Spaces:
Sleeping
Sleeping
| # import gradio as gr | |
| # import torch | |
| # import numpy as np | |
| # from PIL import Image | |
| # import os | |
| # import legacy | |
| # import torch_utils | |
| # # Load the pre-trained StyleGAN model | |
| # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # model_path = 'dress_model.pkl' # Place your .pkl in the same directory or update path | |
| # # Load StyleGAN Generator | |
| # with open(model_path, 'rb') as f: | |
| # G = legacy.load_network_pkl(f)['G_ema'].to(device) | |
| # def mix_styles(image1_path, image2_path, styles_to_mix): | |
| # # Extract image names (without extensions) | |
| # image1_name = os.path.splitext(os.path.basename(image1_path))[0] | |
| # image2_name = os.path.splitext(os.path.basename(image2_path))[0] | |
| # # Load latent vectors from .npz | |
| # latent_vector_1 = np.load(os.path.join("projection_results", image1_name, "projected_w.npz"))['w'] | |
| # latent_vector_2 = np.load(os.path.join("projection_results", image2_name, "projected_w.npz"))['w'] | |
| # # Convert to torch tensors | |
| # latent_1_tensor = torch.from_numpy(latent_vector_1).to(device) | |
| # latent_2_tensor = torch.from_numpy(latent_vector_2).to(device) | |
| # # Mix layers | |
| # mixed_latent = latent_1_tensor.clone() | |
| # mixed_latent[:, styles_to_mix] = latent_2_tensor[:, styles_to_mix] | |
| # # Generate image | |
| # with torch.no_grad(): | |
| # image = G.synthesis(mixed_latent, noise_mode='const') | |
| # # Convert to image | |
| # image = (image.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).cpu().numpy() | |
| # mixed_image = Image.fromarray(image[0], 'RGB') | |
| # return mixed_image | |
| # def style_mixing_interface(image1, image2, mix_value): | |
| # if image1 is None or image2 is None: | |
| # return None | |
| # selected_layers = list(range(mix_value + 1)) | |
| # return mix_styles(image1, image2, selected_layers) | |
| # # Gradio UI | |
| # iface = gr.Interface( | |
| # fn=style_mixing_interface, | |
| # inputs=[ | |
| # gr.Image(label="First Clothing Image", type="filepath"), | |
| # gr.Image(label="Second Clothing Image", type="filepath"), | |
| # gr.Slider(label="Style Mixing Strength (Layers 0 to N)", minimum=0, maximum=9, step=1, value=5) | |
| # ], | |
| # outputs=gr.Image(label="Mixed Clothing Design"), | |
| # live=True, | |
| # title="Style Mixing for Clothing Design", | |
| # description="Upload two projected images and choose how many early layers to mix." | |
| # ) | |
| # iface.launch() | |
| # import gradio as gr | |
| # import torch | |
| # import numpy as np | |
| # from PIL import Image | |
| # import os | |
| # import legacy | |
| # import torch_utils | |
| # import requests | |
| # import io | |
| # import warnings | |
| # warnings.filterwarnings("ignore") | |
| # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # model_path = 'dress_model.pkl' | |
| # with open(model_path, 'rb') as f: | |
| # G = legacy.load_network_pkl(f)['G_ema'].to(device) | |
| # def mix_styles(image1_path, image2_path, styles_to_mix): | |
| # image1_name = os.path.splitext(os.path.basename(image1_path))[0] | |
| # image2_name = os.path.splitext(os.path.basename(image2_path))[0] | |
| # latent_vector_1 = np.load(os.path.join("projection_results", image1_name, "projected_w.npz"))['w'] | |
| # latent_vector_2 = np.load(os.path.join("projection_results", image2_name, "projected_w.npz"))['w'] | |
| # latent_1_tensor = torch.from_numpy(latent_vector_1).to(device) | |
| # latent_2_tensor = torch.from_numpy(latent_vector_2).to(device) | |
| # mixed_latent = latent_1_tensor.clone() | |
| # mixed_latent[:, styles_to_mix] = latent_2_tensor[:, styles_to_mix] | |
| # with torch.no_grad(): | |
| # image = G.synthesis(mixed_latent, noise_mode='const') | |
| # image = (image.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).cpu().numpy() | |
| # mixed_image = Image.fromarray(image[0], 'RGB') | |
| # return mixed_image | |
| # def style_mixing_interface(image1, image2, mix_value): | |
| # if image1 is None or image2 is None: | |
| # return None, None | |
| # selected_layers = list(range(mix_value + 1)) | |
| # mixed_img = mix_styles(image1, image2, selected_layers) | |
| # buffer = io.BytesIO() | |
| # mixed_img.save(buffer, format="PNG") | |
| # buffer.seek(0) | |
| # return mixed_img, buffer | |
| # def send_to_backend(image_buffer, user_id): | |
| # if not user_id: | |
| # return "❌ user_id not found." | |
| # try: | |
| # files = {'file': ('generated_image.png', image_buffer, 'image/png')} | |
| # url = f"https://5a4d-103-40-74-78.ngrok-free.app/customisation/upload/{user_id}" | |
| # response = requests.post(url, files=files) | |
| # if response.status_code == 201: | |
| # return "✅ Image uploaded and saved to database!" | |
| # else: | |
| # return f"❌ Upload failed: {response.status_code} - {response.text}" | |
| # except Exception as e: | |
| # return f"⚠️ Error: {str(e)}" | |
| # # --- Gradio UI --- | |
| # with gr.Blocks(title="Style Mixing for Clothing Design") as iface: | |
| # user_id_state = gr.State() | |
| # @iface.load(inputs=None, outputs=[user_id_state]) | |
| # def on_load(request: gr.Request): | |
| # user_id = request.query_params.get('user_id', '') | |
| # return user_id | |
| # gr.Markdown("## Style Mixing for Clothing Design\nUpload two projected clothing images and mix their styles.") | |
| # with gr.Row(): | |
| # image1_input = gr.Image(label="First Clothing Image", type="filepath") | |
| # image2_input = gr.Image(label="Second Clothing Image", type="filepath") | |
| # mix_slider = gr.Slider(label="Style Mixing Strength (Layers 0 to N)", minimum=0, maximum=9, step=1, value=5) | |
| # with gr.Row(): | |
| # output_image = gr.Image(label="Mixed Clothing Design") | |
| # save_button = gr.Button("Download & Save to Database") | |
| # image_buffer = gr.State() | |
| # save_status = gr.Textbox(label="Save Status", interactive=False) | |
| # def mix_and_store(image1, image2, mix_value): | |
| # result_image, buffer = style_mixing_interface(image1, image2, mix_value) | |
| # return result_image, buffer | |
| # mix_slider.change(mix_and_store, inputs=[image1_input, image2_input, mix_slider], outputs=[output_image, image_buffer]) | |
| # save_button.click(send_to_backend, inputs=[image_buffer, user_id_state], outputs=[save_status]) | |
| # iface.launch() | |
| import gradio as gr | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| import os | |
| import legacy | |
| import torch_utils | |
| import requests | |
| import io | |
| import warnings | |
| import gdown | |
| warnings.filterwarnings("ignore") | |
| # -------- CONFIGURATION -------- | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # Google Drive model setup | |
| file_id = "12_fsSQgUfOCAPQaDtq2QPCLE74qTQEwt" | |
| output_path = "dress_model.pkl" | |
| # Download the model if it's not present | |
| if not os.path.exists(output_path): | |
| print("Downloading StyleGAN2 model from Google Drive...") | |
| gdown.download(f"https://drive.google.com/uc?id={file_id}", output_path, quiet=False) | |
| # Load the model | |
| with open(output_path, 'rb') as f: | |
| G = legacy.load_network_pkl(f)['G_ema'].to(device) | |
| # Save model path for projector.py | |
| NETWORK_PKL = output_path | |
| # -------- ENSURE PROJECTION -------- | |
| def ensure_projection(image_path): | |
| image_name = os.path.splitext(os.path.basename(image_path))[0] | |
| proj_dir = os.path.join("projection_results", image_name) | |
| proj_file = os.path.join(proj_dir, "projected_w.npz") | |
| if not os.path.exists(proj_file): | |
| print(f"Projection for {image_name} not found. Running projector.py...") | |
| os.makedirs(proj_dir, exist_ok=True) | |
| subprocess.run([ | |
| "python", "projector.py", | |
| f"--network={NETWORK_PKL}", | |
| f"--target={image_path}", | |
| f"--outdir={proj_dir}" | |
| ], check=True) | |
| return proj_file | |
| # -------- STYLE MIXING -------- | |
| def mix_styles(image1_path, image2_path, styles_to_mix): | |
| proj_file1 = ensure_projection(image1_path) | |
| proj_file2 = ensure_projection(image2_path) | |
| latent_vector_1 = np.load(proj_file1)['w'] | |
| latent_vector_2 = np.load(proj_file2)['w'] | |
| latent_1_tensor = torch.from_numpy(latent_vector_1).to(device) | |
| latent_2_tensor = torch.from_numpy(latent_vector_2).to(device) | |
| mixed_latent = latent_1_tensor.clone() | |
| mixed_latent[:, styles_to_mix] = latent_2_tensor[:, styles_to_mix] | |
| with torch.no_grad(): | |
| image = G.synthesis(mixed_latent, noise_mode='const') | |
| image = (image.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).cpu().numpy() | |
| mixed_image = Image.fromarray(image[0], 'RGB') | |
| return mixed_image | |
| # Handles style mixing + output buffer | |
| def style_mixing_interface(image1, image2, mix_value): | |
| if image1 is None or image2 is None: | |
| return None, None | |
| selected_layers = list(range(mix_value + 1)) | |
| mixed_img = mix_styles(image1, image2, selected_layers) | |
| buffer = io.BytesIO() | |
| mixed_img.save(buffer, format="PNG") | |
| buffer.seek(0) | |
| return mixed_img, buffer | |
| # Upload to NestJS backend | |
| def send_to_backend(image_buffer, user_id): | |
| if not user_id: | |
| return "❌ user_id not found." | |
| if image_buffer is None: | |
| return "⚠️ No image generated. Please mix styles first." | |
| try: | |
| # Convert BytesIO to raw bytes before sending | |
| file_bytes = image_buffer.getvalue() | |
| files = {'file': ('generated_image.png', file_bytes, 'image/png')} | |
| # Update with your actual ngrok or server URL | |
| url = f" https://68be601de1e4.ngrok-free.app/customisation/upload/{user_id}" | |
| response = requests.post(url, files=files) | |
| if response.status_code == 201: | |
| return "✅ Image uploaded and saved to database!" | |
| else: | |
| return f"❌ Upload failed: {response.status_code} - {response.text}" | |
| except Exception as e: | |
| return f"⚠️ Error: {str(e)}" | |
| # Gradio interface | |
| # with gr.Blocks(title="Style Mixing for Clothing Designs") as iface: | |
| # user_id_state = gr.State() | |
| # @iface.load(inputs=None, outputs=[user_id_state]) | |
| # def on_load(request: gr.Request): | |
| # user_id = request.query_params.get('user_id', '') | |
| # return user_id | |
| # gr.Markdown("## Style Mixing for Clothing Design\nUpload two projected clothing images and mix their styles.") | |
| # with gr.Row(): | |
| # image1_input = gr.Image(label="First Clothing Image", type="filepath") | |
| # image2_input = gr.Image(label="Second Clothing Image", type="filepath") | |
| # mix_slider = gr.Slider(label="Style Mixing Strength", minimum=0, maximum=9, step=1, value=5) | |
| # with gr.Row(): | |
| # output_image = gr.Image(label="Mixed Clothing Design") | |
| # save_button = gr.Button("Download & Save to Database") | |
| # image_buffer = gr.State() | |
| # save_status = gr.Textbox(label="Save Status", interactive=False) | |
| # def mix_and_store(image1, image2, mix_value): | |
| # result_image, buffer = style_mixing_interface(image1, image2, mix_value) | |
| # return result_image, buffer | |
| # mix_button = gr.Button("Mix Styles") | |
| # mix_button.click(mix_and_store, inputs=[image1_input, image2_input, mix_slider], outputs=[output_image, image_buffer]) | |
| # save_button.click(send_to_backend, inputs=[image_buffer, user_id_state], outputs=[save_status]) | |
| # iface.launch() | |
| import gradio as gr | |
| with gr.Blocks(title="Style Mixing for Clothing Designs") as iface: | |
| user_id_state = gr.State() | |
| image_buffer = gr.State() | |
| # Load user ID from URL | |
| def on_load(request: gr.Request): | |
| user_id = request.query_params.get('user_id', '') | |
| return user_id | |
| # Header | |
| gr.Markdown("## 🎨 Style Mixing for Clothing Designs") | |
| gr.Markdown("Upload two projected clothing images and blend their styles using the slider below.") | |
| # Upload Inputs | |
| with gr.Group(): | |
| with gr.Row(): | |
| image1_input = gr.Image(label="👗 First Clothing Image", type="filepath",height=256, width=256) | |
| image2_input = gr.Image(label="👗 Second Clothing Image", type="filepath",height=256, width=256) | |
| mix_slider = gr.Slider(label="🧪 Style Mixing Intensity", minimum=0, maximum=9, step=1, value=5, info="0 = Mostly Left | 9 = Mostly Right") | |
| # Output & Actions | |
| with gr.Group(): | |
| with gr.Row(): | |
| output_image = gr.Image(label="🧵 Mixed Clothing Design",height=256, width=256) | |
| with gr.Row(): | |
| mix_button = gr.Button("✨ Mix Styles") | |
| save_button = gr.Button("💾 Save to Database") | |
| save_status = gr.Textbox(label="Status", interactive=False) | |
| # Functions | |
| def mix_and_store(image1, image2, mix_value): | |
| result_image, buffer = style_mixing_interface(image1, image2, mix_value) | |
| return result_image, buffer | |
| # Button Logic | |
| mix_button.click( | |
| fn=mix_and_store, | |
| inputs=[image1_input, image2_input, mix_slider], | |
| outputs=[output_image, image_buffer] | |
| ) | |
| save_button.click( | |
| fn=send_to_backend, | |
| inputs=[image_buffer, user_id_state], | |
| outputs=[save_status] | |
| ) | |
| iface.launch() | |