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import gradio as gr
import torch
from PIL import Image
from diffusers import PriorTransformer, UNet2DConditionModel, KandinskyV22Pipeline
from huggingface_hub import hf_hub_download
from transformers import CLIPVisionModelWithProjection, CLIPImageProcessor, CLIPTokenizer, CLIPTextModelWithProjection
from model import pops_utils
from model.pipeline_pops import pOpsPipeline
kandinsky_prior_repo: str = 'kandinsky-community/kandinsky-2-2-prior'
kandinsky_decoder_repo: str = 'kandinsky-community/kandinsky-2-2-decoder'
prior_texture_repo: str = 'models/texturing/learned_prior.pth'
prior_instruct_repo: str = 'models/instruct/learned_prior.pth'
prior_scene_repo: str = 'models/scene/learned_prior.pth'
prior_repo = "pOpsPaper/operators"
# gpu = torch.device('cuda')
# cpu = torch.device('cpu')
class PopsPipelines:
def __init__(self):
weight_dtype = torch.float16
self.weight_dtype = weight_dtype
device = 'cpu' #torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.device = 'cuda' #device
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(kandinsky_prior_repo,
subfolder='image_encoder',
torch_dtype=weight_dtype).eval()
self.image_encoder.requires_grad_(False)
self.image_processor = CLIPImageProcessor.from_pretrained(kandinsky_prior_repo,
subfolder='image_processor')
self.tokenizer = CLIPTokenizer.from_pretrained(kandinsky_prior_repo, subfolder='tokenizer')
self.text_encoder = CLIPTextModelWithProjection.from_pretrained(kandinsky_prior_repo,
subfolder='text_encoder',
torch_dtype=weight_dtype).eval().to(device)
# Load full model for vis
self.unet = UNet2DConditionModel.from_pretrained(kandinsky_decoder_repo,
subfolder='unet').to(torch.float16).to(device)
self.decoder = KandinskyV22Pipeline.from_pretrained(kandinsky_decoder_repo, unet=self.unet,
torch_dtype=torch.float16)
self.decoder = self.decoder.to(device)
self.priors_dict = {
'texturing':{'repo':prior_texture_repo},
'instruct': {'repo': prior_instruct_repo},
'scene': {'repo':prior_scene_repo}
}
for prior_type in self.priors_dict:
prior_path = self.priors_dict[prior_type]['repo']
prior = PriorTransformer.from_pretrained(
kandinsky_prior_repo, subfolder="prior"
)
# Load from huggingface
prior_path = hf_hub_download(repo_id=prior_repo, filename=str(prior_path))
prior_state_dict = torch.load(prior_path, map_location=device)
prior.load_state_dict(prior_state_dict, strict=False)
prior.eval()
prior = prior.to(weight_dtype)
prior_pipeline = pOpsPipeline.from_pretrained(kandinsky_prior_repo,
prior=prior,
image_encoder=self.image_encoder,
torch_dtype=torch.float16)
self.priors_dict[prior_type]['pipeline'] = prior_pipeline
def process_image(self, input_path):
if input_path is None:
return None
image_pil = Image.open(input_path).convert("RGB").resize((512, 512))
image = torch.Tensor(self.image_processor(image_pil)['pixel_values'][0]).to(self.device).unsqueeze(0).to(
self.weight_dtype)
return image
def process_text(self, text):
self.text_encoder.to('cuda')
text_inputs = self.tokenizer(
text,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
mask = text_inputs.attention_mask.bool() # [0]
text_encoder_output = self.text_encoder(text_inputs.input_ids.to(self.device))
text_encoder_hidden_states = text_encoder_output.last_hidden_state
text_encoder_concat = text_encoder_hidden_states[:, :mask.sum().item()]
self.text_encoder.to('cpu')
return text_encoder_concat
def run_binary(self, input_a, input_b, prior_type):
# Move pipeline to GPU
pipeline = self.priors_dict[prior_type]['pipeline']
pipeline.to('cuda')
self.image_encoder.to('cuda')
input_image_embeds, input_hidden_state = pops_utils.preprocess(input_a, input_b,
self.image_encoder,
pipeline.prior.clip_mean.detach(),
pipeline.prior.clip_std.detach())
negative_input_embeds = torch.zeros_like(input_image_embeds)
negative_hidden_states = torch.zeros_like(input_hidden_state)
guidance_scale = 1.0
if prior_type == 'texturing':
guidance_scale = 8.0
img_emb = pipeline(input_embeds=input_image_embeds, input_hidden_states=input_hidden_state,
negative_input_embeds=negative_input_embeds,
negative_input_hidden_states=negative_hidden_states,
num_inference_steps=25,
num_images_per_prompt=1,
guidance_scale=guidance_scale)
# Optional
if prior_type == 'scene':
# Scene is the closet to what avg represents for a background image so incorporate that as well
mean_emb = 0.5 * input_hidden_state[:, 0] + 0.5 * input_hidden_state[:, 1]
mean_emb = (mean_emb * pipeline.prior.clip_std) + pipeline.prior.clip_mean
alpha = 0.4
img_emb.image_embeds = (1 - alpha) * img_emb.image_embeds + alpha * mean_emb
# Move pipeline to CPU
pipeline.to('cpu')
self.image_encoder.to('cpu')
return img_emb
def run_instruct(self, input_a, text):
text_encodings = self.process_text(text)
# Move pipeline to GPU
instruct_pipeline = self.priors_dict['instruct']['pipeline']
instruct_pipeline.to('cuda')
self.image_encoder.to('cuda')
input_image_embeds, input_hidden_state = pops_utils.preprocess(input_a, None,
self.image_encoder,
instruct_pipeline.prior.clip_mean.detach(), instruct_pipeline.prior.clip_std.detach(),
concat_hidden_states=text_encodings)
negative_input_embeds = torch.zeros_like(input_image_embeds)
negative_hidden_states = torch.zeros_like(input_hidden_state)
img_emb = instruct_pipeline(input_embeds=input_image_embeds, input_hidden_states=input_hidden_state,
negative_input_embeds=negative_input_embeds,
negative_input_hidden_states=negative_hidden_states,
num_inference_steps=25,
num_images_per_prompt=1,
guidance_scale=1.0)
# Move pipeline to CPU
instruct_pipeline.to('cpu')
self.image_encoder.to('cpu')
return img_emb
def render(self, img_emb):
self.decoder.to('cuda')
images = self.decoder(image_embeds=img_emb.image_embeds, negative_image_embeds=img_emb.negative_image_embeds,
num_inference_steps=50, height=512,
width=512, guidance_scale=4).images
self.decoder.to('cpu')
return images[0]
def run_instruct_texture(self, image_object_path, text_instruct, image_texture_path):
# Process both inputs
image_object = self.process_image(image_object_path)
image_texture = self.process_image(image_texture_path)
if image_object is None:
raise gr.Error('Object image is required')
current_emb = None
if image_texture is None:
instruct_input = image_object
else:
# Run texturing
current_emb = self.run_binary(input_a=image_object, input_b=image_texture,prior_type='texturing')
instruct_input = current_emb.image_embeds
if text_instruct != '':
current_emb = self.run_instruct(input_a=instruct_input, text=text_instruct)
if current_emb is None:
raise gr.Error('At least one of the inputs is required')
# Render as image
image = self.render(current_emb)
return image
def run_texture_scene(self, image_object_path, image_texture_path, image_scene_path):
# Process both inputs
image_object = self.process_image(image_object_path)
image_texture = self.process_image(image_texture_path)
image_scene = self.process_image(image_scene_path)
if image_object is None:
raise gr.Error('Object image is required')
current_emb = None
if image_texture is None:
scene_input = image_object
else:
# Run texturing
current_emb = self.run_binary(input_a=image_object, input_b=image_scene,prior_type='scene')
scene_input = current_emb.image_embeds
# Run scene
if image_scene is not None:
current_emb = self.run_binary(input_a=scene_input, input_b=image_texture,prior_type='texturing')
if current_emb is None:
raise gr.Error('At least one of the images is required')
# Render as image
image = self.render(current_emb)
return image
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