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from diffusers import ( |
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StableDiffusionPipeline, |
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StableDiffusionImg2ImgPipeline, |
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DPMSolverMultistepScheduler, |
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) |
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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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import time |
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import psutil |
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import random |
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from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker |
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start_time = time.time() |
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current_steps = 25 |
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SAFETY_CHECKER = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=torch.float16) |
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class Model: |
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def __init__(self, name, path=""): |
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self.name = name |
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self.path = path |
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if path != "": |
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self.pipe_t2i = StableDiffusionPipeline.from_pretrained( |
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path, torch_dtype=torch.float16, safety_checker=SAFETY_CHECKER |
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) |
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self.pipe_t2i.scheduler = DPMSolverMultistepScheduler.from_config( |
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self.pipe_t2i.scheduler.config |
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) |
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self.pipe_i2i = StableDiffusionImg2ImgPipeline(**self.pipe_t2i.components) |
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else: |
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self.pipe_t2i = None |
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self.pipe_i2i = None |
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models = [ |
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Model("Protogen v2.2 (Anime)", "darkstorm2150/Protogen_v2.2_Official_Release"), |
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Model("Protogen x3.4 (Photorealism)", "darkstorm2150/Protogen_x3.4_Official_Release"), |
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Model("Protogen x5.3 (Photorealism)", "darkstorm2150/Protogen_x5.3_Official_Release"), |
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Model("Protogen x5.8 Rebuilt (Scifi+Anime)", "darkstorm2150/Protogen_x5.8_Official_Release"), |
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Model("Protogen Dragon (RPG Model)", "darkstorm2150/Protogen_Dragon_Official_Release"), |
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Model("Protogen Nova", "darkstorm2150/Protogen_Nova_Official_Release"), |
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Model("Protogen Eclipse", "darkstorm2150/Protogen_Eclipse_Official_Release"), |
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Model("Protogen Infinity", "darkstorm2150/Protogen_Infinity_Official_Release"), |
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] |
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MODELS = {m.name: m for m in models} |
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device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" |
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def error_str(error, title="Error"): |
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return ( |
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f"""#### {title} |
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{error}""" |
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if error |
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else "" |
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) |
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def inference( |
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model_name, |
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prompt, |
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guidance, |
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steps, |
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n_images=1, |
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width=512, |
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height=512, |
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seed=0, |
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img=None, |
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strength=0.5, |
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neg_prompt="", |
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): |
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print(psutil.virtual_memory()) |
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if seed == 0: |
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seed = random.randint(0, 2147483647) |
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generator = torch.Generator("cuda").manual_seed(seed) |
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try: |
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if img is not None: |
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return ( |
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img_to_img( |
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model_name, |
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prompt, |
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n_images, |
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neg_prompt, |
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img, |
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strength, |
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guidance, |
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steps, |
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width, |
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height, |
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generator, |
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seed, |
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), |
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f"Done. Seed: {seed}", |
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) |
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else: |
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return ( |
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txt_to_img( |
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model_name, |
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prompt, |
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n_images, |
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neg_prompt, |
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guidance, |
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steps, |
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width, |
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height, |
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generator, |
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seed, |
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), |
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f"Done. Seed: {seed}", |
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) |
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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( |
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model_name, |
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prompt, |
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n_images, |
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neg_prompt, |
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guidance, |
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steps, |
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width, |
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height, |
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generator, |
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seed, |
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): |
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pipe = MODELS[model_name].pipe_t2i |
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if torch.cuda.is_available(): |
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pipe = pipe.to("cuda") |
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pipe.enable_xformers_memory_efficient_attention() |
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result = pipe( |
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prompt, |
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negative_prompt=neg_prompt, |
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num_images_per_prompt=n_images, |
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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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pipe.to("cpu") |
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return replace_nsfw_images(result) |
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def img_to_img( |
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model_name, |
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prompt, |
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n_images, |
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neg_prompt, |
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img, |
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strength, |
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guidance, |
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steps, |
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width, |
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height, |
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generator, |
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seed, |
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): |
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pipe = MODELS[model_name].pipe_i2i |
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if torch.cuda.is_available(): |
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pipe = pipe.to("cuda") |
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pipe.enable_xformers_memory_efficient_attention() |
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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( |
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prompt, |
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negative_prompt=neg_prompt, |
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num_images_per_prompt=n_images, |
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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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generator=generator, |
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) |
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pipe.to("cpu") |
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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 |
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with gr.Blocks(css="style.css") as demo: |
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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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prompt = gr.Textbox( |
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label="Repo id on Hub", |
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placeholder="Path to model, e.g. CompVis/stable-diffusion-v1-4", |
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) |
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with gr.Box(visible=False) as custom_model_group: |
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custom_model_path = gr.Textbox( |
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label="Custom model path", |
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placeholder="Path to model, e.g. darkstorm2150/Protogen_x3.4_Official_Release", |
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interactive=True, |
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) |
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gr.HTML( |
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"<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>" |
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) |
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with gr.Row(): |
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prompt = gr.Textbox( |
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label="Prompt", |
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show_label=False, |
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max_lines=2, |
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placeholder="Enter prompt.", |
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).style(container=False) |
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generate = gr.Button(value="Generate").style( |
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rounded=(False, True, True, False) |
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) |
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gallery = gr.Gallery( |
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label="Generated images", show_label=False, elem_id="gallery" |
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).style(grid=[2], height="auto") |
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state_info = gr.Textbox(label="State", show_label=False, max_lines=2).style( |
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container=False |
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) |
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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( |
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label="Negative prompt", |
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placeholder="What to exclude from the image", |
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) |
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n_images = gr.Slider( |
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label="Images", value=1, minimum=1, maximum=4, step=1 |
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) |
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with gr.Row(): |
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guidance = gr.Slider( |
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label="Guidance scale", value=7.5, maximum=15 |
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) |
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steps = gr.Slider( |
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label="Steps", |
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value=current_steps, |
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minimum=2, |
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maximum=75, |
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step=1, |
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) |
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with gr.Row(): |
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width = gr.Slider( |
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label="Width", value=512, minimum=64, maximum=1024, step=8 |
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) |
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height = gr.Slider( |
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label="Height", value=512, minimum=64, maximum=1024, step=8 |
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) |
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seed = gr.Slider( |
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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( |
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label="Image", height=256, tool="editor", type="pil" |
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) |
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strength = gr.Slider( |
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label="Transformation strength", |
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minimum=0, |
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maximum=1, |
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step=0.01, |
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value=0.5, |
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) |
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inputs = [ |
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model_name, |
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prompt, |
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guidance, |
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steps, |
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n_images, |
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width, |
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height, |
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seed, |
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image, |
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strength, |
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neg_prompt, |
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] |
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outputs = [gallery, 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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""" |
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<div style="border-top: 1px solid #303030;"> |
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<br> |
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<p>Models by <a href="https://huggingface.co/darkstorm2150">@darkstorm2150</a> and others. ❤️</p> |
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<p>This space uses the <a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver++</a> sampler by <a href="https://arxiv.org/abs/2206.00927">Cheng Lu, et al.</a>.</p> |
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<p>Space by: Darkstorm (Victor Espinoza)<br> |
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<a href="https://www.instagram.com/officialvictorespinoza/">Instagram</a> |
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</div> |
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""" |
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) |
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print(f"Space built in {time.time() - start_time:.2f} seconds") |
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demo.queue(concurrency_count=1) |
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demo.launch() |
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