Update app.py

app.py

@@ -4,25 +4,18 @@ import random
 
 # import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
+from peft import PeftModel, LoraConfig
 import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
 model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
+    model,
     prompt,
     negative_prompt,
     seed,
@@ -38,6 +31,22 @@ def infer(
 
     generator = torch.Generator().manual_seed(seed)
 
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    if torch.cuda.is_available():
+        torch_dtype = torch.float16
+    else:
+        torch_dtype = torch.float32
+
+    if model == "Ramzes":
+        pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch_dtype)
+        pipe.unet = PeftModel.from_pretrained(pipe_v5_2.unet, "Bordoglor/Ramzes_adapter_sd_v1.5/unet")
+        pipe.text_encoder = PeftModel.from_pretrained(pipe_v5_2.text_encoder, "Bordoglor/Ramzes_adapter_sd_v1.5/text_encoder")
+        pipe = pipe.to(device)
+    else:          
+        pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch_dtype)
+        pipe = pipe.to(device)
+
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -50,13 +59,6 @@ def infer(
 
     return image, seed
 
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
 css = """
 #col-container {
     margin: 0 auto;
@@ -68,6 +70,16 @@ with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown(" # Text-to-Image Gradio Template")
 
+
+        with gr.Row():
+            model = gr.Dropdown(
+                choices=["stabilityai/sdxl-turbo", "CompVis/stable-diffusion-v1-4", "Ramzes"],
+                value=model_repo_id,
+                label="Model",
+                info="Choose which diffusion model to use"
+            )
+
+            
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -81,12 +93,13 @@ with gr.Blocks(css=css) as demo:
 
         result = gr.Image(label="Result", show_label=False)
 
-        with gr.Accordion("Advanced Settings", open=False):
+        with gr.Accordion("Advanced Settings", open=True):
             negative_prompt = gr.Text(
                 label="Negative prompt",
+                value="dog, cat",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
+                visible=True,
             )
 
             seed = gr.Slider(
@@ -94,7 +107,7 @@ with gr.Blocks(css=css) as demo:
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=0,
+                value=42,
             )
 
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
@@ -122,7 +135,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    value=7.0,  # Replace with defaults that work for your model
                 )
 
                 num_inference_steps = gr.Slider(
@@ -130,14 +143,14 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=20,  # Replace with defaults that work for your model
                 )
 
-        gr.Examples(examples=examples, inputs=[prompt])
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
         inputs=[
+            model,
             prompt,
             negative_prompt,
             seed,
@@ -151,4 +164,4 @@ with gr.Blocks(css=css) as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()