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β’
b5d5c28
1
Parent(s):
85b8db7
Add advanced option
Browse files
app.py
CHANGED
@@ -1,5 +1,7 @@
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import gradio as gr
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import torch
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from diffusers import DiffusionPipeline
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import streamlit as st
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from transformers import (
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@@ -10,6 +12,8 @@ from transformers import (
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device = "cuda" if torch.cuda.is_available() else "cpu"
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device_dict = {"cuda": 0, "cpu": -1}
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# Add language detection pipeline
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language_detection_model_ckpt = "papluca/xlm-roberta-base-language-detection"
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@@ -30,19 +34,21 @@ pipe = DiffusionPipeline.from_pretrained(
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detection_pipeline=language_detection_pipeline,
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translation_model=trans_model,
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translation_tokenizer=trans_tokenizer,
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-
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-
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)
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pipe.enable_attention_slicing()
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pipe = pipe.to(device)
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#torch.backends.cudnn.benchmark = True
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num_samples = 2
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def infer(prompt):
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-
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css = """
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.gradio-container {
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@@ -100,7 +106,6 @@ css = """
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border-radius: 14px !important;
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}
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#advanced-options {
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display: none;
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margin-bottom: 20px;
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}
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.footer {
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@@ -167,13 +172,19 @@ block = gr.Blocks(css=css)
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examples = [
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[
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'Una casa en la playa en un atardecer lluvioso'
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],
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[
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'Ein Hund, der Orange isst'
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],
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[
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"Photo d'un restaurant parisien"
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],
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]
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@@ -216,14 +227,20 @@ with block as demo:
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)
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gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="generated_id").style(
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grid=[
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)
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-
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ex.dataset.headers = [""]
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text.submit(infer, inputs=[text], outputs=gallery)
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btn.click(infer, inputs=[text], outputs=gallery)
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gr.HTML(
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"""
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from contextlib import nullcontext
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import gradio as gr
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import torch
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from torch import autocast
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from diffusers import DiffusionPipeline
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import streamlit as st
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from transformers import (
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device = "cuda" if torch.cuda.is_available() else "cpu"
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device_dict = {"cuda": 0, "cpu": -1}
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context = autocast if device == "cuda" else nullcontext
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dtype = torch.float16 if device == "cuda" else torch.float32
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# Add language detection pipeline
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language_detection_model_ckpt = "papluca/xlm-roberta-base-language-detection"
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detection_pipeline=language_detection_pipeline,
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translation_model=trans_model,
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translation_tokenizer=trans_tokenizer,
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revision="fp16",
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torch_dtype=dtype,
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)
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pipe = pipe.to(device)
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#torch.backends.cudnn.benchmark = True
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num_samples = 2
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def infer(prompt, scale, steps):
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with context("cuda"):
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images = pipe(num_samples*[prompt], guidance_scale=scale, num_inference_steps=steps).images
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return images
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css = """
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.gradio-container {
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border-radius: 14px !important;
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}
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#advanced-options {
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margin-bottom: 20px;
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}
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.footer {
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examples = [
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[
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'Una casa en la playa en un atardecer lluvioso',
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45,
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7.5,
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],
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[
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'Ein Hund, der Orange isst',
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45,
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7.5,
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],
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[
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"Photo d'un restaurant parisien",
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45,
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7.5,
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],
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]
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)
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gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="generated_id").style(
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grid=[1], height="auto"
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)
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with gr.Row(elem_id="advanced-options"):
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steps = gr.Slider(label="Steps", minimum=5, maximum=50, value=45, step=5)
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scale = gr.Slider(
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label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
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)
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ex = gr.Examples(examples=examples, fn=infer, inputs=[text, steps, scale], outputs=gallery, cache_examples=False)
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ex.dataset.headers = [""]
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text.submit(infer, inputs=[text, steps, scale], outputs=gallery)
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btn.click(infer, inputs=[text, steps, scale], outputs=gallery)
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gr.HTML(
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"""
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