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README.md ADDED
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+ # Work in progress
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+
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+
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+ Similar hacking to our opendiffusionai/stablediffusionxl_t5 model
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+
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+ But, with SD1.5 of course.
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+
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+ Why do this?
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+ Because coming up with a usable finetuning script for SDXL is turning out
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+ to be a pain in the rear. So I thought I might regress to the theoretically
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+ easier experiment.
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+
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+ # Precision
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+
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+ Note that the unet is only bf16 at this time
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+
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+ # Usage
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+
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+ You can use it with the sample code in [demo.py](demo.py)
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+
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+ Note that it will give you an image of SOMETHING...
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+ however, it is sort of random output at this point.
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+ The unet needs to be retrained to get things to match up.
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+
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+
demo.py ADDED
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+
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+
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+ from diffusers import DiffusionPipeline
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+ import torch.nn as nn, torch, types
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+
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+ MODEL_DIR = "opendiffusionai/stablediffusion_t5"
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+
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+ pipe = DiffusionPipeline.from_pretrained(
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+ MODEL_DIR, custom_pipeline=MODEL_DIR, use_safetensors=True,
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+ torch_dtype=torch.bfloat16,
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+ )
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+
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+ print("model initialized. Now moving to CUDA")
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+ pipe.to("cuda")
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+
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+ print("Trying render now...")
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+ images = pipe("a misty Tokyo alley at night",num_inference_steps=30).images
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+ fname="save.png"
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+ print(f"saving to {fname}")
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+ images[0].save(fname)
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+
feature_extractor/preprocessor_config.json ADDED
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+ {
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+ "crop_size": {
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+ "height": 224,
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+ "width": 224
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+ },
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+ "do_center_crop": true,
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+ "do_convert_rgb": true,
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+ "do_normalize": true,
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+ "do_rescale": true,
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+ "do_resize": true,
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+ "image_mean": [
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+ 0.48145466,
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+ 0.4578275,
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+ 0.40821073
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+ ],
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+ "image_processor_type": "CLIPImageProcessor",
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+ "image_std": [
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+ 0.26862954,
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+ 0.26130258,
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+ 0.27577711
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+ ],
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+ "resample": 3,
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+ "rescale_factor": 0.00392156862745098,
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+ "size": {
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+ "shortest_edge": 224
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+ }
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+ }
model_index.json ADDED
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+ {
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+ "_class_name": "StableDiffusionPipeline",
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+ "_diffusers_version": "0.34.0.dev0",
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+ "_name_or_path": "t5-sd",
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+ "feature_extractor": [
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+ "transformers",
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+ "CLIPImageProcessor"
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+ ],
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+ "requires_safety_checker": true,
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+ "image_encoder": [
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+ null,
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+ null
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+ ],
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+ "safety_checker": [
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+ "stable_diffusion",
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+ "StableDiffusionSafetyChecker"
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+ ],
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+ "scheduler": [
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+ "diffusers",
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+ "PNDMScheduler"
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+ ],
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+ "t5_projection": [
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+ null,
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+ null
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+ ],
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+ "text_encoder": [
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+ null,
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+ null
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+ ],
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+ "tokenizer": [
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+ "transformers",
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+ "T5Tokenizer"
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+ ],
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+ "unet": [
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+ "diffusers",
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+ "UNet2DConditionModel"
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+ ],
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+ "vae": [
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+ "diffusers",
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+ "AutoencoderKL"
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+ ]
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+ }
pipeline.py ADDED
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+ # pipeline.py
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+
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+ # subclass SD pipeline to replace CLIP-L with T5
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+
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+ import torch
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+ import torch.nn as nn
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+ from transformers import AutoTokenizer, AutoModel
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+ from transformers import T5Tokenizer, T5EncoderModel
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+ from diffusers import StableDiffusionPipeline
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+ from diffusers.utils import logging
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+
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+ logger = logging.get_logger(__name__)
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+
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+ T5_NAME="mcmonkey/google_t5-v1_1-xxl_encoderonly"
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+
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+ class LinearWithDtype(nn.Linear):
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+ @property
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+ def dtype(self):
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+ return self.weight.dtype
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+
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+ class StableDiffusionT5Pipeline(StableDiffusionPipeline):
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+
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+ # override this so we can auto-init text_encoder
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+ #_optional_components = ["safety_checker", "feature_extractor", "image_encoder", "text_encoder"]
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+
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+ # t5_projection not really optional, but needed it here to stop internal whining
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+ _optional_components = StableDiffusionPipeline._optional_components + ["text_encoder", "t5_projection"]
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+
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+ def __init__(
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+ self,
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+ vae,
32
+ text_encoder,
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+ tokenizer,
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+ unet,
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+ scheduler,
36
+ safety_checker=None,
37
+ feature_extractor=None,
38
+ image_encoder=None,
39
+ requires_safety_checker=True,
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+ t5_projection: LinearWithDtype=None,
41
+ ):
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+ self.tokenizer = (
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+ tokenizer
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+ if tokenizer is not None
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+ else T5Tokenizer.from_pretrained(T5_NAME,torch_dtype=unet.dtype)
46
+ )
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+
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+
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+ if text_encoder is None:
50
+ self.text_encoder = T5EncoderModel.from_pretrained(T5_NAME, torch_dtype=unet.dtype)
51
+ else:
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+ self.text_encoder = text_encoder
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+
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+ super().__init__(
55
+ vae=vae,
56
+ tokenizer=self.tokenizer,
57
+ text_encoder=self.text_encoder,
58
+ unet=unet,
59
+ scheduler=scheduler,
60
+ safety_checker=safety_checker,
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+ feature_extractor=feature_extractor,
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+ image_encoder=image_encoder,
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+ requires_safety_checker=requires_safety_checker,
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+ )
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+
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+ if t5_projection is None:
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+ self.t5_projection = LinearWithDtype(4096, 768).to(unet.device, dtype=unet.dtype)
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+ else:
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+ self.t5_projection = t5_projection
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+
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+ # Ensure everything is properly registered for to("cuda")
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+ self.register_modules(t5_projection=self.t5_projection)
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+
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+ @torch.no_grad()
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+ def encode_prompt(
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+ self,
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+ prompt,
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+ device,
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+ num_images_per_prompt,
80
+ do_classifier_free_guidance,
81
+ negative_prompt = None,
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+ prompt_embeds = None,
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+ negative_prompt_embeds = None,
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+ lora_scale = None,
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+ clip_skip = None, # ignore with T5!!
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+ **kwargs,
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+ ):
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+
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+ def _tok(text):
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+ out = self.tokenizer(
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+ text,
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+ return_tensors="pt",
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+ padding="max_length",
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+ max_length=self.tokenizer.model_max_length,
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+ truncation=True,
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+ )
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+ return out.input_ids.to(device=device, dtype=torch.long), out.attention_mask.to(device)
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+
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+ pos_ids, pos_mask = _tok(prompt)
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+ pos_hidden = self.text_encoder(pos_ids, attention_mask=pos_mask).last_hidden_state
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+ pos_embeds = self.t5_projection(pos_hidden)
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+
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+ if do_classifier_free_guidance:
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+ neg_prompt = negative_prompt if negative_prompt is not None else ""
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+ neg_ids, neg_mask = _tok(neg_prompt)
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+ neg_hidden = self.text_encoder(neg_ids, attention_mask=neg_mask).last_hidden_state
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+ neg_embeds = self.t5_projection(neg_hidden)
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+
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+ # Expand for multiple images per prompt
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+ pos_embeds = pos_embeds.repeat_interleave(num_images_per_prompt, dim=0)
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+ neg_embeds = neg_embeds.repeat_interleave(num_images_per_prompt, dim=0)
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+
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+ return [neg_embeds, pos_embeds]
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+ else:
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+ pos_embeds = pos_embeds.repeat_interleave(num_images_per_prompt, dim=0)
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+ return pos_embeds
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+ "torch_dtype": "float32",
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+ }
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