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marigold-depth-v1-0

This is a model card for the `marigold-depth-v1-0` model for monocular depth estimation from a single image. The model is fine-tuned from the `stable-diffusion-2` model as described in our papers: - CVPR'2024 paper titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation" - Journal extension titled "Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis"

stable-diffusion-safety-checker

- **Developed by:** More information needed - **Shared by [Optional]:** CompVis - **Model type:** Image Identification - **Language(s) (NLP):** More information needed - **License:** More information needed - **Parent Model:** CLIP - **Resources for more information:** - CLIP Paper - Stable Diffusion Model Card The CLIP model devlopers note in their model card : We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.

mo-di-diffusion

This is the fine-tuned Stable Diffusion 1.5 model trained on screenshots from a popular animation studio. Use the tokens **_modern disney style_** in your prompts for the effect. **Videogame Characters rendered with the model:** **Animal Characters rendered with the model:** **Cars and Landscapes rendered with the model:** **modern disney lara croft** _Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 3940025417, Size: 512x768_ **modern disney (baby lion) Negative prompt: person human** _Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 1355059992, Size: 512x512_

control_v11p_sd15_normalbae

**Controlnet v1.1** is the successor model of Controlnet v1.0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} }

controlnet-sd21

Here's the first version of controlnet for stablediffusion 2.1 Trained on a subset of laion/laion-art You can use it with annotator depth/le_res but it works better with ZoeDepth Annotator. My PR is not accepted yet but you can use my fork. My fork: https://github.com/thibaudart/sd-webui-controlnet The PR: https://github.com/Mikubill/sd-webui-controlnet/pull/655#issuecomment-1481724024 The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.

majicmixrealistic

Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs Model link: View model payload = json.dumps({ "key": "", "model_id": "majicmixrealistic", "prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None })

chilloutmix

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs payload = json.dumps({ "key": "", "model_id": "chilloutmix", "prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None })

animagine-xl-3.0

**Animagine XL 3.0** is the latest version of the sophisticated open-source anime text-to-image model, building upon the capabilities of its predecessor, Animagine XL 2.0. Developed based on Stable Diffusion XL, this iteration boasts superior image generation with notable improvements in hand anatomy, efficient tag ordering, and enhanced knowledge about anime concepts. Unlike the previous iteration, we focused on training the model to learn concepts rather than just aesthetics. - **Developed by**: Cagliostro Research Lab - **Model type**: Diffusion-based text-to-image generative model - **Model Description**: Animagine XL 3.0 is engineered to generate high-quality anime images from textual prompts. It features enhanced hand anatomy, better concept understanding, and prompt interpretation, making it the most advanced model in its series. - **License**: CreativeML Open RAIL++-M License - **Finetuned from model**: Animagine XL 2.0 Animagine XL 3.0 is accessible through user-friendly platforms such as Gradio and Google Colab:

stable-diffusion-2-depth

control_v11p_sd15_canny

**Controlnet v1.1** is the successor model of Controlnet v1.0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. For more details, please also have a look at the 🧨 Diffusers docs.