Make a CFG version of flux pipeline

scripts/convert_lora_to_peft_format.py

@@ -78,8 +78,12 @@ for key, value in state_dict.items():
     new_key = new_key.replace('lora_up', 'lora_B')
     new_key = new_key.replace('_lora', '.lora')
     new_key = new_key.replace('attn_', 'attn.')
+    new_key = new_key.replace('ff_', 'ff.')
+    new_key = new_key.replace('context_net_', 'context.net.')
+    new_key = new_key.replace('0_proj', '0.proj')
     new_key = new_key.replace('norm_linear', 'norm.linear')
     new_key = new_key.replace('norm_out_linear', 'norm_out.linear')
+    new_key = new_key.replace('to_out_', 'to_out.')
 
     new_state_dict[new_key] = new_val.to(orig_dtype)
 

toolkit/config_modules.py

@@ -370,6 +370,7 @@ class ModelConfig:
         self.is_flux: bool = kwargs.get('is_flux', False)
         if self.is_pixart_sigma:
             self.is_pixart = True
+        self.use_flux_cfg = kwargs.get('use_flux_cfg', False)
         self.is_ssd: bool = kwargs.get('is_ssd', False)
         self.is_vega: bool = kwargs.get('is_vega', False)
         self.is_v_pred: bool = kwargs.get('is_v_pred', False)

toolkit/pipelines.py

@@ -2,8 +2,11 @@ import importlib
 import inspect
 from typing import Union, List, Optional, Dict, Any, Tuple, Callable
 
+import numpy as np
 import torch
-from diffusers import StableDiffusionXLPipeline, StableDiffusionPipeline, LMSDiscreteScheduler
+from diffusers import StableDiffusionXLPipeline, StableDiffusionPipeline, LMSDiscreteScheduler, FluxPipeline
+from diffusers.pipelines.flux.pipeline_flux import calculate_shift, retrieve_timesteps
+from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 # from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_k_diffusion import ModelWrapper
 from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
@@ -1202,3 +1205,217 @@ class StableDiffusionXLRefinerPipeline(StableDiffusionXLPipeline):
 
         return StableDiffusionXLPipelineOutput(images=image)
 
+
+
+
+# TODO this is rough. Need to properly stack unconditional
+class FluxWithCFGPipeline(FluxPipeline):
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 7.0,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+    ):
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+        (
+            negative_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            negative_text_ids,
+        ) = self.encode_prompt(
+            prompt=negative_prompt,
+            prompt_2=negative_prompt_2,
+            prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        image_seq_len = latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        self._num_timesteps = len(timesteps)
+
+        # 6. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+                # handle guidance
+                if self.transformer.config.guidance_embeds:
+                    guidance = torch.tensor([guidance_scale], device=device)
+                    guidance = guidance.expand(latents.shape[0])
+                else:
+                    guidance = None
+
+                noise_pred_text = self.transformer(
+                    hidden_states=latents,
+                    # 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)
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=pooled_prompt_embeds,
+                    encoder_hidden_states=prompt_embeds,
+                    txt_ids=text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # todo combine these
+                noise_pred_uncond = self.transformer(
+                    hidden_states=latents,
+                    # 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)
+                    timestep=timestep / 1000,
+                    guidance=guidance,
+                    pooled_projections=negative_pooled_prompt_embeds,
+                    encoder_hidden_states=negative_prompt_embeds,
+                    txt_ids=negative_text_ids,
+                    img_ids=latent_image_ids,
+                    joint_attention_kwargs=self.joint_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+            image = self.vae.decode(latents, return_dict=False)[0]
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return FluxPipelineOutput(images=image)

toolkit/stable_diffusion_model.py

@@ -36,7 +36,7 @@ from toolkit.sd_device_states_presets import empty_preset
 from toolkit.train_tools import get_torch_dtype, apply_noise_offset
 import torch
 from toolkit.pipelines import CustomStableDiffusionXLPipeline, CustomStableDiffusionPipeline, \
-    StableDiffusionKDiffusionXLPipeline, StableDiffusionXLRefinerPipeline
+    StableDiffusionKDiffusionXLPipeline, StableDiffusionXLRefinerPipeline, FluxWithCFGPipeline
 from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, T2IAdapter, DDPMScheduler, \
     StableDiffusionXLAdapterPipeline, StableDiffusionAdapterPipeline, DiffusionPipeline, PixArtTransformer2DModel, \
     StableDiffusionXLImg2ImgPipeline, LCMScheduler, Transformer2DModel, AutoencoderTiny, ControlNetModel, \
@@ -797,16 +797,29 @@ class StableDiffusion:
                 ).to(self.device_torch)
                 pipeline.watermark = None
             elif self.is_flux:
-                pipeline = FluxPipeline(
+                if self.model_config.use_flux_cfg:
-                    vae=self.vae,
+                    pipeline = FluxWithCFGPipeline(
-                    transformer=self.unet,
+                        vae=self.vae,
-                    text_encoder=self.text_encoder[0],
+                        transformer=self.unet,
-                    text_encoder_2=self.text_encoder[1],
+                        text_encoder=self.text_encoder[0],
-                    tokenizer=self.tokenizer[0],
+                        text_encoder_2=self.text_encoder[1],
-                    tokenizer_2=self.tokenizer[1],
+                        tokenizer=self.tokenizer[0],
-                    scheduler=noise_scheduler,
+                        tokenizer_2=self.tokenizer[1],
-                    **extra_args
+                        scheduler=noise_scheduler,
-                )
+                        **extra_args
+                    )
+
+                else:
+                    pipeline = FluxPipeline(
+                        vae=self.vae,
+                        transformer=self.unet,
+                        text_encoder=self.text_encoder[0],
+                        text_encoder_2=self.text_encoder[1],
+                        tokenizer=self.tokenizer[0],
+                        tokenizer_2=self.tokenizer[1],
+                        scheduler=noise_scheduler,
+                        **extra_args
+                    )
                 pipeline.watermark = None
             elif self.is_v3:
                 pipeline = Pipe(
@@ -1068,18 +1081,32 @@ class StableDiffusion:
                             **extra
                         ).images[0]
                     elif self.is_flux:
-                        img = pipeline(
+                        if self.model_config.use_flux_cfg:
-                            prompt_embeds=conditional_embeds.text_embeds,
+                            img = pipeline(
-                            pooled_prompt_embeds=conditional_embeds.pooled_embeds,
+                                prompt_embeds=conditional_embeds.text_embeds,
-                            # negative_prompt_embeds=unconditional_embeds.text_embeds,
+                                pooled_prompt_embeds=conditional_embeds.pooled_embeds,
-                            # negative_pooled_prompt_embeds=unconditional_embeds.pooled_embeds,
+                                negative_prompt_embeds=unconditional_embeds.text_embeds,
-                            height=gen_config.height,
+                                negative_pooled_prompt_embeds=unconditional_embeds.pooled_embeds,
-                            width=gen_config.width,
+                                height=gen_config.height,
-                            num_inference_steps=gen_config.num_inference_steps,
+                                width=gen_config.width,
-                            guidance_scale=gen_config.guidance_scale,
+                                num_inference_steps=gen_config.num_inference_steps,
-                            latents=gen_config.latents,
+                                guidance_scale=gen_config.guidance_scale,
-                            **extra
+                                latents=gen_config.latents,
-                        ).images[0]
+                                **extra
+                            ).images[0]
+                        else:
+                            img = pipeline(
+                                prompt_embeds=conditional_embeds.text_embeds,
+                                pooled_prompt_embeds=conditional_embeds.pooled_embeds,
+                                # negative_prompt_embeds=unconditional_embeds.text_embeds,
+                                # negative_pooled_prompt_embeds=unconditional_embeds.pooled_embeds,
+                                height=gen_config.height,
+                                width=gen_config.width,
+                                num_inference_steps=gen_config.num_inference_steps,
+                                guidance_scale=gen_config.guidance_scale,
+                                latents=gen_config.latents,
+                                **extra
+                            ).images[0]
                     elif self.is_pixart:
                         # needs attention masks for some reason
                         img = pipeline(