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| from diffusers import AutoPipelineForImage2Image, AutoPipelineForText2Image, StableDiffusionPipeline, EulerDiscreteScheduler | |
| import torch | |
| import os | |
| try: | |
| import intel_extension_for_pytorch as ipex | |
| except: | |
| pass | |
| from PIL import Image | |
| import numpy as np | |
| import gradio as gr | |
| import psutil | |
| import time | |
| import math | |
| SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None) | |
| TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None) | |
| HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
| # check if MPS is available OSX only M1/M2/M3 chips | |
| mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available() | |
| xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available() | |
| device = torch.device( | |
| "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu" | |
| ) | |
| torch_device = device | |
| torch_dtype = torch.float32 #float16 | |
| print(f"SAFETY_CHECKER: {SAFETY_CHECKER}") | |
| print(f"TORCH_COMPILE: {TORCH_COMPILE}") | |
| print(f"device: {device}") | |
| if mps_available: | |
| device = torch.device("mps") | |
| torch_device = "cpu" | |
| torch_dtype = torch.float32 | |
| repo_id = "runwayml/stable-diffusion-v1-5" | |
| scheduler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler") | |
| t2i_pipe = StableDiffusionPipeline.from_single_file( | |
| "https://huggingface.co/wanghuging/skin_demo/blob/main/skin_demo.safetensors", | |
| scheduler=scheduler, | |
| safety_checker = None, | |
| requires_safety_checker = False | |
| ) | |
| # if SAFETY_CHECKER == "True": | |
| # i2i_pipe = AutoPipelineForImage2Image.from_pretrained( | |
| # "stabilityai/sdxl-turbo", | |
| # torch_dtype=torch_dtype, | |
| # variant="fp16" if torch_dtype == torch.float16 else "fp32", | |
| # ) | |
| # t2i_pipe = AutoPipelineForText2Image.from_pretrained( | |
| # #"stabilityai/sdxl-turbo", | |
| # # "wanghuging/demo_model", | |
| # #"stabilityai/stable-diffusion-xl-base-1.0", | |
| # "stabilityai/stable-diffusion-2-1", | |
| # torch_dtype=torch_dtype, | |
| # variant="fp16" #if torch_dtype == torch.float16 else "fp32", | |
| # ) | |
| # else: | |
| # i2i_pipe = AutoPipelineForImage2Image.from_pretrained( | |
| # "stabilityai/sdxl-turbo", | |
| # safety_checker=None, | |
| # torch_dtype=torch_dtype, | |
| # variant="fp16" if torch_dtype == torch.float16 else "fp32", | |
| # ) | |
| # t2i_pipe = AutoPipelineForText2Image.from_pretrained( | |
| # #"stabilityai/sdxl-turbo", | |
| # # "wanghuging/demo_model", | |
| # # "stabilityai/stable-diffusion-xl-base-1.0", | |
| # "stabilityai/stable-diffusion-2-1", | |
| # safety_checker=None, | |
| # torch_dtype=torch_dtype, | |
| # variant="fp16" #if torch_dtype == torch.float16 else "fp32", | |
| # ) | |
| # t2i_pipe.load_lora_weights("wanghuging/skin_demo", weight_name="skin_demo.safetensors") | |
| t2i_pipe.safety_checker = lambda images, clip_input: (images, False) | |
| t2i_pipe.to(device=torch_device, dtype=torch_dtype).to(device) | |
| t2i_pipe.set_progress_bar_config(disable=True) | |
| # i2i_pipe.to(device=torch_device, dtype=torch_dtype).to(device) | |
| # i2i_pipe.set_progress_bar_config(disable=True) | |
| def resize_crop(image, size=512): | |
| image = image.convert("RGB") | |
| w, h = image.size | |
| image = image.resize((size, int(size * (h / w))), Image.BICUBIC) | |
| return image | |
| # async def predict(init_image, prompt, strength, steps, seed=1231231): | |
| # # init_image = None | |
| # if init_image is not None: | |
| # init_image = resize_crop(init_image) | |
| # generator = torch.manual_seed(seed) | |
| # last_time = time.time() | |
| # if int(steps * strength) < 1: | |
| # steps = math.ceil(1 / max(0.10, strength)) | |
| # results = i2i_pipe( | |
| # prompt=prompt, | |
| # image=init_image, | |
| # generator=generator, | |
| # num_inference_steps=steps, | |
| # guidance_scale=0.0, | |
| # strength=strength, | |
| # width=512, | |
| # height=512, | |
| # output_type="pil", | |
| # ) | |
| # else: | |
| # generator = torch.manual_seed(seed) | |
| # last_time = time.time() | |
| # t2i_pipe.safety_checker = None | |
| # t2i_pipe.requires_safety_checker = False | |
| # results = t2i_pipe( | |
| # prompt=prompt, | |
| # generator=generator, | |
| # num_inference_steps=steps, | |
| # guidance_scale=0.0, | |
| # width=512, | |
| # height=512, | |
| # output_type="pil", | |
| # ) | |
| # print(f"Pipe took {time.time() - last_time} seconds") | |
| # nsfw_content_detected = ( | |
| # results.nsfw_content_detected[0] | |
| # if "nsfw_content_detected" in results | |
| # else False | |
| # ) | |
| # if nsfw_content_detected: | |
| # gr.Warning("NSFW content detected.") | |
| # return Image.new("RGB", (512, 512)) | |
| # return results.images[0] | |
| async def predict(prompt, neg_prompt, strength, steps, seed=1231231): | |
| generator = torch.manual_seed(seed) | |
| last_time = time.time() | |
| t2i_pipe.safety_checker = None | |
| t2i_pipe.requires_safety_checker = False | |
| results = t2i_pipe( | |
| prompt=prompt, | |
| negative_prompt = neg_prompt, | |
| generator=generator, | |
| num_inference_steps=steps, | |
| guidance_scale=0.0, | |
| width=512, | |
| height=512, | |
| output_type="pil", | |
| ) | |
| print(f"Pipe took {time.time() - last_time} seconds") | |
| nsfw_content_detected = ( | |
| results.nsfw_content_detected[0] | |
| if "nsfw_content_detected" in results | |
| else False | |
| ) | |
| if nsfw_content_detected: | |
| gr.Warning("NSFW content detected.") | |
| return Image.new("RGB", (512, 512)) | |
| return results.images[0] | |
| css = """ | |
| #container{ | |
| margin: 0 auto; | |
| max-width: 80rem; | |
| } | |
| #intro{ | |
| max-width: 100%; | |
| text-align: center; | |
| margin: 0 auto; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| # init_image_state = gr.State() | |
| with gr.Column(elem_id="container"): | |
| gr.Markdown( | |
| """# Derm-T2IM Text to Image Skin Cancer | |
| ## Demo | |
| **Model**: https://huggingface.co/wanghuging/skin_demo | |
| """, | |
| elem_id="intro", | |
| ) | |
| with gr.Row(): | |
| prompt = gr.Textbox( | |
| placeholder="Insert your prompt here:", | |
| scale=5, | |
| container=False, | |
| ) | |
| # neg_prompt = gr.Textbox( | |
| # placeholder="Insert your negative prompt here:", | |
| # scale=5, | |
| # container=False, | |
| # ) | |
| generate_bt = gr.Button("Generate", scale=1) | |
| with gr.Row(): | |
| neg_prompt = gr.Textbox( | |
| placeholder="Insert your negative prompt here:", | |
| scale=5, | |
| container=False, | |
| ) | |
| with gr.Row(): | |
| # with gr.Column(): | |
| # neg_prompt = gr.Textbox( | |
| # placeholder="Insert your negative prompt here:", | |
| # scale=5, | |
| # container=False, | |
| # ) | |
| # with gr.Column(): | |
| # image_input = gr.Image( | |
| # sources=["upload", "webcam", "clipboard"], | |
| # label="Webcam", | |
| # type="pil", | |
| # ) | |
| with gr.Column(): | |
| image = gr.Image(type="filepath") | |
| with gr.Column(): | |
| with gr.Accordion("Advanced options", open=False): | |
| strength = gr.Slider( | |
| label="Strength", | |
| value=0.7, | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.001, | |
| ) | |
| steps = gr.Slider( | |
| label="Steps", value=2, minimum=1, maximum=25, step=1 | |
| ) | |
| seed = gr.Slider( | |
| randomize=True, | |
| minimum=0, | |
| maximum=12013012031030, | |
| label="Seed", | |
| step=1, | |
| ) | |
| # with gr.Accordion("Run with diffusers"): | |
| # gr.Markdown( | |
| # """## Running SDXL Turbo with `diffusers` | |
| # ```bash | |
| # pip install diffusers==0.23.1 | |
| # ``` | |
| # ```py | |
| # from diffusers import DiffusionPipeline | |
| # pipe = DiffusionPipeline.from_pretrained( | |
| # "stabilityai/sdxl-turbo" | |
| # ).to("cuda") | |
| # results = pipe( | |
| # prompt="A cinematic shot of a baby racoon wearing an intricate italian priest robe", | |
| # num_inference_steps=1, | |
| # guidance_scale=0.0, | |
| # ) | |
| # imga = results.images[0] | |
| # imga.save("image.png") | |
| # ``` | |
| # """ | |
| # ) | |
| inputs = [prompt, neg_prompt, strength, steps, seed] | |
| # inputs = [image_input, prompt, strength, steps, seed] | |
| generate_bt.click(fn=predict, inputs=inputs, outputs=image, show_progress=False) | |
| prompt.input(fn=predict, inputs=inputs, outputs=image, show_progress=False) | |
| neg_prompt.input(fn=predict, inputs=inputs, outputs=image, show_progress=False) | |
| steps.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) | |
| seed.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) | |
| strength.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) | |
| # image_input.change( | |
| # fn=lambda x: x, | |
| # inputs=image_input, | |
| # outputs=init_image_state, | |
| # show_progress=False, | |
| # queue=False, | |
| # ) | |
| demo.queue() | |
| demo.launch() |