Fixing links

README.md

@@ -28,7 +28,7 @@ We introduce M<sup>3</sup>Face, a unified multi-modal multilingual framework for
 - For face editing, VRAM of 14 GB+ for 512x512 images is required.
 
 ### Pre-trained Models
-You can find the checkpoints for the ControlNet model at [`m3face/ControlnetModels`](https://huggingface.co/m3face/ControlnetModels) and the mask/landmark generator model at [`m3face/FaceConditioning`](https://huggingface.co/m3face/FaceConditioning).
+You can find the checkpoints for the ControlNet model at [`m3face/FaceControlNet`](https://huggingface.co/m3face/FaceControlNet) and the mask/landmark generator model at [`m3face/FaceConditioning`](https://huggingface.co/m3face/FaceConditioning).
 
 ### M<sup>3</sup>CelebA Dataset
 The M<sup>3</sup>CelebA Dataset is available at [`m3face/M3CelebA`](https://huggingface.co/m3face/M3CelebA). You can view or download it from there.

edit.py

@@ -153,14 +153,14 @@ def preprocess(image, condition, prompt, tokenizer):
 def main(args):
     if args.use_english:
         sd_model_name = 'runwayml/stable-diffusion-v1-5'
-        controlnet_model_name = 'm3face/ControlnetModels'
+        controlnet_model_name = 'm3face/FaceControlNet'
         if args.condition == 'mask':
             controlnet_revision = 'segmentation-english'
         elif args.condition == 'landmark':
             controlnet_revision = 'landmark-english'
     else:
         sd_model_name = 'BAAI/AltDiffusion-m18'
-        controlnet_model_name = 'm3face/ControlnetModels'
+        controlnet_model_name = 'm3face/FaceControlNet'
         if args.condition == 'mask':
             controlnet_revision = 'segmentation-mlin'
         elif args.condition == 'landmark':

generate.py

@@ -65,7 +65,7 @@ def parse_args():
 def get_controlnet(args):
     if args.use_english:
         sd_model_name = 'runwayml/stable-diffusion-v1-5'
-        controlnet_model_name = 'm3face/ControlnetModels'
+        controlnet_model_name = 'm3face/FaceControlNet'
         if args.condition == 'mask':
             controlnet_revision = 'segmentation-english'
         elif args.condition == 'landmark':
@@ -79,7 +79,7 @@ def get_controlnet(args):
         pipeline.enable_model_cpu_offload()
     else:
         sd_model_name = 'BAAI/AltDiffusion-m18'
-        controlnet_model_name = 'm3face/ControlnetModels'
+        controlnet_model_name = 'm3face/FaceControlNet'
         if args.condition == 'mask':
             controlnet_revision = 'segmentation-mlin'
         elif args.condition == 'landmark':