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| import os | |
| import torch | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| from einops import rearrange | |
| import requests | |
| import spaces | |
| from huggingface_hub import login | |
| from gradio_imageslider import ImageSlider # Import ImageSlider | |
| from image_datasets.canny_dataset import canny_processor, c_crop | |
| from src.flux.sampling import denoise_controlnet, get_noise, get_schedule, prepare, unpack | |
| from src.flux.util import load_ae, load_clip, load_t5, load_flow_model, load_controlnet, load_safetensors | |
| # Download and load the ControlNet model | |
| model_url = "https://huggingface.co/XLabs-AI/flux-controlnet-canny/resolve/main/controlnet.safetensors?download=true" | |
| model_path = "./controlnet.safetensors" | |
| if not os.path.exists(model_path): | |
| response = requests.get(model_url) | |
| with open(model_path, 'wb') as f: | |
| f.write(response.content) | |
| # Source: https://github.com/XLabs-AI/x-flux.git | |
| name = "flux-dev" | |
| device = torch.device("cuda") | |
| offload = False | |
| is_schnell = name == "flux-schnell" | |
| model, ae, t5, clip, controlnet = None, None, None, None, None | |
| def load_models(): | |
| global model, ae, t5, clip, controlnet | |
| t5 = load_t5(device, max_length=256 if is_schnell else 512) | |
| clip = load_clip(device) | |
| model = load_flow_model(name, device=device) | |
| ae = load_ae(name, device=device) | |
| controlnet = load_controlnet(name, device).to(device).to(torch.bfloat16) | |
| checkpoint = load_safetensors(model_path) | |
| controlnet.load_state_dict(checkpoint, strict=False) | |
| load_models() | |
| def preprocess_image(image, target_width, target_height, crop=True): | |
| if crop: | |
| image = c_crop(image) # Crop the image to square | |
| original_width, original_height = image.size | |
| # Resize to match the target size without stretching | |
| scale = max(target_width / original_width, target_height / original_height) | |
| resized_width = int(scale * original_width) | |
| resized_height = int(scale * original_height) | |
| image = image.resize((resized_width, resized_height), Image.LANCZOS) | |
| # Center crop to match the target dimensions | |
| left = (resized_width - target_width) // 2 | |
| top = (resized_height - target_height) // 2 | |
| image = image.crop((left, top, left + target_width, top + target_height)) | |
| else: | |
| image = image.resize((target_width, target_height), Image.LANCZOS) | |
| return image | |
| def preprocess_canny_image(image, target_width, target_height, crop=True): | |
| image = preprocess_image(image, target_width, target_height, crop=crop) | |
| image = canny_processor(image) | |
| return image | |
| def generate_image(prompt, control_image, num_steps=50, guidance=4, width=512, height=512, seed=42, random_seed=False): | |
| if random_seed: | |
| seed = np.random.randint(0, 10000) | |
| if not os.path.isdir("./controlnet_results/"): | |
| os.makedirs("./controlnet_results/") | |
| torch_device = torch.device("cuda") | |
| model.to(torch_device) | |
| t5.to(torch_device) | |
| clip.to(torch_device) | |
| ae.to(torch_device) | |
| controlnet.to(torch_device) | |
| width = 16 * width // 16 | |
| height = 16 * height // 16 | |
| timesteps = get_schedule(num_steps, (width // 8) * (height // 8) // (16 * 16), shift=(not is_schnell)) | |
| processed_input = preprocess_image(control_image, width, height) | |
| canny_processed = preprocess_canny_image(control_image, width, height) | |
| controlnet_cond = torch.from_numpy((np.array(canny_processed) / 127.5) - 1) | |
| controlnet_cond = controlnet_cond.permute(2, 0, 1).unsqueeze(0).to(torch.bfloat16).to(torch_device) | |
| torch.manual_seed(seed) | |
| with torch.no_grad(): | |
| x = get_noise(1, height, width, device=torch_device, dtype=torch.bfloat16, seed=seed) | |
| inp_cond = prepare(t5=t5, clip=clip, img=x, prompt=prompt) | |
| x = denoise_controlnet(model, **inp_cond, controlnet=controlnet, timesteps=timesteps, guidance=guidance, controlnet_cond=controlnet_cond) | |
| x = unpack(x.float(), height, width) | |
| x = ae.decode(x) | |
| x1 = x.clamp(-1, 1) | |
| x1 = rearrange(x1[-1], "c h w -> h w c") | |
| output_img = Image.fromarray((127.5 * (x1 + 1.0)).cpu().byte().numpy()) | |
| return [processed_input, output_img] # Return both images for slider | |
| interface = gr.Interface( | |
| fn=generate_image, | |
| inputs=[ | |
| gr.Textbox(label="Prompt"), | |
| gr.Image(type="pil", label="Control Image"), | |
| gr.Slider(step=1, minimum=1, maximum=64, value=28, label="Num Steps"), | |
| gr.Slider(minimum=0.1, maximum=10, value=4, label="Guidance"), | |
| gr.Slider(minimum=128, maximum=2048, step=128, value=1024, label="Width"), | |
| gr.Slider(minimum=128, maximum=2048, step=128, value=1024, label="Height"), | |
| gr.Number(value=42, label="Seed"), | |
| gr.Checkbox(label="Random Seed") | |
| ], | |
| outputs=ImageSlider(label="Before / After"), # Use ImageSlider as the output | |
| title="FLUX.1 Controlnet Canny", | |
| description="Generate images using ControlNet and a text prompt.\n[[non-commercial license, Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]" | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch() | |