Create app.py

app.py

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+import gradio as gr
+import torch
+#from torch import autocast // only for GPU
+
+from PIL import Image
+import numpy as np
+from io import BytesIO
+import os
+MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
+
+#from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusionImg2ImgPipeline
+
+print("hello")
+
+YOUR_TOKEN=MY_SECRET_TOKEN
+
+device="cpu"
+
+img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("AkiKagura/mkgen-diffusion", duse_auth_token=YOUR_TOKEN)
+img_pipe.to(device)
+
+source_img = gr.Image(source="upload", type="filepath", label="init_img | 512*512 px")
+gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto")
+
+def resize(value,img):
+  #baseheight = value
+  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)
+  img = img.resize((value,value), Image.Resampling.LANCZOS)
+  return img
+
+
+def infer(source_img, prompt, guide, steps, seed, strength): 
+    generator = torch.Generator('cpu').manual_seed(seed)
+    
+    source_image = resize(512, source_img)
+    source_image.save('source.png')
+    
+    images_list = img_pipe([prompt] * 1, init_image=source_image, strength=strength, guidance_scale=guide, num_inference_steps=steps)
+    images = []
+    
+    for i, image in enumerate(images_list["images"]):
+            images.append(image)    
+    return images
+
+print("done")
+
+title="Marco Generation Img2ing"
+description="<p style='text-align: center;'>Upload your image and input 'mkmk woman' to get Marco image. <br />Warning: Slow process... about 10 min inference time.</p>" 
+
+gr.Interface(fn=infer, inputs=[source_img,
+    "text",
+    gr.Slider(2, 15, value = 7, label = 'Guidence Scale'),
+    gr.Slider(10, 50, value = 25, step = 1, label = 'Number of Iterations'),
+    gr.Slider(label = "Seed", minimum = 0, maximum = 2147483647, step = 1, randomize = True),
+    gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .75)],
+    outputs=gallery,title=title,description=description, allow_flagging="manual", flagging_dir="flagged").queue(max_size=100).launch(enable_queue=True)