Update app.py

app.py

@@ -1,38 +1,57 @@
-import gradio as gr
-import numpy as np
 import random
+import spaces
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import gradio as gr
+import numpy as np
 import torch
+from diffusers import LCMScheduler, PixArtAlphaPipeline, Transformer2DModel
+from peft import PeftModel
+import os
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+IS_SPACE = os.environ.get("SPACE_ID", None) is not None
+
+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")
+
 
 if torch.cuda.is_available():
-    torch_dtype = torch.float16
+    torch.cuda.max_memory_allocated(device=device)
+    pipe = PixArtAlphaPipeline.from_pretrained(
+        "PixArt-alpha/PixArt-XL-2-1024-MS",
+        transformer=transformer,
+        torch_dtype=torch.float16,
+    )
+    if not IS_SPACE:
+        pipe.enable_xformers_memory_efficient_attention()
+    pipe = pipe.to(device)
 else:
-    torch_dtype = torch.float32
+    pipe = PixArtAlphaPipeline.from_pretrained(
+        "PixArt-alpha/PixArt-XL-2-1024-MS",
+        transformer=transformer,
+        torch_dtype=torch.float16,
+    )
+    pipe = pipe.to(device)
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+pipe.text_encoder.to_bettertransformer()
+
+pipe.scheduler = LCMScheduler.from_pretrained(
+    "PixArt-alpha/PixArt-XL-2-1024-MS",
+    subfolder="scheduler",
+    timestep_spacing="trailing",
+)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
+NUM_INFERENCE_STEPS = 4
 
 
-# @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),
-):
+@spaces.GPU
+def infer(prompt, seed, randomize_seed):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
@@ -40,33 +59,55 @@ def infer(
 
     image = pipe(
         prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
+        guidance_scale=0,
+        num_inference_steps=NUM_INFERENCE_STEPS,
         generator=generator,
     ).images[0]
 
-    return image, seed
+    return image
 
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "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.",
+    "A small cactus with a happy face in the Sahara desert.",
+    "A super-realistic close-up of a snake eye",
+    "A cute cheetah looking amazed and surprised",
+    "Pirate ship sailing on a sea with the milky way galaxy in the sky and purple glow lights",
+    "a cute fluffy rabbit pilot walking on a military aircraft carrier, 8k, cinematic",
+    "A close up of an old elderly man with green eyes looking straight at the camera",
+    "A beautiful sunflower in rainy day",
 ]
 
 css = """
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 512px;
 }
 """
 
+if torch.cuda.is_available():
+    power_device = "GPU"
+else:
+    power_device = "CPU"
+
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown(
+            f"""
+        # ⚡ Flash Diffusion: FlashPixart ⚡
+        This is an interactive demo of [Flash Diffusion](https://gojasper.github.io/flash-diffusion-project/), a diffusion distillation method proposed in [Flash Diffusion: Accelerating Any Conditional
+        Diffusion Model for Few Steps Image Generation](http://arxiv.org/abs/2406.02347) *by Clément Chadebec, Onur Tasar, Eyal Benaroche and Benjamin Aubin* from Jasper Research.
+        [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}.
+        """
+        )
+        gr.Markdown(
+            "If you enjoy the space, please also promote *open-source* by giving a ⭐ to the <a href='https://github.com/gojasper/flash-diffusion' target='_blank'>Github Repo</a>."
+        )
+        gr.Markdown(
+            "💡 *Hint:* To better appreciate the low latency of our method, run the demo locally !"
+        )
 
         with gr.Row():
             prompt = gr.Text(
@@ -77,18 +118,11 @@ with gr.Blocks(css=css) as demo:
                 container=False,
             )
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+            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,
@@ -99,56 +133,20 @@ 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,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    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
-                )
-
-            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
-                )
-
-                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
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
+        examples = gr.Examples(examples=examples, inputs=[prompt])
+
+        gr.Markdown("**Disclaimer:**")
+        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."
+        )
     gr.on(
-        triggers=[run_button.click, prompt.submit],
+        [run_button.click, seed.change, randomize_seed.change, prompt.submit],
         fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+        inputs=[prompt, seed, randomize_seed],
+        outputs=[result],
+        show_progress="minimal",
+        show_api=False,
+        trigger_mode="always_last",
     )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.queue().launch(show_api=False)