Add application files

.gitignore

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

README.md

@@ -1,6 +1,6 @@
 ---
-title: Test
-emoji: 📚
+title: Fluxing
+emoji: 🦢
 colorFrom: purple
 colorTo: red
 sdk: gradio

app.py

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+import spaces # type: ignore
+import os
+import time  # noqa: F401
+import uuid
+from PIL import Image
+import gradio as gr # type: ignore
+import numpy as np
+import random
+import torch
+from diffusers import FluxPipeline
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    torch_dtype=dtype,
+)
+pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+
+@spaces.GPU(duration=90)
+def infer(
+    prompt: str,
+    seed=42,
+    randomize_seed=False,
+    width=1024,
+    height=1024,
+    guidance_scale=5.0,
+    num_inference_steps=28,
+    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,
+        width=width,
+        height=height,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        guidance_scale=guidance_scale,
+    ).images[0]
+
+    assert isinstance(
+        image, Image.Image
+    ), "The output is not an instance of Image.Image"
+
+    filepath = os.path.join("images", "{uuid}.png".format(uuid=str(uuid.uuid4().hex)))
+    image.save(filepath)
+
+    return (
+        image,
+        gr.DownloadButton(
+            label="Download PNG", value=filepath, size="sm", visible=True
+        ),
+        seed,
+    )
+
+
+examples = [
+    "a tiny astronaut hatching from an egg on the moon",
+    "a cat holding a sign that says hello world",
+    "an anime illustration of a wiener schnitzel",
+]
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("""# FLUX.1 
+12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
+[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
+        """)
+
+        with gr.Row(equal_height=False):
+            prompt = gr.TextArea(
+                label="Prompt",
+                show_label=False,
+                lines=3,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+
+            run_button = gr.Button("Run", variant="primary", scale=0)
+
+        result = gr.Image(
+            format="webp",
+            type="pil",
+            label="Result",
+            show_label=False,
+            show_download_button=False,
+            show_share_button=False,
+        )
+        download = gr.DownloadButton(size="sm", visible=False)
+
+        with gr.Accordion("Advanced Settings", open=False):
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                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=832,
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1216,
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=0,
+                    maximum=15,
+                    step=0.1,
+                    value=3.5,
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=28,
+                )
+
+        # gr.Examples(
+        #     examples=examples,
+        #     fn=infer,
+        #     inputs=[prompt],
+        #     outputs=[result, seed],
+        #     cache_examples="lazy",
+        #     visible=False,
+        # )
+
+    run_button.click(fn=lambda: gr.update(visible=False),inputs=None, outputs=download)
+    
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+        ],
+        outputs=[result, download, seed],
+    )
+
+# def delete_packed_tensors(
+#     startup_time: int = time.time_ns(),
+#     zerogpu_offload_dir: str = "/home/user/.zerogpu/tensors",
+# ) -> None:
+#     entries = os.scandir(zerogpu_offload_dir)
+#     for entry in entries:
+#         print(f"Found entry {entry.path}")
+#         if entry.stat().st_atime_ns < startup_time:
+#             try:
+#                 file_path = entry.path
+#                 os.remove(file_path)
+#                 print(f"File deleted successfully: {file_path}")
+#             except FileNotFoundError:
+#                 print(f"File not found: {file_path}")
+#             except OSError as e:
+#                 print(f"Error deleting file: {e}")
+
+
+if __name__ == "__main__":
+    # delete_packed_tensors()
+    os.makedirs("images", exist_ok=True)
+    demo.launch()

requirements.txt

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+accelerate
+git+https://github.com/huggingface/diffusers.git
+torch
+transformers==4.42.4
+xformers
+sentencepiece
+bitsandbytes