[Examples/DreamBooth] refactor save_model_card utility in dreambooth examples (huggingface#3543)

refactor save_model_card utility in dreambooth examples.

Author: sayakpaul

examples/dreambooth/train_dreambooth.py

@@ -46,6 +46,7 @@
     DDPMScheduler,
     DiffusionPipeline,
     DPMSolverMultistepScheduler,
+    StableDiffusionPipeline,
     UNet2DConditionModel,
 )
 from diffusers.optimization import get_scheduler
@@ -62,7 +63,15 @@
 logger = get_logger(__name__)
 
 
-def save_model_card(repo_id: str, images=None, base_model=str, train_text_encoder=False, prompt=str, repo_folder=None):
+def save_model_card(
+    repo_id: str,
+    images=None,
+    base_model=str,
+    train_text_encoder=False,
+    prompt=str,
+    repo_folder=None,
+    pipeline: DiffusionPipeline = None,
+):
     img_str = ""
     for i, image in enumerate(images):
         image.save(os.path.join(repo_folder, f"image_{i}.png"))
@@ -74,8 +83,8 @@ def save_model_card(repo_id: str, images=None, base_model=str, train_text_encode
 base_model: {base_model}
 instance_prompt: {prompt}
 tags:
-- stable-diffusion
-- stable-diffusion-diffusers
+- {'stable-diffusion' if isinstance(pipeline, StableDiffusionPipeline) else 'if'}
+- {'stable-diffusion-diffusers' if isinstance(pipeline, StableDiffusionPipeline) else 'if-diffusers'}
 - text-to-image
 - diffusers
 - dreambooth
@@ -1297,6 +1306,7 @@ def compute_text_embeddings(prompt):
                 train_text_encoder=args.train_text_encoder,
                 prompt=args.instance_prompt,
                 repo_folder=args.output_dir,
+                pipeline=pipeline,
             )
             upload_folder(
                 repo_id=repo_id,

examples/dreambooth/train_dreambooth_lora.py

@@ -68,7 +68,15 @@
 logger = get_logger(__name__)
 
 
-def save_model_card(repo_id: str, images=None, base_model=str, train_text_encoder=False, prompt=str, repo_folder=None):
+def save_model_card(
+    repo_id: str,
+    images=None,
+    base_model=str,
+    train_text_encoder=False,
+    prompt=str,
+    repo_folder=None,
+    pipeline: DiffusionPipeline = None,
+):
     img_str = ""
     for i, image in enumerate(images):
         image.save(os.path.join(repo_folder, f"image_{i}.png"))
@@ -80,8 +88,8 @@ def save_model_card(repo_id: str, images=None, base_model=str, train_text_encode
 base_model: {base_model}
 instance_prompt: {prompt}
 tags:
-- stable-diffusion
-- stable-diffusion-diffusers
+- {'stable-diffusion' if isinstance(pipeline, StableDiffusionPipeline) else 'if'}
+- {'stable-diffusion-diffusers' if isinstance(pipeline, StableDiffusionPipeline) else 'if-diffusers'}
 - text-to-image
 - diffusers
 - lora
@@ -844,7 +852,7 @@ def main(args):
                     hidden_size=module.out_features, cross_attention_dim=None
                 )
         text_encoder_lora_layers = AttnProcsLayers(text_lora_attn_procs)
-        temp_pipeline = StableDiffusionPipeline.from_pretrained(
+        temp_pipeline = DiffusionPipeline.from_pretrained(
             args.pretrained_model_name_or_path, text_encoder=text_encoder
         )
         temp_pipeline._modify_text_encoder(text_lora_attn_procs)
@@ -1332,6 +1340,7 @@ def compute_text_embeddings(prompt):
                 train_text_encoder=args.train_text_encoder,
                 prompt=args.instance_prompt,
                 repo_folder=args.output_dir,
+                pipeline=pipeline,
             )
             upload_folder(
                 repo_id=repo_id,