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0c8abab
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Parent(s):
5d45a2a
Upload QRDiffuser.py
Browse files- QRDiffuser.py +227 -0
QRDiffuser.py
ADDED
@@ -0,0 +1,227 @@
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import torch
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import gradio as gr
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from PIL import Image
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4 |
+
import qrcode
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+
import os
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+
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from diffusers import (
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StableDiffusionControlNetPipeline,
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9 |
+
ControlNetModel,
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+
DDIMScheduler,
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+
DPMSolverMultistepScheduler,
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+
UniPCMultistepScheduler,
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+
DEISMultistepScheduler,
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+
HeunDiscreteScheduler,
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+
EulerDiscreteScheduler,
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+
EulerAncestralDiscreteScheduler,
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)
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+
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controlnet = ControlNetModel.from_pretrained(
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"monster-labs/control_v1p_sd15_qrcode_monster",
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+
torch_dtype=torch.float16,
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)
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+
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+
pipe = StableDiffusionControlNetPipeline.from_pretrained(
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+
#"runwayml/stable-diffusion-v1-5",
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"SG161222/Realistic_Vision_V3.0_VAE",
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controlnet=controlnet,
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+
safety_checker=None,
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torch_dtype=torch.float16,
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).to("cuda")
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+
#pipe.enable_xformers_memory_efficient_attention()
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pipe.enable_attention_slicing(1)
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pipe.enable_model_cpu_offload()
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#pipe.enable_vae_tiling()
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pipe.enable_vae_slicing()
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
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+
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+
SAMPLER_MAP = {
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"DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
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"DPM++ Karras": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True),
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"Heun": lambda config: HeunDiscreteScheduler.from_config(config),
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"Euler a": lambda config: EulerAncestralDiscreteScheduler.from_config(config),
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"Euler": lambda config: EulerDiscreteScheduler.from_config(config),
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"DDIM": lambda config: DDIMScheduler.from_config(config),
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"DEIS": lambda config: DEISMultistepScheduler.from_config(config),
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}
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+
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boxsize=16
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def create_code(content: str, errorCorrection: str):
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match errorCorrection:
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case "L 7%":
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errCorr = qrcode.constants.ERROR_CORRECT_L
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case "M 15%":
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errCorr = qrcode.constants.ERROR_CORRECT_M
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case "Q 25%":
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errCorr = qrcode.constants.ERROR_CORRECT_Q
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case "H 30%":
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errCorr = qrcode.constants.ERROR_CORRECT_H
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qr = qrcode.QRCode(
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version=1,
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error_correction=errCorr,
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box_size=boxsize,
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border=0,
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)
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qr.add_data(content)
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qr.make(fit=True)
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img = qr.make_image(fill_color="black", back_color="white")
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# find smallest image size multiple of 256 that can fit qr
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offset_min = 8 * boxsize
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w, h = img.size
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w = (w + 255 + offset_min) // 256 * 256
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h = (h + 255 + offset_min) // 256 * 256
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if w > 1024:
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raise gr.Error("QR code is too large, please use a shorter content")
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bg = Image.new('L', (w, h), 128)
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# align on 16px grid
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coords = ((w - img.size[0]) // 2 // boxsize * boxsize,
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(h - img.size[1]) // 2 // boxsize * boxsize)
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bg.paste(img, coords)
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return bg
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def inference(
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qr_code_content: str,
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errorCorrection: str,
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prompt: str,
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negative_prompt: str,
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inferenceSteps: float,
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guidance_scale: float = 10.0,
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controlnet_conditioning_scale: float = 2.0,
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seed: int = -1,
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sampler="Euler a",
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):
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if prompt is None or prompt == "":
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raise gr.Error("Prompt is required")
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if qr_code_content is None or qr_code_content == "":
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raise gr.Error("QR Code Content is required")
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+
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pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
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+
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generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
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+
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print("Generating QR Code from content")
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qrcode_image = create_code(qr_code_content, errorCorrection)
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+
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# hack due to gradio examples
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init_image = qrcode_image
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init_image.save("c:\\temp\\qr.jpg")
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+
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out = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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image=qrcode_image,
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width=qrcode_image.width,
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height=qrcode_image.height,
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guidance_scale=float(guidance_scale),
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controlnet_conditioning_scale=float(controlnet_conditioning_scale),
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generator=generator,
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num_inference_steps=inferenceSteps,
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)
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return out.images[0]
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+
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+
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css = """
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+
#result_image {
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display: flex;
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place-content: center;
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align-items: center;
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}
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+
#result_image > img {
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height: auto;
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max-width: 100%;
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width: revert;
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}
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+
"""
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+
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+
with gr.Blocks(css=css) as blocks:
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+
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+
with gr.Row():
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+
with gr.Column():
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qr_code_content = gr.Textbox(
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+
label="QR Code Content or URL",
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+
info="The text you want to encode into the QR code",
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+
value="",
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+
)
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+
errorCorrection = gr.Dropdown(
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+
label="QR Code Error Correction Level",
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+
choices=["L 7%", "M 15%", "Q 25%", "H 30%"],
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153 |
+
value="H 30%"
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+
)
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155 |
+
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+
prompt = gr.Textbox(
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+
label="Prompt",
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+
info="Prompt that guides the generation towards",
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+
)
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+
negative_prompt = gr.Textbox(
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+
label="Negative Prompt",
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+
value="ugly, disfigured, low quality, blurry, nsfw",
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+
info="Prompt that guides the generation away from",
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+
)
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165 |
+
inferenceSteps = gr.Slider(
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166 |
+
minimum=10.0,
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167 |
+
maximum=60.0,
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168 |
+
step=1,
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169 |
+
value=20,
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170 |
+
label="Inference Steps",
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171 |
+
info="More steps give better image but longer runtime",
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172 |
+
)
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173 |
+
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174 |
+
with gr.Accordion(
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175 |
+
label="Params: The generated QR Code functionality is largely influenced by the parameters detailed below",
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176 |
+
open=True,
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177 |
+
):
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178 |
+
controlnet_conditioning_scale = gr.Slider(
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179 |
+
minimum=0.5,
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180 |
+
maximum=2.5,
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181 |
+
step=0.01,
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182 |
+
value=1.5,
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183 |
+
label="Controlnet Conditioning Scale",
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184 |
+
info="""Controls the readability/creativity of the QR code.
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+
High values: The generated QR code will be more readable.
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+
Low values: The generated QR code will be more creative.
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+
"""
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+
)
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189 |
+
guidance_scale = gr.Slider(
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190 |
+
minimum=0.0,
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191 |
+
maximum=25.0,
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192 |
+
step=0.25,
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193 |
+
value=7,
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194 |
+
label="Guidance Scale",
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195 |
+
info="Controls the amount of guidance the text prompt guides the image generation"
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196 |
+
)
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197 |
+
sampler = gr.Dropdown(choices=list(
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198 |
+
SAMPLER_MAP.keys()), value="Euler a", label="Sampler")
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199 |
+
seed = gr.Number(
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200 |
+
minimum=-1,
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201 |
+
maximum=9999999999,
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202 |
+
value=-1,
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203 |
+
label="Seed",
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204 |
+
info="Seed for the random number generator. Set to -1 for a random seed"
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205 |
+
)
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206 |
+
with gr.Row():
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207 |
+
run_btn = gr.Button("Run")
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208 |
+
with gr.Column():
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209 |
+
result_image = gr.Image(label="Result Image", elem_id="result_image")
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210 |
+
run_btn.click(
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211 |
+
inference,
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212 |
+
inputs=[
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213 |
+
qr_code_content,
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214 |
+
errorCorrection,
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215 |
+
prompt,
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216 |
+
negative_prompt,
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217 |
+
inferenceSteps,
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218 |
+
guidance_scale,
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219 |
+
controlnet_conditioning_scale,
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220 |
+
seed,
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221 |
+
sampler,
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222 |
+
],
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223 |
+
outputs=[result_image],
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224 |
+
)
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225 |
+
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226 |
+
blocks.queue(concurrency_count=1, max_size=20, api_open=False)
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227 |
+
blocks.launch(share=bool(os.environ.get("SHARE", True)), show_api=False)
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