Add demo files.

README.md

@@ -1,6 +1,6 @@
 ---
 title: Wuerstchen Test
-emoji: 🏢
+emoji: 🌭
 colorFrom: green
 colorTo: red
 sdk: gradio

app.py

@@ -0,0 +1,38 @@
+import torch
+from diffusers import AutoPipelineForText2Image
+from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
+import gradio as gr
+
+if torch.cuda.is_available():
+    pipe = AutoPipelineForText2Image.from_pretrained("warp-ai/wuerstchen", torch_dtype=torch.float16).to("cuda")
+else:
+    pipe = AutoPipelineForText2Image.from_pretrained("warp-ai/wuerstchen")
+
+def gen_image(caption):
+    # caption = "Anthropomorphic fox dressed as a fire fighter"
+    # fantasy art of a band of brothers human cleric by greg rutkowski
+    images = pipe(
+        caption,
+        width=1024,
+        height=1536,
+        prior_timesteps=DEFAULT_STAGE_C_TIMESTEPS,
+        prior_guidance_scale=4.0,
+        num_images_per_prompt=1,
+    ).images
+    return images[0]
+
+
+#  Once primed, 1024 x 1536 images are generated in ~6 seconds on my machine; quality is so-so but similar to SD 1.5
+with gr.Blocks() as demo:
+    gr.Markdown("# Test of the Wueurstchen Model")
+    with gr.Row():
+        with gr.Column():
+            in_text = gr.Textbox(value="Enter a prompt here")
+            run_button = gr.Button(variant="primary")
+        with gr.Column():
+            out_image = gr.Image(label="Image Output")
+
+    run_button.click(gen_image, [in_text], [out_image], None)
+
+if __name__ == '__main__':
+    demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+accelerate
+diffusers
+torch
+transformers