Thafx/sdrdpv1

1from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler2import gradio as gr3import torch4from PIL import Image5 6model_id = 'digiplay/RunDiffusionFXPhotorealistic_v1'7prefix = 'RAW photo,'8     9scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")10 11pipe = StableDiffusionPipeline.from_pretrained(12  model_id,13  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,14  scheduler=scheduler)15 16pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(17  model_id,18  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,19  scheduler=scheduler)20 21if torch.cuda.is_available():22  pipe = pipe.to("cuda")23  pipe_i2i = pipe_i2i.to("cuda")24 25def error_str(error, title="Error"):26    return f"""#### {title}27            {error}"""  if error else ""28 29 30def _parse_args(prompt, generator):31        parser = argparse.ArgumentParser(32            description="making it work."33        )34        parser.add_argument(35            "--no-half-vae", help="no half vae"36        )37 38        cmdline_args = parser.parse_args()39        command = cmdline_args.command40        conf_file = cmdline_args.conf_file41        conf_args = Arguments(conf_file)42        opt = conf_args.readArguments()43 44        if cmdline_args.config_overrides:45            for config_override in cmdline_args.config_overrides.split(";"):46                config_override = config_override.strip()47                if config_override:48                    var_val = config_override.split("=")49                    assert (50                        len(var_val) == 251                    ), f"Config override '{var_val}' does not have the form 'VAR=val'"52                    conf_args.add_opt(opt, var_val[0], var_val[1], force_override=True)53 54def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):55  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None56  prompt = f"{prefix} {prompt}" if auto_prefix else prompt57 58  try:59    if img is not None:60      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None61    else:62      return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None63  except Exception as e:64    return None, error_str(e)65      66      67 68def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):69 70    result = pipe(71      prompt,72      negative_prompt = neg_prompt,73      num_inference_steps = int(steps),74      guidance_scale = guidance,75      width = width,76      height = height,77      generator = generator)78    79    return result.images[0]80 81def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):82 83    ratio = min(height / img.height, width / img.width)84    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)85    result = pipe_i2i(86        prompt,87        negative_prompt = neg_prompt,88        init_image = img,89        num_inference_steps = int(steps),90        strength = strength,91        guidance_scale = guidance,92        width = width,93        height = height,94        generator = generator)95        96    return result.images[0]97 98    def fake_safety_checker(images, **kwargs):99      return result.images[0], [False] * len(images)100    101    pipe.safety_checker = fake_safety_checker102 103css = """.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}104"""105with gr.Blocks(css=css) as demo:106    gr.HTML(107        f"""108            <div class="main-div">109              <div>110                <h1 style="color:orange;">📷 RunDiffusionFXPhotorealistic V1.0 📸</h1>111              </div>112              <p>113               Demo for <a href="https://huggingface.co/digiplay/RunDiffusionFXPhotorealistic_v1">RunDiffusionFXPhotorealistic V1.0</a>114               Stable Diffusion model by <a href="https://huggingface.co/digiplay/"><abbr title="digiplay">digiplay</abbr></a>.  {"" if prefix else ""}  115              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU ⚡</b>"}. 116              </p>117           <p>Please use the prompt template below to get an example of the desired generation results:118           </p>119 120<b>Prompt</b>:121<details><code>122RAW photo, * subject *, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3123<br>124<br>125<q><i>126Example: (hyperrealism:1.2), BREAK fish swimming under the water BREAK (8K UHD:1.2), (photorealistic:1.2)127</i></q>128</code></details>129 130<br>131<b>Negative Prompt</b>:132<details><code>133e.g. : plain background, boring, plain, standard, homogenous, uncreative, unattractive, opaque, grayscale, monochrome, distorted details, low details, grains,134grainy, foggy, dark, blurry, portrait, oversaturated, low contrast, underexposed, overexposed, low-res, low quality, close-up, macro, surreal, multiple views,135multiple angles136</code></details>137<br>138Wide/Tall aspect ratios like '480x832'/'832x480' work great then you can upscale.139 140You can use a CFG scale of 3.5 to 5 to get some softer photorealistic images.141<br>142Have Fun & Enjoy ⚡ <a href="https://www.thafx.com"><abbr title="Website">//THAFX</abbr></a>143<br>144             145            </div>146        """147    )148    with gr.Row():149        150        with gr.Column(scale=55):151          with gr.Group():152              with gr.Row():153                prompt = gr.Textbox(label="Prompt", show_label=False,max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)154                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))155 156              image_out = gr.Image(height=512)157          error_output = gr.Markdown()158 159        with gr.Column(scale=45):160          with gr.Tab("Options"):161            with gr.Group():162              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")163              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (RAW photo,)", value=prefix, visible=prefix)164 165              with gr.Row():166                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)167                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)168 169              with gr.Row():170                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)171                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)172 173              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)174 175          with gr.Tab("Image to image"):176              with gr.Group():177                image = gr.Image(label="Image", height=256, tool="editor", type="pil")178                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)179 180    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)181 182    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]183    outputs = [image_out, error_output]184    prompt.submit(inference, inputs=inputs, outputs=outputs)185    generate.click(inference, inputs=inputs, outputs=outputs)186 187    188 189demo.queue(concurrency_count=1)190demo.launch()191