
dreamlook.ai
dreamlook.ai is a hosted DreamBooth service that finetunes Stable Diffusion 1.5 and SDXL in minutes, then generates images at scale. It is API-first, and the trained checkpoints stay downloadable for use anywhere, from AUTOMATIC1111 to ComfyUI.
What is dreamlook.ai?
dreamlook.ai is a hosted service for finetuning Stable Diffusion models and generating images from them at scale. Its core job is DreamBooth training: you upload a small set of images, pick a base model and write an instance prompt such as photo of ukj person, ukj style or photo of ukj object, and the service returns a model that reproduces that subject, style or object. Stable Diffusion 1.5 and SDXL are both supported, and the training is a full model finetune: the platform trains the text encoder together with the UNet rather than fitting an adapter on top. No regularisation images are used to date, and the service claims its method gives results equivalent to the reference diffusers implementation.
Speed is the stated angle. The pricing grid advertises up to 10.0 steps per second, 1,500 SDXL steps in about ten minutes, training 2.5x faster, image generation at 3.0 to 4.0 seconds per image, and capacity for thousands of runs a day. The other half of the product is generation itself, from your own checkpoints or from 30+ ready-made base models, among them Stable Diffusion XL 1.0, Realistic Vision V6.0, Juggernaut XL, epiCRealism XL v5 Ultimate, Realism Engine SDXL v3.0, Realistic Stock Photo, ICBINP, OpenDalle XL v1.1, ThinkDiffusionXL v1.0 and RunDiffusion XL beta. Native 1024x1024 output, high-res fix, Offset Noise for very dark or very bright images, image captions and ControlNet (OpenPose for body pose, QRCode Monster for patterns) sit alongside the training parameters themselves: number of steps, learning rate, resolution, instance prompt, cropping method and a job completion callback URL.
Access comes in two forms, a web app and a REST API, with the second clearly favoured: We are an API-first product. We build with developers in mind. The pitch to engineering teams is infrastructure avoidance, with no instances to spin up, no GPU quota to hunt for and no CUDA errors to fight. Outputs are portable: full checkpoints in safetensors or in the CompVis format that AUTOMATIC1111 expects, diffusers tarballs, or LoRA files extracted from a full model, all downloadable and usable off-platform. The scope stops there. This is a training and inference back end for Stable Diffusion, not an image editor and not a creative suite. One published collaboration trained a model on thousands of posters for the Alliance Graphique Internationale exhibition Perspectives on AI, and released the result as a download.
What it does
- Finetune a custom Stable Diffusion model on a person, a pet, an object or a style in minutes
- Generate images in batches from your own trained models or from 30+ base models
- Extract a lightweight LoRA file from a full trained checkpoint
- Drive training and generation from your own application through the REST API
- Download the trained weights and run them outside the platform
- Control pose and composition with ControlNet, using OpenPose or QRCode Monster
When to use dreamlook.ai / When not to
A quick filter to help you decide if dreamlook.ai is the right fit.
When to use dreamlook.ai
- Developers embedding image finetuning or generation into a product through an API
- Teams that need to run large volumes of training jobs, up to thousands a day, without managing GPUs
- Builders of avatar, pet avatar and virtual photoshoot applications
- Designers and art directors training a model on an in-house visual style
- AUTOMATIC1111 and ComfyUI users who want checkpoints trained fast in the cloud, then brought back locally
When not to use dreamlook.ai
- Organisations that require an explicit GDPR framework, a data processing agreement or an identified legal entity
- Users who need trained models kept for a long time without a subscription, since the default is 48 hours
- Adult content, nudity or gore, which the terms forbid and the service may block automatically
- Teams that want the newest model generations, beyond Stable Diffusion 1.5 and SDXL
- People looking for a consumer photo editor with no notion of finetuning
How to use dreamlook.ai
A typical end-to-end flow, from setup to results.
- Sign up with a Google account: 50 tokens are credited on signup and no credit card is required
- Pick a base model, from Stable Diffusion 1.5 checkpoints to SDXL variants, among the 30+ available
- Select the image styles you want the trained model to produce
- Upload your training images as JPG, PNG, HEIC or WEBP, 15 MB maximum per image and at least 32x32 pixels
- Optionally add a captions file whose file names match the uploaded images exactly
- Set the instance prompt and the training parameters, or keep the defaults: 1,500 steps, learning rate 1e-5, 1024x1024, guidance 7.5, dpmpp-2m scheduler
- Launch the run and follow it on the jobs page, with consumption tracked on the usage page
- For programmatic use, generate a key on the API keys page, then POST to the API with the headers content-type: application/json and authorization: Bearer $DREAMLOOK_API_KEY
- Register a callback URL to be notified when a job finishes, and test it from the interface
- Download the resulting checkpoint and drop it into models/Stable-diffusion for AUTOMATIC1111, using the docs guides on SD1.5 defaults, result quality and SDXL to refine the next run
Pros & Cons
Pros
- Fast and quantified training: 1,500 SDXL steps in about ten minutes, up to 10 steps per second, and a claimed 2.5x speed-up
- Full model finetuning rather than LoRA only, with LoRA extraction available on top
- No lock-in claimed: the weights are downloadable and free to reuse, and the documentation attributes model ownership to the user
- API-first, documented, with a live OpenAPI specification published at api.dreamlook.ai/docs and /redoc
- Readable unit pricing: from USD 0.75 per SD1.5 run, USD 2.25 per SDXL run, USD 0.01 per SD1.5 image and USD 0.02 per SDXL image
- Genuine free entry: 50 tokens on signup and 400 free images announced, no credit card, and subscriber tokens roll over to the next month
- Unusual transparency for this size: fifteen subprocessors named on a dedicated page, a short 48-hour default retention presented as a privacy choice, and documented interoperability with AUTOMATIC1111, DiffusionBee, ComfyUI, RunDiffusion and ThinkDiffusion
Cons
- No legal entity published: no legal notice, no registered company name, no postal address anywhere on the site
- The GDPR is never mentioned, no data processing agreement is published, and the privacy policy is very short, undated and silent on retention beyond the 48-hour rule
- Uploaded images and trained models are deleted after 48 hours by default, and past that point the operator states it cannot help recover them
- No documented way to exclude your data from model training, and no stated position on whether it is used that way
- Documentation and API reference both display Last updated 2 years ago, even though the service itself is still answering
- Tokens are final and non-refundable, and subscriptions carry no refund unless stated otherwise
- Scope stays on Stable Diffusion 1.5 and SDXL, older model generations than the latest, with no contact page or form, no declared hosting country, and a site rendered entirely in JavaScript, so nothing is readable without executing the bundle
Pricing & Plans
A free tier is available: 50 tokens are credited on signup and the first 400 images are announced as free, with no credit card required. Beyond that, the cheapest paid entry point is a one-off pack of 150 tokens at USD 15.00, and the cheapest subscription is the Enthusiast plan at USD 19.00 per month. Consumption is priced per unit, from USD 0.75 per SD1.5 training run and USD 2.25 per SDXL run, and from USD 0.01 per SD1.5 image and USD 0.02 per SDXL image. Larger packs lower the unit price, down to USD 0.075 per token for the 10,000-token pack at USD 750.00. Prices are also displayed in CNY. Token purchases are final and non-refundable.
- 50 tokens credited on signup
- low queue priority
- one concurrent job
- no ControlNet
- 100 tokens per month
- ControlNet enabled
- normal queue priority
- models stored 30 days
- up to 500 training images
- 1
- 000 tokens per month
- high queue priority
- models stored 90 days
- up to 5
- 000 training images
- 10
- 000 tokens per month
- dedicated capacity
- up to 50
- 000 training images
- sales contact required
- 150 tokens for USD 15
- 300 for USD 27
- 700 for USD 60
- 1
- 200 for USD 100
- 5
- 000 for USD 400 and 10
- 000 for USD 750
- Tiers also differ on maximum training steps (50
- 000
- 500
- 000 then 5 million)
- number of full checkpoints and LoRA files produced per job
- and job concurrency
- subscriptions are billed monthly
- can be cancelled at any time
Data, GDPR & hosting
A consolidated view of how dreamlook.ai handles your data.
GDPR overview
The GDPR is never mentioned on this site: not in the privacy policy, not in the terms, nowhere else. That is silence rather than an explicit refusal. Nothing suggests the operator places itself outside the regulation; it simply never addresses it. There is no Article 27 representative in the EU, no data protection officer, no published or offered data processing agreement, no legal basis for processing, no general retention period and no mention of international transfers. Two elements do exist. The privacy policy grants a right to access, update or delete personal information, exercisable at info@dreamlook.ai. And a dedicated page names fifteen subprocessors with the purpose of each, a level of disclosure rarely seen at this scale. The GDPR compliance flag is set to N because the framework is never described, not because the service declares itself exempt from it.
Who owns the data?
Ownership leans towards the user, but is stated informally rather than in a dedicated clause. For training images, the terms put the burden on the uploader: you warrant that you own the rights to any image you upload, or that you have obtained every permission needed to upload and use it. For trained models, the documentation is explicit that once finetuning is done you can download them and use them for whatever you want, and the models belong to you. For generated images, the collected terms contain no assignment and no licence-back clause, so nothing indicates the operator claims rights over your outputs. It does reserve the right to remove any image that breaches its content policy.
Reuse rights
The privacy policy lists three categories of collected data: site usage, meaning browsing history and pages viewed; device data, meaning IP address, device type and browser; and the images you upload. The stated purposes are delivering the services, improving the site and developing new features, contacting users, and complying with legal obligations. Data may be shared with third-party providers such as hosting and analytics services, contractually limited to providing that service, and with authorities where the law requires it. A cookie banner states that cookies are used so the website runs effectively. On the user's side, reuse needs no permission: the documentation states that trained models can be downloaded and used for whatever you want, and no clause in the terms restricts what you do with the generated images, commercial use included. Two limits apply: your rights over an output can never exceed the rights you hold over the training images, which the terms make your sole responsibility, and the content policy applies throughout. What is left unsaid matters as much: the site never states whether customer data feeds the provider's own model training, and no opt-out from such use is documented anywhere.
Data retention & training
Hosting summary
No hosting country and no region is declared anywhere on the site. What is public is the list of infrastructure subprocessors: Google LLC, for Google Cloud Storage, Firebase and Google Analytics, and Amazon Web Services. Cloudflare is named for CDN, security and performance. None of these entries carries a location, so the storage jurisdiction remains unknown, and the headquarters of a provider says nothing about the region a given bucket sits in. The apex domain resolves to 199.36.158.100, an anycast node operated by Fastly, which is a content delivery point of presence rather than a place where data is stored: no country should be inferred from it. Uploaded images and trained models are deleted after 48 hours by default, which limits exposure but answers nothing about jurisdiction. There is no data processing agreement, no mention of international transfers and no standard contractual clauses. Anyone with a data residency requirement will find nothing here to rely on.
Things to keep in mind
Risks and trade-offs to weigh before adopting dreamlook.ai.
- No legal entity is published: the terms and the privacy policy name only dreamlook.ai, so there is no identified counterparty to turn to in case of a dispute
- The 48-hour automatic deletion is irreversible: a model you do not download in time is lost, and the operator states it cannot help you recover it
- Tokens are non-refundable in every circumstance, including account suspension, and subscriptions renew automatically with cancellation left to the user
- The GDPR is never mentioned anywhere on the site, which makes it hard to judge how personal data is handled before uploading photographs of real people
- Documentation and API reference both display Last updated 2 years ago: check how current the parameters and endpoints are before building on them
- No opt-out from having your data used for model training is documented, and the operator never states its position on the question
- Training a model on a face or on a copyrighted style transfers no rights: what you generate remains subject to the rights over the training images, and the terms place that responsibility on you alone. Generating photorealistic likenesses at a cent per image makes it easy to forget who is depicted and who consented
Setup & Integrations
Technical difficulty
Two levels. Through the web app, setup is easy: sign in with a Google account, follow three steps, base model, image styles then training images, and accept the supplied defaults. The site states that you do not have to be a developer to use it. Through the API, it is standard developer work: a bearer key, a JSON call and a callback endpoint. Getting good results is the harder part, since instance prompt, step count, learning rate, captions and cropping all matter, and the documentation itself concedes that training a Stable Diffusion model is an art.
Deployment
Integrations
Supported languages
Behind dreamlook.ai
Social
Resources
All the official URLs gathered for verification and reference.
Frequently asked questions
Is there an API?
Can I download the model I trained?
Is SDXL supported?
Can I train or extract a LoRA?
Can I train on an object or a style, not just a person?
Do the models work with AUTOMATIC1111?
What happens to the images I upload?
Does the speed cost quality?
What does it cost to start, and are refunds possible?
Which payment methods are accepted?
Should you pick dreamlook.ai?
dreamlook.ai is a narrow and technically credible product: hosted DreamBooth finetuning for Stable Diffusion 1.5 and SDXL, paired with image generation, and built to be driven by an API. What the site shows backs the pitch, with a fully costed pricing grid down to the price of a single run or a single image, nineteen pages of public documentation, a live OpenAPI specification and a named list of fifteen subprocessors that few services of this size publish. The strong points are consistent with each other: speed, full model finetuning rather than adapters alone, and portability, since the weights can be downloaded and used off-platform, with the documentation stating that the models belong to the user.
The reservations are just as concrete. No legal entity is published anywhere: no legal notice, no company name beyond the domain, no postal address. The GDPR is never mentioned, not even in the privacy policy, and no data processing agreement is offered. Data is deleted after 48 hours by default, which is a privacy virtue and an operational risk at once, since a model not downloaded in time is gone. Documentation and API reference both carry Last updated 2 years ago, while the service keeps running.
The result is good value for a technical audience that knows what an instance prompt and a checkpoint are, wants to train fast and take the weights away, and is comfortable with self-service support through email, Discord and a chat widget. It fits far less well a corporate purchase where compliance, contractual guarantees and a named counterparty come first. Anyone planning more than an experiment should check the current pricing, documentation and retention rules before committing.
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