Update app.py

app.py

@@ -1,10 +1,26 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+from diffusers import StableDiffusionPipeline, AutoencoderKL
 
-tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
-model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
+repo = "IDKiro/sdxs-512-0.9"
+seed = 42
+weight_type = torch.float32  # or float16
 
-text = "Tell a story"
+# Load model.
+pipe = StableDiffusionPipeline.from_pretrained(repo, torch_dtype=weight_type)
 
-encoded_input = tokenizer(text, return_tensors='pt')
+# use original VAE
+# pipe.vae = AutoencoderKL.from_pretrained("IDKiro/sdxs-512-0.9/vae_large")
 
-output = model(**encoded_input)
+pipe.to("cuda")
+
+prompt = "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour"
+
+# Ensure using 1 inference step and CFG set to 0.
+image = pipe(
+    prompt,
+    num_inference_steps=1,
+    guidance_scale=0,
+    generator=torch.Generator(device="cuda").manual_seed(seed)
+).images[0]
+
+image.save("output.png")