update

app.py

@@ -12,9 +12,7 @@ def sanitize_prompt(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 = ""
+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:
@@ -34,7 +32,7 @@ def process_images(image, image2=None,prompt="a girl",negative_prompt=None,inpai
          mask = image['layers'][0]
 
 
-    output = flux1_inpaint.process_image(image["background"],mask,prompt,negative_prompt,inpaint_model,strength)
+    output = flux1_inpaint.process_image(image["background"],mask,prompt,inpaint_model,strength,seed)
         
     return output
     
@@ -61,29 +59,28 @@ with demo_blocks as demo:
         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=["#fff"], color_mode="fixed"))
+                    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")
-                    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="black-forest-labs/FLUX.1-schnell") 
+                            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") 
+                    
+                            
                 with gr.Column():
-                    image_out = gr.Image(sources=[],label="Output", elem_id="output-img")
+                    image_out = gr.Image(height=800,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')
+    btn.click(fn=process_images, inputs=[image, image_mask,prompt,inpaint_model,strength,seed], outputs =image_out, api_name='infer')
     gr.Examples(
                examples=[["examples/catman.jpg", "examples/catman_mask.jpg","He's wearing a dog face."]]
 ,

flux1_inpaint.py

@@ -1,64 +1,30 @@
 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 = image.cpu().numpy()  # torch.Tensor を NumPy 配列に変換
-    image = (image * 255).astype(np.uint8)  # 0~1 の範囲を 0~255 に戻し、uint8 型にキャスト
-    image = image.transpose(0, 2, 3, 1)  # (1, 3, H, W) から (1, H, W, 3) に形状を変更
-
-    return image[0]
-
-
-
-
+# I only test with FLUX.1-schnell
 
 @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):
-    
-    
+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
     
-    #control_image=make_inpaint_condition(image,mask_image)
-    #mask_image.save("_mask_image.jpg")
-    #image = control_image
-    
     pipe = FluxInpaintPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
-    pipe.to(device)
-    #batch_size =1
+    pipe.to("cuda")
+
     generators = []
-    generator = torch.Generator(device).manual_seed(seed)
+    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)