Update app.py

app.py

@@ -1,133 +1,33 @@
-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
-controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Union'
-
-# 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(controlnet_model, device).to(device).to(torch.bfloat16)
-
-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
-
-@spaces.GPU(duration=120)
-def generate_image(prompt, control_image, control_mode, 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, control_mode=control_modes.index(control_mode))
-        
-        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
-
-control_modes = [
-    "canny",
-    "tile",
-    "depth",
-    "blur",
-    "pose",
-    "gray",
-    "lq"
-]
-
-interface = gr.Interface(
-    fn=generate_image,
-    inputs=[
-        gr.Textbox(label="Prompt"),
-        gr.Image(type="pil", label="Control Image"),
-        gr.Dropdown(choices=control_modes, value="canny", label="Control Mode"),
-        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(share=True)
+from diffusers.utils import load_image
+from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel
+
+base_model = 'black-forest-labs/FLUX.1-dev'
+controlnet_model_union = 'InstantX/FLUX.1-dev-Controlnet-Union'
+
+controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
+controlnet = FluxMultiControlNetModel([controlnet_union]) # we always recommend loading via FluxMultiControlNetModel
+
+pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+prompt = 'A bohemian-style female travel blogger with sun-kissed skin and messy beach waves.'
+control_image_depth = load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union/resolve/main/images/depth.jpg")
+control_mode_depth = 2
+
+control_image_canny = load_image("https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union/resolve/main/images/canny.jpg")
+control_mode_canny = 0
+
+width, height = control_image.size
+
+image = pipe(
+    prompt, 
+    control_image=[control_image_depth, control_image_canny],
+    control_mode=[control_mode_depth, control_mode_canny],
+    width=width,
+    height=height,
+    controlnet_conditioning_scale=[0.2, 0.4],
+    num_inference_steps=24, 
+    guidance_scale=3.5,
+    generator=torch.manual_seed(42),
+).images[0]