Upload folder using huggingface_hub

README.md

@@ -1,12 +1,14 @@
 ---
-title: Wode
-emoji: 😻
+title: wode
+emoji: 🌻
 colorFrom: pink
-colorTo: yellow
+colorTo: purple
 sdk: gradio
-sdk_version: 6.9.0
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# wode
+
+Anime image generation Space powered by Diffusers.

app.py

@@ -0,0 +1,101 @@
+import gc
+import os
+from typing import Optional
+
+import gradio as gr
+import torch
+from diffusers import AutoPipelineForText2Image
+
+MODEL_ID = os.getenv("MODEL_ID", "Asahina2K/animagine-xl-3.1")
+DEFAULT_NEGATIVE = os.getenv(
+    "DEFAULT_NEGATIVE",
+    "lowres, blurry, bad anatomy, extra fingers, cropped, worst quality, low quality",
+)
+
+_pipe = None
+_device = "cuda" if torch.cuda.is_available() else "cpu"
+_dtype = torch.float16 if _device == "cuda" else torch.float32
+
+
+def load_pipe():
+    global _pipe
+    if _pipe is None:
+        pipe = AutoPipelineForText2Image.from_pretrained(
+            MODEL_ID,
+            torch_dtype=_dtype,
+            use_safetensors=True,
+        )
+        if hasattr(pipe, "enable_attention_slicing"):
+            pipe.enable_attention_slicing()
+        if hasattr(pipe, "vae") and hasattr(pipe.vae, "enable_tiling"):
+            pipe.vae.enable_tiling()
+        pipe = pipe.to(_device)
+        _pipe = pipe
+    return _pipe
+
+
+def unload_pipe():
+    global _pipe
+    if _pipe is not None:
+        _pipe = None
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+
+def generate(
+    prompt: str,
+    negative_prompt: str,
+    steps: int,
+    guidance_scale: float,
+    width: int,
+    height: int,
+    seed: int,
+):
+    if not prompt or not prompt.strip():
+        raise gr.Error("Prompt 不能为空")
+
+    pipe = load_pipe()
+    generator: Optional[torch.Generator] = None
+    if seed >= 0:
+        generator = torch.Generator(device=_device).manual_seed(int(seed))
+
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        num_inference_steps=int(steps),
+        guidance_scale=float(guidance_scale),
+        width=int(width),
+        height=int(height),
+        generator=generator,
+    ).images[0]
+    return image
+
+
+with gr.Blocks(title="wode") as demo:
+    gr.Markdown(f"# wode\n\nModel: `{MODEL_ID}`")
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", lines=5, placeholder="masterpiece, best quality, 1girl, sunflower field, anime style")
+            negative_prompt = gr.Textbox(label="Negative Prompt", lines=3, value=DEFAULT_NEGATIVE)
+            with gr.Row():
+                steps = gr.Slider(10, 50, value=28, step=1, label="Steps")
+                guidance_scale = gr.Slider(1, 12, value=7, step=0.5, label="CFG")
+            with gr.Row():
+                width = gr.Slider(512, 1024, value=832, step=64, label="Width")
+                height = gr.Slider(512, 1536, value=1216, step=64, label="Height")
+            seed = gr.Number(label="Seed (-1 = random)", value=-1, precision=0)
+            run = gr.Button("Generate", variant="primary")
+            unload = gr.Button("Unload model from memory")
+        with gr.Column():
+            out = gr.Image(label="Image", type="pil")
+
+    run.click(
+        fn=generate,
+        inputs=[prompt, negative_prompt, steps, guidance_scale, width, height, seed],
+        outputs=out,
+    )
+    unload.click(fn=unload_pipe, outputs=[])
+
+if __name__ == "__main__":
+    demo.queue(max_size=8).launch()

deploy_space.py

@@ -0,0 +1,53 @@
+import json
+import os
+import sys
+from pathlib import Path
+
+from huggingface_hub import HfApi
+
+SPACE_NAME = os.environ.get("SPACE_NAME", "wode")
+PRIVATE = os.environ.get("PRIVATE", "false").lower() == "true"
+FOLDER = Path(os.environ.get("FOLDER", ".")).resolve()
+
+
+def main():
+    token = sys.stdin.read().strip()
+    if not token:
+        raise SystemExit("missing token on stdin")
+
+    api = HfApi(token=token)
+    me = api.whoami()
+    username = me.get("name") or me.get("fullname") or me.get("email") or "unknown"
+
+    repo_url = api.create_repo(
+        repo_id=SPACE_NAME,
+        repo_type="space",
+        private=PRIVATE,
+        exist_ok=True,
+        space_sdk="gradio",
+    )
+
+    full_repo_id = repo_url.repo_id if hasattr(repo_url, "repo_id") else str(repo_url).replace("https://huggingface.co/", "")
+
+    api.upload_folder(
+        repo_id=full_repo_id,
+        repo_type="space",
+        folder_path=str(FOLDER),
+        ignore_patterns=["*.pyc", "__pycache__/*", ".git/*"],
+    )
+
+    try:
+        api.restart_space(repo_id=full_repo_id)
+    except Exception:
+        pass
+
+    out = {
+        "username": username,
+        "repo_id": full_repo_id,
+        "url": f"https://huggingface.co/spaces/{full_repo_id}",
+    }
+    print(json.dumps(out, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,7 @@
+torch
+transformers
+diffusers
+accelerate
+safetensors
+gradio>=4.44.0
+Pillow