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Merge pull request #15806 from huchenlei/inpaint_fix 

[Performance 4/6] Precompute is_sdxl_inpaint flag
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AUTOMATIC1111 2024-06-08 11:06:39 +03:00 committed by GitHub
parent 64bf57b5ea 3e20b36e8f
commit 6450d24afe
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3 changed files with 22 additions and 22 deletions
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10
modules/processing.py
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@ -115,10 +115,7 @@ def txt2img_image_conditioning(sd_model, x, width, height):
return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device) return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
else: else:
sd = sd_model.model.state_dict() if getattr(sd_model.model, "is_sdxl_inpaint", False):
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9:
# The "masked-image" in this case will just be all 0.5 since the entire image is masked. # The "masked-image" in this case will just be all 0.5 since the entire image is masked.
image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5 image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
image_conditioning = images_tensor_to_samples(image_conditioning, image_conditioning = images_tensor_to_samples(image_conditioning,
@ -392,10 +389,7 @@ class StableDiffusionProcessing:
if self.sampler.conditioning_key == "crossattn-adm": if self.sampler.conditioning_key == "crossattn-adm":
return self.unclip_image_conditioning(source_image) return self.unclip_image_conditioning(source_image)
sd = self.sampler.model_wrap.inner_model.model.state_dict() if getattr(self.sampler.model_wrap.inner_model.model, "is_sdxl_inpaint", False):
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9:
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask) return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
# Dummy zero conditioning if we're not using inpainting or depth model. # Dummy zero conditioning if we're not using inpainting or depth model.

7
modules/sd_models.py
View File

@ -380,6 +380,13 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model') model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
model.is_sd1 = not model.is_sdxl and not model.is_sd2 model.is_sd1 = not model.is_sdxl and not model.is_sd2
model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys() model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys()
# Set is_sdxl_inpaint flag.
diffusion_model_input = state_dict.get('diffusion_model.input_blocks.0.0.weight', None)
model.is_sdxl_inpaint = (
model.is_sdxl and
diffusion_model_input is not None and
diffusion_model_input.shape[1] == 9
)
if model.is_sdxl: if model.is_sdxl:
sd_models_xl.extend_sdxl(model) sd_models_xl.extend_sdxl(model)

7
modules/sd_models_xl.py
View File

@ -35,10 +35,9 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond): def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
sd = self.model.state_dict() """WARNING: This function is called once per denoising iteration. DO NOT add
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None) expensive functionc calls such as `model.state_dict`. """
if diffusion_model_input is not None: if self.is_sdxl_inpaint:
if diffusion_model_input.shape[1] == 9:
x = torch.cat([x] + cond['c_concat'], dim=1) x = torch.cat([x] + cond['c_concat'], dim=1)
return self.model(x, t, cond) return self.model(x, t, cond)