remove demo file

demo.py

@@ -1,53 +0,0 @@
-from diffusers import (
-    StableDiffusionPipeline,
-    PNDMScheduler,
-)
-from diffusers.models import AutoencoderKL
-import os
-import torch
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-vae = AutoencoderKL.from_pretrained(
-    "stabilityai/sd-vae-ft-ema", torch_dtype=torch.float32
-)
-common_config = {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
-SCHEDULER = PNDMScheduler(**common_config, skip_prk_steps=True, steps_offset=1,)
-HF_API_TOKEN = os.getenv("HF_API_TOKEN")
-shared_pipe_kwargs = dict(
-    vae=vae,
-    torch_dtype=torch.float32,
-    revision="fp16",
-    use_auth_token=HF_API_TOKEN,
-    scheduler=SCHEDULER,
-)
-
-
-base_sd_pipe = StableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", **shared_pipe_kwargs
-).to(device)
-
-pai_model_dir = '.'
-
-playground_v1_model_dir = os.path.join(
-    pai_model_dir, "snapshots/36c9e19103f6b897886fb019ebc4d8e86b566032"
-)
-
-pgv1_shared_pipe_kwargs = dict(
-    vae=vae,
-    torch_dtype=torch.float32,
-    tokenizer=base_sd_pipe.tokenizer,
-    feature_extractor=base_sd_pipe.feature_extractor,
-    text_encoder=base_sd_pipe.text_encoder,
-    scheduler=SCHEDULER,
-    local_files_only=True, 
-)
-
-pgv1_pipe = StableDiffusionPipeline.from_pretrained(
-    playground_v1_model_dir,
-    **pgv1_shared_pipe_kwargs,
-).to(device)
-
-img = pgv1_pipe("Frog", num_inference_steps=30)
-
-img[0][0].save('frog.png')