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app.py

@@ -1,6 +1,7 @@
+
 from utils.image2image import stable_diffusion_img2img
 from utils.text2image import stable_diffusion_text2img
-
+from utils.inpaint import stable_diffusion_inpaint
 import gradio as gr
 
 stable_model_list = [
@@ -10,6 +11,12 @@ stable_model_list = [
     "stabilityai/stable-diffusion-2-1",
     "stabilityai/stable-diffusion-2-1-base"
 ]
+
+stable_inpiant_model_list = [
+    "stabilityai/stable-diffusion-2-inpainting",
+    "runwayml/stable-diffusion-inpainting"
+]
+
 stable_prompt_list = [
         "a photo of a man.",
         "a photo of a girl."
@@ -127,6 +134,50 @@ with app:
 
                 image2image_predict = gr.Button(value='Generator')
 
+            with gr.Tab('Inpaint'):
+                inpaint_image_file = gr.Image(
+                    source="upload", 
+                    type="numpy", 
+                    tool="sketch", 
+                    elem_id="source_container"
+                )
+
+                inpaint_model_id = gr.Dropdown(
+                    choices=stable_inpiant_model_list, 
+                    value=stable_inpiant_model_list[0], 
+                    label='Inpaint Model Id'
+                )
+
+                inpaint_prompt = gr.Textbox(
+                    lines=1, 
+                    value=stable_prompt_list[0], 
+                    label='Prompt'
+                )
+
+                inpaint_negative_prompt = gr.Textbox(
+                    lines=1, 
+                    value=stable_negative_prompt_list[0], 
+                    label='Negative Prompt'
+                )
+
+                with gr.Accordion("Advanced Options", open=False):
+                    inpaint_guidance_scale = gr.Slider(
+                        minimum=0.1, 
+                        maximum=15, 
+                        step=0.1, 
+                        value=7.5, 
+                        label='Guidance Scale'
+                    )
+
+                    inpaint_num_inference_step = gr.Slider(
+                        minimum=1, 
+                        maximum=100, 
+                        step=1, 
+                        value=50, 
+                        label='Num Inference Step'
+                    )
+
+                inpaint_predict = gr.Button(value='Generator')
 
     with gr.Tab('Generator'):
         with gr.Column():
@@ -159,4 +210,17 @@ with app:
             outputs = [output_image],
         )  
 
+        inpaint_predict.click(
+            fn = stable_diffusion_inpaint,
+            inputs = [
+                inpaint_image_file,
+                inpaint_model_id, 
+                inpaint_prompt, 
+                inpaint_negative_prompt,
+                inpaint_guidance_scale, 
+                inpaint_num_inference_step,
+            ],
+            outputs = [output_image],
+        )  
+
 app.launch()          

requirements.txt

@@ -2,4 +2,5 @@ transformers
 bitsandbytes==0.35.0 
 xformers 
 controlnet_aux 
-diffusers
+diffusers
+imageio

utils/image2image.py

@@ -1,4 +1,5 @@
 from diffusers import StableDiffusionImg2ImgPipeline, DDIMScheduler
+from IPython.display import display
 from PIL import Image
 import torch
 

utils/inpaint.py

@@ -0,0 +1,53 @@
+from diffusers import DiffusionPipeline, DDIMScheduler
+from PIL import Image
+import imageio
+import torch
+
+# https://huggingface.co/spaces/Manjushri/SD-2.0-Inpainting-CPU/blob/main/app.py
+
+def resize(height,img):
+  baseheight = height
+  img = Image.open(img)
+  hpercent = (baseheight/float(img.size[1]))
+  wsize = int((float(img.size[0])*float(hpercent)))
+  img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS)
+  return img
+
+def img_preprocces(source_img, prompt, negative_prompt):
+    imageio.imwrite("data.png", source_img["image"])
+    imageio.imwrite("data_mask.png", source_img["mask"])
+    src = resize(512, "data.png")
+    src.save("src.png")
+    mask = resize(512, "data_mask.png")  
+    mask.save("mask.png")
+    return src, mask
+
+def stable_diffusion_inpaint(
+    image_path:str,
+    model_path:str,
+    prompt:str,
+    negative_prompt:str,
+    guidance_scale:int,
+    num_inference_step:int,
+    ):
+
+    image, mask_image = img_preprocces(image_path, prompt, negative_prompt)
+    pipe = DiffusionPipeline.from_pretrained(
+        model_path,
+        revision="fp16",
+        torch_dtype=torch.float16,
+    )
+    pipe.to('cuda')
+    pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+    pipe.enable_xformers_memory_efficient_attention()
+
+    output = pipe(
+        prompt = prompt,
+        image = image,
+        mask_image=mask_image,
+        negative_prompt = negative_prompt,
+        num_inference_steps = num_inference_step,
+        guidance_scale = guidance_scale,
+    ).images
+
+    return output