Update pipeline.py

pipeline.py

@@ -66,7 +66,7 @@ def prepare_timesteps(
     return timesteps, num_inference_steps
 
 # FLUX pipeline function
-class FluxWithCFGPipeline(StableDiffusion3Pipeline):
+class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin):
 
     def __init__(
         self,
@@ -244,193 +244,6 @@ class FluxWithCFGPipeline(StableDiffusion3Pipeline):
         self.maybe_free_model_hooks()
         torch.cuda.empty_cache()
 
-    def _decode_latents_to_image(self, latents, height, width, output_type, vae=None):
-        """Decodes the given latents into an image."""
-        vae = vae or self.vae
-        latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
-        latents = (latents / vae.config.scaling_factor) + vae.config.shift_factor
-        image = vae.decode(latents, return_dict=False)[0]
-        return self.image_processor.postprocess(image, output_type=output_type)[0]
-        
-class FluxWithCFGPipeline(StableDiffusion3Pipeline):
-    @torch.inference_mode()
-    def __init__(
-        self,
-        transformer: FluxTransformer2DModel,
-        scheduler: FlowMatchEulerDiscreteScheduler,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        tokenizer_2: T5TokenizerFast,
-        tokenizer_3: None,
-        text_encoder_2: T5EncoderModel,
-        text_encoder_3: None,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            text_encoder_2=text_encoder_2,
-            text_encoder_3=text_encoder_3,
-            tokenizer=tokenizer,
-            tokenizer_2=tokenizer_2,
-            tokenizer_3=tokenizer_3,
-            transformer=transformer,
-            scheduler=scheduler,
-        )
-        self.vae_scale_factor = (
-            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 16
-        )
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.tokenizer_max_length = (
-            self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
-        )
-        self.default_sample_size = 64
-    
-    def generate_image(
-        self,
-        prompt: Union[str, List[str]] = None,
-        prompt_2: Optional[Union[str, List[str]]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        negative_prompt_2: Optional[Union[str, List[str]]] = None,
-        num_inference_steps: int = 4,
-        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,
-        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,
-        max_sequence_length: int = 300,
-    ):
-        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
-        self.check_inputs(
-            prompt,
-            prompt_2,
-            negative_prompt,
-            height,
-            width,
-            prompt_embeds=prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            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
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        # 3. Encode prompt
-        lora_scale = joint_attention_kwargs.get("scale", None) if 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,
-            negative_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_timestep_shift(image_seq_len)
-        timesteps, num_inference_steps = prepare_timesteps(
-            self.scheduler,
-            num_inference_steps,
-            device,
-            timesteps,
-            sigmas,
-            mu=mu,
-        )
-        self._num_timesteps = len(timesteps)
-
-        # Handle guidance
-        guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float16).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
-
-        # 6. Denoising loop
-        for i, t in enumerate(timesteps):
-            if self.interrupt:
-                continue
-
-            timestep = t.expand(latents.shape[0]).to(latents.dtype)
-
-            noise_pred = self.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,
-            )[0]
-            
-            noise_pred_uncond = self.transformer(
-                hidden_states=latents,
-                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)
-
-            latents_dtype = latents.dtype
-            latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-             # Yield intermediate result
-            torch.cuda.empty_cache()
-
-        # Final image
-        return self._decode_latents_to_image(latents, height, width, output_type)
-        self.maybe_free_model_hooks()
-        torch.cuda.empty_cache()
-
     def _decode_latents_to_image(self, latents, height, width, output_type, vae=None):
         """Decodes the given latents into an image."""
         vae = vae or self.vae