import gradio as gr import spaces import os import sys import subprocess import numpy as np from PIL import Image import cv2 from huggingface_hub import login hf_token = os.environ.get("HF_TOKEN_GATED") login(token=hf_token) import torch from diffusers.utils import load_image from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline from diffusers.models.controlnet_flux import FluxControlNetModel base_model = 'black-forest-labs/FLUX.1-dev' controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Canny-alpha' controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16) pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16) # pipe.enable_model_cpu_offload() pipe.to("cuda") def resize_image(input_path, output_path, target_height): # Open the input image img = Image.open(input_path) # Calculate the aspect ratio of the original image original_width, original_height = img.size original_aspect_ratio = original_width / original_height # Calculate the new width while maintaining the aspect ratio and the target height new_width = int(target_height * original_aspect_ratio) # Resize the image while maintaining the aspect ratio and fixing the height img = img.resize((new_width, target_height), Image.LANCZOS) # Save the resized image img.save(output_path) return output_path, new_width, target_height @spaces.GPU() def infer(image_in, prompt, inference_steps, guidance_scale, control_weight, progress=gr.Progress(track_tqdm=True)): n_prompt = 'NSFW, nude, naked, porn, ugly' # Canny preprocessing image_to_canny = load_image(image_in) image_to_canny = np.array(image_to_canny) image_to_canny = cv2.Canny(image_to_canny, 100, 200) image_to_canny = image_to_canny[:, :, None] image_to_canny = np.concatenate([image_to_canny, image_to_canny, image_to_canny], axis=2) image_to_canny = Image.fromarray(image_to_canny) control_image = image_to_canny # infer image = pipe( prompt=prompt, #negative_prompt=n_prompt, control_image=control_image, controlnet_conditioning_scale=control_weight, num_inference_steps=inference_steps, guidance_scale=guidance_scale, ).images[0] image_redim, w, h = resize_image(image_in, "resized_input.jpg", 512) image = image.resize((w, h), Image.LANCZOS) return image, gr.update(value=image_to_canny, visible=True) css=""" #col-container{ margin: 0 auto; max-width: 1080px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(""" # FLUX.1-dev Controlnet Experiment with FLUX.1-dev ControlNet Canny model proposed and maintained by the InstantX team.
Model card: [InstantX/FLUX.1-dev-Controlnet-Canny-alpha](https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny-alpha) """) with gr.Column(): with gr.Row(): with gr.Column(): image_in = gr.Image(label="Image reference", sources=["upload"], type="filepath") prompt = gr.Textbox(label="Prompt") with gr.Accordion("Advanced settings", open=False): with gr.Column(): with gr.Row(): inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=25) guidance_scale = gr.Slider(label="Guidance scale", minimum=1.0, maximum=10.0, step=0.1, value=7.0) control_weight = gr.Slider(label="Control Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.7) submit_canny_btn = gr.Button("Submit") with gr.Column(): result = gr.Image(label="Result") canny_used = gr.Image(label="Preprocessed Canny", visible=False) submit_canny_btn.click( fn = infer, inputs = [image_in, prompt, inference_steps, guidance_scale, control_weight], outputs = [result, canny_used], show_api=False ) demo.queue().launch()