Spaces:
Configuration error
Configuration error
fix error while returning result
Browse files- app.py +40 -61
- demo.ipynb +112 -91
- utils.py +7 -4
app.py
CHANGED
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@@ -83,7 +83,7 @@ def generate(
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upscale_by: float = 1.5,
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add_quality_tags: bool = True,
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progress=gr.Progress(track_tqdm=True),
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)
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generator = utils.seed_everything(seed)
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width, height = utils.aspect_ratio_handler(
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@@ -132,6 +132,7 @@ def generate(
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}
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else:
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metadata["use_upscaler"] = None
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logger.info(json.dumps(metadata, indent=4))
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try:
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@@ -169,12 +170,15 @@ def generate(
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output_type="pil",
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).images
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if images
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except Exception as e:
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logger.exception(f"An error occurred: {e}")
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raise
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@@ -221,14 +225,11 @@ with gr.Blocks(css="style.css") as demo:
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placeholder="Enter your prompt",
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container=False,
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)
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run_button = gr.Button(
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"Generate",
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variant="primary",
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scale=0
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)
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result = gr.Gallery(
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label="Result",
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columns=1,
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preview=True,
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show_label=False
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)
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@@ -239,10 +240,7 @@ with gr.Blocks(css="style.css") as demo:
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placeholder="Enter a negative prompt",
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)
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with gr.Row():
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add_quality_tags = gr.Checkbox(
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label="Add Quality Tags",
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value=True
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)
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quality_selector = gr.Dropdown(
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label="Quality Tags Presets",
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interactive=True,
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@@ -348,49 +346,12 @@ with gr.Blocks(css="style.css") as demo:
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api_name=False,
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)
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guidance_scale,
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num_inference_steps,
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sampler,
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aspect_ratio_selector,
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style_selector,
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quality_selector,
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use_upscaler,
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upscaler_strength,
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upscale_by,
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add_quality_tags,
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]
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prompt.submit(
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fn=utils.randomize_seed_fn,
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inputs=[seed, randomize_seed],
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outputs=seed,
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queue=False,
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api_name=False,
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).then(
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fn=generate,
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inputs=inputs,
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outputs=result,
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api_name="run",
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)
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negative_prompt.submit(
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fn=utils.randomize_seed_fn,
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inputs=[seed, randomize_seed],
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outputs=seed,
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queue=False,
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api_name=False,
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).then(
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fn=generate,
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inputs=inputs,
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outputs=result,
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api_name=False,
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)
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run_button.click(
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fn=utils.randomize_seed_fn,
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inputs=[seed, randomize_seed],
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outputs=seed,
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@@ -398,8 +359,26 @@ with gr.Blocks(css="style.css") as demo:
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api_name=False,
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).then(
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fn=generate,
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inputs=
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outputs=[result, gr_metadata],
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api_name=
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)
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-
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upscale_by: float = 1.5,
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add_quality_tags: bool = True,
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progress=gr.Progress(track_tqdm=True),
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):
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generator = utils.seed_everything(seed)
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width, height = utils.aspect_ratio_handler(
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}
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else:
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metadata["use_upscaler"] = None
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metadata["model"] = "Animagine XL 3.0"
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logger.info(json.dumps(metadata, indent=4))
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try:
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output_type="pil",
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).images
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if images:
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image_paths = [
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utils.save_image(image, metadata, OUTPUT_DIR, IS_COLAB) for image in images
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]
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for image_path in image_paths:
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logger.info(f"Image saved as {image_path} with metadata")
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return image_paths, metadata
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except Exception as e:
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logger.exception(f"An error occurred: {e}")
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raise
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placeholder="Enter your prompt",
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container=False,
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)
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run_button = gr.Button("Generate", variant="primary", scale=0)
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result = gr.Gallery(
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label="Result",
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columns=1,
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height="512px",
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preview=True,
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show_label=False
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)
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placeholder="Enter a negative prompt",
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)
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with gr.Row():
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add_quality_tags = gr.Checkbox(label="Add Quality Tags", value=True)
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quality_selector = gr.Dropdown(
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label="Quality Tags Presets",
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interactive=True,
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api_name=False,
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)
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gr.on(
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triggers=[
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prompt.submit,
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negative_prompt.submit,
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run_button.click,
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],
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fn=utils.randomize_seed_fn,
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inputs=[seed, randomize_seed],
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outputs=seed,
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api_name=False,
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).then(
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fn=generate,
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inputs=[
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prompt,
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negative_prompt,
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seed,
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custom_width,
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custom_height,
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guidance_scale,
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num_inference_steps,
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sampler,
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aspect_ratio_selector,
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style_selector,
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quality_selector,
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use_upscaler,
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upscaler_strength,
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upscale_by,
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add_quality_tags,
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],
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outputs=[result, gr_metadata],
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api_name="run",
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)
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if __name__ == "__main__":
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demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)
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demo.ipynb
CHANGED
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"\n",
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},
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "538a3f0c-50c1-4952-9fcc-070d365c9a0f",
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"metadata": {
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"scrolled": true,
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"id": "538a3f0c-50c1-4952-9fcc-070d365c9a0f"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import subprocess\n",
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"from threading import Timer\n",
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"from queue import Queue\n",
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"\n",
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"def is_colab():\n",
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" try:\n",
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" import google.colab\n",
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" return True\n",
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" except ImportError:\n",
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" return False\n",
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"\n",
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"ROOT_DIR = \"/workspace/\" if not is_colab() else \"/content/\"\n",
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"REPO_URL = \"https://huggingface.co/spaces/Linaqruf/animagine-xl\"\n",
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"REPO_DIR = os.path.join(ROOT_DIR, \"animagine-xl\")\n",
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"\n",
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"NGROK_TOKEN = \"\"\n",
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"NGROK_SUBDOMAIN = \"\"\n",
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"PORT = 7860\n",
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"\n",
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"# os.environ[\"HF_TOKEN\"] = \"\"\n",
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"os.environ[\"IS_COLAB\"] = \"1\"\n",
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"os.environ[\"MODEL\"] = \"https://huggingface.co/cagliostrolab/animagine-xl-3.0/blob/main/animagine-xl-3.0.safetensors\"\n",
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"os.environ[\"CACHE_EXAMPLES\"] = \"1\"\n",
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"\n",
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"def clone_repository(url, directory, branch=None):\n",
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" subprocess.run([\"git\", \"clone\", url, directory], check=True)\n",
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" if branch:\n",
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" subprocess.run([\"git\", \"checkout\", branch], cwd=directory, check=True)\n",
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"\n",
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"def install_dependencies(directory):\n",
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" dependencies = [\"accelerate==0.27.2\", \"diffusers==0.26.3\", \"gradio==4.20.0\",\n",
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" \"invisible-watermark==0.2.0\", \"spaces==0.24.0\", \"omegaconf==2.3.0\", \"timm==0.9.10\"]\n",
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" if is_colab():\n",
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" subprocess.run([\"pip\", \"install\"] + dependencies, check=True)\n",
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" else:\n",
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" requirements_path = os.path.join(directory, \"requirements.txt\")\n",
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" subprocess.run([\"pip\", \"install\", \"-r\", requirements_path], check=True)\n",
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"\n",
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"def setup_ngrok_tunnel(port, queue, auth_token, subdomain):\n",
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" ngrok.set_auth_token(auth_token)\n",
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" url = ngrok.connect(port, bind_tls=True, subdomain=subdomain)\n",
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" queue.put(url)\n",
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"\n",
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"def main():\n",
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" if not os.path.exists(REPO_DIR):\n",
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" print(f\"Cloning repository to {REPO_DIR}\")\n",
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" clone_repository(REPO_URL, REPO_DIR)\n",
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"\n",
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" print(\"Installing required Python libraries\")\n",
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" install_dependencies(REPO_DIR)\n",
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" print(\"Done!\")\n",
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"\n",
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" os.chdir(REPO_DIR)\n",
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"\n",
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" if NGROK_TOKEN:\n",
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" try:\n",
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" from pyngrok import conf, ngrok\n",
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" except ImportError:\n",
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" subprocess.run([\"pip\", \"install\", \"-qqqq\", \"--upgrade\", \"setuptools\"], check=True)\n",
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" subprocess.run([\"pip\", \"install\", \"-qqqq\", \"-U\", \"pyngrok\"], check=True)\n",
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" from pyngrok import conf, ngrok\n",
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"\n",
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" ngrok.kill()\n",
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" ngrok_output_queue = Queue()\n",
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" ngrok_thread = Timer(2, setup_ngrok_tunnel, args=(PORT, ngrok_output_queue, NGROK_TOKEN, NGROK_SUBDOMAIN))\n",
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" ngrok_thread.start()\n",
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" ngrok_thread.join()\n",
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" print(ngrok_output_queue.get())\n",
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"\n",
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" !python app.py\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" main()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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| 104 |
+
"nbconvert_exporter": "python",
|
| 105 |
+
"pygments_lexer": "ipython3",
|
| 106 |
+
"version": "3.10.12"
|
| 107 |
+
},
|
| 108 |
+
"colab": {
|
| 109 |
+
"provenance": []
|
| 110 |
+
}
|
| 111 |
},
|
| 112 |
+
"nbformat": 4,
|
| 113 |
+
"nbformat_minor": 5
|
| 114 |
+
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
utils.py
CHANGED
|
@@ -4,6 +4,7 @@ import random
|
|
| 4 |
import numpy as np
|
| 5 |
import json
|
| 6 |
import torch
|
|
|
|
| 7 |
from PIL import Image, PngImagePlugin
|
| 8 |
from datetime import datetime
|
| 9 |
from dataclasses import dataclass
|
|
@@ -158,12 +159,14 @@ def preprocess_image_dimensions(width, height):
|
|
| 158 |
return width, height
|
| 159 |
|
| 160 |
|
| 161 |
-
def save_image(image, metadata, output_dir):
|
| 162 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 163 |
os.makedirs(output_dir, exist_ok=True)
|
| 164 |
-
filename = f"image_{current_time}.png"
|
| 165 |
filepath = os.path.join(output_dir, filename)
|
| 166 |
-
|
| 167 |
metadata_str = json.dumps(metadata)
|
| 168 |
info = PngImagePlugin.PngInfo()
|
| 169 |
info.add_text("metadata", metadata_str)
|
|
|
|
| 4 |
import numpy as np
|
| 5 |
import json
|
| 6 |
import torch
|
| 7 |
+
import uuid
|
| 8 |
from PIL import Image, PngImagePlugin
|
| 9 |
from datetime import datetime
|
| 10 |
from dataclasses import dataclass
|
|
|
|
| 159 |
return width, height
|
| 160 |
|
| 161 |
|
| 162 |
+
def save_image(image, metadata, output_dir, is_colab):
|
| 163 |
+
if is_colab:
|
| 164 |
+
current_time = datetime.now().strftime("%Y%m%d_%H%M%S")
|
| 165 |
+
filename = f"image_{current_time}.png"
|
| 166 |
+
else:
|
| 167 |
+
filename = str(uuid.uuid4()) + ".png"
|
| 168 |
os.makedirs(output_dir, exist_ok=True)
|
|
|
|
| 169 |
filepath = os.path.join(output_dir, filename)
|
|
|
|
| 170 |
metadata_str = json.dumps(metadata)
|
| 171 |
info = PngImagePlugin.PngInfo()
|
| 172 |
info.add_text("metadata", metadata_str)
|