---
title: "Stable Diffusion 3.5 | Ethical Stack"
url: https://ethicalstack.ai/products/stable-diffusion-3-5/
catalog_version: f6baff2119d0e9158520da7fbe4232011dac79f25bd08757436d7a1dbd593db2
---

# Stable Diffusion 3.5

Downloadable image-generation models that can run locally or through hosted services, under Stability’s version-specific license.

Canonical profile: https://ethicalstack.ai/products/stable-diffusion-3-5/

Scope: Stable Diffusion 3.5: both access. Self-hosting is free under Community License below $1M annual revenue; over that threshold requires Enterprise terms. Ratings describe the stated product, company or configuration in each finding.

Published overall: 5.2; 6/6 dimensions rated.

Round the equal-weight mean of available numerical ratings to one decimal; require at least four of six for an overall score. BDS status stays outside the mean; for eligible profiles, a boycott target caps the final score at 2/10 and a pressure target subtracts one point (minimum 1). Not listed and not checked do not adjust the score.

## Energy practices

Finding: 4/10

Assessment: https://ethicalstack.ai/products/stable-diffusion-3-5/#energy

Stability AI documents open deployment routes, model sizes, a distilled four-step variant, and local hardware requirements. No model-specific electricity, emissions, or water measurements were found.

Reason: 1 + 1 reporting + 0 electricity + 2 controls = 4/10. This grade covers documented practices and transparency; it does not measure the energy or emissions of a request.

Argument: Stability AI documents open deployment routes, model sizes, a distilled four-step variant, and local hardware requirements. No model-specific electricity, emissions, or water measurements were found. The score describes documented measurement transparency and available controls, not environmental performance.

Type: conditional; confidence: medium

Scope (dimension): Stable Diffusion 3.5 model family across local, cloud VM, and third-party hosted routes; no single route assumed.

Research reviewed: 2026-10-24; evidence date: 2026-10-24

Unknowns: Local users choose hardware and electricity, while hosted endpoints have separate operators; neither route has a footprint in the reviewed product sources. Model size, step count, and VRAM needs alone do not establish energy per image.

```json
{
  "method": "practices-transparency-v1",
  "scope": "Stable Diffusion 3.5 model family across local, cloud VM, and third-party hosted routes; no single route assumed.",
  "components": {
    "disclosure": {
      "score": 1,
      "reason": "Stability AI’s model card and self-hosting docs identify the model and deployment boundary, with no quantified environmental reporting for the product.",
      "sourceUrls": [
        "https://huggingface.co/stabilityai/stable-diffusion-3.5-large",
        "https://kb.stability.ai/knowledge-base/self-hosting-101-deploying-stable-diffusion-models"
      ]
    },
    "electricity": {
      "score": 0,
      "reason": "The model supports user-chosen local, cloud VM, and third-party hosted deployments. No electricity source is established for any particular deployment, so no provider/company sourcing is transferred.",
      "sourceUrls": [
        "https://kb.stability.ai/knowledge-base/self-hosting-101-deploying-stable-diffusion-models"
      ]
    },
    "demand": {
      "score": 2,
      "reason": "The SD 3.5 family includes model-size/variant selection, output-size ranges, and a distilled Large Turbo variant documented to generate in four steps. Self-hosting docs also expose device choice. These controls may bound compute, but there are no measured physical-resource reductions.",
      "sourceUrls": [
        "https://stability.ai/news/introducing-stable-diffusion-3-5",
        "https://huggingface.co/stabilityai/stable-diffusion-3.5-large",
        "https://kb.stability.ai/knowledge-base/self-hosting-101-deploying-stable-diffusion-models"
      ]
    }
  },
  "actualImpactMeasured": false,
  "meaning": "Documented environmental practices and transparency. Zero component points mean a reviewed evidence gap."
}
```

- [Introducing Stable Diffusion 3.5](https://stability.ai/news/introducing-stable-diffusion-3-5) (primary; checked not recorded)

- [Stable Diffusion 3.5 Large model card — Stability AI](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) (primary; checked 2026-10-24)

- [Self Hosting 101: Deploying Stable Diffusion Models](https://kb.stability.ai/knowledge-base/self-hosting-101-deploying-stable-diffusion-models) (primary; checked 2026-10-24)

## Training data

Finding: 3/10

Assessment: https://ethicalstack.ai/products/stable-diffusion-3-5/#training-rights

The model card says SD 3.5 used synthetic and filtered publicly available data. Stability separately describes public web data, third-party access and synthetic data, but publishes no release-specific rights ledger.

Reason: 1 + 1 source transparency + 1 permission + 0 creator controls = 3/10. This grades documented training-data safeguards, not a legal ruling.

Argument: The available disclosure provides broad provenance and general sourcing claims while leaving source datasets, licenses, and creator remedies unresolved. The low score measures the record that can be verified for SD 3.5, not a finding that its training was unlawful.

Type: argument; confidence: medium

Scope (dimension): Stable Diffusion 3.5 Large model card; score is for this named release, not other Stability models.

Research reviewed: 2026-10-24; evidence date: 2026-10-24

Unknowns: No named dataset inventory, license compatibility review, or model-specific creator exclusion/remedy is documented.

```json
{
  "method": "training-rights-evidence-v1",
  "scope": "Stable Diffusion 3.5 Large model card; score is for this named release, not other Stability models.",
  "components": {
    "provenance": {
      "score": 1,
      "reason": "The card identifies synthetic and filtered public data, while company reporting adds broad categories but no named inventory.",
      "sourceUrls": [
        "https://huggingface.co/stabilityai/stable-diffusion-3.5-large",
        "https://stability.ai/news-updates/stability-ais-annual-integrity-transparency-report"
      ]
    },
    "permissions": {
      "score": 1,
      "reason": "General sourcing statements exist, but the model card does not connect material sources to licenses or permissions.",
      "sourceUrls": [
        "https://huggingface.co/stabilityai/stable-diffusion-3.5-large",
        "https://stability.ai/news-updates/stability-ais-annual-integrity-transparency-report"
      ]
    },
    "creatorControls": {
      "score": 0,
      "reason": "No corpus-relevant rights-holder exclusion or remedy was found in the reviewed sources.",
      "sourceUrls": [
        "https://huggingface.co/stabilityai/stable-diffusion-3.5-large"
      ]
    }
  },
  "routeDependency": null,
  "modelReference": null,
  "meaning": "Documented training-data rights safeguards. Low points may reflect missing evidence; they do not establish infringement."
}
```

- [Stable Diffusion 3.5 Large model card — Stability AI](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) (primary; checked 2026-10-24)

- [Stability AI annual integrity transparency report](https://stability.ai/news-updates/stability-ais-annual-integrity-transparency-report) (primary; checked 2026-10-24)

## Your data & privacy

Finding: 8/10

Assessment: https://ethicalstack.ai/products/stable-diffusion-3-5/#privacy

When weights are downloaded and run fully offline, prompts and images need not be sent to Stability; hosted deployments have separate privacy boundaries.

Reason: This score applies only to a locally operated, offline configuration. Hardware logs, plugins, custom interfaces, and any cloud-hosted setup can change the data flow.

Argument: This score applies only to a locally operated, offline configuration. Hardware logs, plugins, custom interfaces, and any cloud-hosted setup can change the data flow.

Type: conditional; confidence: medium

Scope (dimension): Selected configuration

Research reviewed: not recorded; evidence date: not recorded

Unknowns: This score applies only to a locally operated, offline configuration. Hardware logs, plugins, custom interfaces, and any cloud-hosted setup can change the data flow.

- [Self Hosting 101: Deploying Stable Diffusion Models](https://kb.stability.ai/knowledge-base/self-hosting-101-deploying-stable-diffusion-models) (primary; checked 2026-10-24)

## Openness

Finding: 7/10

Assessment: https://ethicalstack.ai/products/stable-diffusion-3-5/#openness

SD 3.5 weights are downloadable and can be run locally, under Stability’s Community License with revenue and use restrictions.

Reason: Downloadable weights and self-hosting enable inspection and deployment control; the Community License is not an unrestricted OSI-style model license and includes conditions, so openness is not rated as fully permissive.

Argument: Downloadable weights and self-hosting enable inspection and deployment control; the Community License is not an unrestricted OSI-style model license and includes conditions, so openness is not rated as fully permissive.

Type: evidence; confidence: medium

Scope (dimension): Product

Research reviewed: not recorded; evidence date: not recorded

Unknowns: Downloadable weights and self-hosting enable inspection and deployment control; the Community License is not an unrestricted OSI-style model license and includes conditions, so openness is not rated as fully permissive.

- [Stability AI Core Models](https://stability.ai/core-models) (primary; checked 2026-10-24)

- [Stability AI License](https://stability.ai/license) (primary; checked 2026-10-24)

## Worker protections

Finding: 1/10

Assessment: https://ethicalstack.ai/products/stable-diffusion-3-5/#labour

Stability AI’s model card and license explain model access, deployment, and commercial licensing, but do not report worker pay, exposure protections, grievance routes, supplier coverage, or independent labor outcomes for training-data and moderation work.

Reason: 1 + 0 pay & conditions + 0 worker voice & remedy + 0 evidence & follow-through = 1/10. This grades documented worker safeguards, not a measured rate of mistreatment.

Argument: Stability AI’s model card and license explain model access, deployment, and commercial licensing, but do not report worker pay, exposure protections, grievance routes, supplier coverage, or independent labor outcomes for training-data and moderation work. Downloadable weights make downstream deployments distinct from the hosted service workforce.

Type: argument; confidence: low

Scope (dimension): Stability AI's workforce and disclosed contractors. Coverage of individual product teams may be incomplete.

Research reviewed: 2026-10-24; evidence date: not recorded

Unknowns: Worker pay, exposure and health protections, paid versus volunteer roles, vendor/subcontractor coverage, grievance routes, and independent outcome checks are not established by the reviewed company license and a stability model card checked; neither is a workforce report, and open-weight downstream users are outside the hosted labor scope.

```json
{
  "method": "worker-safeguards-evidence-v1",
  "entity": "stability",
  "company": "Stability AI",
  "scope": "Stability AI's workforce and disclosed contractors. Coverage of individual product teams may be incomplete.",
  "components": {
    "conditions": {
      "score": 0,
      "reason": "No opened source reports worker pay, hours, exposure protections, contractor coverage, or worker-level outcomes for Stability AI. The reviewed material is limited to Company license and a Stability model card checked; neither is a workforce report, and open-weight downstream users are outside the hosted labor scope.; its silence is an information gap, not evidence of poor conditions.",
      "sourceUrls": [
        "https://stability.ai/license",
        "https://huggingface.co/stabilityai/stable-diffusion-3.5-large"
      ]
    },
    "voice": {
      "score": 0,
      "reason": "The reviewed material for Stability AI does not establish worker representation, grievance access, bargaining voice, or documented remedies. Public code contributions, customer controls, or creator rights do not demonstrate worker voice across employees and suppliers.",
      "sourceUrls": [
        "https://stability.ai/license",
        "https://huggingface.co/stabilityai/stable-diffusion-3.5-large"
      ]
    },
    "assurance": {
      "score": 0,
      "reason": "No reviewed source provides an independent audit or corroborated worker-level outcomes for Stability AI’s relevant employees, contractors, and data-work suppliers. The available disclosures are company/project materials rather than independent labor verification.",
      "sourceUrls": [
        "https://stability.ai/license",
        "https://huggingface.co/stabilityai/stable-diffusion-3.5-large"
      ]
    }
  },
  "disclosureLimited": true,
  "meaning": "This grades documented worker safeguards, not a measured rate of mistreatment."
}
```

- [Stability AI License](https://stability.ai/license) (primary; checked 2026-10-24)

- [Stable Diffusion 3.5 Large model card — Stability AI](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) (primary; checked 2026-10-24)

## Military ties

Finding: 8/10

Assessment: https://ethicalstack.ai/products/stable-diffusion-3-5/#military-contracts

Stability AI’s AUP bans illegal or regulated weapons development and multiple harmful surveillance practices, including for community-licensed self-hosted weights, but it does not ban military or intelligence use generally.

Reason: 7 base + 1 safeguard adjustment = 8/10 (4 of 9 safeguard points; every 3 adds 1). No military relationship was established in the cited sources. This ranks above known defense ties without claiming a verified clean record.

Argument: The July 2025 AUP applies to Stability products, APIs, models and materials, explicitly including self-hosting under Non-Commercial Research, Community or Enterprise licenses and third-party hosting. It bans development/manufacture of illegal or regulated weapons, real-time public biometric identification for law enforcement, facial-recognition database expansion without consent and specified harmful profiling. It does not state a blanket military/intelligence ban; this does not necessarily govern independently redistributed permissive releases.

Type: argument; confidence: low

Scope (dimension): Stability AI's relationships and safeguards. A company relationship does not identify every product or release used.

Research reviewed: 2026-10-24; evidence date: not recorded

Unknowns: Unpublished contracts, reseller/subcontractor activity, customer-configured use and enforcement outcomes remain unknown. Hosted AUPs do not necessarily apply to independent local deployments or permissively licensed copies.

```json
{
  "method": "military-ties-priority-v2",
  "entity": "stability",
  "company": "Stability AI",
  "scope": "Stability AI's relationships and safeguards. A company relationship does not identify every product or release used.",
  "components": {
    "restrictions": {
      "score": 2,
      "reason": "Specific weapons and harmful-surveillance safeguards exceed lawful-use-only rules, but no general military/intelligence prohibition exists; published license scope applies to specified Stability-licensed use, not every downstream copy.",
      "sourceUrls": [
        "https://stability.ai/license",
        "https://huggingface.co/stabilityai/stable-diffusion-3.5-large",
        "https://stability.ai/2025-acceptable-use-policy"
      ]
    },
    "oversight": {
      "score": 1,
      "reason": "Stability may suspend or terminate access for AUP violations; no independent military-specific review or enforcement outcomes are disclosed.",
      "sourceUrls": [
        "https://stability.ai/license",
        "https://huggingface.co/stabilityai/stable-diffusion-3.5-large",
        "https://stability.ai/2025-acceptable-use-policy"
      ]
    },
    "transparency": {
      "score": 1,
      "reason": "A public AUP names some prohibited uses, but no defense customer or relationship is disclosed in reviewed material.",
      "sourceUrls": [
        "https://stability.ai/license",
        "https://huggingface.co/stabilityai/stable-diffusion-3.5-large",
        "https://stability.ai/2025-acceptable-use-policy"
      ]
    }
  },
  "relationship": "not-established",
  "relationshipReason": "The bounded official review found no named military/intelligence contract or operational use for this company/project. This evidence limit is not a clean bill of health; third-party model-provider relationships and customers’ independent deployments are separate.",
  "score": 8,
  "baseScore": 7,
  "safeguardPoints": 4,
  "safeguardAdjustment": 1,
  "rawScore": 8,
  "cap": null,
  "disclosureLimited": false,
  "meaning": "Known military relationships take priority over missing safeguards. The rating does not establish the probability of an undisclosed contract."
}
```

- [Stability AI License](https://stability.ai/license) (primary; checked 2026-10-24)

- [Stable Diffusion 3.5 Large model card — Stability AI](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) (primary; checked 2026-10-24)

- [Stability AI Acceptable Use Policy](https://stability.ai/2025-acceptable-use-policy) (acceptable-use-policy; checked 2026-10-24)

## BDS targets

Finding: Not listed

Assessment: https://ethicalstack.ai/products/stable-diffusion-3-5/#israel-palestine

Categorical check of the BNC campaign guide and No Tech campaign page; absence from these named-target pages is not BDS approval.

Reason: No named target for this company was found in the two checked BNC sources as of 2026-10-24. This is a narrow list check, not BDS approval, and it does not capture unlisted relationships or partners.

Argument: No named target for this company was found in the two checked BNC sources as of 2026-10-24. This is a narrow list check, not BDS approval, and it does not capture unlisted relationships or partners.

Type: conditional; confidence: medium

Scope (dimension): Company record

Research reviewed: not recorded; evidence date: not recorded

Unknowns: No named target for this company was found in the two checked BNC sources as of 2026-10-24. This is a narrow list check, not BDS approval, and it does not capture unlisted relationships or partners.

- [Guide to BDS Boycott & Pressure Corporate Priority Targeting](https://bdsmovement.net/Guide-to-BDS-Boycott) (primary; checked not recorded)

- [No Tech for Oppression, Apartheid or Genocide](https://bdsmovement.net/no-tech-oppression-apartheid-or-genocide) (primary; checked not recorded)

## Reading the evidence

- Assessments are provisional editorial judgments. Cite the profile and the underlying sources, and retain confidence, scope and unknowns.

- Unknown, not-applicable and model-dependent are different states. Missing evidence does not establish good or bad conduct.

- Company-level evidence does not establish use of a particular product or model. Account plan, model, host and route can change an assessment.

- Energy scores describe documented practices and transparency, not measured request emissions. Zero component points can mean an evidence gap.

- Published overall scores require at least four of six numerical dimensions. Preserve coverage and BDS adjustments; BDS itself is categorical.

- No named BDS target found is limited to the reviewed campaign sources and is not approval. Personalized priorities do not change the published overall score.

- Review and source-check dates describe the research, not this deployment. Fetch current primary sources when the decision requires current verification.

Methodology: https://ethicalstack.ai/methodology/