Upload testing/shrink_pixart2.py with huggingface_hub

testing/shrink_pixart2.py

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+import torch
+from safetensors.torch import load_file, save_file
+from collections import OrderedDict
+
+model_path = "/home/jaret/Dev/models/hf/PixArt-Sigma-XL-2-1024_tiny/transformer/diffusion_pytorch_model_orig.safetensors"
+output_path = "/home/jaret/Dev/models/hf/PixArt-Sigma-XL-2-1024_tiny/transformer/diffusion_pytorch_model.safetensors"
+
+state_dict = load_file(model_path)
+
+meta = OrderedDict()
+meta["format"] = "pt"
+
+new_state_dict = {}
+
+# Move non-blocks over
+for key, value in state_dict.items():
+    if not key.startswith("transformer_blocks."):
+        new_state_dict[key] = value
+
+block_names = ['transformer_blocks.{idx}.attn1.to_k.bias', 'transformer_blocks.{idx}.attn1.to_k.weight',
+               'transformer_blocks.{idx}.attn1.to_out.0.bias', 'transformer_blocks.{idx}.attn1.to_out.0.weight',
+               'transformer_blocks.{idx}.attn1.to_q.bias', 'transformer_blocks.{idx}.attn1.to_q.weight',
+               'transformer_blocks.{idx}.attn1.to_v.bias', 'transformer_blocks.{idx}.attn1.to_v.weight',
+               'transformer_blocks.{idx}.attn2.to_k.bias', 'transformer_blocks.{idx}.attn2.to_k.weight',
+               'transformer_blocks.{idx}.attn2.to_out.0.bias', 'transformer_blocks.{idx}.attn2.to_out.0.weight',
+               'transformer_blocks.{idx}.attn2.to_q.bias', 'transformer_blocks.{idx}.attn2.to_q.weight',
+               'transformer_blocks.{idx}.attn2.to_v.bias', 'transformer_blocks.{idx}.attn2.to_v.weight',
+               'transformer_blocks.{idx}.ff.net.0.proj.bias', 'transformer_blocks.{idx}.ff.net.0.proj.weight',
+               'transformer_blocks.{idx}.ff.net.2.bias', 'transformer_blocks.{idx}.ff.net.2.weight',
+               'transformer_blocks.{idx}.scale_shift_table']
+
+# Blocks to keep
+# keep_blocks = [0, 1, 2, 6, 10, 14, 18, 22, 26, 27]
+keep_blocks = [0, 1, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 27]
+
+
+def weighted_merge(kept_block, removed_block, weight):
+    return kept_block * (1 - weight) + removed_block * weight
+
+
+# First, copy all kept blocks to new_state_dict
+for i, old_idx in enumerate(keep_blocks):
+    for name in block_names:
+        old_key = name.format(idx=old_idx)
+        new_key = name.format(idx=i)
+        new_state_dict[new_key] = state_dict[old_key].clone()
+
+# Then, merge information from removed blocks
+for i in range(28):
+    if i not in keep_blocks:
+        # Find the nearest kept blocks
+        prev_kept = max([b for b in keep_blocks if b < i])
+        next_kept = min([b for b in keep_blocks if b > i])
+
+        # Calculate the weight based on position
+        weight = (i - prev_kept) / (next_kept - prev_kept)
+
+        for name in block_names:
+            removed_key = name.format(idx=i)
+            prev_new_key = name.format(idx=keep_blocks.index(prev_kept))
+            next_new_key = name.format(idx=keep_blocks.index(next_kept))
+
+            # Weighted merge for previous kept block
+            new_state_dict[prev_new_key] = weighted_merge(new_state_dict[prev_new_key], state_dict[removed_key], weight)
+
+            # Weighted merge for next kept block
+            new_state_dict[next_new_key] = weighted_merge(new_state_dict[next_new_key], state_dict[removed_key],
+                                                          1 - weight)
+
+# Convert to fp16 and move to CPU
+for key, value in new_state_dict.items():
+    new_state_dict[key] = value.to(torch.float16).cpu()
+
+# Save the new state dict
+save_file(new_state_dict, output_path, metadata=meta)
+
+new_param_count = sum([v.numel() for v in new_state_dict.values()])
+old_param_count = sum([v.numel() for v in state_dict.values()])
+
+print(f"Old param count: {old_param_count:,}")
+print(f"New param count: {new_param_count:,}")