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https://github.com/invoke-ai/InvokeAI
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chore: Black linting
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blessedcoolant
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561951ad98
@ -294,11 +294,17 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
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@torch.no_grad()
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def invoke(self, context: InvocationContext) -> CompelOutput:
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c1, c1_pooled, ec1 = self.run_clip_compel(context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=False)
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c1, c1_pooled, ec1 = self.run_clip_compel(
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context, self.clip, self.prompt, False, "lora_te1_", zero_on_empty=False
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)
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if self.style.strip() == "":
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c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.prompt, True, "lora_te2_", zero_on_empty=True)
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c2, c2_pooled, ec2 = self.run_clip_compel(
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context, self.clip2, self.prompt, True, "lora_te2_", zero_on_empty=True
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)
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else:
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c2, c2_pooled, ec2 = self.run_clip_compel(context, self.clip2, self.style, True, "lora_te2_", zero_on_empty=True)
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c2, c2_pooled, ec2 = self.run_clip_compel(
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context, self.clip2, self.style, True, "lora_te2_", zero_on_empty=True
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)
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original_size = (self.original_height, self.original_width)
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crop_coords = (self.crop_top, self.crop_left)
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@ -320,7 +320,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
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def init_scheduler(self, scheduler, device, steps, denoising_start, denoising_end):
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if scheduler.config.get("cpu_only", False):
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device = torch.device("cpu")
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num_inference_steps = steps
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scheduler.set_timesteps(num_inference_steps, device=device)
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timesteps = scheduler.timesteps
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@ -344,7 +344,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
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# calculate step count based on scheduler order
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num_inference_steps = len(timesteps)
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if scheduler.order == 2:
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num_inference_steps += (num_inference_steps % 2)
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num_inference_steps += num_inference_steps % 2
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num_inference_steps = num_inference_steps // 2
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return num_inference_steps, timesteps, init_timestep
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@ -202,8 +202,8 @@ class ControlNetData:
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@dataclass
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class ConditioningData:
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unconditioned_embeddings: Any # TODO: type
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text_embeddings: Any # TODO: type
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unconditioned_embeddings: Any # TODO: type
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text_embeddings: Any # TODO: type
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guidance_scale: Union[float, List[float]]
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"""
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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@ -389,19 +389,17 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
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batched_t = init_timestep.repeat(batch_size)
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if noise is not None:
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#latents = noise * self.scheduler.init_noise_sigma # it's like in t2l according to diffusers
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# latents = noise * self.scheduler.init_noise_sigma # it's like in t2l according to diffusers
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latents = self.scheduler.add_noise(latents, noise, batched_t)
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if mask is not None:
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if is_inpainting_model(self.unet):
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# You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint
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# (that's why there's a mask!) but it seems to really want that blanked out.
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#masked_latents = latents * torch.where(mask < 0.5, 1, 0) TODO: inpaint/outpaint/infill
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# masked_latents = latents * torch.where(mask < 0.5, 1, 0) TODO: inpaint/outpaint/infill
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# TODO: we should probably pass this in so we don't have to try/finally around setting it.
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self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(
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self._unet_forward, mask, orig_latents
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)
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self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(self._unet_forward, mask, orig_latents)
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else:
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# if no noise provided, noisify unmasked area based on seed(or 0 as fallback)
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if noise is None:
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@ -413,7 +411,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
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).to(device=orig_latents.device, dtype=orig_latents.dtype)
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latents = self.scheduler.add_noise(latents, noise, batched_t)
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latents = torch.lerp(orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype))
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latents = torch.lerp(
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orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype)
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)
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additional_guidance.append(AddsMaskGuidance(mask, orig_latents, self.scheduler, noise))
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@ -549,11 +549,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
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uc_noise_pred, c_noise_pred = self.invokeai_diffuser.do_unet_step(
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sample=latent_model_input,
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timestep=t, # TODO: debug how handled batched and non batched timesteps
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timestep=t, # TODO: debug how handled batched and non batched timesteps
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step_index=step_index,
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total_step_count=total_step_count,
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conditioning_data=conditioning_data,
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# extra:
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down_block_additional_residuals=controlnet_down_block_samples, # from controlnet(s)
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mid_block_additional_residual=controlnet_mid_block_sample, # from controlnet(s)
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@ -202,15 +202,21 @@ class InvokeAIDiffuserComponent:
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else:
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if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
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added_cond_kwargs = {
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"text_embeds": torch.cat([
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# TODO: how to pad? just by zeros? or even truncate?
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conditioning_data.unconditioned_embeddings.pooled_embeds,
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conditioning_data.text_embeddings.pooled_embeds,
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], dim=0),
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"time_ids": torch.cat([
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conditioning_data.unconditioned_embeddings.add_time_ids,
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conditioning_data.text_embeddings.add_time_ids,
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], dim=0),
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"text_embeds": torch.cat(
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[
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# TODO: how to pad? just by zeros? or even truncate?
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conditioning_data.unconditioned_embeddings.pooled_embeds,
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conditioning_data.text_embeddings.pooled_embeds,
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],
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dim=0,
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),
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"time_ids": torch.cat(
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[
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conditioning_data.unconditioned_embeddings.add_time_ids,
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conditioning_data.text_embeddings.add_time_ids,
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],
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dim=0,
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),
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}
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(
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encoder_hidden_states,
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@ -260,7 +266,7 @@ class InvokeAIDiffuserComponent:
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self,
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sample: torch.Tensor,
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timestep: torch.Tensor,
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conditioning_data, # TODO: type
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conditioning_data, # TODO: type
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step_index: int,
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total_step_count: int,
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**kwargs,
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@ -380,20 +386,25 @@ class InvokeAIDiffuserComponent:
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added_cond_kwargs = None
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if type(conditioning_data.text_embeddings) is SDXLConditioningInfo:
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added_cond_kwargs = {
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"text_embeds": torch.cat([
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# TODO: how to pad? just by zeros? or even truncate?
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conditioning_data.unconditioned_embeddings.pooled_embeds,
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conditioning_data.text_embeddings.pooled_embeds,
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], dim=0),
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"time_ids": torch.cat([
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conditioning_data.unconditioned_embeddings.add_time_ids,
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conditioning_data.text_embeddings.add_time_ids,
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], dim=0),
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"text_embeds": torch.cat(
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[
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# TODO: how to pad? just by zeros? or even truncate?
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conditioning_data.unconditioned_embeddings.pooled_embeds,
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conditioning_data.text_embeddings.pooled_embeds,
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],
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dim=0,
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),
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"time_ids": torch.cat(
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[
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conditioning_data.unconditioned_embeddings.add_time_ids,
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conditioning_data.text_embeddings.add_time_ids,
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],
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dim=0,
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),
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}
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both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(
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conditioning_data.unconditioned_embeddings.embeds,
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conditioning_data.text_embeddings.embeds
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conditioning_data.unconditioned_embeddings.embeds, conditioning_data.text_embeddings.embeds
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)
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both_results = self.model_forward_callback(
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x_twice,
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