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<hfoption id="Kandinsky 2.2"> |
Copied from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline |
import torch |
prior_pipeline = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16).to("cuda") |
pipeline = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16).to("cuda") |
prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting" |
negative_prompt = "low quality, bad quality" # optional to include a negative prompt, but results are usually better |
image_embeds, negative_image_embeds = prior_pipeline(prompt, guidance_scale=1.0).to_tuple() Pass the image_embeds and negative_image_embeds to the KandinskyV22Pipeline to generate an image: Copied image = pipeline(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=768, width=768).images[0] |
image |
</hfoption> |
<hfoption id="Kandinsky 3"> |
Kandinsky 3 doesn’t require a prior model so you can directly load the Kandinsky3Pipeline and pass a prompt to generate an image: Copied from diffusers import Kandinsky3Pipeline |
import torch |
pipeline = Kandinsky3Pipeline.from_pretrained("kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16) |
pipeline.enable_model_cpu_offload() |
prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting" |
image = pipeline(prompt).images[0] |
image |
</hfoption> |
</hfoptions> |
🤗 Diffusers also provides an end-to-end API with the KandinskyCombinedPipeline and KandinskyV22CombinedPipeline, meaning you don’t have to separately load the prior and text-to-image pipeline. The combined pipeline automatically loads both the prior model and the decoder. You can still set different values for the pri... |
<hfoptions id="text-to-image"> |
<hfoption id="Kandinsky 2.1"> |
Copied from diffusers import AutoPipelineForText2Image |
import torch |
pipeline = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16) |
pipeline.enable_model_cpu_offload() |
prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting" |
negative_prompt = "low quality, bad quality" |
image = pipeline(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale=1.0, guidance_scale=4.0, height=768, width=768).images[0] |
image |
</hfoption> |
<hfoption id="Kandinsky 2.2"> |
Copied from diffusers import AutoPipelineForText2Image |
import torch |
pipeline = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16) |
pipeline.enable_model_cpu_offload() |
prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting" |
negative_prompt = "low quality, bad quality" |
image = pipeline(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale=1.0, guidance_scale=4.0, height=768, width=768).images[0] |
image |
</hfoption> |
</hfoptions> |
Image-to-image For image-to-image, pass the initial image and text prompt to condition the image to the pipeline. Start by loading the prior pipeline: |
<hfoptions id="image-to-image"> |
<hfoption id="Kandinsky 2.1"> |
Copied import torch |
from diffusers import KandinskyImg2ImgPipeline, KandinskyPriorPipeline |
prior_pipeline = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda") |
pipeline = KandinskyImg2ImgPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16, use_safetensors=True).to("cuda") |
</hfoption> |
<hfoption id="Kandinsky 2.2"> |
Copied import torch |
from diffusers import KandinskyV22Img2ImgPipeline, KandinskyPriorPipeline |
prior_pipeline = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda") |
pipeline = KandinskyV22Img2ImgPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16, use_safetensors=True).to("cuda") |
</hfoption> |
<hfoption id="Kandinsky 3"> |
Kandinsky 3 doesn’t require a prior model so you can directly load the image-to-image pipeline: Copied from diffusers import Kandinsky3Img2ImgPipeline |
from diffusers.utils import load_image |
import torch |
pipeline = Kandinsky3Img2ImgPipeline.from_pretrained("kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16) |
pipeline.enable_model_cpu_offload() |
</hfoption> |
</hfoptions> |
Download an image to condition on: Copied from diffusers.utils import load_image |
# download image |
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" |
original_image = load_image(url) |
original_image = original_image.resize((768, 512)) Generate the image_embeds and negative_image_embeds with the prior pipeline: Copied prompt = "A fantasy landscape, Cinematic lighting" |
negative_prompt = "low quality, bad quality" |
image_embeds, negative_image_embeds = prior_pipeline(prompt, negative_prompt).to_tuple() Now pass the original image, and all the prompts and embeddings to the pipeline to generate an image: |
<hfoptions id="image-to-image"> |
<hfoption id="Kandinsky 2.1"> |
Copied from diffusers.utils import make_image_grid |
image = pipeline(prompt, negative_prompt=negative_prompt, image=original_image, image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=768, width=768, strength=0.3).images[0] |
make_image_grid([original_image.resize((512, 512)), image.resize((512, 512))], rows=1, cols=2) |
</hfoption> |
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