# Debugging Custom Stable Diffusion Pipeline for 1D Signal Generation **URL:** \ **Category:** 🧨 Diffusers\ **Created:** [July 2, 2025, 3:39am UTC](https://discuss.huggingface.co/t/debugging-custom-stable-diffusion-pipeline-for-1d-signal-generation/161425 "2025-07-02T03:39:20Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![ThuyVuPhuong](https://avatars.discourse-cdn.com/v4/letter/t/e274bd/32.png) [@ThuyVuPhuong](https://discuss.huggingface.co/u/ThuyVuPhuong)\ **Post date:** [July 2, 2025, 3:39am UTC](https://discuss.huggingface.co/t/debugging-custom-stable-diffusion-pipeline-for-1d-signal-generation/161425/1 "2025-07-02T03:39:20Z")
Hi everyone, I’m building a custom Stable Diffusion pipeline to generate **1D signal data** instead of images. Here’s what I’ve implemented so far: - ✅ Trained a custom **autoencoder** to encode raw 1D signals into a latent space and decode them back. The latent dimension is **4**. - ✅ Trained a **1D UNet** that takes as input the latent signal, text embedding, and timestep embedding, and predicts noise. - ✅ Built a **custom pipeline** using `DDIMScheduler` to iteratively denoise and decode signals from latent space. However, the final **generated signal is just noise** and doesn’t resemble the training data. I’m not sure whether: - My latent space isn’t properly learned, - The UNet training is unstable, - Or my sampling loop has a mistake. Has anyone encountered a similar issue when adapting Stable Diffusion to 1D data? Any help, debugging tips, or pointers would be greatly appreciated! Thanks in advance 🙏 Here’s my sampling pipeline (` __call__ `): > @torch.no\_grad() > def **call** ( > self, > prompt, > signal\_length=None, > latent\_length=None, > num\_inference\_steps=50, > guidance\_scale=7.5, > generator=None, > latents=None, > return\_dict=True, > output\_type=“np”, > sigmas: List[float] = None, > timesteps: List[int] = None, > ): > device = self.\_execution\_device > batch\_size = 1 if isinstance(prompt, str) else len(prompt) > self.\_interrupt = False ``` # 1. Determine latent/signal length if signal_length is None: latent_length = getattr(self.vae.config, "latent_channels", 64) signal_length = getattr(self.vae.config, "input_length", 500) else: latent_length = latent_length signal_length = signal_length # 2. Encode prompt prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, device, guidance_scale=guidance_scale ) if guidance_scale > 1.0: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) # 3. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, timesteps, sigmas ) # 4. Prepare latents if latents is None: latents = randn_tensor( (batch_size, self.unet.config["in_channels"], latent_length), generator=generator, device=device, dtype=prompt_embeds.dtype ) else: latents = latents.to(device) latents *= self.scheduler.init_noise_sigma # 5. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue latent_model_input = torch.cat([latents] * 2) if guidance_scale > 1.0 else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) t_batch = torch.full((batch_size,), t, dtype=torch.long, device=t.device) noise_pred = self.unet( sample=latent_model_input, timestep=t_batch, text_embedding=prompt_embeds ).sample if guidance_scale > 1.0: noise_uncond, noise_text = noise_pred.chunk(2) noise_pred = noise_uncond + guidance_scale * (noise_text - noise_uncond) latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() # 6. Decode self.vae.eval() signal = self.vae.decode(latents)[0] if output_type == "np": signal = signal.squeeze(1).detach().cpu().numpy() return {"signals": signal} if return_dict else signal ``` ---
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\ **Post date:** [July 2, 2025, 6:59am UTC](https://discuss.huggingface.co/t/debugging-custom-stable-diffusion-pipeline-for-1d-signal-generation/161425/2 "2025-07-02T06:59:16Z")
> [@1D Diffusers not behaving as expected, am i retarded?](https://discuss.huggingface.co/t/1d-diffusers-not-behaving-as-expected-am-i-retarded/153021): > > This is my first Post on the Forum, as I’m usually able to solve my issues on my own. But in this very simple Diffusion training of a trivial 1D dataset, the model just cant seem to figure it out. This leads me to think that I’m fundamentally misunderstanding something in the 1D diffusers implementation. Or perhaps it is not supported how i imagined it to be. I have trained 2D models without issues for reference, with close to identical code. The following code is the entire program, which sh… According to several reports, there may be a bug in the newer Diffusers `UNet1DModel`, but the general answer I got from ChatGPT is as follows for now. * * * Below is a **refined sampling pipeline** built on top of Hugging Face Diffusers and adapted for 1D signal generation. It addresses common pitfalls around scheduling, guidance, and debugging, and draws on practices from models like DiffWave and ArchiSound. * * * ```python from diffusers import UNet1DModel, DDIMScheduler import torch class SignalDiffusionPipeline: def __init__ (self, vae, unet: UNet1DModel, scheduler: DDIMScheduler, tokenizer, text_encoder): self.vae = vae.to(vae.device) self.unet = unet.to(vae.device) self.scheduler = scheduler self.tokenizer = tokenizer self.text_encoder = text_encoder @torch.no_grad() def __call__ (self, prompt: str, num_inference_steps=50, guidance_scale=7.5, generator=None, debug=False): device = self.vae.device # 1️⃣ Text encoding & classifier‑free guidance tokens = self.tokenizer([prompt], return_tensors="pt").to(device) text_emb = self.text_encoder(**tokens).last_hidden_state if guidance_scale > 1: null_emb = self.text_encoder( **self.tokenizer([""], return_tensors="pt").to(device) ).last_hidden_state text_emb = torch.cat([null_emb, text_emb], dim=0) # 2️⃣ Correctly set diffusion timesteps self.scheduler.set_timesteps(num_inference_steps) timesteps = self.scheduler.timesteps # 3️⃣ Prepare noisy latent initialization bs = text_emb.shape[0] latent = torch.randn( (bs, self.vae.latent_channels, self.vae.latent_length), generator=generator, device=device ) * self.scheduler.init_noise_sigma # 4️⃣ Reverse diffusion denoising for i, t in enumerate(timesteps): latent_in = latent if guidance_scale > 1: latent_in = torch.cat([latent, latent], dim=0) latent_in = self.scheduler.scale_model_input(latent_in, t) noise_pred = self.unet(latent_in, t, encoder_hidden_states=text_emb).sample if guidance_scale > 1: noise_uncond, noise_text = noise_pred.chunk(2) noise_pred = noise_uncond + guidance_scale * (noise_text - noise_uncond) latent = self.scheduler.step(noise_pred, t, latent).prev_sample # 5️⃣ Debug: Inspect intermediate signals if debug and i in {len(timesteps)//4, len(timesteps)//2, len(timesteps)-1}: mid = self.vae.decode(latent).reshape(bs, self.vae.input_length) print(f"Step {i}: mean={mid.mean():.4f}, std={mid.std():.4f}") # 6️⃣ Decode to final signal signal = self.vae.decode(latent).reshape(bs, self.vae.input_length) return signal.cpu().numpy() ``` * * * ### ✔ Key improvements - **Proper scheduler usage** : `.set_timesteps(...)` aligns inference with training, eliminating τ mismatches that often break denoising. - **Guidance batching** : Following the 2×-batch trick ensures classifier-free guidance is applied correctly. - **`.prev_sample` retrieval** : Matches underlying API expectations, avoiding subtle algorithmic mismatches. - **Intermediate debugging** : Printouts at 25%, 50%, and final steps help verify signal emergence over noise. * * * ### 📚 Reference resources - **Diffusers’ UNet1DModel** docs — details block types and expected signature ([huggingface.co](https://huggingface.co/docs/diffusers/main/en/api/models/unet "UNet1DModel - Hugging Face"), [discuss.huggingface.co](https://discuss.huggingface.co/t/1d-diffusers-not-behaving-as-expected-am-i-retarded/153021 "1D Diffusers not behaving as expected, am i retarded?"), [huggingface.co](https://huggingface.co/docs/diffusers/v0.14.0/en/api/models "Models - Hugging Face")) - **Forum issue** : users reported similar issues with trivial 1D data due to misunderstanding of timesteps or model behavior - **DiffWave** , **ArchiSound** — demonstrate use of stacked 1D UNets with dilated convs and spectral conditioning for audio diffusion ([arxiv.org](https://arxiv.org/abs/2009.09761 "DiffWave: A Versatile Diffusion Model for Audio Synthesis")) * * * ### ✅ What’s next? 1. Run with `debug=True`. Do intermediate mean/std values drift toward training-signal stats? 2. If not, examine: 3. If behavior remains noisy, consider integrating **dilated convs** or **spectral-domain losses** for better structure, following details in DiffWave and ArchiSound. Let me know how the intermediate signals evolve or if you’d like help measuring latent statistics or adjusting architecture!