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app_sd.py

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+from torch import autocast
+import requests
+import torch
+from PIL import Image
+from io import BytesIO
+
+from diffusers import StableDiffusionImg2ImgPipeline
+
+# load the pipeline
+device = "cuda"
+model_id_or_path = "CompVis/stable-diffusion-v1-4"
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    model_id_or_path,
+    revision="fp16", 
+    torch_dtype=torch.float16,
+    use_auth_token=True
+)
+# or download via git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
+# and pass `model_id_or_path="./stable-diffusion-v1-4"` without having to use `use_auth_token=True`.
+pipe = pipe.to(device)
+
+# let's download an initial image
+url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+
+response = requests.get(url)
+init_image = Image.open(BytesIO(response.content)).convert("RGB")
+init_image = init_image.resize((768, 512))
+
+prompt = "Lively, illustration of a [[[<king::4>]]], portrait, fantasy, intricate, Scenic, hyperdetailed, hyper realistic <king-hearthstone>, unreal engine, 4k, smooth, sharp focus, intricate, cinematic lighting, highly detailed, octane, digital painting, artstation, concept art, vibrant colors, Cinema4D, WLOP, 3d render, in the style of hearthstone::5 art by Artgerm and greg rutkowski and magali villeneuve, martina jackova, Giger"
+
+with autocast("cuda"):
+    images = pipe(prompt=prompt, init_image=init_image, strength=0.75, guidance_scale=7.5).images
+
+images[0].save("fantasy_landscape.png")