Text-to-image¶
When you think of diffusion models, text-to-image is usually one of the first things that come to mind. Text-to-image generates an image from a text description (for example, "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k") which is also known as a prompt.
From a very high level, a diffusion model takes a prompt and some random initial noise, and iteratively removes the noise to construct an image. The denoising process is guided by the prompt, and once the denoising process ends after a predetermined number of time steps, the image representation is decoded into an image.
Tip
Read the How does Stable Diffusion work? blog post to learn more about how a latent diffusion model works.
- Load a checkpoint into the
DiffusionPipelineclass, which automatically detects the appropriate pipeline class to use based on the checkpoint:
from mindone.diffusers import DiffusionPipeline
import mindspore as ms
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", mindspore_dtype=ms.float16, variant="fp16"
)
- Pass a prompt to the pipeline to generate an image:
image = pipeline(
"stained glass of darth vader, backlight, centered composition, masterpiece, photorealistic, 8k"
)[0][0]
image
Popular models¶
The most common text-to-image models are Stable Diffusion v1.5, Stable Diffusion XL (SDXL), and Kandinsky 2.2. There are also ControlNet models or adapters that can be used with text-to-image models for more direct control in generating images. The results from each model are slightly different because of their architecture and training process, but no matter which model you choose, their usage is more or less the same. Let's use the same prompt for each model and compare their results.
Stable Diffusion v1.5¶
Stable Diffusion v1.5 is a latent diffusion model initialized from Stable Diffusion v1-4, and finetuned for 595K steps on 512x512 images from the LAION-Aesthetics V2 dataset. You can use this model like:
from mindone.diffusers import DiffusionPipeline
import mindspore as ms
import numpy as np
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", mindspore_dtype=ms.float16, variant="fp16"
)
generator = np.random.Generator(np.random.PCG64(31))
image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", generator=generator)[0][0]
image
Stable Diffusion XL¶
SDXL is a much larger version of the previous Stable Diffusion models, and involves a two-stage model process that adds even more details to an image. It also includes some additional micro-conditionings to generate high-quality images centered subjects. Take a look at the more comprehensive SDXL guide to learn more about how to use it. In general, you can use SDXL like:
from mindone.diffusers import DiffusionPipeline
import mindspore as ms
import numpy as np
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", mindspore_dtype=ms.float16, variant="fp16"
)
generator = np.random.Generator(np.random.PCG64(31))
image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", generator=generator)[0][0]
image
Kandinsky 2.2¶
The Kandinsky model is a bit different from the Stable Diffusion models because it also uses an image prior model to create embeddings that are used to better align text and images in the diffusion model.
The easiest way to use Kandinsky 2.2 is:
from mindone.diffusers import KandinskyV22CombinedPipeline
import mindspore as ms
import numpy as np
pipeline = KandinskyV22CombinedPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder", mindspore_dtype=ms.float16
)
generator = np.random.Generator(np.random.PCG64(31))
image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", generator=generator)[0][0]
image
ControlNet¶
ControlNet models are auxiliary models or adapters that are finetuned on top of text-to-image models, such as Stable Diffusion v1.5. Using ControlNet models in combination with text-to-image models offers diverse options for more explicit control over how to generate an image. With ControlNet, you add an additional conditioning input image to the model. For example, if you provide an image of a human pose (usually represented as multiple keypoints that are connected into a skeleton) as a conditioning input, the model generates an image that follows the pose of the image. Check out the more in-depth ControlNet guide to learn more about other conditioning inputs and how to use them.
In this example, let's condition the ControlNet with a human pose estimation image. Load the ControlNet model pretrained on human pose estimations:
from mindone.diffusers import ControlNetModel, StableDiffusionControlNetPipeline
from mindone.diffusers.utils import load_image
import mindspore as ms
import numpy as np
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/control_v11p_sd15_openpose", mindspore_dtype=ms.float16, variant="fp16"
)
pose_image = load_image("https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png")
Pass the controlnet to the DiffusionPipeline, and provide the prompt and pose estimation image:
pipeline = StableDiffusionControlNetPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, mindspore_dtype=ms.float16, variant="fp16"
)
generator = np.random.Generator(np.random.PCG64(31))
image = pipeline("Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", image=pose_image, generator=generator)[0][0]
image
Configure pipeline parameters¶
There are a number of parameters that can be configured in the pipeline that affect how an image is generated. You can change the image's output size, specify a negative prompt to improve image quality, and more. This section dives deeper into how to use these parameters.
Height and width¶
The height and width parameters control the height and width (in pixels) of the generated image. By default, the Stable Diffusion v1.5 model outputs 512x512 images, but you can change this to any size that is a multiple of 8. For example, to create a rectangular image:
from mindone.diffusers import DiffusionPipeline
import mindspore as ms
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", mindspore_dtype=ms.float16, variant="fp16"
)
image = pipeline(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", height=768, width=512
)[0][0]
image
Warning
Other models may have different default image sizes depending on the image sizes in the training dataset. For example, SDXL's default image size is 1024x1024 and using lower height and width values may result in lower quality images. Make sure you check the model's API reference first!
Guidance scale¶
The guidance_scale parameter affects how much the prompt influences image generation. A lower value gives the model "creativity" to generate images that are more loosely related to the prompt. Higher guidance_scale values push the model to follow the prompt more closely, and if this value is too high, you may observe some artifacts in the generated image.
from mindone.diffusers import DiffusionPipeline
import mindspore as ms
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", mindspore_dtype=ms.float16
)
image = pipeline(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", guidance_scale=3.5
)[0][0]
image
Negative prompt¶
Just like how a prompt guides generation, a negative prompt steers the model away from things you don't want the model to generate. This is commonly used to improve overall image quality by removing poor or bad image features such as "low resolution" or "bad details". You can also use a negative prompt to remove or modify the content and style of an image.
from mindone.diffusers import DiffusionPipeline
import mindspore as ms
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", mindspore_dtype=ms.float16
)
image = pipeline(
prompt="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
negative_prompt="ugly, deformed, disfigured, poor details, bad anatomy",
)[0][0]
image
Generator¶
A numpy.random.Generator object enables reproducibility in a pipeline by setting a manual seed. You can use a Generator to generate batches of images and iteratively improve on an image generated from a seed as detailed in the Improve image quality with deterministic generation guide.
You can set a seed and Generator as shown below. Creating an image with a Generator should return the same result each time instead of randomly generating a new image.
from mindone.diffusers import DiffusionPipeline
import mindspore as ms
import numpy as np
pipeline = DiffusionPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", mindspore_dtype=ms.float16
)
generator = np.random.Generator(np.random.PCG64(30))
image = pipeline(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
generator=generator,
)[0][0]
image
Control image generation¶
There are several ways to exert more control over how an image is generated outside of configuring a pipeline's parameters, such ControlNet models.
ControlNet¶
As you saw in the ControlNet section, these models offer a more flexible and accurate way to generate images by incorporating an additional conditioning image input. Each ControlNet model is pretrained on a particular type of conditioning image to generate new images that resemble it. For example, if you take a ControlNet model pretrained on depth maps, you can give the model a depth map as a conditioning input and it'll generate an image that preserves the spatial information in it. This is quicker and easier than specifying the depth information in a prompt. You can even combine multiple conditioning inputs with a MultiControlNet!
There are many types of conditioning inputs you can use, and ๐ค Diffusers supports ControlNet for Stable Diffusion and SDXL models. Take a look at the more comprehensive ControlNet guide to learn how you can use these models.