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Running
on
CPU Upgrade
Commit
•
63a6e70
1
Parent(s):
38d05ac
hybrid-backend (#256)
Browse files- Swap to hybrid backend (53066e3cf8ddbe2b02bee8a96c1feee01d99a01d)
Co-authored-by: Apolinario <apolinario@users.noreply.huggingface.co>
app.py
CHANGED
@@ -5,7 +5,11 @@ from torch import autocast
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from diffusers import StableDiffusionPipeline
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from datasets import load_dataset
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from PIL import Image
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import re
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from share_btn import community_icon_html, loading_icon_html, share_js
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@@ -21,27 +25,44 @@ torch.backends.cudnn.benchmark = True
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word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True)
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word_list = word_list_dataset["train"]['text']
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#When running locally you can also remove this filter
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for filter in word_list:
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if re.search(rf"\b{filter}\b", prompt):
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raise gr.Error("Unsafe content found. Please try again with different prompts.")
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generator = torch.Generator(device=device).manual_seed(seed)
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images_list = pipe(
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[prompt] * samples,
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num_inference_steps=steps,
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guidance_scale=scale,
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generator=generator,
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)
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images = []
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return images, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
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@@ -298,6 +319,7 @@ with block:
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share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
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with gr.Row(elem_id="advanced-options"):
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samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
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steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
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scale = gr.Slider(
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@@ -311,13 +333,13 @@ with block:
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randomize=True,
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)
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ex = gr.Examples(examples=examples, fn=infer, inputs=
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ex.dataset.headers = [""]
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text.submit(infer, inputs=
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btn.click(infer, inputs=
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advanced_button.click(
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None,
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@@ -350,4 +372,4 @@ Despite how impressive being able to turn text into image is, beware to the fact
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"""
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)
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block.queue(max_size=25).launch()
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from diffusers import StableDiffusionPipeline
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from datasets import load_dataset
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from PIL import Image
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from io import BytesIO
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import base64
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import re
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import os
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import requests
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from share_btn import community_icon_html, loading_icon_html, share_js
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word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True)
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word_list = word_list_dataset["train"]['text']
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is_gpu_busy = False
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def infer(prompt):
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global is_gpu_busy
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samples = 4
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steps = 50
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scale = 7.5
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#When running locally you can also remove this filter
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for filter in word_list:
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if re.search(rf"\b{filter}\b", prompt):
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raise gr.Error("Unsafe content found. Please try again with different prompts.")
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#generator = torch.Generator(device=device).manual_seed(seed)
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print("Is GPU busy? ", is_gpu_busy)
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images = []
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if(not is_gpu_busy):
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is_gpu_busy = True
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images_list = pipe(
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[prompt] * samples,
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num_inference_steps=steps,
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guidance_scale=scale,
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#generator=generator,
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)
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is_gpu_busy = False
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safe_image = Image.open(r"unsafe.png")
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for i, image in enumerate(images_list["sample"]):
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if(images_list["nsfw_content_detected"][i]):
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images.append(safe_image)
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else:
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images.append(image)
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else:
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url = os.getenv('JAX_BACKEND_URL')
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payload = {'prompt': prompt}
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images_request = requests.post(url, json = payload)
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for image in images_request.json()["images"]:
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image_decoded = Image.open(BytesIO(base64.b64decode(image)))
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images.append(image_decoded)
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return images, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
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share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
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with gr.Row(elem_id="advanced-options"):
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gr.Markdown("Advanced settings are temporarily unavailable")
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samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
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steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
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scale = gr.Slider(
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randomize=True,
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)
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ex = gr.Examples(examples=examples, fn=infer, inputs=text, outputs=[gallery, community_icon, loading_icon, share_button], cache_examples=True)
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ex.dataset.headers = [""]
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text.submit(infer, inputs=text, outputs=[gallery, community_icon, loading_icon, share_button])
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btn.click(infer, inputs=text, outputs=[gallery, community_icon, loading_icon, share_button])
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advanced_button.click(
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None,
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"""
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)
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block.queue(max_size=25, concurrency_count=2).launch()
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