Changed to DPMSolverMultistepScheduler

app.py

@@ -20,7 +20,7 @@ generator = torch.Generator(device).manual_seed(lastSeed)
 modelNames = ["stabilityai/stable-diffusion-2-inpainting",
               "runwayml/stable-diffusion-inpainting"]
 modelIndex = 0
-pipeline = None
+outpaintPipeline = None
 oldLatentWalk = None
 activeLatents = None
 
@@ -38,16 +38,18 @@ def GenerateNewLatentsForInference():
 
 def InitializeOutpainting():
     print("Initializing Outpainting")
-    global pipeline
+    global outpaintPipeline
     if deviceStr == "cuda":
-        pipeline = StableDiffusionInpaintPipeline.from_pretrained(modelNames[modelIndex],
+        outpaintPipeline = StableDiffusionInpaintPipeline.from_pretrained(modelNames[modelIndex],
                                                                 torch_dtype=torch.float16)
                                                                 #safety_checker=lambda images, **kwargs: (images, False))
-        pipeline.to(device)
-        pipeline.enable_xformers_memory_efficient_attention()
+        outpaintPipeline.to(device)
+        outpaintPipeline.enable_xformers_memory_efficient_attention()
     else:
-        pipeline = StableDiffusionInpaintPipeline.from_pretrained(modelNames[modelIndex])
+        outpaintPipeline = StableDiffusionInpaintPipeline.from_pretrained(modelNames[modelIndex])
                                                                 #safety_checker=lambda images, **kwargs: (images, False))
+    
+    outpaintPipeline.scheduler = DPMSolverMultistepScheduler.from_config(outpaintPipeline.scheduler.config)
 
 # Based on: https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/4
 # Further optimized to trade a divide operation for a multiply
@@ -77,7 +79,7 @@ def Diffuse(latentWalk, staticLatents, generatorSeed, inputImage, mask, pauseInf
         generator = torch.Generator(device).manual_seed(generatorSeed)
         lastSeed = generatorSeed
         
-    newImage = pipeline(prompt=prompt,
+    newImage = outpaintPipeline(prompt=prompt,
                     negative_prompt=negativePrompt,
                     image=inputImage,
                     mask_image=mask,
@@ -103,7 +105,7 @@ print("Initializing Gradio Interface")
 
 defaultMask = Image.open("assets/masks/diamond.png")
 numInfStepsDesc = "A higher value generally increases quality, but reduces the frames per second of the output stream."
-staticLatentsDesc = "This setting increases the frame to frame determisn of the generation. If this is disabled, then the inerence will take continuous large walks across the latent space between frames."
+staticLatentsDesc = "This setting increases the frame to frame determisn of the generation. If this is disabled, then the inference will take continuous large walks across the latent space between frames."
 generatorSeedDesc = "Identical seeds allow for persistent scene generation between runs, and changing the seed will take a static large walk across the latent space to better control and alter the generation of scene scene content especially when large abberations exist in the reconstruction."
 promptDesc = "This text will condition the generation of the scene to help guide the content creation."
 negPromptDesc = "This text will help deter the generation from converging towards reconstructing the elements described in the text."