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1a1c846be3
Maintains consistency between positive and negative passes to prevent CFG artifacts when using Kontext reference images
184 lines
8.4 KiB
Python
184 lines
8.4 KiB
Python
import math
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from typing import Callable
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import torch
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from tqdm import tqdm
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from invokeai.backend.flux.controlnet.controlnet_flux_output import ControlNetFluxOutput, sum_controlnet_flux_outputs
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from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
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from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
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from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
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from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
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from invokeai.backend.flux.model import Flux
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from invokeai.backend.rectified_flow.rectified_flow_inpaint_extension import RectifiedFlowInpaintExtension
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from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
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def denoise(
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model: Flux,
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# model input
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img: torch.Tensor,
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img_ids: torch.Tensor,
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pos_regional_prompting_extension: RegionalPromptingExtension,
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neg_regional_prompting_extension: RegionalPromptingExtension | None,
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# sampling parameters
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timesteps: list[float],
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step_callback: Callable[[PipelineIntermediateState], None],
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guidance: float,
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cfg_scale: list[float],
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inpaint_extension: RectifiedFlowInpaintExtension | None,
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controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension],
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pos_ip_adapter_extensions: list[XLabsIPAdapterExtension],
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neg_ip_adapter_extensions: list[XLabsIPAdapterExtension],
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# extra img tokens (channel-wise)
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img_cond: torch.Tensor | None,
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# extra img tokens (sequence-wise) - for Kontext conditioning
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img_cond_seq: torch.Tensor | None = None,
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img_cond_seq_ids: torch.Tensor | None = None,
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):
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# step 0 is the initial state
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total_steps = len(timesteps) - 1
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step_callback(
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PipelineIntermediateState(
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step=0,
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order=1,
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total_steps=total_steps,
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timestep=int(timesteps[0]),
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latents=img,
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),
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)
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# guidance_vec is ignored for schnell.
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guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
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# Store original sequence length for slicing predictions
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original_seq_len = img.shape[1]
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for step_index, (t_curr, t_prev) in tqdm(list(enumerate(zip(timesteps[:-1], timesteps[1:], strict=True)))):
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t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
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# Run ControlNet models.
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controlnet_residuals: list[ControlNetFluxOutput] = []
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for controlnet_extension in controlnet_extensions:
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controlnet_residuals.append(
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controlnet_extension.run_controlnet(
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timestep_index=step_index,
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total_num_timesteps=total_steps,
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img=img,
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img_ids=img_ids,
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txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
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txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
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y=pos_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
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timesteps=t_vec,
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guidance=guidance_vec,
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)
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)
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# Merge the ControlNet residuals from multiple ControlNets.
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# TODO(ryand): We may want to calculate the sum just-in-time to keep peak memory low. Keep in mind, that the
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# controlnet_residuals datastructure is efficient in that it likely contains multiple references to the same
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# tensors. Calculating the sum materializes each tensor into its own instance.
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merged_controlnet_residuals = sum_controlnet_flux_outputs(controlnet_residuals)
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# Prepare input for model - concatenate fresh each step
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img_input = img
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img_input_ids = img_ids
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# Add channel-wise conditioning (for ControlNet, FLUX Fill, etc.)
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if img_cond is not None:
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img_input = torch.cat((img_input, img_cond), dim=-1)
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# Add sequence-wise conditioning (for Kontext)
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if img_cond_seq is not None:
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assert img_cond_seq_ids is not None, (
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"You need to provide either both or neither of the sequence conditioning"
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)
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img_input = torch.cat((img_input, img_cond_seq), dim=1)
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img_input_ids = torch.cat((img_input_ids, img_cond_seq_ids), dim=1)
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pred = model(
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img=img_input,
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img_ids=img_input_ids,
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txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
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txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
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y=pos_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
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timesteps=t_vec,
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guidance=guidance_vec,
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timestep_index=step_index,
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total_num_timesteps=total_steps,
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controlnet_double_block_residuals=merged_controlnet_residuals.double_block_residuals,
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controlnet_single_block_residuals=merged_controlnet_residuals.single_block_residuals,
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ip_adapter_extensions=pos_ip_adapter_extensions,
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regional_prompting_extension=pos_regional_prompting_extension,
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)
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# Slice prediction to only include the main image tokens
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if img_cond_seq is not None:
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pred = pred[:, :original_seq_len]
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step_cfg_scale = cfg_scale[step_index]
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# If step_cfg_scale, is 1.0, then we don't need to run the negative prediction.
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if not math.isclose(step_cfg_scale, 1.0):
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# TODO(ryand): Add option to run positive and negative predictions in a single batch for better performance
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# on systems with sufficient VRAM.
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if neg_regional_prompting_extension is None:
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raise ValueError("Negative text conditioning is required when cfg_scale is not 1.0.")
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# For negative prediction with Kontext, we need to include the reference images
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# to maintain consistency between positive and negative passes. Without this,
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# CFG would create artifacts as the attention mechanism would see different
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# spatial structures in each pass
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neg_img_input = img
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neg_img_input_ids = img_ids
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# Add channel-wise conditioning for negative pass if present
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if img_cond is not None:
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neg_img_input = torch.cat((neg_img_input, img_cond), dim=-1)
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# Add sequence-wise conditioning (Kontext) for negative pass
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# This ensures reference images are processed consistently
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if img_cond_seq is not None:
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neg_img_input = torch.cat((neg_img_input, img_cond_seq), dim=1)
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neg_img_input_ids = torch.cat((neg_img_input_ids, img_cond_seq_ids), dim=1)
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neg_pred = model(
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img=neg_img_input,
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img_ids=neg_img_input_ids,
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txt=neg_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
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txt_ids=neg_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
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y=neg_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
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timesteps=t_vec,
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guidance=guidance_vec,
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timestep_index=step_index,
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total_num_timesteps=total_steps,
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controlnet_double_block_residuals=None,
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controlnet_single_block_residuals=None,
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ip_adapter_extensions=neg_ip_adapter_extensions,
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regional_prompting_extension=neg_regional_prompting_extension,
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)
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# Slice negative prediction to match main image tokens
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if img_cond_seq is not None:
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neg_pred = neg_pred[:, :original_seq_len]
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pred = neg_pred + step_cfg_scale * (pred - neg_pred)
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preview_img = img - t_curr * pred
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img = img + (t_prev - t_curr) * pred
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if inpaint_extension is not None:
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img = inpaint_extension.merge_intermediate_latents_with_init_latents(img, t_prev)
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preview_img = inpaint_extension.merge_intermediate_latents_with_init_latents(preview_img, 0.0)
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step_callback(
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PipelineIntermediateState(
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step=step_index + 1,
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order=1,
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total_steps=total_steps,
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timestep=int(t_curr),
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latents=preview_img,
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),
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)
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return img
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