support model cards (#176)

* introduce model cards and support passing FPS.

* updates

* fix

* fix

* updates

* revert changes

* revert args.py changes

* fix

* fix

* fix filename

finetrainers/trainer.py

@@ -50,6 +50,7 @@ from .utils.diffusion_utils import (
     prepare_target,
 )
 from .utils.file_utils import string_to_filename
+from .utils.hub_utils import save_model_card
 from .utils.memory_utils import free_memory, get_memory_statistics, make_contiguous
 from .utils.model_utils import resolve_vae_cls_from_ckpt_path
 from .utils.optimizer_utils import get_optimizer
@@ -923,6 +924,7 @@ class Trainer:
             pipeline.load_lora_weights(self.args.output_dir)
 
         all_processes_artifacts = []
+        prompts_to_filenames = {}
         for i in range(num_validation_samples):
             # Skip current validation on all processes but one
             if i % accelerator.num_processes != accelerator.process_index:
@@ -976,7 +978,10 @@ class Trainer:
                     continue
 
                 extension = "png" if artifact_type == "image" else "mp4"
-                filename = f"validation-{step}-{accelerator.process_index}-{prompt_filename}.{extension}"
+                filename = "validation-" if not final_validation else "final-"
+                filename += f"{step}-{accelerator.process_index}-{prompt_filename}.{extension}"
+                if accelerator.is_main_process and extension == "mp4":
+                    prompts_to_filenames[prompt] = filename
                 filename = os.path.join(self.args.output_dir, filename)
 
                 if artifact_type == "image":
@@ -1005,6 +1010,15 @@ class Trainer:
                         },
                         step=step,
                     )
+            if self.args.push_to_hub and final_validation:
+                video_filenames = list(prompts_to_filenames.values())
+                prompts = list(prompts_to_filenames.keys())
+                save_model_card(
+                    args=self.args,
+                    repo_id=self.state.repo_id,
+                    videos=video_filenames,
+                    validation_prompts=prompts,
+                )
 
         # Remove all hooks that might have been added during pipeline initialization to the models
         pipeline.remove_all_hooks()

finetrainers/utils/hub_utils.py

@@ -0,0 +1,77 @@
+import os
+from typing import List, Union
+
+import numpy as np
+import wandb
+from diffusers.utils import export_to_video
+from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
+from PIL import Image
+
+
+def save_model_card(
+    args,
+    repo_id: str,
+    videos: Union[List[str], Union[List[Image.Image], List[np.ndarray]]],
+    validation_prompts: List[str],
+    fps: int = 30,
+) -> None:
+    widget_dict = []
+    output_dir = str(args.output_dir)
+    if videos is not None and len(videos) > 0:
+        for i, (video, validation_prompt) in enumerate(zip(videos, validation_prompts)):
+            if not isinstance(video, str):
+                export_to_video(video, os.path.join(output_dir, f"final_video_{i}.mp4"), fps=fps)
+            widget_dict.append(
+                {
+                    "text": validation_prompt if validation_prompt else " ",
+                    "output": {"url": video if isinstance(video, str) else f"final_video_{i}.mp4"},
+                }
+            )
+
+    model_description = f"""
+# LoRA Finetune
+
+<Gallery />
+
+## Model description
+
+This is a lora finetune of model: `{args.pretrained_model_name_or_path}`.
+
+The model was trained using [`finetrainers`](https://github.com/a-r-r-o-w/finetrainers).
+
+## Download model
+
+[Download LoRA]({repo_id}/tree/main) in the Files & Versions tab.
+
+## Usage
+
+Requires the [🧨 Diffusers library](https://github.com/huggingface/diffusers) installed.
+
+```py
+TODO
+```
+
+For more details, including weighting, merging and fusing LoRAs, check the [documentation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) on loading LoRAs in diffusers.
+"""
+    if wandb.run.url:
+        model_description += f"""
+Find out the wandb run URL and training configurations [here]({wandb.run.url}).
+"""
+
+    model_card = load_or_create_model_card(
+        repo_id_or_path=repo_id,
+        from_training=True,
+        base_model=args.pretrained_model_name_or_path,
+        model_description=model_description,
+        widget=widget_dict,
+    )
+    tags = [
+        "text-to-video",
+        "diffusers-training",
+        "diffusers",
+        "lora",
+        "template:sd-lora",
+    ]
+
+    model_card = populate_model_card(model_card, tags=tags)
+    model_card.save(os.path.join(args.output_dir, "README.md"))