Update app.py

app.py

@@ -1,3 +1,33 @@
 import gradio as gr
+import torch
+import modin.pandas as pd
+import numpy as np
+from diffusers import DiffusionPipeline 
 
-gr.load("models/mann-e/Mann-E_Dreams").launch()
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+if torch.cuda.is_available():
+    torch.cuda.max_memory_allocated(device=device)
+    torch.cuda.empty_cache()
+    pipe = DiffusionPipeline.from_pretrained("mann-e/Mann-E_Turbo", torch_dtype=torch.float16)
+    pipe.enable_xformers_memory_efficient_attention()
+    pipe = pipe.to(device)
+    torch.cuda.empty_cache()
+else: 
+    pipe = DiffusionPipeline.from_pretrained("mann-e/Mann-E_Turbo", use_safetensors=True)
+    pipe = pipe.to(device)
+    
+def genie (prompt, negative_prompt, steps, seed):
+    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
+    int_image = pipe(prompt=prompt, negative_prompt=negative_prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0]
+    return int_image
+    
+gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 75 Token Limit.'),
+    gr.Textbox(label='What you DO NOT want the AI to generate. 75 Token Limit.'),
+    gr.Slider(1, maximum=8, value=6, step=1, label='Number of Iterations'), 
+    gr.Slider(minimum=0, step=1, maximum=999999999999999999, randomize=True),
+    ],
+    outputs='image', 
+    title="Mann-E Dreams", 
+    description="Mann-E Dreams <br><br><b>WARNING: This model is capable of producing NSFW (Softcore) images.</b>", 
+    article = "").launch(debug=True, max_threads=80)