Upload src/SubjectGeniusPipeline.py with huggingface_hub

src/SubjectGeniusPipeline.py

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+import ipdb
+from accelerate import Accelerator
+from diffusers.configuration_utils import register_to_config
+from diffusers.pipelines import FluxPipeline
+from typing import Any, Callable, Dict, List, Optional, Union
+import torch
+from .condition import Condition
+from diffusers.pipelines.flux.pipeline_flux import (
+    FluxPipelineOutput,
+    calculate_shift,
+    retrieve_timesteps,
+    np,
+)
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.models import AutoencoderKL,FluxTransformer2DModel
+
+
+class SubjectGeniusPipeline(FluxPipeline):
+    @register_to_config
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        text_encoder_2: T5EncoderModel,
+        tokenizer_2: T5TokenizerFast,
+        transformer: FluxTransformer2DModel,
+        image_encoder = None,
+        feature_extractor = None,
+    ):
+        super().__init__(
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            text_encoder_2=text_encoder_2,
+            tokenizer_2=tokenizer_2,
+            transformer=transformer,
+            image_encoder = image_encoder,
+            feature_extractor = feature_extractor,
+        )
+    @property
+    def all_adapters(self):
+        list_adapters = self.get_list_adapters()  # eg {"unet": ["adapter1", "adapter2"], "text_encoder": ["adapter2"]}
+        # eg ["adapter1", "adapter2"]
+        all_adapters = list({adapter for adapters in list_adapters.values() for adapter in adapters})
+        return all_adapters
+
+    @torch.no_grad()
+    def __call__(self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        # additional begin
+        conditions: List[Condition] = None,
+        model_config: Optional[Dict[str, Any]] = {},
+        condition_scale: float = 1.0,
+        # additional over
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 3.5,
+        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,
+        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,
+        accelerator: Accelerator = None,
+    ):
+        # self.block_mask_routers = nn.ModuleList(
+        #     [nn.Sequential(nn.Linear(self.transformer.config.attention_head_dim * self.transformer.config.num_attention_heads, 1, bias=False), nn.Tanh()) for _ in
+        #      range(self.transformer.config.num_layers)]
+        # ).to(accelerator.device,dtype=torch.bfloat16)
+        # self.single_block_mask_routers = nn.ModuleList(
+        #     [nn.Sequential(nn.Linear(self.transformer.config.attention_head_dim * self.transformer.config.num_attention_heads, 1, bias=False), nn.Tanh()) for _ in
+        #      range(self.transformer.config.num_single_layers)]
+        # ).to(accelerator.device,dtype=torch.bfloat16)
+
+        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,
+        )
+
+        # 3. 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,
+        )
+        # 4. Prepare conditions
+        condition_latents, condition_ids, condition_type_ids, condition_types = ([] for _ in range(4))
+        use_condition = conditions is not None
+
+        if use_condition:
+            for condition in conditions:
+                tokens,ids,type_id = condition.encode(self)
+                condition_latents.append(tokens)
+                condition_ids.append(ids)
+                condition_type_ids.append(type_id)
+                condition_types.append(condition.condition_type)
+
+        # 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)
+
+        # handle guidance: Decide whether to enable guidance according to the configuration in base model's config file.
+        # example: Flux-dev: True ; Flux-schnell: False.
+        if self.transformer.config.guidance_embeds:
+            guidance = torch.full([1], guidance_scale, device=device, dtype=latents.dtype)
+            guidance = guidance.expand(latents.shape[0])
+        else:
+            guidance = None
+
+        # 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)
+                noise_pred, conditional_output = self.transformer(
+                    model_config=model_config,
+                    # Inputs of the condition (new feature)
+                    condition_latents=condition_latents if use_condition else None,
+                    condition_ids=condition_ids if use_condition else None,
+                    condition_type_ids=condition_type_ids if use_condition else None, # the condition_type_ids is not used so far.
+                    condition_types = condition_types if use_condition else None,
+                    return_condition_latents = model_config.get("return_condition_latents",False),
+                    # Inputs to the original transformer
+                    hidden_states=latents,
+                    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,
+                )
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                # prepare for callback
+                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()
+
+        # 7 finish denoising process
+        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,conditional_output) if model_config.get("return_condition_latents",False) else (image,)
+
+        return FluxPipelineOutput(images=image)