Update app.py

app.py

@@ -1,18 +1,53 @@
 import gradio as gr
-from transformers import pipeline
+import spaces
+import torch
+import os
+from compel import Compel, ReturnedEmbeddingsType
+from diffusers import DiffusionPipeline
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-def predict(input_img):
-    predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
+model_name = os.environ.get('MODEL_NAME', 'UnfilteredAI/NSFW-gen-v2')
+pipe = DiffusionPipeline.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16
+)
+pipe.to('cuda')
 
-gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-    title="Hot Dog? Or Not?",
+compel = Compel(
+  tokenizer=[pipe.tokenizer, pipe.tokenizer_2] ,
+  text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
+  returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
+  requires_pooled=[False, True]
 )
 
-if __name__ == "__main__":
-    gradio_app.launch()
+
+@spaces.GPU(duration=120)
+def generate(prompt, negative_prompt, num_inference_steps, guidance_scale, width, height, num_samples):
+    embeds, pooled = compel(prompt)
+    neg_embeds, neg_pooled = compel(negative_prompt)
+    return pipe(
+        prompt_embeds=embeds,
+        pooled_prompt_embeds=pooled,
+        negative_prompt_embeds=neg_embeds,
+        negative_pooled_prompt_embeds=neg_pooled,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        width=width,
+        height=height,
+        num_images_per_prompt=num_samples
+    ).images
+
+
+gr.Interface(
+    fn=generate,
+    inputs=[
+        gr.Text(label="Prompt"),
+        gr.Text("", label="Negative Prompt"),
+        gr.Number(7, label="Number inference steps"),
+        gr.Number(3, label="Guidance scale"),
+        gr.Number(512, label="Width"),
+        gr.Number(512, label="Height"),
+        gr.Number(1, label="# images"),
+    ],
+    outputs=gr.Gallery(),
+).launch()