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Anima anime image generator
Generate anime and stylized illustrations from text with the fixed Anima Preview 3 model. No uploaded image is required; the output is an image or a batch of images returned to Sweet Tea. For a first run, keep the supplied cyberpunk eye prompt or replace it with your subject, composition, lighting, and style. Use the negative prompt to describe unwanted traits. Keep the initial settings: 1024 × 1024 pixels, one image, 30 steps, CFG 4, sampler er_sde, scheduler simple, and denoise strength 1. Run the Pipe to receive the generated images; the output stage does not automatically save image files through ComfyUI SaveImage. Width and height are in pixels, in multiples of 8, and together determine the aspect ratio. Images per run sets the batch size. Larger dimensions and batches require more memory. Open Sampling for additional controls. Seed -1 randomizes each run; use a fixed seed when comparing changes while holding other settings constant. Steps set the number of denoising steps. CFG controls prompt guidance; excessive guidance can harm image quality. Sampler selects the sampling algorithm, and Scheduler selects its noise schedule. Denoise strength defaults to 1 for full generation; reducing it changes sampling strength and does not preserve an original image. The workflow uses anima-preview3-base.safetensors, qwen_3_06b_base.safetensors, and qwen_image_vae.safetensors, with Sweet Tea's image output node. These dependencies must be available in the execution environment. Model selection is fixed in this Pipe. This is text-only generation from an empty latent. References to preserving an original composition in the example prompt are text instructions, not image guidance or a guarantee of preservation. There are no reference-image, image-editing, or upscaling stages.
Video green screen
Replace a video's background with a solid green or blue chroma-key backdrop using Bria. Upload the video you want to process, leave Background shade on broadcast_green, and run. Download the resulting video and use your editor's chroma-key tools to remove the colored backdrop for compositing. Background shade offers broadcast_green (#00B140), chroma_green (#00FF00), and blue_screen (#0000FF). Choose a color that contrasts with the foreground. The output has a solid colored background; it is not a transparent video. Seed controls whether Bria processing reruns. Leave -1 to randomize on each run. A fixed seed does not make the provider's results reproducible. Under advanced Video output settings, leave File type and Codec on auto for a first run. Explicit container choices are MP4, MKV, and WebM; codec choices are H.264 and AV1. The supplied evidence does not establish support for every explicit pairing. Audio preservation is also not established by the supplied graph. Processing sends the video to Bria and uses a provider-priced API operation.
Lotus Depth
Upload a source image to generate a Lotus Depth map. Advanced controls let you choose the sampler and schedule, set the step count and denoise amount, and adjust initial sigma. Lotus conditioning is fixed; the default uses one inference step.
Veo video generation
Create one short video with Google Veo from a scene description, optionally guided by an uploaded image and with generated audio. For a first run, replace the example video prompt with your subject, action, setting, camera movement and visual style. Keep the default eight-second, landscape, 720p settings and leave Reference image bypassed for text-only generation. Audio generation is off by default. For image-guided generation, enable Reference image and upload an image. The image guides generation through Veo's optional image input; the prompt and output settings remain available in both modes. Choose 16:9 landscape or 9:16 portrait output and a duration of 4, 6 or 8 seconds. Use the negative prompt for unwanted content or visual traits. Enable Generate audio when wanted and describe intended sounds or speech in the video prompt. People generation lets you allow or block generation of people. Choose a Veo model and resolution. The available resolutions are 720p, 1080p and 4K; 4K is unavailable with Veo 3.1 Lite and Veo 3.0 models. Seed -1 selects a new random seed on each run. A fixed seed is a generation input and does not guarantee identical provider results. Video output contains optional file type and codec choices. Keep both on auto for a first run; these affect the returned video's encoding rather than its generated content. Generation uses an external, provider-priced Veo API. Your prompt and any enabled reference image are sent to the provider.
Love Potion
Do you have a problem with shy subjects in your images? Try this love potion to really break the ice.
Image relight
Create a relit version of an uploaded image using a lighting instruction, Qwen Image Edit, and a dedicated relighting LoRA. For a first run, upload an image and keep the default warm twilight instruction, denoise 1, relighting LoRA strength 1, and the sampling preset: 4 steps, CFG 1, Euler sampler, and simple scheduler. Run the Pipe to receive one edited image. To request another lighting setup, describe direction, time of day, color temperature, softness, and shadows. The relighting trigger is added automatically. The source image is resized before supplying both image conditioning and the initial latent. Output dimensions follow this preprocessing; there are no independent width or height controls. Lower denoise values retain more of the initial image's structure and can limit the lighting change. The default 1 allows a full denoising pass. Objects and details may change along with the light. Uploaded transparency is not preserved because only the loader's RGB image output is connected. Relighting LoRA strength controls the dedicated adapter independently of denoise; 0 removes its contribution while retaining the underlying image-edit model. The negative prompt describes unwanted attributes, but at CFG 1 it has no ordinary classifier-free exclusion effect. Increasing CFG enables its influence and changes sampling behavior. Leave it blank for the source preset. Use seed -1 for a new randomized run, or a fixed seed to compare instructions and settings with the same initial noise. Advanced controls also expose guidance normalization strength and mode, sampling shift, and Lightning adapter strength. Normalization before CFG can amplify the result; the source preset leaves it off. Start with the supplied values and evaluate changes together with CFG, steps, sampler, and scheduler. Removing or changing the Lightning adapter's influence changes the basis of the four-step preset. The workflow requires qwen_image_edit_2509_fp8_e4m3fn.safetensors, qwen_2.5_vl_7b_fp8_scaled.safetensors, qwen_image_vae.safetensors, Qwen-Image-Edit-2509-Relight.safetensors, and Qwen-Image-Edit-2509-Lightning-4steps-V1.0-bf16.safetensors. Installation sources are supplied, but the dependency records mark these artifacts MISSING_MODEL; this review does not establish that they are installed.
Autumn Style
This is a DreamArtist Textual Inversion Style Embedding trained on a single image of fall/autumn like colors. You can get amazing autumn/fall color effects in your images by calling Style-Autumn in the positive...
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.
Illustration to realism
Reinterpret an uploaded illustration as a realistic photographic image using Qwen Image Edit and the Anything2Real adapter. Upload an illustration and keep the default editing instruction for a first run, or describe the photographic lighting, materials, setting, and features you want retained. Begin with Denoise at 1, Realism strength at 1, and the supplied 8 steps, CFG 1, Euler sampler, and beta scheduler. The source image supplies both visual conditioning and the initial latent. This is a generative reinterpretation, so details and identity can change. Lower Denoise to retain more of the original structure, potentially leaving more of its illustrated appearance. Realism strength scales the Anything2Real adapter; setting it to 0 or bypassing the Realism adapter stage keeps prompt-driven image editing active without that adapter's influence. Use Seed -1 for a new random variation each run, or a fixed seed to compare changes with the same starting noise. Negative prompt can be left blank. At CFG 1, ordinary negative-prompt guidance has no effect; increasing CFG makes positive-versus-negative guidance meaningful. Steps, CFG, Sampler, and Scheduler remain independent tuning choices; start with the supplied settings rather than assuming larger values improve the result. Advanced settings expose Lightning strength, guidance normalization, and sampling shift. Lightning strength defaults to 1 for the supplied eight-step adapter; 0 removes its influence without changing Steps automatically. Guidance normalization has its own strength and a before-guidance switch: off uses post-guidance attenuation, while on applies unclamped normalization before guidance combination and can amplify the prediction. Sampling shift defaults to 3 and changes the model's noise schedule independently of the Scheduler choice. Output shape derives from the uploaded image through latent encoding; there are no independent resize controls. Uploaded transparency is not preserved because only the loader's RGB image output is connected.
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.
Chroma1-HD text to image
Generate an image or batch of images from text using the fixed Chroma1-HD model. No uploaded image is required. For a first run, replace the positive prompt with your subject, composition, lighting and style. The example requests a tiger photograph with CHROMA1-HD title lettering. Review the negative prompt: its default discourages sketches and cartoon aesthetics, so remove those exclusions when you want those styles. Begin with 1024 × 1024 pixels, one image, 26 steps and CFG 3.5, then run to receive the generated image batch. Width and height use pixels in multiples of 16; their ratio determines the image aspect ratio. Increasing dimensions or image count increases memory requirements. The selected output returns images through Sweet Tea's preview transport rather than a SaveImage file-saving node. Advanced controls include seed, prompt guidance, steps, sampler, scheduler, flow shift and noise schedule fraction. Seed -1 randomizes each run; use a fixed seed when comparing settings. CFG controls the strength of positive and negative prompt guidance. The sampler chooses the sampling algorithm, and the scheduler distributes noise levels across the steps. Flow shift adjusts the flow model's sampling timestep shift. Euler, beta scheduling and flow shift 1 are the supplied starting settings. Keep noise schedule fraction at 1 for the full generation schedule. Lower values shorten that schedule. This workflow starts from an empty latent and has no uploaded-image or image-to-image mode. Its fixed model setup uses Chroma1-HD-fp8mixed.safetensors, t5xxl_fp8_e4m3fn_scaled.safetensors and ae.safetensors.
NetaYume Lumina Text to Image
Generate anime-style images from text using the fixed NetaYume Lumina model. Enter a positive prompt and adjust the negative prompt, image dimensions, and batch size. Advanced settings provide seed, steps, guidance, sampler, scheduler, denoise, and model shift controls. Fixed model-specific prefixes are added to both prompts.
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.
djz CYBER KAN V0
Curated Collection of Manga Illustrations from my videogame series "BIG KAN", part of the Paradise Universe.
Handsome male models' face (18-30) /realism/
if you want more stronger changes and push the images towards more 18-20 years face, use it around 1.5-2.00
Restyle an image with Seedream 5.0 Lite
Restyle a source image using a second image as a visual reference and a written instruction. Upload both required images: the picture to change as Source image and an example of the desired look as Style reference. In Edit instruction, refer to them as the first and second images respectively. The default instruction requests style transfer; edit it to describe the changes you want and the details to preserve. For a first run, keep the square 2048 × 2048 preset and Maximum images at 1, then run the tool to view the generated image. Select Custom under Output size to activate Width and Height, measured in pixels. Width accepts even values from 1024 to 6240; Height accepts even values from 1024 to 4992. Maximum images at 1 produces one result. Higher values request between one and the chosen maximum number of related images; describe the desired variations in the instruction. With these two reference inputs, use at most 13 generated images because input and generated images together cannot exceed 15. Enable Require complete results to abort on missing or errored requested images; the default accepts available results. Seed -1 randomizes each run; enter a fixed seed to control the generation seed. AI watermark asks the provider to add an AI-generated watermark. Prompt reasoning stays enabled because it cannot be disabled when reference images are supplied. This tool uses the Seedream provider API, sends the reference images and instruction to the provider, and requires configured provider access with provider billing. Generated images are returned through the preview output. Uploaded alpha masks are not connected, so transparency preservation is not supported.
negative_hand Negative Embedding
Currently, there are more and more negative embedding, while many are also very good and easy to use. However, almost all of them currently have a big problem... They change the main or initial artstyle of the used...
