clipnpaper/alcohol_bbq 🖼️🔢❓📝✓ → 🖼️
About
Make outside BBQ party
Example Output
Prompt:
"A refreshing beer marketing scene on a bright summer day. A cold beer glass covered with clear water droplets in the foreground, the beer brand label displayed clearly and accurately with no distortions. Softly blurred outdoor BBQ background with grills, warm lighting, wooden tables, and people enjoying the moment. A vivid blue sky with natural sunlight creating golden reflections on the glass. Cinematic mood, high-detail glass texture, realistic condensation, premium advertisement style, vibrant colors, professional commercial photography."
Output
Performance Metrics
7.73s
Prediction Time
7.74s
Total Time
All Input Parameters
{
"mask": "https://replicate.delivery/pbxt/O8enem2QWUyAZVdghtoQuk5MVyJKi3UZP4qDfMzeixMwPjaF/budweiser.png",
"model": "dev",
"prompt": "A refreshing beer marketing scene on a bright summer day. A cold beer glass covered with clear water droplets in the foreground, the beer brand label displayed clearly and accurately with no distortions. Softly blurred outdoor BBQ background with grills, warm lighting, wooden tables, and people enjoying the moment. A vivid blue sky with natural sunlight creating golden reflections on the glass. Cinematic mood, high-detail glass texture, realistic condensation, premium advertisement style, vibrant colors, professional commercial photography.",
"go_fast": false,
"lora_scale": 1,
"megapixels": "1",
"num_outputs": 1,
"aspect_ratio": "1:1",
"output_format": "webp",
"guidance_scale": 3,
"output_quality": 80,
"prompt_strength": 0.8,
"extra_lora_scale": 1,
"num_inference_steps": 28
}
Input Parameters
- mask
- Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.
- seed
- Random seed. Set for reproducible generation
- image
- Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored.
- model
- Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps.
- width
- Width of generated image. Only works if `aspect_ratio` is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation
- height
- Height of generated image. Only works if `aspect_ratio` is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation
- prompt (required)
- Prompt for generated image. If you include the `trigger_word` used in the training process you are more likely to activate the trained object, style, or concept in the resulting image.
- go_fast
- Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16
- extra_lora
- Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars'
- lora_scale
- Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.
- megapixels
- Approximate number of megapixels for generated image
- num_outputs
- Number of outputs to generate
- aspect_ratio
- Aspect ratio for the generated image. If custom is selected, uses height and width below & will run in bf16 mode
- output_format
- Format of the output images
- guidance_scale
- Guidance scale for the diffusion process. Lower values can give more realistic images. Good values to try are 2, 2.5, 3 and 3.5
- output_quality
- Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs
- prompt_strength
- Prompt strength when using img2img. 1.0 corresponds to full destruction of information in image
- extra_lora_scale
- Determines how strongly the extra LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.
- replicate_weights
- Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars'
- num_inference_steps
- Number of denoising steps. More steps can give more detailed images, but take longer.
- disable_safety_checker
- Disable safety checker for generated images.
Output Schema
Output
Example Execution Logs
Weights already loaded Loaded LoRAs in 0.02s Using seed: 28617 Prompt: A refreshing beer marketing scene on a bright summer day. A cold beer glass covered with clear water droplets in the foreground, the beer brand label displayed clearly and accurately with no distortions. Softly blurred outdoor BBQ background with grills, warm lighting, wooden tables, and people enjoying the moment. A vivid blue sky with natural sunlight creating golden reflections on the glass. Cinematic mood, high-detail glass texture, realistic condensation, premium advertisement style, vibrant colors, professional commercial photography. [!] txt2img mode 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.83it/s] 7%|▋ | 2/28 [00:00<00:05, 4.35it/s] 11%|█ | 3/28 [00:00<00:06, 4.11it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.01it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.95it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.92it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.89it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.88it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.87it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.86it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.86it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.86it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.86it/s] 50%|█████ | 14/28 [00:03<00:03, 3.86it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.86it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.86it/s] 61%|██████ | 17/28 [00:04<00:02, 3.85it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.85it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.85it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.85it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.85it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.86it/s] 82%|████████▏ | 23/28 [00:05<00:01, 3.85it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.85it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.85it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.86it/s] 96%|█████████▋| 27/28 [00:06<00:00, 3.85it/s] 100%|██████████| 28/28 [00:07<00:00, 3.85it/s] 100%|██████████| 28/28 [00:07<00:00, 3.88it/s] Total safe images: 1 out of 1
Version Details
- Version ID
81520f34f3770086c356c923a1101026bf77cbbe0bc84c3d2d9a496fa81735fa- Version Created
- November 28, 2025