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import os
os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
import torch
from diffusers.utils.torch_utils import randn_tensor
from diffusion_model.Processor import AttnProcessorForCallBack, DIFFUSION_LAYERS
from torch import autocast, nn
from diffusers import StableDiffusionPipeline
class diffusion(nn.Module):
def __init__(self,
attention_layers_to_use=None,
model="v2.1",
time_step=45,
dtype=torch.float16,
device='cuda:0'):
super().__init__()
# stabilityai/stable-diffusion-2-1-base runwayml/stable-diffusion-v1-5 CompVis/stable-diffusion-v1-4
if model == "v2.1":
model = "stabilityai/stable-diffusion-2-1-base"
elif model == "v1.5":
model = "runwayml/stable-diffusion-v1-5"
elif model == "v1.4":
model = "CompVis/stable-diffusion-v1-4"
else:
raise ValueError(f"Not supported model {model}")
self.model = StableDiffusionPipeline.from_pretrained(model, torch_dtype=dtype)
self.setup(device)
self.dtype = dtype
self.time_step = time_step
# 获取注意力图
self.attention_maps = {}
if attention_layers_to_use is None:
attention_layers_to_use = [-1]
self.layers = attention_layers_to_use
for layer_idx in attention_layers_to_use:
attn = eval(f"self.model.unet.{DIFFUSION_LAYERS[layer_idx]}")
attn.processor = AttnProcessorForCallBack(self, layer_idx)
def one_step(self, latents, prompts):
self.model._guidance_scale = 1
self.model._clip_skip = None
self.model._joint_attention_kwargs = None
self.model._interrupt = False
self.model.scheduler.set_timesteps(50, device=self.device)
t = self.model.scheduler.timesteps[self.time_step]
noise = randn_tensor(latents.shape, device=latents.device, dtype=latents.dtype)
# get latents
latents = self.model.scheduler.add_noise(latents, noise, t)
prompt_embeds, _ = self.model.encode_prompt(
prompts, self.device, 1, do_classifier_free_guidance=False,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
lora_scale=None,
clip_skip=self.model.clip_skip,
)
noise_pred = self.model.unet(
latents,
t,
encoder_hidden_states=prompt_embeds,
return_dict=False,
)[0]
def generate_image(self, prompts):
with autocast("cuda"):
image = self.model(prompts)["images"][0]
return image
@property
def device(self):
return self.model._execution_device
def setup(self, device):
self.model.to(device)
for param in self.model.vae.parameters():
param.requires_grad = False
for param in self.model.unet.parameters():
param.requires_grad = False
for param in self.model.text_encoder.parameters():
param.requires_grad = False
def forward(self, img, prompts=""):
latent = self.model.image_processor.preprocess(img, height=512, width=512).to(self.dtype)
latent = self.model.vae.encode(latent)[0].mean * self.model.vae.config.scaling_factor
self.one_step(latent, prompts=prompts)
def forward_wo_preprocess(self, img, prompts=""):
latent = img.to(self.dtype)
latent = self.model.vae.encode(latent)[0].mean * self.model.vae.config.scaling_factor
self.one_step(latent, prompts=prompts)
if __name__ == "__main__":
iseg = diffusion(attention_layers_to_use=[-2])
prompt = "two dogs running under the sea. "
iseg.one_step(torch.randn((1, 4, 64, 64), dtype=torch.float16, device='cuda'), prompts='')
img = iseg.generate_image(prompt)
print(iseg.attention_maps[-2].shape)
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