from diffusers import StableDiffusionPipeline
model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionPipeline.from_pretrained(model_id).to("cuda")
prompt = "A realistic photo of an Asian-British mixed-race female model with a curvy body. She has a beautiful blend of Asian and British facial features, medium-length dark hair, and a confident smile. She is dressed in trendy, fashionable clothing suitable for different scenes. The background varies, including urban cityscapes, cozy indoor settings, and natural outdoor environments. The model is posing naturally, showcasing different outfits and expressions."
negative_prompt = "Avoid overly exaggerated features, cartoonish styles, unrealistic proportions, or any artificial-looking elements. Ensure the background and lighting are realistic and natural. No inappropriate or revealing clothing, and avoid monotonous or repetitive backgrounds."
result = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=50)
image = result.images[0]
image.save("output.png")
信息
提示词
from diffusers import StableDiffusionPipeline
model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionPipeline.from_pretrained(model_id).to("cuda")
prompt = "A realistic photo of an Asian-British mixed-race female model with a curvy body. She has a beautiful blend of Asian and British facial features, medium-length dark hair, and a confident smile. She is dressed in trendy, fashionable clothing suitable for different scenes. The background varies, including urban cityscapes, cozy indoor settings, and natural outdoor environments. The model is posing naturally, showcasing different outfits and expressions."
negative_prompt = "Avoid overly exaggerated features, cartoonish styles, unrealistic proportions, or any artificial-looking elements. Ensure the background and lighting are realistic and natural. No inappropriate or revealing clothing, and avoid monotonous or repetitive backgrounds."
result = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=50)
image = result.images[0]
image.save("output.png")
负标签
verybadimagenegative_v1.3, ng_deepnegative_v1_75t, (ugly face:0.8),cross-eyed,sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, bad anatomy, DeepNegative, facing away, tilted head, {Multiple people}, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worstquality, low quality, normal quality, jpegartifacts, signature, watermark, username, blurry, bad feet, cropped, poorly drawn hands, poorly drawn face, mutation, deformed, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, extra fingers, fewer digits, extra limbs, extra arms,extra legs, malformed limbs, fused fingers, too many fingers, long neck, cross-eyed,mutated hands, polar lowres, bad body, bad proportions, gross proportions, text, error, missing fingers, missing arms, missing legs, extra digit, extra arms, extra leg, extra foot, ((repeating hair))
from diffusers import StableDiffusionPipeline model_id = "CompVis/stable-diffusion-v1-4" pipe = StableDiffusionPipeline.from_pretrained(model_id).to("cuda") prompt = "A realistic photo of an Asian-British mixed-race female model with a curvy body. She has a beautiful blend of Asian and British facial features, medium-length dark hair, and a confident smile. She is dressed in trendy, fashionable clothing suitable for different scenes. The background varies, including urban cityscapes, cozy indoor settings, and natural outdoor environments. The model is posing naturally, showcasing different outfits and expressions." negative_prompt = "Avoid overly exaggerated features, cartoonish styles, unrealistic proportions, or any artificial-looking elements. Ensure the background and lighting are realistic and natural. No inappropriate or revealing clothing, and avoid monotonous or repetitive backgrounds." result = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=50) image = result.images[0] image.save("output.png")
CompVis/stable-diffusion-v1-4