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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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model_id = 'theintuitiveye/modernartstyle' |
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prefix = '' |
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scheduler = DPMSolverMultistepScheduler( |
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beta_start=0.00085, |
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beta_end=0.012, |
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beta_schedule="scaled_linear", |
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num_train_timesteps=1000, |
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trained_betas=None, |
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predict_epsilon=True, |
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thresholding=False, |
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algorithm_type="dpmsolver++", |
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solver_type="midpoint", |
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lower_order_final=True, |
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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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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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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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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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def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=True): |
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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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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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def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator): |
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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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return replace_nsfw_images(result) |
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def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): |
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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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return replace_nsfw_images(result) |
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def replace_nsfw_images(results): |
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for i in range(len(results.images)): |
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if results.nsfw_content_detected[i]: |
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results.images[i] = Image.open("nsfw.png") |
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return results.images[0] |
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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>Hassanblend1.4</h1> |
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</div> |
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<p> |
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Demo for <a href="https://huggingface.co/theintuitiveye/modernartstyle">modernartstyle</a> Stable Diffusion model.<br> |
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Add the following tokens to your prompts for the model to work properly: <b></b>. |
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</p> |
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<p>This demo is currently on cpu, to use it upgrade to gpu by going to settings after duplicating this space: <a style="display:inline-block" href="https://huggingface.co/spaces/akhaliq/hassanblend1.4?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a> </p> |
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Running on <b>{"GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"}</b> |
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</div> |
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""" |
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) |
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with gr.Row(): |
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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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image_out = gr.Image(height=512) |
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error_output = gr.Markdown() |
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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 ()", value=True) |
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with gr.Row(): |
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guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) |
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steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1) |
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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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seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) |
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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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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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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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gr.HTML(""" |
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<div style="border-top: 1px solid #303030;"> |
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<br> |
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<p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p> |
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</div> |
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""") |
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demo.queue(concurrency_count=1) |
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demo.launch() |