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initial SDXL refiner support

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AUTOMATIC1111
2023-07-14 09:16:01 +03:00
parent dc39061856
commit 6d8dcdefa0
5 changed files with 71 additions and 19 deletions
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57
modules/sd_models_xl.py
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@@ -14,15 +14,20 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
width = getattr(self, 'target_width', 1024)
height = getattr(self, 'target_height', 1024)
is_negative_prompt = getattr(batch, 'is_negative_prompt', False)
aesthetic_score = shared.opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else shared.opts.sdxl_refiner_high_aesthetic_score
devices_args = dict(device=devices.device, dtype=devices.dtype)
sdxl_conds = {
"txt": batch,
"original_size_as_tuple": torch.tensor([height, width]).repeat(len(batch), 1).to(devices.device, devices.dtype),
"crop_coords_top_left": torch.tensor([shared.opts.sdxl_crop_top, shared.opts.sdxl_crop_left]).repeat(len(batch), 1).to(devices.device, devices.dtype),
"target_size_as_tuple": torch.tensor([height, width]).repeat(len(batch), 1).to(devices.device, devices.dtype),
"original_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
"crop_coords_top_left": torch.tensor([shared.opts.sdxl_crop_top, shared.opts.sdxl_crop_left], **devices_args).repeat(len(batch), 1),
"target_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
"aesthetic_score": torch.tensor([aesthetic_score], **devices_args).repeat(len(batch), 1),
}
force_zero_negative_prompt = getattr(batch, 'is_negative_prompt', False) and all(x == '' for x in batch)
force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in batch)
c = self.conditioner(sdxl_conds, force_zero_embeddings=['txt'] if force_zero_negative_prompt else [])
return c
@@ -35,25 +40,55 @@ def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
def get_first_stage_encoding(self, x): # SDXL's encode_first_stage does everything so get_first_stage_encoding is just there for compatibility
return x
sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
sgm.models.diffusion.DiffusionEngine.get_first_stage_encoding = get_first_stage_encoding
def encode_embedding_init_text(self: sgm.modules.GeneralConditioner, init_text, nvpt):
res = []
for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'encode_embedding_init_text')]:
encoded = embedder.encode_embedding_init_text(init_text, nvpt)
res.append(encoded)
return torch.cat(res, dim=1)
def process_texts(self, texts):
for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'process_texts')]:
return embedder.process_texts(texts)
def get_target_prompt_token_count(self, token_count):
for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'get_target_prompt_token_count')]:
return embedder.get_target_prompt_token_count(token_count)
# those additions to GeneralConditioner make it possible to use it as model.cond_stage_model from SD1.5 in exist
sgm.modules.GeneralConditioner.encode_embedding_init_text = encode_embedding_init_text
sgm.modules.GeneralConditioner.process_texts = process_texts
sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt_token_count
def extend_sdxl(model):
"""this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
dtype = next(model.model.diffusion_model.parameters()).dtype
model.model.diffusion_model.dtype = dtype
model.model.conditioning_key = 'crossattn'
model.cond_stage_model = [x for x in model.conditioner.embedders if 'CLIPEmbedder' in type(x).__name__][0]
model.cond_stage_key = model.cond_stage_model.input_key
model.cond_stage_key = 'txt'
# model.cond_stage_model will be set in sd_hijack
model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
model.conditioner.wrapped = torch.nn.Module()
sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
sgm.models.diffusion.DiffusionEngine.get_first_stage_encoding = get_first_stage_encoding
sgm.modules.attention.print = lambda *args: None
sgm.modules.diffusionmodules.model.print = lambda *args: None
sgm.modules.diffusionmodules.openaimodel.print = lambda *args: None