
|
raise ValueError(f"Unexpected keys when loading prior model: {unexpected_keys}")
|
|
if len(missing_keys) > 0:
|
|
raise ValueError(f"Missing keys when loading prior model: {missing_keys}")
|
|
|
|
|
|
def load_checkpoint_to_model(checkpoint, model, strict=False):
|
|
with tempfile.NamedTemporaryFile() as file:
|
|
torch.save(checkpoint, file.name)
|
|
del checkpoint
|
|
if strict:
|
|
model.load_state_dict(torch.load(file.name), strict=True)
|
|
else:
|
|
load_checkpoint_and_dispatch(model, file.name, device_map="auto")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
|
|
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
|
|
|
|
parser.add_argument(
|
|
"--prior_checkpoint_path",
|
|
default=None,
|
|
type=str,
|
|
required=False,
|
|
help="Path to the prior checkpoint to convert.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--prior_image_checkpoint_path",
|
|
default=None,
|
|
type=str,
|
|
required=False,
|
|
help="Path to the prior_image checkpoint to convert.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--transmitter_checkpoint_path",
|
|
default=None,
|
|
type=str,
|
|
required=False,
|
|
help="Path to the transmitter checkpoint to convert.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--checkpoint_load_device",
|
|
default="cpu",
|
|
type=str,
|
|
required=False,
|
|
help="The device passed to `map_location` when loading checkpoints.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--debug",
|
|
default=None,
|
|
type=str,
|
|
required=False,
|
|
help="Only run a specific stage of the convert script. Used for debugging",
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
print(f"loading checkpoints to {args.checkpoint_load_device}")
|
|
|
|
checkpoint_map_location = torch.device(args.checkpoint_load_device)
|
|
|
|
if args.debug is not None:
|
|
print(f"debug: only executing {args.debug}")
|
|
|
|
if args.debug is None:
|
|
print("YiYi TO-DO")
|
|
elif args.debug == "prior":
|
|
prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location)
|
|
prior_model.save_pretrained(args.dump_path)
|
|
elif args.debug == "prior_image":
|
|
prior_model = prior_image(args=args, checkpoint_map_location=checkpoint_map_location)
|
|
prior_model.save_pretrained(args.dump_path)
|
|
elif args.debug == "renderer":
|
|
renderer_model = renderer(args=args, checkpoint_map_location=checkpoint_map_location)
|
|
renderer_model.save_pretrained(args.dump_path)
|
|
else:
|
|
raise ValueError(f"unknown debug value : {args.debug}")
|