Create app.py

app.py

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+import torch
+import requests
+from PIL import Image
+from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
+
+# Load the pipeline
+pipeline = DiffusionPipeline.from_pretrained(
+    "sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline",
+    torch_dtype=torch.float16
+)
+
+# Feel free to tune the scheduler!
+# `timestep_spacing` parameter is not supported in older versions of `diffusers`
+# so there may be performance degradations
+# We recommend using `diffusers==0.20.2`
+pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
+    pipeline.scheduler.config, timestep_spacing='trailing'
+)
+pipeline.to('cuda:0')
+
+# Download an example image.
+cond = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/lysol.png", stream=True).raw)
+
+# Run the pipeline!
+result = pipeline(cond, num_inference_steps=75).images[0]
+# for general real and synthetic images of general objects
+# usually it is enough to have around 28 inference steps
+# for images with delicate details like faces (real or anime)
+# you may need 75-100 steps for the details to construct
+
+result.show()
+result.save("output.png")
+
+