Add push_to_hub

flux/dreambooth.py

@@ -5,6 +5,7 @@
 import time
 from functools import partial
 from pathlib import Path
+import os
 
 import mlx.core as mx
 import mlx.nn as nn
@@ -15,6 +16,9 @@
 from PIL import Image
 from tqdm import tqdm
 
+from huggingface_hub import HfApi, interpreter_login
+from huggingface_hub.utils import HfFolder
+
 from flux import FluxPipeline
 
 
@@ -156,6 +160,108 @@ def save_adapters(iteration, flux, args):
         },
     )
 
+def push_to_hub(args):
+    if args.hf_token is None:
+        interpreter_login(new_session=False, write_permission=True)
+    else:
+        HfFolder.save_token(args.hf_token)
+
+    repo_id = args.hf_repo_id or f"{HfFolder.get_token_username()}/{args.output_dir}"
+
+    readme_content = generate_readme(args, repo_id)
+    readme_path = os.path.join(args.output_dir, "README.md")
+    with open(readme_path, "w", encoding="utf-8") as f:
+        f.write(readme_content)
+
+    api = HfApi()
+
+    api.create_repo(
+        repo_id,
+        private=args.hf_private,
+        exist_ok=True
+    )
+
+    api.upload_folder(
+        repo_id=repo_id,
+        folder_path=args.output_dir,
+        ignore_patterns=["*.yaml", "*.pt"],
+        repo_type="model",
+    )
+
+def generate_readme(args, repo_id):
+    import yaml
+    import re
+    base_model = f"flux-{args.model}"
+    tags = [
+        "text-to-image",
+        "flux",
+        "lora",
+        "diffusers",
+        "template:sd-lora",
+        "mlx",
+        "mlx-trainer"
+    ]
+
+    widgets = []
+    sample_image_paths = []
+    # Look for progress images directly in the output directory
+    for filename in os.listdir(args.output_dir):
+        match = re.search(r"(\d+)_progress\.png$", filename)
+        if match:
+            iteration = int(match.group(1))
+            sample_image_paths.append((iteration, filename))
+
+    sample_image_paths.sort(key=lambda x: x[0], reverse=True)
+
+    if sample_image_paths:
+        widgets.append(
+            {
+                "text": args.progress_prompt,
+                "output": {
+                    "url": sample_image_paths[0][1]
+                },
+            }
+        )
+
+    readme_content = f"""---
+tags:
+{yaml.dump(tags, indent=4).strip()}
+{"widget:" if sample_image_paths else ""}
+{yaml.dump(widgets, indent=4).strip() if widgets else ""}
+base_model: {base_model}
+license: other
+---
+
+# {os.path.basename(args.output_dir)}
+Model trained with the MLX Flux Dreambooth script
+
+<Gallery />
+
+## Use it with [MLX](https://github.com/ml-explore/mlx-examples)
+```py
+from flux import FluxPipeline
+import mlx.core as mx
+flux = FluxPipeline("flux-{args.model}")
+flux.linear_to_lora_layers({args.lora_rank}, {args.lora_blocks})
+flux.flow.load_weights("{repo_id}")
+image = flux.generate_images("{args.progress_prompt}", n_images=1, num_steps={args.progress_steps})
+image.save("my_image.png")
+```
+
+## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
+```py
+from diffusers import AutoPipelineForText2Image
+import torch
+
+pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/{args.model}', torch_dtype=torch.bfloat16).to('cuda')
+pipeline.load_lora_weights('{repo_id}')
+image = pipeline({args.progress_prompt}').images[0]
+image.save("my_image.png")
+```
+
+For more details on using Flux, check the [Flux documentation](https://github.com/black-forest-labs/flux).
+"""
+    return readme_content
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
@@ -245,7 +351,28 @@ def save_adapters(iteration, flux, args):
     parser.add_argument(
         "--output-dir", default="mlx_output", help="Folder to save the checkpoints in"
     )
-
+    parser.add_argument(
+        "--push_to_hub",
+        action="store_true",
+        help="Push the model to Hugging Face Hub after training",
+    )
+    parser.add_argument(
+        "--hf_token",
+        type=str,
+        default=None,
+        help="Hugging Face token for pushing to Hub",
+    )
+    parser.add_argument(
+        "--hf_repo_id",
+        type=str,
+        default=None,
+        help="Hugging Face repository ID for pushing to Hub",
+    )
+    parser.add_argument(
+        "--hf_private",
+        action="store_true",
+        help="Make the Hugging Face repository private",
+    )
     parser.add_argument("dataset")
 
     args = parser.parse_args()
@@ -376,3 +503,6 @@ def step(x, t5_feat, clip_feat, guidance, prev_grads, perform_step):
         if (i + 1) % 10 == 0:
             losses = []
             tic = time.time()
+
+    if args.push_to_hub:
+        push_to_hub(args)