Update app.py

app.py

@@ -126,9 +126,8 @@ def plex(mput, prompt, neg_prompt, stips, modal_id, dula, blip, blop):
     pipe = accelerator.prepare(pipe.to("cpu"))
     
     tilage = pope(prompt,num_inference_steps=5,height=512,width=512,generator=generator).images[0]
-    tilage.save('./til.png', 'PNG')
-    apol.append(tilage)
-    cannyimage = np.array(apol[0])
+    ##tilage.save('./til.png', 'PNG')
+    cannyimage = np.array(tilage)
     low_threshold = 100
     high_threshold = 200
     cannyimage = cv2.Canny(cannyimage, low_threshold, high_threshold)
@@ -138,17 +137,18 @@ def plex(mput, prompt, neg_prompt, stips, modal_id, dula, blip, blop):
     cannyimage = cannyimage[:, :, None]
     cannyimage = np.concatenate([cannyimage, cannyimage, cannyimage], axis=2)
     canny_image = Image.fromarray(cannyimage)
-    canny_image.save('./can.png', 'PNG')
-    apol.append(canny_image)
+    ##canny_image.save('./can.png', 'PNG')
     pose_image = load_image(mput)
-    pose_image.save('./pos.png', 'PNG')
+    ##pose_image.save('./pos.png', 'PNG')
     openpose_image = openpose(mput)
-    openpose_image.save('./fin.png','PNG')
-    apol.append(openpose_image)
-    images = [apol[2], apol[1]]
+    ##openpose_image.save('./fin.png','PNG')
+    images = [openpose_image, canny_image]
     imoge = pipe([prompt]*2,images,num_inference_steps=stips,generator=generator,negative_prompt=[neg_prompt]*2,controlnet_conditioning_scale=[blip, blop])
     for i, imge in enumerate(imoge["images"]):
         apol.append(imge)
+    apol.append(openpose_image)
+    apol.append(canny_image)
+    apol.append(tilage)
     return apol
 
 iface = gr.Interface(fn=plex,inputs=[gr.Image(type="filepath"), gr.Textbox(label="prompt"), gr.Textbox(label="neg_prompt", value="monochrome, lowres, bad anatomy, worst quality, low quality"), gr.Slider(label="infer_steps", value=5, minimum=1, step=1, maximum=5), gr.Dropdown(choices=models, value=models[0], type="value", label="select a model"), gr.Dropdown(choices=sdulers, value=sdulers[0], type="value", label="schedulrs"), gr.Slider(label="condition_scale_canny", value=0.5, minimum=0.1, step=0.1, maximum=1), gr.Slider(label="condition_scale_pose", value=0.5, minimum=0.1, step=0.1, maximum=1)], outputs=gr.Gallery(columns=1,rows=5), title="Img2Img Guided Multi-Conditioned Canny/Pose Controlnet Selectable StableDiffusion Model Demo", description="by JoPmt.")