| from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler |
| import gradio as gr |
| import torch |
| from PIL import Image |
|
|
| model_id = 'SG161222/Realistic_Vision_V5.0_noVAE' |
| prefix = 'RAW photo,' |
| |
| 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;">📷 Realistic Vision V5.0 📸</h1> |
| </div> |
| <p> |
| Demo for <a href="https://huggingface.co/SG161222/Realistic_Vision_V5.0_noVAE">Realistic Vision V5.0</a> |
| Stable Diffusion model by <a href="https://huggingface.co/SG161222/"><abbr title="SG1611222">Eugene</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 generation results: |
| </p> |
| |
| <b>Prompt</b>: |
| <details><code> |
| * subject *, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3 |
| <br> |
| <br> |
| <q><i> |
| 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> |
| (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3 |
| </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, <br> |
| low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, <br> |
| dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, <br> |
| 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=5, maximum=15) |
| steps = gr.Slider(label="Steps", value=20, minimum=2, maximum=75, step=1) |
|
|
| with gr.Row(): |
| width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8) |
| height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, 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() |
|
|
