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stable-diffusion-v1-5

stable-diffusion-2-1-base

epiCRealism

**emilianJR/epiCRealism** is the HuggingFace diffuser that you can use with **diffusers.StableDiffusionPipeline()**. Examples | Examples | Examples ---- | ---- | ---- ,%20raw%20photo,%20((monochrome)),%20((grayscale)),%20black%20and%20white%20photo.jpeg) | | ,%20lotr,%20fantasy,%20elf,%20female,%20full%20body,%20looking%20at%20viewer,%20portrait,%20phot.jpeg) | ,%20soft%20lighting,%20h.jpeg) | ------- This model can be used just like any other Stable Diffusion model. For more information, please have a look at the Stable Diffusion.

marigold-iid-appearance-v1-1

This is a model card for the `marigold-iid-appearance-v1-1` model for single-image Intrinsic Image Decomposition (IID). 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" This model type (`appearance`) is trained to perform InteriorVerse decomposition into **Albedo** and two **BRDF material** properties: **roughness** and **metallicity**. Both the input image and the output albedo are in the sRGB color space. For an alternative model type (`lighting`) that performs decomposition into Albedo, Diffuse shading, and Non-diffuse residual, click here.

stable-diffusion-3.5-large-gguf

As this is a quantized model not a finetune, all the same restrictions/original license terms still apply. Place model files in `ComfyUI/models/unet` - see the GitHub readme for further install instructions.

mistoonanime

payload = json.dumps({ "key": "your_api_key", "model_id": "mistoonanime", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "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 })

sdols-2.0

stable-diffusion-xl-base-1.0-GGUF

stable-video-diffusion-img2vid-xt-1-1

Stable Video Diffusion (SVD) 1.1 Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it. (SVD 1.1) Image-to-Video is a latent diffusion model trained to generate short video clips from an image conditioning. This model was trained to generate 25 frames at resolution 1024x576 given a context frame of the same size, finetuned from [SVD Image-to-Video [25 frames]](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt).

distilgpt2-stable-diffusion-v2

Fast Anime PromptGen generates descriptive safebooru and danbooru tags for anime text-to-image models. This model was trained on 2,470,000 descriptive stable diffusion prompts on the FredZhang7/distilgpt2-stable-diffusion checkpoint for another 4,270,000 steps. Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM.