added SD3

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import torch
-from diffusers import DiffusionPipeline, StableDiffusionPipeline, StableDiffusionXLPipeline, EulerDiscreteScheduler, UNet2DConditionModel
+from diffusers import DiffusionPipeline, StableDiffusionPipeline, StableDiffusionXLPipeline, EulerDiscreteScheduler, UNet2DConditionModel, StableDiffusion3Pipeline
 from transformers import BlipProcessor, BlipForConditionalGeneration
 from pathlib import Path
 from safetensors.torch import load_file
@@ -44,6 +44,11 @@ def load_model(model_name):
             use_safetensors=True, 
             variant="fp16"
         ).to("cuda")
+    elif model_name == "stabilityai/stable-diffusion-3-medium-diffusers":
+        pipeline = StableDiffusion3Pipeline.from_pretrained(
+            model_name, 
+            torch_dtype=torch.float16
+        ).to("cuda")
     else:
         raise ValueError("Unknown model name")
     return pipeline
@@ -54,6 +59,8 @@ pipeline_text2image = load_model(default_model)
 
 @spaces.GPU
 def getimgen(prompt, model_name):
+    global pipeline_text2image
+    pipeline_text2image = load_model(model_name)
     if model_name == "stabilityai/sdxl-turbo":
         return pipeline_text2image(prompt=prompt, guidance_scale=0.0, num_inference_steps=2).images[0]
     elif model_name == "runwayml/stable-diffusion-v1-5":
@@ -63,6 +70,8 @@ def getimgen(prompt, model_name):
     elif model_name == "segmind/SSD-1B":
         neg_prompt = "ugly, blurry, poor quality"
         return pipeline_text2image(prompt=prompt, negative_prompt=neg_prompt).images[0]
+    elif model_name == "stabilityai/stable-diffusion-3-medium-diffusers":
+        return pipeline_text2image(prompt=prompt, negative_prompt="", num_inference_steps=28, guidance_scale=7.0).images[0]
 
 blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
 blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda")