import spaces import torch from diffusers import FluxInpaintPipeline import gradio as gr import re from PIL import Image import os import numpy as np def convert_to_fit_size(original_width_and_height, maximum_size = 2048): width, height =original_width_and_height if width <= maximum_size and height <= maximum_size: return width,height if width > height: scaling_factor = maximum_size / width else: scaling_factor = maximum_size / height new_width = int(width * scaling_factor) new_height = int(height * scaling_factor) return new_width, new_height def adjust_to_multiple_of_32(width: int, height: int): width = width - (width % 32) height = height - (height % 32) return width, height dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" pipe = FluxInpaintPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(device) def sanitize_prompt(prompt): # Allow only alphanumeric characters, spaces, and basic punctuation allowed_chars = re.compile(r"[^a-zA-Z0-9\s.,!?-]") sanitized_prompt = allowed_chars.sub("", prompt) return sanitized_prompt @spaces.GPU(duration=120) def process_images(image, image2=None,prompt="a girl",inpaint_model="black-forest-labs/FLUX.1-schnell",strength=0.75,seed=0,progress=gr.Progress(track_tqdm=True)): # I'm not sure when this happen progress(0, desc="start-process-images") #print("start-process-images") if not isinstance(image, dict): if image2 == None: print("empty mask") return image,None else: image = dict({'background': image, 'layers': [image2]}) if image2!=None: #print("use image2") mask = image2 else: if len(image['layers']) == 0: print("empty mask") return image print("use layer") mask = image['layers'][0] def process_inpaint(image,mask_image,prompt="a person",model_id="black-forest-labs/FLUX.1-schnell",strength=0.75,seed=0,num_inference_steps=4): if image == None: return None generators = [] generator = torch.Generator("cuda").manual_seed(seed) generators.append(generator) width,height = convert_to_fit_size(image.size) #print(f"fit {width}x{height}") width,height = adjust_to_multiple_of_32(width,height) #print(f"multiple {width}x{height}") image = image.resize((width, height), Image.LANCZOS) mask_image = mask_image.resize((width, height), Image.NEAREST) mask_image = mask_image.convert("RGB") output = pipe(prompt=prompt, image=image, mask_image=mask_image,generator=generator,strength=strength,width=width,height=height, guidance_scale=0,num_inference_steps=num_inference_steps,max_sequence_length=256) return output.images[0],mask_image output,mask_image = process_inpaint(image["background"],mask,prompt,inpaint_model,strength,seed) return output,mask_image def read_file(path: str) -> str: with open(path, 'r', encoding='utf-8') as f: content = f.read() return content css=""" #col-left { margin: 0 auto; max-width: 640px; } #col-right { margin: 0 auto; max-width: 640px; } .grid-container { display: flex; align-items: center; justify-content: center; gap:10px } .image { width: 128px; height: 128px; object-fit: cover; } .text { font-size: 16px; } """ with gr.Blocks(css=css, elem_id="demo-container") as demo: with gr.Column(): gr.HTML(read_file("demo_header.html")) gr.HTML(read_file("demo_tools.html")) with gr.Row(): with gr.Column(): image = gr.ImageEditor(height=800,sources=['upload','clipboard'],transforms=[],image_mode='RGB', layers=False, elem_id="image_upload", type="pil", label="Upload",brush=gr.Brush(colors=["#fff"], color_mode="fixed")) with gr.Row(elem_id="prompt-container", equal_height=False): with gr.Row(): prompt = gr.Textbox(label="Prompt",value="a person",placeholder="Your prompt (what you want in place of what is erased)", elem_id="prompt") btn = gr.Button("Inpaint", elem_id="run_button",variant="primary") image_mask = gr.Image(sources=['upload','clipboard'], elem_id="mask_upload", type="pil", label="Mask_Upload",height=400, value=None) with gr.Accordion(label="Advanced Settings", open=False): with gr.Row( equal_height=True): strength = gr.Number(value=0.75, minimum=0, maximum=1.0, step=0.01, label="Inpaint strength") seed = gr.Number(value=0, minimum=0, step=1, label="Inpaint seed") models = ["black-forest-labs/FLUX.1-schnell"] inpaint_model = gr.Dropdown(label="modes", choices=models, value="black-forest-labs/FLUX.1-schnell") id_input=gr.Text(label="Name", visible=False) with gr.Column(): image_out = gr.Image(height=800,sources=[],label="Output", elem_id="output-img",format="webp") mask_out = gr.Image(height=800,sources=[],label="Mask", elem_id="mask-img",format="jpeg") btn.click(fn=process_images, inputs=[image, image_mask,prompt,inpaint_model,strength,seed], outputs =[image_out,mask_out], api_name='infer') gr.Examples( examples=[ ["examples/00538245.jpg", "examples/normal_mouth_mask.jpg","a beautiful girl,big-smile",0.75,"examples/normal_mouth_mask_result.jpg"], ["examples/00538245.jpg", "examples/expand_mouth_mask.jpg","a beautiful girl,big-smile",0.75,"examples/expand_mouth_mask_result.jpg"], ["examples/00547245_99.jpg", "examples/00547245_99_mask.jpg","a beautiful girl,eyes closed",0.75,"examples/00547245.jpg"], ["examples/00207245_18.jpg", "examples/00207245_18_mask.jpg","a beautiful girl,mouth opened",0.2,"examples/00207245.jpg"] ] , #fn=example_out, inputs=[image,image_mask,prompt,strength,image_out], #outputs=[test_out], #cache_examples=False, ) gr.HTML( gr.HTML(read_file("demo_footer.html")) ) if __name__ == "__main__": demo.launch()