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import numpy as np |
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import gradio as gr |
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import requests |
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import time |
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import json |
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import base64 |
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import os |
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from PIL import Image |
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from io import BytesIO |
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batch_size=1 |
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batch_count=1 |
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class Prodia: |
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def __init__(self, api_key, base=None): |
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self.base = base or "https://api.prodia.com/v1" |
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self.headers = { |
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"X-Prodia-Key": api_key |
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} |
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def generate(self, params): |
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response = self._post(f"{self.base}/sdxl/generate", params) |
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return response.json() |
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def get_job(self, job_id): |
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response = self._get(f"{self.base}/job/{job_id}") |
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return response.json() |
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def wait(self, job): |
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job_result = job |
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while job_result['status'] not in ['succeeded', 'failed']: |
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time.sleep(0.25) |
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job_result = self.get_job(job['job']) |
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return job_result |
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def list_models(self): |
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response = self._get(f"{self.base}/sdxl/models") |
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return response.json() |
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def list_samplers(self): |
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response = self._get(f"{self.base}/sdxl/samplers") |
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return response.json() |
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def _post(self, url, params): |
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headers = { |
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**self.headers, |
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"Content-Type": "application/json" |
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} |
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response = requests.post(url, headers=headers, data=json.dumps(params)) |
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if response.status_code != 200: |
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raise Exception(f"Bad Prodia Response: {response.status_code}") |
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return response |
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def _get(self, url): |
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response = requests.get(url, headers=self.headers) |
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if response.status_code != 200: |
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raise Exception(f"Bad Prodia Response: {response.status_code}") |
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return response |
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def image_to_base64(image_path): |
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with Image.open(image_path) as image: |
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buffered = BytesIO() |
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image.save(buffered, format="PNG") |
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img_str = base64.b64encode(buffered.getvalue()) |
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return img_str.decode('utf-8') |
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prodia_client = Prodia(api_key=os.getenv("API_KEY")) |
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def flip_text(prompt, negative_prompt, steps, cfg_scale, width, height, seed): |
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result = prodia_client.generate({ |
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"prompt": prompt, |
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"negative_prompt": negative_prompt, |
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"model": "sd_xl_base_1.0.safetensors [be9edd61]", |
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"steps": steps, |
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"sampler": "DPM++ 2M Karras", |
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"cfg_scale": cfg_scale, |
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"width": width, |
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"height": height, |
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"seed": seed |
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}) |
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job = prodia_client.wait(result) |
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return job["imageUrl"] |
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css = """ |
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#prompt-container .form{ |
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border-top-right-radius: 0; |
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border-bottom-right-radius: 0; |
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} |
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#prompt-text-input, #negative-prompt-text-input{padding: .45rem 0.625rem} |
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#component-16{border-top-width: 1px!important;margin-top: 1em} |
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.image_duplication{position: absolute; width: 100px; left: 50px} |
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.tabitem{border: 0 !important}.style(mobile_collapse=False, equal_height=True).style(mobile_collapse=False, equal_height=True).style(mobile_collapse=False, equal_height=True).style(mobile_collapse=False, equal_height=True |
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#gen-button{ |
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border-top-left-radius:0; |
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border-bottom-left-radius:0; |
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} |
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#gallery { |
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min-height: 22rem; |
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margin-bottom: 15px; |
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margin-left: auto; |
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margin-right: auto; |
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border-bottom-right-radius: .5rem !important; |
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border-bottom-left-radius: .5rem !important; |
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} |
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#gallery>div>.h-full { |
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min-height: 20rem; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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gr.HTML( |
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""" |
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<div style="text-align: center; margin: 0 auto;"> |
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<div |
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style=" |
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display: inline-flex; |
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align-items: center; |
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gap: 0.8rem; |
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font-size: 1.75rem; |
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" |
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> |
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<svg |
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width="0.65em" |
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height="0.65em" |
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viewBox="0 0 115 115" |
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fill="none" |
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xmlns="http://www.w3.org/2000/svg" |
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> |
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<rect width="23" height="23" fill="white"></rect> |
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<rect y="69" width="23" height="23" fill="white"></rect> |
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<rect x="23" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="69" width="23" height="23" fill="black"></rect> |
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<rect x="69" y="69" width="23" height="23" fill="black"></rect> |
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<rect x="92" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="115" y="46" width="23" height="23" fill="white"></rect> |
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<rect x="115" y="115" width="23" height="23" fill="white"></rect> |
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<rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="92" y="69" width="23" height="23" fill="white"></rect> |
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<rect x="69" y="46" width="23" height="23" fill="white"></rect> |
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<rect x="69" y="115" width="23" height="23" fill="white"></rect> |
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<rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="46" y="46" width="23" height="23" fill="black"></rect> |
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<rect x="46" y="115" width="23" height="23" fill="black"></rect> |
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<rect x="46" y="69" width="23" height="23" fill="black"></rect> |
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<rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect> |
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<rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect> |
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<rect x="23" y="69" width="23" height="23" fill="black"></rect> |
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</svg> |
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<h1 style="font-weight: 900; margin-bottom: 7px;margin-top:5px"> |
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Fast SDXL |
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</h1> |
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</div> |
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</div> |
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""") |
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with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True): |
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prompt = gr.Textbox(label="Prompt", placeholder="a cute cat, 8k", show_label=True, lines=1, elem_id="prompt-text-input") |
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text_button = gr.Button("Generate", variant='primary', elem_id="gen-button") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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image_output = gr.Image(elem_id="gallery") |
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with gr.Row(): |
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with gr.Accordion("Additionals inputs", open=False): |
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with gr.Column(scale=1): |
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negative_prompt = gr.Textbox(label="Negative Prompt", value="text, blurry", placeholder="What you don't want to see in the image", show_label=True, lines=1, elem_id="negative-prompt-text-input") |
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with gr.Column(scale=1): |
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steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1) |
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cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1) |
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seed = gr.Number(label="Seed", value=-1) |
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with gr.Column(scale=1): |
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width = gr.Slider(label="↔️ Width", minimum=1024, maximum=1024, value=1024, step=8) |
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height = gr.Slider(label="↕️ Height", minimum=1024, maximum=1024, value=1024, step=8) |
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text_button.click(flip_text, inputs=[prompt, negative_prompt, steps, cfg_scale, width, height, seed], outputs=image_output) |
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demo.queue(concurrency_count=16, max_size=20, api_open=False).launch(max_threads=64) |
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