DeepFloyd IF
Overview
DeepFloyd IF is a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding. The model is a modular composed of a frozen text encoder and three cascaded pixel diffusion modules:
- Stage 1: a base model that generates 64x64 px image based on text prompt,
- Stage 2: a 64x64 px => 256x256 px super-resolution model, and a
- Stage 3: a 256x256 px => 1024x1024 px super-resolution model Stage 1 and Stage 2 utilize a frozen text encoder based on the T5 transformer to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention pooling. Stage 3 is Stability’s x4 Upscaling model. The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID score of 6.66 on the COCO dataset. Our work underscores the potential of larger UNet architectures in the first stage of cascaded diffusion models and depicts a promising future for text-to-image synthesis.
Usage
Before you can use IF, you need to accept its usage conditions. To do so:
- Make sure to have a Hugging Face account and be logged in
- Accept the license on the model card of DeepFloyd/IF-I-XL-v1.0. Accepting the license on the stage I model card will auto accept for the other IF models.
- Make sure to login locally. Install
huggingface_hub
pip install huggingface_hub --upgrade
run the login function in a Python shell
from huggingface_hub import login
login()
and enter your Hugging Face Hub access token.
Next we install diffusers
and dependencies:
pip install diffusers accelerate transformers safetensors
The following sections give more in-detail examples of how to use IF. Specifically:
- Text-to-Image Generation
- Image-to-Image Generation
- Inpainting
- Reusing model weights
- Speed optimization
- Memory optimization
Available checkpoints
Stage-1
Stage-2
Stage-3
Text-to-Image Generation
By default diffusers makes use of model cpu offloading to run the whole IF pipeline with as little as 14 GB of VRAM.
from diffusers import DiffusionPipeline
from diffusers.utils import pt_to_pil
import torch
# stage 1
stage_1 = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
stage_1.enable_model_cpu_offload()
# stage 2
stage_2 = DiffusionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
)
stage_2.enable_model_cpu_offload()
# stage 3
safety_modules = {
"feature_extractor": stage_1.feature_extractor,
"safety_checker": stage_1.safety_checker,
"watermarker": stage_1.watermarker,
}
stage_3 = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
)
stage_3.enable_model_cpu_offload()
prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
generator = torch.manual_seed(1)
# text embeds
prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)
# stage 1
image = stage_1(
prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, generator=generator, output_type="pt"
).images
pt_to_pil(image)[0].save("./if_stage_I.png")
# stage 2
image = stage_2(
image=image,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
output_type="pt",
).images
pt_to_pil(image)[0].save("./if_stage_II.png")
# stage 3
image = stage_3(prompt=prompt, image=image, noise_level=100, generator=generator).images
image[0].save("./if_stage_III.png")
Text Guided Image-to-Image Generation
The same IF model weights can be used for text-guided image-to-image translation or image variation. In this case just make sure to load the weights using the IFInpaintingPipeline and IFInpaintingSuperResolutionPipeline pipelines.
Note: You can also directly move the weights of the text-to-image pipelines to the image-to-image pipelines
without loading them twice by making use of the ~DiffusionPipeline.components()
function as explained here.
from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline
from diffusers.utils import pt_to_pil
import torch
from PIL import Image
import requests
from io import BytesIO
# download image
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
response = requests.get(url)
original_image = Image.open(BytesIO(response.content)).convert("RGB")
original_image = original_image.resize((768, 512))
# stage 1
stage_1 = IFImg2ImgPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
stage_1.enable_model_cpu_offload()
# stage 2
stage_2 = IFImg2ImgSuperResolutionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
)
stage_2.enable_model_cpu_offload()
# stage 3
safety_modules = {
"feature_extractor": stage_1.feature_extractor,
"safety_checker": stage_1.safety_checker,
"watermarker": stage_1.watermarker,
}
stage_3 = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
)
stage_3.enable_model_cpu_offload()
prompt = "A fantasy landscape in style minecraft"
generator = torch.manual_seed(1)
# text embeds
prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)
# stage 1
image = stage_1(
image=original_image,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
output_type="pt",
).images
pt_to_pil(image)[0].save("./if_stage_I.png")
# stage 2
image = stage_2(
image=image,
original_image=original_image,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
output_type="pt",
).images
pt_to_pil(image)[0].save("./if_stage_II.png")
# stage 3
image = stage_3(prompt=prompt, image=image, generator=generator, noise_level=100).images
image[0].save("./if_stage_III.png")
Text Guided Inpainting Generation
The same IF model weights can be used for text-guided image-to-image translation or image variation. In this case just make sure to load the weights using the IFInpaintingPipeline and IFInpaintingSuperResolutionPipeline pipelines.
Note: You can also directly move the weights of the text-to-image pipelines to the image-to-image pipelines
without loading them twice by making use of the ~DiffusionPipeline.components()
function as explained here.
from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline
from diffusers.utils import pt_to_pil
import torch
from PIL import Image
import requests
from io import BytesIO
# download image
url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png"
response = requests.get(url)
original_image = Image.open(BytesIO(response.content)).convert("RGB")
original_image = original_image
# download mask
url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png"
response = requests.get(url)
mask_image = Image.open(BytesIO(response.content))
mask_image = mask_image
# stage 1
stage_1 = IFInpaintingPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
stage_1.enable_model_cpu_offload()
# stage 2
stage_2 = IFInpaintingSuperResolutionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
)
stage_2.enable_model_cpu_offload()
# stage 3
safety_modules = {
"feature_extractor": stage_1.feature_extractor,
"safety_checker": stage_1.safety_checker,
"watermarker": stage_1.watermarker,
}
stage_3 = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
)
stage_3.enable_model_cpu_offload()
prompt = "blue sunglasses"
generator = torch.manual_seed(1)
# text embeds
prompt_embeds, negative_embeds = stage_1.encode_prompt(prompt)
# stage 1
image = stage_1(
image=original_image,
mask_image=mask_image,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
output_type="pt",
).images
pt_to_pil(image)[0].save("./if_stage_I.png")
# stage 2
image = stage_2(
image=image,
original_image=original_image,
mask_image=mask_image,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
generator=generator,
output_type="pt",
).images
pt_to_pil(image)[0].save("./if_stage_II.png")
# stage 3
image = stage_3(prompt=prompt, image=image, generator=generator, noise_level=100).images
image[0].save("./if_stage_III.png")
Converting between different pipelines
In addition to being loaded with from_pretrained
, Pipelines can also be loaded directly from each other.
from diffusers import IFPipeline, IFSuperResolutionPipeline
pipe_1 = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0")
pipe_2 = IFSuperResolutionPipeline.from_pretrained("DeepFloyd/IF-II-L-v1.0")
from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline
pipe_1 = IFImg2ImgPipeline(**pipe_1.components)
pipe_2 = IFImg2ImgSuperResolutionPipeline(**pipe_2.components)
from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline
pipe_1 = IFInpaintingPipeline(**pipe_1.components)
pipe_2 = IFInpaintingSuperResolutionPipeline(**pipe_2.components)
Optimizing for speed
The simplest optimization to run IF faster is to move all model components to the GPU.
pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.to("cuda")
You can also run the diffusion process for a shorter number of timesteps.
This can either be done with the num_inference_steps
argument
pipe("<prompt>", num_inference_steps=30)
Or with the timesteps
argument
from diffusers.pipelines.deepfloyd_if import fast27_timesteps
pipe("<prompt>", timesteps=fast27_timesteps)
When doing image variation or inpainting, you can also decrease the number of timesteps with the strength argument. The strength argument is the amount of noise to add to the input image which also determines how many steps to run in the denoising process. A smaller number will vary the image less but run faster.
pipe = IFImg2ImgPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.to("cuda")
image = pipe(image=image, prompt="<prompt>", strength=0.3).images
You can also use torch.compile
. Note that we have not exhaustively tested torch.compile
with IF and it might not give expected results.
import torch
pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.to("cuda")
pipe.text_encoder = torch.compile(pipe.text_encoder)
pipe.unet = torch.compile(pipe.unet)
Optimizing for memory
When optimizing for GPU memory, we can use the standard diffusers cpu offloading APIs.
Either the model based CPU offloading,
pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
or the more aggressive layer based CPU offloading.
pipe = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
pipe.enable_sequential_cpu_offload()
Additionally, T5 can be loaded in 8bit precision
from transformers import T5EncoderModel
text_encoder = T5EncoderModel.from_pretrained(
"DeepFloyd/IF-I-XL-v1.0", subfolder="text_encoder", device_map="auto", load_in_8bit=True, variant="8bit"
)
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"DeepFloyd/IF-I-XL-v1.0",
text_encoder=text_encoder, # pass the previously instantiated 8bit text encoder
unet=None,
device_map="auto",
)
prompt_embeds, negative_embeds = pipe.encode_prompt("<prompt>")
For CPU RAM constrained machines like google colab free tier where we can’t load all model components to the CPU at once, we can manually only load the pipeline with the text encoder or unet when the respective model components are needed.
from diffusers import IFPipeline, IFSuperResolutionPipeline
import torch
import gc
from transformers import T5EncoderModel
from diffusers.utils import pt_to_pil
text_encoder = T5EncoderModel.from_pretrained(
"DeepFloyd/IF-I-XL-v1.0", subfolder="text_encoder", device_map="auto", load_in_8bit=True, variant="8bit"
)
# text to image
pipe = DiffusionPipeline.from_pretrained(
"DeepFloyd/IF-I-XL-v1.0",
text_encoder=text_encoder, # pass the previously instantiated 8bit text encoder
unet=None,
device_map="auto",
)
prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
# Remove the pipeline so we can re-load the pipeline with the unet
del text_encoder
del pipe
gc.collect()
torch.cuda.empty_cache()
pipe = IFPipeline.from_pretrained(
"DeepFloyd/IF-I-XL-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16, device_map="auto"
)
generator = torch.Generator().manual_seed(0)
image = pipe(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
output_type="pt",
generator=generator,
).images
pt_to_pil(image)[0].save("./if_stage_I.png")
# Remove the pipeline so we can load the super-resolution pipeline
del pipe
gc.collect()
torch.cuda.empty_cache()
# First super resolution
pipe = IFSuperResolutionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16, device_map="auto"
)
generator = torch.Generator().manual_seed(0)
image = pipe(
image=image,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
output_type="pt",
generator=generator,
).images
pt_to_pil(image)[0].save("./if_stage_II.png")
Available Pipelines:
Pipeline | Tasks | Colab |
---|---|---|
pipeline_if.py | Text-to-Image Generation | - |
pipeline_if_superresolution.py | Text-to-Image Generation | - |
pipeline_if_img2img.py | Image-to-Image Generation | - |
pipeline_if_img2img_superresolution.py | Image-to-Image Generation | - |
pipeline_if_inpainting.py | Image-to-Image Generation | - |
pipeline_if_inpainting_superresolution.py | Image-to-Image Generation | - |
IFPipeline
class diffusers.IFPipeline
< source >( tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler safety_checker: typing.Optional[diffusers.pipelines.deepfloyd_if.safety_checker.IFSafetyChecker] feature_extractor: typing.Optional[transformers.models.clip.image_processing_clip.CLIPImageProcessor] watermarker: typing.Optional[diffusers.pipelines.deepfloyd_if.watermark.IFWatermarker] requires_safety_checker: bool = True )
__call__
< source >(
prompt: typing.Union[str, typing.List[str]] = None
num_inference_steps: int = 100
timesteps: typing.List[int] = None
guidance_scale: float = 7.0
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
height: typing.Optional[int] = None
width: typing.Optional[int] = None
eta: float = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
prompt_embeds: typing.Optional[torch.FloatTensor] = None
negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None
callback_steps: int = 1
clean_caption: bool = True
cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None
)
→
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
Parameters
-
prompt (
str
orList[str]
, optional) — The prompt or prompts to guide the image generation. If not defined, one has to passprompt_embeds
. instead. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
timesteps (
List[int]
, optional) — Custom timesteps to use for the denoising process. If not defined, equal spacednum_inference_steps
timesteps are used. Must be in descending order. -
guidance_scale (
float
, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). -
num_images_per_prompt (
int
, optional, defaults to 1) — The number of images to generate per prompt. -
height (
int
, optional, defaults to self.unet.config.sample_size) — The height in pixels of the generated image. -
width (
int
, optional, defaults to self.unet.config.sample_size) — The width in pixels of the generated image. -
eta (
float
, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others. -
generator (
torch.Generator
orList[torch.Generator]
, optional) — One or a list of torch generator(s) to make generation deterministic. -
prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated fromprompt
input argument. -
negative_prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated fromnegative_prompt
input argument. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a~pipelines.stable_diffusion.IFPipelineOutput
instead of a plain tuple. -
callback (
Callable
, optional) — A function that will be called everycallback_steps
steps during inference. The function will be called with the following arguments:callback(step: int, timestep: int, latents: torch.FloatTensor)
. -
callback_steps (
int
, optional, defaults to 1) — The frequency at which thecallback
function will be called. If not specified, the callback will be called at every step. -
clean_caption (
bool
, optional, defaults toTrue
) — Whether or not to clean the caption before creating embeddings. Requiresbeautifulsoup4
andftfy
to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. -
cross_attention_kwargs (
dict
, optional) — A kwargs dictionary that if specified is passed along to theAttentionProcessor
as defined underself.processor
in diffusers.models.attention_processor.
Returns
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
~pipelines.stable_diffusion.IFPipelineOutput
if return_dict
is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of
bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the
safety_checker`.
Function invoked when calling the pipeline for generation.
Examples:
>>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> pipe = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
>>> pipe.enable_model_cpu_offload()
>>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt").images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt"
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> safety_modules = {
... "feature_extractor": pipe.feature_extractor,
... "safety_checker": pipe.safety_checker,
... "watermarker": pipe.watermarker,
... }
>>> super_res_2_pipe = DiffusionPipeline.from_pretrained(
... "stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16
... )
>>> super_res_2_pipe.enable_model_cpu_offload()
>>> image = super_res_2_pipe(
... prompt=prompt,
... image=image,
... ).images
>>> image[0].save("./if_stage_II.png")
encode_prompt
< source >( prompt do_classifier_free_guidance = True num_images_per_prompt = 1 device = None negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None clean_caption: bool = False )
Encodes the prompt into text encoder hidden states.
device: (torch.device
, optional):
torch device to place the resulting embeddings on
num_images_per_prompt (int
, optional, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (bool
, optional, defaults to True
):
whether to use classifier free guidance or not
negative_prompt (str
or List[str]
, optional):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
negative_prompt_embeds
. instead. If not defined, one has to pass negative_prompt_embeds
. instead.
Ignored when not using guidance (i.e., ignored if guidance_scale
is less than 1
).
prompt_embeds (torch.FloatTensor
, optional):
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
provided, text embeddings will be generated from prompt
input argument.
negative_prompt_embeds (torch.FloatTensor
, optional):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt
input
argument.
IFSuperResolutionPipeline
class diffusers.IFSuperResolutionPipeline
< source >( tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler image_noising_scheduler: DDPMScheduler safety_checker: typing.Optional[diffusers.pipelines.deepfloyd_if.safety_checker.IFSafetyChecker] feature_extractor: typing.Optional[transformers.models.clip.image_processing_clip.CLIPImageProcessor] watermarker: typing.Optional[diffusers.pipelines.deepfloyd_if.watermark.IFWatermarker] requires_safety_checker: bool = True )
__call__
< source >(
prompt: typing.Union[str, typing.List[str]] = None
height: int = None
width: int = None
image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.FloatTensor] = None
num_inference_steps: int = 50
timesteps: typing.List[int] = None
guidance_scale: float = 4.0
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
eta: float = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
prompt_embeds: typing.Optional[torch.FloatTensor] = None
negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None
callback_steps: int = 1
cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None
noise_level: int = 250
clean_caption: bool = True
)
→
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
Parameters
-
prompt (
str
orList[str]
, optional) — The prompt or prompts to guide the image generation. If not defined, one has to passprompt_embeds
. instead. -
height (
int
, optional, defaults to self.unet.config.sample_size) — The height in pixels of the generated image. -
width (
int
, optional, defaults to self.unet.config.sample_size) — The width in pixels of the generated image. -
image (
PIL.Image.Image
,np.ndarray
,torch.FloatTensor
) — The image to be upscaled. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
timesteps (
List[int]
, optional) — Custom timesteps to use for the denoising process. If not defined, equal spacednum_inference_steps
timesteps are used. Must be in descending order. -
guidance_scale (
float
, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). -
num_images_per_prompt (
int
, optional, defaults to 1) — The number of images to generate per prompt. -
eta (
float
, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others. -
generator (
torch.Generator
orList[torch.Generator]
, optional) — One or a list of torch generator(s) to make generation deterministic. -
prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated fromprompt
input argument. -
negative_prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated fromnegative_prompt
input argument. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a~pipelines.stable_diffusion.IFPipelineOutput
instead of a plain tuple. -
callback (
Callable
, optional) — A function that will be called everycallback_steps
steps during inference. The function will be called with the following arguments:callback(step: int, timestep: int, latents: torch.FloatTensor)
. -
callback_steps (
int
, optional, defaults to 1) — The frequency at which thecallback
function will be called. If not specified, the callback will be called at every step. -
cross_attention_kwargs (
dict
, optional) — A kwargs dictionary that if specified is passed along to theAttentionProcessor
as defined underself.processor
in diffusers.models.attention_processor. -
noise_level (
int
, optional, defaults to 250) — The amount of noise to add to the upscaled image. Must be in the range[0, 1000)
-
clean_caption (
bool
, optional, defaults toTrue
) — Whether or not to clean the caption before creating embeddings. Requiresbeautifulsoup4
andftfy
to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt.
Returns
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
~pipelines.stable_diffusion.IFPipelineOutput
if return_dict
is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of
bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the
safety_checker`.
Function invoked when calling the pipeline for generation.
Examples:
>>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> pipe = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
>>> pipe.enable_model_cpu_offload()
>>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"'
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt").images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds
... ).images
>>> image[0].save("./if_stage_II.png")
encode_prompt
< source >( prompt do_classifier_free_guidance = True num_images_per_prompt = 1 device = None negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None clean_caption: bool = False )
Encodes the prompt into text encoder hidden states.
device: (torch.device
, optional):
torch device to place the resulting embeddings on
num_images_per_prompt (int
, optional, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (bool
, optional, defaults to True
):
whether to use classifier free guidance or not
negative_prompt (str
or List[str]
, optional):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
negative_prompt_embeds
. instead. If not defined, one has to pass negative_prompt_embeds
. instead.
Ignored when not using guidance (i.e., ignored if guidance_scale
is less than 1
).
prompt_embeds (torch.FloatTensor
, optional):
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
provided, text embeddings will be generated from prompt
input argument.
negative_prompt_embeds (torch.FloatTensor
, optional):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt
input
argument.
IFImg2ImgPipeline
class diffusers.IFImg2ImgPipeline
< source >( tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler safety_checker: typing.Optional[diffusers.pipelines.deepfloyd_if.safety_checker.IFSafetyChecker] feature_extractor: typing.Optional[transformers.models.clip.image_processing_clip.CLIPImageProcessor] watermarker: typing.Optional[diffusers.pipelines.deepfloyd_if.watermark.IFWatermarker] requires_safety_checker: bool = True )
__call__
< source >(
prompt: typing.Union[str, typing.List[str]] = None
image: typing.Union[PIL.Image.Image, torch.Tensor, numpy.ndarray, typing.List[PIL.Image.Image], typing.List[torch.Tensor], typing.List[numpy.ndarray]] = None
strength: float = 0.7
num_inference_steps: int = 80
timesteps: typing.List[int] = None
guidance_scale: float = 10.0
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
eta: float = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
prompt_embeds: typing.Optional[torch.FloatTensor] = None
negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None
callback_steps: int = 1
clean_caption: bool = True
cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None
)
→
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
Parameters
-
prompt (
str
orList[str]
, optional) — The prompt or prompts to guide the image generation. If not defined, one has to passprompt_embeds
. instead. -
image (
torch.FloatTensor
orPIL.Image.Image
) —Image
, or tensor representing an image batch, that will be used as the starting point for the process. -
strength (
float
, optional, defaults to 0.8) — Conceptually, indicates how much to transform the referenceimage
. Must be between 0 and 1.image
will be used as a starting point, adding more noise to it the larger thestrength
. The number of denoising steps depends on the amount of noise initially added. Whenstrength
is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified innum_inference_steps
. A value of 1, therefore, essentially ignoresimage
. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
timesteps (
List[int]
, optional) — Custom timesteps to use for the denoising process. If not defined, equal spacednum_inference_steps
timesteps are used. Must be in descending order. -
guidance_scale (
float
, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). -
num_images_per_prompt (
int
, optional, defaults to 1) — The number of images to generate per prompt. -
eta (
float
, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others. -
generator (
torch.Generator
orList[torch.Generator]
, optional) — One or a list of torch generator(s) to make generation deterministic. -
prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated fromprompt
input argument. -
negative_prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated fromnegative_prompt
input argument. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a~pipelines.stable_diffusion.IFPipelineOutput
instead of a plain tuple. -
callback (
Callable
, optional) — A function that will be called everycallback_steps
steps during inference. The function will be called with the following arguments:callback(step: int, timestep: int, latents: torch.FloatTensor)
. -
callback_steps (
int
, optional, defaults to 1) — The frequency at which thecallback
function will be called. If not specified, the callback will be called at every step. -
clean_caption (
bool
, optional, defaults toTrue
) — Whether or not to clean the caption before creating embeddings. Requiresbeautifulsoup4
andftfy
to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. -
cross_attention_kwargs (
dict
, optional) — A kwargs dictionary that if specified is passed along to theAttentionProcessor
as defined underself.processor
in diffusers.models.attention_processor.
Returns
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
~pipelines.stable_diffusion.IFPipelineOutput
if return_dict
is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of
bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the
safety_checker`.
Function invoked when calling the pipeline for generation.
Examples:
>>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from io import BytesIO
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> response = requests.get(url)
>>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> original_image = original_image.resize((768, 512))
>>> pipe = IFImg2ImgPipeline.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0",
... variant="fp16",
... torch_dtype=torch.float16,
... )
>>> pipe.enable_model_cpu_offload()
>>> prompt = "A fantasy landscape in style minecraft"
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(
... image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... output_type="pt",
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0",
... text_encoder=None,
... variant="fp16",
... torch_dtype=torch.float16,
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image,
... original_image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... ).images
>>> image[0].save("./if_stage_II.png")
encode_prompt
< source >( prompt do_classifier_free_guidance = True num_images_per_prompt = 1 device = None negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None clean_caption: bool = False )
Encodes the prompt into text encoder hidden states.
device: (torch.device
, optional):
torch device to place the resulting embeddings on
num_images_per_prompt (int
, optional, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (bool
, optional, defaults to True
):
whether to use classifier free guidance or not
negative_prompt (str
or List[str]
, optional):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
negative_prompt_embeds
. instead. If not defined, one has to pass negative_prompt_embeds
. instead.
Ignored when not using guidance (i.e., ignored if guidance_scale
is less than 1
).
prompt_embeds (torch.FloatTensor
, optional):
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
provided, text embeddings will be generated from prompt
input argument.
negative_prompt_embeds (torch.FloatTensor
, optional):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt
input
argument.
IFImg2ImgSuperResolutionPipeline
class diffusers.IFImg2ImgSuperResolutionPipeline
< source >( tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler image_noising_scheduler: DDPMScheduler safety_checker: typing.Optional[diffusers.pipelines.deepfloyd_if.safety_checker.IFSafetyChecker] feature_extractor: typing.Optional[transformers.models.clip.image_processing_clip.CLIPImageProcessor] watermarker: typing.Optional[diffusers.pipelines.deepfloyd_if.watermark.IFWatermarker] requires_safety_checker: bool = True )
__call__
< source >(
image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.FloatTensor]
original_image: typing.Union[PIL.Image.Image, torch.Tensor, numpy.ndarray, typing.List[PIL.Image.Image], typing.List[torch.Tensor], typing.List[numpy.ndarray]] = None
strength: float = 0.8
prompt: typing.Union[str, typing.List[str]] = None
num_inference_steps: int = 50
timesteps: typing.List[int] = None
guidance_scale: float = 4.0
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
eta: float = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
prompt_embeds: typing.Optional[torch.FloatTensor] = None
negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None
callback_steps: int = 1
cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None
noise_level: int = 250
clean_caption: bool = True
)
→
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
Parameters
-
image (
torch.FloatTensor
orPIL.Image.Image
) —Image
, or tensor representing an image batch, that will be used as the starting point for the process. -
original_image (
torch.FloatTensor
orPIL.Image.Image
) — The original image thatimage
was varied from. -
strength (
float
, optional, defaults to 0.8) — Conceptually, indicates how much to transform the referenceimage
. Must be between 0 and 1.image
will be used as a starting point, adding more noise to it the larger thestrength
. The number of denoising steps depends on the amount of noise initially added. Whenstrength
is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified innum_inference_steps
. A value of 1, therefore, essentially ignoresimage
. -
prompt (
str
orList[str]
, optional) — The prompt or prompts to guide the image generation. If not defined, one has to passprompt_embeds
. instead. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
timesteps (
List[int]
, optional) — Custom timesteps to use for the denoising process. If not defined, equal spacednum_inference_steps
timesteps are used. Must be in descending order. -
guidance_scale (
float
, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). -
num_images_per_prompt (
int
, optional, defaults to 1) — The number of images to generate per prompt. -
eta (
float
, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others. -
generator (
torch.Generator
orList[torch.Generator]
, optional) — One or a list of torch generator(s) to make generation deterministic. -
prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated fromprompt
input argument. -
negative_prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated fromnegative_prompt
input argument. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a~pipelines.stable_diffusion.IFPipelineOutput
instead of a plain tuple. -
callback (
Callable
, optional) — A function that will be called everycallback_steps
steps during inference. The function will be called with the following arguments:callback(step: int, timestep: int, latents: torch.FloatTensor)
. -
callback_steps (
int
, optional, defaults to 1) — The frequency at which thecallback
function will be called. If not specified, the callback will be called at every step. -
cross_attention_kwargs (
dict
, optional) — A kwargs dictionary that if specified is passed along to theAttentionProcessor
as defined underself.processor
in diffusers.models.attention_processor. -
noise_level (
int
, optional, defaults to 250) — The amount of noise to add to the upscaled image. Must be in the range[0, 1000)
-
clean_caption (
bool
, optional, defaults toTrue
) — Whether or not to clean the caption before creating embeddings. Requiresbeautifulsoup4
andftfy
to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt.
Returns
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
~pipelines.stable_diffusion.IFPipelineOutput
if return_dict
is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of
bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the
safety_checker`.
Function invoked when calling the pipeline for generation.
Examples:
>>> from diffusers import IFImg2ImgPipeline, IFImg2ImgSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from io import BytesIO
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
>>> response = requests.get(url)
>>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> original_image = original_image.resize((768, 512))
>>> pipe = IFImg2ImgPipeline.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0",
... variant="fp16",
... torch_dtype=torch.float16,
... )
>>> pipe.enable_model_cpu_offload()
>>> prompt = "A fantasy landscape in style minecraft"
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(
... image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... output_type="pt",
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0",
... text_encoder=None,
... variant="fp16",
... torch_dtype=torch.float16,
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image,
... original_image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... ).images
>>> image[0].save("./if_stage_II.png")
encode_prompt
< source >( prompt do_classifier_free_guidance = True num_images_per_prompt = 1 device = None negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None clean_caption: bool = False )
Encodes the prompt into text encoder hidden states.
device: (torch.device
, optional):
torch device to place the resulting embeddings on
num_images_per_prompt (int
, optional, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (bool
, optional, defaults to True
):
whether to use classifier free guidance or not
negative_prompt (str
or List[str]
, optional):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
negative_prompt_embeds
. instead. If not defined, one has to pass negative_prompt_embeds
. instead.
Ignored when not using guidance (i.e., ignored if guidance_scale
is less than 1
).
prompt_embeds (torch.FloatTensor
, optional):
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
provided, text embeddings will be generated from prompt
input argument.
negative_prompt_embeds (torch.FloatTensor
, optional):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt
input
argument.
IFInpaintingPipeline
class diffusers.IFInpaintingPipeline
< source >( tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler safety_checker: typing.Optional[diffusers.pipelines.deepfloyd_if.safety_checker.IFSafetyChecker] feature_extractor: typing.Optional[transformers.models.clip.image_processing_clip.CLIPImageProcessor] watermarker: typing.Optional[diffusers.pipelines.deepfloyd_if.watermark.IFWatermarker] requires_safety_checker: bool = True )
__call__
< source >(
prompt: typing.Union[str, typing.List[str]] = None
image: typing.Union[PIL.Image.Image, torch.Tensor, numpy.ndarray, typing.List[PIL.Image.Image], typing.List[torch.Tensor], typing.List[numpy.ndarray]] = None
mask_image: typing.Union[PIL.Image.Image, torch.Tensor, numpy.ndarray, typing.List[PIL.Image.Image], typing.List[torch.Tensor], typing.List[numpy.ndarray]] = None
strength: float = 1.0
num_inference_steps: int = 50
timesteps: typing.List[int] = None
guidance_scale: float = 7.0
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
eta: float = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
prompt_embeds: typing.Optional[torch.FloatTensor] = None
negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None
callback_steps: int = 1
clean_caption: bool = True
cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None
)
→
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
Parameters
-
prompt (
str
orList[str]
, optional) — The prompt or prompts to guide the image generation. If not defined, one has to passprompt_embeds
. instead. -
image (
torch.FloatTensor
orPIL.Image.Image
) —Image
, or tensor representing an image batch, that will be used as the starting point for the process. -
mask_image (
PIL.Image.Image
) —Image
, or tensor representing an image batch, to maskimage
. White pixels in the mask will be repainted, while black pixels will be preserved. Ifmask_image
is a PIL image, it will be converted to a single channel (luminance) before use. If it’s a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be(B, H, W, 1)
. -
strength (
float
, optional, defaults to 0.8) — Conceptually, indicates how much to transform the referenceimage
. Must be between 0 and 1.image
will be used as a starting point, adding more noise to it the larger thestrength
. The number of denoising steps depends on the amount of noise initially added. Whenstrength
is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified innum_inference_steps
. A value of 1, therefore, essentially ignoresimage
. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
timesteps (
List[int]
, optional) — Custom timesteps to use for the denoising process. If not defined, equal spacednum_inference_steps
timesteps are used. Must be in descending order. -
guidance_scale (
float
, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). -
num_images_per_prompt (
int
, optional, defaults to 1) — The number of images to generate per prompt. -
eta (
float
, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others. -
generator (
torch.Generator
orList[torch.Generator]
, optional) — One or a list of torch generator(s) to make generation deterministic. -
prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated fromprompt
input argument. -
negative_prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated fromnegative_prompt
input argument. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a~pipelines.stable_diffusion.IFPipelineOutput
instead of a plain tuple. -
callback (
Callable
, optional) — A function that will be called everycallback_steps
steps during inference. The function will be called with the following arguments:callback(step: int, timestep: int, latents: torch.FloatTensor)
. -
callback_steps (
int
, optional, defaults to 1) — The frequency at which thecallback
function will be called. If not specified, the callback will be called at every step. -
clean_caption (
bool
, optional, defaults toTrue
) — Whether or not to clean the caption before creating embeddings. Requiresbeautifulsoup4
andftfy
to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. -
cross_attention_kwargs (
dict
, optional) — A kwargs dictionary that if specified is passed along to theAttentionProcessor
as defined underself.processor
in diffusers.models.attention_processor.
Returns
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
~pipelines.stable_diffusion.IFPipelineOutput
if return_dict
is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of
bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the
safety_checker`.
Function invoked when calling the pipeline for generation.
Examples:
>>> from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from io import BytesIO
>>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png"
>>> response = requests.get(url)
>>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> original_image = original_image
>>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png"
>>> response = requests.get(url)
>>> mask_image = Image.open(BytesIO(response.content))
>>> mask_image = mask_image
>>> pipe = IFInpaintingPipeline.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16
... )
>>> pipe.enable_model_cpu_offload()
>>> prompt = "blue sunglasses"
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(
... image=original_image,
... mask_image=mask_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... output_type="pt",
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFInpaintingSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image,
... mask_image=mask_image,
... original_image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... ).images
>>> image[0].save("./if_stage_II.png")
encode_prompt
< source >( prompt do_classifier_free_guidance = True num_images_per_prompt = 1 device = None negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None clean_caption: bool = False )
Encodes the prompt into text encoder hidden states.
device: (torch.device
, optional):
torch device to place the resulting embeddings on
num_images_per_prompt (int
, optional, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (bool
, optional, defaults to True
):
whether to use classifier free guidance or not
negative_prompt (str
or List[str]
, optional):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
negative_prompt_embeds
. instead. If not defined, one has to pass negative_prompt_embeds
. instead.
Ignored when not using guidance (i.e., ignored if guidance_scale
is less than 1
).
prompt_embeds (torch.FloatTensor
, optional):
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
provided, text embeddings will be generated from prompt
input argument.
negative_prompt_embeds (torch.FloatTensor
, optional):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt
input
argument.
IFInpaintingSuperResolutionPipeline
class diffusers.IFInpaintingSuperResolutionPipeline
< source >( tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: UNet2DConditionModel scheduler: DDPMScheduler image_noising_scheduler: DDPMScheduler safety_checker: typing.Optional[diffusers.pipelines.deepfloyd_if.safety_checker.IFSafetyChecker] feature_extractor: typing.Optional[transformers.models.clip.image_processing_clip.CLIPImageProcessor] watermarker: typing.Optional[diffusers.pipelines.deepfloyd_if.watermark.IFWatermarker] requires_safety_checker: bool = True )
__call__
< source >(
image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.FloatTensor]
original_image: typing.Union[PIL.Image.Image, torch.Tensor, numpy.ndarray, typing.List[PIL.Image.Image], typing.List[torch.Tensor], typing.List[numpy.ndarray]] = None
mask_image: typing.Union[PIL.Image.Image, torch.Tensor, numpy.ndarray, typing.List[PIL.Image.Image], typing.List[torch.Tensor], typing.List[numpy.ndarray]] = None
strength: float = 0.8
prompt: typing.Union[str, typing.List[str]] = None
num_inference_steps: int = 100
timesteps: typing.List[int] = None
guidance_scale: float = 4.0
negative_prompt: typing.Union[str, typing.List[str], NoneType] = None
num_images_per_prompt: typing.Optional[int] = 1
eta: float = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
prompt_embeds: typing.Optional[torch.FloatTensor] = None
negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None
callback_steps: int = 1
cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None
noise_level: int = 0
clean_caption: bool = True
)
→
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
Parameters
-
image (
torch.FloatTensor
orPIL.Image.Image
) —Image
, or tensor representing an image batch, that will be used as the starting point for the process. -
original_image (
torch.FloatTensor
orPIL.Image.Image
) — The original image thatimage
was varied from. -
mask_image (
PIL.Image.Image
) —Image
, or tensor representing an image batch, to maskimage
. White pixels in the mask will be repainted, while black pixels will be preserved. Ifmask_image
is a PIL image, it will be converted to a single channel (luminance) before use. If it’s a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be(B, H, W, 1)
. -
strength (
float
, optional, defaults to 0.8) — Conceptually, indicates how much to transform the referenceimage
. Must be between 0 and 1.image
will be used as a starting point, adding more noise to it the larger thestrength
. The number of denoising steps depends on the amount of noise initially added. Whenstrength
is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified innum_inference_steps
. A value of 1, therefore, essentially ignoresimage
. -
prompt (
str
orList[str]
, optional) — The prompt or prompts to guide the image generation. If not defined, one has to passprompt_embeds
. instead. -
num_inference_steps (
int
, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. -
timesteps (
List[int]
, optional) — Custom timesteps to use for the denoising process. If not defined, equal spacednum_inference_steps
timesteps are used. Must be in descending order. -
guidance_scale (
float
, optional, defaults to 7.5) — Guidance scale as defined in Classifier-Free Diffusion Guidance.guidance_scale
is defined asw
of equation 2. of Imagen Paper. Guidance scale is enabled by settingguidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the textprompt
, usually at the expense of lower image quality. -
negative_prompt (
str
orList[str]
, optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds
instead. Ignored when not using guidance (i.e., ignored ifguidance_scale
is less than1
). -
num_images_per_prompt (
int
, optional, defaults to 1) — The number of images to generate per prompt. -
eta (
float
, optional, defaults to 0.0) — Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to schedulers.DDIMScheduler, will be ignored for others. -
generator (
torch.Generator
orList[torch.Generator]
, optional) — One or a list of torch generator(s) to make generation deterministic. -
prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated fromprompt
input argument. -
negative_prompt_embeds (
torch.FloatTensor
, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated fromnegative_prompt
input argument. -
output_type (
str
, optional, defaults to"pil"
) — The output format of the generate image. Choose between PIL:PIL.Image.Image
ornp.array
. -
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a~pipelines.stable_diffusion.IFPipelineOutput
instead of a plain tuple. -
callback (
Callable
, optional) — A function that will be called everycallback_steps
steps during inference. The function will be called with the following arguments:callback(step: int, timestep: int, latents: torch.FloatTensor)
. -
callback_steps (
int
, optional, defaults to 1) — The frequency at which thecallback
function will be called. If not specified, the callback will be called at every step. -
cross_attention_kwargs (
dict
, optional) — A kwargs dictionary that if specified is passed along to theAttentionProcessor
as defined underself.processor
in diffusers.models.attention_processor. -
noise_level (
int
, optional, defaults to 0) — The amount of noise to add to the upscaled image. Must be in the range[0, 1000)
-
clean_caption (
bool
, optional, defaults toTrue
) — Whether or not to clean the caption before creating embeddings. Requiresbeautifulsoup4
andftfy
to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt.
Returns
~pipelines.stable_diffusion.IFPipelineOutput
or tuple
~pipelines.stable_diffusion.IFPipelineOutput
if return_dict
is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of
bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) or watermarked content, according to the
safety_checker`.
Function invoked when calling the pipeline for generation.
Examples:
>>> from diffusers import IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, DiffusionPipeline
>>> from diffusers.utils import pt_to_pil
>>> import torch
>>> from PIL import Image
>>> import requests
>>> from io import BytesIO
>>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/person.png"
>>> response = requests.get(url)
>>> original_image = Image.open(BytesIO(response.content)).convert("RGB")
>>> original_image = original_image
>>> url = "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/if/glasses_mask.png"
>>> response = requests.get(url)
>>> mask_image = Image.open(BytesIO(response.content))
>>> mask_image = mask_image
>>> pipe = IFInpaintingPipeline.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16
... )
>>> pipe.enable_model_cpu_offload()
>>> prompt = "blue sunglasses"
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt)
>>> image = pipe(
... image=original_image,
... mask_image=mask_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... output_type="pt",
... ).images
>>> # save intermediate image
>>> pil_image = pt_to_pil(image)
>>> pil_image[0].save("./if_stage_I.png")
>>> super_res_1_pipe = IFInpaintingSuperResolutionPipeline.from_pretrained(
... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16
... )
>>> super_res_1_pipe.enable_model_cpu_offload()
>>> image = super_res_1_pipe(
... image=image,
... mask_image=mask_image,
... original_image=original_image,
... prompt_embeds=prompt_embeds,
... negative_prompt_embeds=negative_embeds,
... ).images
>>> image[0].save("./if_stage_II.png")
encode_prompt
< source >( prompt do_classifier_free_guidance = True num_images_per_prompt = 1 device = None negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None clean_caption: bool = False )
Encodes the prompt into text encoder hidden states.
device: (torch.device
, optional):
torch device to place the resulting embeddings on
num_images_per_prompt (int
, optional, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (bool
, optional, defaults to True
):
whether to use classifier free guidance or not
negative_prompt (str
or List[str]
, optional):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
negative_prompt_embeds
. instead. If not defined, one has to pass negative_prompt_embeds
. instead.
Ignored when not using guidance (i.e., ignored if guidance_scale
is less than 1
).
prompt_embeds (torch.FloatTensor
, optional):
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
provided, text embeddings will be generated from prompt
input argument.
negative_prompt_embeds (torch.FloatTensor
, optional):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt
input
argument.