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Co-authored-by: shaya khair <shayakh@users.noreply.huggingface.co>

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  1. .gitattributes +34 -0
  2. README.md +20 -0
  3. app.py +189 -0
  4. requirements.txt +10 -0
.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ title: Realistic Vision v5.0
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+ emoji: 📷
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+ colorFrom: yellow
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+ colorTo: red
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+ sdk: gradio
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+ sdk_version: 3.18.0
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+ app_file: app.py
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+ pinned: true
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+ duplicated_from: shayakh/sdrv50
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+ tags:
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+ - stable-diffusion
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+ - stable-diffusion-diffusers
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+ - text-to-image
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+ - realistic-vision
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+ models:
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+ - SG161222/Realistic_Vision_V5.0_noVAE
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
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+ import gradio as gr
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+ import torch
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+ from PIL import Image
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+
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+ model_id = 'SG161222/Realistic_Vision_V5.0_noVAE'
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+ prefix = 'RAW photo,'
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+
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+ scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
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+
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+ pipe = StableDiffusionPipeline.from_pretrained(
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+ model_id,
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+ torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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+ scheduler=scheduler)
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+
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+ pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
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+ model_id,
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+ torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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+ scheduler=scheduler)
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+
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+ if torch.cuda.is_available():
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+ pipe = pipe.to("cuda")
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+ pipe_i2i = pipe_i2i.to("cuda")
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+
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+ def error_str(error, title="Error"):
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+ return f"""#### {title}
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+ {error}""" if error else ""
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+
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+
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+ def _parse_args(prompt, generator):
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+ parser = argparse.ArgumentParser(
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+ description="making it work."
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+ )
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+ parser.add_argument(
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+ "--no-half-vae", help="no half vae"
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+ )
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+
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+ cmdline_args = parser.parse_args()
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+ command = cmdline_args.command
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+ conf_file = cmdline_args.conf_file
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+ conf_args = Arguments(conf_file)
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+ opt = conf_args.readArguments()
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+
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+ if cmdline_args.config_overrides:
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+ for config_override in cmdline_args.config_overrides.split(";"):
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+ config_override = config_override.strip()
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+ if config_override:
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+ var_val = config_override.split("=")
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+ assert (
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+ len(var_val) == 2
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+ ), f"Config override '{var_val}' does not have the form 'VAR=val'"
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+ conf_args.add_opt(opt, var_val[0], var_val[1], force_override=True)
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+
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+ def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
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+ generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
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+ prompt = f"{prefix} {prompt}" if auto_prefix else prompt
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+
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+ try:
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+ if img is not None:
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+ return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
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+ else:
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+ return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
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+ except Exception as e:
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+ return None, error_str(e)
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+
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+
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+
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+ def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
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+
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+ result = pipe(
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+ prompt,
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+ negative_prompt = neg_prompt,
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+ num_inference_steps = int(steps),
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+ guidance_scale = guidance,
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+ width = width,
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+ height = height,
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+ generator = generator)
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+
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+ return result.images[0]
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+
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+ def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
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+
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+ ratio = min(height / img.height, width / img.width)
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+ img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
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+ result = pipe_i2i(
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+ prompt,
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+ negative_prompt = neg_prompt,
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+ init_image = img,
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+ num_inference_steps = int(steps),
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+ strength = strength,
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+ guidance_scale = guidance,
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+ width = width,
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+ height = height,
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+ generator = generator)
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+
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+ return result.images[0]
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+
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+ def fake_safety_checker(images, **kwargs):
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+ return result.images[0], [False] * len(images)
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+
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+ pipe.safety_checker = fake_safety_checker
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+
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+ css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
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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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+ f"""
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+ <div class="main-div">
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+ <div>
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+ <h1 style="color:orange;">📷 Realistic Vision V5.0 📸</h1>
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+ </div>
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+ <p>
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+ Demo for <a href="https://huggingface.co/SG161222/Realistic_Vision_V5.0_noVAE">Realistic Vision V5.0</a>
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+ Stable Diffusion model by <a href="https://huggingface.co/SG161222/"><abbr title="SG1611222">Eugene</abbr></a>. {"" if prefix else ""}
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+ Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU ⚡</b>"}.
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+ </p>
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+ <p>Please use the prompt template below to get an example of the desired generation results:
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+ </p>
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+
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+ <b>Prompt</b>:
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+ <details><code>
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+ * subject *, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
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+ <br>
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+ <br>
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+ <q><i>
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+ Example: a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, <br>
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+ (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
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+ </i></q>
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+ </code></details>
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+
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+ <br>
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+ <b>Negative Prompt</b>:
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+ <details><code>
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+ (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, <br>
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+ low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, <br>
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+ dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, <br>
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+ extra legs, fused fingers, too many fingers, long neck
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+ </code></details>
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+
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+ <br>
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+ Have Fun & Enjoy ⚡ <a href="https://www.thafx.com"><abbr title="Website">//THAFX</abbr></a>
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+ <br>
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+
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+ </div>
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+ """
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+ )
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+ with gr.Row():
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+
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+ with gr.Column(scale=55):
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+ with gr.Group():
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+ with gr.Row():
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+ prompt = gr.Textbox(label="Prompt", show_label=False,max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
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+ generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
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+
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+ image_out = gr.Image(height=512)
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+ error_output = gr.Markdown()
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+
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+ with gr.Column(scale=45):
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+ with gr.Tab("Options"):
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+ with gr.Group():
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+ neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
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+ auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (RAW photo,)", value=prefix, visible=prefix)
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+
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+ with gr.Row():
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+ guidance = gr.Slider(label="Guidance scale", value=5, maximum=15)
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+ steps = gr.Slider(label="Steps", value=20, minimum=2, maximum=75, step=1)
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+
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+ with gr.Row():
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+ width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
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+ height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
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+
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+ seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
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+
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+ with gr.Tab("Image to image"):
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+ with gr.Group():
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+ image = gr.Image(label="Image", height=256, tool="editor", type="pil")
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+ strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
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+
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+ auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
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+
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+ inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
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+ outputs = [image_out, error_output]
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+ prompt.submit(inference, inputs=inputs, outputs=outputs)
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+ generate.click(inference, inputs=inputs, outputs=outputs)
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+
186
+
187
+
188
+ demo.queue(concurrency_count=1)
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+ demo.launch()
requirements.txt ADDED
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+ --extra-index-url https://download.pytorch.org/whl/cu113
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+ invisible-watermark
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+ fonts
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+ font-roboto
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+ torch
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+ diffusers
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+ transformers
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+ git+https://github.com/huggingface/transformers
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+ accelerate
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+ ftfy