Create app.py

app.py

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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
+
+hf_token = os.getenv("HF_TOKEN")
+# Login to Hugging Face
+login(token=hf_token)
+
+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)
+
+# 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_canny_image(image, width=1024, height=1024):
+    image = c_crop(image)
+    image = image.resize((width, height))
+    image = canny_processor(image)
+    return image
+
+@spaces.GPU()
+def generate_image(prompt, control_image, num_steps=50, guidance=4, width=512, height=512, seed=42):
+    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))
+    
+    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 output_img
+
+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")
+    ],
+    outputs=gr.Image(type="pil", label="Generated Image"),
+    title="FLUX.1 Controlnet Cany",
+    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()