| from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| model_id = 'SG161222/Paragon_V1.0' | |
| prefix = '' | |
| scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler") | |
| pipe = StableDiffusionPipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| scheduler=scheduler) | |
| pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| scheduler=scheduler) | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| pipe_i2i = pipe_i2i.to("cuda") | |
| def error_str(error, title="Error"): | |
| return f"""#### {title} | |
| {error}""" if error else "" | |
| def _parse_args(prompt, generator): | |
| parser = argparse.ArgumentParser( | |
| description="making it work." | |
| ) | |
| parser.add_argument( | |
| "--no-half-vae", help="no half vae" | |
| ) | |
| cmdline_args = parser.parse_args() | |
| command = cmdline_args.command | |
| conf_file = cmdline_args.conf_file | |
| conf_args = Arguments(conf_file) | |
| opt = conf_args.readArguments() | |
| if cmdline_args.config_overrides: | |
| for config_override in cmdline_args.config_overrides.split(";"): | |
| config_override = config_override.strip() | |
| if config_override: | |
| var_val = config_override.split("=") | |
| assert ( | |
| len(var_val) == 2 | |
| ), f"Config override '{var_val}' does not have the form 'VAR=val'" | |
| conf_args.add_opt(opt, var_val[0], var_val[1], force_override=True) | |
| def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False): | |
| generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None | |
| prompt = f"{prefix} {prompt}" if auto_prefix else prompt | |
| try: | |
| if img is not None: | |
| return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None | |
| else: | |
| return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None | |
| except Exception as e: | |
| return None, error_str(e) | |
| def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator): | |
| result = pipe( | |
| prompt, | |
| negative_prompt = neg_prompt, | |
| num_inference_steps = int(steps), | |
| guidance_scale = guidance, | |
| width = width, | |
| height = height, | |
| generator = generator) | |
| return result.images[0] | |
| def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): | |
| ratio = min(height / img.height, width / img.width) | |
| img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) | |
| result = pipe_i2i( | |
| prompt, | |
| negative_prompt = neg_prompt, | |
| init_image = img, | |
| num_inference_steps = int(steps), | |
| strength = strength, | |
| guidance_scale = guidance, | |
| width = width, | |
| height = height, | |
| generator = generator) | |
| return result.images[0] | |
| def fake_safety_checker(images, **kwargs): | |
| return result.images[0], [False] * len(images) | |
| pipe.safety_checker = fake_safety_checker | |
| 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} | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| gr.HTML( | |
| f""" | |
| <div class="main-div"> | |
| <div> | |
| <h1 style="color:orange;">📷 Paragon 📸</h1> | |
| </div> | |
| <p> | |
| Demo for <a href="https://huggingface.co/SG161222/Paragon_V1.0">Paragon</a> | |
| Stable Diffusion model by <a href="https://huggingface.co/SG161222/"><abbr title="SG161222">SG161222</abbr></a>. {"" if prefix else ""} | |
| Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU ⚡</b>"}. | |
| </p> | |
| <p>Please use the prompt template below to get an example of the desired results: | |
| </p> | |
| <b>Prompt</b>: | |
| <details><code> | |
| <q><i> | |
| Example: close up of a young woman wearing a black and gold liquid splash dress, pretty face, detailed eyes, soft lips, floating in outer space and planets in the background, | |
| fluid, wet, dripping, waxy, smooth, realistic, octane render | |
| </i></q> | |
| </code></details> | |
| <br> | |
| <b>Negative Prompt</b>: | |
| <details><code> | |
| (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, | |
| jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, | |
| bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, | |
| too many fingers, long neck | |
| </code></details> | |
| <br> | |
| Have Fun & Enjoy ⚡ <a href="https://www.thafx.com"><abbr title="Website">//THAFX</abbr></a> | |
| <br> | |
| </div> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=55): | |
| with gr.Group(): | |
| with gr.Row(): | |
| prompt = gr.Textbox(label="Prompt", show_label=False,max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False) | |
| generate = gr.Button(value="Generate").style(rounded=(False, True, True, False)) | |
| image_out = gr.Image(height=512) | |
| error_output = gr.Markdown() | |
| with gr.Column(scale=45): | |
| with gr.Tab("Options"): | |
| with gr.Group(): | |
| neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") | |
| auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (RAW photo,)", value=prefix, visible=prefix) | |
| with gr.Row(): | |
| guidance = gr.Slider(label="Guidance scale", value=7.0, maximum=15) | |
| steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1) | |
| with gr.Row(): | |
| width = gr.Slider(label="Width", value=512, minimum=384, maximum=640, step=8) | |
| height = gr.Slider(label="Height", value=768, minimum=512, maximum=960, step=8) | |
| seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) | |
| with gr.Tab("Image to image"): | |
| with gr.Group(): | |
| image = gr.Image(label="Image", height=256, tool="editor", type="pil") | |
| strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5) | |
| auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False) | |
| inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix] | |
| outputs = [image_out, error_output] | |
| prompt.submit(inference, inputs=inputs, outputs=outputs) | |
| generate.click(inference, inputs=inputs, outputs=outputs) | |
| demo.queue(concurrency_count=1) | |
| demo.launch() | |