Update app.py

app.py

@@ -1,60 +1,20 @@
 import gradio as gr
 import numpy as np
 import random
-import spaces
-from diffusers import AuraFlowPipeline
 import torch
-from gradio_imageslider import ImageSlider
+from diffusers import AuraFlowPipeline
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-#torch.set_float32_matmul_precision("high")
-
-#torch._inductor.config.conv_1x1_as_mm = True
-#torch._inductor.config.coordinate_descent_tuning = True
-#torch._inductor.config.epilogue_fusion = False
-#torch._inductor.config.coordinate_descent_check_all_directions = True
-
-#pipe_v1 = AuraFlowPipeline.from_pretrained(
-#	"fal/AuraFlow",
-#    torch_dtype=torch.float16
-#).to("cuda")
-
-pipe_v2 = AuraFlowPipeline.from_pretrained(
-    "fal/AuraFlow-v0.2",
-    torch_dtype=torch.float16
-).to("cuda")
-
+# Initialize the AuraFlow v0.3 pipeline
 pipe = AuraFlowPipeline.from_pretrained(
-	"fal/AuraFlow-v0.3",
+    "fal/AuraFlow-v0.3",
     torch_dtype=torch.float16
-).to("cuda")
-#pipe.transformer.to(memory_format=torch.channels_last)
-#pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True)
-#pipe.transformer.to(memory_format=torch.channels_last)
-#pipe.vae.to(memory_format=torch.channels_last)
-
-#pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
-#pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune", fullgraph=True)
+).to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-@spaces.GPU()
-def infer_example(prompt, negative_prompt="", seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, model_version="0.2", comparison_mode=False, progress=gr.Progress(track_tqdm=True)):
-    generator = torch.Generator().manual_seed(seed)
-    image = pipe(
-            prompt = prompt, 
-            negative_prompt = negative_prompt,
-            width = width,
-            height = height,
-            guidance_scale = guidance_scale, 
-            num_inference_steps = num_inference_steps, 
-            generator = generator
-    ).images[0] 
-    return image, seed
-    
-@spaces.GPU(duration=95)
 def infer(prompt,
           negative_prompt="",
           seed=42,
@@ -63,68 +23,24 @@ def infer(prompt,
           height=1024,
           guidance_scale=5.0,
           num_inference_steps=28,
-          model_version="0.3",
-          comparison_mode=False,
-          progress=gr.Progress(track_tqdm=True)
-         ):
+          progress=gr.Progress(track_tqdm=True)):
     
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
-    generator = torch.Generator().manual_seed(seed)
-    if(comparison_mode):
-        image_1 = pipe_v2(
-            prompt = prompt, 
-            negative_prompt = negative_prompt,
-            width=width,
-            height=height,
-            guidance_scale = guidance_scale, 
-            num_inference_steps = num_inference_steps, 
-            generator = generator
-        ).images[0] 
-        generator = torch.Generator().manual_seed(seed)
-        image_2 = pipe(
-            prompt = prompt, 
-            negative_prompt = negative_prompt,
-            width=width,
-            height=height,
-            guidance_scale = guidance_scale, 
-            num_inference_steps = num_inference_steps, 
-            generator = generator
-        ).images[0]
-        return gr.update(visible=False), gr.update(visible=True, value=(image_1, image_2)), seed
-    if(model_version == "0.1"):
-        image = pipe_v1(
-            prompt = prompt, 
-            negative_prompt = negative_prompt,
-            width=width,
-            height=height,
-            guidance_scale = guidance_scale, 
-            num_inference_steps = num_inference_steps, 
-            generator = generator
-        ).images[0]
-    elif(model_version == "0.2"):
-        image = pipe_v2(
-            prompt = prompt, 
-            negative_prompt = negative_prompt,
-            width=width,
-            height=height,
-            guidance_scale = guidance_scale, 
-            num_inference_steps = num_inference_steps, 
-            generator = generator
-        ).images[0]
-    else:
-        image = pipe(
-            prompt = prompt, 
-            negative_prompt = negative_prompt,
-            width=width,
-            height=height,
-            guidance_scale = guidance_scale, 
-            num_inference_steps = num_inference_steps, 
-            generator = generator
-        ).images[0]
+    generator = torch.Generator(device=device).manual_seed(seed)
+    
+    image = pipe(
+        prompt=prompt, 
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale, 
+        num_inference_steps=num_inference_steps, 
+        generator=generator
+    ).images[0]
         
-    return gr.update(visible=True, value=image), gr.update(visible=False), seed
+    return image, seed
 
 examples = [
     "A photo of a lavender cat",
@@ -141,16 +57,24 @@ css="""
 """
 
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # AuraFlow 0.3
-        Demo of the [AuraFlow 0.3](https://huggingface.co/fal/AuraFlow-v0.3) 6.8B parameters open source diffusion transformer model
-        [[blog](https://blog.fal.ai/auraflow/)] [[model](https://huggingface.co/fal/AuraFlow)] [[fal](https://fal.ai/models/fal-ai/aura-flow)]
-        """)
+        gr.HTML(
+            """
+            <h1 style='text-align: center'>
+            AuraFlow v0.3
+            </h1>
+            """
+        )
+        gr.HTML(
+            """
+            <h3 style='text-align: center'>
+            Follow me for more!
+            <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>  | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
+            </h3>
+            """
+        )
         
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -158,18 +82,11 @@ 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)
-        result_compare = ImageSlider(visible=False, label="Left 0.2, Right 0.3")
-        comparison_mode = gr.Checkbox(label="Comparison mode", info="Compare v0.2 with v0.3", value=False)
+        
         with gr.Accordion("Advanced Settings", open=False):
-            
-            model_version = gr.Dropdown(
-                ["0.2", "0.3"], label="Model version", value="0.3"
-            )
-            
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
@@ -187,7 +104,6 @@ with gr.Blocks(css=css) as demo:
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
@@ -195,7 +111,6 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
@@ -205,7 +120,6 @@ with gr.Blocks(css=css) as demo:
                 )
             
             with gr.Row():
-                
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
@@ -213,7 +127,6 @@ with gr.Blocks(css=css) as demo:
                     step=0.1,
                     value=5.0,
                 )
-                
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -223,18 +136,18 @@ with gr.Blocks(css=css) as demo:
                 )
         
         gr.Examples(
-            examples = examples,
-            fn = infer_example,
-            inputs = [prompt],
-            outputs = [result, seed],
+            examples=examples,
+            fn=infer,
+            inputs=[prompt],
+            outputs=[result, seed],
             cache_examples="lazy"
         )
 
     gr.on(
         triggers=[run_button.click, prompt.submit, negative_prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, model_version, comparison_mode],
-        outputs = [result, result_compare, 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()