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β’
5ddef29
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Parent(s):
π
Browse files- app.py +152 -0
- readme.md +13 -0
- requirements.txt +3 -0
app.py
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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}/job", params)
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return response.json()
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def transform(self, params):
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response = self._post(f"{self.base}/transform", params)
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return response.json()
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def controlnet(self, params):
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response = self._post(f"{self.base}/controlnet", 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}/models/list")
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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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# Open the image with PIL
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with Image.open(image_path) as image:
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# Convert the image to bytes
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buffered = BytesIO()
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image.save(buffered, format="PNG") # You can change format to PNG if needed
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# Encode the bytes to base64
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img_str = base64.b64encode(buffered.getvalue())
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return img_str.decode('utf-8') # Convert bytes to string
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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):
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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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})
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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.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(placeholder="Prompt", show_label=False, lines=3)
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negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3)
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with gr.Column(equal_height=True):
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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=1):
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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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model = gr.Dropdown(interactive=True,value="v1-5-pruned-emaonly.safetensors [d7049739]", show_label=False, choices=prodia_client.list_models())
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sampler = gr.Dropdown(value="Euler a", show_label=False, choices=[
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"Euler",
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"Euler a",
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"LMS",
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"Heun",
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"DPM2",
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"DPM2 a",
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"DPM++ 2S a",
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"DPM++ 2M",
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"DPM++ SDE",
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"DPM fast",
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"DPM adaptive",
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"LMS Karras",
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"DPM2 Karras",
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"DPM2 a Karras",
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"DPM++ 2S a Karras",
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"DPM++ 2M Karras",
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"DPM++ SDE Karras",
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"DDIM",
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"PLMS",
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])
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with gr.Column(scale=1):
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steps = gr.Slider(label="Steps", miniumum=1, maximum=50, value=25)
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cfg_scale = gr.Slider(label="CFG Scale", miniumum=1, maximum=20, value=7)
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with gr.Column(scale=1):
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image_output = gr.Image()
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text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale], outputs=image_output)
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demo.launch()
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readme.md
ADDED
@@ -0,0 +1,13 @@
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---
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title: stable-diffusion
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emoji: π₯
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colorFrom: yellow
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colorTo: gray
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sdk: gradio
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sdk_version: 3.11.0
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app_file: app.py
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pinned: false
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license: mit
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---
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Prodia's Stable Diffusion Space.
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requirements.txt
ADDED
@@ -0,0 +1,3 @@
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numpy
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2 |
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gradio
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requests
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