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Quality Embeddings for Anime models
I made 2 basic quality embeddings as an alternative to the quality tags that are normally used with my checkpoints. They work of course also with any other IL/NAI-based anime checkpoint.
Product camera tour
Generate one silent product camera-tour video by joining three Kling clips between four Seedream-generated views. For a first run, upload a product photo in Source image and leave that stage enabled. Review the four required view prompts, then run with the default sizes, transition modes and encoding settings. The prompts request an opening close-up, a low wide view, a side view and a rear view against a white studio background. The closing prompt includes car-specific details; replace those for another product. Four images are generated internally, but the selected result is the combined video. For text-only generation, disable the Source image stage and explicitly describe the same subject and setting in all four prompts. The views are generated independently from those prompts, so subject consistency may decrease. Even with a reference, consistent appearance and camera-only movement are generation goals rather than guarantees. Edit each view's framing, camera position and setting to change the shot sequence. View seeds are independent; -1 requests randomization. Gemini compares each adjacent image pair and automatically supplies positive and negative motion prompts to Kling. Direct motion-prompt overrides are not exposed. Each transition has its own mode string in mode / duration / model_name format, guidance value from 0 to 1, and aspect ratio. Defaults use pro mode / 5s duration / kling-v2-5-turbo and 16:9 for all three clips. Use configurations supported by the source node; this draft does not enumerate additional mode strings. Planning seeds offer best-effort repeatability of Gemini responses. Keep all four generated images at matching dimensions because adjacent images are batched for planning. Custom width and height are in pixels and apply only when that view's size preset is Custom. When changing the video shape, set all three transition aspect ratios consistently and keep their resulting frame dimensions compatible. Mode changes must also preserve compatible clip dimensions and frame rates. The final assembly uses the first clip's frame rate for all frames, so differing clip frame rates can alter playback timing. Each view remains limited to one generated image to provide an unambiguous start and end frame. The clips are joined in opening-to-wide, wide-to-side, then side-to-rear order. Audio is not carried into the result. File type, codec and CRF control final encoding; auto and CRF -1 retain transport defaults. Image watermark toggles affect the generated keyframes used downstream. Automatic planning relies on Gemini returning a single line with the expected --no separator; a differently formatted response can prevent usable prompt extraction.
Four-reference portrait with Flux.2 Pro
Create one prompted group portrait from four character references with Flux.2 Pro. For a first run, upload one character image into each of the four required reference inputs, keep the supplied prompt and default settings, then run the tool. The prompt requests a softly lit editorial portrait with all four characters looking up toward an elevated camera. References reach the generator in the displayed order. Describe the composition, setting, lighting and style in the prompt, including which details from each reference should carry into the result. Rewrite the prompt to request other scenes or compositions using the same four references. Character fidelity and placement are guided by the references and prompt rather than guaranteed. Uploaded alpha masks are not used by this workflow. Width and height set output dimensions in pixels, each from 256 to 2048 in increments of 32. The default is 1920 × 1280; adjust both to choose the frame's aspect ratio. Prompt expansion is enabled by default and allows the provider to modify the prompt for more creative generation. Disable it to avoid automatic prompt expansion. Seed -1 selects a fresh random seed for each run. Set a fixed nonnegative seed to hold the noise seed constant while comparing changes to the prompt or other settings.
Love Potion
Do you have a problem with shy subjects in your images? Try this love potion to really break the ice.
Krea 2 Turbo image generator
Create an image or batch of images from a text description using Krea 2 Turbo. No uploaded image is required. For a first run, replace the example image prompt, choose an aspect ratio, and keep the defaults: 1 target megapixel, one image, prompt refinement enabled, and Darkbrush disabled. Run the Pipe to receive the generated image batch in Sweet Tea. Prompt refinement uses the local text model with fixed prompt-expansion instructions. Turn it off to use your wording directly. Refinement can change the interpretation of a detailed prompt. Its token limit, sampling settings, seed, optional thinking mode, and model template are available under Refinement tuning. Thinking applies only if the model supports it. Sampling-specific controls apply when refinement sampling is on. Image sampling and prompt refinement have separate seeds. Use -1 for a new random seed each run. To repeat a setup, fix the image seed and either disable refinement or also fix its seed and settings. Enable Darkbrush to apply the fixed style LoRA and append the Style phrase to the direct or refined prompt. LoRA strength changes the model adjustment; the phrase separately changes text conditioning. Setting strength to zero still leaves the phrase appended. Disabling Darkbrush selects the base model and omits the phrase. This interface uses the fixed Darkbrush LoRA and does not select other style LoRAs. Aspect ratio and target megapixels jointly determine pixel dimensions. At the square default, 1 target megapixel is approximately 1024 × 1024. Increasing resolution or image count increases resource use. The selected output returns the whole image batch through Sweet Tea's preview transport; it does not permanently save images through ComfyUI SaveImage. Image sampling starts from an empty latent with 8 steps, CFG 1, Euler sampling, the simple scheduler, and denoise 1 by default. Advanced Image sampling controls change this process. Lower denoise values do not preserve an uploaded image because this workflow has no image input. Negative conditioning is zeroed, so there is no independent negative prompt.
Edit character expressions
Edit a character image with one of 12 preset expressions or a custom expression. Upload an image, choose preset 1–12 or select 13 and enter a custom instruction. Batch mode generates all 12 preset expressions. Set output size and expression strength; advanced controls include editable prompt text and sampling options.
Qwen-Image: Text to image
Generate images from text with Qwen-Image, using standard sampling or an optional Lightning LoRA mode. No source image is required. For a first run, replace the example positive prompt with your subject, composition, lighting and style. Include desired written text in the prompt. Leave the negative prompt empty unless you want to discourage particular elements. Keep 1328 × 1328 pixels, image count 1 and Lightning off. Standard mode defaults to 20 steps and CFG 4. Lightning mode selects Qwen-Image-Lightning-8steps-V1.0.safetensors and its separate defaults of 8 steps and CFG 1. The toggle switches the model, steps and guidance together. Each mode has independently editable steps and CFG; only the active mode's settings affect the result. Lightning strength applies only in Lightning mode. Negative-prompt influence depends on CFG; Lightning's default CFG is 1. Width and height are pixels in multiples of 16. Set them together to choose the aspect ratio; 1664 × 928 is landscape and 928 × 1664 is portrait. Increasing dimensions or image count increases memory use. Seed -1 selects a fresh random seed each run; use a fixed seed when comparing settings. Sampler, scheduler, denoise and sampling shift are shared by both modes. The supplied defaults are Euler, simple, full denoising at 1 and sampling shift approximately 3.1. This graph starts from an empty latent and does not edit uploaded images. The fixed model files must be available: qwen_image_fp8_e4m3fn.safetensors in diffusion_models, qwen_2.5_vl_7b_fp8_scaled.safetensors in text_encoders and qwen_image_vae.safetensors in vae. Their source is https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files. Lightning additionally uses Qwen-Image-Lightning-8steps-V1.0.safetensors in loras, supplied at https://huggingface.co/lightx2v/Qwen-Image-Lightning/resolve/main/Qwen-Image-Lightning-8steps-V1.0.safetensors. Dependency evidence currently marks these files as missing; ensure they are available before running the corresponding setup.
HiDream I1 Full text to image
Generate images from text using the fixed HiDream I1 Full FP8 model. No uploaded image is required. Describe the subject, composition, lighting and style in the positive prompt; use the negative prompt for unwanted attributes. For a first run, replace the example positive prompt or run it as supplied. Keep 1024 × 1024 pixels, image count 1 and the supplied advanced settings: 50 steps, CFG 5, uni_pc sampler, simple scheduler, sampling shift 3 and denoise 1. Run the Pipe to receive the generated image batch. Width and height must be multiples of 16; set them together to choose the aspect ratio. Image count controls how many images are generated per run. Increasing dimensions or image count increases resource use. Seed -1 randomizes each run; use a fixed seed to reuse the initial noise when comparing settings. Advanced sampling exposes steps, CFG, sampler, scheduler, denoise and sampling shift. CFG controls prompt guidance; at CFG 1 the negative prompt has no effect. Sampler and scheduler change the sampling process, while sampling shift adjusts the model's noise schedule. Denoise defaults to 1; lowering it reduces the effective noise schedule. Because generation starts from an empty latent, it does not preserve an existing image. The model and text encoders are fixed. Dev and Fast models are not selectable modes, and this Pipe does not offer image-to-image input.
Coloring Book Style
Trained on a variety of coloring book images, it produces clear line drawings with plenty of white space for your crayons. Adding the prompts white background, b&w and the negatives: color, gradient, grey seem to...
Pure Eros Face
This embedding will generate good-looking girl faces , close concept of kpop idols or Instagram girls . For SD1.5 based models.
