Develop first demo

app.py

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+import os
+import torch
+import gradio as gr
+from torch import autocast
+from diffusers import StableDiffusionPipeline
+
+# get hf user access token as an environment variable
+TOKEN_KEY = os.getenv('AUTH_TOKEN')
+
+# setup pipeline
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16, use_auth_token=TOKEN_KEY)
+# pipe = pipe.to('cuda')
+
+# define gradio function
+def generate(prompt:str, seed:int, guidance:float, steps:int):
+    generator = torch.Generator("cuda").manual_seed(int(seed))
+    with autocast("cuda"):
+        image = pipe(prompt=prompt, generator=generator, guidance_scale=guidance, steps=50).images[0]
+    return image
+
+# create the gradio UI
+demo = gr.Interface(
+    fn=generate,
+    inputs=[gr.Textbox(placeholder="castle on a mountain"), gr.Number(value=123456), gr.Slider(0,10)],
+    outputs="image",
+    allow_flagging="never",
+)
+
+# allow queueing or incoming requests, max=3 
+demo.queue(concurrency_count=3)
+
+# launch demo
+demo.launch()

requirements.txt

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+scipy
+ftfy
+torch>=1.10.0
+transformers
+diffusers==0.3.0