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README.md

@@ -1,12 +1,13 @@
----
-title: Votepurchase-AnythingXL Xl
-emoji: ⚡
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 4.38.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Votepurchase-AnythingXL Xl
+emoji: 🖼
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 4.26.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import numpy as np
+import PIL.Image
+from PIL import Image
+import random
+from diffusers import ControlNetModel, StableDiffusionXLPipeline, AutoencoderKL
+from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
+import cv2
+import torch
+import spaces
+from hf import upload_image
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "votepurchase/AnythingXL_xl",
+    torch_dtype=torch.float16,
+)
+
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1216
+
+    
+@spaces.GPU
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, filename=""):
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator().manual_seed(seed)
+
+    output_image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator
+    ).images[0]
+
+    if filename: upload_image(output_image, filename)
+
+    return output_image
+
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+
+    with gr.Column(elem_id="col-container"):
+
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                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):
+
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                value="nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
+            )
+
+            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=1024,#832,
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,#1216,
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=20.0,
+                    step=0.1,
+                    value=7,
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=28,
+                    step=1,
+                    value=28,
+                )
+
+    run_button.click(#lambda x: None, inputs=None, outputs=result).then(
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result]
+    )
+
+demo.queue().launch()

hf.py

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+import os
+from pathlib import Path
+
+
+os.environ['HF_OUTPUT_REPO'] = 'votepurchase/pony' # The name of the 'model' repo to upload to. private repo recommended.
+# Set HF write token to HF_TOKEN as SECRET (never as environment)
+
+
+def upload_to_repo(path, filename):
+    from huggingface_hub import HfApi, hf_hub_url
+    hf_token = os.environ.get("HF_TOKEN")
+    repo_id = os.environ.get("HF_OUTPUT_REPO")
+    api = HfApi()
+    try:
+        api.upload_file(path_or_fileobj=path, path_in_repo=filename, repo_id=repo_id, token=hf_token)
+        url = hf_hub_url(repo_id=repo_id, filename=filename)
+    except Exception as e:
+        print(f"Error: Failed to upload to {repo_id}. ")
+        return None
+    return url
+
+def upload_image(path, filename):
+    url = upload_to_repo(path, filename)
+    print(url) # for debug
+    return url
+
+
+def compose_image_url(repo_id, filename): # Can be used in local environment
+    from huggingface_hub import hf_hub_url
+    url = hf_hub_url(repo_id=repo_id, filename=filename)
+    return url
+
+
+def is_exists_image(filename): # Can be used in local environment
+    from huggingface_hub import HfApi
+    hf_token = os.environ.get("HF_TOKEN")
+    repo_id = os.environ.get("HF_OUTPUT_REPO")
+    api = HfApi()
+    try:
+        is_exists = api.file_exists(repo_id=repo_id, filename=filename, token=hf_token)
+    except Exception as e:
+        return None
+    return is_exists
+
+
+def download_image(filename): # Can be used in local environment
+    from huggingface_hub import HfApi
+    hf_token = os.environ.get("HF_TOKEN")
+    repo_id = os.environ.get("HF_OUTPUT_REPO")
+    api = HfApi()
+    try:
+        if is_exists_image:
+            api.hf_hub_download(repo_id=repo_id, filename=filename, local_dir=".", token=hf_token)
+    except Exception as e:
+        return
+
+#  HfApi Client Document: https://huggingface.co/docs/huggingface_hub/package_reference/hf_api

requirements.txt

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+accelerate
+diffusers
+invisible_watermark
+torch
+transformers
+xformers