last

app.py

@@ -133,8 +133,9 @@ num_images_per_prompt = st.slider('Image Count to be Produced:', min_value=1, ma
 # use seed with torch generator
 #torch.manual_seed(0)
 # seed
-#seed = st.slider('Seed:', min_value=0, max_value=100, value=1)
-#generator = [torch.Generator(device="cuda").manual_seed(seed) for i in range(num_images_per_prompt)]
+
+seed = st.slider('Seed:', min_value=0, max_value=100, value=1)
+generator = [torch.Generator(device="cuda").manual_seed(seed) for i in range(num_images_per_prompt)]
 
 #generator = torch.Generator(device="cuda").manual_seed(0)
 run_model_button = st.button("Run Model") 
@@ -143,9 +144,9 @@ run_model_button = st.button("Run Model")
 def initialize_pipe():
     pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting",
             revision="fp16",
-            torch_dtype=torch.float32, #16 for gpu
+            torch_dtype=torch.float16, #16 for gpu
             safety_checker = None,
-            requires_safety_checker = False) #.to("cuda")
+            requires_safety_checker = False).to("cuda")
 
     pipe.safety_checker = None
     pipe.requires_safety_checker = False
@@ -190,10 +191,10 @@ if run_model_button == True and input_prompt is not None :
 
     pipe = initialize_pipe()
 
-    output_height = 128
-    output_width = 128
+    #output_height = 128
+    #output_width = 128
 
-    x5 = pipe(image=dif_image, mask_image=inverted_image, num_inference_steps=num_inference_steps,  # , generator= generator
+    x5 = pipe(image=dif_image, mask_image=inverted_image, num_inference_steps=num_inference_steps,generator= generator,
                 num_images_per_prompt=num_images_per_prompt, prompt=prompt, negative_prompt=input_negative_prompt,
                 height=output_height, width=output_width).images