Update sd3 controlnet example

examples/controlnet/README_sd3.md

@@ -104,7 +104,7 @@ from diffusers.utils import load_image
 import torch
 
 base_model_path = "stabilityai/stable-diffusion-3-medium-diffusers"
-controlnet_path = "sd3-controlnet-out/checkpoint-6500/controlnet"
+controlnet_path = "DavyMorgan/sd3-controlnet-out"
 
 controlnet = SD3ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
 pipe = StableDiffusion3ControlNetPipeline.from_pretrained(

examples/controlnet/train_controlnet_sd3.py

@@ -50,7 +50,7 @@
 )
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import compute_density_for_timestep_sampling, compute_loss_weighting_for_sd3, free_memory
-from diffusers.utils import check_min_version, is_wandb_available
+from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.torch_utils import is_compiled_module
 
@@ -64,17 +64,6 @@
 logger = get_logger(__name__)
 
 
-def image_grid(imgs, rows, cols):
-    assert len(imgs) == rows * cols
-
-    w, h = imgs[0].size
-    grid = Image.new("RGB", size=(cols * w, rows * h))
-
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i % cols * w, i // cols * h))
-    return grid
-
-
 def log_validation(controlnet, args, accelerator, weight_dtype, step, is_final_validation=False):
     logger.info("Running validation... ")
 
@@ -224,7 +213,7 @@ def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=N
             validation_image.save(os.path.join(repo_folder, "image_control.png"))
             img_str += f"prompt: {validation_prompt}\n"
             images = [validation_image] + images
-            image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
+            make_image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
             img_str += f"![images_{i})](./images_{i}.png)\n"
 
     model_description = f"""