Text-to-Image
Diffusers
TensorBoard
stable-diffusion
diffusion
distillation
flow-matching
geometric-deep-learning
research
Instructions to use AbstractPhil/sd15-flow-matching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AbstractPhil/sd15-flow-matching with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AbstractPhil/sd15-flow-matching", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Update README.md
Browse files
README.md
CHANGED
|
@@ -29,6 +29,13 @@ David simply wasn't ready to teach, and the model is too bulky to learn much mor
|
|
| 29 |
|
| 30 |
The only answer is a smaller and more concise model specific to the task.
|
| 31 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 32 |
|
| 33 |
# I've decided to name this model
|
| 34 |
|
|
|
|
| 29 |
|
| 30 |
The only answer is a smaller and more concise model specific to the task.
|
| 31 |
|
| 32 |
+
The next David will require a much higher % timestep accuracy with direct pattern association.
|
| 33 |
+
|
| 34 |
+
Instead of attempting to perform multiple tasks, the prototype Zephyr will be issued a new form of attention that I've been
|
| 35 |
+
prototyping and planning that enables much higher sequences with less vram to associate attention controllers.
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
|
| 39 |
|
| 40 |
# I've decided to name this model
|
| 41 |
|