initial

app.py

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+import spaces
+import gradio as gr
+import re
+from PIL import Image
+import flux1_inpaint
+
+
+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=180)
+def process_images(image, image2=None,prompt="a girl",negative_prompt=None,inpaint_model="black-forest-labs/FLUX.1-schnell",strength=0.75):
+    if negative_prompt == None:
+         negative_prompt = ""
+    # I'm not sure when this happen
+    if not isinstance(image, dict):
+        if image2 == None:
+             print("empty mask")
+             return image
+        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]
+
+
+    output = flux1_inpaint.process_image(image["background"],mask,prompt,negative_prompt,inpaint_model,strength)
+        
+    return output
+    
+
+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;
+}
+"""
+demo_blocks = gr.Blocks(css=css, elem_id="demo-container")
+with demo_blocks as demo:
+    with gr.Column():
+        gr.HTML(read_file("demo_header.html"))
+    with gr.Row():
+                with gr.Column():
+                    image = gr.ImageEditor(height=1000,sources=['upload','clipboard'],transforms=[],image_mode='RGB', layers=False,  elem_id="image_upload", type="pil", label="Upload",brush=gr.Brush(colors=["#999"], color_mode="fixed"))
+                    with gr.Row(elem_id="prompt-container",  equal_height=False):
+                        with gr.Row():
+                            prompt = gr.Textbox(label="Prompt",placeholder="Your prompt (what you want in place of what is erased)", elem_id="prompt")
+                            
+                    btn = gr.Button("Inpaint!", elem_id="run_button")
+                    negative_prompt = gr.Textbox(label="Negative Prompt",placeholder="negative prompt",value="worst quality", elem_id="negative_prompt")
+                    
+                    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=0.8, minimum=0, maximum=1.0, step=0.01, label="Inpaint strength")
+                            blur_radius = gr.Number(value=25, minimum=0.0, maximum=50.0, step=1, label="Blur Radius")
+                            edge_expand = gr.Number(value=8, minimum=0.0, maximum=20.0, step=1, label="Edge Expand")
+                    with gr.Row(equal_height=True):
+                            models = ["black-forest-labs/FLUX.1-schnell"]
+                            inpaint_model = gr.Dropdown(label="modes", choices=models, value="stablediffusionapi/bracingevomix-v2") 
+                with gr.Column():
+                    image_out = gr.Image(sources=[],label="Output", elem_id="output-img")
+                    
+            
+
+    btn.click(fn=process_images, inputs=[image, image_mask,prompt,negative_prompt,inpaint_model,strength], outputs =image_out, api_name='infer')
+    gr.Examples(
+               examples=[["examples/catman.jpg", "examples/catman_mask.jpg","He's wearing a dog face."]]
+,
+                #fn=predict,
+                inputs=[image,image_mask,prompt],
+                cache_examples=False,
+    )
+    gr.HTML(
+        """
+            <div style="text-align: center;">
+                <p>Inpaint Code <a href="https://github.com/opencv/opencv/blob/da3debda6d233af90e421e95700c63fc08b83b75/samples/python/inpaint.py" style="text-decoration: underline;" target="_blank">OpenCV inpaint example</a> - Gradio Demo by 🤗 Hugging Face
+                </p>
+            </div>
+        """
+    )
+
+demo_blocks.queue(max_size=25).launch(share=False,debug=True)

demo_header.html

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+<div style="text-align: center;">
+    <h1>
+        Inpaint
+    </h1>
+    <p>
+        
+    </p>
+</div>

flux1_inpaint.py

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+import torch
+from diffusers import FluxInpaintPipeline
+from diffusers.utils import load_image
+
+from PIL import Image
+import sys
+import numpy as np
+
+import json
+import os
+import spaces
+
+device = "cuda"
+pipeline_device = 0 if torch.cuda.is_available() else -1 # TODO mix above
+torch_dtype = torch.float16
+debug = True
+@spaces.GPU
+def make_inpaint_condition(image, image_mask):
+    image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
+    image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
+
+    if image.shape[0:1] != image_mask.shape[0:1]:
+        print("error image and image_mask must have the same image size")
+        return None
+    
+    image[image_mask > 0.5] = -1.0  # set as masked pixel
+    image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+    return image
+
+
+
+
+
+@spaces.GPU
+def process_image(image,mask_image,prompt="a girl",negative_prompt="",model_id="black-forest-labs/FLUX.1-schnell",strength=0.75,seed=0,num_inference_steps=4):
+    
+    
+
+    #control_image=make_inpaint_condition(image,mask_image)
+    #image.save("_control.jpg")
+    if image == None:
+        return None
+    
+    pipe = FluxInpaintPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
+
+    #batch_size =1
+    generators = []
+    generator = torch.Generator(device).manual_seed(seed)
+    generators.append(generator)
+    
+    output = pipe(prompt=prompt, image=image, mask_image=mask_image,generator=generator)
+
+    return output.images[0]
+
+
+if __name__ == "__main__":
+    image = Image.open(sys.argv[1])
+    mask  = Image.open(sys.argv[2])
+    output = process_image(image,mask)
+    output.save(sys.argv[3])

requirements.txt

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+safetensors 
+numpy
+torch
+diffusers
+spaces
+accelerate
+transformers