update app readme + disclaimer

app.py

@@ -1,38 +1,35 @@
+import random
+
 import gradio as gr
 import numpy as np
-import random
-from diffusers import PixArtAlphaPipeline, Transformer2DModel, LCMScheduler
 import torch
+from diffusers import LCMScheduler, PixArtAlphaPipeline, Transformer2DModel
 from peft import PeftModel
 
-
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 transformer = Transformer2DModel.from_pretrained(
-  "PixArt-alpha/PixArt-XL-2-1024-MS",
-  subfolder="transformer",
-  torch_dtype=torch.float16
-)
-transformer = PeftModel.from_pretrained(
-  transformer,
-  "jasperai/flash-pixart"
+    "PixArt-alpha/PixArt-XL-2-1024-MS",
+    subfolder="transformer",
+    torch_dtype=torch.float16,
 )
+transformer = PeftModel.from_pretrained(transformer, "jasperai/flash-pixart")
 
 
 if torch.cuda.is_available():
     torch.cuda.max_memory_allocated(device=device)
     pipe = PixArtAlphaPipeline.from_pretrained(
-      "PixArt-alpha/PixArt-XL-2-1024-MS",
-      transformer=transformer,
-      torch_dtype=torch.float16
+        "PixArt-alpha/PixArt-XL-2-1024-MS",
+        transformer=transformer,
+        torch_dtype=torch.float16,
     )
     pipe.enable_xformers_memory_efficient_attention()
     pipe = pipe.to(device)
-else: 
+else:
     pipe = PixArtAlphaPipeline.from_pretrained(
-      "PixArt-alpha/PixArt-XL-2-1024-MS",
-      transformer=transformer,
-      torch_dtype=torch.float16
+        "PixArt-alpha/PixArt-XL-2-1024-MS",
+        transformer=transformer,
+        torch_dtype=torch.float16,
     )
     pipe = pipe.to(device)
 
@@ -48,22 +45,24 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 NUM_INFERENCE_STEPS = 4
 
+
 def infer(prompt, seed, randomize_seed):
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
+
     generator = torch.Generator().manual_seed(seed)
-    
+
     image = pipe(
-        prompt = prompt, 
-        guidance_scale = 0, 
-        num_inference_steps = NUM_INFERENCE_STEPS, 
-        generator = generator
-    ).images[0] 
-    
+        prompt=prompt,
+        guidance_scale=0,
+        num_inference_steps=NUM_INFERENCE_STEPS,
+        generator=generator,
+    ).images[0]
+
     return image
 
+
 examples = [
     "The image showcases a freshly baked bread, possibly focaccia, with rosemary sprigs and red pepper flakes sprinkled on top. It's sliced and placed on a wire cooling rack, with a bowl of mixed peppercorns beside it.",
     "A raccoon reading a book in a lush forest.",
@@ -76,7 +75,7 @@ examples = [
     "A beautiful sunflower in rainy day",
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 512px;
@@ -89,17 +88,19 @@ else:
     power_device = "CPU"
 
 with gr.Blocks(css=css) as demo:
-    
+
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
+        gr.Markdown(
+            f"""
         # ⚡ FlashDiffusion: FlashPixart ⚡
-        This is an interactive demo of [Flash Diffusion](https://huggingface.co/jasperai/flash-pixart), a diffusion distillation method proposed in [ADD ARXIV]() *by Clément Chadebec, Onur Tasar and Benjamin Aubin.*
-        This model is a **66.5M** LoRA distilled version of [Pixart-α](https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS) model that is able to generate 1024x1024 images in **4 steps**. 
+        This is an interactive demo of [Flash Diffusion](https://flash-diffusion.gojasper.github.io), a diffusion distillation method proposed in [ADD ARXIV]() *by Clément Chadebec, Onur Tasar, Eyal Benaroche and Benjamin Aubin.*
+        [This model](https://huggingface.co/jasperai/flash-pixart) is a **66.5M** LoRA distilled version of [Pixart-α](https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS) model that is able to generate 1024x1024 images in **4 steps**. 
         Currently running on {power_device}.
-        """)
-        
+        """
+        )
+
         with gr.Row():
-            
+
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -107,13 +108,13 @@ 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):
-            
+
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -121,24 +122,20 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-            
+
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+
+        examples = gr.Examples(examples=examples, inputs=[prompt])
+
+        gr.Markdown(
+            "This demo is only for research purpose. Jasper cannot be held responsible for the generation of NSFW (Not Safe For Work) content through the use of this demo. Users are solely responsible for any content they create, and it is their obligation to ensure that it adheres to appropriate and ethical standards. Jasper provides the tools, but the responsibility for their use lies with the individual user."
         )
 
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed],
-        outputs = [result]
-    )
-    seed.change(
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed],
-        outputs = [result]
-    )
+        gr.Markdown(
+            "To better appreciate the low latency of our method, run the demo locally !"
+        )
+
+    run_button.click(fn=infer, inputs=[prompt, seed, randomize_seed], outputs=[result])
+    seed.change(fn=infer, inputs=[prompt, seed, randomize_seed], outputs=[result])
 
-demo.queue().launch()
+demo.queue().launch(show_api=False)