The two prompt additions each changed the style; the base prompt did a cartoon; the realistic prompt addition made it more of a 3D render, and the Midjourney token made it an artsy approach. However, when negative prompts are added, each image becomes more clear, with less blurriness, more...
Let's make sure that Stable Diffusion returns the image# in latent spacelow_res_latents=pipeline(prompt,generator=generator,output_type="latent").imagesupscaled_image=upscaler(prompt=prompt,image=low_res_latents,num_inference_steps=20,guidance_scale=0,generator=generator, ).images[0]# Let's sa...
validation_prompt = "easyphoto_face, easyphoto, 1person" DEFAULT_POSITIVE = '(cloth:1.5), (best quality), (realistic, photo-realistic:1.3), (detailed skin:1.3), (rough skin:1.3), (beautiful eyes:1.3), (sparkling eyes:1.3), (beautiful mouth:1.3), finely detail, light smile, extremely de...
Validate Environment: Ensure that the testing environment closely mirrors the production environment. This ensures that the test results are realistic and applicable. Document Everything: Keep thorough documentation of your negative test cases, procedures, and results. This aids in understanding the test ...
Negative prompts are important for v2 models Negative prompt with Stable Diffusion v2.1 Consistent withMax Woolf’s finding, my experience is that the negative prompt is very important for v2 models. Below, I used the positive prompt for generating realistic humans but with the Stable Diffusion 2....
Works with very few training images and yields more precise segmentations; see Figure 1. The main idea in ![] is to supplement a usual contracting network by Successive layers, where pooling operators are replaced by upsampling operators. ...