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| #!/usr/bin/env python | |
| #patch 0.01 | |
| import os | |
| import random | |
| import uuid | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import spaces | |
| import torch | |
| from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler | |
| huggingface_token = os.getenv("HUGGINGFACE_TOKEN") | |
| examples = [ | |
| ["assets/1.png", "Change the picture to black and white."], | |
| ["assets/2.png", "Add the chocolate topping to the ice cream."], | |
| ["assets/3.png", "Make the burger look spicy."], | |
| ["assets/4.png", "Change the color of the jacket to white."], | |
| ] | |
| model_id = "timbrooks/instruct-pix2pix" | |
| pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None) | |
| pipe.to("cuda") | |
| pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) | |
| DESCRIPTION = """ | |
| """ | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>" | |
| MAX_SEED = np.iinfo(np.int32).max | |
| CACHE_EXAMPLES = False | |
| MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096")) | |
| USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" | |
| ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| def save_image(img): | |
| unique_name = str(uuid.uuid4()) + ".png" | |
| img.save(unique_name) | |
| return unique_name | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| @spaces.GPU | |
| def img2img_generate( | |
| prompt: str, | |
| init_image: gr.Image, | |
| negative_prompt: str = "", | |
| use_negative_prompt: bool = False, | |
| seed: int = 0, | |
| guidance_scale: float = 7, | |
| randomize_seed: bool = False, | |
| num_inference_steps=30, | |
| strength: float = 0.8, | |
| NUM_IMAGES_PER_PROMPT=1, | |
| use_resolution_binning: bool = True, | |
| progress=gr.Progress(track_tqdm=True), | |
| ): | |
| pipe.to(device) | |
| seed = int(randomize_seed_fn(seed, randomize_seed)) | |
| generator = torch.Generator().manual_seed(seed) | |
| if not use_negative_prompt: | |
| negative_prompt = None # type: ignore | |
| init_image = init_image.resize((768, 768)) | |
| output = pipe( | |
| prompt=prompt, | |
| image=init_image, | |
| negative_prompt=negative_prompt, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| generator=generator, | |
| strength=strength, | |
| num_images_per_prompt=NUM_IMAGES_PER_PROMPT, | |
| output_type="pil", | |
| ).images | |
| return output | |
| css = ''' | |
| .gradio-container{max-width: 800px !important} | |
| h1{text-align:center} | |
| ''' | |
| with gr.Blocks(css=css, theme="xiaobaiyuan/theme_brief") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Group(): | |
| with gr.Row(equal_height=True): | |
| with gr.Column(scale=1): | |
| img2img_prompt = gr.Text( | |
| label="Instruct", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| init_image = gr.Image(label="Image", type="pil") | |
| with gr.Row(): | |
| img2img_run_button = gr.Button("Generate", variant="primary") | |
| with gr.Column(scale=1): | |
| img2img_output = gr.Gallery(label="Result", elem_id="gallery") | |
| with gr.Accordion("Advanced options", open=False, visible=False): | |
| with gr.Row(): | |
| img2img_use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) | |
| img2img_negative_prompt = gr.Text( | |
| label="Negative prompt", | |
| max_lines=1, | |
| value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation", | |
| visible=True, | |
| ) | |
| img2img_seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| img2img_steps = gr.Slider( | |
| label="Steps", | |
| minimum=0, | |
| maximum=60, | |
| step=1, | |
| value=25, | |
| ) | |
| img2img_number_image = gr.Slider( | |
| label="No.of.Images", | |
| minimum=1, | |
| maximum=4, | |
| step=1, | |
| value=1, | |
| ) | |
| img2img_randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(): | |
| img2img_guidance_scale = gr.Slider( | |
| label="Guidance Scale", | |
| minimum=0.1, | |
| maximum=10, | |
| step=0.1, | |
| value=5.0, | |
| ) | |
| strength = gr.Slider(label="Confidence", minimum=0.0, maximum=1.0, step=0.01, value=0.8) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[init_image, img2img_prompt], | |
| outputs=img2img_output, | |
| fn=img2img_generate, | |
| cache_examples=CACHE_EXAMPLES, | |
| ) | |
| img2img_use_negative_prompt.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=img2img_use_negative_prompt, | |
| outputs=img2img_negative_prompt, | |
| api_name=False, | |
| ) | |
| gr.on( | |
| triggers=[ | |
| img2img_prompt.submit, | |
| img2img_negative_prompt.submit, | |
| img2img_run_button.click, | |
| ], | |
| fn=img2img_generate, | |
| inputs=[ | |
| img2img_prompt, | |
| init_image, | |
| img2img_negative_prompt, | |
| img2img_use_negative_prompt, | |
| img2img_seed, | |
| img2img_guidance_scale, | |
| img2img_randomize_seed, | |
| img2img_steps, | |
| strength, | |
| img2img_number_image, | |
| ], | |
| outputs=[img2img_output], | |
| api_name="img-to-img", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue().launch(show_api=False, debug=False#!/usr/bin/env python | |
| #patch 0.01 | |
| import os | |
| import random | |
| import uuid | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import spaces | |
| import torch | |
| from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler | |
| huggingface_token = os.getenv("HUGGINGFACE_TOKEN") | |
| examples = [ | |
| ["assets/1.png", "Change the picture to black and white."], | |
| ["assets/2.png", "Add the chocolate topping to the ice cream."], | |
| ["assets/3.png", "Make the burger look spicy."], | |
| ["assets/4.png", "Change the color of the jacket to white."], | |
| ] | |
| model_id = "timbrooks/instruct-pix2pix" | |
| pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None) | |
| pipe.to("cuda") | |
| pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) | |
| DESCRIPTION = """ | |
| """ | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>" | |
| MAX_SEED = np.iinfo(np.int32).max | |
| CACHE_EXAMPLES = False | |
| MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096")) | |
| USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" | |
| ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| def save_image(img): | |
| unique_name = str(uuid.uuid4()) + ".png" | |
| img.save(unique_name) | |
| return unique_name | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| @spaces.GPU | |
| def img2img_generate( | |
| prompt: str, | |
| init_image: gr.Image, | |
| negative_prompt: str = "", | |
| use_negative_prompt: bool = False, | |
| seed: int = 0, | |
| guidance_scale: float = 7, | |
| randomize_seed: bool = False, | |
| num_inference_steps=30, | |
| strength: float = 0.8, | |
| NUM_IMAGES_PER_PROMPT=1, | |
| use_resolution_binning: bool = True, | |
| progress=gr.Progress(track_tqdm=True), | |
| ): | |
| pipe.to(device) | |
| seed = int(randomize_seed_fn(seed, randomize_seed)) | |
| generator = torch.Generator().manual_seed(seed) | |
| if not use_negative_prompt: | |
| negative_prompt = None # type: ignore | |
| init_image = init_image.resize((768, 768)) | |
| output = pipe( | |
| prompt=prompt, | |
| image=init_image, | |
| negative_prompt=negative_prompt, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| generator=generator, | |
| strength=strength, | |
| num_images_per_prompt=NUM_IMAGES_PER_PROMPT, | |
| output_type="pil", | |
| ).images | |
| return output | |
| css = ''' | |
| .gradio-container{max-width: 800px !important} | |
| h1{text-align:center} | |
| ''' | |
| with gr.Blocks(css=css, theme="xiaobaiyuan/theme_brief") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Group(): | |
| with gr.Row(equal_height=True): | |
| with gr.Column(scale=1): | |
| img2img_prompt = gr.Text( | |
| label="Instruct", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| init_image = gr.Image(label="Image", type="pil") | |
| with gr.Row(): | |
| img2img_run_button = gr.Button("Generate", variant="primary") | |
| with gr.Column(scale=1): | |
| img2img_output = gr.Gallery(label="Result", elem_id="gallery") | |
| with gr.Accordion("Advanced options", open=False, visible=False): | |
| with gr.Row(): | |
| img2img_use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) | |
| img2img_negative_prompt = gr.Text( | |
| label="Negative prompt", | |
| max_lines=1, | |
| value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation", | |
| visible=True, | |
| ) | |
| img2img_seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| img2img_steps = gr.Slider( | |
| label="Steps", | |
| minimum=0, | |
| maximum=60, | |
| step=1, | |
| value=25, | |
| ) | |
| img2img_number_image = gr.Slider( | |
| label="No.of.Images", | |
| minimum=1, | |
| maximum=4, | |
| step=1, | |
| value=1, | |
| ) | |
| img2img_randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(): | |
| img2img_guidance_scale = gr.Slider( | |
| label="Guidance Scale", | |
| minimum=0.1, | |
| maximum=10, | |
| step=0.1, | |
| value=5.0, | |
| ) | |
| strength = gr.Slider(label="Confidence", minimum=0.0, maximum=1.0, step=0.01, value=0.8) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[init_image, img2img_prompt], | |
| outputs=img2img_output, | |
| fn=img2img_generate, | |
| cache_examples=CACHE_EXAMPLES, | |
| ) | |
| img2img_use_negative_prompt.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=img2img_use_negative_prompt, | |
| outputs=img2img_negative_prompt, | |
| api_name=False, | |
| ) | |
| gr.on( | |
| triggers=[ | |
| img2img_prompt.submit, | |
| img2img_negative_prompt.submit, | |
| img2img_run_button.click, | |
| ], | |
| fn=img2img_generate, | |
| inputs=[ | |
| img2img_prompt, | |
| init_image, | |
| img2img_negative_prompt, | |
| img2img_use_negative_prompt, | |
| img2img_seed, | |
| img2img_guidance_scale, | |
| img2img_randomize_seed, | |
| img2img_steps, | |
| strength, | |
| img2img_number_image, | |
| ], | |
| outputs=[img2img_output], | |
| api_name="img-to-img", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue().launch(show_api=False, debug=False | |