---
license: openrail
tags:
  - stable-diffusion
  - sd-1.5
  - diffusers
  - ferrotorch
  - real-artifact
---

# `ferrotorch/sd-v1-5-generation-trajectory`

End-to-end SD-1.5 text-to-image generation trajectory pinned for the
ferrotorch real-artifact parity harness (Phase F, #1163).

## Provenance

* Upstream model: `runwayml/stable-diffusion-v1-5` (`StableDiffusionPipeline`
  composed from `text_encoder/`, `unet/`, `vae/` subfolders, plus
  `DDIMScheduler.from_config(pipe.scheduler.config)`).
* Conversion script:
  [`scripts/pin_pretrained_sd_pipeline.py`](https://github.com/dollspace-gay/ferrotorch/blob/main/scripts/pin_pretrained_sd_pipeline.py).
* Ferrotorch issue: <https://github.com/dollspace-gay/ferrotorch/issues/1163>.
* SHA-256 of `bundle.tar` (pinned in
  `ferrotorch-hub/src/registry.rs`): `5fa7bd809e3aaa120a79c744801de44342a2e22ab82137cd5fe0d43302924c6e`.

## Files

* `cond_embeds.bin` — `[1, 77, 768]` f32 CLIP text embedding of
  `PROMPT = "a photograph of an astronaut riding a horse"`.
* `uncond_embeds.bin` — `[1, 77, 768]` f32 CLIP text embedding of
  the empty negative prompt.
* `init_latent.bin` — `[1, 4, 64, 64]` f32 Gaussian noise drawn
  via `torch.Generator(device='cpu').manual_seed(42).randn`.
  The rust pipeline reads this file directly because the rust
  PRNG (`rand::StdRng`) does not match `torch.Generator`.
* `final_image.bin` — `[1, 3, 512, 512]` f32 decoded image in
  `[-1, 1]` from `pipe.vae.decode(latent / 0.18215).sample`.
* `step_K_noise_pred_uncond.bin` — `[1, 4, 64, 64]` f32 UNet
  forward pass with the unconditional embedding, for `K=0..3`.
* `step_K_noise_pred_cond.bin` — same but with the conditional
  embedding.
* `step_K_guided_noise.bin` — `noise_uncond + 7.5 *
  (noise_cond - noise_uncond)`.
* `step_K_latent_after.bin` — latent after the scheduler step,
  i.e. the input to step `K+1` (or the VAE for the final step).
* `meta.json` — prompt, negative prompt, seed, step count,
  guidance scale, and the exact timestep list.
* `bundle.tar` — single-file convenience archive carrying every
  fixture above (so the registry pin has one SHA-256 to track).

## Settings

* `prompt              = "a photograph of an astronaut riding a horse"`
* `negative_prompt     = ""`
* `seed                = 42`
* `num_inference_steps = 4`
* `guidance_scale      = 7.5`
* `scheduler           = DDIMScheduler` (scaled_linear,
  beta_start=0.00085, beta_end=0.012,
  clip_sample=False, set_alpha_to_one=False,
  prediction_type="epsilon", timestep_spacing="leading",
  steps_offset=1)
* `timesteps           = [751, 501, 251, 1]`

## How the rust side consumes this

The rust dump example
[`ferrotorch-diffusion/examples/sd_pipeline_dump.rs`](https://github.com/dollspace-gay/ferrotorch/blob/main/ferrotorch-diffusion/examples/sd_pipeline_dump.rs)
loads the three sub-models from `ferrotorch/sd-v1-5-{clip-text-encoder,unet,vae-decoder}`,
loads `init_latent.bin` and the two text embeddings from this
mirror (so the rust↔torch PRNG mismatch and tokenizer absence
are routed around), runs the same 4-step CFG loop with a rust
DDIMScheduler whose constants mirror diffusers byte-for-byte,
and dumps the equivalent intermediates. The python harness
[`scripts/verify_sd_pipeline_inference.py`](https://github.com/dollspace-gay/ferrotorch/blob/main/scripts/verify_sd_pipeline_inference.py)
then compares each rust intermediate against the corresponding
file shipped here, per-stage tolerances.

## Upstream license

Stable Diffusion v1.5 is distributed under the CreativeML Open RAIL-M license. This pipeline-trajectory bundle inherits that license — see https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/LICENSE for the full terms.