Update app.py

app.py

@@ -8,7 +8,7 @@ from io import BytesIO
 import os
 MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
 
-from diffusers import StableDiffusionPipeline
+#from diffusers import StableDiffusionPipeline
 from diffusers import StableDiffusionImg2ImgPipeline
 
 def empty_checker(images, **kwargs): return images, False
@@ -24,11 +24,6 @@ img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("AkiKagura/mkgen-diffu
 img_pipe.safety_checker = empty_checker
 img_pipe.to(device)
 
-# txt2img pipeline
-pipe = StableDiffusionPipeline.from_pretrained("AkiKagura/mkgen-diffusion", duse_auth_token=YOUR_TOKEN)
-pipe.safety_checker = empty_checker
-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")
 
@@ -45,12 +40,9 @@ def resize(value,img):
 def infer(source_img, prompt, guide, steps, seed, strength): 
     generator = torch.Generator('cpu').manual_seed(seed)
     
-    if source_image is None:
-        images_list = pipe([prompt] * 1, guidance_scale=guide, num_inference_steps=steps)
-    else:
-        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)
+    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"]):