add xavy model

.gitignore

@@ -0,0 +1 @@
+.venv/

app.py

@@ -1,51 +1,59 @@
 import gradio as gr
 import numpy as np
 import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+# import spaces #[uncomment to use ZeroGPU]
+from diffusers import AutoPipelineForText2Image
 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
+# 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
+# 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)
+
+pipeline = AutoPipelineForText2Image.from_pretrained(
+    'black-forest-labs/FLUX.1-schnell', torch_dtype=torch.bfloat16).to('cuda')
+pipeline.load_lora_weights('Roomie/xavyy', weight_name='xavyy.safetensors')
 
-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]
+# @spaces.GPU #[uncomment to use ZeroGPU]
+
+
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
+
     generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
+
+    image = pipeline(
+        prompt=prompt,
+        # negative_prompt=negative_prompt,
+        # guidance_scale=guidance_scale,
+        # num_inference_steps=num_inference_steps,
+        # width=width,
+        # height=height,
+        # generator=generator
+    ).images[0]
+
     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="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 640px;
@@ -53,14 +61,14 @@ css="""
 """
 
 with gr.Blocks(css=css) as demo:
-    
+
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
         # Text-to-Image Gradio Template
         """)
-        
+
         with gr.Row():
-            
+
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -68,20 +76,20 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-            
+
             run_button = gr.Button("Run", scale=0)
-        
+
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-            
+
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
                 visible=False,
             )
-            
+
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -89,54 +97,55 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-            
+
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
+
             with gr.Row():
-                
+
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
+                    value=1024,  # Replace with defaults that work for your model
                 )
-                
+
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
+                    value=1024,  # Replace with defaults that work for your model
                 )
-            
+
             with gr.Row():
-                
+
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0, #Replace with defaults that work for your model
+                    value=0.0,  # Replace with defaults that work for your model
                 )
-                
+
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2, #Replace with defaults that work for your model
+                    value=2,  # Replace with defaults that work for your model
                 )
-        
+
         gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+            examples=examples,
+            inputs=[prompt]
         )
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed,
+                width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed]
     )
 
-demo.queue().launch()
+demo.queue().launch()