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| import time | |
| import torch | |
| import streamlit as st | |
| from PIL import Image, ImageDraw | |
| from streamlit_image_coordinates import streamlit_image_coordinates | |
| import draggan | |
| import utils | |
| ## Default to CPU if no GPU is available | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| else: | |
| device = torch.device("cpu") | |
| ### Streamlit setup ### | |
| st.set_page_config( | |
| page_title="DragGAN Demo", | |
| page_icon="🐉", | |
| layout="wide", | |
| ) | |
| st.markdown( | |
| """### 🐉 DragGAN Streamlit Demo | |
| Unofficial implementation of [DragGAN](https://vcai.mpi-inf.mpg.de/projects/DragGAN/) in PyTorch & Streamlit by [Skim AI](https://skimai.com). See also [GitHub repo](https://github.com/skimai/draggan). | |
| ### To Use: | |
| 1) Select StyleGAN2 **Model** from dropdown | |
| 2) Change **Seed** to generate a new random latent vector | |
| 2) Click on image to add "handle" (red dot) and "target" (blue dot) pairs | |
| 3) Click ***Run*** to optimize the latent vector to move handle points to the targets | |
| 4) ***Reset*** to clear points and revert to initial latent | |
| """) | |
| message_container = st.empty() | |
| col1, col2 = st.columns([1, 2]) | |
| def reset(): | |
| st.session_state.clear() | |
| def reset_rerun(): | |
| reset() | |
| st.experimental_rerun() | |
| ### Run/Reset buttons in right col ### | |
| with col2: | |
| st.markdown("") | |
| but_col1, but_col2 = st.columns([1,7]) | |
| run_button = but_col1.button("▶️ Run") | |
| reset_button = but_col2.button("🔁 Reset") | |
| ### Settings panel in left col ### | |
| with col1: | |
| # st.header("🐉 DragGAN") | |
| st.header("") | |
| settings_col1, settings_col2 = st.columns([1,1]) | |
| # Models from Self-Distilled SG https://github.com/self-distilled-stylegan/self-distilled-internet-photos | |
| model_options = { | |
| "Lions": "https://storage.googleapis.com/self-distilled-stylegan/lions_512_pytorch.pkl", | |
| "Faces (FFHQ)": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl", | |
| "Elephants": "https://storage.googleapis.com/self-distilled-stylegan/elephants_512_pytorch.pkl", | |
| "Parrots": "https://storage.googleapis.com/self-distilled-stylegan/parrots_512_pytorch.pkl", | |
| "Horses": "https://storage.googleapis.com/self-distilled-stylegan/horses_256_pytorch.pkl", | |
| "Bicycles": "https://storage.googleapis.com/self-distilled-stylegan/bicycles_256_pytorch.pkl", | |
| "Giraffes": "https://storage.googleapis.com/self-distilled-stylegan/giraffes_512_pytorch.pkl", | |
| "Dogs (1)": "https://storage.googleapis.com/self-distilled-stylegan/dogs_1024_pytorch.pkl", | |
| "Dogs (2)": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/afhqdog.pkl", | |
| "Cats": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/afhqcat.pkl", | |
| "Wildlife": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/afhqwild.pkl", | |
| "MetFaces": "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl", | |
| } | |
| model_name = str(settings_col1.selectbox("Model", list(model_options.keys()), on_change=reset, help="StyleGAN2 model to use, downloaded and cached on first run")) | |
| model_url = model_options[model_name] | |
| seed = settings_col2.number_input("Seed", value=22, step=1, min_value=0, on_change=reset, help="Random seed for generating W+ latent") | |
| target_resolution = int(settings_col1.selectbox("Resolution", [256, 512, 1024], index=1, on_change=reset, help="Resize generated image to this resolution (may be different than native model resolution)")) | |
| n_iter = int(settings_col1.number_input("Iterations", value=200, step=5, help="Number of iterations to run optimization", on_change=reset)) | |
| step_size = settings_col2.number_input("Step Size", value=1e-3, step=1e-4, min_value=1e-4, format="%.4f", help="Step size (Learning Rate) for gradient descent") | |
| multiplier = settings_col1.number_input("Speed", value=1.0, step=0.05, min_value=0.05, help="Multiplier for target patch movement") | |
| tolerance = settings_col2.number_input("Tolerance", value=2, step=1, min_value=1, help="Number of pixels away from target to stop") | |
| display_every = settings_col2.number_input("Display Every", value=25, step=1, min_value=1, help="Display image during optimization every n iterations") | |
| truncation_psi = settings_col1.number_input("Truncation", value=0.8, step=0.1, min_value=0.0, on_change=reset, help="Truncation trick value to control diversity (higher = more diverse)") | |
| truncation_cutoff = settings_col2.number_input( | |
| "Truncation Cutoff", value=8, step=1, min_value=-1, max_value=18, on_change=reset, help="Number of layers to apply truncation to (-1 = all layers)" | |
| ) | |
| if reset_button: | |
| reset_rerun() | |
| if "points" not in st.session_state: | |
| st.session_state["points"] = [] | |
| st.session_state["points_types"] = [] | |
| # State variable to track whether the next click should be a 'handle' or 'target' | |
| st.session_state["next_click"] = "handle" | |
| s = time.perf_counter() | |
| G = draggan.load_model(model_url, device=device) | |
| if "W" not in st.session_state: | |
| W = draggan.generate_W( | |
| G, | |
| seed=int(seed), | |
| truncation_psi=truncation_psi, | |
| truncation_cutoff=int(truncation_cutoff), | |
| network_pkl=model_url, | |
| device=device, | |
| ) | |
| else: | |
| W = st.session_state["W"] | |
| img, F0 = draggan.generate_image(W, G, network_pkl=model_url, device=device) | |
| if img.size[0] != target_resolution: | |
| img = img.resize((target_resolution, target_resolution)) | |
| print(f"Generated image in {(time.perf_counter() - s)*1000:.0f}ms") | |
| # Draw an ellipse at each coordinate in points | |
| if "points" in st.session_state and "points_types" in st.session_state: | |
| handles, targets = [], [] | |
| for point, point_type in zip( | |
| st.session_state["points"], st.session_state["points_types"] | |
| ): | |
| if point_type == "handle": | |
| handles.append(point) | |
| else: | |
| targets.append(point) | |
| if len(handles) > 0: | |
| utils.draw_handle_target_points(img, handles, targets) | |
| ### Right column image container ### | |
| with col2: | |
| empty = st.empty() | |
| with empty.container(): | |
| value = streamlit_image_coordinates(img, key="pil") | |
| # New point is clicked | |
| if value is not None: | |
| point = value["x"], value["y"] | |
| if point not in st.session_state["points"]: | |
| # st.session_state["points"].append(point) | |
| st.session_state["points"].append(point) | |
| st.session_state["points_types"].append(st.session_state["next_click"]) | |
| st.session_state["next_click"] = ( | |
| "target" if st.session_state["next_click"] == "handle" else "handle" | |
| ) | |
| st.experimental_rerun() | |
| ## Optimization loop | |
| if run_button: | |
| if len(handles) > 0 and len(targets) > 0 and len(handles) == len(targets) and all(targets): | |
| W = draggan.optimize( | |
| W, | |
| G, | |
| handle_points=handles, | |
| target_points=targets, | |
| r1=3, | |
| r2=12, | |
| tolerance=tolerance, | |
| max_iter=n_iter, | |
| lr=step_size, | |
| multiplier=multiplier, | |
| empty=empty, | |
| display_every=display_every, | |
| target_resolution=target_resolution, | |
| device=device, | |
| ) | |
| # st.write(handles) | |
| # st.write(targets) | |
| st.session_state.clear() | |
| st.session_state["W"] = W | |
| st.experimental_rerun() | |
| else: | |
| message_container.warning("Please add at least one handle and one target point.") | |