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import safetensors.torch as sf
import torch
from huggingface_guess import model_list
from backend import memory_management, utils
from backend.args import dynamic_args
from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
from backend.nn.unet import Timestep
from backend.patcher.clip import CLIP
from backend.patcher.unet import UnetPatcher
from backend.patcher.vae import VAE
from backend.text_processing.classic_engine import ClassicTextProcessingEngine
from modules.shared import opts
class StableDiffusionXL(ForgeDiffusionEngine):
matched_guesses = [model_list.SDXL]
def __init__(self, estimated_config, huggingface_components):
super().__init__(estimated_config, huggingface_components)
clip = CLIP(model_dict={"clip_l": huggingface_components["text_encoder"], "clip_g": huggingface_components["text_encoder_2"]}, tokenizer_dict={"clip_l": huggingface_components["tokenizer"], "clip_g": huggingface_components["tokenizer_2"]})
vae = VAE(model=huggingface_components["vae"])
unet = UnetPatcher.from_model(model=huggingface_components["unet"], diffusers_scheduler=huggingface_components["scheduler"], config=estimated_config)
self.text_processing_engine_l = ClassicTextProcessingEngine(
text_encoder=clip.cond_stage_model.clip_l,
tokenizer=clip.tokenizer.clip_l,
embedding_dir=dynamic_args["embedding_dir"],
embedding_key="clip_l",
embedding_expected_shape=2048,
text_projection=False,
minimal_clip_skip=2,
clip_skip=2,
return_pooled=False,
final_layer_norm=False,
)
self.text_processing_engine_g = ClassicTextProcessingEngine(
text_encoder=clip.cond_stage_model.clip_g,
tokenizer=clip.tokenizer.clip_g,
embedding_dir=dynamic_args["embedding_dir"],
embedding_key="clip_g",
embedding_expected_shape=2048,
text_projection=True,
minimal_clip_skip=2,
clip_skip=2,
return_pooled=True,
final_layer_norm=False,
)
self.embedder = Timestep(256)
self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
self.forge_objects_original = self.forge_objects.shallow_copy()
self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
# WebUI Legacy
self.is_sdxl = True
def set_clip_skip(self, clip_skip):
self.text_processing_engine_l.clip_skip = clip_skip
self.text_processing_engine_g.clip_skip = clip_skip
@torch.inference_mode()
def get_learned_conditioning(self, prompt: list[str]):
memory_management.load_model_gpu(self.forge_objects.clip.patcher)
cond_l = self.text_processing_engine_l(prompt)
cond_g, clip_pooled = self.text_processing_engine_g(prompt)
width = getattr(prompt, "width", 1024) or 1024
height = getattr(prompt, "height", 1024) or 1024
is_negative_prompt = getattr(prompt, "is_negative_prompt", False)
crop_w = opts.sdxl_crop_left
crop_h = opts.sdxl_crop_top
target_width = width
target_height = height
out = [self.embedder(torch.Tensor([height])), self.embedder(torch.Tensor([width])), self.embedder(torch.Tensor([crop_h])), self.embedder(torch.Tensor([crop_w])), self.embedder(torch.Tensor([target_height])), self.embedder(torch.Tensor([target_width]))]
flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1).to(clip_pooled)
force_zero_negative_prompt = is_negative_prompt and all(x == "" for x in prompt)
if force_zero_negative_prompt:
clip_pooled = torch.zeros_like(clip_pooled)
cond_l = torch.zeros_like(cond_l)
cond_g = torch.zeros_like(cond_g)
# Ensure cond_l and cond_g have the same size
max_len = max(cond_l.shape[1], cond_g.shape[1])
cond_l = torch.cat([cond_l, cond_l.new_zeros(cond_l.size(0), max_len - cond_l.shape[1], cond_l.size(2))], dim=1)
cond_g = torch.cat([cond_g, cond_g.new_zeros(cond_g.size(0), max_len - cond_g.shape[1], cond_g.size(2))], dim=1)
cond = dict(
crossattn=torch.cat([cond_l, cond_g], dim=2),
vector=torch.cat([clip_pooled, flat], dim=1),
)
return cond
@torch.inference_mode()
def get_prompt_lengths_on_ui(self, prompt):
_, token_count = self.text_processing_engine_l.process_texts([prompt])
return token_count, self.text_processing_engine_l.get_target_prompt_token_count(token_count)
@torch.inference_mode()
def encode_first_stage(self, x):
sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
sample = self.forge_objects.vae.first_stage_model.process_in(sample)
return sample.to(x)
@torch.inference_mode()
def decode_first_stage(self, x):
sample = self.forge_objects.vae.first_stage_model.process_out(x)
sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
return sample.to(x)
def save_checkpoint(self, filename):
sd = {}
sd.update(utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix="model.diffusion_model."))
sd.update(model_list.SDXL.process_clip_state_dict_for_saving(self, utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix="")))
sd.update(utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix="first_stage_model."))
sf.save_file(sd, filename)
return filename
class StableDiffusionXLRefiner(ForgeDiffusionEngine):
matched_guesses = [model_list.SDXLRefiner]
def __init__(self, estimated_config, huggingface_components):
super().__init__(estimated_config, huggingface_components)
clip = CLIP(
model_dict={"clip_g": huggingface_components["text_encoder"]},
tokenizer_dict={
"clip_g": huggingface_components["tokenizer"],
},
)
vae = VAE(model=huggingface_components["vae"])
unet = UnetPatcher.from_model(model=huggingface_components["unet"], diffusers_scheduler=huggingface_components["scheduler"], config=estimated_config)
self.text_processing_engine_g = ClassicTextProcessingEngine(
text_encoder=clip.cond_stage_model.clip_g,
tokenizer=clip.tokenizer.clip_g,
embedding_dir=dynamic_args["embedding_dir"],
embedding_key="clip_g",
embedding_expected_shape=2048,
text_projection=True,
minimal_clip_skip=2,
clip_skip=2,
return_pooled=True,
final_layer_norm=False,
)
self.embedder = Timestep(256)
self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
self.forge_objects_original = self.forge_objects.shallow_copy()
self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
# WebUI Legacy
self.is_sdxl = True
def set_clip_skip(self, clip_skip):
self.text_processing_engine_g.clip_skip = clip_skip
@torch.inference_mode()
def get_learned_conditioning(self, prompt: list[str]):
memory_management.load_model_gpu(self.forge_objects.clip.patcher)
cond_g, clip_pooled = self.text_processing_engine_g(prompt)
width = getattr(prompt, "width", 1024) or 1024
height = getattr(prompt, "height", 1024) or 1024
is_negative_prompt = getattr(prompt, "is_negative_prompt", False)
crop_w = opts.sdxl_crop_left
crop_h = opts.sdxl_crop_top
aesthetic = opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else opts.sdxl_refiner_high_aesthetic_score
out = [self.embedder(torch.Tensor([height])), self.embedder(torch.Tensor([width])), self.embedder(torch.Tensor([crop_h])), self.embedder(torch.Tensor([crop_w])), self.embedder(torch.Tensor([aesthetic]))]
flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1).to(clip_pooled)
force_zero_negative_prompt = is_negative_prompt and all(x == "" for x in prompt)
if force_zero_negative_prompt:
clip_pooled = torch.zeros_like(clip_pooled)
cond_g = torch.zeros_like(cond_g)
cond = dict(
crossattn=cond_g,
vector=torch.cat([clip_pooled, flat], dim=1),
)
return cond
@torch.inference_mode()
def get_prompt_lengths_on_ui(self, prompt):
_, token_count = self.text_processing_engine_g.process_texts([prompt])
return token_count, self.text_processing_engine_g.get_target_prompt_token_count(token_count)
@torch.inference_mode()
def encode_first_stage(self, x):
sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
sample = self.forge_objects.vae.first_stage_model.process_in(sample)
return sample.to(x)
@torch.inference_mode()
def decode_first_stage(self, x):
sample = self.forge_objects.vae.first_stage_model.process_out(x)
sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
return sample.to(x)
def save_checkpoint(self, filename):
sd = {}
sd.update(utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix="model.diffusion_model."))
sd.update(model_list.SDXLRefiner.process_clip_state_dict_for_saving(self, utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix="")))
sd.update(utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix="first_stage_model."))
sf.save_file(sd, filename)
return filename
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