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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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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("PRODIA_API_KEY")) |
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def flip_text(prompt, negative_prompt, model, steps, sampler, 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": model, |
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"steps": steps, |
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"sampler": sampler, |
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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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#generate { |
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height: 100%; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Row(): |
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with gr.Column(scale=6): |
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model = gr.Dropdown(interactive=True,value="sd_xl_base_1.0.safetensors [be9edd61]", show_label=True, label="Stable Diffusion Checkpoint", choices=prodia_client.list_models()) |
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with gr.Column(scale=1): |
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gr.Markdown(elem_id="powered-by-prodia", value="AUTOMATIC1111 Stable Diffusion Web UI for SDXL V1.0.<br>Powered by [Prodia](https://prodia.com).") |
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with gr.Tab("txt2img"): |
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with gr.Row(): |
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with gr.Column(scale=6, min_width=600): |
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prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3) |
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negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") |
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with gr.Column(): |
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text_button = gr.Button("Generate", variant='primary', elem_id="generate") |
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with gr.Row(): |
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with gr.Column(scale=3): |
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with gr.Tab("Generation"): |
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with gr.Row(): |
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with gr.Column(scale=1): |
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sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers()) |
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with gr.Column(scale=1): |
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steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1) |
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with gr.Row(): |
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with gr.Column(scale=1): |
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width = gr.Slider(label="Width", minimum=512, maximum=1536, value=1024, step=8) |
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height = gr.Slider(label="Height", minimum=512, maximum=1536, value=1024, step=8) |
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gr.Markdown(elem_id="resolution", value="*Resolution Maximum: 1MP (1048576 px)*") |
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with gr.Column(scale=1): |
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batch_size = gr.Slider(label="Batch Size", maximum=1, value=1) |
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batch_count = gr.Slider(label="Batch Count", maximum=1, value=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=2): |
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image_output = gr.Image(value="https://cdn-uploads.huggingface.co/production/uploads/noauth/XWJyh9DhMGXrzyRJk7SfP.png") |
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text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output) |
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demo.queue(concurrency_count=24, max_size=32, api_open=False).launch(max_threads=128) |
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