301 lines
10 KiB
Python
301 lines
10 KiB
Python
import torch
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import yaml, os
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from diffusers.pipelines import FluxPipeline
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from typing import List, Union, Optional, Dict, Any, Callable
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from .transformer import tranformer_forward
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from .condition import Condition
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from diffusers.pipelines.flux.pipeline_flux import (
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FluxPipelineOutput,
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calculate_shift,
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retrieve_timesteps,
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np,
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)
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def prepare_params(
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prompt: Union[str, List[str]] = None,
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prompt_2: Optional[Union[str, List[str]]] = None,
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height: Optional[int] = 512,
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width: Optional[int] = 512,
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num_inference_steps: int = 28,
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timesteps: List[int] = None,
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guidance_scale: float = 3.5,
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num_images_per_prompt: Optional[int] = 1,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.FloatTensor] = None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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joint_attention_kwargs: Optional[Dict[str, Any]] = None,
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callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
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callback_on_step_end_tensor_inputs: List[str] = ["latents"],
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max_sequence_length: int = 512,
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**kwargs: dict,
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):
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return (
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prompt,
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prompt_2,
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height,
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width,
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num_inference_steps,
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timesteps,
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guidance_scale,
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num_images_per_prompt,
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generator,
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latents,
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prompt_embeds,
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pooled_prompt_embeds,
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output_type,
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return_dict,
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joint_attention_kwargs,
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callback_on_step_end,
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callback_on_step_end_tensor_inputs,
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max_sequence_length,
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)
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def seed_everything(seed: int = 42):
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torch.backends.cudnn.deterministic = True
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torch.manual_seed(seed)
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np.random.seed(seed)
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@torch.no_grad()
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def generate(
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pipeline: FluxPipeline,
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conditions: List[Condition] = None,
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model_config: Optional[Dict[str, Any]] = {},
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condition_scale: float = 1.0,
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**params: dict,
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):
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# print(params)
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# return
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# model_config = model_config or get_config(config_path).get("model", {})
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if condition_scale != 1:
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for name, module in pipeline.transformer.named_modules():
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if not name.endswith(".attn"):
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continue
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module.c_factor = torch.ones(1, 1) * condition_scale
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self = pipeline
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(
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prompt,
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prompt_2,
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height,
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width,
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num_inference_steps,
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timesteps,
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guidance_scale,
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num_images_per_prompt,
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generator,
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latents,
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prompt_embeds,
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pooled_prompt_embeds,
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output_type,
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return_dict,
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joint_attention_kwargs,
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callback_on_step_end,
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callback_on_step_end_tensor_inputs,
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max_sequence_length,
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) = prepare_params(**params)
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height = height or self.default_sample_size * self.vae_scale_factor
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width = width or self.default_sample_size * self.vae_scale_factor
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(
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prompt,
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prompt_2,
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height,
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width,
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prompt_embeds=prompt_embeds,
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pooled_prompt_embeds=pooled_prompt_embeds,
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callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
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max_sequence_length=max_sequence_length,
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)
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self._guidance_scale = guidance_scale
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self._joint_attention_kwargs = joint_attention_kwargs
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self._interrupt = False
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# 2. Define call parameters
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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device = self._execution_device
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# print('execution device --->', device)
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# device = 'cpu'
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lora_scale = (
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self.joint_attention_kwargs.get("scale", None)
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if self.joint_attention_kwargs is not None
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else None
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)
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(
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prompt_embeds,
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pooled_prompt_embeds,
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text_ids,
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) = self.encode_prompt(
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prompt=prompt,
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prompt_2=prompt_2,
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prompt_embeds=prompt_embeds,
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pooled_prompt_embeds=pooled_prompt_embeds,
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device=device,
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num_images_per_prompt=num_images_per_prompt,
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max_sequence_length=max_sequence_length,
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lora_scale=lora_scale,
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)
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# print('-----> real prompt embeds:')
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# print(prompt_embeds[0])
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# print(pooled_prompt_embeds[0])
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# print(text_ids)
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# 4. Prepare latent variables
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num_channels_latents = self.transformer.config.in_channels // 4
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latents, latent_image_ids = self.prepare_latents(
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batch_size * num_images_per_prompt,
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num_channels_latents,
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height,
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width,
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prompt_embeds.dtype,
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device,
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generator,
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latents,
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)
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# 4.1. Prepare conditions
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condition_latents, condition_ids, condition_type_ids = ([] for _ in range(3))
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use_condition = conditions is not None or []
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if use_condition:
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assert len(conditions) <= 1, "Only one condition is supported for now."
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pipeline.set_adapters(conditions[0].condition_type)
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for condition in conditions:
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# tokens, ids, type_id = condition.encode(self)
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tokens, ids, type_id = params['encoded_condition']
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tokens.to(torch.bfloat16)
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ids.to(torch.bfloat16)
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type_id.to(torch.bfloat16)
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condition_latents.append(tokens) # [batch_size, token_n, token_dim]
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condition_ids.append(ids) # [token_n, id_dim(3)]
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condition_type_ids.append(type_id) # [token_n, 1]
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condition_latents = torch.cat(condition_latents, dim=1)
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condition_ids = torch.cat(condition_ids, dim=0)
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if condition.condition_type == "subject":
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condition_ids[:, 2] += width // 16
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condition_type_ids = torch.cat(condition_type_ids, dim=0)
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# 5. Prepare timesteps
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sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
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image_seq_len = latents.shape[1]
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mu = calculate_shift(
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image_seq_len,
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self.scheduler.config.base_image_seq_len,
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self.scheduler.config.max_image_seq_len,
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self.scheduler.config.base_shift,
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self.scheduler.config.max_shift,
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)
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timesteps, num_inference_steps = retrieve_timesteps(
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self.scheduler,
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num_inference_steps,
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device,
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timesteps,
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sigmas,
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mu=mu,
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)
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num_warmup_steps = max(
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len(timesteps) - num_inference_steps * self.scheduler.order, 0
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)
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self._num_timesteps = len(timesteps)
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# 6. Denoising loop
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with self.progress_bar(total=num_inference_steps) as progress_bar:
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for i, t in enumerate(timesteps):
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if self.interrupt:
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continue
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# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
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timestep = t.expand(latents.shape[0]).to(latents.dtype)
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# handle guidance
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if self.transformer.config.guidance_embeds:
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guidance = torch.tensor([guidance_scale], device=device)
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guidance = guidance.expand(latents.shape[0])
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else:
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guidance = None
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noise_pred = tranformer_forward(
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self.transformer,
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model_config=model_config,
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# Inputs of the condition (new feature)
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condition_latents=condition_latents if use_condition else None,
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condition_ids=condition_ids if use_condition else None,
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condition_type_ids=condition_type_ids if use_condition else None,
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# Inputs to the original transformer
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hidden_states=latents,
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# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
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timestep=timestep / 1000,
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guidance=guidance,
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pooled_projections=pooled_prompt_embeds,
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encoder_hidden_states=prompt_embeds,
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txt_ids=text_ids,
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img_ids=latent_image_ids,
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joint_attention_kwargs=self.joint_attention_kwargs,
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return_dict=False,
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)[0]
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# compute the previous noisy sample x_t -> x_t-1
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latents_dtype = latents.dtype
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latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
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if latents.dtype != latents_dtype:
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if torch.backends.mps.is_available():
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# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
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latents = latents.to(latents_dtype)
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if callback_on_step_end is not None:
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callback_kwargs = {}
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for k in callback_on_step_end_tensor_inputs:
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callback_kwargs[k] = locals()[k]
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callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
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latents = callback_outputs.pop("latents", latents)
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prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
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# call the callback, if provided
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if i == len(timesteps) - 1 or (
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(i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
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):
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progress_bar.update()
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if output_type == "latent":
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image = latents
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else:
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latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
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latents = (
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latents / self.vae.config.scaling_factor
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) + self.vae.config.shift_factor
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image = self.vae.decode(latents, return_dict=False)[0]
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image = self.image_processor.postprocess(image, output_type=output_type)
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# Offload all models
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self.maybe_free_model_hooks()
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if condition_scale != 1:
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for name, module in pipeline.transformer.named_modules():
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if not name.endswith(".attn"):
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continue
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del module.c_factor
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if not return_dict:
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return (image,)
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return FluxPipelineOutput(images=image)
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