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https://github.com/ostris/ai-toolkit.git
synced 2026-03-13 22:49:48 +00:00
Bug fixes. allow for random negative prompts
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@@ -1,5 +1,9 @@
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import argparse
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from collections import OrderedDict
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import sys
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import os
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ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.append(ROOT_DIR)
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import torch
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42
scripts/patch_te_adapter.py
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42
scripts/patch_te_adapter.py
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import torch
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from safetensors.torch import save_file, load_file
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from collections import OrderedDict
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meta = OrderedDict()
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meta["format"] ="pt"
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attn_dict = load_file("/mnt/Train/out/ip_adapter/sd15_bigG/sd15_bigG_000266000.safetensors")
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state_dict = load_file("/home/jaret/Dev/models/hf/OstrisDiffusionV1/unet/diffusion_pytorch_model.safetensors")
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attn_list = []
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for key, value in state_dict.items():
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if "attn1" in key:
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attn_list.append(key)
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attn_names = ['down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor', 'down_blocks.0.attentions.1.transformer_blocks.0.attn2.processor', 'down_blocks.1.attentions.0.transformer_blocks.0.attn2.processor', 'down_blocks.1.attentions.1.transformer_blocks.0.attn2.processor', 'down_blocks.2.attentions.0.transformer_blocks.0.attn2.processor', 'down_blocks.2.attentions.1.transformer_blocks.0.attn2.processor', 'up_blocks.1.attentions.0.transformer_blocks.0.attn2.processor', 'up_blocks.1.attentions.1.transformer_blocks.0.attn2.processor', 'up_blocks.1.attentions.2.transformer_blocks.0.attn2.processor', 'up_blocks.2.attentions.0.transformer_blocks.0.attn2.processor', 'up_blocks.2.attentions.1.transformer_blocks.0.attn2.processor', 'up_blocks.2.attentions.2.transformer_blocks.0.attn2.processor', 'up_blocks.3.attentions.0.transformer_blocks.0.attn2.processor', 'up_blocks.3.attentions.1.transformer_blocks.0.attn2.processor', 'up_blocks.3.attentions.2.transformer_blocks.0.attn2.processor', 'mid_block.attentions.0.transformer_blocks.0.attn2.processor']
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adapter_names = []
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for i in range(100):
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if f'te_adapter.adapter_modules.{i}.to_k_adapter.weight' in attn_dict:
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adapter_names.append(f"te_adapter.adapter_modules.{i}.adapter")
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for i in range(len(adapter_names)):
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adapter_name = adapter_names[i]
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attn_name = attn_names[i]
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adapter_k_name = adapter_name[:-8] + '.to_k_adapter.weight'
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adapter_v_name = adapter_name[:-8] + '.to_v_adapter.weight'
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state_k_name = attn_name.replace(".processor", ".to_k.weight")
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state_v_name = attn_name.replace(".processor", ".to_v.weight")
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if adapter_k_name in attn_dict:
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state_dict[state_k_name] = attn_dict[adapter_k_name]
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state_dict[state_v_name] = attn_dict[adapter_v_name]
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else:
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print("adapter_k_name", adapter_k_name)
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print("state_k_name", state_k_name)
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for key, value in state_dict.items():
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state_dict[key] = value.cpu().to(torch.float16)
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save_file(state_dict, "/home/jaret/Dev/models/hf/OstrisDiffusionV1/unet/diffusion_pytorch_model.safetensors", metadata=meta)
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print("Done")
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65
scripts/repair_dataset_folder.py
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65
scripts/repair_dataset_folder.py
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@@ -0,0 +1,65 @@
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import argparse
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from PIL import Image
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from PIL.ImageOps import exif_transpose
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from tqdm import tqdm
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import os
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parser = argparse.ArgumentParser(description='Process some images.')
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parser.add_argument("input_folder", type=str, help="Path to folder containing images")
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args = parser.parse_args()
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img_types = ['.jpg', '.jpeg', '.png', '.webp']
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# find all images in the input folder
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images = []
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for root, _, files in os.walk(args.input_folder):
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for file in files:
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if file.lower().endswith(tuple(img_types)):
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images.append(os.path.join(root, file))
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print(f"Found {len(images)} images")
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num_skipped = 0
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num_repaired = 0
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num_deleted = 0
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pbar = tqdm(total=len(images), desc=f"Repaired {num_repaired} images", unit="image")
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for img_path in images:
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filename = os.path.basename(img_path)
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filename_no_ext, file_extension = os.path.splitext(filename)
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# if it is jpg, ignore
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if file_extension.lower() == '.jpg':
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num_skipped += 1
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pbar.update(1)
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continue
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try:
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img = Image.open(img_path)
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except Exception as e:
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print(f"Error opening {img_path}: {e}")
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# delete it
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os.remove(img_path)
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num_deleted += 1
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pbar.update(1)
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pbar.set_description(f"Repaired {num_repaired} images, Skipped {num_skipped}, Deleted {num_deleted}")
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continue
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try:
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img = exif_transpose(img)
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except Exception as e:
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print(f"Error rotating {img_path}: {e}")
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new_path = os.path.join(os.path.dirname(img_path), filename_no_ext + '.jpg')
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img = img.convert("RGB")
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img.save(new_path, quality=95)
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# remove the old file
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os.remove(img_path)
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num_repaired += 1
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pbar.update(1)
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# update pbar
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pbar.set_description(f"Repaired {num_repaired} images, Skipped {num_skipped}, Deleted {num_deleted}")
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print("Done")
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