:tada: add negative embedding

app.py

@@ -1,7 +1,6 @@
 import dataclasses
 
 import gradio as gr
-import requests
 import spaces
 import torch
 from PIL import Image
@@ -25,15 +24,17 @@ EXTERNAL_MODEL_MAPPING = {
 
 MODEL_CHOICES = DIFFUSERS_MODEL_IDS + list(EXTERNAL_MODEL_MAPPING.keys())
 
-# Global Variables
 current_model_id = "stabilityai/stable-diffusion-3-medium-diffusers"
-device = "cuda" if torch.cuda.is_available() else "cpu"
 
+device = "cuda" if torch.cuda.is_available() else "cpu"
 if device == 'cuda':
+    dtype = torch.float16
     pipe = DiffusionPipeline.from_pretrained(
         current_model_id,
-        torch_dtype=torch.float16,
+        torch_dtype=dtype,
     ).to(device)
+else:
+    dtype = torch.float32
 
 
 @dataclasses.dataclass
@@ -61,6 +62,15 @@ EXAMPLES = [
         prompt='Beautiful pixel art of a Wizard with hovering text "Achivement unlocked: Diffusion models can spell now"'
     ).to_list(),
     Input(prompt='A corgi wearing sunglasses says "U-Net is OVER!!"').to_list(),
+    Input(
+        prompt='Cinematic Photo of a beautiful korean fashion model bokeh train',
+        model_id='Beautiful Realistic Asians',
+        negative_prompt='(worst_quality:2.0) (MajicNegative_V2:0.8) BadNegAnatomyV1-neg bradhands cartoon, cgi, render, illustration, painting, drawing',
+        width=512,
+        height=512,
+        guidance_scale=5.0,
+        num_inference_step=50,
+    ).to_list()
 ]
 
 
@@ -79,24 +89,28 @@ def inference(
 ) -> Image.Image:
     progress(0, "Starting inference...")
 
-    if device != 'cuda':
-        raise gr.Error("This model requires a GPU to run. Please switch to a GPU runtime.")
-
     global current_model_id, pipe
 
     if model_id != current_model_id:
         try:
+            # For NOT Diffusers' Models
             if model_id not in DIFFUSERS_MODEL_IDS:
                 model_id = EXTERNAL_MODEL_MAPPING[model_id]
 
             pipe = DiffusionPipeline.from_pretrained(
                 model_id,
-                torch_dtype=torch.float16,
+                torch_dtype=dtype,
             ).to(device)
+
             current_model_id = model_id
         except Exception as e:
             raise gr.Error(str(e))
 
+    # Load Textual Inversion
+    pipe.load_textual_inversion(
+        "checkpoints/embeddings/BadNegAnatomyV1 neg.pt", token='BadNegAnatomyV1-neg'
+    )
+
     # Generation
     images = pipe(
         prompt,
@@ -141,8 +155,9 @@ if __name__ == "__main__":
                         num_images = gr.Number(label="Num Images", value=4, minimum=1, maximum=10, step=1)
 
                     guidance_scale = gr.Slider(label="Guidance Scale", value=7.5, step=0.5, minimum=0, maximum=10)
-                    num_inference_step = gr.Slider(label="Num Inference Steps", value=50, minimum=1, maximum=100,
-                                                   step=1)
+                    num_inference_step = gr.Slider(
+                        label="Num Inference Steps", value=50, minimum=1, maximum=100, step=2
+                    )
 
             with gr.Column():
                 output_image = gr.Image(label="Image", type="pil")

checkpoints/embeddings/BadNegAnatomyV1 neg.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b389c1e5f96e23612272607e980d4ddab62183b7bfecc631293ebd700ce8f96a
+size 215915