from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import *
import PIL
class StableDiffusionXL_lowlevel(StableDiffusionXLPipeline):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def prepare_latents_img2img(
self,
image,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
):
image = image.to(device=device, dtype=dtype)
latents = self.vae.encode(image).latent_dist.sample(generator=generator)
print("latents", latents.shape)
latents = latents * self.vae.config.scaling_factor
noise = torch.randn(latents.shape, generator=generator, device=device, dtype=dtype)
print("noise", noise.shape)
latents = latents + noise
return latents
def prepare_latents_latent2img(
self,
latents,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
):
print("latents", latents.shape)
latents = latents.to(device, dtype=dtype) * self.vae.config.scaling_factor
noise = torch.randn(latents.shape, generator=generator, device=device, dtype=dtype)
print("noise", noise.shape)
latents = latents + noise
return latents
StableDiffusionXLPipeline.prepare_latents_img2img = StableDiffusionXL_lowlevel.prepare_latents_img2img
StableDiffusionXLPipeline.prepare_latents_latent2img = StableDiffusionXL_lowlevel.prepare_latents_latent2img
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def generate_ip_adapter_embeds(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
timesteps: List[int] = None,
denoising_end: Optional[float] = None,
guidance_scale: float = 5.0,
img2img_strength: float = 0.85, # 新增参数
low_level_image: Optional[PipelineImageInput] = None, # 新增参数
negative_prompt: Optional[Union[str, List[str]]] = None,
negative_prompt_2: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
low_level_latent: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
original_size: Optional[Tuple[int, int]] = None,
crops_coords_top_left: Tuple[int, int] = (0, 0),
target_size: Optional[Tuple[int, int]] = None,
negative_original_size: Optional[Tuple[int, int]] = None,
negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
negative_target_size: Optional[Tuple[int, int]] = None,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
Function invoked when calling the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in both text-encoders
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image. This is set to 1024 by default for the best results.
Anything below 512 pixels won't work well for
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
and checkpoints that are not specifically fine-tuned on low resolutions.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image. This is set to 1024 by default for the best results.
Anything below 512 pixels won't work well for
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
and checkpoints that are not specifically fine-tuned on low resolutions.
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 with schedulers which support a `timesteps` argument
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
passed will be used. Must be in descending order.
denoising_end (`float`, *optional*):
When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
completed before it is intentionally prematurely terminated. As a result, the returned sample will
still retain a substantial amount of noise as determined by the discrete timesteps selected by the
scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
"Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
guidance_scale (`float`, *optional*, defaults to 5.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
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` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
low_level_latent (`torch.FloatTensor`, *optional*):
Pre-generated noisy low_level_latent, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
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.
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
ip_adapter_embeds: (`FloatTensor`, *optional*): Optional image embeddings to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
of a plain tuple.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
guidance_rescale (`float`, *optional*, defaults to 0.0):
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
[Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
Guidance rescale factor should fix overexposure when using zero terminal SNR.
original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
`original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
For most cases, `target_size` should be set to the desired height and width of the generated image. If
not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
To negatively condition the generation process based on a specific image resolution. Part of SDXL's
micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
To negatively condition the generation process based on a target image resolution. It should be as same
as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
Examples:
Returns:
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the generated images.
"""
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"callback_steps",
"1.0.0",
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
# 0. Default height and width to unet
height = height or self.default_sample_size * self.vae_scale_factor
width = width or self.default_sample_size * self.vae_scale_factor
original_size = original_size or (height, width)
target_size = target_size or (height, width)
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
height,
width,
callback_steps,
negative_prompt,
negative_prompt_2,
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
callback_on_step_end_tensor_inputs,
)
self._guidance_scale = guidance_scale
self._guidance_rescale = guidance_rescale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
self._denoising_end = denoising_end
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# 新增：处理 low_level_image
if low_level_image is not None:
# 确保 low_level_image 已经被处理为适当的格式
if isinstance(low_level_image, PIL.Image.Image):
low_level_image = self.image_processor.preprocess(low_level_image)
elif isinstance(low_level_image, torch.Tensor):
low_level_image = low_level_image.to(device)
else:
raise ValueError("low_level_image should be a PIL image or a torch tensor.")
# 3. Encode input prompt
lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(
prompt=prompt,
prompt_2=prompt_2,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=self.do_classifier_free_guidance,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
lora_scale=lora_scale,
clip_skip=self.clip_skip,
)
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
# 新增：根据 img2img_strength 计算初始步数
if low_level_image is not None:
# 根据 img2img_strength 计算需要跳过的时间步数
init_timestep = min(int(num_inference_steps * img2img_strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = timesteps[t_start:]
else:
t_start = 0 # 如果没有 low_level_image，从头开始
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
# latents = self.prepare_latents(
# batch_size * num_images_per_prompt,
# num_channels_latents,
# height,
# width,
# prompt_embeds.dtype,
# device,
# generator,
# )
# 新增：准备初始 latents
if low_level_image is not None and low_level_latent is None:
# 编码 low_level_image 到潜在空间
latents = self.prepare_latents_img2img(
low_level_image,
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
)
elif low_level_latent is not None:
latents = self.prepare_latents_latent2img(
low_level_latent,
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
)
else:
# 如果没有 low_level_image，随机初始化 latents
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Prepare added time ids & embeddings
add_text_embeds = pooled_prompt_embeds
if self.text_encoder_2 is None:
text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
else:
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
add_time_ids = self._get_add_time_ids(
original_size,
crops_coords_top_left,
target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
if negative_original_size is not None and negative_target_size is not None:
negative_add_time_ids = self._get_add_time_ids(
negative_original_size,
negative_crops_coords_top_left,
negative_target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
else:
negative_add_time_ids = add_time_ids
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
prompt_embeds = prompt_embeds.to(device)
add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
image_embeds = image_embeds.to(device)
if ip_adapter_embeds is not None:
image_embeds = ip_adapter_embeds.to(device=device, dtype=prompt_embeds.dtype)
if self.do_classifier_free_guidance:
negative_image_embeds = torch.zeros_like(image_embeds)
image_embeds = torch.cat([negative_image_embeds, image_embeds])
image_embeds = image_embeds.to(device)
# 8. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
# 8.1 Apply denoising_end
if (
self.denoising_end is not None
and isinstance(self.denoising_end, float)
and self.denoising_end > 0
and self.denoising_end < 1
):
discrete_timestep_cutoff = int(
round(
self.scheduler.config.num_train_timesteps
- (self.denoising_end * self.scheduler.config.num_train_timesteps)
)
)
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
timesteps = timesteps[:num_inference_steps]
# 9. Optionally get Guidance Scale Embedding
timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
timestep_cond = self.get_guidance_scale_embedding(
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
).to(device=device, dtype=latents.dtype)
self._num_timesteps = len(timesteps)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
if ip_adapter_image is not None or ip_adapter_embeds is not None:
added_cond_kwargs["image_embeds"] = image_embeds
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# perform guidance
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
negative_pooled_prompt_embeds = callback_outputs.pop(
"negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
)
add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step()
if not output_type == "latent":
# make sure the VAE is in float32 mode, as it overflows in bfloat16
needs_upcasting = self.vae.dtype == torch.bfloat16 and self.vae.config.force_upcast
if needs_upcasting:
self.upcast_vae()
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
# cast back to fp16 if needed
if needs_upcasting:
self.vae.to(dtype=torch.bfloat16)
else:
image = latents
if not output_type == "latent":
# apply watermark if available
if self.watermark is not None:
image = self.watermark.apply_watermark(image)
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return StableDiffusionXLPipelineOutput(images=image)
def encode_image(image, image_encoder, feature_extractor, num_images_per_prompt=1, device='cuda'):
dtype = next(image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = feature_extractor(image, return_tensors="pt").pixel_values # [1, 3, 224, 224]
image = image.to(device=device, dtype=dtype)
image_embeds = image_encoder(image).image_embeds # (1, 1024)
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) # (num_images_per_prompt, 1024)
return image_embeds
class Generator4Embeds:
def __init__(self, num_inference_steps=1, device='cuda', img2img_strength=1, low_level_image=None, low_level_latent = None) -> None:
# import os
# proxy = 'http://10.16.118.59:13390'
# os.environ['http_proxy'] = proxy
# os.environ['https_proxy'] = proxy
self.num_inference_steps = num_inference_steps
self.dtype = torch.bfloat16
self.device = device
self.img2img_strength = img2img_strength
self.low_level_image = low_level_image
self.low_level_latent = low_level_latent
# path = '/home/weichen/.cache/huggingface/hub/models--stabilityai--sdxl-turbo/snapshots/f4b0486b498f84668e828044de1d0c8ba486e05b'
# path = "/home/ldy/Workspace/sdxl-turbo/f4b0486b498f84668e828044de1d0c8ba486e05b"
pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.bfloat16, variant="fp16")
# pipe = DiffusionPipeline.from_pretrained(path, torch_dtype=torch.bfloat16, variant="fp16")
pipe.to(device)
pipe.generate_ip_adapter_embeds = generate_ip_adapter_embeds.__get__(pipe)
# load ip adapter
pipe.load_ip_adapter(
"h94/IP-Adapter", subfolder="sdxl_models",
weight_name="ip-adapter_sdxl_vit-h.bin",
torch_dtype=torch.bfloat16)
# set ip_adapter scale (defauld is 1)
pipe.set_ip_adapter_scale(1)
self.pipe = pipe
def generate(self, image_embeds, text_prompt='', generator=None):
image_embeds = image_embeds.to(device=self.device, dtype=self.dtype)
pipe = self.pipe
# generate image with image prompt - ip_adapter_embeds
image = pipe.generate_ip_adapter_embeds(
prompt=text_prompt,
ip_adapter_embeds=image_embeds,
num_inference_steps=self.num_inference_steps,
guidance_scale=0.0,
generator=generator,
img2img_strength=self.img2img_strength,
low_level_image=self.low_level_image,
low_level_latent=self.low_level_latent,
).images[0]
return image
if __name__ == "__main__":
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
# import os
# os.environ['http_proxy'] = 'http://10.16.35.10:13390'
# os.environ['https_proxy'] = 'http://10.16.35.10:13390'
# # path = '/home/weichen/.cache/huggingface/hub/models--stabilityai--sdxl-turbo/snapshots/f4b0486b498f84668e828044de1d0c8ba486e05b'
# pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.bfloat16, variant="fp16")
# # pipe = DiffusionPipeline.from_pretrained(path, torch_dtype=torch.bfloat16, variant="fp16")
# pipe.to("cuda")
# from IPython.display import Image, display
# pipe.generate_ip_adapter_embeds = generate_ip_adapter_embeds.__get__(pipe)
# prompt = ""
# # # 1. Generate image
# # image = pipe.generate_ip_adapter_embeds(
# # prompt=prompt,
# # num_inference_steps=4,
# # guidance_scale=0.0,
# # ).images[0]
# # # show this image
# # display(image)
# # load ip adapter
# pipe.load_ip_adapter(
# "h94/IP-Adapter", subfolder="sdxl_models",
# weight_name="ip-adapter_sdxl_vit-h.safetensors",
# torch_dtype=torch.bfloat16)
# pipe.set_ip_adapter_scale(0.5)
# # 2. Load image encoder
# from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
# image_encoder = CLIPVisionModelWithProjection.from_pretrained(
# "h94/IP-Adapter",
# subfolder="models/image_encoder",
# torch_dtype=torch.bfloat16,
# ).to("cuda")
# feature_extractor = CLIPImageProcessor()
# from diffusers.utils import load_image
# image_prompt = load_image("/mnt/dataset0/weichen/projects/visobj/proposals/mise/data/things-images/THINGSplus/images/images_resized/apple.jpg")
# display(image_prompt)
# # encode image
# image_embeds = encode_image(image_prompt, image_encoder, feature_extractor, 1, "cuda")
# # 3. Generate image with image prompt - ip_adapter_image
# pipe.image_encoder = image_encoder
# image = pipe.generate_ip_adapter_embeds(
# prompt=prompt,
# ip_adapter_image=image_prompt,
# num_inference_steps=4,
# guidance_scale=0.0,
# ).images[0]
# # show this image
# display(image)
# # 4. Generate image with image prompt - ip_adapter_embeds
# image = pipe.generate_ip_adapter_embeds(
# prompt=prompt,
# ip_adapter_embeds=image_embeds,
# num_inference_steps=4,
# guidance_scale=0.0,
# ).images[0]
# # show this image
# display(image)
from IPython.display import Image, display
generator = Generator4Embeds(num_inference_steps=4)
# 2. Load image encoder
# from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor
# image_encoder = CLIPVisionModelWithProjection.from_pretrained(
# "h94/IP-Adapter",
# subfolder="models/image_encoder",
# torch_dtype=torch.bfloat16,
# ).to("cuda")
# feature_extractor = CLIPImageProcessor()
# 2.2 Load image encoder from open_clip
import open_clip
image_encoder, _, feature_extractor = open_clip.create_model_and_transforms(
'ViT-H-14', pretrained='laion2b_s32b_b79k', precision='fp16', device='cuda')
from diffusers.utils import load_image
image_prompt = load_image("/mnt/dataset0/weichen/projects/visobj/proposals/mise/data/things-images/THINGSplus/images/images_resized/apple.jpg")
# image_prompt = load_image("https://th.bing.com/th/id/OIP.BGo1V-YM46ZrqSo5N_edWAHaE7?rs=1&pid=ImgDetMain")
display(image_prompt)
# encode image
# image_embeds = encode_image(image_prompt, image_encoder, feature_extractor, 1, "cuda")
image_embeds = image_encoder.encode_image(feature_extractor(image_prompt)[None, ...].to("cuda", dtype=torch.bfloat16))
image = generator.generate(image_embeds)
display(image)
Datasets
Models
