demo

app.py

@@ -34,6 +34,7 @@ if os.environ.get("HF_HUB_ENABLE_HF_TRANSFER") == "1":
 import json
 from pathlib import Path
 from datetime import datetime
+import random
 
 import gradio as gr
 import numpy as np
@@ -353,7 +354,10 @@ class FaceLiftPipeline:
                 nearfar_percent=(0.0001, 1.0),
             )
             
-            ply_path = output_dir / "gaussians.ply"
+            # Generate random filename to support multiple concurrent users
+            random_id = random.randint(0, 99)
+            ply_filename = f"gaussians_{random_id:03d}.ply"
+            ply_path = output_dir / ply_filename
             filtered_gaussians.save_ply(str(ply_path))
             
             # Save output image
@@ -431,9 +435,9 @@ class FaceLiftPipeline:
                 """
             
             return (
+                viewer_html,                   # Viewer HTML (top)
                 str(output_path),              # Reconstruction grid
                 str(turntable_path),           # Turntable video
-                viewer_html,                   # Viewer HTML
                 str(ply_path),                 # Download file
             )
             
@@ -482,9 +486,9 @@ def main():
                     )
 
             with gr.Column(scale=1):
+                out_viewer = gr.HTML(label="🎮 Interactive 3D Viewer")
                 out_recon  = gr.Image(label="3D Reconstruction Views")
                 out_video  = gr.PlayableVideo(label="Turntable Animation (360° View)", height=600)
-                out_viewer = gr.HTML(label="🎮 Interactive 3D Viewer")
                 out_ply    = gr.File(label="Download 3D Model (.ply)")
 
         # Wrapper: call the pipeline and forward outputs in the exact order expected
@@ -495,7 +499,7 @@ def main():
         run_btn.click(
             fn=_generate_and_filter_outputs,
             inputs=[in_image, auto_crop, guidance, seed, steps],
-            outputs=[out_recon, out_video, out_viewer, out_ply],
+            outputs=[out_viewer, out_recon, out_video, out_ply],
         )
 
         demo.queue(max_size=10)

app_old.py

@@ -1,398 +0,0 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
-#
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
-#
-# For inquiries contact: [email protected]
-
-"""
-FaceLift: Single Image 3D Face Reconstruction
-Generates 3D head models from single images using multi-view diffusion and GS-LRM.
-"""
-
-# Disable HF fast transfer if hf_transfer is not installed
-# This MUST be done before importing huggingface_hub
-import os
-if os.environ.get("HF_HUB_ENABLE_HF_TRANSFER") == "1":
-    try:
-        import hf_transfer
-    except ImportError:
-        print("⚠️  hf_transfer not available, disabling fast download")
-        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "0"
-
-import json
-from pathlib import Path
-from datetime import datetime
-import uuid
-import time
-import shutil
-
-import gradio as gr
-import numpy as np
-import torch
-import yaml
-from easydict import EasyDict as edict
-from einops import rearrange
-from PIL import Image
-from huggingface_hub import snapshot_download
-import spaces
-
-# Install diff-gaussian-rasterization at runtime (requires GPU)
-import subprocess
-import sys
-
-# Outputs directory for generated files
-OUTPUTS_DIR = Path.cwd() / "outputs"
-OUTPUTS_DIR.mkdir(exist_ok=True)
-
-# -----------------------------
-# Ensure diff-gaussian-rasterization builds for current GPU
-# -----------------------------
-try:
-    import diff_gaussian_rasterization  # noqa: F401
-except ImportError:
-    print("Installing diff-gaussian-rasterization (compiling for detected CUDA arch)...")
-    env = os.environ.copy()
-    try:
-        import torch as _torch
-        if _torch.cuda.is_available():
-            maj, minr = _torch.cuda.get_device_capability()
-            arch = f"{maj}.{minr}"                 # e.g., "9.0" on H100/H200, "8.0" on A100
-            env["TORCH_CUDA_ARCH_LIST"] = f"{arch}+PTX"
-        else:
-            # Build stage may not see a GPU on HF Spaces: compile a cross-arch set
-            env["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6;8.9;9.0+PTX"
-    except Exception:
-        env["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6;8.9;9.0+PTX"
-
-    # (Optional) side-step allocator+NVML quirks in restrictive containers
-    env.setdefault("PYTORCH_NO_CUDA_MEMORY_CACHING", "1")
-
-    subprocess.check_call(
-        [sys.executable, "-m", "pip", "install",
-         "git+https://github.com/graphdeco-inria/diff-gaussian-rasterization"],
-        env=env,
-    )
-    import diff_gaussian_rasterization  # noqa: F401
-
-
-from gslrm.model.gaussians_renderer import render_turntable, imageseq2video
-from mvdiffusion.pipelines.pipeline_mvdiffusion_unclip import StableUnCLIPImg2ImgPipeline
-from utils_folder.face_utils import preprocess_image, preprocess_image_without_cropping
-
-# HuggingFace repository configuration
-HF_REPO_ID = "wlyu/OpenFaceLift"
-
-def download_weights_from_hf() -> Path:
-    """Download model weights from HuggingFace if not already present.
-    
-    Returns:
-        Path to the downloaded repository
-    """
-    workspace_dir = Path(__file__).parent
-    
-    # Check if weights already exist locally
-    mvdiffusion_path = workspace_dir / "checkpoints/mvdiffusion/pipeckpts"
-    gslrm_path = workspace_dir / "checkpoints/gslrm/ckpt_0000000000021125.pt"
-    
-    if mvdiffusion_path.exists() and gslrm_path.exists():
-        print("Using local model weights")
-        return workspace_dir
-    
-    print(f"Downloading model weights from HuggingFace: {HF_REPO_ID}")
-    print("This may take a few minutes on first run...")
-    
-    # Download to local directory
-    snapshot_download(
-        repo_id=HF_REPO_ID,
-        local_dir=str(workspace_dir / "checkpoints"),
-        local_dir_use_symlinks=False,
-    )
-    
-    print("Model weights downloaded successfully!")
-    return workspace_dir
-
-class FaceLiftPipeline:
-    """Pipeline for FaceLift 3D head generation from single images."""
-    
-    def __init__(self):
-        # Download weights from HuggingFace if needed
-        workspace_dir = download_weights_from_hf()
-        
-        # Setup paths
-        self.output_dir = workspace_dir / "outputs"
-        self.examples_dir = workspace_dir / "examples"
-        self.output_dir.mkdir(exist_ok=True)
-        
-        # Parameters
-        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        self.image_size = 512
-        self.camera_indices = [2, 1, 0, 5, 4, 3]
-        
-        # Load models (keep on CPU for ZeroGPU compatibility)
-        print("Loading models...")
-        try:
-            self.mvdiffusion_pipeline = StableUnCLIPImg2ImgPipeline.from_pretrained(
-                str(workspace_dir / "checkpoints/mvdiffusion/pipeckpts"),
-                torch_dtype=torch.float16,
-            )
-            # Don't move to device or enable xformers here - will be done in GPU-decorated function
-            self._models_on_gpu = False
-            
-            with open(workspace_dir / "configs/gslrm.yaml", "r") as f:
-                config = edict(yaml.safe_load(f))
-            
-            module_name, class_name = config.model.class_name.rsplit(".", 1)
-            module = __import__(module_name, fromlist=[class_name])
-            ModelClass = getattr(module, class_name)
-            
-            self.gs_lrm_model = ModelClass(config)
-            checkpoint = torch.load(
-                workspace_dir / "checkpoints/gslrm/ckpt_0000000000021125.pt",
-                map_location="cpu"
-            )
-            # Filter out loss_calculator weights (training-only, not needed for inference)
-            state_dict = {k: v for k, v in checkpoint["model"].items() 
-                          if not k.startswith("loss_calculator.")}
-            self.gs_lrm_model.load_state_dict(state_dict)
-            # Keep on CPU initially - will move to GPU in decorated function
-            
-            self.color_prompt_embedding = torch.load(
-                workspace_dir / "mvdiffusion/fixed_prompt_embeds_6view/clr_embeds.pt",
-                map_location="cpu"
-            )
-            
-            with open(workspace_dir / "utils_folder/opencv_cameras.json", 'r') as f:
-                self.cameras_data = json.load(f)["frames"]
-            
-            print("Models loaded successfully!")
-        except Exception as e:
-            print(f"Error loading models: {e}")
-            import traceback
-            traceback.print_exc()
-            raise
-    
-    def _move_models_to_gpu(self):
-        """Move models to GPU and enable optimizations. Called within @spaces.GPU context."""
-        if not self._models_on_gpu and torch.cuda.is_available():
-            print("Moving models to GPU...")
-            self.device = torch.device("cuda:0")
-            self.mvdiffusion_pipeline.to(self.device)
-            self.mvdiffusion_pipeline.unet.enable_xformers_memory_efficient_attention()
-            self.gs_lrm_model.to(self.device)
-            self.gs_lrm_model.eval()  # Set to eval mode
-            self.color_prompt_embedding = self.color_prompt_embedding.to(self.device)
-            self._models_on_gpu = True
-            torch.cuda.empty_cache()  # Clear cache after moving models
-            print("Models on GPU, xformers enabled!")
-    
-    @spaces.GPU(duration=120)
-    def generate_3d_head(self, image_path, auto_crop=True, guidance_scale=3.0, 
-                         random_seed=4, num_steps=50):
-        """Generate 3D head from single image."""
-        try:
-            # Move models to GPU now that we're in the GPU context
-            self._move_models_to_gpu()
-            # Setup output directory
-            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-            output_dir = self.output_dir / timestamp
-            output_dir.mkdir(exist_ok=True)
-            
-            # Preprocess input
-            original_img = np.array(Image.open(image_path))
-            input_image = preprocess_image(original_img) if auto_crop else \
-                         preprocess_image_without_cropping(original_img)
-            
-            if input_image.size != (self.image_size, self.image_size):
-                input_image = input_image.resize((self.image_size, self.image_size))
-            
-            input_path = output_dir / "input.png"
-            input_image.save(input_path)
-            
-            # Generate multi-view images
-            generator = torch.Generator(device=self.mvdiffusion_pipeline.unet.device)
-            generator.manual_seed(random_seed)
-            
-            result = self.mvdiffusion_pipeline(
-                input_image, None,
-                prompt_embeds=self.color_prompt_embedding,
-                height=self.image_size,
-                width=self.image_size,
-                guidance_scale=guidance_scale,
-                num_images_per_prompt=1,
-                num_inference_steps=num_steps,
-                generator=generator,
-                eta=1.0,
-            )
-            
-            selected_views = result.images[:6]
-            
-            # Save multi-view composite
-            multiview_image = Image.new("RGB", (self.image_size * 6, self.image_size))
-            for i, view in enumerate(selected_views):
-                multiview_image.paste(view, (self.image_size * i, 0))
-            
-            multiview_path = output_dir / "multiview.png"
-            multiview_image.save(multiview_path)
-            
-            # Move diffusion model to CPU to free GPU memory for GS-LRM
-            print("Moving diffusion model to CPU to free memory...")
-            self.mvdiffusion_pipeline.to("cpu")
-            
-            # Delete intermediate variables to free memory
-            del result, generator
-            torch.cuda.empty_cache()
-            torch.cuda.synchronize()
-            
-            # Prepare 3D reconstruction input
-            view_arrays = [np.array(view) for view in selected_views]
-            lrm_input = torch.from_numpy(np.stack(view_arrays, axis=0)).float()
-            lrm_input = lrm_input[None].to(self.device) / 255.0
-            lrm_input = rearrange(lrm_input, "b v h w c -> b v c h w")
-            
-            # Prepare camera parameters
-            selected_cameras = [self.cameras_data[i] for i in self.camera_indices]
-            fxfycxcy_list = [[c["fx"], c["fy"], c["cx"], c["cy"]] for c in selected_cameras]
-            c2w_list = [np.linalg.inv(np.array(c["w2c"])) for c in selected_cameras]
-            
-            fxfycxcy = torch.from_numpy(np.stack(fxfycxcy_list, axis=0).astype(np.float32))
-            c2w = torch.from_numpy(np.stack(c2w_list, axis=0).astype(np.float32))
-            fxfycxcy = fxfycxcy[None].to(self.device)
-            c2w = c2w[None].to(self.device)
-            
-            batch_indices = torch.stack([
-                torch.zeros(lrm_input.size(1)).long(),
-                torch.arange(lrm_input.size(1)).long(),
-            ], dim=-1)[None].to(self.device)
-            
-            batch = edict({
-                "image": lrm_input,
-                "c2w": c2w,
-                "fxfycxcy": fxfycxcy,
-                "index": batch_indices,
-            })
-            
-            # Ensure GS-LRM model is on GPU
-            if next(self.gs_lrm_model.parameters()).device.type == "cpu":
-                print("Moving GS-LRM model to GPU...")
-                self.gs_lrm_model.to(self.device)
-                torch.cuda.empty_cache()
-
-            # Final memory cleanup before reconstruction
-            torch.cuda.empty_cache()
-            
-            # Run 3D reconstruction
-            with torch.no_grad(), torch.autocast(enabled=True, device_type="cuda", dtype=torch.float16):
-                result = self.gs_lrm_model.forward(batch, create_visual=False, split_data=True)
-            
-            comp_image = result.render[0].unsqueeze(0).detach()
-            gaussians = result.gaussians[0]
-            
-            # Clear CUDA cache after reconstruction
-            torch.cuda.empty_cache()
-            
-            # Save filtered gaussians
-            filtered_gaussians = gaussians.apply_all_filters(
-                cam_origins=None,
-                opacity_thres=0.04,
-                scaling_thres=0.2,
-                floater_thres=0.75,
-                crop_bbx=[-0.91, 0.91, -0.91, 0.91, -1.0, 1.0],
-                nearfar_percent=(0.0001, 1.0),
-            )
-            
-            ply_path = output_dir / "gaussians.ply"
-            filtered_gaussians.save_ply(str(ply_path))
-            
-            # Save output image
-            comp_image = rearrange(comp_image, "x v c h w -> (x h) (v w) c")
-            comp_image = (comp_image.cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8)
-            output_path = output_dir / "output.png"
-            Image.fromarray(comp_image).save(output_path)
-            
-            # Generate turntable video
-            turntable_resolution = 512
-            num_turntable_views = 180
-            turntable_frames = render_turntable(gaussians, rendering_resolution=turntable_resolution, 
-                                               num_views=num_turntable_views)
-            turntable_frames = rearrange(turntable_frames, "h (v w) c -> v h w c", v=num_turntable_views)
-            turntable_frames = np.ascontiguousarray(turntable_frames)
-            
-            turntable_path = output_dir / "turntable.mp4"
-            imageseq2video(turntable_frames, str(turntable_path), fps=30)
-            
-            # Final CUDA cache clear
-            torch.cuda.empty_cache()
-            
-            return str(input_path), str(multiview_path), str(output_path), \
-                   str(turntable_path), str(ply_path)
-            
-        except Exception as e:
-            import traceback
-            error_details = traceback.format_exc()
-            print(f"Error details:\n{error_details}")
-            raise gr.Error(f"Generation failed: {str(e)}")
-
-def main():
-    """Run the FaceLift application."""
-    pipeline = FaceLiftPipeline()
-
-    # Prepare examples (same as before)
-    examples = []
-    if pipeline.examples_dir.exists():
-        examples = [[str(f), True, 3.0, 4, 50] for f in sorted(pipeline.examples_dir.iterdir()) 
-                   if f.suffix.lower() in {'.png', '.jpg', '.jpeg'}]
-
-    with gr.Blocks(title="FaceLift: Single Image 3D Face Reconstruction") as demo:
-
-        # Wrapper to return outputs for display
-        def _generate_and_filter_outputs(image_path, auto_crop, guidance_scale, random_seed, num_steps):
-            input_path, multiview_path, output_path, turntable_path, ply_path = \
-                pipeline.generate_3d_head(image_path, auto_crop, guidance_scale, random_seed, num_steps)
-            
-            return output_path, turntable_path, ply_path
-
-        gr.Markdown("## FaceLift: Single Image 3D Face Reconstruction.")
-        
-        gr.Markdown("""
-        ### 💡 Tips for Best Results
-        - Works best with near-frontal portrait images
-        - The provided checkpoints were not trained with accessories (glasses, hats, etc.). Portraits containing accessories may produce suboptimal results.
-        - If face detection fails, try disabling auto-cropping and manually crop to square
-        """)
-        
-        with gr.Row():
-            with gr.Column(scale=1):
-                in_image  = gr.Image(type="filepath", label="Input Portrait Image")
-                auto_crop = gr.Checkbox(value=True, label="Auto Cropping")
-                guidance  = gr.Slider(1.0, 10.0, 3.0, step=0.1, label="Guidance Scale")
-                seed      = gr.Number(value=4, label="Random Seed")
-                steps     = gr.Slider(10, 100, 50, step=5, label="Generation Steps")
-                run_btn   = gr.Button("Generate 3D Head", variant="primary")
-
-                # Examples (match input signature)
-                if examples:
-                    gr.Examples(
-                        examples=examples,
-                        inputs=[in_image, auto_crop, guidance, seed, steps],
-                        examples_per_page=10,
-                    )
-
-            with gr.Column(scale=1):
-                out_recon = gr.Image(label="3D Reconstruction Views")
-                out_video = gr.PlayableVideo(label="Turntable Animation (360° View)", height=600)
-                out_ply   = gr.File(label="Download 3D Model (.ply)")
-
-        # Run generation and display all outputs
-        run_btn.click(
-            fn=_generate_and_filter_outputs,
-            inputs=[in_image, auto_crop, guidance, seed, steps],
-            outputs=[out_recon, out_video, out_ply],
-        )
-
-        demo.queue(max_size=10)
-        demo.launch(share=True, server_name="0.0.0.0", server_port=7860, show_error=True)
-
-if __name__ == "__main__":
-    main()

gslrm/__init__.py

@@ -1,8 +1,14 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# For inquiries contact: wlyu3@ucmerced.edu
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 

gslrm/model/__init__.py

@@ -1,8 +1,14 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# For inquiries contact: wlyu3@ucmerced.edu
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 

gslrm/model/gaussians_renderer.py

@@ -1,10 +1,16 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# For inquiries contact: wlyu3@ucmerced.edu
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 import math
 import os

gslrm/model/gslrm.py

@@ -1,10 +1,16 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# For inquiries contact: wlyu3@ucmerced.edu
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 """
 GSLRM (Gaussian Splatting Large Reconstruction Model)

gslrm/model/transform_data.py

@@ -1,10 +1,16 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# For inquiries contact: wlyu3@ucmerced.edu
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 """
 Data transformation utilities for GSLRM model.

gslrm/model/utils_transformer.py

@@ -1,10 +1,16 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# For inquiries contact: wlyu3@ucmerced.edu
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 """
 Transformer utilities for GSLRM.

mvdiffusion/__init__.py

@@ -1,8 +1,14 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# For inquiries contact: wlyu3@ucmerced.edu
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 

mvdiffusion/models/__init__.py

@@ -1,8 +1,14 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# For inquiries contact: wlyu3@ucmerced.edu
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 

mvdiffusion/pipelines/__init__.py

@@ -1,8 +1,14 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# For inquiries contact: wlyu3@ucmerced.edu
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 

mvdiffusion/pipelines/pipeline_mvdiffusion_unclip.py

@@ -1,12 +1,18 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 # 
 # Modified from https://github.com/pengHTYX/Era3D/blob/main/mvdiffusion/pipelines/pipeline_mvdiffusion_unclip.py
-#
-# For inquiries contact: [email protected]
 
 import inspect
 import warnings

utils_folder/__init__.py

@@ -1,8 +1,14 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# For inquiries contact: wlyu3@ucmerced.edu
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 

utils_folder/face_utils.py

@@ -1,10 +1,16 @@
-# Copyright (C) 2025, FaceLift Research Group
-# https://github.com/weijielyu/FaceLift
+# Copyright 2025 Adobe Inc.
 #
-# This software is free for non-commercial, research and evaluation use 
-# under the terms of the LICENSE.md file.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# For inquiries contact: wlyu3@ucmerced.edu
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 """
 Face detection and cropping utilities for 3D face reconstruction.