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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import requests | |
| import os | |
| import random | |
| import gradio as gr | |
| import numpy as np | |
| import spaces | |
| import torch | |
| import cv2 | |
| from PIL import Image | |
| from io import BytesIO | |
| from diffusers.utils import load_image | |
| from diffusers import StableDiffusionXLControlNetPipeline, StableDiffusionXLControlNetInpaintPipeline, ControlNetModel, AutoencoderKL, DiffusionPipeline, AutoPipelineForImage2Image, AutoPipelineForInpainting, UNet2DConditionModel | |
| from controlnet_aux import HEDdetector | |
| DESCRIPTION = "# Run any LoRA or SD Model" | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>⚠️ This space is running on the CPU. This demo doesn't work on CPU 😞! Run on a GPU by duplicating this space or test our website for free and unlimited by <a href='https://squaadai.com'>clicking here</a>, which provides these and more options.</p>" | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1824")) | |
| USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1" | |
| ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1" | |
| ENABLE_USE_LORA = os.getenv("ENABLE_USE_LORA", "1") == "1" | |
| ENABLE_USE_LORA2 = os.getenv("ENABLE_USE_LORA2", "1") == "1" | |
| ENABLE_USE_VAE = os.getenv("ENABLE_USE_VAE", "1") == "1" | |
| ENABLE_USE_IMG2IMG = os.getenv("ENABLE_USE_IMG2IMG", "1") == "1" | |
| ENABLE_USE_CONTROLNET = os.getenv("ENABLE_USE_CONTROLNET", "1") == "1" | |
| ENABLE_USE_CONTROLNETINPAINT = os.getenv("ENABLE_USE_CONTROLNETINPAINT", "1") == "1" | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| def generate( | |
| prompt: str = "", | |
| negative_prompt: str = "", | |
| prompt_2: str = "", | |
| negative_prompt_2: str = "", | |
| use_negative_prompt: bool = False, | |
| use_prompt_2: bool = False, | |
| use_negative_prompt_2: bool = False, | |
| seed: int = 0, | |
| width: int = 1024, | |
| height: int = 1024, | |
| guidance_scale_base: float = 5.0, | |
| num_inference_steps_base: int = 25, | |
| controlnet_conditioning_scale: float = 1, | |
| control_guidance_start: float = 0, | |
| control_guidance_end: float = 1, | |
| strength_img2img: float = 0.7, | |
| use_vae: bool = False, | |
| use_lora: bool = False, | |
| use_lora2: bool = False, | |
| model = 'stabilityai/stable-diffusion-xl-base-1.0', | |
| vaecall = 'madebyollin/sdxl-vae-fp16-fix', | |
| lora = '', | |
| lora2 = '', | |
| controlnet_model = 'diffusers/controlnet-canny-sdxl-1.0', | |
| lora_scale: float = 0.7, | |
| lora_scale2: float = 0.7, | |
| use_img2img: bool = False, | |
| use_controlnet: bool = False, | |
| use_controlnetinpaint: bool = False, | |
| url = '', | |
| controlnet_img = '', | |
| controlnet_inpaint = '', | |
| ): | |
| if torch.cuda.is_available(): | |
| if not use_img2img: | |
| pipe = DiffusionPipeline.from_pretrained(model, safety_checker=None, requires_safety_checker=False, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| if use_vae: | |
| vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| pipe = DiffusionPipeline.from_pretrained(model, vae=vae, safety_checker=None, requires_safety_checker=False, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| if use_img2img: | |
| pipe = AutoPipelineForImage2Image.from_pretrained(model, safety_checker=None, requires_safety_checker=False, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| init_image = load_image(url) | |
| if use_vae: | |
| vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| pipe = AutoPipelineForImage2Image.from_pretrained(model, vae=vae, safety_checker=None, requires_safety_checker=False, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| if use_controlnet: | |
| controlnet = ControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| pipe = StableDiffusionXLControlNetPipeline.from_pretrained(model, safety_checker=None, requires_safety_checker=False, controlnet=controlnet, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| image = load_image(controlnet_img) | |
| image = np.array(image) | |
| image = cv2.Canny(image, 250, 255) | |
| image = image[:, :, None] | |
| image = np.concatenate([image, image, image], axis=2) | |
| image = Image.fromarray(image) | |
| if use_vae: | |
| vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| pipe = StableDiffusionXLControlNetPipeline.from_pretrained(model, safety_checker=None, requires_safety_checker=False, controlnet=controlnet, vae=vae, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| if use_controlnetinpaint: | |
| controlnet = ControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| pipe = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(model, safety_checker=None, requires_safety_checker=False, controlnet=controlnet, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| image_start = load_image(controlnet_img) | |
| image = load_image(controlnet_img) | |
| image_mask = load_image(controlnet_img2img) | |
| image = np.array(image) | |
| image = cv2.Canny(image, 100, 200) | |
| image = image[:, :, None] | |
| image = np.concatenate([image, image, image], axis=2) | |
| image = Image.fromarray(image) | |
| if use_vae: | |
| vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| pipe = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(model, safety_checker=None, requires_safety_checker=False, controlnet=controlnet, vae=vae, torch_dtype=torch.float16, device_map="balanced", low_cpu_mem_usage=True) | |
| if use_lora: | |
| pipe.load_lora_weights(lora, adapter_name="1") | |
| pipe.set_adapters("1", adapter_weights=[lora_scale]) | |
| if use_lora2: | |
| pipe.load_lora_weights(lora, adapter_name="1") | |
| pipe.load_lora_weights(lora2, adapter_name="2") | |
| pipe.set_adapters(["1", "2"], adapter_weights=[lora_scale, lora_scale2]) | |
| reset_device_map() | |
| pipe.enable_model_cpu_offload() | |
| generator = torch.Generator().manual_seed(seed) | |
| if not use_negative_prompt: | |
| negative_prompt = None # type: ignore | |
| if not use_prompt_2: | |
| prompt_2 = None # type: ignore | |
| if not use_negative_prompt_2: | |
| negative_prompt_2 = None # type: ignore | |
| if use_controlnetinpaint: | |
| image = pipe( | |
| prompt=prompt, | |
| strength=strength_img2img, | |
| controlnet_conditioning_scale=controlnet_conditioning_scale, | |
| eta=0.0, | |
| mask_image=image_mask, | |
| image=image_start, | |
| control_image=image, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale_base, | |
| num_inference_steps=num_inference_steps_base, | |
| generator=generator, | |
| ).images[0] | |
| return image | |
| if use_controlnet: | |
| image = pipe( | |
| prompt=prompt, | |
| controlnet_conditioning_scale=controlnet_conditioning_scale, | |
| control_guidance_start=control_guidance_start, | |
| control_guidance_end=control_guidance_end, | |
| image=image, | |
| negative_prompt=negative_prompt, | |
| prompt_2=prompt_2, | |
| width=width, | |
| height=height, | |
| negative_prompt_2=negative_prompt_2, | |
| guidance_scale=guidance_scale_base, | |
| num_inference_steps=num_inference_steps_base, | |
| generator=generator, | |
| ).images[0] | |
| return image | |
| elif use_img2img: | |
| images = pipe( | |
| prompt=prompt, | |
| image=init_image, | |
| strength=strength_img2img, | |
| negative_prompt=negative_prompt, | |
| prompt_2=prompt_2, | |
| negative_prompt_2=negative_prompt_2, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale_base, | |
| num_inference_steps=num_inference_steps_base, | |
| generator=generator, | |
| ).images[0] | |
| return images | |
| else: | |
| return pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| prompt_2=prompt_2, | |
| negative_prompt_2=negative_prompt_2, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale_base, | |
| num_inference_steps=num_inference_steps_base, | |
| generator=generator, | |
| ).images[0] | |
| with gr.Blocks(theme=gr.themes.Soft(), css="style.css") as demo: | |
| gr.HTML( | |
| "<p><center>📙 For any additional support, join our <a href='https://discord.gg/JprjXpjt9K'>Discord</a></center></p>" | |
| ) | |
| gr.Markdown(DESCRIPTION, elem_id="description") | |
| with gr.Group(): | |
| model = gr.Text(label='Model', placeholder='e.g. stabilityai/stable-diffusion-xl-base-1.0') | |
| vaecall = gr.Text(label='VAE', placeholder='e.g. madebyollin/sdxl-vae-fp16-fix') | |
| lora = gr.Text(label='LoRA 1', placeholder='e.g. nerijs/pixel-art-xl') | |
| lora2 = gr.Text(label='LoRA 2', placeholder='e.g. nerijs/pixel-art-xl') | |
| controlnet_model = gr.Text(label='Controlnet', placeholder='e.g diffusers/controlnet-canny-sdxl-1.0') | |
| lora_scale = gr.Slider( | |
| info="The closer to 1, the more it will resemble LoRA, but errors may be visible.", | |
| label="Lora Scale 1", | |
| minimum=0.01, | |
| maximum=1, | |
| step=0.01, | |
| value=0.7, | |
| ) | |
| lora_scale2 = gr.Slider( | |
| info="The closer to 1, the more it will resemble LoRA, but errors may be visible.", | |
| label="Lora Scale 2", | |
| minimum=0.01, | |
| maximum=1, | |
| step=0.01, | |
| value=0.7, | |
| ) | |
| url = gr.Text(label='URL (Img2Img)') | |
| controlnet_img = gr.Text(label='URL (Controlnet)', placeholder='e.g https://example.com/image.png') | |
| controlnet_inpaint = gr.Text(label='URL (Controlnet - IMG2IMG)', placeholder='e.g https://example.com/image.png') | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| placeholder="Input prompt", | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| container=False, | |
| ) | |
| run_button = gr.Button("Run", scale=0) | |
| result = gr.Image(label="Result", show_label=False) | |
| with gr.Accordion("Advanced options", open=False): | |
| with gr.Row(): | |
| use_controlnet = gr.Checkbox(label='Use Controlnet', value=False, visible=ENABLE_USE_CONTROLNET) | |
| use_controlnetinpaint = gr.Checkbox(label='Use Controlnet Img2Img', value=False, visible=ENABLE_USE_CONTROLNETINPAINT) | |
| use_img2img = gr.Checkbox(label='Use Img2Img', value=False, visible=ENABLE_USE_IMG2IMG) | |
| use_vae = gr.Checkbox(label='Use VAE', value=False, visible=ENABLE_USE_VAE) | |
| use_lora = gr.Checkbox(label='Use Lora 1', value=False, visible=ENABLE_USE_LORA) | |
| use_lora2 = gr.Checkbox(label='Use Lora 2', value=False, visible=ENABLE_USE_LORA2) | |
| use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False) | |
| use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False) | |
| use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False) | |
| negative_prompt = gr.Text( | |
| placeholder="Input Negative Prompt", | |
| label="Negative prompt", | |
| max_lines=1, | |
| visible=False, | |
| ) | |
| prompt_2 = gr.Text( | |
| placeholder="Input Prompt 2", | |
| label="Prompt 2", | |
| max_lines=1, | |
| visible=False, | |
| ) | |
| negative_prompt_2 = gr.Text( | |
| placeholder="Input Negative Prompt 2", | |
| label="Negative prompt 2", | |
| max_lines=1, | |
| visible=False, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1024, | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=32, | |
| value=1024, | |
| ) | |
| with gr.Row(): | |
| guidance_scale_base = gr.Slider( | |
| info="Scale for classifier-free guidance", | |
| label="Guidance scale", | |
| minimum=1, | |
| maximum=20, | |
| step=0.1, | |
| value=5.0, | |
| ) | |
| with gr.Row(): | |
| num_inference_steps_base = gr.Slider( | |
| info="Number of denoising steps", | |
| label="Number of inference steps", | |
| minimum=10, | |
| maximum=100, | |
| step=1, | |
| value=25, | |
| ) | |
| with gr.Row(): | |
| controlnet_conditioning_scale = gr.Slider( | |
| info="controlnet_conditioning_scale", | |
| label="controlnet_conditioning_scale", | |
| minimum=0.01, | |
| maximum=2, | |
| step=0.01, | |
| value=1, | |
| ) | |
| with gr.Row(): | |
| control_guidance_start = gr.Slider( | |
| info="control_guidance_start", | |
| label="control_guidance_start", | |
| minimum=0.01, | |
| maximum=1, | |
| step=0.01, | |
| value=0, | |
| ) | |
| with gr.Row(): | |
| control_guidance_end = gr.Slider( | |
| info="control_guidance_end", | |
| label="control_guidance_end", | |
| minimum=0.01, | |
| maximum=1, | |
| step=0.01, | |
| value=1, | |
| ) | |
| with gr.Row(): | |
| strength_img2img = gr.Slider( | |
| info="Strength for Img2Img", | |
| label="Strength", | |
| minimum=0, | |
| maximum=1, | |
| step=0.01, | |
| value=0.7, | |
| ) | |
| use_negative_prompt.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_negative_prompt, | |
| outputs=negative_prompt, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| use_prompt_2.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_prompt_2, | |
| outputs=prompt_2, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| use_negative_prompt_2.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_negative_prompt_2, | |
| outputs=negative_prompt_2, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| use_vae.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_vae, | |
| outputs=vaecall, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| use_lora.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_lora, | |
| outputs=lora, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| use_lora2.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_lora2, | |
| outputs=lora2, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| use_img2img.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_img2img, | |
| outputs=url, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| use_controlnet.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_controlnet, | |
| outputs=controlnet_img, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| use_controlnetinpaint.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_controlnetinpaint, | |
| outputs=controlnet_inpaint, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| gr.on( | |
| triggers=[ | |
| prompt.submit, | |
| negative_prompt.submit, | |
| prompt_2.submit, | |
| negative_prompt_2.submit, | |
| run_button.click, | |
| ], | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=generate, | |
| inputs=[ | |
| prompt, | |
| negative_prompt, | |
| prompt_2, | |
| negative_prompt_2, | |
| use_negative_prompt, | |
| use_prompt_2, | |
| use_negative_prompt_2, | |
| seed, | |
| width, | |
| height, | |
| guidance_scale_base, | |
| num_inference_steps_base, | |
| controlnet_conditioning_scale, | |
| control_guidance_start, | |
| control_guidance_end, | |
| strength_img2img, | |
| use_vae, | |
| use_lora, | |
| use_lora2, | |
| model, | |
| vaecall, | |
| lora, | |
| lora2, | |
| controlnet_model, | |
| lora_scale, | |
| lora_scale2, | |
| use_img2img, | |
| use_controlnet, | |
| use_controlnetinpaint, | |
| url, | |
| controlnet_img, | |
| controlnet_inpaint, | |
| ], | |
| outputs=result, | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20, default_concurrency_limit=2).launch() |