initial

.gitignore

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+__pycache__
+*.egg-info
+.ipynb_checkpoints
+dist
+.gradio
+build

app.py

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+import spaces
+import gradio as gr
+import re
+from PIL import Image
+import flux1_img2img
+import os
+import numpy as np
+import shutil
+#shutil.rmtree("/home/user/app/.gradio/cached_examples/23")
+
+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",inpaint_model="black-forest-labs/FLUX.1-schnell",strength=0.75,seed=0):
+    # 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,inpaint_model,strength,seed)
+        
+    return output
+    
+
+def read_file(path: str) -> str:
+    with open(path, 'r', encoding='utf-8') as f:
+        content = f.read()
+
+    return content
+
+def example_out(image,image_mask,prompt,strength,example_id):
+    # input
+    #parent,file=os.path.split(image_mask) # image is complex dict
+    #base,ext = os.path.splitext(file)
+    #key = base.split("_")[0]
+    return f"images/{example_id}.jpg"
+    #loaded_image =  Image.open(f"images/{example_id}.jpg")
+    #return loaded_image
+    #return np.array(loaded_image)
+
+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=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 eyes closed girl,shut eyes",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="jpg")
+
+                    
+            
+
+    btn.click(fn=process_images, inputs=[image, image_mask,prompt,inpaint_model,strength,seed], outputs =image_out, api_name='infer')
+    gr.Examples(
+               examples=[
+                    #["images/00547245_99.jpg", "images/00547245_99_mask.jpg","a beautiful girl,eyes closed",0.8,"images/00547245.jpg"],
+                    #["images/00538245_paint.jpg", "images/00538245_mask.jpg","a beautiful girl,wearing t-shirt",0.7,"images/00538245.jpg"],
+                    #["images/00207245_18.jpg", "images/00207245_18_mask.jpg","a beautiful girl,mouth opened",0.2,"images/00207245.jpg"]
+                         ]
+,
+                #fn=example_out,
+                inputs=[image,image_mask,prompt,strength,image_out],
+                #outputs=[test_out],
+                #cache_examples=False,
+    )
+    gr.HTML(
+        """
+            
+        """
+    )
+
+demo_blocks.queue(max_size=25).launch(share=False,debug=True)

demo_header.html

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+<div style="text-align: center;">
+    <h1>
+        Flux.1-schnell mask-inpaint
+    </h1>
+    <p>Making face images for <a href="https://huggingface.co/spaces/Akjava/godot-huggingface-chain">AI Diagram Chat with Voice/Face Character</a></p>
+    <p>
+        
+        See examples.
+        The image requires guide painting.(To generate an image with closed eyes, fill the eye area with skin color.)
+        You can draw mask or upload mask.BE CAREFUL UPLOAEDED MASK(If the uploaded mask exist,ignore your painted mask)
+    </p>
+    <p>Hint:step by step,reduce strength.blur guide-paint and mask</p>
+</div>

flux1_img2img.py

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+import torch
+from diffusers import FluxInpaintPipeline
+
+from PIL import Image
+import sys
+import spaces
+
+# I only test with FLUX.1-schnell
+
+@spaces.GPU
+def process_image(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
+    
+    pipe = FluxInpaintPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
+    pipe.to("cuda")
+
+    generators = []
+    generator = torch.Generator("cuda").manual_seed(seed)
+    generators.append(generator)
+    # more parameter see https://huggingface.co/docs/diffusers/api/pipelines/flux#diffusers.FluxInpaintPipeline
+    output = pipe(prompt=prompt, image=image, mask_image=mask_image,generator=generator,strength=strength)
+
+    return output.images[0]
+
+if __name__ == "__main__":
+    #args input-image input-mask output
+    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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+spaces
+numpy
+torch
+diffusers
+accelerate
+transformers
+sentencepiece